code stringlengths 2k 1.04M | repo_path stringlengths 5 517 | parsed_code stringlengths 0 1.04M | quality_prob float64 0.02 0.95 | learning_prob float64 0.02 0.93 |
|---|---|---|---|---|
MENUS = {
'MAIN':
[['[&File]', 'FILE', None],
['[&Edit]', 'EDIT', None],
['[&Code]', 'CODE', None],
['[&Macro]', 'MACRO', None],
['[&Tools]', 'TOOLS', None],
['[&Git]', 'GIT', None],
['[&Window]', 'WINDOW', None]],
'FILE':
[['&New', None, 'file.new'],
['&Open', None, 'file.open'],
['File &Info', None, 'file.info'],
['View &Diff', None, 'file.viewdiff'],
['&Save', None, 'file.save'],
['Save &As', None, 'file.saveas'],
['&Close', None, 'file.close'],
['Save a&ll', None, 'file.save.all'],
['Clos&e all', None, 'file.close.all'],
['[&Recently]', 'RECENTLY-USED-FILES', None],
['&Quit', None, 'file.quit']],
'RECENTLY-USED-FILES':
[['Recently used &Files', None, 'file.recently-used-files'],
['Recently used &Dirs', None, 'file.recently-used-directories']],
'EDIT':
[['&Cut', None, 'edit.cut'],
['C&opy', None, 'edit.copy'],
['&Paste', None, 'edit.paste'],
['Paste &History', None, 'edit.clipboard-history'],
['&Undo', None, 'edit.undo'],
['&Redo', None, 'edit.redo'],
['&Search', None, 'search.showsearch'],
['R&eplace', None, 'search.showreplace'],
['Co&mplete', None, 'edit.word-complete'],
['[&Convert]', 'EDIT-CONVERT', None]],
'EDIT-CONVERT':
[['&Upper', None, 'edit.conv.upper'],
['&Lower', None, 'edit.conv.lower'],
['&Normalization', None, 'edit.conv.nfkc'],
['&Full-width', None, 'edit.conv.full-width']],
'MACRO':
[['&Start Record', None, 'macro.start-record'],
['&End Record', None, 'macro.end-record'],
['&Run Macro', None, 'macro.run']],
'TOOLS':
[['&Grep', None, 'tools.grep'],
['Paste &Lines', None, 'edit.paste-lines'],
['&Shell command', None, 'tools.execute-shell-command'],
['&Make', None, 'tools.make'],
['Spell &Checker', None, 'tools.spellchecker'],
['&Python console', None, 'python.console'],
['Python &Debugger', None, 'python.debugger.run'],
['Python debugger s&erver', None, 'python.debugger.server'],
],
'GIT':
[['&Status', None, 'git.status'],
['&Log', None, 'git.log'],
],
'WINDOW':
[['&Frame list', None, 'app.show-framelist'],
['Split &vert', None, 'editor.splitvert'],
['Split &horz', None, 'editor.splithorz'],
['&Move separator', None, 'editor.moveseparator'],
['&Next window', None, 'editor.nextwindow'],
['&Prev window', None, 'editor.prevwindow'],
['&Join window', None, 'editor.joinwindow'],
['[&Switch file]', 'CHANGE-WINDOW', None]],
'CHANGE-WINDOW':
[['&Switch file', None, 'editor.switchfile'],
['&New file here', None, 'file.new-to'],
['&Open file here', None, 'file.open-to'],
['Recently used &Files', None, 'file.recently-used-files-to'],
['Recently used &Dirs', None, 'file.recently-used-directories-to']],
} | kaa/filetype/default/menu.py | MENUS = {
'MAIN':
[['[&File]', 'FILE', None],
['[&Edit]', 'EDIT', None],
['[&Code]', 'CODE', None],
['[&Macro]', 'MACRO', None],
['[&Tools]', 'TOOLS', None],
['[&Git]', 'GIT', None],
['[&Window]', 'WINDOW', None]],
'FILE':
[['&New', None, 'file.new'],
['&Open', None, 'file.open'],
['File &Info', None, 'file.info'],
['View &Diff', None, 'file.viewdiff'],
['&Save', None, 'file.save'],
['Save &As', None, 'file.saveas'],
['&Close', None, 'file.close'],
['Save a&ll', None, 'file.save.all'],
['Clos&e all', None, 'file.close.all'],
['[&Recently]', 'RECENTLY-USED-FILES', None],
['&Quit', None, 'file.quit']],
'RECENTLY-USED-FILES':
[['Recently used &Files', None, 'file.recently-used-files'],
['Recently used &Dirs', None, 'file.recently-used-directories']],
'EDIT':
[['&Cut', None, 'edit.cut'],
['C&opy', None, 'edit.copy'],
['&Paste', None, 'edit.paste'],
['Paste &History', None, 'edit.clipboard-history'],
['&Undo', None, 'edit.undo'],
['&Redo', None, 'edit.redo'],
['&Search', None, 'search.showsearch'],
['R&eplace', None, 'search.showreplace'],
['Co&mplete', None, 'edit.word-complete'],
['[&Convert]', 'EDIT-CONVERT', None]],
'EDIT-CONVERT':
[['&Upper', None, 'edit.conv.upper'],
['&Lower', None, 'edit.conv.lower'],
['&Normalization', None, 'edit.conv.nfkc'],
['&Full-width', None, 'edit.conv.full-width']],
'MACRO':
[['&Start Record', None, 'macro.start-record'],
['&End Record', None, 'macro.end-record'],
['&Run Macro', None, 'macro.run']],
'TOOLS':
[['&Grep', None, 'tools.grep'],
['Paste &Lines', None, 'edit.paste-lines'],
['&Shell command', None, 'tools.execute-shell-command'],
['&Make', None, 'tools.make'],
['Spell &Checker', None, 'tools.spellchecker'],
['&Python console', None, 'python.console'],
['Python &Debugger', None, 'python.debugger.run'],
['Python debugger s&erver', None, 'python.debugger.server'],
],
'GIT':
[['&Status', None, 'git.status'],
['&Log', None, 'git.log'],
],
'WINDOW':
[['&Frame list', None, 'app.show-framelist'],
['Split &vert', None, 'editor.splitvert'],
['Split &horz', None, 'editor.splithorz'],
['&Move separator', None, 'editor.moveseparator'],
['&Next window', None, 'editor.nextwindow'],
['&Prev window', None, 'editor.prevwindow'],
['&Join window', None, 'editor.joinwindow'],
['[&Switch file]', 'CHANGE-WINDOW', None]],
'CHANGE-WINDOW':
[['&Switch file', None, 'editor.switchfile'],
['&New file here', None, 'file.new-to'],
['&Open file here', None, 'file.open-to'],
['Recently used &Files', None, 'file.recently-used-files-to'],
['Recently used &Dirs', None, 'file.recently-used-directories-to']],
} | 0.213787 | 0.204759 |
from keras.engine.topology import Layer
class SpatialPyramidPoling(Layer):
"""Spatial pyramid pooling layer for 2D inputs.
See Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,
<NAME>, <NAME>, <NAME>, <NAME>
# Arguments
pool_list: list of int
# Input shape
or 4D tensor with shape:
(samples, rows, cols, channels)` if dim_ordering='tf'.
# Output shape
2D tensor with shape:
(samples, channels * sum([i * i for i in pool_list])
"""
def __init__(self, pool_list, **kwargs):
self.pool_list = pool_list
self.num_outputs_per_channel = sum([i**2 for i in pool_list])
super(SpatialPyramidPolinhttps, self).__init__(**kwargs)
def build(self, input_shape):
self.num_channels = input_shape[3]
def compute_output_shape(self, input_shape):
return (input_shape[0], self.num_channels*self.num_outputs_per_channel)
def get_config(self):
config = {
'pool_list':self.pool_list
}
base_config = super(SpatialPyramidPoling, self).get_config()
return dict(list(base_config.items())+list(config.items()))
def call(self, x, mask=None):
input_shape = K.shape(x)
num_rows = input_shape[1]
num_cols = input_shape[2]
row_lenght = [K.cast(num_rows, 'float32')/i for i in self.pool_list]
col_lenght = [K.cast(num_cols, 'float32')/i for i in self.pool_list]
results = []
for ip, p in enumerate(self.pool_list):
for j in range(p):
for i in range(p):
x1 = i * col_length[ip]
x2 = i * col_length[ip] + col_length[ip]
y1 = j * row_length[ip]
y2 = j * row_length[ip] + row_length[ip]
x1 = K.cast(K.round(x1), 'int32')
x2 = K.cast(K.round(x2), 'int32')
y1 = K.cast(K.round(y1), 'int32')
y2 = K.cast(K.round(y2), 'int32')
new_shape = [input_shape[0], y2 - y1,
x2 - x1, input_shape[3]]
x_crop = x[:, y1:y2, x1:x2, :]
xm = K.reshape(x_crop, new_shape)
pooled_val = K.max(xm, axis=(1, 2))
results.append(pooled_val)
results = K.concatenate(results)
return results | spp_layer.py | from keras.engine.topology import Layer
class SpatialPyramidPoling(Layer):
"""Spatial pyramid pooling layer for 2D inputs.
See Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,
<NAME>, <NAME>, <NAME>, <NAME>
# Arguments
pool_list: list of int
# Input shape
or 4D tensor with shape:
(samples, rows, cols, channels)` if dim_ordering='tf'.
# Output shape
2D tensor with shape:
(samples, channels * sum([i * i for i in pool_list])
"""
def __init__(self, pool_list, **kwargs):
self.pool_list = pool_list
self.num_outputs_per_channel = sum([i**2 for i in pool_list])
super(SpatialPyramidPolinhttps, self).__init__(**kwargs)
def build(self, input_shape):
self.num_channels = input_shape[3]
def compute_output_shape(self, input_shape):
return (input_shape[0], self.num_channels*self.num_outputs_per_channel)
def get_config(self):
config = {
'pool_list':self.pool_list
}
base_config = super(SpatialPyramidPoling, self).get_config()
return dict(list(base_config.items())+list(config.items()))
def call(self, x, mask=None):
input_shape = K.shape(x)
num_rows = input_shape[1]
num_cols = input_shape[2]
row_lenght = [K.cast(num_rows, 'float32')/i for i in self.pool_list]
col_lenght = [K.cast(num_cols, 'float32')/i for i in self.pool_list]
results = []
for ip, p in enumerate(self.pool_list):
for j in range(p):
for i in range(p):
x1 = i * col_length[ip]
x2 = i * col_length[ip] + col_length[ip]
y1 = j * row_length[ip]
y2 = j * row_length[ip] + row_length[ip]
x1 = K.cast(K.round(x1), 'int32')
x2 = K.cast(K.round(x2), 'int32')
y1 = K.cast(K.round(y1), 'int32')
y2 = K.cast(K.round(y2), 'int32')
new_shape = [input_shape[0], y2 - y1,
x2 - x1, input_shape[3]]
x_crop = x[:, y1:y2, x1:x2, :]
xm = K.reshape(x_crop, new_shape)
pooled_val = K.max(xm, axis=(1, 2))
results.append(pooled_val)
results = K.concatenate(results)
return results | 0.920039 | 0.485844 |
import pandas as pd
from datetime import datetime
import math
import pathlib
import sys
import os
# Read data downloaded from the crawler
def read_data(path):
try:
data = pd.read_excel(path, engine="odf")
return data
except Exception as excep:
sys.stderr.write(
"'Não foi possível ler o arquivo: "
+ path
+ ". O seguinte erro foi gerado: "
+ excep
)
os._exit(1)
# Strange way to check nan. Only I managed to make work
# Source: https://stackoverflow.com/a/944712/5822594
def isNaN(string):
return string != string
def get_begin_row(rows, begin_string):
begin_row = 0
for row in rows:
begin_row += 1
if row[0] == begin_string:
break
# We need to continue interate until wee a value that is not
# whitespace. That happen due to the spreadsheet formatting.
while isNaN(rows[begin_row][0]):
begin_row += 1
return begin_row
def get_end_row(rows, begin_row):
end_row = 0
for row in rows:
# First goes to begin_row.
if end_row < begin_row:
end_row += 1
continue
# Then keep moving until find a blank row.
if isNaN(row[0]):
break
end_row += 1
end_row -= 1
return end_row
def format_value(element):
# A value was found with incorrect formatting. (3,045.99 instead of 3045.99)
if isNaN(element):
return 0.0
if type(element) == str:
if "." in element and "," in element:
element = element.replace(".", "").replace(",", ".")
elif "," in element:
element = element.replace(",", ".")
return float(element)
# Used when the employee is not on the indemnity list
def parse_employees(file_name):
rows = read_data(file_name).to_numpy()
begin_string = "Matrícula"
begin_row = get_begin_row(rows, begin_string)
end_row = get_end_row(rows, begin_row)
employees = {}
curr_row = 0
for row in rows:
if curr_row < begin_row:
curr_row += 1
continue
matricula = row[0]
if type(matricula) != str:
matricula = str(matricula)
nome = row[1].split("/")[0]
cargo_efetivo = row[1].split("/")[1]
lotacao = row[2]
if isNaN(lotacao):
lotacao = "Não informado"
remuneracao_cargo_efetivo = format_value(row[4])
outras_verbas_remuneratorias = format_value(row[5])
confianca_comissao = format_value(
row[6]
) # Função de Confiança ou Cargo em Comissão
grat_natalina = format_value(row[7]) # Gratificação Natalina
ferias = format_value(row[8])
permanencia = format_value(row[9]) # Abono de Permanência
previdencia = format_value(row[13]) # Contribuição Previdenciária
imp_renda = format_value(row[14]) # Imposto de Renda
teto_constitucional = format_value(row[15]) # Retenção por Teto Constitucional
total_desconto = previdencia + imp_renda + teto_constitucional
total_gratificacoes = grat_natalina + ferias + permanencia + confianca_comissao
total_bruto = (
remuneracao_cargo_efetivo
+ outras_verbas_remuneratorias
+ total_gratificacoes
)
employees[matricula] = {
"reg": matricula,
"name": nome,
"role": cargo_efetivo,
"type": "membro",
"workplace": lotacao,
"active": True,
"income": {
"total": round(total_bruto, 2),
# REMUNERAÇÃO BÁSICA = Remuneração Cargo Efetivo + Outras Verbas Remuneratórias, Legais ou Judiciais
"wage": round(
remuneracao_cargo_efetivo + outras_verbas_remuneratorias, 2
),
"other": { # Gratificações
"total": round(total_gratificacoes, 2),
"trust_position": confianca_comissao,
"others_total": round(grat_natalina + ferias + permanencia, 2),
"others": {
"Gratificação Natalina": grat_natalina,
"Férias (1/3 constitucional)": ferias,
"Abono de Permanência": permanencia,
},
},
},
"discounts": { # Discounts Object. Using abs to garantee numbers are positivo (spreadsheet have negative discounts).
"total": round(total_desconto, 2),
"prev_contribution": previdencia,
# Retenção por teto constitucional
"ceil_retention": teto_constitucional,
"income_tax": imp_renda,
},
}
curr_row += 1
if curr_row > end_row:
break
return employees
def update_employee_indemnity(file_name, employees):
rows = read_data(file_name).to_numpy()
begin_string = "Matrícula" # word before starting data
begin_row = get_begin_row(rows, begin_string)
end_row = get_end_row(rows, begin_row)
curr_row = 0
# If the spreadsheet does not contain employees
for row in rows:
if curr_row < begin_row:
curr_row += 1
continue
matricula = row[0]
if type(matricula) != str:
matricula = str(matricula)
abono_pecuniario = format_value(row[2])
creche = format_value(row[3])
ajuda_de_custo = format_value(row[4])
natalidade = format_value(row[5])
alimentacao = format_value(row[6])
transporte = format_value(row[7])
ferias_indenizada = format_value(row[8])
banco_de_horas_indenizado = format_value(row[9])
moradia = format_value(row[10])
lp_pecunia = format_value(row[11])
total_indenizacoes = (
abono_pecuniario
+ creche
+ ajuda_de_custo
+ natalidade
+ alimentacao
+ transporte
+ ferias_indenizada
+ banco_de_horas_indenizado
+ moradia
+ lp_pecunia
)
emp = employees[matricula]
emp["income"].update(
{
"total": round(emp["income"]["total"] + total_indenizacoes, 2),
}
)
emp["income"].update(
{
"perks": {
"total": round(total_indenizacoes, 2),
"food": alimentacao,
"pre_school": creche,
"transportation": transporte,
"housing_aid": moradia,
"vacation": ferias_indenizada,
"pecuniary": round(abono_pecuniario + banco_de_horas_indenizado, 2),
"subsistence": ajuda_de_custo,
"birth_aid": natalidade,
"premium_license_pecuniary": lp_pecunia,
}
}
)
employees[matricula] = emp
curr_row += 1
if curr_row > end_row:
break
return employees
def update_employee_temporary_remuneration(file_name, employees):
rows = read_data(file_name).to_numpy()
begin_string = "Matrícula" # word before starting data
begin_row = get_begin_row(rows, begin_string)
end_row = get_end_row(rows, begin_row)
curr_row = 0
# If the spreadsheet does not contain employees
for row in rows:
if curr_row < begin_row:
curr_row += 1
continue
matricula = row[0]
if type(matricula) != str:
matricula = str(matricula)
# PSSS = Plano de Seguridade Social do Servidor Público
substituicao_membros = format_value(row[2]) # Substituição de Membros
funcao_substituicao = format_value(row[3]) # Função de Substituição
grat_encargo_curso = format_value(row[4]) # Gratificação por Encargo de Curso
insalubridade = format_value(row[5]) # Adicional de Insalubridade
grat_encargo_concurso = format_value(
row[6]
) # Gratificação por Encargo de Concurso
periculosidade = format_value(row[7]) # Periculosidade
exercicio_cumulativo_sem_psss = format_value(
row[8]
) # Gratificação de Exercício Cumulativo com Ofício Sem Psss
exercicio_cumulativo_com_psss = format_value(
row[9]
) # Gratificação Exercício Cumulativo com Ofício Com Psss
membros_substituicao = format_value(row[10]) # Membros Substituição
hora_extra_sem_pass = format_value(row[11]) # Hora Extra Sem Psss
adic_noturno_sem_pass = format_value(row[12]) # Adicional Noturno Sem Psss
subs_membros_ms2013 = format_value(row[13]) # Substituição Membros MS2013
adic_penosidade = format_value(row[14]) # Adicional Penosidade
total_temporario = (
substituicao_membros
+ funcao_substituicao
+ grat_encargo_curso
+ insalubridade
+ grat_encargo_concurso
+ periculosidade
+ exercicio_cumulativo_sem_psss
+ exercicio_cumulativo_com_psss
+ membros_substituicao
+ hora_extra_sem_pass
+ adic_noturno_sem_pass
+ subs_membros_ms2013
+ adic_penosidade
)
emp = employees[matricula]
emp["income"].update(
{
"total": round(emp["income"]["total"] + total_temporario, 2),
}
)
emp["income"]["other"].update(
{
"others_total": round(
emp["income"]["other"]["others_total"] + total_temporario, 2
),
"total": round(emp["income"]["other"]["total"] + total_temporario, 2),
}
)
emp["income"]["other"]["others"].update(
{
"Substituição de Membros": substituicao_membros,
"Função de Substituição": funcao_substituicao,
"Gratificação por Encargo de Curso": grat_encargo_curso,
"Adicional de Insalubridade": insalubridade,
"Gratificação por Encargo de Concurso": grat_encargo_concurso,
"Adicional de Periculosidade": periculosidade,
"Gratificação de Exercício Cumulativo com Ofício Sem Psss": exercicio_cumulativo_sem_psss,
"Gratificação Exercício Cumulativo com Ofício Com Psss": exercicio_cumulativo_com_psss,
"Membros Substituição": membros_substituicao,
"Hora Extra Sem Psss": hora_extra_sem_pass,
"Adicional Noturno Sem Psss": adic_noturno_sem_pass,
"Substituição Membros MS2013": subs_membros_ms2013,
"Adicional Penosidade": adic_penosidade,
}
)
employees[matricula] = emp
curr_row += 1
if curr_row > end_row:
break
return employees
def parse(file_names):
employees = {}
for fn in file_names:
if "Verbas Indenizatorias" not in fn and "Verbas Temporarias" not in fn:
# Puts all parsed employees in the big map
employees.update(parse_employees(fn))
try:
for fn in file_names:
if "Verbas Indenizatorias" in fn:
update_employee_indemnity(fn, employees)
elif "Verbas Temporarias" in fn:
update_employee_temporary_remuneration(fn, employees)
except KeyError as e:
sys.stderr.write(
"Registro inválido ao processar verbas indenizatórias: {}".format(e)
)
os._exit(1)
return list(employees.values()) | mpdft/src/parser.py | import pandas as pd
from datetime import datetime
import math
import pathlib
import sys
import os
# Read data downloaded from the crawler
def read_data(path):
try:
data = pd.read_excel(path, engine="odf")
return data
except Exception as excep:
sys.stderr.write(
"'Não foi possível ler o arquivo: "
+ path
+ ". O seguinte erro foi gerado: "
+ excep
)
os._exit(1)
# Strange way to check nan. Only I managed to make work
# Source: https://stackoverflow.com/a/944712/5822594
def isNaN(string):
return string != string
def get_begin_row(rows, begin_string):
begin_row = 0
for row in rows:
begin_row += 1
if row[0] == begin_string:
break
# We need to continue interate until wee a value that is not
# whitespace. That happen due to the spreadsheet formatting.
while isNaN(rows[begin_row][0]):
begin_row += 1
return begin_row
def get_end_row(rows, begin_row):
end_row = 0
for row in rows:
# First goes to begin_row.
if end_row < begin_row:
end_row += 1
continue
# Then keep moving until find a blank row.
if isNaN(row[0]):
break
end_row += 1
end_row -= 1
return end_row
def format_value(element):
# A value was found with incorrect formatting. (3,045.99 instead of 3045.99)
if isNaN(element):
return 0.0
if type(element) == str:
if "." in element and "," in element:
element = element.replace(".", "").replace(",", ".")
elif "," in element:
element = element.replace(",", ".")
return float(element)
# Used when the employee is not on the indemnity list
def parse_employees(file_name):
rows = read_data(file_name).to_numpy()
begin_string = "Matrícula"
begin_row = get_begin_row(rows, begin_string)
end_row = get_end_row(rows, begin_row)
employees = {}
curr_row = 0
for row in rows:
if curr_row < begin_row:
curr_row += 1
continue
matricula = row[0]
if type(matricula) != str:
matricula = str(matricula)
nome = row[1].split("/")[0]
cargo_efetivo = row[1].split("/")[1]
lotacao = row[2]
if isNaN(lotacao):
lotacao = "Não informado"
remuneracao_cargo_efetivo = format_value(row[4])
outras_verbas_remuneratorias = format_value(row[5])
confianca_comissao = format_value(
row[6]
) # Função de Confiança ou Cargo em Comissão
grat_natalina = format_value(row[7]) # Gratificação Natalina
ferias = format_value(row[8])
permanencia = format_value(row[9]) # Abono de Permanência
previdencia = format_value(row[13]) # Contribuição Previdenciária
imp_renda = format_value(row[14]) # Imposto de Renda
teto_constitucional = format_value(row[15]) # Retenção por Teto Constitucional
total_desconto = previdencia + imp_renda + teto_constitucional
total_gratificacoes = grat_natalina + ferias + permanencia + confianca_comissao
total_bruto = (
remuneracao_cargo_efetivo
+ outras_verbas_remuneratorias
+ total_gratificacoes
)
employees[matricula] = {
"reg": matricula,
"name": nome,
"role": cargo_efetivo,
"type": "membro",
"workplace": lotacao,
"active": True,
"income": {
"total": round(total_bruto, 2),
# REMUNERAÇÃO BÁSICA = Remuneração Cargo Efetivo + Outras Verbas Remuneratórias, Legais ou Judiciais
"wage": round(
remuneracao_cargo_efetivo + outras_verbas_remuneratorias, 2
),
"other": { # Gratificações
"total": round(total_gratificacoes, 2),
"trust_position": confianca_comissao,
"others_total": round(grat_natalina + ferias + permanencia, 2),
"others": {
"Gratificação Natalina": grat_natalina,
"Férias (1/3 constitucional)": ferias,
"Abono de Permanência": permanencia,
},
},
},
"discounts": { # Discounts Object. Using abs to garantee numbers are positivo (spreadsheet have negative discounts).
"total": round(total_desconto, 2),
"prev_contribution": previdencia,
# Retenção por teto constitucional
"ceil_retention": teto_constitucional,
"income_tax": imp_renda,
},
}
curr_row += 1
if curr_row > end_row:
break
return employees
def update_employee_indemnity(file_name, employees):
rows = read_data(file_name).to_numpy()
begin_string = "Matrícula" # word before starting data
begin_row = get_begin_row(rows, begin_string)
end_row = get_end_row(rows, begin_row)
curr_row = 0
# If the spreadsheet does not contain employees
for row in rows:
if curr_row < begin_row:
curr_row += 1
continue
matricula = row[0]
if type(matricula) != str:
matricula = str(matricula)
abono_pecuniario = format_value(row[2])
creche = format_value(row[3])
ajuda_de_custo = format_value(row[4])
natalidade = format_value(row[5])
alimentacao = format_value(row[6])
transporte = format_value(row[7])
ferias_indenizada = format_value(row[8])
banco_de_horas_indenizado = format_value(row[9])
moradia = format_value(row[10])
lp_pecunia = format_value(row[11])
total_indenizacoes = (
abono_pecuniario
+ creche
+ ajuda_de_custo
+ natalidade
+ alimentacao
+ transporte
+ ferias_indenizada
+ banco_de_horas_indenizado
+ moradia
+ lp_pecunia
)
emp = employees[matricula]
emp["income"].update(
{
"total": round(emp["income"]["total"] + total_indenizacoes, 2),
}
)
emp["income"].update(
{
"perks": {
"total": round(total_indenizacoes, 2),
"food": alimentacao,
"pre_school": creche,
"transportation": transporte,
"housing_aid": moradia,
"vacation": ferias_indenizada,
"pecuniary": round(abono_pecuniario + banco_de_horas_indenizado, 2),
"subsistence": ajuda_de_custo,
"birth_aid": natalidade,
"premium_license_pecuniary": lp_pecunia,
}
}
)
employees[matricula] = emp
curr_row += 1
if curr_row > end_row:
break
return employees
def update_employee_temporary_remuneration(file_name, employees):
rows = read_data(file_name).to_numpy()
begin_string = "Matrícula" # word before starting data
begin_row = get_begin_row(rows, begin_string)
end_row = get_end_row(rows, begin_row)
curr_row = 0
# If the spreadsheet does not contain employees
for row in rows:
if curr_row < begin_row:
curr_row += 1
continue
matricula = row[0]
if type(matricula) != str:
matricula = str(matricula)
# PSSS = Plano de Seguridade Social do Servidor Público
substituicao_membros = format_value(row[2]) # Substituição de Membros
funcao_substituicao = format_value(row[3]) # Função de Substituição
grat_encargo_curso = format_value(row[4]) # Gratificação por Encargo de Curso
insalubridade = format_value(row[5]) # Adicional de Insalubridade
grat_encargo_concurso = format_value(
row[6]
) # Gratificação por Encargo de Concurso
periculosidade = format_value(row[7]) # Periculosidade
exercicio_cumulativo_sem_psss = format_value(
row[8]
) # Gratificação de Exercício Cumulativo com Ofício Sem Psss
exercicio_cumulativo_com_psss = format_value(
row[9]
) # Gratificação Exercício Cumulativo com Ofício Com Psss
membros_substituicao = format_value(row[10]) # Membros Substituição
hora_extra_sem_pass = format_value(row[11]) # Hora Extra Sem Psss
adic_noturno_sem_pass = format_value(row[12]) # Adicional Noturno Sem Psss
subs_membros_ms2013 = format_value(row[13]) # Substituição Membros MS2013
adic_penosidade = format_value(row[14]) # Adicional Penosidade
total_temporario = (
substituicao_membros
+ funcao_substituicao
+ grat_encargo_curso
+ insalubridade
+ grat_encargo_concurso
+ periculosidade
+ exercicio_cumulativo_sem_psss
+ exercicio_cumulativo_com_psss
+ membros_substituicao
+ hora_extra_sem_pass
+ adic_noturno_sem_pass
+ subs_membros_ms2013
+ adic_penosidade
)
emp = employees[matricula]
emp["income"].update(
{
"total": round(emp["income"]["total"] + total_temporario, 2),
}
)
emp["income"]["other"].update(
{
"others_total": round(
emp["income"]["other"]["others_total"] + total_temporario, 2
),
"total": round(emp["income"]["other"]["total"] + total_temporario, 2),
}
)
emp["income"]["other"]["others"].update(
{
"Substituição de Membros": substituicao_membros,
"Função de Substituição": funcao_substituicao,
"Gratificação por Encargo de Curso": grat_encargo_curso,
"Adicional de Insalubridade": insalubridade,
"Gratificação por Encargo de Concurso": grat_encargo_concurso,
"Adicional de Periculosidade": periculosidade,
"Gratificação de Exercício Cumulativo com Ofício Sem Psss": exercicio_cumulativo_sem_psss,
"Gratificação Exercício Cumulativo com Ofício Com Psss": exercicio_cumulativo_com_psss,
"Membros Substituição": membros_substituicao,
"Hora Extra Sem Psss": hora_extra_sem_pass,
"Adicional Noturno Sem Psss": adic_noturno_sem_pass,
"Substituição Membros MS2013": subs_membros_ms2013,
"Adicional Penosidade": adic_penosidade,
}
)
employees[matricula] = emp
curr_row += 1
if curr_row > end_row:
break
return employees
def parse(file_names):
employees = {}
for fn in file_names:
if "Verbas Indenizatorias" not in fn and "Verbas Temporarias" not in fn:
# Puts all parsed employees in the big map
employees.update(parse_employees(fn))
try:
for fn in file_names:
if "Verbas Indenizatorias" in fn:
update_employee_indemnity(fn, employees)
elif "Verbas Temporarias" in fn:
update_employee_temporary_remuneration(fn, employees)
except KeyError as e:
sys.stderr.write(
"Registro inválido ao processar verbas indenizatórias: {}".format(e)
)
os._exit(1)
return list(employees.values()) | 0.338186 | 0.371878 |
import torch
import torchmetrics
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from . import backbone
from siamese.trainer.loss import ContrastiveLoss
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torchmetrics
import torch.optim as optim
class SiameseTask(pl.LightningModule):
def __init__(self, feature_extractor: nn.Module):
super().__init__()
self.feature_extractor = feature_extractor
self.criterion = ContrastiveLoss()
self.trn_loss: torchmetrics.AverageMeter = torchmetrics.AverageMeter()
self.val_loss: torchmetrics.AverageMeter = torchmetrics.AverageMeter()
def _forward_once(self, x: torch.Tensor):
return self.feature_extractor(x)
def _forward(self, x: torch.Tensor, y:torch.Tensor):
# x,y = data
main = self._forward_once(x)
comp = self._forward_once(y)
return main, comp
def backward(self, loss, optimizer, optimizer_idx):
loss.backward()
def shared_step(self, batch, batch_idx):
main_imgs, comp_imgs, labels = batch
main_feature, comp_feature = self._forward(main_imgs, comp_imgs)
loss = self.criterion(main_feature, comp_feature, labels)
return loss
def training_step(self, batch, batch_idx):
loss = self.shared_step(batch, batch_idx)
trn_loss = self.trn_loss(loss)
self.log('trn_step_loss', trn_loss, prog_bar=True, logger=True)
return loss
def training_epoch_end(self, outs):
self.log('trn_epoch_loss', self.trn_loss.compute(), logger=True)
def validation_step(self, batch, batch_idx):
loss = self.shared_step(batch, batch_idx)
val_loss = self.val_loss(loss)
self.log('val_step_loss', val_loss, prog_bar=True, logger=True)
return loss
def validation_epoch_end(self, outs):
self.log('val_epoch_loss', self.val_loss.compute(), logger=True)
def configure_optimizers(self):
return optim.RMSprop(self.parameters(), lr=0.0001, alpha=0.99, eps=1e-8, weight_decay=0.0005, momentum=0.9)
def siamese_net(pretrained=True, backbone_name="mobilenetv2", encoder_digit=64, **kwargs):
if backbone_name.startswith("resnet"):
version = int(backbone_name.split('resnet')[-1])
backbone_model = backbone.resnet_backbone(pretrained_backbone=pretrained,
encoder_digit=encoder_digit,
version=version, **kwargs)
elif backbone_name=="siamese":
backbone_model = backbone.siamese_backbone(encoder_digit=encoder_digit)
else:
backbone_model = backbone.mobilenetv2_backbone(pretrained_backbone=pretrained,
encoder_digit=encoder_digit, **kwargs)
siamese_net = SiameseTask(feature_extractor=backbone_model)
return siamese_net
if __name__=="__main__":
module = siamese_net(backbone_name='siamese') | siamese/modules/model.py | import torch
import torchmetrics
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from . import backbone
from siamese.trainer.loss import ContrastiveLoss
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torchmetrics
import torch.optim as optim
class SiameseTask(pl.LightningModule):
def __init__(self, feature_extractor: nn.Module):
super().__init__()
self.feature_extractor = feature_extractor
self.criterion = ContrastiveLoss()
self.trn_loss: torchmetrics.AverageMeter = torchmetrics.AverageMeter()
self.val_loss: torchmetrics.AverageMeter = torchmetrics.AverageMeter()
def _forward_once(self, x: torch.Tensor):
return self.feature_extractor(x)
def _forward(self, x: torch.Tensor, y:torch.Tensor):
# x,y = data
main = self._forward_once(x)
comp = self._forward_once(y)
return main, comp
def backward(self, loss, optimizer, optimizer_idx):
loss.backward()
def shared_step(self, batch, batch_idx):
main_imgs, comp_imgs, labels = batch
main_feature, comp_feature = self._forward(main_imgs, comp_imgs)
loss = self.criterion(main_feature, comp_feature, labels)
return loss
def training_step(self, batch, batch_idx):
loss = self.shared_step(batch, batch_idx)
trn_loss = self.trn_loss(loss)
self.log('trn_step_loss', trn_loss, prog_bar=True, logger=True)
return loss
def training_epoch_end(self, outs):
self.log('trn_epoch_loss', self.trn_loss.compute(), logger=True)
def validation_step(self, batch, batch_idx):
loss = self.shared_step(batch, batch_idx)
val_loss = self.val_loss(loss)
self.log('val_step_loss', val_loss, prog_bar=True, logger=True)
return loss
def validation_epoch_end(self, outs):
self.log('val_epoch_loss', self.val_loss.compute(), logger=True)
def configure_optimizers(self):
return optim.RMSprop(self.parameters(), lr=0.0001, alpha=0.99, eps=1e-8, weight_decay=0.0005, momentum=0.9)
def siamese_net(pretrained=True, backbone_name="mobilenetv2", encoder_digit=64, **kwargs):
if backbone_name.startswith("resnet"):
version = int(backbone_name.split('resnet')[-1])
backbone_model = backbone.resnet_backbone(pretrained_backbone=pretrained,
encoder_digit=encoder_digit,
version=version, **kwargs)
elif backbone_name=="siamese":
backbone_model = backbone.siamese_backbone(encoder_digit=encoder_digit)
else:
backbone_model = backbone.mobilenetv2_backbone(pretrained_backbone=pretrained,
encoder_digit=encoder_digit, **kwargs)
siamese_net = SiameseTask(feature_extractor=backbone_model)
return siamese_net
if __name__=="__main__":
module = siamese_net(backbone_name='siamese') | 0.880328 | 0.386242 |
from __future__ import annotations
import numpy as np
import pandas as pd
from sklearn import datasets
from IMLearn.metrics import mean_square_error
from IMLearn.utils import split_train_test
from IMLearn.model_selection import cross_validate
from IMLearn.learners.regressors import PolynomialFitting, LinearRegression, RidgeRegression
from sklearn.linear_model import Lasso
from utils import *
import plotly.graph_objects as go
from plotly.subplots import make_subplots
pio.renderers.default = "browser"
def select_polynomial_degree(n_samples: int = 100, noise: float = 5):
"""
Simulate data from a polynomial model and use cross-validation to select the best fitting degree
Parameters
----------
n_samples: int, default=100
Number of samples to generate
noise: float, default = 5
Noise level to simulate in responses
"""
# Question 1 - Generate dataset for model f(x)=(x+3)(x+2)(x+1)(x-1)(x-2) + eps for eps Gaussian noise
# and split into training- and testing portions
X = np.linspace(-1.2, 2, n_samples)
eps = np.random.normal(0, noise, n_samples)
noiseless_y = (X + 3) * (X + 2) * (X + 1) * (X - 1) * (X - 2)
y = noiseless_y + eps
train_X, train_y, test_X, test_y = split_train_test(pd.DataFrame(X), pd.Series(y), 2 / 3)
fig = go.Figure([go.Scatter(x=np.concatenate(np.asarray(train_X)), y=np.asarray(train_y), mode='markers',
marker=dict(color='red'),
name='Train set'),
go.Scatter(x=np.concatenate(np.asarray(test_X)), y=np.asarray(test_y), mode='markers',
marker=dict(color='blue'),
name='Test set'),
go.Scatter(x=X, y=noiseless_y, mode='lines+markers', marker=dict(color='black'), name='True model')])
fig.update_layout(title=f'Question 1 - Dataset of model f(x) of size {n_samples} with noise {noise}')
fig.show()
# Question 2 - Perform CV for polynomial fitting with degrees 0,1,...,10
train_avg_lst = []
val_avg_lst = []
x = np.linspace(0, 10, 11)
for k in range(11):
train_avg, val_avg = cross_validate(PolynomialFitting(k), np.asarray(train_X), np.asarray(train_y), mean_square_error)
train_avg_lst.append(train_avg)
val_avg_lst.append(val_avg)
fig2 = go.Figure([go.Scatter(x=x, y=train_avg_lst, mode='lines+markers',
marker=dict(color='red'),
name='Average train error'),
go.Scatter(x=x, y=val_avg_lst, mode='lines+markers',
marker=dict(color='blue'),
name='Average validation error')])
fig2.update_layout(title=f'Question 2 - Cross Validation with Polyfit of sizes 0-10 of size {n_samples} with noise {noise}')
fig2.show()
# Question 3 - Using best value of k, fit a k-degree polynomial model and report test error
min_k = np.argmin(val_avg_lst, axis=0)
poly = PolynomialFitting(min_k)
poly.fit(np.concatenate(np.asarray(train_X)), np.asarray(train_y))
y_pred = poly.predict(np.concatenate(np.asarray(test_X)))
error = mean_square_error(np.asarray(test_y), y_pred)
print(f'Question 3 - Polynomial degree with lowest validation error: {min_k}, with test error: {np.round(error, 2)}'
f' of size {n_samples} with noise {noise}')
def select_regularization_parameter(n_samples: int = 50, n_evaluations: int = 500):
"""
Using sklearn's diabetes dataset use cross-validation to select the best fitting regularization parameter
values for Ridge and Lasso regressions
Parameters
----------
n_samples: int, default=50
Number of samples to generate
n_evaluations: int, default = 500
Number of regularization parameter values to evaluate for each of the algorithms
"""
# Question 6 - Load diabetes dataset and split into training and testing portions
data_X, data_y = datasets.load_diabetes(return_X_y=True)
train_X, train_y, test_X, test_y = split_train_test(pd.DataFrame(data_X), pd.Series(data_y), n_samples / data_y.size)
# Question 7 - Perform CV for different values of the regularization parameter for Ridge and Lasso regressions
lambdas = 10 ** np.linspace(-4, 0.5, n_evaluations)
ridge_scores = []
lasso_scores = []
ridge_vals = []
lasso_vals = []
for lam in lambdas:
train_avg_ridge, val_avg_ridge = cross_validate(RidgeRegression(lam), np.asarray(train_X), np.asarray(train_y), mean_square_error)
train_avg_lasso, val_avg_lasso = cross_validate(Lasso(lam, max_iter=10000), np.asarray(train_X), np.asarray(train_y), mean_square_error)
ridge_scores.append(train_avg_ridge)
ridge_vals.append(val_avg_ridge)
lasso_scores.append(train_avg_lasso)
lasso_vals.append(val_avg_lasso)
fig = go.Figure([go.Scatter(x=lambdas,
y=ridge_scores, mode='markers',
marker=dict(color='red'),
name='Ridge train error'),
go.Scatter(x=lambdas,
y=ridge_vals, mode='markers',
marker=dict(color='orange'),
name='Ridge validation error'),
go.Scatter(x=lambdas,
y=lasso_scores, mode='markers',
marker=dict(color='purple'),
name='Lasso train error'),
go.Scatter(x=lambdas,
y=lasso_vals, mode='markers',
marker=dict(color='blue'),
name='Lasso validation error')])
fig.update_layout(title='Question 7 - Train and validation error as a funcion of different regularization parameter')
fig.show()
# Question 8 - Compare best Ridge model, best Lasso model and Least Squares model
min_lam_ridge = lambdas[np.argmin(ridge_vals)]
min_lam_lasso = lambdas[np.argmin(lasso_vals)]
train_X = np.asarray(train_X)
train_y = np.asarray(train_y)
test_X = np.asarray(test_X)
test_y = np.asarray(test_y)
ridge = RidgeRegression(min_lam_ridge)
ridge.fit(train_X, train_y)
ridge_loss = ridge.loss(test_X, test_y)
lasso = Lasso(min_lam_lasso)
lasso.fit(train_X, train_y)
y_pred_lasso = lasso.predict(test_X)
lasso_loss = mean_square_error(test_y, y_pred_lasso)
lin = LinearRegression()
lin.fit(train_X, train_y)
lin_loss = lin.loss(test_X, test_y)
print('Question 8 -')
print(f'Regularization parameter with lowest validation error for Ridge: {np.round(min_lam_ridge, 3)}, with error: {np.round(ridge_loss, 2)}')
print(f'Regularization parameter with lowest validation error for Lasso: {np.round(min_lam_lasso, 3)}, with error: {np.round(lasso_loss, 2)}')
print(f'Error for LS: {np.round(lin_loss, 2)}')
if __name__ == '__main__':
np.random.seed(0)
np.warnings.filterwarnings('ignore',
category=np.VisibleDeprecationWarning)
select_polynomial_degree()
select_polynomial_degree(noise=0)
select_polynomial_degree(n_samples=1500, noise=10)
select_regularization_parameter() | exercises/perform_model_selection.py | from __future__ import annotations
import numpy as np
import pandas as pd
from sklearn import datasets
from IMLearn.metrics import mean_square_error
from IMLearn.utils import split_train_test
from IMLearn.model_selection import cross_validate
from IMLearn.learners.regressors import PolynomialFitting, LinearRegression, RidgeRegression
from sklearn.linear_model import Lasso
from utils import *
import plotly.graph_objects as go
from plotly.subplots import make_subplots
pio.renderers.default = "browser"
def select_polynomial_degree(n_samples: int = 100, noise: float = 5):
"""
Simulate data from a polynomial model and use cross-validation to select the best fitting degree
Parameters
----------
n_samples: int, default=100
Number of samples to generate
noise: float, default = 5
Noise level to simulate in responses
"""
# Question 1 - Generate dataset for model f(x)=(x+3)(x+2)(x+1)(x-1)(x-2) + eps for eps Gaussian noise
# and split into training- and testing portions
X = np.linspace(-1.2, 2, n_samples)
eps = np.random.normal(0, noise, n_samples)
noiseless_y = (X + 3) * (X + 2) * (X + 1) * (X - 1) * (X - 2)
y = noiseless_y + eps
train_X, train_y, test_X, test_y = split_train_test(pd.DataFrame(X), pd.Series(y), 2 / 3)
fig = go.Figure([go.Scatter(x=np.concatenate(np.asarray(train_X)), y=np.asarray(train_y), mode='markers',
marker=dict(color='red'),
name='Train set'),
go.Scatter(x=np.concatenate(np.asarray(test_X)), y=np.asarray(test_y), mode='markers',
marker=dict(color='blue'),
name='Test set'),
go.Scatter(x=X, y=noiseless_y, mode='lines+markers', marker=dict(color='black'), name='True model')])
fig.update_layout(title=f'Question 1 - Dataset of model f(x) of size {n_samples} with noise {noise}')
fig.show()
# Question 2 - Perform CV for polynomial fitting with degrees 0,1,...,10
train_avg_lst = []
val_avg_lst = []
x = np.linspace(0, 10, 11)
for k in range(11):
train_avg, val_avg = cross_validate(PolynomialFitting(k), np.asarray(train_X), np.asarray(train_y), mean_square_error)
train_avg_lst.append(train_avg)
val_avg_lst.append(val_avg)
fig2 = go.Figure([go.Scatter(x=x, y=train_avg_lst, mode='lines+markers',
marker=dict(color='red'),
name='Average train error'),
go.Scatter(x=x, y=val_avg_lst, mode='lines+markers',
marker=dict(color='blue'),
name='Average validation error')])
fig2.update_layout(title=f'Question 2 - Cross Validation with Polyfit of sizes 0-10 of size {n_samples} with noise {noise}')
fig2.show()
# Question 3 - Using best value of k, fit a k-degree polynomial model and report test error
min_k = np.argmin(val_avg_lst, axis=0)
poly = PolynomialFitting(min_k)
poly.fit(np.concatenate(np.asarray(train_X)), np.asarray(train_y))
y_pred = poly.predict(np.concatenate(np.asarray(test_X)))
error = mean_square_error(np.asarray(test_y), y_pred)
print(f'Question 3 - Polynomial degree with lowest validation error: {min_k}, with test error: {np.round(error, 2)}'
f' of size {n_samples} with noise {noise}')
def select_regularization_parameter(n_samples: int = 50, n_evaluations: int = 500):
"""
Using sklearn's diabetes dataset use cross-validation to select the best fitting regularization parameter
values for Ridge and Lasso regressions
Parameters
----------
n_samples: int, default=50
Number of samples to generate
n_evaluations: int, default = 500
Number of regularization parameter values to evaluate for each of the algorithms
"""
# Question 6 - Load diabetes dataset and split into training and testing portions
data_X, data_y = datasets.load_diabetes(return_X_y=True)
train_X, train_y, test_X, test_y = split_train_test(pd.DataFrame(data_X), pd.Series(data_y), n_samples / data_y.size)
# Question 7 - Perform CV for different values of the regularization parameter for Ridge and Lasso regressions
lambdas = 10 ** np.linspace(-4, 0.5, n_evaluations)
ridge_scores = []
lasso_scores = []
ridge_vals = []
lasso_vals = []
for lam in lambdas:
train_avg_ridge, val_avg_ridge = cross_validate(RidgeRegression(lam), np.asarray(train_X), np.asarray(train_y), mean_square_error)
train_avg_lasso, val_avg_lasso = cross_validate(Lasso(lam, max_iter=10000), np.asarray(train_X), np.asarray(train_y), mean_square_error)
ridge_scores.append(train_avg_ridge)
ridge_vals.append(val_avg_ridge)
lasso_scores.append(train_avg_lasso)
lasso_vals.append(val_avg_lasso)
fig = go.Figure([go.Scatter(x=lambdas,
y=ridge_scores, mode='markers',
marker=dict(color='red'),
name='Ridge train error'),
go.Scatter(x=lambdas,
y=ridge_vals, mode='markers',
marker=dict(color='orange'),
name='Ridge validation error'),
go.Scatter(x=lambdas,
y=lasso_scores, mode='markers',
marker=dict(color='purple'),
name='Lasso train error'),
go.Scatter(x=lambdas,
y=lasso_vals, mode='markers',
marker=dict(color='blue'),
name='Lasso validation error')])
fig.update_layout(title='Question 7 - Train and validation error as a funcion of different regularization parameter')
fig.show()
# Question 8 - Compare best Ridge model, best Lasso model and Least Squares model
min_lam_ridge = lambdas[np.argmin(ridge_vals)]
min_lam_lasso = lambdas[np.argmin(lasso_vals)]
train_X = np.asarray(train_X)
train_y = np.asarray(train_y)
test_X = np.asarray(test_X)
test_y = np.asarray(test_y)
ridge = RidgeRegression(min_lam_ridge)
ridge.fit(train_X, train_y)
ridge_loss = ridge.loss(test_X, test_y)
lasso = Lasso(min_lam_lasso)
lasso.fit(train_X, train_y)
y_pred_lasso = lasso.predict(test_X)
lasso_loss = mean_square_error(test_y, y_pred_lasso)
lin = LinearRegression()
lin.fit(train_X, train_y)
lin_loss = lin.loss(test_X, test_y)
print('Question 8 -')
print(f'Regularization parameter with lowest validation error for Ridge: {np.round(min_lam_ridge, 3)}, with error: {np.round(ridge_loss, 2)}')
print(f'Regularization parameter with lowest validation error for Lasso: {np.round(min_lam_lasso, 3)}, with error: {np.round(lasso_loss, 2)}')
print(f'Error for LS: {np.round(lin_loss, 2)}')
if __name__ == '__main__':
np.random.seed(0)
np.warnings.filterwarnings('ignore',
category=np.VisibleDeprecationWarning)
select_polynomial_degree()
select_polynomial_degree(noise=0)
select_polynomial_degree(n_samples=1500, noise=10)
select_regularization_parameter() | 0.939429 | 0.482734 |
import math
def multiply(quaternion1, quaternion0):
x0, y0, z0, w0 = quaternion0
x1, y1, z1, w1 = quaternion1
return [ x1*w0 + y1*z0 - z1*y0 + w1*x0,
-x1*z0 + y1*w0 + z1*x0 + w1*y0,
x1*y0 - y1*x0 + z1*w0 + w1*z0,
-x1*x0 - y1*y0 - z1*z0 + w1*w0]
def conjugate(quaternion):
x, y, z, w = quaternion
return [-x, -y, -z, w]
def identity():
return [0, 0, 0, 1]
def rotate_vector(vector, quaternion):
# https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation
qvector = vector + [0]
con = conjugate(quaternion)
part1 = multiply(quaternion, qvector)
return multiply(part1, con)[:-1]
def transform(vector, transformation):
translation, rotation = transformation
rotated = rotate_vector(vector, rotation)
return [sum(v) for v in zip(rotated, translation)]
def normalize(quaternion):
x0, y0, z0, w0 = quaternion
sqr_size = x0*x0 + y0*y0 + z0*z0 + w0*w0
if abs(sqr_size - 1.0) > 0.00001:
k = math.sqrt(sqr_size)
x0, y0, z0, w0 = x0/k, y0/k, z0/k, w0/k
return [x0, y0, z0, w0]
def euler_to_quaternion(yaw, pitch, roll):
qx = math.sin(roll/2) * math.cos(pitch/2) * math.cos(yaw/2) - math.cos(roll/2) * math.sin(pitch/2) * math.sin(yaw/2)
qy = math.cos(roll/2) * math.sin(pitch/2) * math.cos(yaw/2) + math.sin(roll/2) * math.cos(pitch/2) * math.sin(yaw/2)
qz = math.cos(roll/2) * math.cos(pitch/2) * math.sin(yaw/2) - math.sin(roll/2) * math.sin(pitch/2) * math.cos(yaw/2)
qw = math.cos(roll/2) * math.cos(pitch/2) * math.cos(yaw/2) + math.sin(roll/2) * math.sin(pitch/2) * math.sin(yaw/2)
return [qx, qy, qz, qw]
def euler_zyx(quaternion):
x0, y0, z0, w0 = normalize(quaternion)
ax = math.atan2(2*(w0*x0+y0*z0), 1-2*(x0*x0+y0*y0))
ay = math.asin(max(-1, min(1, 2*(w0*y0-z0*x0)))) # cliping the range because of posible small overflows
az = math.atan2(2*(w0*z0+x0*y0), 1-2*(y0*y0+z0*z0))
return [az, ay, ax]
def heading(quaternion):
x0, y0, z0, w0 = quaternion
az = math.atan2(2*(w0*z0+x0*y0), 1-2*(y0*y0+z0*z0))
return az
def from_axis_angle(axis, angle):
ax, ay, az = axis
qx = ax * math.sin(angle / 2)
qy = ay * math.sin(angle / 2)
qz = az * math.sin(angle / 2)
qw = math.cos(angle / 2)
return [qx, qy, qz, qw]
def rotation_matrix(quaternion):
qx, qy, qz, qw = quaternion
r1 = [1 - 2*qy**2 - 2*qz**2, 2*qx*qy - 2*qz*qw, 2*qx*qz + 2*qy*qw]
r2 = [2*qx*qy + 2*qz*qw, 1 - 2*qx**2 - 2*qz**2, 2*qy*qz - 2*qx*qw]
r3 = [2*qx*qz - 2*qy*qw, 2*qy*qz + 2*qx*qw, 1 - 2*qx**2 - 2*qy**2]
return [r1, r2, r3]
def from_rotation_matrix(rotation_matrix):
# http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToQuaternion/index.htm
# https://en.wikipedia.org/wiki/Rotation_matrix#Quaternion
m00, m01, m02 = rotation_matrix[0]
m10, m11, m12 = rotation_matrix[1]
m20, m21, m22 = rotation_matrix[2]
qw = math.sqrt(max(0, 1 + m00 + m11 + m22)) / 2
qx = math.sqrt(max(0, 1 + m00 - m11 - m22)) / 2
qy = math.sqrt(max(0, 1 - m00 + m11 - m22)) / 2
qz = math.sqrt(max(0, 1 - m00 - m11 + m22)) / 2
qx = math.copysign(qx, m21 - m12)
qy = math.copysign(qy, m02 - m20)
qz = math.copysign(qz, m10 - m01)
return [qx, qy, qz, qw]
def angle_between(quaternion0, quaternion1):
# https://math.stackexchange.com/questions/90081/quaternion-distance
inner_product = sum(a * b for (a, b) in zip(quaternion0, quaternion1))
val = 2 * inner_product**2 - 1
return math.acos(max(-1.0, min(1.0, val)))
# vim: expandtab sw=4 ts=4 | osgar/lib/quaternion.py | import math
def multiply(quaternion1, quaternion0):
x0, y0, z0, w0 = quaternion0
x1, y1, z1, w1 = quaternion1
return [ x1*w0 + y1*z0 - z1*y0 + w1*x0,
-x1*z0 + y1*w0 + z1*x0 + w1*y0,
x1*y0 - y1*x0 + z1*w0 + w1*z0,
-x1*x0 - y1*y0 - z1*z0 + w1*w0]
def conjugate(quaternion):
x, y, z, w = quaternion
return [-x, -y, -z, w]
def identity():
return [0, 0, 0, 1]
def rotate_vector(vector, quaternion):
# https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation
qvector = vector + [0]
con = conjugate(quaternion)
part1 = multiply(quaternion, qvector)
return multiply(part1, con)[:-1]
def transform(vector, transformation):
translation, rotation = transformation
rotated = rotate_vector(vector, rotation)
return [sum(v) for v in zip(rotated, translation)]
def normalize(quaternion):
x0, y0, z0, w0 = quaternion
sqr_size = x0*x0 + y0*y0 + z0*z0 + w0*w0
if abs(sqr_size - 1.0) > 0.00001:
k = math.sqrt(sqr_size)
x0, y0, z0, w0 = x0/k, y0/k, z0/k, w0/k
return [x0, y0, z0, w0]
def euler_to_quaternion(yaw, pitch, roll):
qx = math.sin(roll/2) * math.cos(pitch/2) * math.cos(yaw/2) - math.cos(roll/2) * math.sin(pitch/2) * math.sin(yaw/2)
qy = math.cos(roll/2) * math.sin(pitch/2) * math.cos(yaw/2) + math.sin(roll/2) * math.cos(pitch/2) * math.sin(yaw/2)
qz = math.cos(roll/2) * math.cos(pitch/2) * math.sin(yaw/2) - math.sin(roll/2) * math.sin(pitch/2) * math.cos(yaw/2)
qw = math.cos(roll/2) * math.cos(pitch/2) * math.cos(yaw/2) + math.sin(roll/2) * math.sin(pitch/2) * math.sin(yaw/2)
return [qx, qy, qz, qw]
def euler_zyx(quaternion):
x0, y0, z0, w0 = normalize(quaternion)
ax = math.atan2(2*(w0*x0+y0*z0), 1-2*(x0*x0+y0*y0))
ay = math.asin(max(-1, min(1, 2*(w0*y0-z0*x0)))) # cliping the range because of posible small overflows
az = math.atan2(2*(w0*z0+x0*y0), 1-2*(y0*y0+z0*z0))
return [az, ay, ax]
def heading(quaternion):
x0, y0, z0, w0 = quaternion
az = math.atan2(2*(w0*z0+x0*y0), 1-2*(y0*y0+z0*z0))
return az
def from_axis_angle(axis, angle):
ax, ay, az = axis
qx = ax * math.sin(angle / 2)
qy = ay * math.sin(angle / 2)
qz = az * math.sin(angle / 2)
qw = math.cos(angle / 2)
return [qx, qy, qz, qw]
def rotation_matrix(quaternion):
qx, qy, qz, qw = quaternion
r1 = [1 - 2*qy**2 - 2*qz**2, 2*qx*qy - 2*qz*qw, 2*qx*qz + 2*qy*qw]
r2 = [2*qx*qy + 2*qz*qw, 1 - 2*qx**2 - 2*qz**2, 2*qy*qz - 2*qx*qw]
r3 = [2*qx*qz - 2*qy*qw, 2*qy*qz + 2*qx*qw, 1 - 2*qx**2 - 2*qy**2]
return [r1, r2, r3]
def from_rotation_matrix(rotation_matrix):
# http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToQuaternion/index.htm
# https://en.wikipedia.org/wiki/Rotation_matrix#Quaternion
m00, m01, m02 = rotation_matrix[0]
m10, m11, m12 = rotation_matrix[1]
m20, m21, m22 = rotation_matrix[2]
qw = math.sqrt(max(0, 1 + m00 + m11 + m22)) / 2
qx = math.sqrt(max(0, 1 + m00 - m11 - m22)) / 2
qy = math.sqrt(max(0, 1 - m00 + m11 - m22)) / 2
qz = math.sqrt(max(0, 1 - m00 - m11 + m22)) / 2
qx = math.copysign(qx, m21 - m12)
qy = math.copysign(qy, m02 - m20)
qz = math.copysign(qz, m10 - m01)
return [qx, qy, qz, qw]
def angle_between(quaternion0, quaternion1):
# https://math.stackexchange.com/questions/90081/quaternion-distance
inner_product = sum(a * b for (a, b) in zip(quaternion0, quaternion1))
val = 2 * inner_product**2 - 1
return math.acos(max(-1.0, min(1.0, val)))
# vim: expandtab sw=4 ts=4 | 0.785391 | 0.66939 |
from abc import abstractmethod, ABC
from typing import Optional, Sequence, Hashable, Any, Union, Iterable, overload, Dict
from coba.utilities import HashableDict
from coba.pipes import Source
Action = Union[Hashable, HashableDict]
Context = Union[None, Hashable, HashableDict]
class SimulatedInteraction:
"""A class to contain all data needed to represent an interaction in a simulated bandit interaction."""
@overload
def __init__(self,
context: Context,
actions: Sequence[Action],
*,
rewards: Sequence[float],
**kwargs: Sequence[Any]) -> None:
...
"""Instantiate Interaction.
Args
context : Features describing the interaction's context. This should be `None` for multi-armed bandit simulations.
actions : Features describing available actions in the interaction.
rewards : The reward that will be revealed to learners based on the taken action. We require len(rewards) == len(actions).
**kwargs: Additional information that should be recorded in the interactions table of an experiment result. If any
data is a sequence with length equal to actions only the data at the selected action index will be recorded.
"""
@overload
def __init__(self,
context: Context,
actions: Sequence[Action],
*,
reveals: Sequence[Any],
**kwargs: Sequence[Any]) -> None:
...
"""Instantiate Interaction.
Args
context : Features describing the interaction's context. Will be `None` for multi-armed bandit simulations.
actions : Features describing available actions in the interaction.
reveals : The data that will be revealed to learners based on the selected action. We require len(reveals) == len(actions).
When working with non-scalar data use "reveals" instead of "rewards" to make it clear to Coba the data is non-scalar.
**kwargs: Additional information that should be recorded in the interactions table of an experiment result. If any
data is a sequence with length equal to actions only the data at the selected action index will be recorded.
"""
@overload
def __init__(self,
context: Context,
actions: Sequence[Action],
*,
rewards : Sequence[float],
reveals : Sequence[Any],
**kwargs: Sequence[Any]) -> None:
...
"""Instantiate Interaction.
Args
context : Features describing the interaction's context. Will be `None` for multi-armed bandit simulations.
actions : Features describing available actions in the interaction.
rewards : A sequence of scalar values representing reward. When both rewards and reveals are provided only
reveals will be shown to the learner when an action is selected. The reward values will only be used
by Coba when plotting experimental results. We require that len(rewards) == len(actions).
reveals : The data that will be revealed to learners based on the selected action. We require len(reveals) == len(actions).
When working with non-scalar data use "reveals" instead of "rewards" to make it clear to Coba the data is non-scalar.
**kwargs: Additional information that should be recorded in the interactions table of an experiment result. If any
data is a sequence with length equal to actions only the data at the selected action index will be recorded.
"""
def __init__(self, *args, **kwargs) -> None:
assert len(args) == 2, "An unexpected number of positional arguments was supplied to Interaction."
assert kwargs.keys() & {"rewards", "reveals"}, "Interaction requires either a rewards or reveals keyword warg."
assert "rewards" not in kwargs or len(args[1]) == len(kwargs["rewards"]), "Interaction rewards must match action length."
assert "reveals" not in kwargs or len(args[1]) == len(kwargs["reveals"]), "Interaction reveals must match action length."
self._context = self._hashable(args[0])
self._actions = [ self._hashable(action) for action in args[1] ]
self._kwargs = kwargs
def _hashable(self, feats):
if isinstance(feats, dict):
return HashableDict(feats)
if isinstance(feats,list):
return tuple(feats)
return feats
@property
def context(self) -> Context:
"""The interaction's context description."""
return self._context
@property
def actions(self) -> Sequence[Action]:
"""The interaction's available actions."""
return self._actions
@property
def kwargs(self) -> Dict[str,Any]:
return self._kwargs
class LoggedInteraction:
def __init__(self, context: Context, action: Action, reward: float, actions: Sequence[Action] = None, probability: float = None) -> None:
self._context = context
self._action = action
self._reward = reward
self._actions = actions
self._probability = probability
@property
def context(self) -> Context:
"""The context in which an action was taken."""
return self._context
@property
def action(self) -> Action:
"""The action that was taken."""
return self._context
@property
def reward(self) -> float:
"""The reward that was observed when the action was taken."""
return self._reward
@property
def actions(self) -> Sequence[Action]:
"""The actions that were available to take."""
return self._actions
@property
def probability(self) -> Optional[Sequence[float]]:
"""The probability that the given action was taken."""
return self._probability
class SimulatedEnvironment(Source[Iterable[SimulatedInteraction]], ABC):
"""The interface for a simulated environment."""
@property
@abstractmethod
def params(self) -> Dict[str,Any]:
"""Paramaters describing the simulation.
Remarks:
These will be simulation columns in coba.benchmark.Result.
"""
...
@abstractmethod
def read(self) -> Iterable[SimulatedInteraction]:
"""The sequence of interactions in a simulation.
Remarks:
This function should always be "re-iterable".
"""
...
class LoggedEnvironment(Source[Iterable[LoggedInteraction]], ABC):
"""The simulation interface."""
@property
@abstractmethod
def params(self) -> Dict[str,Any]:
"""Paramaters describing the simulation.
Remarks:
These will be simulation columns in coba.benchmark.Result.
"""
...
@abstractmethod
def read(self) -> Iterable[SimulatedInteraction]:
"""The sequence of interactions in a simulation.
Remarks:
This function should always be "re-iterable".
"""
...
class WarmStartEnvironment(Source[Iterable[Union[LoggedInteraction, SimulatedInteraction]]], ABC):
"""
TODO: docs
"""
@property
@abstractmethod
def params(self) -> Dict[str,Any]:
"""Paramaters describing the simulation.
Remarks:
These will be simulation columns in coba.benchmark.Result.
"""
...
@abstractmethod
def read(self) -> Iterable[SimulatedInteraction]:
"""The sequence of interactions in a simulation.
Remarks:
This function should always be "re-iterable".
"""
... | coba/environments/core.py | from abc import abstractmethod, ABC
from typing import Optional, Sequence, Hashable, Any, Union, Iterable, overload, Dict
from coba.utilities import HashableDict
from coba.pipes import Source
Action = Union[Hashable, HashableDict]
Context = Union[None, Hashable, HashableDict]
class SimulatedInteraction:
"""A class to contain all data needed to represent an interaction in a simulated bandit interaction."""
@overload
def __init__(self,
context: Context,
actions: Sequence[Action],
*,
rewards: Sequence[float],
**kwargs: Sequence[Any]) -> None:
...
"""Instantiate Interaction.
Args
context : Features describing the interaction's context. This should be `None` for multi-armed bandit simulations.
actions : Features describing available actions in the interaction.
rewards : The reward that will be revealed to learners based on the taken action. We require len(rewards) == len(actions).
**kwargs: Additional information that should be recorded in the interactions table of an experiment result. If any
data is a sequence with length equal to actions only the data at the selected action index will be recorded.
"""
@overload
def __init__(self,
context: Context,
actions: Sequence[Action],
*,
reveals: Sequence[Any],
**kwargs: Sequence[Any]) -> None:
...
"""Instantiate Interaction.
Args
context : Features describing the interaction's context. Will be `None` for multi-armed bandit simulations.
actions : Features describing available actions in the interaction.
reveals : The data that will be revealed to learners based on the selected action. We require len(reveals) == len(actions).
When working with non-scalar data use "reveals" instead of "rewards" to make it clear to Coba the data is non-scalar.
**kwargs: Additional information that should be recorded in the interactions table of an experiment result. If any
data is a sequence with length equal to actions only the data at the selected action index will be recorded.
"""
@overload
def __init__(self,
context: Context,
actions: Sequence[Action],
*,
rewards : Sequence[float],
reveals : Sequence[Any],
**kwargs: Sequence[Any]) -> None:
...
"""Instantiate Interaction.
Args
context : Features describing the interaction's context. Will be `None` for multi-armed bandit simulations.
actions : Features describing available actions in the interaction.
rewards : A sequence of scalar values representing reward. When both rewards and reveals are provided only
reveals will be shown to the learner when an action is selected. The reward values will only be used
by Coba when plotting experimental results. We require that len(rewards) == len(actions).
reveals : The data that will be revealed to learners based on the selected action. We require len(reveals) == len(actions).
When working with non-scalar data use "reveals" instead of "rewards" to make it clear to Coba the data is non-scalar.
**kwargs: Additional information that should be recorded in the interactions table of an experiment result. If any
data is a sequence with length equal to actions only the data at the selected action index will be recorded.
"""
def __init__(self, *args, **kwargs) -> None:
assert len(args) == 2, "An unexpected number of positional arguments was supplied to Interaction."
assert kwargs.keys() & {"rewards", "reveals"}, "Interaction requires either a rewards or reveals keyword warg."
assert "rewards" not in kwargs or len(args[1]) == len(kwargs["rewards"]), "Interaction rewards must match action length."
assert "reveals" not in kwargs or len(args[1]) == len(kwargs["reveals"]), "Interaction reveals must match action length."
self._context = self._hashable(args[0])
self._actions = [ self._hashable(action) for action in args[1] ]
self._kwargs = kwargs
def _hashable(self, feats):
if isinstance(feats, dict):
return HashableDict(feats)
if isinstance(feats,list):
return tuple(feats)
return feats
@property
def context(self) -> Context:
"""The interaction's context description."""
return self._context
@property
def actions(self) -> Sequence[Action]:
"""The interaction's available actions."""
return self._actions
@property
def kwargs(self) -> Dict[str,Any]:
return self._kwargs
class LoggedInteraction:
def __init__(self, context: Context, action: Action, reward: float, actions: Sequence[Action] = None, probability: float = None) -> None:
self._context = context
self._action = action
self._reward = reward
self._actions = actions
self._probability = probability
@property
def context(self) -> Context:
"""The context in which an action was taken."""
return self._context
@property
def action(self) -> Action:
"""The action that was taken."""
return self._context
@property
def reward(self) -> float:
"""The reward that was observed when the action was taken."""
return self._reward
@property
def actions(self) -> Sequence[Action]:
"""The actions that were available to take."""
return self._actions
@property
def probability(self) -> Optional[Sequence[float]]:
"""The probability that the given action was taken."""
return self._probability
class SimulatedEnvironment(Source[Iterable[SimulatedInteraction]], ABC):
"""The interface for a simulated environment."""
@property
@abstractmethod
def params(self) -> Dict[str,Any]:
"""Paramaters describing the simulation.
Remarks:
These will be simulation columns in coba.benchmark.Result.
"""
...
@abstractmethod
def read(self) -> Iterable[SimulatedInteraction]:
"""The sequence of interactions in a simulation.
Remarks:
This function should always be "re-iterable".
"""
...
class LoggedEnvironment(Source[Iterable[LoggedInteraction]], ABC):
"""The simulation interface."""
@property
@abstractmethod
def params(self) -> Dict[str,Any]:
"""Paramaters describing the simulation.
Remarks:
These will be simulation columns in coba.benchmark.Result.
"""
...
@abstractmethod
def read(self) -> Iterable[SimulatedInteraction]:
"""The sequence of interactions in a simulation.
Remarks:
This function should always be "re-iterable".
"""
...
class WarmStartEnvironment(Source[Iterable[Union[LoggedInteraction, SimulatedInteraction]]], ABC):
"""
TODO: docs
"""
@property
@abstractmethod
def params(self) -> Dict[str,Any]:
"""Paramaters describing the simulation.
Remarks:
These will be simulation columns in coba.benchmark.Result.
"""
...
@abstractmethod
def read(self) -> Iterable[SimulatedInteraction]:
"""The sequence of interactions in a simulation.
Remarks:
This function should always be "re-iterable".
"""
... | 0.943217 | 0.721694 |
import pygame
import math
class Planet:
def __init__(self, surface, color, position, radius, center):
self.radius = radius
self.surface = surface
self.color = color
self.setPosition(position)
self.center = center
self.setOrbitOffset(0)
self.setOrbitPeriod(1)
self.setOrbitRadius(0)
self.year = 0
self.mass = 0
self.velocity = 0
self.angle = 0
self.name = ''
def drawPlanet(self):
x = int(self.position[0])
y = int(self.position[1])
pygame.draw.circle(self.surface, self.color, (x, y), self.radius)
def getRadius(self): return self.radius
def setPosition(self, newPos):
self.position = newPos
return self
def getPosition(self): return self.position
def setVelocity(self, vel):
self.velocity = vel
return self
def getVelocity(self): return self.velocity
def setAngle(self, angle):
self.angle = angle
return self
def getAngle(self): return self.angle
def setName(self, name):
self.name = name
return self
def getName(self): return self.name
def setGravity(self, gravity):
self.gravity = gravity
return self
def getGravity(self): return self.gravity
def setOrbitRadius(self, radius):
self.orbitRadius = radius
return self
def getOrbitRadius(self): return self.orbitRadius
def setOrbitOffset(self, offset):
self.orbitOffset = offset
return self
def getOrbitOffset(self): return self.orbitOffset
def setOrbitPeriod(self, period):
self.orbitPeriod = period
return self
def getOrbitPeriod(self): return self.orbitPeriod
def advancePosition(self, sun):
x, y = self.position
# get new point with no gravity
v = self.velocity
angle = self.angle
vx = v * math.sin(angle)
vy = v * math.cos(angle)
# get the pull fromt he sun
gravitaionalConstant = 14 # this is the number that made it work well
# i don't know why this number and not another
sunX, sunY = sun.getPosition()
sunX -= x
sunY -= y
d = math.sqrt(sunX**2 + sunY**2)
g = sun.getGravity() * gravitaionalConstant / (d ** 2)
ax = (g * sunX) / d
ay = (g * sunY) / d
# add these vectors together
dx = vx + ax
dy = vy + ay
newV = math.sqrt(dx**2 + dy**2)
# using law of cosines to get the angle
# by getting the cosine first, then using arccos to find the angle
ac = (g**2 - v**2 - newV**2)/(-2 * v * newV)
A = math.acos(ac)
#update attributes
self.angle += A
self.velocity = newV
x += newV * math.sin(self.angle)
y += newV * math.cos(self.angle)
self.setPosition((x, y))
return self
def distanceFrom(self, pos):
x1 = self.position[0]
y1 = self.position[1]
x2 = pos[0]
y2 = pos[1]
return math.sqrt((x1 - x2)**2 + (y1 - y2)**2)
# EOF for planets | space/planet.py | import pygame
import math
class Planet:
def __init__(self, surface, color, position, radius, center):
self.radius = radius
self.surface = surface
self.color = color
self.setPosition(position)
self.center = center
self.setOrbitOffset(0)
self.setOrbitPeriod(1)
self.setOrbitRadius(0)
self.year = 0
self.mass = 0
self.velocity = 0
self.angle = 0
self.name = ''
def drawPlanet(self):
x = int(self.position[0])
y = int(self.position[1])
pygame.draw.circle(self.surface, self.color, (x, y), self.radius)
def getRadius(self): return self.radius
def setPosition(self, newPos):
self.position = newPos
return self
def getPosition(self): return self.position
def setVelocity(self, vel):
self.velocity = vel
return self
def getVelocity(self): return self.velocity
def setAngle(self, angle):
self.angle = angle
return self
def getAngle(self): return self.angle
def setName(self, name):
self.name = name
return self
def getName(self): return self.name
def setGravity(self, gravity):
self.gravity = gravity
return self
def getGravity(self): return self.gravity
def setOrbitRadius(self, radius):
self.orbitRadius = radius
return self
def getOrbitRadius(self): return self.orbitRadius
def setOrbitOffset(self, offset):
self.orbitOffset = offset
return self
def getOrbitOffset(self): return self.orbitOffset
def setOrbitPeriod(self, period):
self.orbitPeriod = period
return self
def getOrbitPeriod(self): return self.orbitPeriod
def advancePosition(self, sun):
x, y = self.position
# get new point with no gravity
v = self.velocity
angle = self.angle
vx = v * math.sin(angle)
vy = v * math.cos(angle)
# get the pull fromt he sun
gravitaionalConstant = 14 # this is the number that made it work well
# i don't know why this number and not another
sunX, sunY = sun.getPosition()
sunX -= x
sunY -= y
d = math.sqrt(sunX**2 + sunY**2)
g = sun.getGravity() * gravitaionalConstant / (d ** 2)
ax = (g * sunX) / d
ay = (g * sunY) / d
# add these vectors together
dx = vx + ax
dy = vy + ay
newV = math.sqrt(dx**2 + dy**2)
# using law of cosines to get the angle
# by getting the cosine first, then using arccos to find the angle
ac = (g**2 - v**2 - newV**2)/(-2 * v * newV)
A = math.acos(ac)
#update attributes
self.angle += A
self.velocity = newV
x += newV * math.sin(self.angle)
y += newV * math.cos(self.angle)
self.setPosition((x, y))
return self
def distanceFrom(self, pos):
x1 = self.position[0]
y1 = self.position[1]
x2 = pos[0]
y2 = pos[1]
return math.sqrt((x1 - x2)**2 + (y1 - y2)**2)
# EOF for planets | 0.714827 | 0.409132 |
import glob
import logging
import os.path
import shutil
import signal
import sys
def patch_crypto():
# This is needed to help pyinstaller find the right backend
from cryptography.hazmat import backends
from cryptography.hazmat.backends.openssl.backend import backend as be_cc
backends._available_backends_list =[be_cc]
def copy_resources():
# pylint: disable=maybe-no-member
if not hasattr(sys, "_MEIPASS") or not getattr(sys, 'frozen', False):
return
src_dir = os.path.join(sys._MEIPASS, "public")
tgt_dir = os.path.join(os.path.dirname(sys.executable), "public")
src_cfg = os.path.join(sys._MEIPASS, "example_config.json")
tgt_cfg = os.path.join(os.path.dirname(sys.executable), "config.json")
print("Creating web directories")
shutil.copytree(src_dir, tgt_dir, dirs_exist_ok=True)
if not os.path.isfile(tgt_cfg):
print("Creating config.json")
print(src_cfg, tgt_cfg)
shutil.copy(src_cfg, tgt_cfg)
if __name__ == '__main__':
copy_resources()
# These must only be imported after the resources are in place
from ashnasbot import socket_server
from ashnasbot import config
print("Loading config")
cfg = config.Config()
try:
lvl = cfg["log_level"].upper()
log_level = getattr(logging, lvl)
print("Log level set to:", lvl)
except:
print("Log level not set, defaulting to INFO")
log_level = logging.INFO
# set up logging to file and screen
root_logger = logging.getLogger()
root_logger.setLevel(logging.DEBUG)
patch_crypto()
console = logging.StreamHandler()
console.setLevel(log_level)
formatter = logging.Formatter('%(name)-30s: %(levelname)-6s %(message)s')
console.setFormatter(formatter)
root_logger.addHandler(console)
logfile = logging.FileHandler('debug.log', 'w', 'utf-8')
logfile.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s %(name)-30s %(levelname)-6s %(message)s', "%H:%M:%S")
logfile.setFormatter(formatter)
root_logger.addHandler(logfile)
socket_thread = socket_server.SocketServer()
signal.signal(signal.SIGINT, socket_thread.stop)
# blocks forever
socket_thread.run() | __main__.py | import glob
import logging
import os.path
import shutil
import signal
import sys
def patch_crypto():
# This is needed to help pyinstaller find the right backend
from cryptography.hazmat import backends
from cryptography.hazmat.backends.openssl.backend import backend as be_cc
backends._available_backends_list =[be_cc]
def copy_resources():
# pylint: disable=maybe-no-member
if not hasattr(sys, "_MEIPASS") or not getattr(sys, 'frozen', False):
return
src_dir = os.path.join(sys._MEIPASS, "public")
tgt_dir = os.path.join(os.path.dirname(sys.executable), "public")
src_cfg = os.path.join(sys._MEIPASS, "example_config.json")
tgt_cfg = os.path.join(os.path.dirname(sys.executable), "config.json")
print("Creating web directories")
shutil.copytree(src_dir, tgt_dir, dirs_exist_ok=True)
if not os.path.isfile(tgt_cfg):
print("Creating config.json")
print(src_cfg, tgt_cfg)
shutil.copy(src_cfg, tgt_cfg)
if __name__ == '__main__':
copy_resources()
# These must only be imported after the resources are in place
from ashnasbot import socket_server
from ashnasbot import config
print("Loading config")
cfg = config.Config()
try:
lvl = cfg["log_level"].upper()
log_level = getattr(logging, lvl)
print("Log level set to:", lvl)
except:
print("Log level not set, defaulting to INFO")
log_level = logging.INFO
# set up logging to file and screen
root_logger = logging.getLogger()
root_logger.setLevel(logging.DEBUG)
patch_crypto()
console = logging.StreamHandler()
console.setLevel(log_level)
formatter = logging.Formatter('%(name)-30s: %(levelname)-6s %(message)s')
console.setFormatter(formatter)
root_logger.addHandler(console)
logfile = logging.FileHandler('debug.log', 'w', 'utf-8')
logfile.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s %(name)-30s %(levelname)-6s %(message)s', "%H:%M:%S")
logfile.setFormatter(formatter)
root_logger.addHandler(logfile)
socket_thread = socket_server.SocketServer()
signal.signal(signal.SIGINT, socket_thread.stop)
# blocks forever
socket_thread.run() | 0.259263 | 0.055311 |
from django.contrib.auth.models import User
from rest_framework import status
from rest_framework.authtoken.models import Token
from rest_framework.test import APITestCase
from users.tests import token_auth
from .models import BeverageType, Purchase
class PurchasesTest(APITestCase):
api_uri = '/api/purchases'
beverage_type_api_uri = '/api/beverage-types'
user_api_uri = '/api/users'
password: str
user1: User
user2: User
staff: User
user1_token: str
staff_token: str
purchase1: Purchase
purchase2: Purchase
beverage_type: BeverageType
@classmethod
def setUpTestData(cls) -> None:
cls.password = '<PASSWORD>'
cls.user1 = User.objects.create_user(username='erni', password=<PASSWORD>)
cls.user1.profile.bio = 'hi there'
cls.user2 = User.objects.create_user(username='ducky', password=<PASSWORD>)
cls.user2.profile.bio = 'hello'
cls.staff = User.objects.create_superuser(
username='staff', password=<PASSWORD>
)
cls.user1_token = Token.objects.get(user=cls.user1).key
cls.staff_token = Token.objects.get(user=cls.staff).key
cls.beverage_type = BeverageType.objects.create(name='coffee', price=2.22)
# Without refreshing the type will be float
cls.beverage_type.refresh_from_db()
cls.purchase1 = Purchase.objects.create(
beverage_type=cls.beverage_type, user=cls.user1
)
cls.purchase2 = Purchase.objects.create(
beverage_type=cls.beverage_type, user=cls.user2
)
@property
def purchase1_uri(self) -> str:
return f'{self.api_uri}/{self.purchase2.id}/'
@property
def purchase2_uri(self) -> str:
return f'{self.api_uri}/{self.purchase2.id}/'
@property
def beverage_type_uri(self) -> str:
return f'{self.beverage_type_api_uri}/{self.beverage_type.id}/'
@property
def user1_uri(self) -> str:
return f'{self.user_api_uri}/{self.user1.id}/'
@property
def user2_uri(self) -> str:
return f'{self.user_api_uri}/{self.user2.id}/'
def test_users_can_create_purchases_for_themselves(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.post(
f'{self.api_uri}/',
{'beverage_type': self.beverage_type_uri, 'user': self.user1_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_users_cant_create_purchases_for_others(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.post(
f'{self.api_uri}/',
{'beverage_type': self.beverage_type_uri, 'user': self.user2_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_staff_can_create_purchases_for_others(self) -> None:
with token_auth(self, self.staff_token):
response = self.client.post(
f'{self.api_uri}/',
{'beverage_type': self.beverage_type_uri, 'user': self.user2_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_users_can_list_and_retrieve_purchases(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/')
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(self.purchase1_uri)
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_users_cant_delete_or_update_purchases(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.put(
self.purchase1_uri,
{'beverage_type': self.beverage_type_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.patch(
self.purchase1_uri,
{'beverage_type': self.beverage_type_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.delete(self.purchase1_uri)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_admins_can_delete_and_update_purchases(self) -> None:
with token_auth(self, self.staff_token):
response = self.client.put(
self.purchase1_uri,
{'beverage_type': self.beverage_type_uri, 'user': self.user1_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.patch(
self.purchase1_uri,
{'beverage_type': self.beverage_type_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.delete(self.purchase1_uri)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
# Restore purchase
self.purchase1.save()
def test_purchase_creation_updates_profile_balance(self) -> None:
with token_auth(self, self.user1_token):
self.user1.refresh_from_db()
previous_balance = self.user1.profile.balance
response = self.client.post(
f'{self.api_uri}/',
{'beverage_type': self.beverage_type_uri, 'user': self.user1_uri},
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.user1.refresh_from_db()
new_balance = self.user1.profile.balance
self.assertEqual(new_balance, previous_balance - self.beverage_type.price)
def test_user_query(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/?user=1')
self.assertEqual(response.status_code, status.HTTP_200_OK)
for purchase in response.data:
self.assertIn(self.user1_uri, purchase['user'])
def test_user_query_ignores_wrong_type(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/?user=has-to-be-0-or-1')
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_beverage_type_query(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/?beverage_type=1')
self.assertEqual(response.status_code, status.HTTP_200_OK)
for purchase in response.data:
self.assertIn(self.beverage_type_uri, purchase['beverage_type'])
def test_beverage_type_query_ignores_wrong_type(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(
f'{self.api_uri}/?beverage_type=has-to-be-0-or-1'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
class BeverageTypesTest(APITestCase):
api_uri = '/api/beverage-types'
password = '<PASSWORD>'
user1: User
user2: User
staff: User
user1_token: str
staff_token: str
beverage_type1: BeverageType
@classmethod
def setUpTestData(cls) -> None:
cls.user1 = User.objects.create_user(username='erni', password=cls.password)
cls.user2 = User.objects.create_user(username='ducky', password=cls.password)
cls.staff = User.objects.create_superuser(
username='staff', password=<PASSWORD>
)
cls.user1_token = Token.objects.get(user=cls.user1).key
cls.staff_token = Token.objects.get(user=cls.staff).key
cls.beverage_type1 = BeverageType.objects.create(name='coffee', price='2.20')
cls.beverage_type2 = BeverageType.objects.create(
name='latte macchiato', price='2.40'
)
@property
def beverage_type_uri(self) -> str:
return f'{self.api_uri}/{self.beverage_type1.id}/'
def test_users_can_list_and_retrieve(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/')
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(self.beverage_type_uri)
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_users_cant_create_delete_or_update(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.post(
f'{self.api_uri}/', {'name': 'beer', 'price': '3.20'}, format='json'
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.put(
self.beverage_type_uri,
{'name': 'espresso', 'price': '3.20'},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.patch(
self.beverage_type_uri, {'price': '3.30'}, format='json'
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.delete(self.beverage_type_uri)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_staff_can_create_delete_and_update(self) -> None:
with token_auth(self, self.staff_token):
response = self.client.post(
f'{self.api_uri}/', {'name': 'beer', 'price': '3.20'}, format='json'
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client.put(
self.beverage_type_uri,
{'name': 'espresso', 'price': '3.20'},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.patch(
self.beverage_type_uri, {'price': '3.30'}, format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.delete(self.beverage_type_uri)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
# Restore beverage type
self.beverage_type1.save()
def test_name_query(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/?name=coff')
self.assertEqual(response.status_code, status.HTTP_200_OK)
for user in response.data:
self.assertIn('coff', user['name'].lower()) | purchases/tests.py | from django.contrib.auth.models import User
from rest_framework import status
from rest_framework.authtoken.models import Token
from rest_framework.test import APITestCase
from users.tests import token_auth
from .models import BeverageType, Purchase
class PurchasesTest(APITestCase):
api_uri = '/api/purchases'
beverage_type_api_uri = '/api/beverage-types'
user_api_uri = '/api/users'
password: str
user1: User
user2: User
staff: User
user1_token: str
staff_token: str
purchase1: Purchase
purchase2: Purchase
beverage_type: BeverageType
@classmethod
def setUpTestData(cls) -> None:
cls.password = '<PASSWORD>'
cls.user1 = User.objects.create_user(username='erni', password=<PASSWORD>)
cls.user1.profile.bio = 'hi there'
cls.user2 = User.objects.create_user(username='ducky', password=<PASSWORD>)
cls.user2.profile.bio = 'hello'
cls.staff = User.objects.create_superuser(
username='staff', password=<PASSWORD>
)
cls.user1_token = Token.objects.get(user=cls.user1).key
cls.staff_token = Token.objects.get(user=cls.staff).key
cls.beverage_type = BeverageType.objects.create(name='coffee', price=2.22)
# Without refreshing the type will be float
cls.beverage_type.refresh_from_db()
cls.purchase1 = Purchase.objects.create(
beverage_type=cls.beverage_type, user=cls.user1
)
cls.purchase2 = Purchase.objects.create(
beverage_type=cls.beverage_type, user=cls.user2
)
@property
def purchase1_uri(self) -> str:
return f'{self.api_uri}/{self.purchase2.id}/'
@property
def purchase2_uri(self) -> str:
return f'{self.api_uri}/{self.purchase2.id}/'
@property
def beverage_type_uri(self) -> str:
return f'{self.beverage_type_api_uri}/{self.beverage_type.id}/'
@property
def user1_uri(self) -> str:
return f'{self.user_api_uri}/{self.user1.id}/'
@property
def user2_uri(self) -> str:
return f'{self.user_api_uri}/{self.user2.id}/'
def test_users_can_create_purchases_for_themselves(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.post(
f'{self.api_uri}/',
{'beverage_type': self.beverage_type_uri, 'user': self.user1_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_users_cant_create_purchases_for_others(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.post(
f'{self.api_uri}/',
{'beverage_type': self.beverage_type_uri, 'user': self.user2_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_staff_can_create_purchases_for_others(self) -> None:
with token_auth(self, self.staff_token):
response = self.client.post(
f'{self.api_uri}/',
{'beverage_type': self.beverage_type_uri, 'user': self.user2_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_users_can_list_and_retrieve_purchases(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/')
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(self.purchase1_uri)
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_users_cant_delete_or_update_purchases(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.put(
self.purchase1_uri,
{'beverage_type': self.beverage_type_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.patch(
self.purchase1_uri,
{'beverage_type': self.beverage_type_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.delete(self.purchase1_uri)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_admins_can_delete_and_update_purchases(self) -> None:
with token_auth(self, self.staff_token):
response = self.client.put(
self.purchase1_uri,
{'beverage_type': self.beverage_type_uri, 'user': self.user1_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.patch(
self.purchase1_uri,
{'beverage_type': self.beverage_type_uri},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.delete(self.purchase1_uri)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
# Restore purchase
self.purchase1.save()
def test_purchase_creation_updates_profile_balance(self) -> None:
with token_auth(self, self.user1_token):
self.user1.refresh_from_db()
previous_balance = self.user1.profile.balance
response = self.client.post(
f'{self.api_uri}/',
{'beverage_type': self.beverage_type_uri, 'user': self.user1_uri},
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.user1.refresh_from_db()
new_balance = self.user1.profile.balance
self.assertEqual(new_balance, previous_balance - self.beverage_type.price)
def test_user_query(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/?user=1')
self.assertEqual(response.status_code, status.HTTP_200_OK)
for purchase in response.data:
self.assertIn(self.user1_uri, purchase['user'])
def test_user_query_ignores_wrong_type(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/?user=has-to-be-0-or-1')
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_beverage_type_query(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/?beverage_type=1')
self.assertEqual(response.status_code, status.HTTP_200_OK)
for purchase in response.data:
self.assertIn(self.beverage_type_uri, purchase['beverage_type'])
def test_beverage_type_query_ignores_wrong_type(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(
f'{self.api_uri}/?beverage_type=has-to-be-0-or-1'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
class BeverageTypesTest(APITestCase):
api_uri = '/api/beverage-types'
password = '<PASSWORD>'
user1: User
user2: User
staff: User
user1_token: str
staff_token: str
beverage_type1: BeverageType
@classmethod
def setUpTestData(cls) -> None:
cls.user1 = User.objects.create_user(username='erni', password=cls.password)
cls.user2 = User.objects.create_user(username='ducky', password=cls.password)
cls.staff = User.objects.create_superuser(
username='staff', password=<PASSWORD>
)
cls.user1_token = Token.objects.get(user=cls.user1).key
cls.staff_token = Token.objects.get(user=cls.staff).key
cls.beverage_type1 = BeverageType.objects.create(name='coffee', price='2.20')
cls.beverage_type2 = BeverageType.objects.create(
name='latte macchiato', price='2.40'
)
@property
def beverage_type_uri(self) -> str:
return f'{self.api_uri}/{self.beverage_type1.id}/'
def test_users_can_list_and_retrieve(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/')
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(self.beverage_type_uri)
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_users_cant_create_delete_or_update(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.post(
f'{self.api_uri}/', {'name': 'beer', 'price': '3.20'}, format='json'
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.put(
self.beverage_type_uri,
{'name': 'espresso', 'price': '3.20'},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.patch(
self.beverage_type_uri, {'price': '3.30'}, format='json'
)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
response = self.client.delete(self.beverage_type_uri)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_staff_can_create_delete_and_update(self) -> None:
with token_auth(self, self.staff_token):
response = self.client.post(
f'{self.api_uri}/', {'name': 'beer', 'price': '3.20'}, format='json'
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client.put(
self.beverage_type_uri,
{'name': 'espresso', 'price': '3.20'},
format='json',
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.patch(
self.beverage_type_uri, {'price': '3.30'}, format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.delete(self.beverage_type_uri)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
# Restore beverage type
self.beverage_type1.save()
def test_name_query(self) -> None:
with token_auth(self, self.user1_token):
response = self.client.get(f'{self.api_uri}/?name=coff')
self.assertEqual(response.status_code, status.HTTP_200_OK)
for user in response.data:
self.assertIn('coff', user['name'].lower()) | 0.637821 | 0.166032 |
import pytest
import pyomo.environ as pyo
from idaes.apps.caprese.categorize import (
categorize_dae_variables_and_constraints,
)
from idaes.apps.caprese.tests.test_simple_model import (
make_model,
initialize_t0,
)
from idaes.apps.caprese.estimator import (
_EstimatorBlockData,
EstimatorBlock,
SimpleEstimatorBlock,
IndexedEstimatorBlock,
)
from idaes.apps.caprese.common.config import (
VariableCategory,
ConstraintCategory,
)
from idaes.apps.caprese.dynamic_var import (
DiffVar,
DerivVar,
AlgVar,
InputVar,
ModelDisturbanceVar,
MeasurementErrorVar,
ActualMeasurementVar,
)
from idaes.core.util.model_statistics import degrees_of_freedom
from pyomo.common.collections import ComponentSet
from pyomo.dae.flatten import (
flatten_components_along_sets,
flatten_dae_components,
)
__author__ = "<NAME>"
solver_available = pyo.SolverFactory('ipopt').available()
if solver_available:
solver = pyo.SolverFactory('ipopt')
else:
solver = None
class TestEstimatorBlock(object):
@pytest.mark.unit
def test_construct(self, sample_time=0.5, horizon=1., nfe=2):
model = make_model(horizon=horizon, nfe=nfe)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[0,'A']]
estimator = EstimatorBlock(
model=model,
time=time,
inputs=inputs,
measurements=measurements,
sample_time=sample_time,
)
assert type(estimator) is SimpleEstimatorBlock
assert isinstance(estimator, EstimatorBlock)
assert isinstance(estimator, _EstimatorBlockData)
estimator.construct()
assert estimator[None] is estimator
assert estimator.mod is model
assert estimator.time is time
assert estimator._sample_time
assert all(i1 is i2 for i1, i2 in zip(estimator._inputs, inputs))
assert all(i1 is i2 for i1, i2 in
zip(estimator._measurements, measurements))
assert hasattr(estimator, "category_dict")
assert hasattr(estimator, "con_category_dict")
assert hasattr(estimator, "MHE_VARS_CONS_BLOCK")
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MEASUREMENT_SET")
assert len(MHEBlock.MEASUREMENT_SET.ordered_data()) == len(measurements)
assert hasattr(MHEBlock, "DIFFERENTIAL_SET")
assert len(MHEBlock.DIFFERENTIAL_SET.ordered_data()) == \
len(estimator.category_dict[VariableCategory.DIFFERENTIAL])
@pytest.mark.unit
def test_construct_indexed(self, sample_time=0.5):#, horizon=1., nfe=2):
estimator_set = pyo.Set(initialize=[0, 1, 2])
estimator_set.construct()
horizon_map = {0: 1., 1: 3., 2: 5.}
nfe_map = {0: 2, 1: 6, 2: 10}
model_map = {i: make_model(horizon_map[i], nfe_map[i])
for i in estimator_set}
time_map = {i: model_map[i].time for i in estimator_set}
inputs_map = {i: [model_map[i].flow_in[0]] for i in estimator_set}
measurements_map = {i: [model_map[i].conc[0, 'A']]
for i in estimator_set}
sample_time_map = {i: sample_time for i in estimator_set}
estimator = EstimatorBlock(
estimator_set,
model=model_map,
time=time_map,
inputs=inputs_map,
measurements=measurements_map,
sample_time=sample_time_map,
)
assert type(estimator) is IndexedEstimatorBlock
assert isinstance(estimator, IndexedEstimatorBlock)
estimator.construct()
assert all(b.parent_component() is estimator
for b in estimator.values())
for i in estimator_set:
assert i in estimator
for i, c in estimator.items():
assert c.mod is model_map[i]
assert c.time is time_map[i]
assert c._sample_time is sample_time_map[i]
assert all(i1 is i2 for i1, i2 in zip(c._inputs, inputs_map[i]))
assert all(i1 is i2
for i1, i2 in zip(c._measurements, measurements_map[i]))
assert hasattr(c, "category_dict")
assert hasattr(c, "con_category_dict")
assert hasattr(c, "MHE_VARS_CONS_BLOCK")
MHEBlock = c.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MEASUREMENT_SET")
assert len(MHEBlock.MEASUREMENT_SET.ordered_data()) == \
len(measurements_map[i])
assert hasattr(MHEBlock, "DIFFERENTIAL_SET")
assert len(MHEBlock.DIFFERENTIAL_SET.ordered_data()) == \
len(c.category_dict[VariableCategory.DIFFERENTIAL])
@pytest.mark.unit
def make_estimator(self, sample_time=0.5, horizon=1., nfe=2):
model = make_model(horizon=horizon, nfe=nfe)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[0,'A']]
estimator = EstimatorBlock(
model=model,
time=time,
inputs=inputs,
measurements=measurements,
sample_time=sample_time,
)
estimator.construct()
estimator.set_sample_time(sample_time)
return estimator
@pytest.mark.unit
def test_use_user_given_var_con_categ_dict(self):
model = make_model(horizon=1, nfe=2)
time = model.time
t0 = time.first()
sample_time = 0.5
inputs = [model.flow_in[t0]]
measurements = [model.conc[t0,'A']]
scalar_vars, dae_vars = flatten_dae_components(model,
time,
ctype=pyo.Var)
scalar_cons, dae_cons = flatten_dae_components(model,
time,
ctype = pyo.Constraint)
input_ids = [id(vart0) for vart0 in inputs]
input_vars = [var for var in dae_vars if id(var[t0]) in input_ids]
measurement_ids = [id(vart0) for vart0 in measurements]
measurement_vars = [var for var in dae_vars
if id(var[t0]) in measurement_ids]
category_dict, con_category_dict = \
categorize_dae_variables_and_constraints(
model,
dae_vars,
dae_cons,
time,
input_vars = input_vars,
)
# Provide the category_dict when constructing EstimatorBlock
estimator = EstimatorBlock(
model=model,
time=time,
category_dict={None: category_dict},
con_category_dict={None: con_category_dict},
inputs=input_vars,
measurements=measurement_vars,
sample_time=sample_time,
)
estimator.construct()
# Make sure _category_dict is not None
assert estimator._category_dict
# Make sure dae variables are saved in estimator.dae_vars
vart0_id_dae_vars = [id(var[t0]) for var in estimator.dae_vars]
for categ, varlist in estimator.category_dict.items():
if categ not in [VariableCategory.MEASUREMENT,
VariableCategory.ACTUALMEASUREMENT,
VariableCategory.MEASUREMENTERROR,
VariableCategory.MODELDISTURBANCE]:
for var in varlist:
assert id(var[t0]) in vart0_id_dae_vars
vart0_id_dae_vars.remove(id(var[t0]))
assert vart0_id_dae_vars == []
# Make sure con_category_dict is not None
assert estimator._con_category_dict
assert estimator.con_category_dict == estimator._con_category_dict
@pytest.mark.unit
def test_categorize_var_con_for_MHE(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
mod = estimator.mod
# line227-261: make sure variables are categoried in correct cateogries.
pred_diff_vars = [mod.conc[t0, "A"], mod.conc[t0, "B"]]
diff_vars = estimator.category_dict[VariableCategory.DIFFERENTIAL]
diff_vart0_ids = [id(var[t0]) for var in diff_vars]
assert len(diff_vars) == len(pred_diff_vars)
for var in pred_diff_vars:
assert id(var) in diff_vart0_ids
diff_vart0_ids.remove(id(var))
assert diff_vart0_ids == []
pred_alg_vars = [mod.flow_out[t0], mod.rate[t0, "A"], mod.rate[t0, "B"]]
alg_vars = estimator.category_dict[VariableCategory.ALGEBRAIC]
alg_vart0_ids = [id(var[t0]) for var in alg_vars]
assert len(alg_vars) == len(pred_alg_vars)
for var in pred_alg_vars:
assert id(var) in alg_vart0_ids
alg_vart0_ids.remove(id(var))
assert alg_vart0_ids == []
pred_deri_vars = [mod.dcdt[t0, "A"], mod.dcdt[t0, "B"]]
deri_vars = estimator.category_dict[VariableCategory.DERIVATIVE]
deri_vart0_ids = [id(var[t0]) for var in deri_vars]
assert len(deri_vars) == len(pred_deri_vars)
for var in pred_deri_vars:
assert id(var) in deri_vart0_ids
deri_vart0_ids.remove(id(var))
assert deri_vart0_ids == []
pred_input_vars = [mod.flow_in[t0]]
input_vars = estimator.category_dict[VariableCategory.INPUT]
input_vart0_ids = [id(var[t0]) for var in input_vars]
assert len(input_vars) == len(pred_input_vars)
for var in pred_input_vars:
assert id(var) in input_vart0_ids
input_vart0_ids.remove(id(var))
assert input_vart0_ids == []
# line265-296: make sure constraints are categoried in correct cateogries.
tlast = time.last()
pred_diff_cons = [mod.material_balance[tlast, "A"],
mod.material_balance[tlast, "B"]]
diff_cons = estimator.con_category_dict[ConstraintCategory.DIFFERENTIAL]
diff_contlast_ids = [id(con[tlast]) for con in diff_cons]
assert len(diff_cons) == len(pred_diff_cons)
for con in pred_diff_cons:
assert id(con) in diff_contlast_ids
diff_contlast_ids.remove(id(con))
assert diff_contlast_ids == []
pred_alg_cons = [mod.flow_eqn[tlast],
mod.rate_eqn[tlast, "A"],
mod.rate_eqn[tlast, "B"]]
alg_cons = estimator.con_category_dict[ConstraintCategory.ALGEBRAIC]
alg_contlast_ids = [id(con[tlast]) for con in alg_cons]
assert len(alg_cons) == len(pred_alg_cons)
for con in pred_alg_cons:
assert id(con) in alg_contlast_ids
alg_contlast_ids.remove(id(con))
assert alg_contlast_ids == []
pred_disc_cons = [mod.dcdt_disc_eq[tlast, "A"],
mod.dcdt_disc_eq[tlast, "B"]]
disc_cons = estimator.con_category_dict[ConstraintCategory.DISCRETIZATION]
disc_contlast_ids = [id(con[tlast]) for con in disc_cons]
assert len(disc_cons) == len(pred_disc_cons)
for con in pred_disc_cons:
assert id(con) in disc_contlast_ids
disc_contlast_ids.remove(id(con))
assert disc_contlast_ids == []
@pytest.mark.unit
def test_reference_var_category_dict(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
assert VariableCategory.DIFFERENTIAL in estimator.category_dict
assert VariableCategory.ALGEBRAIC in estimator.category_dict
assert VariableCategory.DERIVATIVE in estimator.category_dict
assert VariableCategory.INPUT in estimator.category_dict
CATEGORY_MAP = {VariableCategory.DIFFERENTIAL: DiffVar,
VariableCategory.ALGEBRAIC: AlgVar,
VariableCategory.DERIVATIVE: DerivVar,
VariableCategory.INPUT: InputVar}
# Make sure variables are references and their ctype are correct.
for categ, ctype in CATEGORY_MAP.items():
varlist = estimator.category_dict[categ]
for var in varlist:
assert var.is_reference()
assert var.ctype is ctype
@pytest.mark.unit
def test_add_sample_point_set(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
assert hasattr(estimator, 'SAMPLEPOINT_SET')
assert all(i1 is i2 for i1, i2
in zip(estimator.SAMPLEPOINT_SET.ordered_data(),
estimator.sample_points))
@pytest.mark.unit
def test_add_actual_measurement_param(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "ACTUAL_MEASUREMENT_BLOCK")
acemeablock = MHEBlock.ACTUAL_MEASUREMENT_BLOCK
assert acemeablock.dim() == 1
assert acemeablock.index_set() == estimator.MEASUREMENT_SET
for bind in estimator.MEASUREMENT_SET:
curr_block = acemeablock[bind]
assert hasattr(curr_block, "actual_measurement")
assert curr_block.actual_measurement.index_set() == \
estimator.SAMPLEPOINT_SET
var = curr_block.actual_measurement
for sp in estimator.SAMPLEPOINT_SET:
assert var[sp].value == 0.0
assert var[sp].fixed
@pytest.mark.unit
def test_add_measurement_error(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MEASUREMENT_ERROR_BLOCK")
meaerrblock = MHEBlock.MEASUREMENT_ERROR_BLOCK
assert meaerrblock.dim() == 1
assert meaerrblock.index_set() == estimator.MEASUREMENT_SET
for bind in estimator.MEASUREMENT_SET:
curr_block = meaerrblock[bind]
assert hasattr(curr_block, "measurement_error")
assert curr_block.measurement_error.index_set() == \
estimator.SAMPLEPOINT_SET
var = curr_block.measurement_error
for sp in estimator.SAMPLEPOINT_SET:
assert var[sp].value == 0.0
@pytest.mark.unit
def test_add_model_disturbance(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MODEL_DISTURBANCE_BLOCK")
moddisblock = MHEBlock.MODEL_DISTURBANCE_BLOCK
assert moddisblock.dim() == 1
assert moddisblock.index_set() == estimator.DIFFERENTIAL_SET
for bind in estimator.MEASUREMENT_SET:
curr_block = moddisblock[bind]
assert hasattr(curr_block, "model_disturbance")
assert curr_block.model_disturbance.index_set() == \
estimator.SAMPLEPOINT_SET
var = curr_block.model_disturbance
for sp in estimator.SAMPLEPOINT_SET:
if sp == t0:
assert var[sp].fixed
assert var[sp].value == 0.0
@pytest.mark.unit
def test_new_MHE_vars_in_category_dict(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
# Make sure category dict contains these categories
assert VariableCategory.DIFFERENTIAL in estimator.category_dict
assert VariableCategory.ALGEBRAIC in estimator.category_dict
assert VariableCategory.DERIVATIVE in estimator.category_dict
assert VariableCategory.INPUT in estimator.category_dict
assert VariableCategory.ACTUALMEASUREMENT in estimator.category_dict
assert VariableCategory.MEASUREMENTERROR in estimator.category_dict
assert VariableCategory.MODELDISTURBANCE in estimator.category_dict
# Make sure MHE categories contains variables we expect
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
actmeablock = MHEBlock.ACTUAL_MEASUREMENT_BLOCK
pred_actmea_vars = ComponentSet((actmeablock[ind].actual_measurement[t0]
for ind in estimator.MEASUREMENT_SET))
actmea_vars = estimator.category_dict[VariableCategory.ACTUALMEASUREMENT]
for var in actmea_vars:
assert var.is_reference()
assert var.ctype is ActualMeasurementVar
assert var._attr == "actualmeasurement"
assert var[t0] in pred_actmea_vars
pred_actmea_vars.remove(var[t0])
assert pred_actmea_vars._data == {}
meaerrblock = MHEBlock.MEASUREMENT_ERROR_BLOCK
pred_meaerr_vars = ComponentSet((meaerrblock[ind].measurement_error[t0]
for ind in estimator.MEASUREMENT_SET))
meaerr_vars = estimator.category_dict[VariableCategory.MEASUREMENTERROR]
for var in meaerr_vars:
assert var.is_reference()
assert var.ctype is MeasurementErrorVar
assert var._attr == "measurementerror"
assert var[t0] in pred_meaerr_vars
pred_meaerr_vars.remove(var[t0])
assert pred_meaerr_vars._data == {}
moddisblock = MHEBlock.MODEL_DISTURBANCE_BLOCK
pred_moddis_vars = ComponentSet((moddisblock[ind].model_disturbance[t0]
for ind in estimator.DIFFERENTIAL_SET))
moddis_vars = estimator.category_dict[VariableCategory.MODELDISTURBANCE]
for var in moddis_vars:
assert var.is_reference()
assert var.ctype is ModelDisturbanceVar
assert var._attr == "modeldisturbance"
assert var[t0] in pred_moddis_vars
pred_moddis_vars.remove(var[t0])
assert pred_moddis_vars._data == {}
@pytest.mark.unit
def test_add_mea_moddis_componentmap(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
moddis_block = estimator.MODELDISTURBANCE_BLOCK
diffvar_map_moddis = estimator.diffvar_map_moddis
for ind, var in enumerate(estimator.differential_vars):
assert diffvar_map_moddis[var[t0]] is moddis_block[ind].var
derivar_map_moddis = estimator.derivar_map_moddis
for ind, var in enumerate(estimator.derivative_vars):
assert derivar_map_moddis[var[t0]] is moddis_block[ind].var
meaerr_block = estimator.MEASUREMENTERROR_BLOCK
meavar_map_meaerr = estimator.meavar_map_meaerr
for ind, var in enumerate(estimator.measurement_vars):
assert meavar_map_meaerr[var[t0]] is meaerr_block[ind].var
actmea_block = estimator.ACTUALMEASUREMENT_BLOCK
meavar_map_actmea = estimator.meavar_map_actmea
for ind, var in enumerate(estimator.measurement_vars):
assert meavar_map_actmea[var[t0]] is actmea_block[ind].var
@pytest.mark.unit
def test_add_measurement_constraint(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MEASUREMENT_CONSTRAINT_BLOCK")
meacon_block = MHEBlock.MEASUREMENT_CONSTRAINT_BLOCK
assert meacon_block.dim() == 1
assert meacon_block.index_set() == estimator.MEASUREMENT_SET
actmea_block = estimator.MHE_VARS_CONS_BLOCK.ACTUAL_MEASUREMENT_BLOCK
meaerr_block = estimator.MHE_VARS_CONS_BLOCK.MEASUREMENT_ERROR_BLOCK
mea_block = estimator.MEASUREMENT_BLOCK
for bind in estimator.MEASUREMENT_SET:
assert len(list(meacon_block[bind].component_objects(pyo.Constraint))) == 1
assert hasattr(meacon_block[bind], "con_mea_err")
curr_con = meacon_block[bind].con_mea_err
assert curr_con.index_set() == time
assert all(i1 is i2 for i1, i2 in
zip(curr_con.keys(), estimator.SAMPLEPOINT_SET))
for t in time:
if t in estimator.SAMPLEPOINT_SET:
pred_expr = actmea_block[bind].actual_measurement[t] ==\
(mea_block[bind].var[t] + \
meaerr_block[bind].measurement_error[t])
assert curr_con[t].expr.to_string() == pred_expr.to_string()
@pytest.mark.unit
def test_add_disturbance_to_differential_cons(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "DISTURBED_DIFFERENTIAL_CONSTRAINT_BLOCK")
ddc_block = MHEBlock.DISTURBED_DIFFERENTIAL_CONSTRAINT_BLOCK
assert ddc_block.dim() == 1
assert len(list(ddc_block.keys())) == \
len(estimator.con_category_dict[ConstraintCategory.DIFFERENTIAL])
def map_t_to_sp(tind):
for item in estimator.sample_point_indices:
if item >= tind:
return estimator.time[item]
diff_cons = estimator.con_category_dict[ConstraintCategory.DIFFERENTIAL]
moddis_block = estimator.MODELDISTURBANCE_BLOCK
for bind in estimator.DIFFERENTIAL_SET:
curr_block = ddc_block[bind]
assert len(list(curr_block.component_objects(pyo.Constraint))) == 1
assert hasattr(curr_block, "disturbed_diff_con")
curr_con = curr_block.disturbed_diff_con
assert curr_con.index_set() == time
for t in time:
tind = time.find_nearest_index(t)
sp = map_t_to_sp(tind)
pred_expr = diff_cons[bind][t].body - \
moddis_block[bind].var[sp] == 0.0
assert curr_con[t].expr.to_string() == pred_expr.to_string()
assert not diff_cons[bind][t].active
@pytest.mark.unit
def test_add_steady_state_objective_for_MHE_initialization(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
desiredss = [
(estimator.mod.flow_in[t0], 3.0),
]
weights = [
(estimator.mod.flow_in[t0], 2.0),
]
estimator.mod.flow_in[:].set_value(3.0)
initialize_t0(estimator.mod)
estimator.add_single_time_optimization_objective(desiredss, weights)
assert hasattr(estimator, 'single_time_optimization_objective')
pred_obj_expr = (2.0*(estimator.mod.flow_in[t0] - 3.0)**2)
obj_expr = estimator.single_time_optimization_objective.expr
assert pyo.value(pred_obj_expr) == pyo.value(obj_expr)
assert pred_obj_expr.to_string() == obj_expr.to_string()
estimator.del_component(estimator.single_time_optimization_objective)
desiredss = [
(estimator.mod.flow_in[t0], 3.0),
(estimator.mod.conc[t0,'A'], 1.5),
]
weights = [
(estimator.mod.flow_in[t0], 1.0),
(estimator.mod.conc[t0,'A'], 5.0),
]
estimator.add_single_time_optimization_objective(desiredss, weights)
pred_obj_expr = (
1.0*(estimator.mod.flow_in[t0] - 3.0)**2 +
5.0*(estimator.mod.conc[t0,'A'] - 1.5)**2
)
obj_expr = estimator.single_time_optimization_objective.expr
assert pyo.value(pred_obj_expr) == pyo.value(obj_expr)
assert pred_obj_expr.to_string() == obj_expr.to_string()
@pytest.mark.component
@pytest.mark.skipif(not solver_available, reason='IPOPT is not available')
def test_solve_steady_state_for_MHE_initialization(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
desiredss = [
(estimator.mod.flow_in[t0], 3.0),
]
weights = [
(estimator.mod.flow_in[t0], 1.0),
]
estimator.add_single_time_optimization_objective(desiredss, weights)
estimator.mod.flow_in[:].set_value(3.0)
initialize_t0(estimator.mod)
dof_prior = degrees_of_freedom(estimator)
estimator.solve_single_time_optimization(solver,
ic_type = "differential_var",
require_steady = True,
load_setpoints = False,
restore_ic_input_after_solve = False,
isMHE_block = True,)
dof_post = degrees_of_freedom(estimator)
assert dof_prior == dof_post
assert estimator.differential_vars[0][t0].value == \
pytest.approx(3.75, abs=1e-3)
assert estimator.differential_vars[1][t0].value == \
pytest.approx(1.25, abs=1e-3)
assert estimator.algebraic_vars[0][t0].value == \
pytest.approx(3.0, abs=1e-3)
assert estimator.algebraic_vars[1][t0].value == \
pytest.approx(-3.75, abs=1e-3)
assert estimator.algebraic_vars[2][t0].value == \
pytest.approx(3.75, abs=1e-3)
assert estimator.input_vars[0][t0].value == \
pytest.approx(3.0, abs=1e-3)
# Make sure all undisturbed differential equations are deactivated
diff_cons = estimator.con_category_dict[ConstraintCategory.DIFFERENTIAL]
assert not all(con[t].active for con in diff_cons for t in time)
# Make sure MHE block is active
assert estimator.MHE_VARS_CONS_BLOCK.active
# Make sure derivatives are not fixed
deri_vars = estimator.category_dict[VariableCategory.DERIVATIVE]
assert not all(var[t].fixed for var in deri_vars for t in time)
# Make sure all inputs are fixed
input_vars = estimator.category_dict[VariableCategory.INPUT]
assert all(var[t].fixed for var in input_vars for t in time)
# Make sure differential variables at t0 are fixed
diff_vars = estimator.category_dict[VariableCategory.DIFFERENTIAL]
assert all(var[t0].fixed for var in diff_vars)
@pytest.mark.unit
def test_initialize_actualmeasurements_at_t0(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
# Re-initialize measurements and actualmeasurements to make sure they
# are different before assertion
estimator.vectors.measurement[...].set_value(20.)
estimator.vectors.actualmeasurement[...].set_value(100.)
estimator.initialize_actualmeasurements_at_t0()
for ind in estimator.ACTUALMEASUREMENT_SET:
var_ind = (ind, t0)
actmea_var = estimator.vectors.actualmeasurement
assert actmea_var[var_ind].value == 20.
@pytest.mark.component
@pytest.mark.skipif(not solver_available, reason='IPOPT is not available')
def test_initialize_past_info_with_steady_state(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
desiredss = [
(estimator.mod.flow_in[t0], 3.0),
]
weights = [
(estimator.mod.flow_in[t0], 1.0),
]
estimator.mod.flow_in[:].set_value(3.0)
initialize_t0(estimator.mod)
estimator.initialize_past_info_with_steady_state(desiredss,
weights,
solver)
for tp in time:
assert estimator.differential_vars[0][tp].value == \
pytest.approx(3.75, abs=1e-3)
assert estimator.differential_vars[1][tp].value == \
pytest.approx(1.25, abs=1e-3)
assert estimator.algebraic_vars[0][tp].value == \
pytest.approx(3.0, abs=1e-3)
assert estimator.algebraic_vars[1][tp].value == \
pytest.approx(-3.75, abs=1e-3)
assert estimator.algebraic_vars[2][tp].value == \
pytest.approx(3.75, abs=1e-3)
assert estimator.input_vars[0][tp].value == \
pytest.approx(3.0, abs=1e-3)
for sp in estimator.sample_points:
curr_var = estimator.vectors.actualmeasurement[0, sp]
assert curr_var.value == 3.75
assert curr_var.fixed
@pytest.mark.unit
def test_add_noise_minimize_objective(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
# Re-initialize noises so they are not zero for objective evalutaion
estimator.vectors.modeldisturbance[...].set_value(0.5)
estimator.vectors.measurementerror[...].set_value(2.5)
model_disturbance_weights = [
(estimator.mod.conc[t0,'A'], 0.1),
(estimator.mod.conc[t0,'B'], 0.2),
]
measurement_error_weights = [(estimator.mod.conc[0,'A'], 10.),]
estimator.add_noise_minimize_objective(model_disturbance_weights,
measurement_error_weights,
givenform = "weight")
assert hasattr(estimator, 'noise_minimize_objective')
MD_block = estimator.MHE_VARS_CONS_BLOCK.MODEL_DISTURBANCE_BLOCK
ME_block = estimator.MHE_VARS_CONS_BLOCK.MEASUREMENT_ERROR_BLOCK
pred_obj_expr = sum(0.1*(MD_block[0].model_disturbance[t])**2
for t in estimator.sample_points if t!=t0) + \
sum(0.2*(MD_block[1].model_disturbance[t])**2
for t in estimator.sample_points if t!=t0) + \
sum(10.*(ME_block[0].measurement_error[t])**2
for t in estimator.sample_points)
obj_expr = estimator.noise_minimize_objective.expr
assert pyo.value(pred_obj_expr) == pyo.value(obj_expr)
assert pyo.value(obj_expr) > 0
estimator.del_component("noise_minimize_objective")
model_disturbance_variances = [
(estimator.mod.conc[t0,'A'], 10.),
(estimator.mod.conc[t0,'B'], 20.),
]
measurement_error_variances = [(estimator.mod.conc[0,'A'], 0.1),]
estimator.add_noise_minimize_objective(model_disturbance_variances,
measurement_error_variances,
givenform = "variance")
assert hasattr(estimator, 'noise_minimize_objective')
pred_obj_expr = sum(1/10.*(MD_block[0].model_disturbance[t])**2
for t in estimator.sample_points if t!=t0) + \
sum(1/20.*(MD_block[1].model_disturbance[t])**2
for t in estimator.sample_points if t!=t0) + \
sum(1/0.1*(ME_block[0].measurement_error[t])**2
for t in estimator.sample_points)
obj_expr = estimator.noise_minimize_objective.expr
assert pyo.value(pred_obj_expr) == pyo.value(obj_expr)
assert pyo.value(obj_expr) > 0
@pytest.mark.unit
def test_check_var_con_dof(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
estimator.check_var_con_dof(skip_dof_check = False)
input_vars = estimator.vectors.input
for ind in input_vars.keys():
assert input_vars[ind].fixed
diffvars = estimator.vectors.differential
for ind in diffvars.keys():
assert not diffvars[ind].fixed
for ind in estimator.MODELDISTURBANCE_SET:
assert estimator.vectors.modeldisturbance[ind, 0].fixed
assert pyo.value(estimator.vectors.modeldisturbance[ind, 0]) == 0.0
@pytest.mark.unit
def load_inputs_into_last_sample(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
# Re-initialize inputs so they are not 100.
estimator.vectors.input[...].set_value(0.5)
input = [100.]
estimator.load_inputs_into_last_sample(input)
spi = estimator.sample_point_indices
check_t_list = [tp for tp in time if tp > time[spi[-2]]
and tp <= time[spi[-1]]]
for tp in check_t_list:
assert pyo.value(estimator.vectors.input[0, tp]) == 100.
@pytest.mark.unit
def test_generate_estimates_at_time(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
tlast = time.last()
# Re-set values for differential variables
estimator.vectors.differential[0,tlast].set_value(105.)
estimator.vectors.differential[1,tlast].set_value(205.)
estimates = estimator.generate_estimates_at_time(tlast)
assert estimates == [105., 205.]
@pytest.mark.unit
def test_load_measurements(self):
blk = self.make_estimator()
time = blk.time
t0 = time.first()
vals = [0.75]
t_last = time.last()
blk.load_measurements(vals, timepoint = t_last)
for b, val in zip(blk.ACTUALMEASUREMENT_BLOCK.values(), vals):
assert b.var[t_last].value == val | idaes/apps/caprese/tests/test_estimator.py | import pytest
import pyomo.environ as pyo
from idaes.apps.caprese.categorize import (
categorize_dae_variables_and_constraints,
)
from idaes.apps.caprese.tests.test_simple_model import (
make_model,
initialize_t0,
)
from idaes.apps.caprese.estimator import (
_EstimatorBlockData,
EstimatorBlock,
SimpleEstimatorBlock,
IndexedEstimatorBlock,
)
from idaes.apps.caprese.common.config import (
VariableCategory,
ConstraintCategory,
)
from idaes.apps.caprese.dynamic_var import (
DiffVar,
DerivVar,
AlgVar,
InputVar,
ModelDisturbanceVar,
MeasurementErrorVar,
ActualMeasurementVar,
)
from idaes.core.util.model_statistics import degrees_of_freedom
from pyomo.common.collections import ComponentSet
from pyomo.dae.flatten import (
flatten_components_along_sets,
flatten_dae_components,
)
__author__ = "<NAME>"
solver_available = pyo.SolverFactory('ipopt').available()
if solver_available:
solver = pyo.SolverFactory('ipopt')
else:
solver = None
class TestEstimatorBlock(object):
@pytest.mark.unit
def test_construct(self, sample_time=0.5, horizon=1., nfe=2):
model = make_model(horizon=horizon, nfe=nfe)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[0,'A']]
estimator = EstimatorBlock(
model=model,
time=time,
inputs=inputs,
measurements=measurements,
sample_time=sample_time,
)
assert type(estimator) is SimpleEstimatorBlock
assert isinstance(estimator, EstimatorBlock)
assert isinstance(estimator, _EstimatorBlockData)
estimator.construct()
assert estimator[None] is estimator
assert estimator.mod is model
assert estimator.time is time
assert estimator._sample_time
assert all(i1 is i2 for i1, i2 in zip(estimator._inputs, inputs))
assert all(i1 is i2 for i1, i2 in
zip(estimator._measurements, measurements))
assert hasattr(estimator, "category_dict")
assert hasattr(estimator, "con_category_dict")
assert hasattr(estimator, "MHE_VARS_CONS_BLOCK")
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MEASUREMENT_SET")
assert len(MHEBlock.MEASUREMENT_SET.ordered_data()) == len(measurements)
assert hasattr(MHEBlock, "DIFFERENTIAL_SET")
assert len(MHEBlock.DIFFERENTIAL_SET.ordered_data()) == \
len(estimator.category_dict[VariableCategory.DIFFERENTIAL])
@pytest.mark.unit
def test_construct_indexed(self, sample_time=0.5):#, horizon=1., nfe=2):
estimator_set = pyo.Set(initialize=[0, 1, 2])
estimator_set.construct()
horizon_map = {0: 1., 1: 3., 2: 5.}
nfe_map = {0: 2, 1: 6, 2: 10}
model_map = {i: make_model(horizon_map[i], nfe_map[i])
for i in estimator_set}
time_map = {i: model_map[i].time for i in estimator_set}
inputs_map = {i: [model_map[i].flow_in[0]] for i in estimator_set}
measurements_map = {i: [model_map[i].conc[0, 'A']]
for i in estimator_set}
sample_time_map = {i: sample_time for i in estimator_set}
estimator = EstimatorBlock(
estimator_set,
model=model_map,
time=time_map,
inputs=inputs_map,
measurements=measurements_map,
sample_time=sample_time_map,
)
assert type(estimator) is IndexedEstimatorBlock
assert isinstance(estimator, IndexedEstimatorBlock)
estimator.construct()
assert all(b.parent_component() is estimator
for b in estimator.values())
for i in estimator_set:
assert i in estimator
for i, c in estimator.items():
assert c.mod is model_map[i]
assert c.time is time_map[i]
assert c._sample_time is sample_time_map[i]
assert all(i1 is i2 for i1, i2 in zip(c._inputs, inputs_map[i]))
assert all(i1 is i2
for i1, i2 in zip(c._measurements, measurements_map[i]))
assert hasattr(c, "category_dict")
assert hasattr(c, "con_category_dict")
assert hasattr(c, "MHE_VARS_CONS_BLOCK")
MHEBlock = c.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MEASUREMENT_SET")
assert len(MHEBlock.MEASUREMENT_SET.ordered_data()) == \
len(measurements_map[i])
assert hasattr(MHEBlock, "DIFFERENTIAL_SET")
assert len(MHEBlock.DIFFERENTIAL_SET.ordered_data()) == \
len(c.category_dict[VariableCategory.DIFFERENTIAL])
@pytest.mark.unit
def make_estimator(self, sample_time=0.5, horizon=1., nfe=2):
model = make_model(horizon=horizon, nfe=nfe)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[0,'A']]
estimator = EstimatorBlock(
model=model,
time=time,
inputs=inputs,
measurements=measurements,
sample_time=sample_time,
)
estimator.construct()
estimator.set_sample_time(sample_time)
return estimator
@pytest.mark.unit
def test_use_user_given_var_con_categ_dict(self):
model = make_model(horizon=1, nfe=2)
time = model.time
t0 = time.first()
sample_time = 0.5
inputs = [model.flow_in[t0]]
measurements = [model.conc[t0,'A']]
scalar_vars, dae_vars = flatten_dae_components(model,
time,
ctype=pyo.Var)
scalar_cons, dae_cons = flatten_dae_components(model,
time,
ctype = pyo.Constraint)
input_ids = [id(vart0) for vart0 in inputs]
input_vars = [var for var in dae_vars if id(var[t0]) in input_ids]
measurement_ids = [id(vart0) for vart0 in measurements]
measurement_vars = [var for var in dae_vars
if id(var[t0]) in measurement_ids]
category_dict, con_category_dict = \
categorize_dae_variables_and_constraints(
model,
dae_vars,
dae_cons,
time,
input_vars = input_vars,
)
# Provide the category_dict when constructing EstimatorBlock
estimator = EstimatorBlock(
model=model,
time=time,
category_dict={None: category_dict},
con_category_dict={None: con_category_dict},
inputs=input_vars,
measurements=measurement_vars,
sample_time=sample_time,
)
estimator.construct()
# Make sure _category_dict is not None
assert estimator._category_dict
# Make sure dae variables are saved in estimator.dae_vars
vart0_id_dae_vars = [id(var[t0]) for var in estimator.dae_vars]
for categ, varlist in estimator.category_dict.items():
if categ not in [VariableCategory.MEASUREMENT,
VariableCategory.ACTUALMEASUREMENT,
VariableCategory.MEASUREMENTERROR,
VariableCategory.MODELDISTURBANCE]:
for var in varlist:
assert id(var[t0]) in vart0_id_dae_vars
vart0_id_dae_vars.remove(id(var[t0]))
assert vart0_id_dae_vars == []
# Make sure con_category_dict is not None
assert estimator._con_category_dict
assert estimator.con_category_dict == estimator._con_category_dict
@pytest.mark.unit
def test_categorize_var_con_for_MHE(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
mod = estimator.mod
# line227-261: make sure variables are categoried in correct cateogries.
pred_diff_vars = [mod.conc[t0, "A"], mod.conc[t0, "B"]]
diff_vars = estimator.category_dict[VariableCategory.DIFFERENTIAL]
diff_vart0_ids = [id(var[t0]) for var in diff_vars]
assert len(diff_vars) == len(pred_diff_vars)
for var in pred_diff_vars:
assert id(var) in diff_vart0_ids
diff_vart0_ids.remove(id(var))
assert diff_vart0_ids == []
pred_alg_vars = [mod.flow_out[t0], mod.rate[t0, "A"], mod.rate[t0, "B"]]
alg_vars = estimator.category_dict[VariableCategory.ALGEBRAIC]
alg_vart0_ids = [id(var[t0]) for var in alg_vars]
assert len(alg_vars) == len(pred_alg_vars)
for var in pred_alg_vars:
assert id(var) in alg_vart0_ids
alg_vart0_ids.remove(id(var))
assert alg_vart0_ids == []
pred_deri_vars = [mod.dcdt[t0, "A"], mod.dcdt[t0, "B"]]
deri_vars = estimator.category_dict[VariableCategory.DERIVATIVE]
deri_vart0_ids = [id(var[t0]) for var in deri_vars]
assert len(deri_vars) == len(pred_deri_vars)
for var in pred_deri_vars:
assert id(var) in deri_vart0_ids
deri_vart0_ids.remove(id(var))
assert deri_vart0_ids == []
pred_input_vars = [mod.flow_in[t0]]
input_vars = estimator.category_dict[VariableCategory.INPUT]
input_vart0_ids = [id(var[t0]) for var in input_vars]
assert len(input_vars) == len(pred_input_vars)
for var in pred_input_vars:
assert id(var) in input_vart0_ids
input_vart0_ids.remove(id(var))
assert input_vart0_ids == []
# line265-296: make sure constraints are categoried in correct cateogries.
tlast = time.last()
pred_diff_cons = [mod.material_balance[tlast, "A"],
mod.material_balance[tlast, "B"]]
diff_cons = estimator.con_category_dict[ConstraintCategory.DIFFERENTIAL]
diff_contlast_ids = [id(con[tlast]) for con in diff_cons]
assert len(diff_cons) == len(pred_diff_cons)
for con in pred_diff_cons:
assert id(con) in diff_contlast_ids
diff_contlast_ids.remove(id(con))
assert diff_contlast_ids == []
pred_alg_cons = [mod.flow_eqn[tlast],
mod.rate_eqn[tlast, "A"],
mod.rate_eqn[tlast, "B"]]
alg_cons = estimator.con_category_dict[ConstraintCategory.ALGEBRAIC]
alg_contlast_ids = [id(con[tlast]) for con in alg_cons]
assert len(alg_cons) == len(pred_alg_cons)
for con in pred_alg_cons:
assert id(con) in alg_contlast_ids
alg_contlast_ids.remove(id(con))
assert alg_contlast_ids == []
pred_disc_cons = [mod.dcdt_disc_eq[tlast, "A"],
mod.dcdt_disc_eq[tlast, "B"]]
disc_cons = estimator.con_category_dict[ConstraintCategory.DISCRETIZATION]
disc_contlast_ids = [id(con[tlast]) for con in disc_cons]
assert len(disc_cons) == len(pred_disc_cons)
for con in pred_disc_cons:
assert id(con) in disc_contlast_ids
disc_contlast_ids.remove(id(con))
assert disc_contlast_ids == []
@pytest.mark.unit
def test_reference_var_category_dict(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
assert VariableCategory.DIFFERENTIAL in estimator.category_dict
assert VariableCategory.ALGEBRAIC in estimator.category_dict
assert VariableCategory.DERIVATIVE in estimator.category_dict
assert VariableCategory.INPUT in estimator.category_dict
CATEGORY_MAP = {VariableCategory.DIFFERENTIAL: DiffVar,
VariableCategory.ALGEBRAIC: AlgVar,
VariableCategory.DERIVATIVE: DerivVar,
VariableCategory.INPUT: InputVar}
# Make sure variables are references and their ctype are correct.
for categ, ctype in CATEGORY_MAP.items():
varlist = estimator.category_dict[categ]
for var in varlist:
assert var.is_reference()
assert var.ctype is ctype
@pytest.mark.unit
def test_add_sample_point_set(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
assert hasattr(estimator, 'SAMPLEPOINT_SET')
assert all(i1 is i2 for i1, i2
in zip(estimator.SAMPLEPOINT_SET.ordered_data(),
estimator.sample_points))
@pytest.mark.unit
def test_add_actual_measurement_param(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "ACTUAL_MEASUREMENT_BLOCK")
acemeablock = MHEBlock.ACTUAL_MEASUREMENT_BLOCK
assert acemeablock.dim() == 1
assert acemeablock.index_set() == estimator.MEASUREMENT_SET
for bind in estimator.MEASUREMENT_SET:
curr_block = acemeablock[bind]
assert hasattr(curr_block, "actual_measurement")
assert curr_block.actual_measurement.index_set() == \
estimator.SAMPLEPOINT_SET
var = curr_block.actual_measurement
for sp in estimator.SAMPLEPOINT_SET:
assert var[sp].value == 0.0
assert var[sp].fixed
@pytest.mark.unit
def test_add_measurement_error(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MEASUREMENT_ERROR_BLOCK")
meaerrblock = MHEBlock.MEASUREMENT_ERROR_BLOCK
assert meaerrblock.dim() == 1
assert meaerrblock.index_set() == estimator.MEASUREMENT_SET
for bind in estimator.MEASUREMENT_SET:
curr_block = meaerrblock[bind]
assert hasattr(curr_block, "measurement_error")
assert curr_block.measurement_error.index_set() == \
estimator.SAMPLEPOINT_SET
var = curr_block.measurement_error
for sp in estimator.SAMPLEPOINT_SET:
assert var[sp].value == 0.0
@pytest.mark.unit
def test_add_model_disturbance(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MODEL_DISTURBANCE_BLOCK")
moddisblock = MHEBlock.MODEL_DISTURBANCE_BLOCK
assert moddisblock.dim() == 1
assert moddisblock.index_set() == estimator.DIFFERENTIAL_SET
for bind in estimator.MEASUREMENT_SET:
curr_block = moddisblock[bind]
assert hasattr(curr_block, "model_disturbance")
assert curr_block.model_disturbance.index_set() == \
estimator.SAMPLEPOINT_SET
var = curr_block.model_disturbance
for sp in estimator.SAMPLEPOINT_SET:
if sp == t0:
assert var[sp].fixed
assert var[sp].value == 0.0
@pytest.mark.unit
def test_new_MHE_vars_in_category_dict(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
# Make sure category dict contains these categories
assert VariableCategory.DIFFERENTIAL in estimator.category_dict
assert VariableCategory.ALGEBRAIC in estimator.category_dict
assert VariableCategory.DERIVATIVE in estimator.category_dict
assert VariableCategory.INPUT in estimator.category_dict
assert VariableCategory.ACTUALMEASUREMENT in estimator.category_dict
assert VariableCategory.MEASUREMENTERROR in estimator.category_dict
assert VariableCategory.MODELDISTURBANCE in estimator.category_dict
# Make sure MHE categories contains variables we expect
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
actmeablock = MHEBlock.ACTUAL_MEASUREMENT_BLOCK
pred_actmea_vars = ComponentSet((actmeablock[ind].actual_measurement[t0]
for ind in estimator.MEASUREMENT_SET))
actmea_vars = estimator.category_dict[VariableCategory.ACTUALMEASUREMENT]
for var in actmea_vars:
assert var.is_reference()
assert var.ctype is ActualMeasurementVar
assert var._attr == "actualmeasurement"
assert var[t0] in pred_actmea_vars
pred_actmea_vars.remove(var[t0])
assert pred_actmea_vars._data == {}
meaerrblock = MHEBlock.MEASUREMENT_ERROR_BLOCK
pred_meaerr_vars = ComponentSet((meaerrblock[ind].measurement_error[t0]
for ind in estimator.MEASUREMENT_SET))
meaerr_vars = estimator.category_dict[VariableCategory.MEASUREMENTERROR]
for var in meaerr_vars:
assert var.is_reference()
assert var.ctype is MeasurementErrorVar
assert var._attr == "measurementerror"
assert var[t0] in pred_meaerr_vars
pred_meaerr_vars.remove(var[t0])
assert pred_meaerr_vars._data == {}
moddisblock = MHEBlock.MODEL_DISTURBANCE_BLOCK
pred_moddis_vars = ComponentSet((moddisblock[ind].model_disturbance[t0]
for ind in estimator.DIFFERENTIAL_SET))
moddis_vars = estimator.category_dict[VariableCategory.MODELDISTURBANCE]
for var in moddis_vars:
assert var.is_reference()
assert var.ctype is ModelDisturbanceVar
assert var._attr == "modeldisturbance"
assert var[t0] in pred_moddis_vars
pred_moddis_vars.remove(var[t0])
assert pred_moddis_vars._data == {}
@pytest.mark.unit
def test_add_mea_moddis_componentmap(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
moddis_block = estimator.MODELDISTURBANCE_BLOCK
diffvar_map_moddis = estimator.diffvar_map_moddis
for ind, var in enumerate(estimator.differential_vars):
assert diffvar_map_moddis[var[t0]] is moddis_block[ind].var
derivar_map_moddis = estimator.derivar_map_moddis
for ind, var in enumerate(estimator.derivative_vars):
assert derivar_map_moddis[var[t0]] is moddis_block[ind].var
meaerr_block = estimator.MEASUREMENTERROR_BLOCK
meavar_map_meaerr = estimator.meavar_map_meaerr
for ind, var in enumerate(estimator.measurement_vars):
assert meavar_map_meaerr[var[t0]] is meaerr_block[ind].var
actmea_block = estimator.ACTUALMEASUREMENT_BLOCK
meavar_map_actmea = estimator.meavar_map_actmea
for ind, var in enumerate(estimator.measurement_vars):
assert meavar_map_actmea[var[t0]] is actmea_block[ind].var
@pytest.mark.unit
def test_add_measurement_constraint(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "MEASUREMENT_CONSTRAINT_BLOCK")
meacon_block = MHEBlock.MEASUREMENT_CONSTRAINT_BLOCK
assert meacon_block.dim() == 1
assert meacon_block.index_set() == estimator.MEASUREMENT_SET
actmea_block = estimator.MHE_VARS_CONS_BLOCK.ACTUAL_MEASUREMENT_BLOCK
meaerr_block = estimator.MHE_VARS_CONS_BLOCK.MEASUREMENT_ERROR_BLOCK
mea_block = estimator.MEASUREMENT_BLOCK
for bind in estimator.MEASUREMENT_SET:
assert len(list(meacon_block[bind].component_objects(pyo.Constraint))) == 1
assert hasattr(meacon_block[bind], "con_mea_err")
curr_con = meacon_block[bind].con_mea_err
assert curr_con.index_set() == time
assert all(i1 is i2 for i1, i2 in
zip(curr_con.keys(), estimator.SAMPLEPOINT_SET))
for t in time:
if t in estimator.SAMPLEPOINT_SET:
pred_expr = actmea_block[bind].actual_measurement[t] ==\
(mea_block[bind].var[t] + \
meaerr_block[bind].measurement_error[t])
assert curr_con[t].expr.to_string() == pred_expr.to_string()
@pytest.mark.unit
def test_add_disturbance_to_differential_cons(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
MHEBlock = estimator.MHE_VARS_CONS_BLOCK
assert hasattr(MHEBlock, "DISTURBED_DIFFERENTIAL_CONSTRAINT_BLOCK")
ddc_block = MHEBlock.DISTURBED_DIFFERENTIAL_CONSTRAINT_BLOCK
assert ddc_block.dim() == 1
assert len(list(ddc_block.keys())) == \
len(estimator.con_category_dict[ConstraintCategory.DIFFERENTIAL])
def map_t_to_sp(tind):
for item in estimator.sample_point_indices:
if item >= tind:
return estimator.time[item]
diff_cons = estimator.con_category_dict[ConstraintCategory.DIFFERENTIAL]
moddis_block = estimator.MODELDISTURBANCE_BLOCK
for bind in estimator.DIFFERENTIAL_SET:
curr_block = ddc_block[bind]
assert len(list(curr_block.component_objects(pyo.Constraint))) == 1
assert hasattr(curr_block, "disturbed_diff_con")
curr_con = curr_block.disturbed_diff_con
assert curr_con.index_set() == time
for t in time:
tind = time.find_nearest_index(t)
sp = map_t_to_sp(tind)
pred_expr = diff_cons[bind][t].body - \
moddis_block[bind].var[sp] == 0.0
assert curr_con[t].expr.to_string() == pred_expr.to_string()
assert not diff_cons[bind][t].active
@pytest.mark.unit
def test_add_steady_state_objective_for_MHE_initialization(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
desiredss = [
(estimator.mod.flow_in[t0], 3.0),
]
weights = [
(estimator.mod.flow_in[t0], 2.0),
]
estimator.mod.flow_in[:].set_value(3.0)
initialize_t0(estimator.mod)
estimator.add_single_time_optimization_objective(desiredss, weights)
assert hasattr(estimator, 'single_time_optimization_objective')
pred_obj_expr = (2.0*(estimator.mod.flow_in[t0] - 3.0)**2)
obj_expr = estimator.single_time_optimization_objective.expr
assert pyo.value(pred_obj_expr) == pyo.value(obj_expr)
assert pred_obj_expr.to_string() == obj_expr.to_string()
estimator.del_component(estimator.single_time_optimization_objective)
desiredss = [
(estimator.mod.flow_in[t0], 3.0),
(estimator.mod.conc[t0,'A'], 1.5),
]
weights = [
(estimator.mod.flow_in[t0], 1.0),
(estimator.mod.conc[t0,'A'], 5.0),
]
estimator.add_single_time_optimization_objective(desiredss, weights)
pred_obj_expr = (
1.0*(estimator.mod.flow_in[t0] - 3.0)**2 +
5.0*(estimator.mod.conc[t0,'A'] - 1.5)**2
)
obj_expr = estimator.single_time_optimization_objective.expr
assert pyo.value(pred_obj_expr) == pyo.value(obj_expr)
assert pred_obj_expr.to_string() == obj_expr.to_string()
@pytest.mark.component
@pytest.mark.skipif(not solver_available, reason='IPOPT is not available')
def test_solve_steady_state_for_MHE_initialization(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
desiredss = [
(estimator.mod.flow_in[t0], 3.0),
]
weights = [
(estimator.mod.flow_in[t0], 1.0),
]
estimator.add_single_time_optimization_objective(desiredss, weights)
estimator.mod.flow_in[:].set_value(3.0)
initialize_t0(estimator.mod)
dof_prior = degrees_of_freedom(estimator)
estimator.solve_single_time_optimization(solver,
ic_type = "differential_var",
require_steady = True,
load_setpoints = False,
restore_ic_input_after_solve = False,
isMHE_block = True,)
dof_post = degrees_of_freedom(estimator)
assert dof_prior == dof_post
assert estimator.differential_vars[0][t0].value == \
pytest.approx(3.75, abs=1e-3)
assert estimator.differential_vars[1][t0].value == \
pytest.approx(1.25, abs=1e-3)
assert estimator.algebraic_vars[0][t0].value == \
pytest.approx(3.0, abs=1e-3)
assert estimator.algebraic_vars[1][t0].value == \
pytest.approx(-3.75, abs=1e-3)
assert estimator.algebraic_vars[2][t0].value == \
pytest.approx(3.75, abs=1e-3)
assert estimator.input_vars[0][t0].value == \
pytest.approx(3.0, abs=1e-3)
# Make sure all undisturbed differential equations are deactivated
diff_cons = estimator.con_category_dict[ConstraintCategory.DIFFERENTIAL]
assert not all(con[t].active for con in diff_cons for t in time)
# Make sure MHE block is active
assert estimator.MHE_VARS_CONS_BLOCK.active
# Make sure derivatives are not fixed
deri_vars = estimator.category_dict[VariableCategory.DERIVATIVE]
assert not all(var[t].fixed for var in deri_vars for t in time)
# Make sure all inputs are fixed
input_vars = estimator.category_dict[VariableCategory.INPUT]
assert all(var[t].fixed for var in input_vars for t in time)
# Make sure differential variables at t0 are fixed
diff_vars = estimator.category_dict[VariableCategory.DIFFERENTIAL]
assert all(var[t0].fixed for var in diff_vars)
@pytest.mark.unit
def test_initialize_actualmeasurements_at_t0(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
# Re-initialize measurements and actualmeasurements to make sure they
# are different before assertion
estimator.vectors.measurement[...].set_value(20.)
estimator.vectors.actualmeasurement[...].set_value(100.)
estimator.initialize_actualmeasurements_at_t0()
for ind in estimator.ACTUALMEASUREMENT_SET:
var_ind = (ind, t0)
actmea_var = estimator.vectors.actualmeasurement
assert actmea_var[var_ind].value == 20.
@pytest.mark.component
@pytest.mark.skipif(not solver_available, reason='IPOPT is not available')
def test_initialize_past_info_with_steady_state(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
desiredss = [
(estimator.mod.flow_in[t0], 3.0),
]
weights = [
(estimator.mod.flow_in[t0], 1.0),
]
estimator.mod.flow_in[:].set_value(3.0)
initialize_t0(estimator.mod)
estimator.initialize_past_info_with_steady_state(desiredss,
weights,
solver)
for tp in time:
assert estimator.differential_vars[0][tp].value == \
pytest.approx(3.75, abs=1e-3)
assert estimator.differential_vars[1][tp].value == \
pytest.approx(1.25, abs=1e-3)
assert estimator.algebraic_vars[0][tp].value == \
pytest.approx(3.0, abs=1e-3)
assert estimator.algebraic_vars[1][tp].value == \
pytest.approx(-3.75, abs=1e-3)
assert estimator.algebraic_vars[2][tp].value == \
pytest.approx(3.75, abs=1e-3)
assert estimator.input_vars[0][tp].value == \
pytest.approx(3.0, abs=1e-3)
for sp in estimator.sample_points:
curr_var = estimator.vectors.actualmeasurement[0, sp]
assert curr_var.value == 3.75
assert curr_var.fixed
@pytest.mark.unit
def test_add_noise_minimize_objective(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
# Re-initialize noises so they are not zero for objective evalutaion
estimator.vectors.modeldisturbance[...].set_value(0.5)
estimator.vectors.measurementerror[...].set_value(2.5)
model_disturbance_weights = [
(estimator.mod.conc[t0,'A'], 0.1),
(estimator.mod.conc[t0,'B'], 0.2),
]
measurement_error_weights = [(estimator.mod.conc[0,'A'], 10.),]
estimator.add_noise_minimize_objective(model_disturbance_weights,
measurement_error_weights,
givenform = "weight")
assert hasattr(estimator, 'noise_minimize_objective')
MD_block = estimator.MHE_VARS_CONS_BLOCK.MODEL_DISTURBANCE_BLOCK
ME_block = estimator.MHE_VARS_CONS_BLOCK.MEASUREMENT_ERROR_BLOCK
pred_obj_expr = sum(0.1*(MD_block[0].model_disturbance[t])**2
for t in estimator.sample_points if t!=t0) + \
sum(0.2*(MD_block[1].model_disturbance[t])**2
for t in estimator.sample_points if t!=t0) + \
sum(10.*(ME_block[0].measurement_error[t])**2
for t in estimator.sample_points)
obj_expr = estimator.noise_minimize_objective.expr
assert pyo.value(pred_obj_expr) == pyo.value(obj_expr)
assert pyo.value(obj_expr) > 0
estimator.del_component("noise_minimize_objective")
model_disturbance_variances = [
(estimator.mod.conc[t0,'A'], 10.),
(estimator.mod.conc[t0,'B'], 20.),
]
measurement_error_variances = [(estimator.mod.conc[0,'A'], 0.1),]
estimator.add_noise_minimize_objective(model_disturbance_variances,
measurement_error_variances,
givenform = "variance")
assert hasattr(estimator, 'noise_minimize_objective')
pred_obj_expr = sum(1/10.*(MD_block[0].model_disturbance[t])**2
for t in estimator.sample_points if t!=t0) + \
sum(1/20.*(MD_block[1].model_disturbance[t])**2
for t in estimator.sample_points if t!=t0) + \
sum(1/0.1*(ME_block[0].measurement_error[t])**2
for t in estimator.sample_points)
obj_expr = estimator.noise_minimize_objective.expr
assert pyo.value(pred_obj_expr) == pyo.value(obj_expr)
assert pyo.value(obj_expr) > 0
@pytest.mark.unit
def test_check_var_con_dof(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
estimator.check_var_con_dof(skip_dof_check = False)
input_vars = estimator.vectors.input
for ind in input_vars.keys():
assert input_vars[ind].fixed
diffvars = estimator.vectors.differential
for ind in diffvars.keys():
assert not diffvars[ind].fixed
for ind in estimator.MODELDISTURBANCE_SET:
assert estimator.vectors.modeldisturbance[ind, 0].fixed
assert pyo.value(estimator.vectors.modeldisturbance[ind, 0]) == 0.0
@pytest.mark.unit
def load_inputs_into_last_sample(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
# Re-initialize inputs so they are not 100.
estimator.vectors.input[...].set_value(0.5)
input = [100.]
estimator.load_inputs_into_last_sample(input)
spi = estimator.sample_point_indices
check_t_list = [tp for tp in time if tp > time[spi[-2]]
and tp <= time[spi[-1]]]
for tp in check_t_list:
assert pyo.value(estimator.vectors.input[0, tp]) == 100.
@pytest.mark.unit
def test_generate_estimates_at_time(self):
estimator = self.make_estimator()
time = estimator.time
t0 = time.first()
tlast = time.last()
# Re-set values for differential variables
estimator.vectors.differential[0,tlast].set_value(105.)
estimator.vectors.differential[1,tlast].set_value(205.)
estimates = estimator.generate_estimates_at_time(tlast)
assert estimates == [105., 205.]
@pytest.mark.unit
def test_load_measurements(self):
blk = self.make_estimator()
time = blk.time
t0 = time.first()
vals = [0.75]
t_last = time.last()
blk.load_measurements(vals, timepoint = t_last)
for b, val in zip(blk.ACTUALMEASUREMENT_BLOCK.values(), vals):
assert b.var[t_last].value == val | 0.531939 | 0.541288 |
from datetime import timedelta
import re
from typing import Any, Dict, Optional, Union
multiple_map = {
"K": 1024 ** 0,
"M": 1024 ** 1,
"G": 1024 ** 2,
"T": 1024 ** 3,
"E": 1024 ** 4,
}
state_colors = {
"FAILED": "red",
"TIMEOUT": "red",
"OUT_OF_MEMORY": "red",
"RUNNING": "cyan",
"CANCELLED": "yellow",
"COMPLETED": "green",
"PENDING": "blue",
}
#: Regex for DDHHMMSS style timestamps
DDHHMMSS_RE = re.compile(
r"(?P<days>\d+)-(?P<hours>\d{2}):(?P<minutes>\d{2}):(?P<seconds>\d{2})"
)
#: Regex for HHMMSS style timestamps
HHMMSS_RE = re.compile(r"(?P<hours>\d{2}):(?P<minutes>\d{2}):(?P<seconds>\d{2})")
#: Regex for HHMMmmm style timestamps
MMSSMMM_RE = re.compile(
r"(?P<minutes>\d{2}):(?P<seconds>\d{2}).(?P<milliseconds>\d{3})"
)
#: Regex for maxRSS and reqmem
MEM_RE = re.compile(
r"(?P<memory>[-+]?\d*\.\d+|\d+)(?P<multiple>[KMGTE]?)(?P<type>[nc]?)"
)
class Job:
"""Representation of scheduler job."""
def __init__(self, job: str, jobid: str, filename: Optional[str]) -> None:
"""Initialize new job.
Args:
job: the base job number
jobid: same as job unless an array job
filename: the output file associated with this job
"""
self.job = job
self.jobid = jobid
self.filename = filename
self.stepmem = 0.0
self.totalmem: Optional[float] = None
self.time: Optional[str] = "---"
self.time_eff: Union[str, float] = "---"
self.cpu: Optional[Union[str, float]] = "---"
self.mem: Union[str, float] = "---"
self.state: Optional[str] = None
self.other_entries: Dict[str, str] = {}
def __eq__(self, other: Any) -> bool:
"""Test for equality.
Args:
other: the other object
Returns:
true if the other object is a Job and all attributes match
"""
if not isinstance(other, Job):
return False
return self.__dict__ == other.__dict__
def __repr__(self) -> str:
"""Job representation.
Returns:
The job string representation
"""
return f"Job(job={self.job}, jobid={self.jobid}, filename={self.filename})"
def update(self, entry: Dict) -> None:
"""Update the job properties based on the db_inquirer entry.
Args:
entry: the db_inquirer entry for the matching job
"""
if "." not in entry["JobID"]:
self.state = entry["State"].split()[0]
if self.state == "PENDING":
return
# main job id
if self.jobid == entry["JobID"]:
self._update_main_job(entry)
elif self.state != "RUNNING":
for k, value in entry.items():
if k not in self.other_entries or not self.other_entries[k]:
self.other_entries[k] = value
self.stepmem += parsemem(entry["MaxRSS"]) if "MaxRSS" in entry else 0
def _update_main_job(self, entry: Dict) -> None:
"""Update properties for the main job.
Args:
entry: the entry where the jobid matches exactly
"""
self.other_entries = entry
self.time = entry["Elapsed"] if "Elapsed" in entry else None
requested = (
_parse_slurm_timedelta(entry["Timelimit"]) if "Timelimit" in entry else 1
)
wall = _parse_slurm_timedelta(entry["Elapsed"]) if "Elapsed" in entry else 0
if requested != 0:
self.time_eff = round(wall / requested * 100, 1)
if self.state == "RUNNING":
return
cpus = (
_parse_slurm_timedelta(entry["TotalCPU"]) / int(entry["AllocCPUS"])
if "TotalCPU" in entry and "AllocCPUS" in entry
else 0
)
if wall == 0:
self.cpu = None
else:
self.cpu = round(cpus / wall * 100, 1)
if "REQMEM" in entry and "NNodes" in entry and "AllocCPUS" in entry:
self.totalmem = parsemem(
entry["REQMEM"], int(entry["NNodes"]), int(entry["AllocCPUS"])
)
def name(self) -> str:
"""The name of the job.
Returns:
The filename (if set) or jobid
"""
if self.filename:
return self.filename
return self.jobid
def get_entry(self, key: str) -> Any:
"""Get an attribute by name.
Args:
key: the attribute to query
Returns:
The value of that attribute or "---" if not found
"""
if key == "JobID":
return self.name()
if key == "State":
return self.state
if key == "MemEff":
if self.totalmem:
return round(self.stepmem / self.totalmem * 100, 1)
return "---"
if key == "TimeEff":
return self.time_eff
if key == "CPUEff":
return self.cpu if self.cpu else "---"
else:
return self.other_entries.get(key, "---")
def _parse_slurm_timedelta(delta: str) -> int:
"""Parse one of the three formats used in TotalCPU.
Based on which regex matches, convert into a timedelta
and return total seconds.
Args:
delta: The time duration
Returns:
Number of seconds elapsed during the delta
Raises:
ValueError: if unable to parse delta
"""
match = re.match(DDHHMMSS_RE, delta)
if match:
return int(
timedelta(
days=int(match.group("days")),
hours=int(match.group("hours")),
minutes=int(match.group("minutes")),
seconds=int(match.group("seconds")),
).total_seconds()
)
match = re.match(HHMMSS_RE, delta)
if match:
return int(
timedelta(
hours=int(match.group("hours")),
minutes=int(match.group("minutes")),
seconds=int(match.group("seconds")),
).total_seconds()
)
match = re.match(MMSSMMM_RE, delta)
if match:
return int(
timedelta(
minutes=int(match.group("minutes")),
seconds=int(match.group("seconds")),
milliseconds=int(match.group("milliseconds")),
).total_seconds()
)
raise ValueError(f'Failed to parse time "{delta}"')
def parsemem(mem: str, nodes: int = 1, cpus: int = 1) -> float:
"""Parse memory representations of reqmem and maxrss.
Args:
mem: the memory representation
nodes: the number of nodes in the job
cpus: the number of cpus in the job
Returns:
The number of bytes for the job.
if mem is empty, return 0.
if mem ends with n or c, scale by the provided nodes or cpus respecitvely
the multiple of memory (e.g. M or G) is always scaled if provided
Raises:
ValueError: if unable to parse mem
"""
if mem == "" or mem == "0":
return 0
match = re.fullmatch(MEM_RE, mem)
if not match:
raise ValueError(f'Failed to parse memory "{mem}"')
memory = float(match.group("memory"))
if match.group("multiple") != "":
memory *= multiple_map[match.group("multiple")]
if match.group("type") != "":
if match.group("type") == "n":
memory *= nodes
else:
memory *= cpus
return memory | src/reportseff/job.py | from datetime import timedelta
import re
from typing import Any, Dict, Optional, Union
multiple_map = {
"K": 1024 ** 0,
"M": 1024 ** 1,
"G": 1024 ** 2,
"T": 1024 ** 3,
"E": 1024 ** 4,
}
state_colors = {
"FAILED": "red",
"TIMEOUT": "red",
"OUT_OF_MEMORY": "red",
"RUNNING": "cyan",
"CANCELLED": "yellow",
"COMPLETED": "green",
"PENDING": "blue",
}
#: Regex for DDHHMMSS style timestamps
DDHHMMSS_RE = re.compile(
r"(?P<days>\d+)-(?P<hours>\d{2}):(?P<minutes>\d{2}):(?P<seconds>\d{2})"
)
#: Regex for HHMMSS style timestamps
HHMMSS_RE = re.compile(r"(?P<hours>\d{2}):(?P<minutes>\d{2}):(?P<seconds>\d{2})")
#: Regex for HHMMmmm style timestamps
MMSSMMM_RE = re.compile(
r"(?P<minutes>\d{2}):(?P<seconds>\d{2}).(?P<milliseconds>\d{3})"
)
#: Regex for maxRSS and reqmem
MEM_RE = re.compile(
r"(?P<memory>[-+]?\d*\.\d+|\d+)(?P<multiple>[KMGTE]?)(?P<type>[nc]?)"
)
class Job:
"""Representation of scheduler job."""
def __init__(self, job: str, jobid: str, filename: Optional[str]) -> None:
"""Initialize new job.
Args:
job: the base job number
jobid: same as job unless an array job
filename: the output file associated with this job
"""
self.job = job
self.jobid = jobid
self.filename = filename
self.stepmem = 0.0
self.totalmem: Optional[float] = None
self.time: Optional[str] = "---"
self.time_eff: Union[str, float] = "---"
self.cpu: Optional[Union[str, float]] = "---"
self.mem: Union[str, float] = "---"
self.state: Optional[str] = None
self.other_entries: Dict[str, str] = {}
def __eq__(self, other: Any) -> bool:
"""Test for equality.
Args:
other: the other object
Returns:
true if the other object is a Job and all attributes match
"""
if not isinstance(other, Job):
return False
return self.__dict__ == other.__dict__
def __repr__(self) -> str:
"""Job representation.
Returns:
The job string representation
"""
return f"Job(job={self.job}, jobid={self.jobid}, filename={self.filename})"
def update(self, entry: Dict) -> None:
"""Update the job properties based on the db_inquirer entry.
Args:
entry: the db_inquirer entry for the matching job
"""
if "." not in entry["JobID"]:
self.state = entry["State"].split()[0]
if self.state == "PENDING":
return
# main job id
if self.jobid == entry["JobID"]:
self._update_main_job(entry)
elif self.state != "RUNNING":
for k, value in entry.items():
if k not in self.other_entries or not self.other_entries[k]:
self.other_entries[k] = value
self.stepmem += parsemem(entry["MaxRSS"]) if "MaxRSS" in entry else 0
def _update_main_job(self, entry: Dict) -> None:
"""Update properties for the main job.
Args:
entry: the entry where the jobid matches exactly
"""
self.other_entries = entry
self.time = entry["Elapsed"] if "Elapsed" in entry else None
requested = (
_parse_slurm_timedelta(entry["Timelimit"]) if "Timelimit" in entry else 1
)
wall = _parse_slurm_timedelta(entry["Elapsed"]) if "Elapsed" in entry else 0
if requested != 0:
self.time_eff = round(wall / requested * 100, 1)
if self.state == "RUNNING":
return
cpus = (
_parse_slurm_timedelta(entry["TotalCPU"]) / int(entry["AllocCPUS"])
if "TotalCPU" in entry and "AllocCPUS" in entry
else 0
)
if wall == 0:
self.cpu = None
else:
self.cpu = round(cpus / wall * 100, 1)
if "REQMEM" in entry and "NNodes" in entry and "AllocCPUS" in entry:
self.totalmem = parsemem(
entry["REQMEM"], int(entry["NNodes"]), int(entry["AllocCPUS"])
)
def name(self) -> str:
"""The name of the job.
Returns:
The filename (if set) or jobid
"""
if self.filename:
return self.filename
return self.jobid
def get_entry(self, key: str) -> Any:
"""Get an attribute by name.
Args:
key: the attribute to query
Returns:
The value of that attribute or "---" if not found
"""
if key == "JobID":
return self.name()
if key == "State":
return self.state
if key == "MemEff":
if self.totalmem:
return round(self.stepmem / self.totalmem * 100, 1)
return "---"
if key == "TimeEff":
return self.time_eff
if key == "CPUEff":
return self.cpu if self.cpu else "---"
else:
return self.other_entries.get(key, "---")
def _parse_slurm_timedelta(delta: str) -> int:
"""Parse one of the three formats used in TotalCPU.
Based on which regex matches, convert into a timedelta
and return total seconds.
Args:
delta: The time duration
Returns:
Number of seconds elapsed during the delta
Raises:
ValueError: if unable to parse delta
"""
match = re.match(DDHHMMSS_RE, delta)
if match:
return int(
timedelta(
days=int(match.group("days")),
hours=int(match.group("hours")),
minutes=int(match.group("minutes")),
seconds=int(match.group("seconds")),
).total_seconds()
)
match = re.match(HHMMSS_RE, delta)
if match:
return int(
timedelta(
hours=int(match.group("hours")),
minutes=int(match.group("minutes")),
seconds=int(match.group("seconds")),
).total_seconds()
)
match = re.match(MMSSMMM_RE, delta)
if match:
return int(
timedelta(
minutes=int(match.group("minutes")),
seconds=int(match.group("seconds")),
milliseconds=int(match.group("milliseconds")),
).total_seconds()
)
raise ValueError(f'Failed to parse time "{delta}"')
def parsemem(mem: str, nodes: int = 1, cpus: int = 1) -> float:
"""Parse memory representations of reqmem and maxrss.
Args:
mem: the memory representation
nodes: the number of nodes in the job
cpus: the number of cpus in the job
Returns:
The number of bytes for the job.
if mem is empty, return 0.
if mem ends with n or c, scale by the provided nodes or cpus respecitvely
the multiple of memory (e.g. M or G) is always scaled if provided
Raises:
ValueError: if unable to parse mem
"""
if mem == "" or mem == "0":
return 0
match = re.fullmatch(MEM_RE, mem)
if not match:
raise ValueError(f'Failed to parse memory "{mem}"')
memory = float(match.group("memory"))
if match.group("multiple") != "":
memory *= multiple_map[match.group("multiple")]
if match.group("type") != "":
if match.group("type") == "n":
memory *= nodes
else:
memory *= cpus
return memory | 0.919439 | 0.343342 |
import torch
from torch import nn
import torch.nn.functional as F
from typing import Sequence, Tuple, Dict
class DistogramHead(nn.Module):
"""
https://github.com/lupoglaz/alphafold/blob/2d53ad87efedcbbda8e67ab3be96af769dbeae7d/alphafold/model/modules.py#L1348
"""
def __init__(self, config, global_config, num_feat_2d:int) -> None:
super(DistogramHead, self).__init__()
self.config = config
self.global_config = global_config
self.half_logits = nn.Linear(num_feat_2d, config.num_bins)
self.loss_function = nn.CrossEntropyLoss(reduction='none')
def load_weights_from_af2(self, data, rel_path: str='distogram_head', ind:int=None):
modules=[self.half_logits]
names=['half_logits']
nums=[1]
for module, name, num in zip(modules, names, nums):
for i in range(num):
if i==0:
add_str = ''
else:
add_str = f'_{i}'
if ind is None:
w = data[f'{rel_path}/{name}{add_str}']['weights'].transpose(-1,-2)
b = data[f'{rel_path}/{name}{add_str}']['bias']
else:
w = data[f'{rel_path}/{name}{add_str}']['weights'][ind,...].transpose(-1,-2)
b = data[f'{rel_path}/{name}{add_str}']['bias'][ind,...]
if isinstance(module, nn.ModuleList):
print(f'Loading {name}{add_str}.weight: {w.shape} -> {module[i].weight.size()}')
print(f'Loading {name}{add_str}.bias: {b.shape} -> {module[i].bias.size()}')
module[i].weight.data.copy_(torch.from_numpy(w))
module[i].bias.data.copy_(torch.from_numpy(b))
else:
print(f'Loading {name}{add_str}.weight: {w.shape} -> {module.weight.size()}')
print(f'Loading {name}{add_str}.bias: {b.shape} -> {module.bias.size()}')
module.weight.data.copy_(torch.from_numpy(w))
module.bias.data.copy_(torch.from_numpy(b))
def forward(self, representations:Dict[str,torch.Tensor], batch:Dict[str,torch.Tensor], is_training:bool=False):
half_logits = self.half_logits(representations['pair'])
logits = half_logits + half_logits.transpose(-2, -3)
breaks = torch.linspace(start=self.config.first_break, end=self.config.last_break, steps=self.config.num_bins-1, device=logits.device, dtype=logits.dtype)
return dict(logits=logits, bin_edges=breaks)
def loss(self, value:Dict[str,torch.Tensor], batch:Dict[str,torch.Tensor])->Dict[str,torch.Tensor]:
logits = value['logits']
bin_edges = value['bin_edges']
positions = batch['pseudo_beta']
mask = batch['pseudo_beta_mask']
assert logits.ndimension() == 3
assert positions.size(-1) == 3
sq_breaks = torch.square(bin_edges)
dist2 = torch.sum(torch.square(positions.unsqueeze(dim=-2) - positions.unsqueeze(dim=-3)), dim=-1, keepdim=True)
true_bins = torch.sum(dist2>sq_breaks, dim=-1)
errors = self.loss_function(logits.view(-1, self.config.num_bins), true_bins.view(-1))
square_mask = mask.unsqueeze(dim=-2) * mask.unsqueeze(dim=-1)
errors = errors.view(positions.size(-2), positions.size(-2))
avg_error = torch.sum(errors*square_mask, dim=(-2,-1))/(1e-8 + torch.sum(square_mask, dim=(-2,-1)))
dist2 = dist2[...,0]
return dict(loss=avg_error, true_dist=torch.sqrt(1e-6 + dist2)) | alphafold/Model/Heads/distogram.py | import torch
from torch import nn
import torch.nn.functional as F
from typing import Sequence, Tuple, Dict
class DistogramHead(nn.Module):
"""
https://github.com/lupoglaz/alphafold/blob/2d53ad87efedcbbda8e67ab3be96af769dbeae7d/alphafold/model/modules.py#L1348
"""
def __init__(self, config, global_config, num_feat_2d:int) -> None:
super(DistogramHead, self).__init__()
self.config = config
self.global_config = global_config
self.half_logits = nn.Linear(num_feat_2d, config.num_bins)
self.loss_function = nn.CrossEntropyLoss(reduction='none')
def load_weights_from_af2(self, data, rel_path: str='distogram_head', ind:int=None):
modules=[self.half_logits]
names=['half_logits']
nums=[1]
for module, name, num in zip(modules, names, nums):
for i in range(num):
if i==0:
add_str = ''
else:
add_str = f'_{i}'
if ind is None:
w = data[f'{rel_path}/{name}{add_str}']['weights'].transpose(-1,-2)
b = data[f'{rel_path}/{name}{add_str}']['bias']
else:
w = data[f'{rel_path}/{name}{add_str}']['weights'][ind,...].transpose(-1,-2)
b = data[f'{rel_path}/{name}{add_str}']['bias'][ind,...]
if isinstance(module, nn.ModuleList):
print(f'Loading {name}{add_str}.weight: {w.shape} -> {module[i].weight.size()}')
print(f'Loading {name}{add_str}.bias: {b.shape} -> {module[i].bias.size()}')
module[i].weight.data.copy_(torch.from_numpy(w))
module[i].bias.data.copy_(torch.from_numpy(b))
else:
print(f'Loading {name}{add_str}.weight: {w.shape} -> {module.weight.size()}')
print(f'Loading {name}{add_str}.bias: {b.shape} -> {module.bias.size()}')
module.weight.data.copy_(torch.from_numpy(w))
module.bias.data.copy_(torch.from_numpy(b))
def forward(self, representations:Dict[str,torch.Tensor], batch:Dict[str,torch.Tensor], is_training:bool=False):
half_logits = self.half_logits(representations['pair'])
logits = half_logits + half_logits.transpose(-2, -3)
breaks = torch.linspace(start=self.config.first_break, end=self.config.last_break, steps=self.config.num_bins-1, device=logits.device, dtype=logits.dtype)
return dict(logits=logits, bin_edges=breaks)
def loss(self, value:Dict[str,torch.Tensor], batch:Dict[str,torch.Tensor])->Dict[str,torch.Tensor]:
logits = value['logits']
bin_edges = value['bin_edges']
positions = batch['pseudo_beta']
mask = batch['pseudo_beta_mask']
assert logits.ndimension() == 3
assert positions.size(-1) == 3
sq_breaks = torch.square(bin_edges)
dist2 = torch.sum(torch.square(positions.unsqueeze(dim=-2) - positions.unsqueeze(dim=-3)), dim=-1, keepdim=True)
true_bins = torch.sum(dist2>sq_breaks, dim=-1)
errors = self.loss_function(logits.view(-1, self.config.num_bins), true_bins.view(-1))
square_mask = mask.unsqueeze(dim=-2) * mask.unsqueeze(dim=-1)
errors = errors.view(positions.size(-2), positions.size(-2))
avg_error = torch.sum(errors*square_mask, dim=(-2,-1))/(1e-8 + torch.sum(square_mask, dim=(-2,-1)))
dist2 = dist2[...,0]
return dict(loss=avg_error, true_dist=torch.sqrt(1e-6 + dist2)) | 0.860398 | 0.475971 |
from typing import List
import argparse
import pandas as pd
from ulfs import stats_utils, tex_utils
def reduce(df_l: List[pd.DataFrame], out_csv: str, out_tex: str):
# drop_columns = [
# 'seed', 'b', 'terminate_reason', 'train_acc',
# 'val_same_gnd_clusters', 'val_same_pred_clusters', 'val_new_gnd_clusters', 'val_new_pred_clusters']
keep_columns = [
'ds_family', 'sampler_model', 't', 'augment',
'train_acc',
'test_same_acc', 'test_same_rho', 'test_same_prec', 'test_same_rec',
'test_new_acc', 'test_new_rho', 'test_new_prec', 'test_new_rec']
# df_l = [df.drop(columns=drop_columns) for df in df_l]
df_l = [df[keep_columns].copy() for df in df_l]
for i, df in enumerate(list(df_l)):
df = df.rename(columns={'sampler_model': 'train_sampler'})
columns = list(df.columns)
print('columns', columns)
columns = ['code'] + columns
df['code'] = df.ds_family
df.t = df.t / 60
df.loc[df.code != 'lsl_shapeworld', 'code'] = 'ours'
df.loc[df.code == 'lsl_shapeworld', 'code'] = 'LSL'
df.ds_family = df.ds_family.str.replace('lsl_', '').replace('texrel', '\\textsc{TexRel}').replace(
'shapeworld', 'Shapeworld')
df['test_sampler'] = df.train_sampler.copy()
df.loc[df.code == 'LSL', 'train_sampler'] = 'soft'
df.train_sampler = df.train_sampler.str.replace('Softmax', 'soft').replace('Gumbel', 'gumb')
df.test_sampler = df.test_sampler.str.replace('Softmax', 'soft').replace('Gumbel', 'discr').replace(
'discrete', 'discr')
columns = columns[:3] + ['test_sampler'] + columns[3:]
# df.loc[(df.code == 'lsl_shapeworld') & (df.augment), 'ds_family'] = 'Shapeworld+aug'
df.loc[(df.code == 'LSL') & (df.augment), 'ds_family'] = 'Shapeworld+aug'
df = df[columns]
df = df.drop(columns=['augment'])
df_l[i] = df
averaged_str_no_ci, average, ci, counts = stats_utils.average_of_dfs(df_l, show_ci=False, show_mean=True, max_sig=2)
averaged_str_with_ci, average, ci, counts = stats_utils.average_of_dfs(
df_l, show_ci=False, show_mean=True, max_sig=2)
averaged_str_with_ci.t = averaged_str_no_ci.t
print(averaged_str_with_ci)
averaged_str_with_ci.to_csv(out_csv)
highlight = tex_utils.get_best_by_column(
df_str_no_ci=averaged_str_no_ci, df_mean=average, maximize=[
'train_acc',
'test_same_acc', 'test_same_rho', 'test_same_prec', 'test_same_rec',
'test_new_acc', 'test_new_rho', 'test_new_prec', 'test_new_rec'
]
)
titles = {
'ds family': 'Dataset',
'code': 'Code',
't': 'Time',
'train sampler': 'Train sampler',
'test sampler': 'Test sampler',
'train acc': '$\\acc_{train}$',
'test same acc': '$\\acc_{same}$',
'test same rho': '$\\rho_{same}$',
'test same prec': '$\\text{prec}_{same}$',
'test same rec': '$\\text{rec}_{same}$',
'test new acc': '$\\acc_{new}$',
'test new rho': '$\\rho_{new}$',
'test new prec': '$\\text{prec}_{new}$',
'test new rec': '$\\text{rec}_{new}$',
}
tex_utils.write_tex(
df_str=averaged_str_with_ci, df_mean=average, filepath=out_tex,
highlight=highlight,
titles=titles,
latex_defines='\\def\\acc{\\text{acc}}',
add_arrows=False,
caption='Comparison between \\textsc{{TexRel}} and ShapeWorld datasets for emergent communications scenario',
label='tab:vs_shapeworld')
def run(args):
df_l = []
for ref in args.in_refs:
df = pd.read_csv(f'pull/{ref}.csv')
df_l.append(df)
reduce(
df_l,
out_csv=args.out_csv_templ.format(out_ref=args.out_ref),
out_tex=args.out_tex_templ.format(out_ref=args.out_ref))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--in-refs', type=str, nargs='+')
parser.add_argument('--out-ref', type=str, required=True)
parser.add_argument('--out-csv-templ', type=str, default='pull/{out_ref}.csv')
parser.add_argument('--out-tex-templ', type=str, default='pull/{out_ref}.tex')
args = parser.parse_args()
run(args) | ref_task/analysis/texrel/reduce_vs_shapeworld.py | from typing import List
import argparse
import pandas as pd
from ulfs import stats_utils, tex_utils
def reduce(df_l: List[pd.DataFrame], out_csv: str, out_tex: str):
# drop_columns = [
# 'seed', 'b', 'terminate_reason', 'train_acc',
# 'val_same_gnd_clusters', 'val_same_pred_clusters', 'val_new_gnd_clusters', 'val_new_pred_clusters']
keep_columns = [
'ds_family', 'sampler_model', 't', 'augment',
'train_acc',
'test_same_acc', 'test_same_rho', 'test_same_prec', 'test_same_rec',
'test_new_acc', 'test_new_rho', 'test_new_prec', 'test_new_rec']
# df_l = [df.drop(columns=drop_columns) for df in df_l]
df_l = [df[keep_columns].copy() for df in df_l]
for i, df in enumerate(list(df_l)):
df = df.rename(columns={'sampler_model': 'train_sampler'})
columns = list(df.columns)
print('columns', columns)
columns = ['code'] + columns
df['code'] = df.ds_family
df.t = df.t / 60
df.loc[df.code != 'lsl_shapeworld', 'code'] = 'ours'
df.loc[df.code == 'lsl_shapeworld', 'code'] = 'LSL'
df.ds_family = df.ds_family.str.replace('lsl_', '').replace('texrel', '\\textsc{TexRel}').replace(
'shapeworld', 'Shapeworld')
df['test_sampler'] = df.train_sampler.copy()
df.loc[df.code == 'LSL', 'train_sampler'] = 'soft'
df.train_sampler = df.train_sampler.str.replace('Softmax', 'soft').replace('Gumbel', 'gumb')
df.test_sampler = df.test_sampler.str.replace('Softmax', 'soft').replace('Gumbel', 'discr').replace(
'discrete', 'discr')
columns = columns[:3] + ['test_sampler'] + columns[3:]
# df.loc[(df.code == 'lsl_shapeworld') & (df.augment), 'ds_family'] = 'Shapeworld+aug'
df.loc[(df.code == 'LSL') & (df.augment), 'ds_family'] = 'Shapeworld+aug'
df = df[columns]
df = df.drop(columns=['augment'])
df_l[i] = df
averaged_str_no_ci, average, ci, counts = stats_utils.average_of_dfs(df_l, show_ci=False, show_mean=True, max_sig=2)
averaged_str_with_ci, average, ci, counts = stats_utils.average_of_dfs(
df_l, show_ci=False, show_mean=True, max_sig=2)
averaged_str_with_ci.t = averaged_str_no_ci.t
print(averaged_str_with_ci)
averaged_str_with_ci.to_csv(out_csv)
highlight = tex_utils.get_best_by_column(
df_str_no_ci=averaged_str_no_ci, df_mean=average, maximize=[
'train_acc',
'test_same_acc', 'test_same_rho', 'test_same_prec', 'test_same_rec',
'test_new_acc', 'test_new_rho', 'test_new_prec', 'test_new_rec'
]
)
titles = {
'ds family': 'Dataset',
'code': 'Code',
't': 'Time',
'train sampler': 'Train sampler',
'test sampler': 'Test sampler',
'train acc': '$\\acc_{train}$',
'test same acc': '$\\acc_{same}$',
'test same rho': '$\\rho_{same}$',
'test same prec': '$\\text{prec}_{same}$',
'test same rec': '$\\text{rec}_{same}$',
'test new acc': '$\\acc_{new}$',
'test new rho': '$\\rho_{new}$',
'test new prec': '$\\text{prec}_{new}$',
'test new rec': '$\\text{rec}_{new}$',
}
tex_utils.write_tex(
df_str=averaged_str_with_ci, df_mean=average, filepath=out_tex,
highlight=highlight,
titles=titles,
latex_defines='\\def\\acc{\\text{acc}}',
add_arrows=False,
caption='Comparison between \\textsc{{TexRel}} and ShapeWorld datasets for emergent communications scenario',
label='tab:vs_shapeworld')
def run(args):
df_l = []
for ref in args.in_refs:
df = pd.read_csv(f'pull/{ref}.csv')
df_l.append(df)
reduce(
df_l,
out_csv=args.out_csv_templ.format(out_ref=args.out_ref),
out_tex=args.out_tex_templ.format(out_ref=args.out_ref))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--in-refs', type=str, nargs='+')
parser.add_argument('--out-ref', type=str, required=True)
parser.add_argument('--out-csv-templ', type=str, default='pull/{out_ref}.csv')
parser.add_argument('--out-tex-templ', type=str, default='pull/{out_ref}.tex')
args = parser.parse_args()
run(args) | 0.562777 | 0.404449 |
import logging
class Result:
def __init__(self, value, pos: int):
self.value = value
self.pos = pos
class Parser:
def __call__(self, tokens: list, pos: int) -> Result:
return None
def __add__(self, other):
return Concat(self, other)
def __or__(self, other):
return Alternate(self, other)
def __xor__(self, function):
return Process(self, function)
def __pos__(self):
return Opt(self)
def __neg__(self):
return Rep(self)
class Reserved(Parser):
def __init__(self, value: str, tag: str):
self.value = value
self.tag = tag
def __call__(self, tokens: list, pos: int) -> Result:
if pos < len(tokens) and tokens[pos].text == self.value and tokens[pos].tag is self.tag:
return Result(tokens[pos].text, pos + 1)
else:
return None
class Tag(Parser):
def __init__(self, tag: str):
self.tag = tag
def __call__(self, tokens: list, pos: int) -> Result:
if pos < len(tokens) and tokens[pos].tag is self.tag:
return Result(tokens[pos].text, pos + 1)
else:
return None
class Concat(Parser):
def __init__(self, left: Parser, right: Parser):
self.left = left
self.right = right
def __call__(self, tokens: list, pos: int) -> Result:
left_result = self.left(tokens, pos)
if left_result:
right_result = self.right(tokens, left_result.pos)
if right_result:
combined_value = (left_result.value, right_result.value)
return Result(combined_value, right_result.pos)
return None
class Alternate(Parser):
def __init__(self, left: Parser, right: Parser):
self.left = left
self.right = right
def __call__(self, tokens: list, pos: int) -> Result:
left_result = self.left(tokens, pos)
if left_result:
return left_result
else:
right_result = self.right(tokens, pos)
return right_result
class Process(Parser):
def __init__(self, parser: Parser, function):
self.parser = parser
self.function = function
def __call__(self, tokens: list, pos: int) -> Result:
result = self.parser(tokens, pos)
if result:
result.value = self.function(result.value)
return result
class Opt(Parser):
def __init__(self, parser: Parser):
self.parser = parser
def __call__(self, tokens: list, pos: int) -> Result:
result = self.parser(tokens, pos)
if result:
return result
else:
return Result(None, pos)
class Rep(Parser):
def __init__(self, parser: Parser):
self.parser = parser
def __call__(self, tokens: list, pos: int) -> Result:
results = []
result = self.parser(tokens, pos)
while result:
results.append(result.value)
pos = result.pos
result = self.parser(tokens, pos)
return Result(results, pos)
class Lazy(Parser):
def __init__(self, parser_func):
self.parser = None
self.parser_func = parser_func
def __call__(self, tokens: list, pos: int) -> Result:
if not self.parser:
self.parser = self.parser_func()
return self.parser(tokens, pos)
class Phrase(Parser):
def __init__(self, parser: Parser):
self.parser = parser
def __call__(self, tokens: list, pos: int) -> Result:
result = self.parser(tokens, pos)
if result and result.pos < len(tokens):
logging.error("Not all tokens parses: %s (%s)", result.value, result.pos)
return Result(None, 0)
return result | makehex/combinators.py | import logging
class Result:
def __init__(self, value, pos: int):
self.value = value
self.pos = pos
class Parser:
def __call__(self, tokens: list, pos: int) -> Result:
return None
def __add__(self, other):
return Concat(self, other)
def __or__(self, other):
return Alternate(self, other)
def __xor__(self, function):
return Process(self, function)
def __pos__(self):
return Opt(self)
def __neg__(self):
return Rep(self)
class Reserved(Parser):
def __init__(self, value: str, tag: str):
self.value = value
self.tag = tag
def __call__(self, tokens: list, pos: int) -> Result:
if pos < len(tokens) and tokens[pos].text == self.value and tokens[pos].tag is self.tag:
return Result(tokens[pos].text, pos + 1)
else:
return None
class Tag(Parser):
def __init__(self, tag: str):
self.tag = tag
def __call__(self, tokens: list, pos: int) -> Result:
if pos < len(tokens) and tokens[pos].tag is self.tag:
return Result(tokens[pos].text, pos + 1)
else:
return None
class Concat(Parser):
def __init__(self, left: Parser, right: Parser):
self.left = left
self.right = right
def __call__(self, tokens: list, pos: int) -> Result:
left_result = self.left(tokens, pos)
if left_result:
right_result = self.right(tokens, left_result.pos)
if right_result:
combined_value = (left_result.value, right_result.value)
return Result(combined_value, right_result.pos)
return None
class Alternate(Parser):
def __init__(self, left: Parser, right: Parser):
self.left = left
self.right = right
def __call__(self, tokens: list, pos: int) -> Result:
left_result = self.left(tokens, pos)
if left_result:
return left_result
else:
right_result = self.right(tokens, pos)
return right_result
class Process(Parser):
def __init__(self, parser: Parser, function):
self.parser = parser
self.function = function
def __call__(self, tokens: list, pos: int) -> Result:
result = self.parser(tokens, pos)
if result:
result.value = self.function(result.value)
return result
class Opt(Parser):
def __init__(self, parser: Parser):
self.parser = parser
def __call__(self, tokens: list, pos: int) -> Result:
result = self.parser(tokens, pos)
if result:
return result
else:
return Result(None, pos)
class Rep(Parser):
def __init__(self, parser: Parser):
self.parser = parser
def __call__(self, tokens: list, pos: int) -> Result:
results = []
result = self.parser(tokens, pos)
while result:
results.append(result.value)
pos = result.pos
result = self.parser(tokens, pos)
return Result(results, pos)
class Lazy(Parser):
def __init__(self, parser_func):
self.parser = None
self.parser_func = parser_func
def __call__(self, tokens: list, pos: int) -> Result:
if not self.parser:
self.parser = self.parser_func()
return self.parser(tokens, pos)
class Phrase(Parser):
def __init__(self, parser: Parser):
self.parser = parser
def __call__(self, tokens: list, pos: int) -> Result:
result = self.parser(tokens, pos)
if result and result.pos < len(tokens):
logging.error("Not all tokens parses: %s (%s)", result.value, result.pos)
return Result(None, 0)
return result | 0.739986 | 0.457137 |
from quex.engine.state_machine.core import DFA
import quex.engine.state_machine.construction.parallelize as parallelize
def stem(Dfa):
"""RETURNS: DFA consisting only of branches until the first acceptance
state.
"""
return __clone_until_acceptance(Dfa, Dfa.init_state_index)
def crown(Dfa):
"""RETURNS: DFA consisting of all branchest starting from any acceptance
state.
"""
branch_list = __all_paths_between_acceptance_states(Dfa)
if not branch_list: return DFA.Empty()
else: return parallelize.do(branch_list)
def __all_paths_between_acceptance_states(Dfa):
"""Generates for each front acceptance state a copy of the complete
graph which can be reached inside 'Dfa' starting from it until the next
acceptance state.
RETURNS: List of DFAs containing a tail for each found acceptance states.
"""
def _get_branch(Dfa, acceptance_si):
result = Dfa.clone_subset(acceptance_si, Dfa.get_successors(acceptance_si))
# Clone acceptance state as init state, which does not accept.
# Take over all transitions of the acceptance state.
new_state = result.get_init_state().clone()
new_state.set_acceptance(False)
result.set_new_init_state(new_state)
# Original acceptance only remains in place, if it is the target of a
# transition.
if not result.has_transition_to(acceptance_si):
result.delete_state(acceptance_si)
return result
return [
_get_branch(Dfa, acceptance_si)
for acceptance_si in Dfa.acceptance_state_index_list()
]
def __clone_until_acceptance(Dfa, StartSi):
"""Make a new DFA from the graph between the given 'StartSi' to the
until an acceptance state is reached. Walks from a given 'StartSi'
along all paths until an acceptance state is reached.
RETURNS: DFA containing the graph.
"""
result = DFA(InitStateIndex = StartSi,
AcceptanceF = Dfa.states[StartSi].is_acceptance())
work_set = set([StartSi])
done_set = set([StartSi])
orphans_possible_f = False
while work_set:
si = work_set.pop()
state = Dfa.states[si]
done_set.add(si)
if state.is_acceptance():
target_si_iterable = []
result.states[si] = state.clone()
result.states[si].target_map.clear()
orphans_possible_f = True
else:
result.states[si] = state
target_si_iterable = state.target_map.get_target_state_index_list()
work_set.update(
target_si
for target_si in target_si_iterable if target_si not in done_set
)
return result.clone() | quex/engine/state_machine/cut/stem_and_branches.py | from quex.engine.state_machine.core import DFA
import quex.engine.state_machine.construction.parallelize as parallelize
def stem(Dfa):
"""RETURNS: DFA consisting only of branches until the first acceptance
state.
"""
return __clone_until_acceptance(Dfa, Dfa.init_state_index)
def crown(Dfa):
"""RETURNS: DFA consisting of all branchest starting from any acceptance
state.
"""
branch_list = __all_paths_between_acceptance_states(Dfa)
if not branch_list: return DFA.Empty()
else: return parallelize.do(branch_list)
def __all_paths_between_acceptance_states(Dfa):
"""Generates for each front acceptance state a copy of the complete
graph which can be reached inside 'Dfa' starting from it until the next
acceptance state.
RETURNS: List of DFAs containing a tail for each found acceptance states.
"""
def _get_branch(Dfa, acceptance_si):
result = Dfa.clone_subset(acceptance_si, Dfa.get_successors(acceptance_si))
# Clone acceptance state as init state, which does not accept.
# Take over all transitions of the acceptance state.
new_state = result.get_init_state().clone()
new_state.set_acceptance(False)
result.set_new_init_state(new_state)
# Original acceptance only remains in place, if it is the target of a
# transition.
if not result.has_transition_to(acceptance_si):
result.delete_state(acceptance_si)
return result
return [
_get_branch(Dfa, acceptance_si)
for acceptance_si in Dfa.acceptance_state_index_list()
]
def __clone_until_acceptance(Dfa, StartSi):
"""Make a new DFA from the graph between the given 'StartSi' to the
until an acceptance state is reached. Walks from a given 'StartSi'
along all paths until an acceptance state is reached.
RETURNS: DFA containing the graph.
"""
result = DFA(InitStateIndex = StartSi,
AcceptanceF = Dfa.states[StartSi].is_acceptance())
work_set = set([StartSi])
done_set = set([StartSi])
orphans_possible_f = False
while work_set:
si = work_set.pop()
state = Dfa.states[si]
done_set.add(si)
if state.is_acceptance():
target_si_iterable = []
result.states[si] = state.clone()
result.states[si].target_map.clear()
orphans_possible_f = True
else:
result.states[si] = state
target_si_iterable = state.target_map.get_target_state_index_list()
work_set.update(
target_si
for target_si in target_si_iterable if target_si not in done_set
)
return result.clone() | 0.639286 | 0.547948 |
import math
from utils import *
class TAG(object):
"""
Implementation of our proposed TAG optimizer
"""
def __init__(self, model, args, num_tasks, optim='rms', lr=None, b=5):
"""
Gets all the necessary arguments for initialization
:param model: Current model
:param args: All arguments for experiment configuration
:param num_tasks: Total number of tasks
:param optim: Base optimizers to be used: {'rms':TAG-RMSProp, 'adagrad':TAG-Adagrad, 'adam': TAG-Adam}
:param lr: Learning rate (eta)
:param b: Hyperparameter for regulating alpha - high b value implies more focus on preventing forgetting
"""
self.optim = optim
self.args = args
self.iters = 0
self.model = model
self.b = b
self.weight_decay = 0.0
if self.optim=='adam':
self.beta1, self.beta2 = 0.9, 0.999
else:
self.beta1, self.beta2 = 0.9, 0.99
self.lr = lr
self.alpha_add_ = {}
self.v, self.v_t = {}, {}
self.m, self.m_t = {}, {}
self.m_t_norms = {}
for task in range(num_tasks):
self.v_t[task] = {}
self.m_t[task] = {}
self.m_t_norms[task] = {}
self.alpha_add_[task] = {}
for (name, param) in model.named_parameters():
if task == 0:
self.v[name] = torch.zeros_like(param).to(args.device)
self.m[name] = torch.zeros_like(param).to(args.device)
self.alpha_add_[task][name] = np.array([1])
self.v_t[task][name] = torch.zeros_like(param).to(args.device)
self.m_t[task][name] = torch.zeros_like(param).to(args.device)
self.m_t_norms[task][name] = torch.zeros_like(param).to(args.device)
def zero_grad(self):
return self.model.zero_grad()
def update_all(self, task_id):
"""
Normalize the current task-based first moments (that will remain fixed)
"""
for name, v in self.model.named_parameters():
self.m_t_norms[task_id][name] = self.m_t[task_id][name].reshape(-1) / torch.norm(self.m_t[task_id][name])
def update_naive(self, param_name, param_grad):
"""
Use the naive-optimizer update
:param param_name: Parameter identity
:param param_grad: Gradient associated with the given parameter
:return: New update to the given parameter
"""
if self.optim=='rms':
self.v[param_name] = self.beta2 * self.v[param_name] + (1 - self.beta2) * param_grad ** 2
else:
self.v[param_name] += param_grad ** 2
denom = torch.sqrt(self.v[param_name]) + 1e-8
return - (self.lr * param_grad / denom)
def update_tag(self, param_name, param_grad, task_id):
"""
Update Task-based accumulated gradients, calculate alpha and return the new updates
:param param_name: Parameter identity
:param param_grad: Gradient associated with the given parameter
:param task_id: Current task identity
:return: New update to the given parameter
"""
bias_corr1, bias_corr2 = 1, 1
new_v = None
# Update task-based first moment
self.m_t[task_id][param_name] = self.beta1 * self.m_t[task_id][param_name] + (1 - self.beta1) * param_grad
# Change numerator based on the optimizer
if self.optim=='adam':
bias_corr1, bias_corr2 = 1 - self.beta1 ** (self.iters + 1), 1 - self.beta2 ** (self.iters + 1)
numer = self.m_t[task_id][param_name] / bias_corr1
else:
numer = param_grad
# Update task-based second moments based on the optimizer
if self.optim=='rms' or self.optim=='adam':
self.v_t[task_id][param_name] = self.beta2 * self.v_t[task_id][param_name] + (1 - self.beta2) * param_grad ** 2
else:
self.v_t[task_id][param_name] = self.v_t[task_id][param_name] + param_grad ** 2
# Get new alphas by computing correlation using task-based first moments
if task_id>0:
alpha_add = []
for t in range(task_id):
corr = torch.dot(self.m_t[task_id][param_name].reshape(-1) / torch.norm(self.m_t[task_id][param_name]),
self.m_t_norms[t][param_name])
alpha_add += [(-corr).cpu().numpy()]
alpha_add += [-1.]
alpha_add = torch.from_numpy(np.array(alpha_add)).to(DEVICE)
alpha_add_ = torch.exp(self.b*alpha_add).float()
else:
alpha_add_ = torch.from_numpy(np.array([1.0] * (task_id + 1))).to(DEVICE)
self.alpha_add_[task_id][param_name] = alpha_add_.cpu().numpy()
# Concatenate all task-based second moments
for t in range(task_id):
new_v = self.v_t[t][param_name].unsqueeze(0) \
if t==0 \
else torch.cat((new_v, self.v_t[t][param_name].unsqueeze(0)), dim=0)
new_v = self.v_t[task_id][param_name].unsqueeze(0) \
if new_v is None \
else torch.cat((new_v, self.v_t[task_id][param_name].unsqueeze(0)), dim=0)
# Compute inner product of alphas and task-based second moments using torch.einsum() function.
# eq takes care of varying the dimensions of parameter variable with each layer.
eq = {1:'n,nh->h', 2:'n,nhw->hw', 3:'n,nhwc->hwc', 4: 'n,nhwvd->hwvd', 5:'n,nhwzxc->hwzxc'}[len(param_grad.shape)]
denom = (torch.sqrt(torch.einsum(eq, alpha_add_.float(), new_v))/ math.sqrt(bias_corr2)) + 1e-8
return - (self.lr * numer / denom)
def step(self, model, task_id, step):
"""
Perform update over the parameters
:param model: Current model
:param task_id: Current task id (t)
:param step: Current Step (n)
:return:
"""
self.iters = step
state_dict = model.state_dict()
for i, (name, param) in enumerate(state_dict.items()):
if name.split('.')[-1] in ['running_mean', 'num_batches_tracked', 'running_var']:
continue
for n, v in model.named_parameters():
if n == name:
break
if v.grad is None:
continue
update = self.update_tag(name, v.grad, task_id)
state_dict[name].data.copy_(param + update.reshape(param.shape))
return state_dict
def store_alpha(tag_optimizer, task_id, iter, alpha_mean=None):
"""
Collects alpha values for given task (t) and current step (n)
:param tag_optimizer: Object of the class tag_opt()
:param task_id: Current task identity
:param iter: Current step in the epoch
:return: alpha_mean: Dictionary with previous task ids as keys
"""
for tau in tag_optimizer.alpha_add_[task_id]:
alphas = tag_optimizer.alpha_add_[task_id][tau]
if iter==0:
alpha_mean[tau] = alphas
else:
alpha_mean[tau] = (alpha_mean[tau]*iter + alphas)/(iter+1)
return alpha_mean | tag_update.py | import math
from utils import *
class TAG(object):
"""
Implementation of our proposed TAG optimizer
"""
def __init__(self, model, args, num_tasks, optim='rms', lr=None, b=5):
"""
Gets all the necessary arguments for initialization
:param model: Current model
:param args: All arguments for experiment configuration
:param num_tasks: Total number of tasks
:param optim: Base optimizers to be used: {'rms':TAG-RMSProp, 'adagrad':TAG-Adagrad, 'adam': TAG-Adam}
:param lr: Learning rate (eta)
:param b: Hyperparameter for regulating alpha - high b value implies more focus on preventing forgetting
"""
self.optim = optim
self.args = args
self.iters = 0
self.model = model
self.b = b
self.weight_decay = 0.0
if self.optim=='adam':
self.beta1, self.beta2 = 0.9, 0.999
else:
self.beta1, self.beta2 = 0.9, 0.99
self.lr = lr
self.alpha_add_ = {}
self.v, self.v_t = {}, {}
self.m, self.m_t = {}, {}
self.m_t_norms = {}
for task in range(num_tasks):
self.v_t[task] = {}
self.m_t[task] = {}
self.m_t_norms[task] = {}
self.alpha_add_[task] = {}
for (name, param) in model.named_parameters():
if task == 0:
self.v[name] = torch.zeros_like(param).to(args.device)
self.m[name] = torch.zeros_like(param).to(args.device)
self.alpha_add_[task][name] = np.array([1])
self.v_t[task][name] = torch.zeros_like(param).to(args.device)
self.m_t[task][name] = torch.zeros_like(param).to(args.device)
self.m_t_norms[task][name] = torch.zeros_like(param).to(args.device)
def zero_grad(self):
return self.model.zero_grad()
def update_all(self, task_id):
"""
Normalize the current task-based first moments (that will remain fixed)
"""
for name, v in self.model.named_parameters():
self.m_t_norms[task_id][name] = self.m_t[task_id][name].reshape(-1) / torch.norm(self.m_t[task_id][name])
def update_naive(self, param_name, param_grad):
"""
Use the naive-optimizer update
:param param_name: Parameter identity
:param param_grad: Gradient associated with the given parameter
:return: New update to the given parameter
"""
if self.optim=='rms':
self.v[param_name] = self.beta2 * self.v[param_name] + (1 - self.beta2) * param_grad ** 2
else:
self.v[param_name] += param_grad ** 2
denom = torch.sqrt(self.v[param_name]) + 1e-8
return - (self.lr * param_grad / denom)
def update_tag(self, param_name, param_grad, task_id):
"""
Update Task-based accumulated gradients, calculate alpha and return the new updates
:param param_name: Parameter identity
:param param_grad: Gradient associated with the given parameter
:param task_id: Current task identity
:return: New update to the given parameter
"""
bias_corr1, bias_corr2 = 1, 1
new_v = None
# Update task-based first moment
self.m_t[task_id][param_name] = self.beta1 * self.m_t[task_id][param_name] + (1 - self.beta1) * param_grad
# Change numerator based on the optimizer
if self.optim=='adam':
bias_corr1, bias_corr2 = 1 - self.beta1 ** (self.iters + 1), 1 - self.beta2 ** (self.iters + 1)
numer = self.m_t[task_id][param_name] / bias_corr1
else:
numer = param_grad
# Update task-based second moments based on the optimizer
if self.optim=='rms' or self.optim=='adam':
self.v_t[task_id][param_name] = self.beta2 * self.v_t[task_id][param_name] + (1 - self.beta2) * param_grad ** 2
else:
self.v_t[task_id][param_name] = self.v_t[task_id][param_name] + param_grad ** 2
# Get new alphas by computing correlation using task-based first moments
if task_id>0:
alpha_add = []
for t in range(task_id):
corr = torch.dot(self.m_t[task_id][param_name].reshape(-1) / torch.norm(self.m_t[task_id][param_name]),
self.m_t_norms[t][param_name])
alpha_add += [(-corr).cpu().numpy()]
alpha_add += [-1.]
alpha_add = torch.from_numpy(np.array(alpha_add)).to(DEVICE)
alpha_add_ = torch.exp(self.b*alpha_add).float()
else:
alpha_add_ = torch.from_numpy(np.array([1.0] * (task_id + 1))).to(DEVICE)
self.alpha_add_[task_id][param_name] = alpha_add_.cpu().numpy()
# Concatenate all task-based second moments
for t in range(task_id):
new_v = self.v_t[t][param_name].unsqueeze(0) \
if t==0 \
else torch.cat((new_v, self.v_t[t][param_name].unsqueeze(0)), dim=0)
new_v = self.v_t[task_id][param_name].unsqueeze(0) \
if new_v is None \
else torch.cat((new_v, self.v_t[task_id][param_name].unsqueeze(0)), dim=0)
# Compute inner product of alphas and task-based second moments using torch.einsum() function.
# eq takes care of varying the dimensions of parameter variable with each layer.
eq = {1:'n,nh->h', 2:'n,nhw->hw', 3:'n,nhwc->hwc', 4: 'n,nhwvd->hwvd', 5:'n,nhwzxc->hwzxc'}[len(param_grad.shape)]
denom = (torch.sqrt(torch.einsum(eq, alpha_add_.float(), new_v))/ math.sqrt(bias_corr2)) + 1e-8
return - (self.lr * numer / denom)
def step(self, model, task_id, step):
"""
Perform update over the parameters
:param model: Current model
:param task_id: Current task id (t)
:param step: Current Step (n)
:return:
"""
self.iters = step
state_dict = model.state_dict()
for i, (name, param) in enumerate(state_dict.items()):
if name.split('.')[-1] in ['running_mean', 'num_batches_tracked', 'running_var']:
continue
for n, v in model.named_parameters():
if n == name:
break
if v.grad is None:
continue
update = self.update_tag(name, v.grad, task_id)
state_dict[name].data.copy_(param + update.reshape(param.shape))
return state_dict
def store_alpha(tag_optimizer, task_id, iter, alpha_mean=None):
"""
Collects alpha values for given task (t) and current step (n)
:param tag_optimizer: Object of the class tag_opt()
:param task_id: Current task identity
:param iter: Current step in the epoch
:return: alpha_mean: Dictionary with previous task ids as keys
"""
for tau in tag_optimizer.alpha_add_[task_id]:
alphas = tag_optimizer.alpha_add_[task_id][tau]
if iter==0:
alpha_mean[tau] = alphas
else:
alpha_mean[tau] = (alpha_mean[tau]*iter + alphas)/(iter+1)
return alpha_mean | 0.566258 | 0.549218 |
import unittest
from flask import json
from mock import patch, Mock
from app import app
class StorageResourceTestCase(unittest.TestCase):
def setUp(self):
self.app = app.test_client()
mock = patch('flask_login.AnonymousUserMixin.is_authenticated').start()
mock.return_value = True
def tearDown(self):
patch.stopall()
def test_list_storages_given_volume_api_error(self):
list_storage_mock = self.mock_cloudstack_list_storages({"errortext": "Unable to find project"})
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(400, response.status_code)
self.assertEquals("Unable to find project", json.loads(response.data)['message'])
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282', 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
def test_list_storages_given_snapshot_api_error(self):
list_storage_mock = self.mock_cloudstack_list_storages(
{"count": 1, "volume":[{
"name": 'ROOT-3145', "state": 'Ready',"size": '1287589', "zonename": 'zone', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "type": 'ROOT', 'virtualmachineid': 1
}]},
{"errortext": "Unable to find project"}
)
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(400, response.status_code)
self.assertEquals("Unable to find project", json.loads(response.data)['message'])
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282' , 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
list_storage_mock.listSnapshots.assert_called_with(expect_resp)
def test_list_storages_given_template_api_error(self):
list_storage_mock = self.mock_cloudstack_list_storages(
{"count": 1, "volume":[{
"name": 'ROOT-3145', "state": 'Ready',"size": '1287589', "zonename": 'zone', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "type": 'ROOT', 'virtualmachineid': 1
}]},
{"count": 1, "snapshot":[{
"name": 'Snapshot-3145', "state": 'BackedUp',"size": '1287589', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "snapshottype": 'MANUAL', 'vmid': 1, "volumename": "name"
}]},
{"errortext": "Unable to find project"}
)
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(400, response.status_code)
self.assertEquals("Unable to find project", json.loads(response.data)['message'])
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282', 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
list_storage_mock.listSnapshots.assert_called_with(expect_resp)
list_storage_mock.listTemplates.assert_called_with(expect_resp)
def test_list_storages_given_empty_data(self):
list_storage_mock = self.mock_cloudstack_list_storages({},{}, {})
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(200, response.status_code)
self.assertEquals([], json.loads(response.data)['storage'])
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282', 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
list_storage_mock.listSnapshots.assert_called_with(expect_resp)
list_storage_mock.listTemplates.assert_called_with(expect_resp)
def test_list_storages(self):
list_storage_mock = self.mock_cloudstack_list_storages(
{"count": 1, "volume":[{
"name": 'ROOT-3145', "state": 'Ready',"size": '1287589', "zonename": 'zone', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "type": 'ROOT', 'virtualmachineid': 1
}]},
{"count": 1, "snapshot":[{
"name": 'Snapshot-3145', "state": 'BackedUp',"size": '1287589', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "snapshottype": 'MANUAL', 'vmid': 1, "volumename": "name"
}]},
{"count": 1, "template":[{
"name": 'Template-3147', "status": 'DownloadComplete',"size": '1287589', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "templatetype": 'USER'
}]}
)
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(200, response.status_code)
self.assertEquals(3, len(json.loads(response.data)['storage']))
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282', 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
list_storage_mock.listSnapshots.assert_called_with(expect_resp)
list_storage_mock.listTemplates.assert_called_with(expect_resp)
def mock_cloudstack_list_storages(self, volumes=None, snapshots=None, templates=None):
acs_mock = patch('app.storage.resource.StorageResource.get_cloudstack').start()
list_storage = Mock()
if volumes is not None:
list_storage.listVolumes.return_value = volumes
if snapshots is not None:
list_storage.listSnapshots.return_value = snapshots
if templates is not None:
list_storage.listTemplates.return_value = templates
acs_mock.return_value = list_storage
return list_storage | app/storage/tests.py | import unittest
from flask import json
from mock import patch, Mock
from app import app
class StorageResourceTestCase(unittest.TestCase):
def setUp(self):
self.app = app.test_client()
mock = patch('flask_login.AnonymousUserMixin.is_authenticated').start()
mock.return_value = True
def tearDown(self):
patch.stopall()
def test_list_storages_given_volume_api_error(self):
list_storage_mock = self.mock_cloudstack_list_storages({"errortext": "Unable to find project"})
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(400, response.status_code)
self.assertEquals("Unable to find project", json.loads(response.data)['message'])
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282', 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
def test_list_storages_given_snapshot_api_error(self):
list_storage_mock = self.mock_cloudstack_list_storages(
{"count": 1, "volume":[{
"name": 'ROOT-3145', "state": 'Ready',"size": '1287589', "zonename": 'zone', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "type": 'ROOT', 'virtualmachineid': 1
}]},
{"errortext": "Unable to find project"}
)
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(400, response.status_code)
self.assertEquals("Unable to find project", json.loads(response.data)['message'])
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282' , 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
list_storage_mock.listSnapshots.assert_called_with(expect_resp)
def test_list_storages_given_template_api_error(self):
list_storage_mock = self.mock_cloudstack_list_storages(
{"count": 1, "volume":[{
"name": 'ROOT-3145', "state": 'Ready',"size": '1287589', "zonename": 'zone', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "type": 'ROOT', 'virtualmachineid': 1
}]},
{"count": 1, "snapshot":[{
"name": 'Snapshot-3145', "state": 'BackedUp',"size": '1287589', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "snapshottype": 'MANUAL', 'vmid': 1, "volumename": "name"
}]},
{"errortext": "Unable to find project"}
)
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(400, response.status_code)
self.assertEquals("Unable to find project", json.loads(response.data)['message'])
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282', 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
list_storage_mock.listSnapshots.assert_called_with(expect_resp)
list_storage_mock.listTemplates.assert_called_with(expect_resp)
def test_list_storages_given_empty_data(self):
list_storage_mock = self.mock_cloudstack_list_storages({},{}, {})
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(200, response.status_code)
self.assertEquals([], json.loads(response.data)['storage'])
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282', 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
list_storage_mock.listSnapshots.assert_called_with(expect_resp)
list_storage_mock.listTemplates.assert_called_with(expect_resp)
def test_list_storages(self):
list_storage_mock = self.mock_cloudstack_list_storages(
{"count": 1, "volume":[{
"name": 'ROOT-3145', "state": 'Ready',"size": '1287589', "zonename": 'zone', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "type": 'ROOT', 'virtualmachineid': 1
}]},
{"count": 1, "snapshot":[{
"name": 'Snapshot-3145', "state": 'BackedUp',"size": '1287589', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "snapshottype": 'MANUAL', 'vmid': 1, "volumename": "name"
}]},
{"count": 1, "template":[{
"name": 'Template-3147', "status": 'DownloadComplete',"size": '1287589', 'zoneid': 1,
"created": "2015-09-18T14:08:30-0300", "templatetype": 'USER'
}]}
)
query = dict(project_id='28f40084-2aed-11e5-8fce-76b2dd27c282')
response = self.app.get('/api/v1/lab/storage/', query_string=query)
self.assertEquals(200, response.status_code)
self.assertEquals(3, len(json.loads(response.data)['storage']))
expect_resp = {'projectid': '28f40084-2aed-11e5-8fce-76b2dd27c282', 'templatefilter':'self',
'pagesize': '-1', 'listall': 'true'}
list_storage_mock.listVolumes.assert_called_with(expect_resp)
list_storage_mock.listSnapshots.assert_called_with(expect_resp)
list_storage_mock.listTemplates.assert_called_with(expect_resp)
def mock_cloudstack_list_storages(self, volumes=None, snapshots=None, templates=None):
acs_mock = patch('app.storage.resource.StorageResource.get_cloudstack').start()
list_storage = Mock()
if volumes is not None:
list_storage.listVolumes.return_value = volumes
if snapshots is not None:
list_storage.listSnapshots.return_value = snapshots
if templates is not None:
list_storage.listTemplates.return_value = templates
acs_mock.return_value = list_storage
return list_storage | 0.497803 | 0.306073 |
from pymodm.errors import ValidationError
def together(*funcs):
"""Run several validators successively on the same value."""
def validator(value):
for func in funcs:
func(value)
return validator
def validator_for_func(func):
"""Return a validator that re-raises any errors from the given function."""
def validator(value):
try:
func(value)
except Exception as exc:
raise ValidationError(exc)
return validator
def validator_for_type(types, value_name=None):
"""Return a validator that ensures its value is among the given `types`."""
def validator(value):
if not isinstance(value, types):
if isinstance(types, tuple): # multiple types
type_names = tuple(t.__name__ for t in types)
err = 'must be one of %r' % (type_names,)
else:
err = 'must be a %s' % types.__name__
raise ValidationError(
'%s %s, not %r'
% (value_name or 'Value', err, value))
return validator
def validator_for_geojson_type(geojson_type):
"""Return a validator that validates its value as having the given GeoJSON
``type``.
"""
def validator(value):
if value.get('type') != geojson_type:
raise ValidationError(
'GeoJSON type must be %r, not %r'
% (geojson_type, value.get('type')))
return validator
def validator_for_min_max(min, max):
"""Return a validator that validates its value against a minimum/maximum."""
def validator(value):
if min is not None and value < min:
raise ValidationError(
'%s is less than minimum value of %s.' % (value, min))
if max is not None and value > max:
raise ValidationError(
'%s is greater than maximum value of %s.' % (value, max))
return validator
def validator_for_length(min, max):
"""Return a validator that validates a given value's length."""
def validator(value):
len_value = len(value)
if min is not None and len_value < min:
raise ValidationError(
'%s is under the minimum length of %d.' % (value, min))
if max is not None and len_value > max:
raise ValidationError(
'value exceeds the maximum length of %d.' % (max,))
return validator | env/lib/python3.6/site-packages/pymodm/validators.py |
from pymodm.errors import ValidationError
def together(*funcs):
"""Run several validators successively on the same value."""
def validator(value):
for func in funcs:
func(value)
return validator
def validator_for_func(func):
"""Return a validator that re-raises any errors from the given function."""
def validator(value):
try:
func(value)
except Exception as exc:
raise ValidationError(exc)
return validator
def validator_for_type(types, value_name=None):
"""Return a validator that ensures its value is among the given `types`."""
def validator(value):
if not isinstance(value, types):
if isinstance(types, tuple): # multiple types
type_names = tuple(t.__name__ for t in types)
err = 'must be one of %r' % (type_names,)
else:
err = 'must be a %s' % types.__name__
raise ValidationError(
'%s %s, not %r'
% (value_name or 'Value', err, value))
return validator
def validator_for_geojson_type(geojson_type):
"""Return a validator that validates its value as having the given GeoJSON
``type``.
"""
def validator(value):
if value.get('type') != geojson_type:
raise ValidationError(
'GeoJSON type must be %r, not %r'
% (geojson_type, value.get('type')))
return validator
def validator_for_min_max(min, max):
"""Return a validator that validates its value against a minimum/maximum."""
def validator(value):
if min is not None and value < min:
raise ValidationError(
'%s is less than minimum value of %s.' % (value, min))
if max is not None and value > max:
raise ValidationError(
'%s is greater than maximum value of %s.' % (value, max))
return validator
def validator_for_length(min, max):
"""Return a validator that validates a given value's length."""
def validator(value):
len_value = len(value)
if min is not None and len_value < min:
raise ValidationError(
'%s is under the minimum length of %d.' % (value, min))
if max is not None and len_value > max:
raise ValidationError(
'value exceeds the maximum length of %d.' % (max,))
return validator | 0.889162 | 0.500366 |
import locale
import time
import re
from dateutil.parser import parse
from datetime import datetime, time as ltime
from time import mktime as mktime, time as ttime
'''
Магический ооочень простой объект времени
'''
class magictime:
tformat = '%d %b %Y %H:%M:%S'
mysqlformat = '%Y%m%d%H%M%S'
def __cmp__(self, other):
if not isinstance(other, magictime):
other = magictime(other)
if self.unixtime > other.unixtime:
return 1
elif self.unixtime < other.unixtime:
return -1
else:
return 0
def __init__(self, time='0', **kwargs):
self.original = 0
self.asctime = ''
self.unixtime = 0
self.msectime = 0
self.mcsectime = 0
self.t = time
self.mysql = ''
rexp = re.match('^([12][90][012]\d)([01][0-9])([0123][0-9])(\d\d)(\d\d)(\d\d)', str(time))
if time is None: # Nonetype. Считаем, что время равно нулю
time = 0
self.asctime = datetime.fromtimestamp(time).strftime(self.tformat)
self.mysql = datetime.fromtimestamp(time).strftime(self.mysqlformat)
self.unixtime = 0
self.msectime = 0
elif isinstance(time, datetime): # datetime тип (нафик он нам тут?)
self.asctime = time.strftime(self.tformat)
self.mysql = time.strftime(self.mysqlformat)
self.unixtime = int(mktime(time.timetuple()))
self.msectime = self.unixtime * 1000000
elif re.match('^\d+$', str(time)):
time = int(time)
if time == 0: # Текущее время
time = int(ttime())
self.original = str(time)
self.asctime = datetime.fromtimestamp(time).strftime(self.tformat)
self.mysql = datetime.fromtimestamp(time).strftime(self.mysqlformat)
self.unixtime = time
self.msectime = time * 1000000
elif rexp and int(rexp.group(1)) > 1970 and int(rexp.group(1)) < 2025: # Это mysql timestamp
time = str(time)
parsed = parse(time, fuzzy_with_tokens=True)
restime = int(mktime(parsed[0].timetuple()))
self.original = time
self.unixtime = restime
self.msectime = restime * 1000000
self.mysql = time
self.asctime = parsed[0].strftime(self.tformat).encode('utf-8')
elif time > 0 and time < 4294967296: # Это наверняка unixtime
self.original = time
self.asctime = datetime.fromtimestamp(time).strftime(self.tformat)
self.mysql = datetime.fromtimestamp(time).strftime(self.mysqlformat)
self.unixtime = time
self.msectime = time * 1000000
elif time > 9999999999 and time < 99999999999999999: # Скорее всего это время в микросекундах
self.original = time
self.unixtime = int(time/1000000)
self.asctime = datetime.fromtimestamp(self.unixtime).strftime(self.tformat)
self.msectime = time
self.mysql = time
else:
return None
else: # Это, предположительно,
time = str(time.decode('utf-8')) # время в текстовом формате
parsed = parse(time, fuzzy_with_tokens=True)
restime = int(mktime(parsed[0].timetuple()))
self.original = time.encode('utf-8')
self.unixtime = restime
self.msectime = restime * 1000000
self.mysql = parsed[0].strftime(self.mysqlformat).encode('utf-8')
self.asctime = parsed[0].strftime(self.tformat).encode('utf-8')
return None | magictime.py |
import locale
import time
import re
from dateutil.parser import parse
from datetime import datetime, time as ltime
from time import mktime as mktime, time as ttime
'''
Магический ооочень простой объект времени
'''
class magictime:
tformat = '%d %b %Y %H:%M:%S'
mysqlformat = '%Y%m%d%H%M%S'
def __cmp__(self, other):
if not isinstance(other, magictime):
other = magictime(other)
if self.unixtime > other.unixtime:
return 1
elif self.unixtime < other.unixtime:
return -1
else:
return 0
def __init__(self, time='0', **kwargs):
self.original = 0
self.asctime = ''
self.unixtime = 0
self.msectime = 0
self.mcsectime = 0
self.t = time
self.mysql = ''
rexp = re.match('^([12][90][012]\d)([01][0-9])([0123][0-9])(\d\d)(\d\d)(\d\d)', str(time))
if time is None: # Nonetype. Считаем, что время равно нулю
time = 0
self.asctime = datetime.fromtimestamp(time).strftime(self.tformat)
self.mysql = datetime.fromtimestamp(time).strftime(self.mysqlformat)
self.unixtime = 0
self.msectime = 0
elif isinstance(time, datetime): # datetime тип (нафик он нам тут?)
self.asctime = time.strftime(self.tformat)
self.mysql = time.strftime(self.mysqlformat)
self.unixtime = int(mktime(time.timetuple()))
self.msectime = self.unixtime * 1000000
elif re.match('^\d+$', str(time)):
time = int(time)
if time == 0: # Текущее время
time = int(ttime())
self.original = str(time)
self.asctime = datetime.fromtimestamp(time).strftime(self.tformat)
self.mysql = datetime.fromtimestamp(time).strftime(self.mysqlformat)
self.unixtime = time
self.msectime = time * 1000000
elif rexp and int(rexp.group(1)) > 1970 and int(rexp.group(1)) < 2025: # Это mysql timestamp
time = str(time)
parsed = parse(time, fuzzy_with_tokens=True)
restime = int(mktime(parsed[0].timetuple()))
self.original = time
self.unixtime = restime
self.msectime = restime * 1000000
self.mysql = time
self.asctime = parsed[0].strftime(self.tformat).encode('utf-8')
elif time > 0 and time < 4294967296: # Это наверняка unixtime
self.original = time
self.asctime = datetime.fromtimestamp(time).strftime(self.tformat)
self.mysql = datetime.fromtimestamp(time).strftime(self.mysqlformat)
self.unixtime = time
self.msectime = time * 1000000
elif time > 9999999999 and time < 99999999999999999: # Скорее всего это время в микросекундах
self.original = time
self.unixtime = int(time/1000000)
self.asctime = datetime.fromtimestamp(self.unixtime).strftime(self.tformat)
self.msectime = time
self.mysql = time
else:
return None
else: # Это, предположительно,
time = str(time.decode('utf-8')) # время в текстовом формате
parsed = parse(time, fuzzy_with_tokens=True)
restime = int(mktime(parsed[0].timetuple()))
self.original = time.encode('utf-8')
self.unixtime = restime
self.msectime = restime * 1000000
self.mysql = parsed[0].strftime(self.mysqlformat).encode('utf-8')
self.asctime = parsed[0].strftime(self.tformat).encode('utf-8')
return None | 0.260954 | 0.138026 |
import logging
import aiogram.utils.markdown as md
from aiogram import Bot, Dispatcher, executor, types
from aiogram.contrib.fsm_storage.memory import MemoryStorage
from aiogram.dispatcher import FSMContext
from aiogram.dispatcher.filters import Text
from aiogram.dispatcher.filters.state import State, StatesGroup
import pb
import keyboards
import config
logging.basicConfig(level=logging.INFO)
bot = Bot(token=config.TOKEN)
dp = Dispatcher(bot, storage=MemoryStorage())
class Company(StatesGroup):
inn = State()
invoise = State()
class Newspneumax(StatesGroup):
text = State()
class Pesrson(StatesGroup):
invoise = State()
@dp.message_handler(commands=["start"])
async def cmd_start(message: types.Message):
keyboard = await keyboards.main_keyboard()
await message.answer(f"Вас приветствует компания {config.COMPANY}.\n"
"Чтобы получить информацию о статусе заказа, нажмите 'Заказ '", reply_markup=keyboard)
@dp.message_handler(commands=["adminmenumesseng"])
async def cmd_start(message: types.Message):
keyboard = await keyboards.admistrator_menu_keyboard()
await message.answer(f"Админское меню данного бота", reply_markup=keyboard)
@dp.message_handler(lambda message: message.text is not None, state=Newspneumax.text)
async def process_callback_news_all_users(message: types.Message, state: FSMContext):
# Update state and data
await state.update_data(text=message.text)
if message.text != '/cancel':
markup = await keyboards.main_keyboard()
all_users = pb.get_all_users()
for i in all_users:
try:
await bot.send_message(i[0], message.text, reply_markup=markup)
except:
print("Пользователь удалился")
await state.finish()
@dp.callback_query_handler(lambda c: c.data == 'getAllUsers')
async def process_callback_getAllUsers(callback_query: types.CallbackQuery):
keyboard = await keyboards.admistrator_menu_keyboard()
alluser = len(pb.get_all_users())
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, f'Сейчас на бота подписано {alluser} человек', reply_markup=keyboard)
@dp.callback_query_handler(lambda c: c.data == 'getNewsMesseng')
async def process_callback_getNewsMesseng(callback_query: types.CallbackQuery):
keyboard = await keyboards.cancel_button_keyboard()
await Newspneumax.text.set()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, f'Введите новость для рассылки:', reply_markup=keyboard)
@dp.callback_query_handler(lambda c: c.data == 'getInvoice')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
keyboard = await keyboards.invoice_keyboard()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, 'Получить информацию по заказу на:', reply_markup=keyboard)
@dp.callback_query_handler(lambda c: c.data == 'getInvoiceCompany')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
userinn = pb.find_user(callback_query.from_user.id)
if userinn[0][1] == 0:
await Company.inn.set()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, 'Введите ИНН организации:')
else:
keyboard = await keyboards.confirmation_keyboard()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, f'Это ваш инн {userinn[0][1]}?', reply_markup=keyboard)
@dp.callback_query_handler(lambda c: c.data == 'getСonfirmationYes')
async def process_callback_getInvouce(callback_query: types.CallbackQuery, state: FSMContext):
userinn = pb.find_user(callback_query.from_user.id)
await Company.inn.set()
await state.update_data(inn=int(userinn[0][1]))
await Company.next()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, 'Введите последние 5 цифр заказа:')
@dp.callback_query_handler(lambda c: c.data == 'getConfirmationNo')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
await Company.inn.set()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, 'Введите ИНН организации:')
@dp.callback_query_handler(lambda c: c.data == 'getInvoicePerson')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
await bot.answer_callback_query(callback_query.id)
await Pesrson.invoise.set()
await bot.send_message(callback_query.from_user.id, 'Введите последние 5 цифр заказа')
# You can use state '*' if you need to handle all states
@dp.message_handler(state='*', commands='cancel')
@dp.message_handler(Text(equals='cancel', ignore_case=True), state='*')
async def cancel_handler(message: types.Message, state: FSMContext):
"""
Allow user to cancel any action
"""
current_state = await state.get_state()
if current_state is None:
keyboard = await keyboards.main_keyboard()
await message.reply('Действие отменено.', reply_markup=types.ReplyKeyboardRemove())
await message.answer(f"Вас приветствует компания {config.COMPANY}.\n"
"Чтобы получить информацию о статусе заказа, нажмите 'Заказ '", reply_markup=keyboard)
return
logging.info('Cancelling state %r', current_state)
# Cancel state and inform user about it
await state.finish()
# And remove keyboard (just in case)
await message.reply('Cancelled.', reply_markup=types.ReplyKeyboardRemove())
@dp.callback_query_handler(lambda c: c.data == 'getContacts')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, config.CONTACT)
@dp.message_handler(lambda message: message.text.isdigit(), state=Company.inn)
async def process_inn(message: types.Message, state: FSMContext):
"""
Process inn Company
"""
await state.update_data(inn=int(message.text))
pb.save_users_inn(message.from_user.id, int(message.text))
await Company.next()
await message.reply("Введите последние 5 цифр заказа:")
@dp.message_handler(lambda message: not message.text.isdigit() or len(message.text) < 10, state=Company.inn)
async def process_inn_invalid(message: types.Message):
"""
If Company is invalid
"""
return await message.reply("ИНН компании должен быть цифровой и более 9 цифр.\nВаш ИНН:")
@dp.message_handler(lambda message: not message.text.isdigit(), state=Company.invoise)
async def process_invoise_invalid(message: types.Message):
"""
If invoise is invalid
"""
return await message.reply("Номер заказа должен быть цифровой.\nВведите последние 5 цифр заказа:")
@dp.message_handler(lambda message: not message.text.isdigit(), state=Pesrson.invoise)
async def process_invoise_invalid(message: types.Message):
"""
If invoise is invalid
"""
return await message.reply("Номер заказа должен быть цифровой.\nВведите последние 5 цифр заказа:")
@dp.message_handler(lambda message: message.text.isdigit(), state=Company.invoise)
async def process_invoise(message: types.Message, state: FSMContext):
# Update state and data
await state.update_data(invoise=int(message.text))
async with state.proxy() as data:
inn = str(md.bold(data['inn']))
invoise = str(md.bold(data['invoise']))
all_invoce = pb.get_invoice(inn.replace("*", ""), invoise.replace("*", ""))
if all_invoce == False:
text = f'По организации {inn} не найден заказ с номером {invoise} '
else:
products = pb.get_products(inn.replace("*", ""), invoise.replace("*", ""))
text = f'Ваш заказ № {invoise.replace("*", "")}\n' \
f'Статус: {all_invoce[0][3]}\n' \
f'Cумма заказа: {all_invoce[0][6]} \n' \
f'Доставка: {all_invoce[0][4]} \n' \
f'TK : {all_invoce[0][5]}\n\n' \
f'{products}'
markup = await keyboards.main_keyboard()
await bot.send_message(
message.chat.id,
md.text(
md.text(text),
sep='\n',
),
reply_markup=markup
)
# Finish conversation
await state.finish()
@dp.message_handler(lambda message: message.text.isdigit(), state=Pesrson.invoise)
async def process_invoise_person(message: types.Message, state: FSMContext):
# Update state and data
await state.update_data(invoise=int(message.text))
async with state.proxy() as data:
invoise = str(md.bold(data['invoise']))
all_invoce = pb.get_invoice_person(invoise.replace("*", ""))
if all_invoce == False:
text = f'Не найден заказ с номером {invoise} '
else:
products = pb.get_goods(invoise.replace("*", ""))
text = f'Ваш заказ № {invoise.replace("*", "")}\n' \
f'Статус: {all_invoce[0][3]}\n' \
f'Cумма заказа: {all_invoce[0][6]} \n' \
f'Доставка: {all_invoce[0][4]} \n' \
f'TK : {all_invoce[0][5]}\n\n' \
f'{products}'
markup = await keyboards.main_keyboard()
await bot.send_message(
message.chat.id,
md.text(
md.text(text),
sep='\n',
),
reply_markup=markup
)
# Finish conversation
await state.finish()
if __name__ == "__main__":
executor.start_polling(dp, skip_updates=True) #skip_updates=True позволяет пропустить накопившиеся входящие сообщения | aibot.py | import logging
import aiogram.utils.markdown as md
from aiogram import Bot, Dispatcher, executor, types
from aiogram.contrib.fsm_storage.memory import MemoryStorage
from aiogram.dispatcher import FSMContext
from aiogram.dispatcher.filters import Text
from aiogram.dispatcher.filters.state import State, StatesGroup
import pb
import keyboards
import config
logging.basicConfig(level=logging.INFO)
bot = Bot(token=config.TOKEN)
dp = Dispatcher(bot, storage=MemoryStorage())
class Company(StatesGroup):
inn = State()
invoise = State()
class Newspneumax(StatesGroup):
text = State()
class Pesrson(StatesGroup):
invoise = State()
@dp.message_handler(commands=["start"])
async def cmd_start(message: types.Message):
keyboard = await keyboards.main_keyboard()
await message.answer(f"Вас приветствует компания {config.COMPANY}.\n"
"Чтобы получить информацию о статусе заказа, нажмите 'Заказ '", reply_markup=keyboard)
@dp.message_handler(commands=["adminmenumesseng"])
async def cmd_start(message: types.Message):
keyboard = await keyboards.admistrator_menu_keyboard()
await message.answer(f"Админское меню данного бота", reply_markup=keyboard)
@dp.message_handler(lambda message: message.text is not None, state=Newspneumax.text)
async def process_callback_news_all_users(message: types.Message, state: FSMContext):
# Update state and data
await state.update_data(text=message.text)
if message.text != '/cancel':
markup = await keyboards.main_keyboard()
all_users = pb.get_all_users()
for i in all_users:
try:
await bot.send_message(i[0], message.text, reply_markup=markup)
except:
print("Пользователь удалился")
await state.finish()
@dp.callback_query_handler(lambda c: c.data == 'getAllUsers')
async def process_callback_getAllUsers(callback_query: types.CallbackQuery):
keyboard = await keyboards.admistrator_menu_keyboard()
alluser = len(pb.get_all_users())
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, f'Сейчас на бота подписано {alluser} человек', reply_markup=keyboard)
@dp.callback_query_handler(lambda c: c.data == 'getNewsMesseng')
async def process_callback_getNewsMesseng(callback_query: types.CallbackQuery):
keyboard = await keyboards.cancel_button_keyboard()
await Newspneumax.text.set()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, f'Введите новость для рассылки:', reply_markup=keyboard)
@dp.callback_query_handler(lambda c: c.data == 'getInvoice')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
keyboard = await keyboards.invoice_keyboard()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, 'Получить информацию по заказу на:', reply_markup=keyboard)
@dp.callback_query_handler(lambda c: c.data == 'getInvoiceCompany')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
userinn = pb.find_user(callback_query.from_user.id)
if userinn[0][1] == 0:
await Company.inn.set()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, 'Введите ИНН организации:')
else:
keyboard = await keyboards.confirmation_keyboard()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, f'Это ваш инн {userinn[0][1]}?', reply_markup=keyboard)
@dp.callback_query_handler(lambda c: c.data == 'getСonfirmationYes')
async def process_callback_getInvouce(callback_query: types.CallbackQuery, state: FSMContext):
userinn = pb.find_user(callback_query.from_user.id)
await Company.inn.set()
await state.update_data(inn=int(userinn[0][1]))
await Company.next()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, 'Введите последние 5 цифр заказа:')
@dp.callback_query_handler(lambda c: c.data == 'getConfirmationNo')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
await Company.inn.set()
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, 'Введите ИНН организации:')
@dp.callback_query_handler(lambda c: c.data == 'getInvoicePerson')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
await bot.answer_callback_query(callback_query.id)
await Pesrson.invoise.set()
await bot.send_message(callback_query.from_user.id, 'Введите последние 5 цифр заказа')
# You can use state '*' if you need to handle all states
@dp.message_handler(state='*', commands='cancel')
@dp.message_handler(Text(equals='cancel', ignore_case=True), state='*')
async def cancel_handler(message: types.Message, state: FSMContext):
"""
Allow user to cancel any action
"""
current_state = await state.get_state()
if current_state is None:
keyboard = await keyboards.main_keyboard()
await message.reply('Действие отменено.', reply_markup=types.ReplyKeyboardRemove())
await message.answer(f"Вас приветствует компания {config.COMPANY}.\n"
"Чтобы получить информацию о статусе заказа, нажмите 'Заказ '", reply_markup=keyboard)
return
logging.info('Cancelling state %r', current_state)
# Cancel state and inform user about it
await state.finish()
# And remove keyboard (just in case)
await message.reply('Cancelled.', reply_markup=types.ReplyKeyboardRemove())
@dp.callback_query_handler(lambda c: c.data == 'getContacts')
async def process_callback_getInvouce(callback_query: types.CallbackQuery):
await bot.answer_callback_query(callback_query.id)
await bot.send_message(callback_query.from_user.id, config.CONTACT)
@dp.message_handler(lambda message: message.text.isdigit(), state=Company.inn)
async def process_inn(message: types.Message, state: FSMContext):
"""
Process inn Company
"""
await state.update_data(inn=int(message.text))
pb.save_users_inn(message.from_user.id, int(message.text))
await Company.next()
await message.reply("Введите последние 5 цифр заказа:")
@dp.message_handler(lambda message: not message.text.isdigit() or len(message.text) < 10, state=Company.inn)
async def process_inn_invalid(message: types.Message):
"""
If Company is invalid
"""
return await message.reply("ИНН компании должен быть цифровой и более 9 цифр.\nВаш ИНН:")
@dp.message_handler(lambda message: not message.text.isdigit(), state=Company.invoise)
async def process_invoise_invalid(message: types.Message):
"""
If invoise is invalid
"""
return await message.reply("Номер заказа должен быть цифровой.\nВведите последние 5 цифр заказа:")
@dp.message_handler(lambda message: not message.text.isdigit(), state=Pesrson.invoise)
async def process_invoise_invalid(message: types.Message):
"""
If invoise is invalid
"""
return await message.reply("Номер заказа должен быть цифровой.\nВведите последние 5 цифр заказа:")
@dp.message_handler(lambda message: message.text.isdigit(), state=Company.invoise)
async def process_invoise(message: types.Message, state: FSMContext):
# Update state and data
await state.update_data(invoise=int(message.text))
async with state.proxy() as data:
inn = str(md.bold(data['inn']))
invoise = str(md.bold(data['invoise']))
all_invoce = pb.get_invoice(inn.replace("*", ""), invoise.replace("*", ""))
if all_invoce == False:
text = f'По организации {inn} не найден заказ с номером {invoise} '
else:
products = pb.get_products(inn.replace("*", ""), invoise.replace("*", ""))
text = f'Ваш заказ № {invoise.replace("*", "")}\n' \
f'Статус: {all_invoce[0][3]}\n' \
f'Cумма заказа: {all_invoce[0][6]} \n' \
f'Доставка: {all_invoce[0][4]} \n' \
f'TK : {all_invoce[0][5]}\n\n' \
f'{products}'
markup = await keyboards.main_keyboard()
await bot.send_message(
message.chat.id,
md.text(
md.text(text),
sep='\n',
),
reply_markup=markup
)
# Finish conversation
await state.finish()
@dp.message_handler(lambda message: message.text.isdigit(), state=Pesrson.invoise)
async def process_invoise_person(message: types.Message, state: FSMContext):
# Update state and data
await state.update_data(invoise=int(message.text))
async with state.proxy() as data:
invoise = str(md.bold(data['invoise']))
all_invoce = pb.get_invoice_person(invoise.replace("*", ""))
if all_invoce == False:
text = f'Не найден заказ с номером {invoise} '
else:
products = pb.get_goods(invoise.replace("*", ""))
text = f'Ваш заказ № {invoise.replace("*", "")}\n' \
f'Статус: {all_invoce[0][3]}\n' \
f'Cумма заказа: {all_invoce[0][6]} \n' \
f'Доставка: {all_invoce[0][4]} \n' \
f'TK : {all_invoce[0][5]}\n\n' \
f'{products}'
markup = await keyboards.main_keyboard()
await bot.send_message(
message.chat.id,
md.text(
md.text(text),
sep='\n',
),
reply_markup=markup
)
# Finish conversation
await state.finish()
if __name__ == "__main__":
executor.start_polling(dp, skip_updates=True) #skip_updates=True позволяет пропустить накопившиеся входящие сообщения | 0.202364 | 0.180233 |
from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^date/$', views.DateListView.as_view(),
name='date_list'),
url(
r'^date/(?P<pk>[0-9]+)$', views.DateDetailView.as_view(),
name='date_detail'),
url(
r'^date/create/$', views.DateCreate.as_view(),
name='date_create'),
url(
r'^date/update/(?P<pk>[0-9]+)$', views.DateUpdate.as_view(),
name='date_update'),
url(r'^corpus/$', views.CorpusListView.as_view(),
name='corpus_list'),
url(
r'^corpus/(?P<pk>[0-9]+)$', views.CorpusDetailView.as_view(),
name='corpus_detail'),
url(
r'^corpus/create/$', views.CorpusCreate.as_view(),
name='corpus_create'),
url(
r'^corpus/update/(?P<pk>[0-9]+)$', views.CorpusUpdate.as_view(),
name='corpus_update'),
url(r'^text/$', views.TextListView.as_view(),
name='text_list'),
url(
r'^text/(?P<pk>[0-9]+)$', views.TextDetailView.as_view(),
name='text_detail'),
url(
r'^text/create/$', views.TextCreate.as_view(),
name='text_create'),
url(
r'^text/update/(?P<pk>[0-9]+)$', views.TextUpdate.as_view(),
name='text_update'),
url(r'^consonant/$', views.ConsonantListView.as_view(),
name='consonant_list'),
url(
r'^consonant/(?P<pk>[0-9]+)$', views.ConsonantDetailView.as_view(),
name='consonant_detail'),
url(
r'^consonant/create/$', views.ConsonantCreate.as_view(),
name='consonant_create'),
url(
r'^consonant/update/(?P<pk>[0-9]+)$', views.ConsonantUpdate.as_view(),
name='consonant_update'),
url(r'^cluster/$', views.ClusterListView.as_view(),
name='cluster_list'),
url(
r'^cluster/(?P<pk>[0-9]+)$', views.ClusterDetailView.as_view(),
name='cluster_detail'),
url(
r'^cluster/create/$', views.ClusterCreate.as_view(),
name='cluster_create'),
url(
r'^cluster/update/(?P<pk>[0-9]+)$', views.ClusterUpdate.as_view(),
name='cluster_update'),
url(r'^tokenlabel/$', views.TokenLabelListView.as_view(),
name='tokenlabel_list'),
url(
r'^tokenlabel/(?P<pk>[0-9]+)$', views.TokenLabelDetailView.as_view(),
name='tokenlabel_detail'),
url(
r'^tokenlabel/create/$', views.TokenLabelCreate.as_view(),
name='tokenlabel_create'),
url(
r'^tokenlabel/update/(?P<pk>[0-9]+)$', views.TokenLabelUpdate.as_view(),
name='tokenlabel_update'),
url(r'^schwapresent/$', views.SchwaPresentListView.as_view(),
name='schwapresent_list'),
url(
r'^schwapresent/(?P<pk>[0-9]+)$', views.SchwaPresentDetailView.as_view(),
name='schwapresent_detail'),
url(
r'^schwapresent/create/$', views.SchwaPresentCreate.as_view(),
name='schwapresent_create'),
url(
r'^schwapresent/update/(?P<pk>[0-9]+)$', views.SchwaPresentUpdate.as_view(),
name='schwapresent_update'),
url(r'^onset/$', views.OnSetListView.as_view(),
name='onset_list'),
url(
r'^onset/(?P<pk>[0-9]+)$', views.OnSetDetailView.as_view(),
name='onset_detail'),
url(
r'^onset/create/$', views.OnSetCreate.as_view(),
name='onset_create'),
url(
r'^onset/update/(?P<pk>[0-9]+)$', views.OnSetUpdate.as_view(),
name='onset_update'),
url(r'^token/$', views.TokenListView.as_view(),
name='token_list'),
url(
r'^token/(?P<pk>[0-9]+)$', views.TokenDetailView.as_view(),
name='token_detail'),
url(
r'^token/create/$', views.TokenCreate.as_view(),
name='token_create'),
url(
r'^token/update/(?P<pk>[0-9]+)$', views.TokenUpdate.as_view(),
name='token_update'),
url(
r'^lemma/(?P<pk>[0-9]+)$', views.LemmaDetailView.as_view(),
name='lemma_detail'),
] | tokens/urls.py | from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^date/$', views.DateListView.as_view(),
name='date_list'),
url(
r'^date/(?P<pk>[0-9]+)$', views.DateDetailView.as_view(),
name='date_detail'),
url(
r'^date/create/$', views.DateCreate.as_view(),
name='date_create'),
url(
r'^date/update/(?P<pk>[0-9]+)$', views.DateUpdate.as_view(),
name='date_update'),
url(r'^corpus/$', views.CorpusListView.as_view(),
name='corpus_list'),
url(
r'^corpus/(?P<pk>[0-9]+)$', views.CorpusDetailView.as_view(),
name='corpus_detail'),
url(
r'^corpus/create/$', views.CorpusCreate.as_view(),
name='corpus_create'),
url(
r'^corpus/update/(?P<pk>[0-9]+)$', views.CorpusUpdate.as_view(),
name='corpus_update'),
url(r'^text/$', views.TextListView.as_view(),
name='text_list'),
url(
r'^text/(?P<pk>[0-9]+)$', views.TextDetailView.as_view(),
name='text_detail'),
url(
r'^text/create/$', views.TextCreate.as_view(),
name='text_create'),
url(
r'^text/update/(?P<pk>[0-9]+)$', views.TextUpdate.as_view(),
name='text_update'),
url(r'^consonant/$', views.ConsonantListView.as_view(),
name='consonant_list'),
url(
r'^consonant/(?P<pk>[0-9]+)$', views.ConsonantDetailView.as_view(),
name='consonant_detail'),
url(
r'^consonant/create/$', views.ConsonantCreate.as_view(),
name='consonant_create'),
url(
r'^consonant/update/(?P<pk>[0-9]+)$', views.ConsonantUpdate.as_view(),
name='consonant_update'),
url(r'^cluster/$', views.ClusterListView.as_view(),
name='cluster_list'),
url(
r'^cluster/(?P<pk>[0-9]+)$', views.ClusterDetailView.as_view(),
name='cluster_detail'),
url(
r'^cluster/create/$', views.ClusterCreate.as_view(),
name='cluster_create'),
url(
r'^cluster/update/(?P<pk>[0-9]+)$', views.ClusterUpdate.as_view(),
name='cluster_update'),
url(r'^tokenlabel/$', views.TokenLabelListView.as_view(),
name='tokenlabel_list'),
url(
r'^tokenlabel/(?P<pk>[0-9]+)$', views.TokenLabelDetailView.as_view(),
name='tokenlabel_detail'),
url(
r'^tokenlabel/create/$', views.TokenLabelCreate.as_view(),
name='tokenlabel_create'),
url(
r'^tokenlabel/update/(?P<pk>[0-9]+)$', views.TokenLabelUpdate.as_view(),
name='tokenlabel_update'),
url(r'^schwapresent/$', views.SchwaPresentListView.as_view(),
name='schwapresent_list'),
url(
r'^schwapresent/(?P<pk>[0-9]+)$', views.SchwaPresentDetailView.as_view(),
name='schwapresent_detail'),
url(
r'^schwapresent/create/$', views.SchwaPresentCreate.as_view(),
name='schwapresent_create'),
url(
r'^schwapresent/update/(?P<pk>[0-9]+)$', views.SchwaPresentUpdate.as_view(),
name='schwapresent_update'),
url(r'^onset/$', views.OnSetListView.as_view(),
name='onset_list'),
url(
r'^onset/(?P<pk>[0-9]+)$', views.OnSetDetailView.as_view(),
name='onset_detail'),
url(
r'^onset/create/$', views.OnSetCreate.as_view(),
name='onset_create'),
url(
r'^onset/update/(?P<pk>[0-9]+)$', views.OnSetUpdate.as_view(),
name='onset_update'),
url(r'^token/$', views.TokenListView.as_view(),
name='token_list'),
url(
r'^token/(?P<pk>[0-9]+)$', views.TokenDetailView.as_view(),
name='token_detail'),
url(
r'^token/create/$', views.TokenCreate.as_view(),
name='token_create'),
url(
r'^token/update/(?P<pk>[0-9]+)$', views.TokenUpdate.as_view(),
name='token_update'),
url(
r'^lemma/(?P<pk>[0-9]+)$', views.LemmaDetailView.as_view(),
name='lemma_detail'),
] | 0.259544 | 0.141015 |
import copy
from typing import Tuple, List, Optional
from aparse import Literal
from dataclasses import dataclass, fields, field, is_dataclass
from viewformer.utils.schedules import Schedule
ModelType = Literal['codebook', 'transformer']
def asdict(obj):
dict_factory = dict
def _asdict_inner(obj, dict_factory):
if hasattr(obj, 'from_str'):
return str(obj)
elif is_dataclass(obj):
result = []
for f in fields(obj):
value = _asdict_inner(getattr(obj, f.name), dict_factory)
result.append((f.name, value))
return dict_factory(result)
elif isinstance(obj, tuple) and hasattr(obj, '_fields'):
return type(obj)(*[_asdict_inner(v, dict_factory) for v in obj])
elif isinstance(obj, (list, tuple)):
return type(obj)(_asdict_inner(v, dict_factory) for v in obj)
elif isinstance(obj, dict):
return type(obj)((_asdict_inner(k, dict_factory),
_asdict_inner(v, dict_factory))
for k, v in obj.items())
else:
return copy.deepcopy(obj)
if not is_dataclass(obj):
raise TypeError("asdict() should be called on dataclass instances")
return _asdict_inner(obj, dict_factory)
@dataclass
class ModelConfig:
model: str = field(init=False)
def __post_init__(self):
cls_name = type(self).__name__
assert cls_name.endswith('Config')
cls_name = cls_name[:-len('Config')]
cls_name = cls_name.lower()
self.model = cls_name
def asdict(self):
return asdict(self)
@classmethod
def supported_config_dict(cls):
configs = {}
if cls != ModelConfig:
configs[cls.__name__.lower()[:-len('config')]] = cls
for c in cls.__subclasses__():
configs.update(c.supported_config_dict())
return configs
@dataclass
class MIGTConfig(ModelConfig):
n_embeddings: int = 1024
n_head: int = 12
d_model: int = 768
dropout: float = 0.1
n_layer: int = 12
weight_decay: float = 0.01
label_smoothing: float = 0.0
learning_rate: float = 6.4e-4
batch_size: int = 64
gradient_clip_val: float = 0.0
sequence_size: int = 20
token_image_size: int = 8
total_steps: int = 300000
n_loss_skip: int = 4
augment_poses: Literal['no', 'relative', 'simple', 'advanced'] = 'relative'
use_dynamic_pose_loss: bool = False
localization_weight: Schedule = Schedule.from_str('1')
image_generation_weight: float = 1.
pose_multiplier: float = 1.
random_pose_multiplier: float = 1.
@property
def model_type(self):
return 'transformer'
@dataclass
class VQGANConfig(ModelConfig):
learning_rate: float = 1.584e-3
embed_dim: int = 256
n_embed: int = 1024
z_channels: int = 256
resolution: int = 256
in_channels: int = 3
out_ch: int = 3
ch: int = 128
num_res_blocks: int = 2
ch_mult: List[int] = field(default_factory=lambda: [1, 1, 2, 2, 4])
attn_resolutions: List[int] = field(default_factory=lambda: [16])
gradient_clip_val: float = .0
batch_size: int = 352
image_size: int = 128
total_steps: int = 200000
codebook_weight: float = 1.0
pixelloss_weight: float = 1.0
perceptual_weight: float = 1.0
@property
def stride(self):
return 2 ** (len(self.ch_mult) - 1)
@property
def model_type(self):
return 'codebook' | viewformer/models/config.py | import copy
from typing import Tuple, List, Optional
from aparse import Literal
from dataclasses import dataclass, fields, field, is_dataclass
from viewformer.utils.schedules import Schedule
ModelType = Literal['codebook', 'transformer']
def asdict(obj):
dict_factory = dict
def _asdict_inner(obj, dict_factory):
if hasattr(obj, 'from_str'):
return str(obj)
elif is_dataclass(obj):
result = []
for f in fields(obj):
value = _asdict_inner(getattr(obj, f.name), dict_factory)
result.append((f.name, value))
return dict_factory(result)
elif isinstance(obj, tuple) and hasattr(obj, '_fields'):
return type(obj)(*[_asdict_inner(v, dict_factory) for v in obj])
elif isinstance(obj, (list, tuple)):
return type(obj)(_asdict_inner(v, dict_factory) for v in obj)
elif isinstance(obj, dict):
return type(obj)((_asdict_inner(k, dict_factory),
_asdict_inner(v, dict_factory))
for k, v in obj.items())
else:
return copy.deepcopy(obj)
if not is_dataclass(obj):
raise TypeError("asdict() should be called on dataclass instances")
return _asdict_inner(obj, dict_factory)
@dataclass
class ModelConfig:
model: str = field(init=False)
def __post_init__(self):
cls_name = type(self).__name__
assert cls_name.endswith('Config')
cls_name = cls_name[:-len('Config')]
cls_name = cls_name.lower()
self.model = cls_name
def asdict(self):
return asdict(self)
@classmethod
def supported_config_dict(cls):
configs = {}
if cls != ModelConfig:
configs[cls.__name__.lower()[:-len('config')]] = cls
for c in cls.__subclasses__():
configs.update(c.supported_config_dict())
return configs
@dataclass
class MIGTConfig(ModelConfig):
n_embeddings: int = 1024
n_head: int = 12
d_model: int = 768
dropout: float = 0.1
n_layer: int = 12
weight_decay: float = 0.01
label_smoothing: float = 0.0
learning_rate: float = 6.4e-4
batch_size: int = 64
gradient_clip_val: float = 0.0
sequence_size: int = 20
token_image_size: int = 8
total_steps: int = 300000
n_loss_skip: int = 4
augment_poses: Literal['no', 'relative', 'simple', 'advanced'] = 'relative'
use_dynamic_pose_loss: bool = False
localization_weight: Schedule = Schedule.from_str('1')
image_generation_weight: float = 1.
pose_multiplier: float = 1.
random_pose_multiplier: float = 1.
@property
def model_type(self):
return 'transformer'
@dataclass
class VQGANConfig(ModelConfig):
learning_rate: float = 1.584e-3
embed_dim: int = 256
n_embed: int = 1024
z_channels: int = 256
resolution: int = 256
in_channels: int = 3
out_ch: int = 3
ch: int = 128
num_res_blocks: int = 2
ch_mult: List[int] = field(default_factory=lambda: [1, 1, 2, 2, 4])
attn_resolutions: List[int] = field(default_factory=lambda: [16])
gradient_clip_val: float = .0
batch_size: int = 352
image_size: int = 128
total_steps: int = 200000
codebook_weight: float = 1.0
pixelloss_weight: float = 1.0
perceptual_weight: float = 1.0
@property
def stride(self):
return 2 ** (len(self.ch_mult) - 1)
@property
def model_type(self):
return 'codebook' | 0.850049 | 0.180829 |
import popart
import pytest
import numpy as np
import test_util as tu
import tempfile
import os
# Test that you can train a model and then use the weight in a inference run
@tu.requires_ipu_model
def test_train_then_infer_via_file():
builder = popart.Builder()
input_shape = popart.TensorInfo("FLOAT", [1, 2, 4, 4])
weight_shape = popart.TensorInfo("FLOAT", [3, 2, 3, 3])
weight_data = np.ones([3, 2, 3, 3], np.float32)
input = builder.addInputTensor(input_shape)
weights = builder.addInitializedInputTensor(weight_data)
act = builder.aiOnnx.conv([input, weights],
dilations=[1, 1],
pads=[1, 1, 1, 1],
strides=[1, 1])
o = builder.aiOnnx.relu([act])
l1 = builder.aiGraphcore.l1loss([o], 0.1)
anchor_names = [
o,
popart.reservedGradientPrefix() + input,
popart.reservedGradientPrefix() + weights
]
training_dataFlow = popart.DataFlow(
1, {
anchor_names[0]: popart.AnchorReturnType("All"),
anchor_names[1]: popart.AnchorReturnType("All"),
anchor_names[2]: popart.AnchorReturnType("All")
})
opts = popart.SessionOptions()
opts.constantWeights = False # Allow the weights to be updated
tempDir = tempfile.TemporaryDirectory()
opts.engineOptions["autoReport.directory"] = tempDir.name
opts.engineOptions["autoReport.all"] = "true"
# ----------------------------------------------
# Create the device
device = tu.create_test_device(1, opts={"compileIPUCode": True})
device.attach()
# ----------------------------------------------
# Prepare the input data
input_data = np.ones(input_shape.shape(), dtype=np.float32)
# ----------------------------------------------
# Prepare the Inference session
inference_dataFlow = popart.DataFlow(1,
{o: popart.AnchorReturnType("All")})
inference_session = popart.InferenceSession(
fnModel=builder.getModelProto(),
dataFlow=inference_dataFlow,
userOptions=opts,
deviceInfo=device)
# Compile the inference graph
inference_session.prepareDevice()
# ----------------------------------------------
# Prepare the Training session
training_session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=training_dataFlow,
loss=l1,
optimizer=popart.ConstSGD(0.01),
userOptions=opts,
deviceInfo=device,
name="ivor")
# Compile the training graph
training_session.prepareDevice()
# ----------------------------------------------
# Run the training session
training_session.weightsFromHost()
training_anchors = training_session.initAnchorArrays()
training_inputs = {input: input_data}
for i in range(4):
training_session.run(popart.PyStepIO(training_inputs,
training_anchors))
# Save the trained weights
training_session.modelToHost("test.onnx")
# ----------------------------------------------
# Run the inference session
## Load the updated weights from the training session
inference_session.resetHostWeights("test.onnx")
inference_session.weightsFromHost()
inference_anchors = inference_session.initAnchorArrays()
inference_inputs = {input: input_data}
inference_session.run(popart.PyStepIO(inference_inputs, inference_anchors))
# check that the profile.pop as been created in the subdirectories
assert (os.path.isfile(tempDir.name + "/inference/profile.pop"), True)
assert (os.path.isfile(tempDir.name + "/ivor/profile.pop"), True)
@tu.requires_ipu_model
def test_cannot_call_resethostweights_with_constant_weights():
builder = popart.Builder()
input_shape = popart.TensorInfo("FLOAT", [1, 2, 4, 4])
weight_data = np.ones([3, 2, 3, 3], np.float32)
input = builder.addInputTensor(input_shape)
weights = builder.addInitializedInputTensor(weight_data)
act = builder.aiOnnx.conv([input, weights],
dilations=[1, 1],
pads=[1, 1, 1, 1],
strides=[1, 1])
o = builder.aiOnnx.relu([act])
builder.addOutputTensor(o)
opts = popart.SessionOptions()
opts.constantWeights = True # Fix weights in inference session
# ----------------------------------------------
# Create the device
device = tu.create_test_device(1, opts={"compileIPUCode": True})
device.attach()
# ----------------------------------------------
# Prepare the input data
input_data = np.ones(input_shape.shape(), dtype=np.float32)
# ----------------------------------------------
# Prepare the Inference session
inference_dataFlow = popart.DataFlow(1,
{o: popart.AnchorReturnType("All")})
inference_session = popart.InferenceSession(
fnModel=builder.getModelProto(),
dataFlow=inference_dataFlow,
userOptions=opts,
deviceInfo=device)
# Compile the inference graph
inference_session.prepareDevice()
# Create a file with some weights
inference_session.modelToHost("test.onnx")
## Load the updated weights from the training session
with pytest.raises(popart.popart_exception) as e_info:
inference_session.resetHostWeights("test.onnx")
assert (e_info.value.args[0].startswith(
"Cannot call resetHostWeights when constantWeights is set"))
@tu.requires_ipu_model
def test_modelToHost_calls_resetHostWeights():
builder = popart.Builder()
input_shape = popart.TensorInfo("FLOAT", [1, 1, 4, 4])
weight_shape = popart.TensorInfo("FLOAT", [1, 1, 3, 3])
input_data = np.ones(input_shape.shape(), dtype=np.float32)
weight_data = np.ones(weight_shape.shape(), np.float32)
input = builder.addInputTensor(input_shape)
weights = builder.addInitializedInputTensor(weight_data)
act = builder.aiOnnx.conv([input, weights],
dilations=[1, 1],
pads=[1, 1, 1, 1],
strides=[1, 1])
o = builder.aiOnnx.relu([act])
l1 = builder.aiGraphcore.l1loss([o], 0.1)
builder.addOutputTensor(o)
anchor_names = [o]
data_flow = popart.DataFlow(
1, {i: popart.AnchorReturnType("All")
for i in anchor_names})
opts = popart.SessionOptions()
opts.constantWeights = False # Allow the weights to be updated
# Create the device
device = tu.create_test_device(1, opts={"compileIPUCode": True})
device.attach()
# Prepare the Training session
session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=data_flow,
loss=l1,
optimizer=popart.ConstSGD(0.1),
userOptions=opts,
deviceInfo=device)
# Compile the training graph
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
inputs = {input: input_data}
outputs = []
for i in range(2):
session.run(popart.PyStepIO(inputs, anchors))
outputs.append(np.copy(anchors[o]))
# The outputs of the two training runs should not be close
print('Checking first two outputs differ')
assert not np.allclose(outputs[0], outputs[1])
# Write weights from device to host
session.modelToHost("test.onnx")
# Write weights from host to device
session.weightsFromHost()
# Run the training session and get the output
session.run(popart.PyStepIO(inputs, anchors))
outputs.append(np.copy(anchors[o]))
# Neither of the previous outputs should be close to the new output
print('Checking third output differs from first two')
assert not np.allclose(outputs[2], outputs[0])
assert not np.allclose(outputs[2], outputs[1])
# Last output should be close to second output minus
# the difference between first and second outputs.
delta_outputs = outputs[0] - outputs[1]
expected_out = outputs[1] - delta_outputs
print('Checking third output is close to expected value')
assert np.allclose(outputs[2], expected_out) | tests/integration/train_then_infer_test.py | import popart
import pytest
import numpy as np
import test_util as tu
import tempfile
import os
# Test that you can train a model and then use the weight in a inference run
@tu.requires_ipu_model
def test_train_then_infer_via_file():
builder = popart.Builder()
input_shape = popart.TensorInfo("FLOAT", [1, 2, 4, 4])
weight_shape = popart.TensorInfo("FLOAT", [3, 2, 3, 3])
weight_data = np.ones([3, 2, 3, 3], np.float32)
input = builder.addInputTensor(input_shape)
weights = builder.addInitializedInputTensor(weight_data)
act = builder.aiOnnx.conv([input, weights],
dilations=[1, 1],
pads=[1, 1, 1, 1],
strides=[1, 1])
o = builder.aiOnnx.relu([act])
l1 = builder.aiGraphcore.l1loss([o], 0.1)
anchor_names = [
o,
popart.reservedGradientPrefix() + input,
popart.reservedGradientPrefix() + weights
]
training_dataFlow = popart.DataFlow(
1, {
anchor_names[0]: popart.AnchorReturnType("All"),
anchor_names[1]: popart.AnchorReturnType("All"),
anchor_names[2]: popart.AnchorReturnType("All")
})
opts = popart.SessionOptions()
opts.constantWeights = False # Allow the weights to be updated
tempDir = tempfile.TemporaryDirectory()
opts.engineOptions["autoReport.directory"] = tempDir.name
opts.engineOptions["autoReport.all"] = "true"
# ----------------------------------------------
# Create the device
device = tu.create_test_device(1, opts={"compileIPUCode": True})
device.attach()
# ----------------------------------------------
# Prepare the input data
input_data = np.ones(input_shape.shape(), dtype=np.float32)
# ----------------------------------------------
# Prepare the Inference session
inference_dataFlow = popart.DataFlow(1,
{o: popart.AnchorReturnType("All")})
inference_session = popart.InferenceSession(
fnModel=builder.getModelProto(),
dataFlow=inference_dataFlow,
userOptions=opts,
deviceInfo=device)
# Compile the inference graph
inference_session.prepareDevice()
# ----------------------------------------------
# Prepare the Training session
training_session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=training_dataFlow,
loss=l1,
optimizer=popart.ConstSGD(0.01),
userOptions=opts,
deviceInfo=device,
name="ivor")
# Compile the training graph
training_session.prepareDevice()
# ----------------------------------------------
# Run the training session
training_session.weightsFromHost()
training_anchors = training_session.initAnchorArrays()
training_inputs = {input: input_data}
for i in range(4):
training_session.run(popart.PyStepIO(training_inputs,
training_anchors))
# Save the trained weights
training_session.modelToHost("test.onnx")
# ----------------------------------------------
# Run the inference session
## Load the updated weights from the training session
inference_session.resetHostWeights("test.onnx")
inference_session.weightsFromHost()
inference_anchors = inference_session.initAnchorArrays()
inference_inputs = {input: input_data}
inference_session.run(popart.PyStepIO(inference_inputs, inference_anchors))
# check that the profile.pop as been created in the subdirectories
assert (os.path.isfile(tempDir.name + "/inference/profile.pop"), True)
assert (os.path.isfile(tempDir.name + "/ivor/profile.pop"), True)
@tu.requires_ipu_model
def test_cannot_call_resethostweights_with_constant_weights():
builder = popart.Builder()
input_shape = popart.TensorInfo("FLOAT", [1, 2, 4, 4])
weight_data = np.ones([3, 2, 3, 3], np.float32)
input = builder.addInputTensor(input_shape)
weights = builder.addInitializedInputTensor(weight_data)
act = builder.aiOnnx.conv([input, weights],
dilations=[1, 1],
pads=[1, 1, 1, 1],
strides=[1, 1])
o = builder.aiOnnx.relu([act])
builder.addOutputTensor(o)
opts = popart.SessionOptions()
opts.constantWeights = True # Fix weights in inference session
# ----------------------------------------------
# Create the device
device = tu.create_test_device(1, opts={"compileIPUCode": True})
device.attach()
# ----------------------------------------------
# Prepare the input data
input_data = np.ones(input_shape.shape(), dtype=np.float32)
# ----------------------------------------------
# Prepare the Inference session
inference_dataFlow = popart.DataFlow(1,
{o: popart.AnchorReturnType("All")})
inference_session = popart.InferenceSession(
fnModel=builder.getModelProto(),
dataFlow=inference_dataFlow,
userOptions=opts,
deviceInfo=device)
# Compile the inference graph
inference_session.prepareDevice()
# Create a file with some weights
inference_session.modelToHost("test.onnx")
## Load the updated weights from the training session
with pytest.raises(popart.popart_exception) as e_info:
inference_session.resetHostWeights("test.onnx")
assert (e_info.value.args[0].startswith(
"Cannot call resetHostWeights when constantWeights is set"))
@tu.requires_ipu_model
def test_modelToHost_calls_resetHostWeights():
builder = popart.Builder()
input_shape = popart.TensorInfo("FLOAT", [1, 1, 4, 4])
weight_shape = popart.TensorInfo("FLOAT", [1, 1, 3, 3])
input_data = np.ones(input_shape.shape(), dtype=np.float32)
weight_data = np.ones(weight_shape.shape(), np.float32)
input = builder.addInputTensor(input_shape)
weights = builder.addInitializedInputTensor(weight_data)
act = builder.aiOnnx.conv([input, weights],
dilations=[1, 1],
pads=[1, 1, 1, 1],
strides=[1, 1])
o = builder.aiOnnx.relu([act])
l1 = builder.aiGraphcore.l1loss([o], 0.1)
builder.addOutputTensor(o)
anchor_names = [o]
data_flow = popart.DataFlow(
1, {i: popart.AnchorReturnType("All")
for i in anchor_names})
opts = popart.SessionOptions()
opts.constantWeights = False # Allow the weights to be updated
# Create the device
device = tu.create_test_device(1, opts={"compileIPUCode": True})
device.attach()
# Prepare the Training session
session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=data_flow,
loss=l1,
optimizer=popart.ConstSGD(0.1),
userOptions=opts,
deviceInfo=device)
# Compile the training graph
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
inputs = {input: input_data}
outputs = []
for i in range(2):
session.run(popart.PyStepIO(inputs, anchors))
outputs.append(np.copy(anchors[o]))
# The outputs of the two training runs should not be close
print('Checking first two outputs differ')
assert not np.allclose(outputs[0], outputs[1])
# Write weights from device to host
session.modelToHost("test.onnx")
# Write weights from host to device
session.weightsFromHost()
# Run the training session and get the output
session.run(popart.PyStepIO(inputs, anchors))
outputs.append(np.copy(anchors[o]))
# Neither of the previous outputs should be close to the new output
print('Checking third output differs from first two')
assert not np.allclose(outputs[2], outputs[0])
assert not np.allclose(outputs[2], outputs[1])
# Last output should be close to second output minus
# the difference between first and second outputs.
delta_outputs = outputs[0] - outputs[1]
expected_out = outputs[1] - delta_outputs
print('Checking third output is close to expected value')
assert np.allclose(outputs[2], expected_out) | 0.678859 | 0.545891 |
from flask import Flask, render_template, url_for,jsonify
import json
# python modules created for apis
from ormQueries import getSampleNames,getOTUbySamples,getSampleMetaData,getWashingFreq
from sqlalchemy import create_engine
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
import pandas as pd
app = Flask(__name__)
# dashboard homepage
@app.route('/')
def hello():
"""Return the dashboard homepage."""
return render_template('index.html')
@app.route("/names")
def sampleNames():
"""List of sample names.
Returns a list of sample names in the format
[
"BB_940",
"BB_941",
"BB_943",
"BB_944",
"BB_945",
"BB_946",
"BB_947",
...
]
"""
sampleNames = getSampleNames()
return json.dumps(sampleNames)
@app.route("/otu")
def otu():
"""List of OTU descriptions.
Returns a list of OTU descriptions in the following format
[
"Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Halobacteriaceae;Halococcus",
"Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Halobacteriaceae;Halococcus",
"Bacteria",
"Bacteria",
"Bacteria",
...
]
"""
@app.route("/metadata/<sample_id>")
def metaDataSample(sample_id):
"""MetaData for a given sample.
Args: Sample in the format: `BB_940`
Returns a json dictionary of sample metadata in the format
{
AGE: 24,
BBTYPE: "I",
ETHNICITY: "Caucasian",
GENDER: "F",
LOCATION: "Beaufort/NC",
SAMPLEID: 940
}
"""
meta = getSampleMetaData(sample_id)
return jsonify(meta)
@app.route("/wfreq/<sample_id>")
def washingFreq(sample_id):
"""Weekly Washing Frequency as a number.
Args: Sample in the format: `BB_940`
Returns an integer value for the weekly washing frequency `WFREQ`
"""
wfreq = getWashingFreq(sample_id)
return jsonify(wfreq)
@app.route("/samples/<sample_id>")
def check(sample_id):
"""OTU IDs and Sample Values for a given sample.
Sort your Pandas DataFrame (OTU ID and Sample Value)
in Descending Order by Sample Value
Return a list of dictionaries containing sorted lists for `otu_ids`
and `sample_values`
[
{
otu_ids: [
1166,
2858,
481,
...
],
sample_values: [
163,
126,
113,
...
]
}
]
"""
return getOTUbySamples(sample_id)
if __name__ == "__main__":
app.run() | app.py | from flask import Flask, render_template, url_for,jsonify
import json
# python modules created for apis
from ormQueries import getSampleNames,getOTUbySamples,getSampleMetaData,getWashingFreq
from sqlalchemy import create_engine
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
import pandas as pd
app = Flask(__name__)
# dashboard homepage
@app.route('/')
def hello():
"""Return the dashboard homepage."""
return render_template('index.html')
@app.route("/names")
def sampleNames():
"""List of sample names.
Returns a list of sample names in the format
[
"BB_940",
"BB_941",
"BB_943",
"BB_944",
"BB_945",
"BB_946",
"BB_947",
...
]
"""
sampleNames = getSampleNames()
return json.dumps(sampleNames)
@app.route("/otu")
def otu():
"""List of OTU descriptions.
Returns a list of OTU descriptions in the following format
[
"Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Halobacteriaceae;Halococcus",
"Archaea;Euryarchaeota;Halobacteria;Halobacteriales;Halobacteriaceae;Halococcus",
"Bacteria",
"Bacteria",
"Bacteria",
...
]
"""
@app.route("/metadata/<sample_id>")
def metaDataSample(sample_id):
"""MetaData for a given sample.
Args: Sample in the format: `BB_940`
Returns a json dictionary of sample metadata in the format
{
AGE: 24,
BBTYPE: "I",
ETHNICITY: "Caucasian",
GENDER: "F",
LOCATION: "Beaufort/NC",
SAMPLEID: 940
}
"""
meta = getSampleMetaData(sample_id)
return jsonify(meta)
@app.route("/wfreq/<sample_id>")
def washingFreq(sample_id):
"""Weekly Washing Frequency as a number.
Args: Sample in the format: `BB_940`
Returns an integer value for the weekly washing frequency `WFREQ`
"""
wfreq = getWashingFreq(sample_id)
return jsonify(wfreq)
@app.route("/samples/<sample_id>")
def check(sample_id):
"""OTU IDs and Sample Values for a given sample.
Sort your Pandas DataFrame (OTU ID and Sample Value)
in Descending Order by Sample Value
Return a list of dictionaries containing sorted lists for `otu_ids`
and `sample_values`
[
{
otu_ids: [
1166,
2858,
481,
...
],
sample_values: [
163,
126,
113,
...
]
}
]
"""
return getOTUbySamples(sample_id)
if __name__ == "__main__":
app.run() | 0.739516 | 0.182225 |
from urllib.parse import quote
import hmac
import time
import requests
import logging
from base64 import b64encode
from hashlib import sha256
from com.data.abstract_calls import AbstractApiCall
from properties import AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_ASSOCIATE_TAG, \
AWS_PRODUCT_API_SERVICE, AWS_PRODUCT_API_VERSION
ENDPOINT = {
'US': 'webservices.amazon.com',
'CA': 'webservices.amazon.ca',
'CN': 'webservices.amazon.cn',
'DE': 'webservices.amazon.de',
'ES': 'webservices.amazon.es',
'FR': 'webservices.amazon.fr',
'IN': 'webservices.amazon.in',
'IT': 'webservices.amazon.it',
'JP': 'webservices.amazon.co.jp',
'UK': 'webservices.amazon.co.uk',
'BR': 'webservices.amazon.com.br',
'MX': 'webservices.amazon.com.mx',
}
REQUEST_URI = "/onca/xml"
log = logging.getLogger(__name__)
class ProductAPI(AbstractApiCall):
def __init__(self, region='US', timeout=10, max_rate=0.9):
self.region = region
self._last_request_time = None
super().__init__(timeout, max_rate)
def _api_url(self, extra_params):
params = {
'AWSAccessKeyId': AWS_ACCESS_KEY_ID,
'AssociateTag': AWS_ASSOCIATE_TAG,
'Service': AWS_PRODUCT_API_SERVICE,
'Timestamp': time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime()),
'Version': AWS_PRODUCT_API_VERSION,
}
params.update(extra_params)
service_domain = ENDPOINT[self.region]
quoted_strings = self._canonical_query_string(params)
# Generate the string to be signed
data = 'GET\n' + service_domain + '\n' + REQUEST_URI + '\n' + quoted_strings
# Generate the signature required by the Product Advertising API
digest = hmac.new(AWS_SECRET_ACCESS_KEY.encode(), data.encode(), sha256).digest()
# base64 encode and urlencode
signature = quote(b64encode(digest))
return ("https://" + service_domain + REQUEST_URI + "?" +
quoted_strings + "&Signature=%s" % signature)
def _call_api(self, api_url):
try:
with requests.get(api_url, timeout=self.timeout) as resp:
return resp
except Exception as e:
log.error(e)
pass
__all__ = ["ProductAPI"] | com/amazon/amazon_api.py | from urllib.parse import quote
import hmac
import time
import requests
import logging
from base64 import b64encode
from hashlib import sha256
from com.data.abstract_calls import AbstractApiCall
from properties import AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_ASSOCIATE_TAG, \
AWS_PRODUCT_API_SERVICE, AWS_PRODUCT_API_VERSION
ENDPOINT = {
'US': 'webservices.amazon.com',
'CA': 'webservices.amazon.ca',
'CN': 'webservices.amazon.cn',
'DE': 'webservices.amazon.de',
'ES': 'webservices.amazon.es',
'FR': 'webservices.amazon.fr',
'IN': 'webservices.amazon.in',
'IT': 'webservices.amazon.it',
'JP': 'webservices.amazon.co.jp',
'UK': 'webservices.amazon.co.uk',
'BR': 'webservices.amazon.com.br',
'MX': 'webservices.amazon.com.mx',
}
REQUEST_URI = "/onca/xml"
log = logging.getLogger(__name__)
class ProductAPI(AbstractApiCall):
def __init__(self, region='US', timeout=10, max_rate=0.9):
self.region = region
self._last_request_time = None
super().__init__(timeout, max_rate)
def _api_url(self, extra_params):
params = {
'AWSAccessKeyId': AWS_ACCESS_KEY_ID,
'AssociateTag': AWS_ASSOCIATE_TAG,
'Service': AWS_PRODUCT_API_SERVICE,
'Timestamp': time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime()),
'Version': AWS_PRODUCT_API_VERSION,
}
params.update(extra_params)
service_domain = ENDPOINT[self.region]
quoted_strings = self._canonical_query_string(params)
# Generate the string to be signed
data = 'GET\n' + service_domain + '\n' + REQUEST_URI + '\n' + quoted_strings
# Generate the signature required by the Product Advertising API
digest = hmac.new(AWS_SECRET_ACCESS_KEY.encode(), data.encode(), sha256).digest()
# base64 encode and urlencode
signature = quote(b64encode(digest))
return ("https://" + service_domain + REQUEST_URI + "?" +
quoted_strings + "&Signature=%s" % signature)
def _call_api(self, api_url):
try:
with requests.get(api_url, timeout=self.timeout) as resp:
return resp
except Exception as e:
log.error(e)
pass
__all__ = ["ProductAPI"] | 0.509276 | 0.037117 |
import hashlib
# Third party library imports
import click
import requests
# Needed to access API
PASS_URL = "https://api.pwnedpasswords.com/range/"
# Script information
VERSION = "0.1.2"
LOGO = r"""
__ __ __
| |--.---.-.--.--.-----. |__| | |--.-----.-----.-----.
| | _ | | | -__| | | | _ | -__| -__| |
|__|__|___._|\___/|_____| |__| |_____|_____|_____|__|__|
__ _____
.-----.--.--.--.-----.-----.--| ||__ |
| _ | | | | | -__| _ |', ,-'
| __|________|__|__|_____|_____| |--|
|__| '--' """
def print_info(context, _, value): # `param` (_) required by `click`
"""Print information about the program and exit."""
if not value or context.resilient_parsing:
return
click.secho(
f'{LOGO}\n{"Python 3.6+ Checker".rjust(60)}\n'
f'{("v" + VERSION).rjust(60)}',
fg="blue",
bold=True,
)
context.exit()
def pass_to_sha1(password):
"""Will convert `password` with SHA-1 algorithm and return the short
version with the first five characters and the long version with
everything but the first five characters as a tuple."""
my_password = password.encode() # converts to byte string
complete_hash = hashlib.sha1(my_password).hexdigest().upper()
long_hash = complete_hash[5:]
short_hash = complete_hash[:5]
return short_hash, long_hash
def check_password(short_hash, long_hash):
"""Consult haveibeenpwned.com to see how many times the password has
been breached."""
click.secho(
"Please note that only the 5 first characters from your "
"SHA-1 ENCRYPTED password are sent to haveibeenpwned.com.",
fg="yellow",
bold=True,
)
response = requests.get(PASS_URL + short_hash)
lines = response.text.split()
for line in lines:
if long_hash in line:
num_times = "occurrences" if int(line[36:]) > 1 else "occurence"
click.secho("Your password was found!", fg="red", bold=True)
click.echo(f"→ {line[36:]} {num_times}")
break
else:
click.secho("Your password was NOT found!", fg="green")
@click.group()
@click.option(
"--info",
is_flag=True,
callback=print_info,
expose_value=False,
is_eager=True,
help="Print some information about the program and exit.",
)
@click.version_option(version=VERSION, message="%(version)s")
def hibp():
"""A simple command-line interface to make use of
haveibeenpwned.com's data.
Type `python pwned.py usage` for more info (or whatever way you
call Python 3)."""
@hibp.command()
def usage():
"""Give examples on how to use this script."""
click.secho("Examples:", fg="blue")
click.secho("python pwned.py check --help for more info.", fg="green")
click.secho("python pwned.py check -p MyPasswordHere", fg="yellow")
click.secho("python pwned.py check --password MyPasswordHere", fg="green")
click.echo("")
click.secho(
"For more complicated passwords, you have to use quotes and ",
fg="blue",
)
click.secho("escape symbols with \\ where appropriate:", fg="blue")
click.secho(
'python pwned.py check -p "as0d9\\"asg0\'\'A=)SYD"', fg="green"
)
@hibp.command()
@click.option(
"-p",
"--password",
default=None,
help="Reveal if match exists with `password`.",
)
def check(password):
"""Tell if your password has been breached."""
if password:
short_hash, long_hash = pass_to_sha1(password)
check_password(short_hash, long_hash)
if not password:
click.secho("Run `pwned.py check --help` for more info.", fg="red")
if __name__ == "__main__":
hibp() | haveibeenpwnwed_password_checker/pwned.py | import hashlib
# Third party library imports
import click
import requests
# Needed to access API
PASS_URL = "https://api.pwnedpasswords.com/range/"
# Script information
VERSION = "0.1.2"
LOGO = r"""
__ __ __
| |--.---.-.--.--.-----. |__| | |--.-----.-----.-----.
| | _ | | | -__| | | | _ | -__| -__| |
|__|__|___._|\___/|_____| |__| |_____|_____|_____|__|__|
__ _____
.-----.--.--.--.-----.-----.--| ||__ |
| _ | | | | | -__| _ |', ,-'
| __|________|__|__|_____|_____| |--|
|__| '--' """
def print_info(context, _, value): # `param` (_) required by `click`
"""Print information about the program and exit."""
if not value or context.resilient_parsing:
return
click.secho(
f'{LOGO}\n{"Python 3.6+ Checker".rjust(60)}\n'
f'{("v" + VERSION).rjust(60)}',
fg="blue",
bold=True,
)
context.exit()
def pass_to_sha1(password):
"""Will convert `password` with SHA-1 algorithm and return the short
version with the first five characters and the long version with
everything but the first five characters as a tuple."""
my_password = password.encode() # converts to byte string
complete_hash = hashlib.sha1(my_password).hexdigest().upper()
long_hash = complete_hash[5:]
short_hash = complete_hash[:5]
return short_hash, long_hash
def check_password(short_hash, long_hash):
"""Consult haveibeenpwned.com to see how many times the password has
been breached."""
click.secho(
"Please note that only the 5 first characters from your "
"SHA-1 ENCRYPTED password are sent to haveibeenpwned.com.",
fg="yellow",
bold=True,
)
response = requests.get(PASS_URL + short_hash)
lines = response.text.split()
for line in lines:
if long_hash in line:
num_times = "occurrences" if int(line[36:]) > 1 else "occurence"
click.secho("Your password was found!", fg="red", bold=True)
click.echo(f"→ {line[36:]} {num_times}")
break
else:
click.secho("Your password was NOT found!", fg="green")
@click.group()
@click.option(
"--info",
is_flag=True,
callback=print_info,
expose_value=False,
is_eager=True,
help="Print some information about the program and exit.",
)
@click.version_option(version=VERSION, message="%(version)s")
def hibp():
"""A simple command-line interface to make use of
haveibeenpwned.com's data.
Type `python pwned.py usage` for more info (or whatever way you
call Python 3)."""
@hibp.command()
def usage():
"""Give examples on how to use this script."""
click.secho("Examples:", fg="blue")
click.secho("python pwned.py check --help for more info.", fg="green")
click.secho("python pwned.py check -p MyPasswordHere", fg="yellow")
click.secho("python pwned.py check --password MyPasswordHere", fg="green")
click.echo("")
click.secho(
"For more complicated passwords, you have to use quotes and ",
fg="blue",
)
click.secho("escape symbols with \\ where appropriate:", fg="blue")
click.secho(
'python pwned.py check -p "as0d9\\"asg0\'\'A=)SYD"', fg="green"
)
@hibp.command()
@click.option(
"-p",
"--password",
default=None,
help="Reveal if match exists with `password`.",
)
def check(password):
"""Tell if your password has been breached."""
if password:
short_hash, long_hash = pass_to_sha1(password)
check_password(short_hash, long_hash)
if not password:
click.secho("Run `pwned.py check --help` for more info.", fg="red")
if __name__ == "__main__":
hibp() | 0.63023 | 0.318989 |
from textwrap import dedent
from typing import Any, Dict, List
from shared.di import service_as_factory
from shared.postgresql_backend import ConnectionHandler
from shared.util import ModelLocked, collectionfield_and_fqid_from_fqfield
# FQID LOCKING
# positions: <1> <2> <3> <4> <5>
# a/1 modified: X X
# Lock a/1 with pos 4, 5: OK
# Lock a/1 with pos 3, 2, ..: not OK
# Lock a/1 with pos P: Exists an event with pos>P -> not OK
@service_as_factory
class SqlOccLockerBackendService:
connection: ConnectionHandler
def assert_fqid_positions(self, fqids: Dict[str, int]) -> None:
if not fqids:
return
query_arguments: List[Any] = []
filter_parts = []
for fqid, position in fqids.items():
query_arguments.extend((fqid, position,))
filter_parts.append("(fqid=%s and position>%s)")
query = (
"select fqid from events where " + " or ".join(filter_parts) + " limit 1"
)
self.raise_model_locked_if_match(query, query_arguments)
def assert_fqfield_positions(self, fqfields: Dict[str, int]) -> None:
if not fqfields:
return
event_query_arguments: List[Any] = []
event_filter_parts = []
collectionfield_query_arguments: List[str] = []
collectionfield_filter_parts = []
for fqfield, position in fqfields.items():
collectionfield, fqid = collectionfield_and_fqid_from_fqfield(fqfield)
event_query_arguments.extend((fqid, position,))
event_filter_parts.append("(fqid=%s and position>%s)")
collectionfield = collectionfield.replace("_", r"\_")
collectionfield = collectionfield.replace("$", "_%")
collectionfield_query_arguments.extend((fqid, collectionfield,))
collectionfield_filter_parts.append(
"(e.fqid=%s and cf.collectionfield LIKE %s)"
)
event_filter = " or ".join(event_filter_parts)
collectionfield_filter = " or ".join(collectionfield_filter_parts)
query = dedent(
f"""\
select e.fqid from (
select id, fqid from events where {event_filter}
) e
inner join events_to_collectionfields ecf on e.id=ecf.event_id
inner join collectionfields cf on ecf.collectionfield_id=cf.id
where {collectionfield_filter} limit 1"""
)
query_arguments = event_query_arguments + collectionfield_query_arguments
self.raise_model_locked_if_match(query, query_arguments)
def assert_collectionfield_positions(
self, collectionfields: Dict[str, int]
) -> None:
if not collectionfields:
return
query_arguments: List[Any] = []
filter_parts = []
for collectionfield, position in collectionfields.items():
query_arguments.extend((collectionfield, position,))
filter_parts.append("(collectionfield=%s and position>%s)")
query = (
"select collectionfield from collectionfields where "
+ " or ".join(filter_parts)
+ " limit 1"
)
self.raise_model_locked_if_match(query, query_arguments)
def raise_model_locked_if_match(self, query, arguments):
""" returns str (the only response) or None if there is no row """
locked_key = self.connection.query_single_value(query, arguments)
if locked_key is not None:
raise ModelLocked(locked_key) | writer/writer/postgresql_backend/sql_occ_locker_backend_service.py | from textwrap import dedent
from typing import Any, Dict, List
from shared.di import service_as_factory
from shared.postgresql_backend import ConnectionHandler
from shared.util import ModelLocked, collectionfield_and_fqid_from_fqfield
# FQID LOCKING
# positions: <1> <2> <3> <4> <5>
# a/1 modified: X X
# Lock a/1 with pos 4, 5: OK
# Lock a/1 with pos 3, 2, ..: not OK
# Lock a/1 with pos P: Exists an event with pos>P -> not OK
@service_as_factory
class SqlOccLockerBackendService:
connection: ConnectionHandler
def assert_fqid_positions(self, fqids: Dict[str, int]) -> None:
if not fqids:
return
query_arguments: List[Any] = []
filter_parts = []
for fqid, position in fqids.items():
query_arguments.extend((fqid, position,))
filter_parts.append("(fqid=%s and position>%s)")
query = (
"select fqid from events where " + " or ".join(filter_parts) + " limit 1"
)
self.raise_model_locked_if_match(query, query_arguments)
def assert_fqfield_positions(self, fqfields: Dict[str, int]) -> None:
if not fqfields:
return
event_query_arguments: List[Any] = []
event_filter_parts = []
collectionfield_query_arguments: List[str] = []
collectionfield_filter_parts = []
for fqfield, position in fqfields.items():
collectionfield, fqid = collectionfield_and_fqid_from_fqfield(fqfield)
event_query_arguments.extend((fqid, position,))
event_filter_parts.append("(fqid=%s and position>%s)")
collectionfield = collectionfield.replace("_", r"\_")
collectionfield = collectionfield.replace("$", "_%")
collectionfield_query_arguments.extend((fqid, collectionfield,))
collectionfield_filter_parts.append(
"(e.fqid=%s and cf.collectionfield LIKE %s)"
)
event_filter = " or ".join(event_filter_parts)
collectionfield_filter = " or ".join(collectionfield_filter_parts)
query = dedent(
f"""\
select e.fqid from (
select id, fqid from events where {event_filter}
) e
inner join events_to_collectionfields ecf on e.id=ecf.event_id
inner join collectionfields cf on ecf.collectionfield_id=cf.id
where {collectionfield_filter} limit 1"""
)
query_arguments = event_query_arguments + collectionfield_query_arguments
self.raise_model_locked_if_match(query, query_arguments)
def assert_collectionfield_positions(
self, collectionfields: Dict[str, int]
) -> None:
if not collectionfields:
return
query_arguments: List[Any] = []
filter_parts = []
for collectionfield, position in collectionfields.items():
query_arguments.extend((collectionfield, position,))
filter_parts.append("(collectionfield=%s and position>%s)")
query = (
"select collectionfield from collectionfields where "
+ " or ".join(filter_parts)
+ " limit 1"
)
self.raise_model_locked_if_match(query, query_arguments)
def raise_model_locked_if_match(self, query, arguments):
""" returns str (the only response) or None if there is no row """
locked_key = self.connection.query_single_value(query, arguments)
if locked_key is not None:
raise ModelLocked(locked_key) | 0.714827 | 0.212681 |
import json
def json_output(response: dict) -> str:
"""
Output API response in prettified JSON format
Parameters
----------
response : dict
Returns
-------
str
"""
return json.dumps(response, indent=2, sort_keys=True)
def table_output(response: dict) -> str:
"""
Output API response in prettified Table format
Parameters
----------
response : dict
Returns
-------
str
"""
# TODO: How to find screen width
width = 120
# NOTE: We assume that results are a flat list of dictionaries inside an object
header = list(response.keys())[0]
rows = list(response[header])
# NOTE: We assume that all items in list have the same number of keys
cols = list(rows[0].keys())
table = ['-' * width]
table.append("|{content:^{width}}|".format(content=header, width=width - 2))
table.append('-' * width)
col_width = width // len(cols)
col_headers = []
for col in cols:
col_headers.append("|{content:^{width}}|".format(content=col, width=col_width - 2))
table.append(''.join(col_headers))
table.append('-' * width)
for row in rows:
row_data = [''] * len(row)
for key, value in row.items():
ind = cols.index(key)
if value is None:
value = "null"
row_data[ind] = "|{content:^{width}}|".format(content=value, width=col_width - 2)
table.append(''.join(row_data))
return '\n'.join(table)
def output(response: dict, format: str = "json") -> str:
"""
Output API response in requested format
Parameters
----------
response : dict
The API response JSON
format : {'json', 'table'}, optional
The format to convert the API response into
Default value is 'json'
Returns
-------
str
The API response in the requested format
"""
if format == "json":
return json_output(response)
if format == "table":
return table_output(response)
raise ValueError("Supported formats are 'json' and 'table'") | beeminder_sync/output/output.py | import json
def json_output(response: dict) -> str:
"""
Output API response in prettified JSON format
Parameters
----------
response : dict
Returns
-------
str
"""
return json.dumps(response, indent=2, sort_keys=True)
def table_output(response: dict) -> str:
"""
Output API response in prettified Table format
Parameters
----------
response : dict
Returns
-------
str
"""
# TODO: How to find screen width
width = 120
# NOTE: We assume that results are a flat list of dictionaries inside an object
header = list(response.keys())[0]
rows = list(response[header])
# NOTE: We assume that all items in list have the same number of keys
cols = list(rows[0].keys())
table = ['-' * width]
table.append("|{content:^{width}}|".format(content=header, width=width - 2))
table.append('-' * width)
col_width = width // len(cols)
col_headers = []
for col in cols:
col_headers.append("|{content:^{width}}|".format(content=col, width=col_width - 2))
table.append(''.join(col_headers))
table.append('-' * width)
for row in rows:
row_data = [''] * len(row)
for key, value in row.items():
ind = cols.index(key)
if value is None:
value = "null"
row_data[ind] = "|{content:^{width}}|".format(content=value, width=col_width - 2)
table.append(''.join(row_data))
return '\n'.join(table)
def output(response: dict, format: str = "json") -> str:
"""
Output API response in requested format
Parameters
----------
response : dict
The API response JSON
format : {'json', 'table'}, optional
The format to convert the API response into
Default value is 'json'
Returns
-------
str
The API response in the requested format
"""
if format == "json":
return json_output(response)
if format == "table":
return table_output(response)
raise ValueError("Supported formats are 'json' and 'table'") | 0.613121 | 0.386648 |
from game.nodes.actor import Actor
from game.nodes.base import Base
from game.nodes.bullet import (
BulletBoxShape,
BulletCapsuleShape,
BulletDebugNode,
BulletPlaneShape,
BulletRigidBodyNode,
BulletSphereShape,
BulletWorld,
)
from game.nodes.camera import Camera
from game.nodes.collision import (
CollisionBox,
CollisionCapsule,
CollisionInvSphere,
CollisionNode,
CollisionRay,
CollisionSphere,
)
from game.nodes.constants import TAG_NODE_TYPE
from game.nodes.fog import Fog
from game.nodes.lensnode import LensNode
from game.nodes.lights import (
AmbientLight,
DirectionalLight,
PointLight,
Spotlight
)
from game.nodes.modelnode import ModelNode
from game.nodes.modelroot import ModelRoot
from game.nodes.nodepath import NodePath
from game.nodes.nongraphobject import NonGraphObject
from game.nodes.pandanode import PandaNode
from game.nodes.particleeffect import ParticleEffect
from game.nodes.sceneroot import SceneRoot
from game.nodes.showbasedefaults import (
Aspect2d,
BaseCam,
BaseCamera,
Cam2d,
Camera2d,
Pixel2d,
Render,
Render2d,
)
from game.nodes.texture import Texture
class Manager:
def __init__(self):
self.wrappers = {
'Actor': Actor,
'AmbientLight': AmbientLight,
'Aspect2d': Aspect2d,
'Base': Base,
'BaseCam': BaseCam,
'BaseCamera': BaseCamera,
'BulletBoxShape': BulletBoxShape,
'BulletCapsuleShape': BulletCapsuleShape,
'BulletDebugNode': BulletDebugNode,
'BulletPlaneShape': BulletPlaneShape,
'BulletSphereShape': BulletSphereShape,
'BulletRigidBodyNode': BulletRigidBodyNode,
'BulletWorld': BulletWorld,
'Cam2d': Cam2d,
'Camera': Camera,
'Camera2d': Camera2d,
'CollisionBox': CollisionBox,
'CollisionCapsule': CollisionCapsule,
'CollisionInvSphere': CollisionInvSphere,
'CollisionNode': CollisionNode,
'CollisionRay': CollisionRay,
'CollisionSphere': CollisionSphere,
'DirectionalLight': DirectionalLight,
'Fog': Fog,
'LensNode': LensNode,
'ModelNode': ModelNode,
'ModelRoot': ModelRoot,
'NodePath': NodePath,
'NonGraphObject': NonGraphObject,
'PandaNode': PandaNode,
'ParticleEffect': ParticleEffect,
'Pixel2d': Pixel2d,
'PointLight': PointLight,
'Render': Render,
'Render2d': Render2d,
'SceneRoot': SceneRoot,
'Spotlight': Spotlight,
'Texture': Texture,
}
def wrap(self, obj):
"""
Return a wrapper suitable for the indicated object. If the correct
wrapper cannot be found, return a NodePath wrapper for NodePaths and
a Base wrapper for everything else.
"""
comp_cls = self.get_wrapper(obj)
if comp_cls is not None:
return comp_cls(obj)
else:
comp_cls = self.get_default_wrapper(obj)
return comp_cls(obj)
def get_wrapper(self, obj):
type_ = self.get_type_string(obj)
return self.wrappers.get(type_)
def get_component_by_name(self, c_type):
return self.wrappers.get(c_type)
def get_type_string(self, comp):
"""
Return the type of the component as a string. Components are
identified in the following method (in order):
- If the component has the class variable 'cType' then this string
will be used as the type.
- Use the component's type's name as the type.
- If this is 'NodePath' then look for a overriding tag on the node
for the type.
- If this tag is missing, use the NodePath's node as the type.
"""
if hasattr(comp.__class__, 'cType'):
return comp.cType
type_str = type(comp).__name__
if type_str == 'NodePath':
type_str = comp.node().get_tag(TAG_NODE_TYPE)
if not type_str:
type_str = type(comp.node()).__name__
return type_str | src/pandaEditor/game/nodes/manager.py | from game.nodes.actor import Actor
from game.nodes.base import Base
from game.nodes.bullet import (
BulletBoxShape,
BulletCapsuleShape,
BulletDebugNode,
BulletPlaneShape,
BulletRigidBodyNode,
BulletSphereShape,
BulletWorld,
)
from game.nodes.camera import Camera
from game.nodes.collision import (
CollisionBox,
CollisionCapsule,
CollisionInvSphere,
CollisionNode,
CollisionRay,
CollisionSphere,
)
from game.nodes.constants import TAG_NODE_TYPE
from game.nodes.fog import Fog
from game.nodes.lensnode import LensNode
from game.nodes.lights import (
AmbientLight,
DirectionalLight,
PointLight,
Spotlight
)
from game.nodes.modelnode import ModelNode
from game.nodes.modelroot import ModelRoot
from game.nodes.nodepath import NodePath
from game.nodes.nongraphobject import NonGraphObject
from game.nodes.pandanode import PandaNode
from game.nodes.particleeffect import ParticleEffect
from game.nodes.sceneroot import SceneRoot
from game.nodes.showbasedefaults import (
Aspect2d,
BaseCam,
BaseCamera,
Cam2d,
Camera2d,
Pixel2d,
Render,
Render2d,
)
from game.nodes.texture import Texture
class Manager:
def __init__(self):
self.wrappers = {
'Actor': Actor,
'AmbientLight': AmbientLight,
'Aspect2d': Aspect2d,
'Base': Base,
'BaseCam': BaseCam,
'BaseCamera': BaseCamera,
'BulletBoxShape': BulletBoxShape,
'BulletCapsuleShape': BulletCapsuleShape,
'BulletDebugNode': BulletDebugNode,
'BulletPlaneShape': BulletPlaneShape,
'BulletSphereShape': BulletSphereShape,
'BulletRigidBodyNode': BulletRigidBodyNode,
'BulletWorld': BulletWorld,
'Cam2d': Cam2d,
'Camera': Camera,
'Camera2d': Camera2d,
'CollisionBox': CollisionBox,
'CollisionCapsule': CollisionCapsule,
'CollisionInvSphere': CollisionInvSphere,
'CollisionNode': CollisionNode,
'CollisionRay': CollisionRay,
'CollisionSphere': CollisionSphere,
'DirectionalLight': DirectionalLight,
'Fog': Fog,
'LensNode': LensNode,
'ModelNode': ModelNode,
'ModelRoot': ModelRoot,
'NodePath': NodePath,
'NonGraphObject': NonGraphObject,
'PandaNode': PandaNode,
'ParticleEffect': ParticleEffect,
'Pixel2d': Pixel2d,
'PointLight': PointLight,
'Render': Render,
'Render2d': Render2d,
'SceneRoot': SceneRoot,
'Spotlight': Spotlight,
'Texture': Texture,
}
def wrap(self, obj):
"""
Return a wrapper suitable for the indicated object. If the correct
wrapper cannot be found, return a NodePath wrapper for NodePaths and
a Base wrapper for everything else.
"""
comp_cls = self.get_wrapper(obj)
if comp_cls is not None:
return comp_cls(obj)
else:
comp_cls = self.get_default_wrapper(obj)
return comp_cls(obj)
def get_wrapper(self, obj):
type_ = self.get_type_string(obj)
return self.wrappers.get(type_)
def get_component_by_name(self, c_type):
return self.wrappers.get(c_type)
def get_type_string(self, comp):
"""
Return the type of the component as a string. Components are
identified in the following method (in order):
- If the component has the class variable 'cType' then this string
will be used as the type.
- Use the component's type's name as the type.
- If this is 'NodePath' then look for a overriding tag on the node
for the type.
- If this tag is missing, use the NodePath's node as the type.
"""
if hasattr(comp.__class__, 'cType'):
return comp.cType
type_str = type(comp).__name__
if type_str == 'NodePath':
type_str = comp.node().get_tag(TAG_NODE_TYPE)
if not type_str:
type_str = type(comp.node()).__name__
return type_str | 0.679817 | 0.499146 |
import h5py
import random
import numpy as np
class DataGenerator:
"""
Class for a generator that reads in data from the HDF5 file, one batch at
a time, converts it into the jigsaw, and then returns the data
"""
def __init__(self, conf, maxHammingSet):
"""
Explain
"""
self.data_path = conf.data_path
self.numChannels = conf.numChannels
self.numCrops = conf.numCrops
self.cropSize = conf.cropSize
self.cellSize = conf.cellSize
self.tileSize = conf.tileSize
self.colorJitter = conf.colorJitter
self.batchSize = conf.batchSize
self.meanTensor, self.stdTensor = self.get_stats()
self.maxHammingSet = np.array(maxHammingSet, dtype=np.uint8)
self.batch_counter()
self.numClasses = self.maxHammingSet.shape[0] # i.e. number of jigsaw types
def get_stats(self):
h5f = h5py.File(self.data_path, 'r')
mean = h5f['train_mean'][:].astype(np.float32)
std = h5f['train_std'][:].astype(np.float32)
h5f.close()
if self.numChannels == 1:
mean = np.expand_dims(mean, axis=-1)
std = np.expand_dims(std, axis=-1)
return mean, std
def batch_counter(self):
h5f = h5py.File(self.data_path, 'r')
self.numTrainBatch = h5f['train_img'][:].shape[0] // self.batchSize
self.numValBatch = h5f['val_img'][:].shape[0] // self.batchSize
self.numTestBatch = h5f['test_img'][:].shape[0] // self.batchSize
h5f.close()
self.batchIndexTrain = 0
self.batchIndexVal = 0
self.batchIndexTest = 0
def __batch_generation_normalized(self, x):
"""
Explain
"""
x -= self.meanTensor
x /= self.stdTensor
# This implementation modifies each image individually
y = np.empty(self.batchSize)
# Python list of 4D numpy tensors for each channel
X = [np.empty((self.batchSize, self.tileSize, self.tileSize, self.numChannels), np.float32)
for _ in range(self.numCrops)]
for image_num in range(self.batchSize):
# Transform the image into its nine crops
single_image, y[image_num] = self.create_croppings(x[image_num])
for image_location in range(self.numCrops):
X[image_location][image_num, :, :, :] = single_image[:, :, :, image_location]
return X, y
def one_hot(self, y):
"""
Explain
"""
return np.array([[1 if y[i] == j else 0 for j in range(self.numClasses)] for i in range(y.shape[0])])
def generate(self, mode='train'):
"""
Explain
"""
if mode == 'train':
h5f = h5py.File(self.data_path, 'r')
x = h5f['train_img'][self.batchIndexTrain * self.batchSize:(self.batchIndexTrain + 1) * self.batchSize, ...]
h5f.close()
if self.numChannels == 1:
x = np.expand_dims(x, axis=-1)
X, y = self.__batch_generation_normalized(x.astype(np.float32))
self.batchIndexTrain += 1 # Increment the batch index
if self.batchIndexTrain == self.numTrainBatch:
self.batchIndexTrain = 0
elif mode == 'valid':
h5f = h5py.File(self.data_path, 'r')
x = h5f['val_img'][self.batchIndexVal * self.batchSize:(self.batchIndexVal + 1) * self.batchSize, ...]
h5f.close()
if self.numChannels == 1:
x = np.expand_dims(x, axis=-1)
X, y = self.__batch_generation_normalized(x.astype(np.float32))
self.batchIndexVal += 1 # Increment the batch index
if self.batchIndexVal == self.numValBatch:
self.batchIndexVal = 0
elif mode == 'test':
h5f = h5py.File(self.data_path, 'r')
x = h5f['test_img'][self.batchIndexTest * self.batchSize:(self.batchIndexTest + 1) * self.batchSize, ...]
h5f.close()
if self.numChannels == 1:
x = np.expand_dims(x, axis=-1)
X, y = self.__batch_generation_normalized(x.astype(np.float32))
self.batchIndexTest += 1 # Increment the batch index
if self.batchIndexTest == self.numTestBatch:
self.batchIndexTest = 0
return np.transpose(np.array(X), axes=[1, 2, 3, 4, 0]), self.one_hot(y)
def randomize(self):
""" Randomizes the order of data samples"""
h5f = h5py.File(self.data_path, 'a')
train_img = h5f['train_img'][:].astype(np.float32)
permutation = np.random.permutation(train_img.shape[0])
train_img = train_img[permutation, :, :, :]
del h5f['train_img']
h5f.create_dataset('train_img', data=train_img)
h5f.close()
def create_croppings(self, image):
"""
Take in a 3D numpy array (256x256x3) and a 4D numpy array containing 9 "jigsaw" puzzles.
Dimensions of the output array is 64 (height) x 64 (width) x 3 (colour channels) x 9(each cropping)
The 3x3 grid is numbered as follows:
0 1 2
3 4 5
6 7 8
"""
# Jitter the colour channel
# image = self.color_channel_jitter(image)
y_dim, x_dim = image.shape[:2]
# Have the x & y coordinate of the crop
if x_dim != self.cropSize:
crop_x = random.randrange(x_dim - self.cropSize)
crop_y = random.randrange(y_dim - self.cropSize)
else:
crop_x, crop_y = 0, 0
# Select which image ordering we'll use from the maximum hamming set
perm_index = random.randrange(self.numClasses)
final_crops = np.zeros((self.tileSize, self.tileSize, self.numChannels, self.numCrops), dtype=np.float32)
n_crops = int(np.sqrt(self.numCrops))
for row in range(n_crops):
for col in range(n_crops):
x_start = crop_x + col * self.cellSize + random.randrange(self.cellSize - self.tileSize)
y_start = crop_y + row * self.cellSize + random.randrange(self.cellSize - self.tileSize)
# Put the crop in the list of pieces randomly according to the number picked
final_crops[:, :, :, self.maxHammingSet[perm_index, row * n_crops + col]] = \
image[y_start:y_start + self.tileSize, x_start:x_start + self.tileSize, :]
return final_crops, perm_index
def color_channel_jitter(self, image):
"""
Explain
"""
# Determine the dimensions of the array, minus the crop around the border
# of 4 pixels (threshold margin due to 2 pixel jitter)
x_dim = image.shape[0] - self.colorJitter * 2
y_dim = image.shape[1] - self.colorJitter * 2
# Determine the jitters in all directions
R_xjit = random.randrange(self.colorJitter * 2 + 1)
R_yjit = random.randrange(self.colorJitter * 2 + 1)
# Seperate the colour channels
return_array = np.empty((x_dim, y_dim, 3), np.float32)
for colour_channel in range(3):
return_array[:, :, colour_channel] = \
image[R_xjit:x_dim +R_xjit, R_yjit:y_dim + R_yjit, colour_channel]
return return_array | legacy/example/DataLoader/DataGenerator.py | import h5py
import random
import numpy as np
class DataGenerator:
"""
Class for a generator that reads in data from the HDF5 file, one batch at
a time, converts it into the jigsaw, and then returns the data
"""
def __init__(self, conf, maxHammingSet):
"""
Explain
"""
self.data_path = conf.data_path
self.numChannels = conf.numChannels
self.numCrops = conf.numCrops
self.cropSize = conf.cropSize
self.cellSize = conf.cellSize
self.tileSize = conf.tileSize
self.colorJitter = conf.colorJitter
self.batchSize = conf.batchSize
self.meanTensor, self.stdTensor = self.get_stats()
self.maxHammingSet = np.array(maxHammingSet, dtype=np.uint8)
self.batch_counter()
self.numClasses = self.maxHammingSet.shape[0] # i.e. number of jigsaw types
def get_stats(self):
h5f = h5py.File(self.data_path, 'r')
mean = h5f['train_mean'][:].astype(np.float32)
std = h5f['train_std'][:].astype(np.float32)
h5f.close()
if self.numChannels == 1:
mean = np.expand_dims(mean, axis=-1)
std = np.expand_dims(std, axis=-1)
return mean, std
def batch_counter(self):
h5f = h5py.File(self.data_path, 'r')
self.numTrainBatch = h5f['train_img'][:].shape[0] // self.batchSize
self.numValBatch = h5f['val_img'][:].shape[0] // self.batchSize
self.numTestBatch = h5f['test_img'][:].shape[0] // self.batchSize
h5f.close()
self.batchIndexTrain = 0
self.batchIndexVal = 0
self.batchIndexTest = 0
def __batch_generation_normalized(self, x):
"""
Explain
"""
x -= self.meanTensor
x /= self.stdTensor
# This implementation modifies each image individually
y = np.empty(self.batchSize)
# Python list of 4D numpy tensors for each channel
X = [np.empty((self.batchSize, self.tileSize, self.tileSize, self.numChannels), np.float32)
for _ in range(self.numCrops)]
for image_num in range(self.batchSize):
# Transform the image into its nine crops
single_image, y[image_num] = self.create_croppings(x[image_num])
for image_location in range(self.numCrops):
X[image_location][image_num, :, :, :] = single_image[:, :, :, image_location]
return X, y
def one_hot(self, y):
"""
Explain
"""
return np.array([[1 if y[i] == j else 0 for j in range(self.numClasses)] for i in range(y.shape[0])])
def generate(self, mode='train'):
"""
Explain
"""
if mode == 'train':
h5f = h5py.File(self.data_path, 'r')
x = h5f['train_img'][self.batchIndexTrain * self.batchSize:(self.batchIndexTrain + 1) * self.batchSize, ...]
h5f.close()
if self.numChannels == 1:
x = np.expand_dims(x, axis=-1)
X, y = self.__batch_generation_normalized(x.astype(np.float32))
self.batchIndexTrain += 1 # Increment the batch index
if self.batchIndexTrain == self.numTrainBatch:
self.batchIndexTrain = 0
elif mode == 'valid':
h5f = h5py.File(self.data_path, 'r')
x = h5f['val_img'][self.batchIndexVal * self.batchSize:(self.batchIndexVal + 1) * self.batchSize, ...]
h5f.close()
if self.numChannels == 1:
x = np.expand_dims(x, axis=-1)
X, y = self.__batch_generation_normalized(x.astype(np.float32))
self.batchIndexVal += 1 # Increment the batch index
if self.batchIndexVal == self.numValBatch:
self.batchIndexVal = 0
elif mode == 'test':
h5f = h5py.File(self.data_path, 'r')
x = h5f['test_img'][self.batchIndexTest * self.batchSize:(self.batchIndexTest + 1) * self.batchSize, ...]
h5f.close()
if self.numChannels == 1:
x = np.expand_dims(x, axis=-1)
X, y = self.__batch_generation_normalized(x.astype(np.float32))
self.batchIndexTest += 1 # Increment the batch index
if self.batchIndexTest == self.numTestBatch:
self.batchIndexTest = 0
return np.transpose(np.array(X), axes=[1, 2, 3, 4, 0]), self.one_hot(y)
def randomize(self):
""" Randomizes the order of data samples"""
h5f = h5py.File(self.data_path, 'a')
train_img = h5f['train_img'][:].astype(np.float32)
permutation = np.random.permutation(train_img.shape[0])
train_img = train_img[permutation, :, :, :]
del h5f['train_img']
h5f.create_dataset('train_img', data=train_img)
h5f.close()
def create_croppings(self, image):
"""
Take in a 3D numpy array (256x256x3) and a 4D numpy array containing 9 "jigsaw" puzzles.
Dimensions of the output array is 64 (height) x 64 (width) x 3 (colour channels) x 9(each cropping)
The 3x3 grid is numbered as follows:
0 1 2
3 4 5
6 7 8
"""
# Jitter the colour channel
# image = self.color_channel_jitter(image)
y_dim, x_dim = image.shape[:2]
# Have the x & y coordinate of the crop
if x_dim != self.cropSize:
crop_x = random.randrange(x_dim - self.cropSize)
crop_y = random.randrange(y_dim - self.cropSize)
else:
crop_x, crop_y = 0, 0
# Select which image ordering we'll use from the maximum hamming set
perm_index = random.randrange(self.numClasses)
final_crops = np.zeros((self.tileSize, self.tileSize, self.numChannels, self.numCrops), dtype=np.float32)
n_crops = int(np.sqrt(self.numCrops))
for row in range(n_crops):
for col in range(n_crops):
x_start = crop_x + col * self.cellSize + random.randrange(self.cellSize - self.tileSize)
y_start = crop_y + row * self.cellSize + random.randrange(self.cellSize - self.tileSize)
# Put the crop in the list of pieces randomly according to the number picked
final_crops[:, :, :, self.maxHammingSet[perm_index, row * n_crops + col]] = \
image[y_start:y_start + self.tileSize, x_start:x_start + self.tileSize, :]
return final_crops, perm_index
def color_channel_jitter(self, image):
"""
Explain
"""
# Determine the dimensions of the array, minus the crop around the border
# of 4 pixels (threshold margin due to 2 pixel jitter)
x_dim = image.shape[0] - self.colorJitter * 2
y_dim = image.shape[1] - self.colorJitter * 2
# Determine the jitters in all directions
R_xjit = random.randrange(self.colorJitter * 2 + 1)
R_yjit = random.randrange(self.colorJitter * 2 + 1)
# Seperate the colour channels
return_array = np.empty((x_dim, y_dim, 3), np.float32)
for colour_channel in range(3):
return_array[:, :, colour_channel] = \
image[R_xjit:x_dim +R_xjit, R_yjit:y_dim + R_yjit, colour_channel]
return return_array | 0.758063 | 0.381421 |
import os
import sys
import tempfile
from glob import glob
from copy import deepcopy
from collections import defaultdict
from typing import List, NamedTuple, Union, Dict, Any, Set, Tuple
import click
import pandas as pd
import numpy as np
from tabulate import tabulate
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from sklearn.metrics import classification_report
from pawls.commands.export import export
def get_unique_image_ids(coco: COCO) -> Set[int]:
return set([anno["image_id"] for anno in coco.dataset["annotations"]])
def get_mutually_annotated_image_ids(coco1: COCO, coco2: COCO) -> Set[int]:
return get_unique_image_ids(coco1).intersection(get_unique_image_ids(coco2))
def filter_annotation_with_image_ids(coco: COCO, image_ids: Set[int]) -> COCO:
coco = deepcopy(coco)
coco.dataset["annotations"] = [
anno for anno in coco.dataset["annotations"] if anno["image_id"] in image_ids
]
coco.dataset["images"] = [
image for image in coco.dataset["images"] if image["id"] in image_ids
]
return coco
def print_results(calculation_method_msg, df: pd.DataFrame) -> pd.DataFrame:
# TODO: Might need to change the function name, see in https://github.com/allenai/pawls/pull/112
cleaned_table = df[sorted(df.columns)].loc[sorted(df.columns)]
print(calculation_method_msg)
print("-" * 45)
print(
"The (i,j)-th element in the table is calculated by treating the annotations from\n"
"i as the 'ground-truth's, and those from j are considered as 'predictions'."
)
print(
tabulate(
cleaned_table,
headers="keys",
tablefmt="psql",
)
)
print("\n")
return cleaned_table
class PythonLiteralOption(click.Option):
"""Used for parsing list-like stings from the input.
A technique adapted from https://stackoverflow.com/a/47730333
"""
def type_cast_value(self, ctx, value):
try:
return [ele.strip() for ele in value.split(",")] if value else []
except:
raise click.BadParameter(value)
class HiddenPrints:
"""Used for hiding unnecessary prints of COCO Eval
A trick learned from https://stackoverflow.com/a/45669280
"""
def __enter__(self):
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stdout.close()
sys.stdout = self._original_stdout
class COCOEvaluator:
COCO_METRICS = ["AP", "AP50", "AP75", "APs", "APm", "APl"]
def __init__(self, coco_save_path: str, class_names: List[str] = None):
all_cocos = {}
annotators = []
for userfile in sorted(glob(f"{coco_save_path}/*.json")):
with HiddenPrints():
username = userfile.split("/")[-1].replace(".json", "")
annotators.append(username)
all_cocos[username] = COCO(userfile)
# A hack to make use of the current COCOEval API
for coco in all_cocos.values():
for ele in coco.dataset["annotations"]:
ele["score"] = 1
self.all_cocos = all_cocos
self.annotators = annotators
self.class_names = class_names or [
val["name"] for _, val in all_cocos[annotators[0]].cats.items()
]
def calculate_scores_for_two_cocos(
self, coco1: COCO, coco2: COCO, class_names: List[str]
) -> Tuple[Dict, Dict]:
coco_eval = COCOeval(coco1, coco2, iouType="bbox")
with HiddenPrints():
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
results = {
metric: float(
coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan"
)
for idx, metric in enumerate(self.COCO_METRICS)
}
precisions = coco_eval.eval["precision"]
results_per_category = []
for idx, name in enumerate(class_names):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
precision = precisions[:, :, idx, 0, -1]
precision = precision[precision > -1]
ap = np.mean(precision) if precision.size else float("nan")
results_per_category.append(("{}".format(name), float(ap * 100)))
results_per_category = {name: ap for name, ap in results_per_category}
return results, results_per_category
def calculate_ap_scores(self) -> Tuple[Dict, Dict]:
coco_results = defaultdict(dict)
coco_category_results = defaultdict(dict)
for name1, coco1 in self.all_cocos.items():
for name2, coco2 in self.all_cocos.items():
image_ids = get_mutually_annotated_image_ids(coco1, coco2)
coco1 = filter_annotation_with_image_ids(coco1, image_ids)
coco2 = filter_annotation_with_image_ids(coco2, image_ids)
results, results_per_category = self.calculate_scores_for_two_cocos(
coco1, coco2, self.class_names
)
coco_results[name1][name2] = results
coco_category_results[name1][name2] = results_per_category
return coco_results, coco_category_results
def show_results(
self, results: Dict, metric_names: List[str] = None
) -> pd.DataFrame:
"""Show COCO Eval results for the given metric names.
Args:
results (Dict):
The coco_results dict generated by `COCOEvaluator.calculate_ap_scores`.
metric_names (List[str], optional):
Metric report of the specified `metric_names` will be displayed.
If not set, all metrics in `COCOEvaluator.COCO_METRICS` will be displayed.
"""
# TODO: Might need to change the function name, see in https://github.com/allenai/pawls/pull/112
if metric_names is None:
metric_names = self.COCO_METRICS
cleaned_tables = {}
for metric_name in metric_names:
df = pd.DataFrame(results).applymap(
lambda ele: ele.get(metric_name) if not pd.isna(ele) else ele
)
cleaned_table = print_results(
f"Inter-annotator agreement based on {metric_name} scores.", df
)
cleaned_tables[metric_name] = cleaned_table
return cleaned_tables
def show_category_results(
self, results: Dict, class_names: List[str] = None
) -> pd.DataFrame:
"""Show COCO Eval results for the given class_names.
Args:
results (Dict):
The coco_category_results dict generated by `COCOEvaluator.calculate_ap_scores`.
class_names (List[str], optional):
Metric report of the specified `class_names` will be displayed.
If not set, all classes in `self.class_names` will be displayed.
"""
# TODO: Might need to change the function name, see in https://github.com/allenai/pawls/pull/112
if class_names is None:
class_names = self.class_names
cleaned_tables = {}
for class_name in class_names:
df = pd.DataFrame(results).applymap(
lambda ele: ele.get(class_name) if not pd.isna(ele) else ele
)
cleaned_table = print_results(
f"Inter-annotator agreement of the {class_name} class based on AP scores.",
df,
)
cleaned_tables[class_name] = cleaned_table
class TokenEvaluator:
PDF_FEATURES_IN_SAVED_TABLES = ["pdf", "page_index", "index", "text"]
DUMMY_CATEGORY_NAME = "NO-LABEL"
def __init__(self, token_save_path: str):
self.df = pd.read_csv(token_save_path)
self.annotators = list(
self.df.columns[len(self.PDF_FEATURES_IN_SAVED_TABLES) :]
)
# Assuming all users are stored in email address
def calculate_scores_for_two_annotators(
self, ground_truth: str, predictions: str
):
cur_df = (
self.df[[ground_truth, predictions]]
.dropna(subset=[ground_truth])
.fillna(-1)
)
acc = (cur_df[ground_truth] == cur_df[predictions]).mean()
return acc
def create_categorical_report(
self, ground_truth: str, predictions: str, table_per_category: dict
):
gt = self.df[ground_truth].fillna("").values
pred = self.df[predictions].fillna("").values
labels = list(table_per_category.keys())
report = classification_report(
gt, pred, labels=labels + [""], output_dict=True, zero_division=0
)
for cat_name, table in table_per_category.items():
table[ground_truth][predictions] = report[cat_name]["f1-score"]
def calculate_token_accuracy(self, categories: List = None):
table = defaultdict(dict)
for i, ground_truth in enumerate(self.annotators):
for j, predictions in enumerate(self.annotators):
if i == j:
continue
# The token_acc table is symmetric
table[ground_truth][
predictions
] = self.calculate_scores_for_two_annotators(
ground_truth, predictions
)
if categories is None:
return table
table_per_category = {cat_name: defaultdict(dict) for cat_name in categories}
for i, ground_truth in enumerate(self.annotators):
for j, predictions in enumerate(self.annotators):
if i == j:
continue
self.create_categorical_report(
ground_truth, predictions, table_per_category
)
return table, table_per_category
@staticmethod
def show_results(results: Dict) -> pd.DataFrame:
df = pd.DataFrame(results)
calculation_method_msg = (
"The token accuracy is calculated by simply comparing"
"the compatibility of tokens labels agasin two annotators."
)
cleaned_table = print_results(calculation_method_msg, df)
return cleaned_table
@staticmethod
def show_category_results(token_category_results: Dict) -> pd.DataFrame:
for cat_name, results in token_category_results.items():
df = pd.DataFrame(results)
calculation_method_msg = (
f"We compute the category-level f1-scores for the category {cat_name}"
)
cleaned_table = print_results(calculation_method_msg, df)
@click.command(context_settings={"help_option_names": ["--help", "-h"]})
@click.argument("path", type=click.Path(exists=True, file_okay=False))
@click.argument("config", type=str)
@click.option(
"--annotator",
"-u",
multiple=True,
help="Export annotations of the specified annotator.",
)
@click.option(
"--textual-categories",
cls=PythonLiteralOption,
help="The annotations of textual categories will be evaluated based on token accuracy.",
)
@click.option(
"--non-textual-categories",
cls=PythonLiteralOption,
help="The annotations of non-textual categories will be evaluated based on AP scores based on box overlapping.",
)
@click.option(
"--pdf-shas",
multiple=True,
default=[],
help="Specify only exporting the selected PDF shas.",
)
@click.option(
"--include-unfinished",
"-i",
is_flag=True,
help="A flag to export all annotation by the specified annotator including unfinished ones.",
)
@click.option(
"--verbose",
"-v",
is_flag=True,
help="A flag to show detailed reports.",
)
@click.option(
"--save",
type=click.Path(),
help="If set, PAWLS will save the reports in the given folder.",
)
@click.pass_context
def metric(
ctx,
path: click.Path,
config: str,
annotator: List,
textual_categories: List = [],
non_textual_categories: List = [],
pdf_shas: List = [],
include_unfinished: bool = False,
verbose: bool = False,
save: click.Path = None,
):
"""Calculate the inter-annotator agreement for the annotation project for both textual-categories
Usage:
Run evaluation for textual-categories cat1 and cat2, and for non-textual-categories cat3 and cat4:
pawls metric <labeling_folder> <labeling_config> --textual-categories cat1,cat2 --non-textual-categories cat3,cat4
Specifying annotators for evaluation, and include any unfinished annotations:
pawls metric <labeling_folder> <labeling_config> -u annotator1 -u annotator2 --textual-categories cat1,cat2 --non-textual-categories cat3,cat4 --include-unfinished
Generating detailed reports:
pawls metric <labeling_folder> <labeling_config> --textual-categories cat1,cat2 --non-textual-categories cat3,cat4 --verbose
"""
if save is not None:
save = str(save)
if not os.path.exists(save):
os.makedirs(save)
invoke_export = lambda tempdir, format, categories: ctx.invoke(
export,
path=path,
config=config,
output=tempdir,
format=format,
annotator=annotator,
categories=categories,
pdf_shas=pdf_shas,
include_unfinished=include_unfinished,
export_images=False,
)
if len(non_textual_categories) > 0:
print(
f"Generating Accuracy report for non-textual categories {non_textual_categories}"
)
with tempfile.TemporaryDirectory() as tempdir:
invoke_export(
tempdir=tempdir, format="coco", categories=non_textual_categories
)
coco_eval = COCOEvaluator(tempdir)
coco_results, coco_category_results = coco_eval.calculate_ap_scores()
if verbose:
save_table = coco_eval.show_results(coco_results)
coco_eval.show_category_results(coco_category_results)
else:
save_table = coco_eval.show_results(coco_results, ["AP"])
if save is not None:
save_table["AP"].to_csv(f"{save}/block-eval.csv")
if len(textual_categories) > 0:
print(f"Generating Accuracy report for textual categories {textual_categories}")
with tempfile.TemporaryDirectory() as tempdir:
tempdir = os.path.join(tempdir, "annotation.csv")
invoke_export(
tempdir=tempdir, format="token", categories=textual_categories
)
token_eval = TokenEvaluator(tempdir)
if verbose:
# As calculating the classification report is slow, we only
# do this if user specifies the verbose mode.
(
token_results,
token_category_results,
) = token_eval.calculate_token_accuracy(textual_categories)
save_table = token_eval.show_results(token_results)
token_eval.show_category_results(token_category_results)
else:
token_results = token_eval.calculate_token_accuracy()
save_table = token_eval.show_results(token_results)
if save is not None:
save_table.to_csv(f"{save}/textual-eval.csv") | cli/pawls/commands/metric.py | import os
import sys
import tempfile
from glob import glob
from copy import deepcopy
from collections import defaultdict
from typing import List, NamedTuple, Union, Dict, Any, Set, Tuple
import click
import pandas as pd
import numpy as np
from tabulate import tabulate
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from sklearn.metrics import classification_report
from pawls.commands.export import export
def get_unique_image_ids(coco: COCO) -> Set[int]:
return set([anno["image_id"] for anno in coco.dataset["annotations"]])
def get_mutually_annotated_image_ids(coco1: COCO, coco2: COCO) -> Set[int]:
return get_unique_image_ids(coco1).intersection(get_unique_image_ids(coco2))
def filter_annotation_with_image_ids(coco: COCO, image_ids: Set[int]) -> COCO:
coco = deepcopy(coco)
coco.dataset["annotations"] = [
anno for anno in coco.dataset["annotations"] if anno["image_id"] in image_ids
]
coco.dataset["images"] = [
image for image in coco.dataset["images"] if image["id"] in image_ids
]
return coco
def print_results(calculation_method_msg, df: pd.DataFrame) -> pd.DataFrame:
# TODO: Might need to change the function name, see in https://github.com/allenai/pawls/pull/112
cleaned_table = df[sorted(df.columns)].loc[sorted(df.columns)]
print(calculation_method_msg)
print("-" * 45)
print(
"The (i,j)-th element in the table is calculated by treating the annotations from\n"
"i as the 'ground-truth's, and those from j are considered as 'predictions'."
)
print(
tabulate(
cleaned_table,
headers="keys",
tablefmt="psql",
)
)
print("\n")
return cleaned_table
class PythonLiteralOption(click.Option):
"""Used for parsing list-like stings from the input.
A technique adapted from https://stackoverflow.com/a/47730333
"""
def type_cast_value(self, ctx, value):
try:
return [ele.strip() for ele in value.split(",")] if value else []
except:
raise click.BadParameter(value)
class HiddenPrints:
"""Used for hiding unnecessary prints of COCO Eval
A trick learned from https://stackoverflow.com/a/45669280
"""
def __enter__(self):
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stdout.close()
sys.stdout = self._original_stdout
class COCOEvaluator:
COCO_METRICS = ["AP", "AP50", "AP75", "APs", "APm", "APl"]
def __init__(self, coco_save_path: str, class_names: List[str] = None):
all_cocos = {}
annotators = []
for userfile in sorted(glob(f"{coco_save_path}/*.json")):
with HiddenPrints():
username = userfile.split("/")[-1].replace(".json", "")
annotators.append(username)
all_cocos[username] = COCO(userfile)
# A hack to make use of the current COCOEval API
for coco in all_cocos.values():
for ele in coco.dataset["annotations"]:
ele["score"] = 1
self.all_cocos = all_cocos
self.annotators = annotators
self.class_names = class_names or [
val["name"] for _, val in all_cocos[annotators[0]].cats.items()
]
def calculate_scores_for_two_cocos(
self, coco1: COCO, coco2: COCO, class_names: List[str]
) -> Tuple[Dict, Dict]:
coco_eval = COCOeval(coco1, coco2, iouType="bbox")
with HiddenPrints():
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
results = {
metric: float(
coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan"
)
for idx, metric in enumerate(self.COCO_METRICS)
}
precisions = coco_eval.eval["precision"]
results_per_category = []
for idx, name in enumerate(class_names):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
precision = precisions[:, :, idx, 0, -1]
precision = precision[precision > -1]
ap = np.mean(precision) if precision.size else float("nan")
results_per_category.append(("{}".format(name), float(ap * 100)))
results_per_category = {name: ap for name, ap in results_per_category}
return results, results_per_category
def calculate_ap_scores(self) -> Tuple[Dict, Dict]:
coco_results = defaultdict(dict)
coco_category_results = defaultdict(dict)
for name1, coco1 in self.all_cocos.items():
for name2, coco2 in self.all_cocos.items():
image_ids = get_mutually_annotated_image_ids(coco1, coco2)
coco1 = filter_annotation_with_image_ids(coco1, image_ids)
coco2 = filter_annotation_with_image_ids(coco2, image_ids)
results, results_per_category = self.calculate_scores_for_two_cocos(
coco1, coco2, self.class_names
)
coco_results[name1][name2] = results
coco_category_results[name1][name2] = results_per_category
return coco_results, coco_category_results
def show_results(
self, results: Dict, metric_names: List[str] = None
) -> pd.DataFrame:
"""Show COCO Eval results for the given metric names.
Args:
results (Dict):
The coco_results dict generated by `COCOEvaluator.calculate_ap_scores`.
metric_names (List[str], optional):
Metric report of the specified `metric_names` will be displayed.
If not set, all metrics in `COCOEvaluator.COCO_METRICS` will be displayed.
"""
# TODO: Might need to change the function name, see in https://github.com/allenai/pawls/pull/112
if metric_names is None:
metric_names = self.COCO_METRICS
cleaned_tables = {}
for metric_name in metric_names:
df = pd.DataFrame(results).applymap(
lambda ele: ele.get(metric_name) if not pd.isna(ele) else ele
)
cleaned_table = print_results(
f"Inter-annotator agreement based on {metric_name} scores.", df
)
cleaned_tables[metric_name] = cleaned_table
return cleaned_tables
def show_category_results(
self, results: Dict, class_names: List[str] = None
) -> pd.DataFrame:
"""Show COCO Eval results for the given class_names.
Args:
results (Dict):
The coco_category_results dict generated by `COCOEvaluator.calculate_ap_scores`.
class_names (List[str], optional):
Metric report of the specified `class_names` will be displayed.
If not set, all classes in `self.class_names` will be displayed.
"""
# TODO: Might need to change the function name, see in https://github.com/allenai/pawls/pull/112
if class_names is None:
class_names = self.class_names
cleaned_tables = {}
for class_name in class_names:
df = pd.DataFrame(results).applymap(
lambda ele: ele.get(class_name) if not pd.isna(ele) else ele
)
cleaned_table = print_results(
f"Inter-annotator agreement of the {class_name} class based on AP scores.",
df,
)
cleaned_tables[class_name] = cleaned_table
class TokenEvaluator:
PDF_FEATURES_IN_SAVED_TABLES = ["pdf", "page_index", "index", "text"]
DUMMY_CATEGORY_NAME = "NO-LABEL"
def __init__(self, token_save_path: str):
self.df = pd.read_csv(token_save_path)
self.annotators = list(
self.df.columns[len(self.PDF_FEATURES_IN_SAVED_TABLES) :]
)
# Assuming all users are stored in email address
def calculate_scores_for_two_annotators(
self, ground_truth: str, predictions: str
):
cur_df = (
self.df[[ground_truth, predictions]]
.dropna(subset=[ground_truth])
.fillna(-1)
)
acc = (cur_df[ground_truth] == cur_df[predictions]).mean()
return acc
def create_categorical_report(
self, ground_truth: str, predictions: str, table_per_category: dict
):
gt = self.df[ground_truth].fillna("").values
pred = self.df[predictions].fillna("").values
labels = list(table_per_category.keys())
report = classification_report(
gt, pred, labels=labels + [""], output_dict=True, zero_division=0
)
for cat_name, table in table_per_category.items():
table[ground_truth][predictions] = report[cat_name]["f1-score"]
def calculate_token_accuracy(self, categories: List = None):
table = defaultdict(dict)
for i, ground_truth in enumerate(self.annotators):
for j, predictions in enumerate(self.annotators):
if i == j:
continue
# The token_acc table is symmetric
table[ground_truth][
predictions
] = self.calculate_scores_for_two_annotators(
ground_truth, predictions
)
if categories is None:
return table
table_per_category = {cat_name: defaultdict(dict) for cat_name in categories}
for i, ground_truth in enumerate(self.annotators):
for j, predictions in enumerate(self.annotators):
if i == j:
continue
self.create_categorical_report(
ground_truth, predictions, table_per_category
)
return table, table_per_category
@staticmethod
def show_results(results: Dict) -> pd.DataFrame:
df = pd.DataFrame(results)
calculation_method_msg = (
"The token accuracy is calculated by simply comparing"
"the compatibility of tokens labels agasin two annotators."
)
cleaned_table = print_results(calculation_method_msg, df)
return cleaned_table
@staticmethod
def show_category_results(token_category_results: Dict) -> pd.DataFrame:
for cat_name, results in token_category_results.items():
df = pd.DataFrame(results)
calculation_method_msg = (
f"We compute the category-level f1-scores for the category {cat_name}"
)
cleaned_table = print_results(calculation_method_msg, df)
@click.command(context_settings={"help_option_names": ["--help", "-h"]})
@click.argument("path", type=click.Path(exists=True, file_okay=False))
@click.argument("config", type=str)
@click.option(
"--annotator",
"-u",
multiple=True,
help="Export annotations of the specified annotator.",
)
@click.option(
"--textual-categories",
cls=PythonLiteralOption,
help="The annotations of textual categories will be evaluated based on token accuracy.",
)
@click.option(
"--non-textual-categories",
cls=PythonLiteralOption,
help="The annotations of non-textual categories will be evaluated based on AP scores based on box overlapping.",
)
@click.option(
"--pdf-shas",
multiple=True,
default=[],
help="Specify only exporting the selected PDF shas.",
)
@click.option(
"--include-unfinished",
"-i",
is_flag=True,
help="A flag to export all annotation by the specified annotator including unfinished ones.",
)
@click.option(
"--verbose",
"-v",
is_flag=True,
help="A flag to show detailed reports.",
)
@click.option(
"--save",
type=click.Path(),
help="If set, PAWLS will save the reports in the given folder.",
)
@click.pass_context
def metric(
ctx,
path: click.Path,
config: str,
annotator: List,
textual_categories: List = [],
non_textual_categories: List = [],
pdf_shas: List = [],
include_unfinished: bool = False,
verbose: bool = False,
save: click.Path = None,
):
"""Calculate the inter-annotator agreement for the annotation project for both textual-categories
Usage:
Run evaluation for textual-categories cat1 and cat2, and for non-textual-categories cat3 and cat4:
pawls metric <labeling_folder> <labeling_config> --textual-categories cat1,cat2 --non-textual-categories cat3,cat4
Specifying annotators for evaluation, and include any unfinished annotations:
pawls metric <labeling_folder> <labeling_config> -u annotator1 -u annotator2 --textual-categories cat1,cat2 --non-textual-categories cat3,cat4 --include-unfinished
Generating detailed reports:
pawls metric <labeling_folder> <labeling_config> --textual-categories cat1,cat2 --non-textual-categories cat3,cat4 --verbose
"""
if save is not None:
save = str(save)
if not os.path.exists(save):
os.makedirs(save)
invoke_export = lambda tempdir, format, categories: ctx.invoke(
export,
path=path,
config=config,
output=tempdir,
format=format,
annotator=annotator,
categories=categories,
pdf_shas=pdf_shas,
include_unfinished=include_unfinished,
export_images=False,
)
if len(non_textual_categories) > 0:
print(
f"Generating Accuracy report for non-textual categories {non_textual_categories}"
)
with tempfile.TemporaryDirectory() as tempdir:
invoke_export(
tempdir=tempdir, format="coco", categories=non_textual_categories
)
coco_eval = COCOEvaluator(tempdir)
coco_results, coco_category_results = coco_eval.calculate_ap_scores()
if verbose:
save_table = coco_eval.show_results(coco_results)
coco_eval.show_category_results(coco_category_results)
else:
save_table = coco_eval.show_results(coco_results, ["AP"])
if save is not None:
save_table["AP"].to_csv(f"{save}/block-eval.csv")
if len(textual_categories) > 0:
print(f"Generating Accuracy report for textual categories {textual_categories}")
with tempfile.TemporaryDirectory() as tempdir:
tempdir = os.path.join(tempdir, "annotation.csv")
invoke_export(
tempdir=tempdir, format="token", categories=textual_categories
)
token_eval = TokenEvaluator(tempdir)
if verbose:
# As calculating the classification report is slow, we only
# do this if user specifies the verbose mode.
(
token_results,
token_category_results,
) = token_eval.calculate_token_accuracy(textual_categories)
save_table = token_eval.show_results(token_results)
token_eval.show_category_results(token_category_results)
else:
token_results = token_eval.calculate_token_accuracy()
save_table = token_eval.show_results(token_results)
if save is not None:
save_table.to_csv(f"{save}/textual-eval.csv") | 0.564819 | 0.300271 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import logging
import time
import subprocess
import re
from pykakasi import kakasi
from arduino_timer import Yukkuri
import grove_gesture_sensor
import ap_music
import ap_music_server_conf
MENU_IDLE = 0
MENU_PLAYING = 1
MENU_ONMENU = 2
class ApMenu:
MENU_TIMEOUT_SET = 20*5
def __init__(self,ledobj,volumebj,timerobj):
self.conf = ap_music_server_conf.MusicServerConfig().get_conf()
self.loop1 = 0
self.led = ledobj
self.volume = volumebj
self.timer = timerobj
self.music = ap_music.ApMusic()
self.mpdstat = "" #mpd status
self.menu_stat = MENU_ONMENU
self.stat_chage = True
self.menu_item = ["PLAY","STOP","CANCEL","RELOAD","WEATHER","NEWS"]
self.station_list = []
self.menu_cursor = 0
self.current_station = 0
list = self.music.get_playlist()
for station in list:
if station != "":
self.station_list.append(station)
print("station",station)
self.menu_timeout = 0
self.kakasi = kakasi()
self.kakasi.setMode('H', 'a')
self.kakasi.setMode('K', 'a')
self.kakasi.setMode('J', 'a')
self.conv = self.kakasi.getConverter()
self.yukkuri = Yukkuri()
return
def update(self,cnt,x,y,button,ges):
if self.menu_stat == MENU_IDLE:
self.mode_idle(cnt,x,y,button,ges)
elif self.menu_stat == MENU_PLAYING:
self.mode_playing(cnt,x,y,button,ges)
elif self.menu_stat == MENU_ONMENU:
self.mode_onmenu(cnt,x,y,button,ges)
return
def mode_idle(self,cnt,x,y,button,ges):
return
def mode_playing(self,cnt,x,y,button,ges):
if x!=0 or y!=0 or button != 0 or ges == grove_gesture_sensor.gesture.FORWARD:
self.menu_stat = MENU_ONMENU
self.stat_chage = True
self.menu_timeout = 20*5
return
if 0 == cnt % 40 :
self.mpdstat = self.music.check_mpd_status()
self.mpdstat = self.conv.do(self.mpdstat)
self.mpdstat = self.music.pickup_first_line(self.mpdstat)
self.mpdstat = self.mpdstat.replace('\n','')
if 0 == cnt % 10:
if self.loop1 > len(self.mpdstat) :
self.loop1=0
if len(self.mpdstat)-self.loop1 < 32:
t=self.mpdstat[self.loop1:]+" / "+self.mpdstat
self.led.put_text(t)
else:
self.led.put_text(self.mpdstat[self.loop1:])
self.loop1+=1
if ges == grove_gesture_sensor.gesture.CLOCKWISE:
play_stat = self.music.check_mpc_status()
if play_stat:
self.music.stop()
self.yukkuri.wether_speech()
if play_stat:
self.music.play()
return
def reload_schedule(self):
self.timer.reload_schedule()
def mode_onmenu(self,cnt,x,y,button,ges):
if y != 0:
x = 0
condition_update = False
if x!=0 or y!=0 or button != 0 or ges != 0:
self.menu_timeout = self.MENU_TIMEOUT_SET
self.menu_timeout -= 1
if self.menu_timeout <= 0:
self.menu_stat = MENU_PLAYING
if ges == grove_gesture_sensor.gesture.FORWARD:
button = 1
if ges == grove_gesture_sensor.gesture.UP:
y = -1
if ges == grove_gesture_sensor.gesture.DOWN:
y = 1
if ges == grove_gesture_sensor.gesture.RIGHT:
x = 1
if ges == grove_gesture_sensor.gesture.LEFT:
x = -1
if self.stat_chage:
#menu will be updated when state is changed
condition_update = True
self.stat_chage=False
if y == 1:
self.menu_cursor+=1
condition_update = True
if self.menu_cursor>=len(self.menu_item):
self.menu_cursor=0
if y == -1:
self.menu_cursor-=1
condition_update = True
if self.menu_cursor<0:
self.menu_cursor=len(self.menu_item)-1
if x == 1:
self.current_station+=1
condition_update = True
if self.current_station>=len(self.station_list):
self.current_station=0
if x == -1:
self.current_station-=1
condition_update = True
if self.current_station<0:
self.current_station=len(self.station_list)-1
if condition_update:
self.led.clear_display()
volume = self.volume.get_current_volset()
output = "["+self.menu_item[self.menu_cursor]+"]"+" V:"+str(volume)+"\n"
output += self.station_list[self.current_station]
self.led.put_text(output)
if button==1:
logging.debug("button press: cursor="+str(self.menu_cursor))
if self.menu_item[self.menu_cursor] == "PLAY":
r = self.music.play_item(str(self.current_station+1))
logging.debug(r)
elif self.menu_item[self.menu_cursor] == "STOP":
r = self.music.stop()
logging.debug(r)
elif self.menu_item[self.menu_cursor] == "NEXT":
r = self.music.next()
logging.debug(r)
elif self.menu_item[self.menu_cursor] == "CANCEL":
self.menu_stat = MENU_PLAYING
self.stat_chage = True
elif self.menu_item[self.menu_cursor] == "RELOAD":
self.reload_schedule()
elif self.menu_item[self.menu_cursor] == "WEATHER":
def local_yukkuri():
self.yukkuri.dayofweek_info_speech()
self.yukkuri.wether_speech()
self.music.mute_play_action(local_yukkuri)
elif self.menu_item[self.menu_cursor] == "NEWS":
def local_yukkuri():
self.yukkuri.rss_speech(self.conf['rss01'])
self.yukkuri.rss_speech(self.conf['rss02'])
self.yukkuri.rss_speech(self.conf['rss03'])
self.music.mute_play_action(local_yukkuri)
return | audioserver/ap_menu.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import logging
import time
import subprocess
import re
from pykakasi import kakasi
from arduino_timer import Yukkuri
import grove_gesture_sensor
import ap_music
import ap_music_server_conf
MENU_IDLE = 0
MENU_PLAYING = 1
MENU_ONMENU = 2
class ApMenu:
MENU_TIMEOUT_SET = 20*5
def __init__(self,ledobj,volumebj,timerobj):
self.conf = ap_music_server_conf.MusicServerConfig().get_conf()
self.loop1 = 0
self.led = ledobj
self.volume = volumebj
self.timer = timerobj
self.music = ap_music.ApMusic()
self.mpdstat = "" #mpd status
self.menu_stat = MENU_ONMENU
self.stat_chage = True
self.menu_item = ["PLAY","STOP","CANCEL","RELOAD","WEATHER","NEWS"]
self.station_list = []
self.menu_cursor = 0
self.current_station = 0
list = self.music.get_playlist()
for station in list:
if station != "":
self.station_list.append(station)
print("station",station)
self.menu_timeout = 0
self.kakasi = kakasi()
self.kakasi.setMode('H', 'a')
self.kakasi.setMode('K', 'a')
self.kakasi.setMode('J', 'a')
self.conv = self.kakasi.getConverter()
self.yukkuri = Yukkuri()
return
def update(self,cnt,x,y,button,ges):
if self.menu_stat == MENU_IDLE:
self.mode_idle(cnt,x,y,button,ges)
elif self.menu_stat == MENU_PLAYING:
self.mode_playing(cnt,x,y,button,ges)
elif self.menu_stat == MENU_ONMENU:
self.mode_onmenu(cnt,x,y,button,ges)
return
def mode_idle(self,cnt,x,y,button,ges):
return
def mode_playing(self,cnt,x,y,button,ges):
if x!=0 or y!=0 or button != 0 or ges == grove_gesture_sensor.gesture.FORWARD:
self.menu_stat = MENU_ONMENU
self.stat_chage = True
self.menu_timeout = 20*5
return
if 0 == cnt % 40 :
self.mpdstat = self.music.check_mpd_status()
self.mpdstat = self.conv.do(self.mpdstat)
self.mpdstat = self.music.pickup_first_line(self.mpdstat)
self.mpdstat = self.mpdstat.replace('\n','')
if 0 == cnt % 10:
if self.loop1 > len(self.mpdstat) :
self.loop1=0
if len(self.mpdstat)-self.loop1 < 32:
t=self.mpdstat[self.loop1:]+" / "+self.mpdstat
self.led.put_text(t)
else:
self.led.put_text(self.mpdstat[self.loop1:])
self.loop1+=1
if ges == grove_gesture_sensor.gesture.CLOCKWISE:
play_stat = self.music.check_mpc_status()
if play_stat:
self.music.stop()
self.yukkuri.wether_speech()
if play_stat:
self.music.play()
return
def reload_schedule(self):
self.timer.reload_schedule()
def mode_onmenu(self,cnt,x,y,button,ges):
if y != 0:
x = 0
condition_update = False
if x!=0 or y!=0 or button != 0 or ges != 0:
self.menu_timeout = self.MENU_TIMEOUT_SET
self.menu_timeout -= 1
if self.menu_timeout <= 0:
self.menu_stat = MENU_PLAYING
if ges == grove_gesture_sensor.gesture.FORWARD:
button = 1
if ges == grove_gesture_sensor.gesture.UP:
y = -1
if ges == grove_gesture_sensor.gesture.DOWN:
y = 1
if ges == grove_gesture_sensor.gesture.RIGHT:
x = 1
if ges == grove_gesture_sensor.gesture.LEFT:
x = -1
if self.stat_chage:
#menu will be updated when state is changed
condition_update = True
self.stat_chage=False
if y == 1:
self.menu_cursor+=1
condition_update = True
if self.menu_cursor>=len(self.menu_item):
self.menu_cursor=0
if y == -1:
self.menu_cursor-=1
condition_update = True
if self.menu_cursor<0:
self.menu_cursor=len(self.menu_item)-1
if x == 1:
self.current_station+=1
condition_update = True
if self.current_station>=len(self.station_list):
self.current_station=0
if x == -1:
self.current_station-=1
condition_update = True
if self.current_station<0:
self.current_station=len(self.station_list)-1
if condition_update:
self.led.clear_display()
volume = self.volume.get_current_volset()
output = "["+self.menu_item[self.menu_cursor]+"]"+" V:"+str(volume)+"\n"
output += self.station_list[self.current_station]
self.led.put_text(output)
if button==1:
logging.debug("button press: cursor="+str(self.menu_cursor))
if self.menu_item[self.menu_cursor] == "PLAY":
r = self.music.play_item(str(self.current_station+1))
logging.debug(r)
elif self.menu_item[self.menu_cursor] == "STOP":
r = self.music.stop()
logging.debug(r)
elif self.menu_item[self.menu_cursor] == "NEXT":
r = self.music.next()
logging.debug(r)
elif self.menu_item[self.menu_cursor] == "CANCEL":
self.menu_stat = MENU_PLAYING
self.stat_chage = True
elif self.menu_item[self.menu_cursor] == "RELOAD":
self.reload_schedule()
elif self.menu_item[self.menu_cursor] == "WEATHER":
def local_yukkuri():
self.yukkuri.dayofweek_info_speech()
self.yukkuri.wether_speech()
self.music.mute_play_action(local_yukkuri)
elif self.menu_item[self.menu_cursor] == "NEWS":
def local_yukkuri():
self.yukkuri.rss_speech(self.conf['rss01'])
self.yukkuri.rss_speech(self.conf['rss02'])
self.yukkuri.rss_speech(self.conf['rss03'])
self.music.mute_play_action(local_yukkuri)
return | 0.049314 | 0.082328 |
import tempfile
import unittest
import healpy as hp
import numpy as np
import jang.conversions
import jang.gw
import jang.limits
import jang.results
import jang.significance
import jang.stacking
from jang.neutrinos import BackgroundFixed, Detector
from jang.parameters import Parameters
class TestExamples(unittest.TestCase):
def setUp(self):
self.tmpdir = tempfile.mkdtemp()
config_str = """
analysis:
nside: 8
apply_det_systematics: 0
ntoys_det_systematics: 0
search_region: region_90_excludezero
prior_signal: flat
range:
log10_flux: [-5, 5, 1000]
log10_etot: [48, 62, 1400]
log10_fnu: [-5, 10, 1500]
neutrino_energy_GeV: [0.1, 1e8]
"""
self.config_file = f"{self.tmpdir}/config.yaml"
with open(self.config_file, "w") as f:
f.write(config_str)
#
detector_str = """
name: TestDet
nsamples: 2
samples:
names: ["sampleA", "sampleB"]
shortnames: ["A", "B"]
energyrange: [0, 100]
earth_location:
latitude: 10.0
longitude: 50.0
units: deg
errors:
acceptance: 0.00
acceptance_corr: 1
background: 0.00
"""
self.det_file = f"{self.tmpdir}/detector.yaml"
with open(self.det_file, "w") as f:
f.write(detector_str)
self.accs = [np.ones(hp.nside2npix(8)), np.ones(hp.nside2npix(8))]
#
self.gwdb_file = "examples/input_files/gw_catalogs/database_example.csv"
self.db_file = f"{self.tmpdir}/db.csv"
# configuration
self.pars = Parameters(self.config_file)
self.pars.set_models("x**-2", jang.conversions.JetIsotropic())
# GW database
database_gw = jang.gw.Database(self.gwdb_file)
self.gw = database_gw.find_gw("GW190412")
# detector
self.det = Detector(self.det_file)
self.det.set_acceptances(self.accs, self.pars.spectrum, self.pars.nside)
bkg = [BackgroundFixed(b) for b in [0.1, 0.3]]
self.det.set_observations([0, 0], bkg)
def test_example(self):
# compute limits
limit_flux = jang.limits.get_limit_flux(
self.det, self.gw, self.pars, f"{self.tmpdir}/flux"
)
limit_etot = jang.limits.get_limit_etot(
self.det, self.gw, self.pars, f"{self.tmpdir}/etot"
)
limit_fnu = jang.limits.get_limit_fnu(
self.det, self.gw, self.pars, f"{self.tmpdir}/fnu"
)
jang.significance.compute_prob_null_hypothesis(self.det, self.gw, self.pars)
# compute limits (with systematics)
self.pars.apply_det_systematics = True
self.pars.ntoys_det_systematics = 10
limit_flux = jang.limits.get_limit_flux(
self.det, self.gw, self.pars, f"{self.tmpdir}/flux"
)
limit_etot = jang.limits.get_limit_etot(
self.det, self.gw, self.pars, f"{self.tmpdir}/etot"
)
limit_fnu = jang.limits.get_limit_fnu(
self.det, self.gw, self.pars, f"{self.tmpdir}/fnu"
)
jang.significance.compute_prob_null_hypothesis(self.det, self.gw, self.pars)
# save in database
database_res = jang.results.Database(self.db_file)
database_res.add_entry(
self.det,
self.gw,
self.pars,
1,
1e55,
1,
f"{self.tmpdir}/flux",
f"{self.tmpdir}/etot",
f"{self.tmpdir}/fnu",
custom={"test": 0},
)
database_res.add_entry(
self.det,
self.gw,
self.pars,
2,
2e55,
2,
f"{self.tmpdir}/flux",
f"{self.tmpdir}/etot",
f"{self.tmpdir}/fnu",
custom={"test": 0},
)
database_res.save()
# open database
database_res = jang.results.Database(self.db_file)
with self.assertLogs(level="INFO"):
database_res = database_res.select()
database_res = database_res.select(self.det, self.pars.spectrum, self.pars.jet)
# make plots
cat = {
"column": "GW.type",
"categories": ["BBH", "BNS", "NSBH"],
"labels": ["BBH", "BNS", "NSBH"],
"colors": ["black", "blue", "orange"],
"markers": ["+", "x", "^"],
}
database_res.plot_energy_vs_distance(f"{self.tmpdir}/eiso.png")
database_res.plot_energy_vs_distance(f"{self.tmpdir}/eiso.png", cat=cat)
database_res.plot_fnu_vs_distance(f"{self.tmpdir}/fnu.png")
database_res.plot_fnu_vs_distance(f"{self.tmpdir}/fnu.png", cat=cat)
database_res.plot_flux(f"{self.tmpdir}/flux.png")
database_res.plot_flux(f"{self.tmpdir}/flux.png", cat=cat)
database_res.plot_summary_observations(
f"{self.tmpdir}/obs.png", {s.shortname: "black" for s in self.det.samples}
)
#
jang.stacking.stack_events(database_res, self.pars)
with self.assertLogs(level="ERROR"):
jang.stacking.stack_events_listgw(
database_res, ["GW190412", "missing_ev"], self.pars
)
jang.stacking.stack_events_weightedevents(
database_res,
{"GW190412": 1},
self.pars,
outfile=f"{self.tmpdir}/stacking.png",
)
with self.assertLogs(level="ERROR"):
jang.stacking.stack_events_weightedevents(
database_res,
{"GW190412": 1, "missing_ev": 0.5},
self.pars,
outfile=f"{self.tmpdir}/stacking.png",
) | tests/test_examples.py |
import tempfile
import unittest
import healpy as hp
import numpy as np
import jang.conversions
import jang.gw
import jang.limits
import jang.results
import jang.significance
import jang.stacking
from jang.neutrinos import BackgroundFixed, Detector
from jang.parameters import Parameters
class TestExamples(unittest.TestCase):
def setUp(self):
self.tmpdir = tempfile.mkdtemp()
config_str = """
analysis:
nside: 8
apply_det_systematics: 0
ntoys_det_systematics: 0
search_region: region_90_excludezero
prior_signal: flat
range:
log10_flux: [-5, 5, 1000]
log10_etot: [48, 62, 1400]
log10_fnu: [-5, 10, 1500]
neutrino_energy_GeV: [0.1, 1e8]
"""
self.config_file = f"{self.tmpdir}/config.yaml"
with open(self.config_file, "w") as f:
f.write(config_str)
#
detector_str = """
name: TestDet
nsamples: 2
samples:
names: ["sampleA", "sampleB"]
shortnames: ["A", "B"]
energyrange: [0, 100]
earth_location:
latitude: 10.0
longitude: 50.0
units: deg
errors:
acceptance: 0.00
acceptance_corr: 1
background: 0.00
"""
self.det_file = f"{self.tmpdir}/detector.yaml"
with open(self.det_file, "w") as f:
f.write(detector_str)
self.accs = [np.ones(hp.nside2npix(8)), np.ones(hp.nside2npix(8))]
#
self.gwdb_file = "examples/input_files/gw_catalogs/database_example.csv"
self.db_file = f"{self.tmpdir}/db.csv"
# configuration
self.pars = Parameters(self.config_file)
self.pars.set_models("x**-2", jang.conversions.JetIsotropic())
# GW database
database_gw = jang.gw.Database(self.gwdb_file)
self.gw = database_gw.find_gw("GW190412")
# detector
self.det = Detector(self.det_file)
self.det.set_acceptances(self.accs, self.pars.spectrum, self.pars.nside)
bkg = [BackgroundFixed(b) for b in [0.1, 0.3]]
self.det.set_observations([0, 0], bkg)
def test_example(self):
# compute limits
limit_flux = jang.limits.get_limit_flux(
self.det, self.gw, self.pars, f"{self.tmpdir}/flux"
)
limit_etot = jang.limits.get_limit_etot(
self.det, self.gw, self.pars, f"{self.tmpdir}/etot"
)
limit_fnu = jang.limits.get_limit_fnu(
self.det, self.gw, self.pars, f"{self.tmpdir}/fnu"
)
jang.significance.compute_prob_null_hypothesis(self.det, self.gw, self.pars)
# compute limits (with systematics)
self.pars.apply_det_systematics = True
self.pars.ntoys_det_systematics = 10
limit_flux = jang.limits.get_limit_flux(
self.det, self.gw, self.pars, f"{self.tmpdir}/flux"
)
limit_etot = jang.limits.get_limit_etot(
self.det, self.gw, self.pars, f"{self.tmpdir}/etot"
)
limit_fnu = jang.limits.get_limit_fnu(
self.det, self.gw, self.pars, f"{self.tmpdir}/fnu"
)
jang.significance.compute_prob_null_hypothesis(self.det, self.gw, self.pars)
# save in database
database_res = jang.results.Database(self.db_file)
database_res.add_entry(
self.det,
self.gw,
self.pars,
1,
1e55,
1,
f"{self.tmpdir}/flux",
f"{self.tmpdir}/etot",
f"{self.tmpdir}/fnu",
custom={"test": 0},
)
database_res.add_entry(
self.det,
self.gw,
self.pars,
2,
2e55,
2,
f"{self.tmpdir}/flux",
f"{self.tmpdir}/etot",
f"{self.tmpdir}/fnu",
custom={"test": 0},
)
database_res.save()
# open database
database_res = jang.results.Database(self.db_file)
with self.assertLogs(level="INFO"):
database_res = database_res.select()
database_res = database_res.select(self.det, self.pars.spectrum, self.pars.jet)
# make plots
cat = {
"column": "GW.type",
"categories": ["BBH", "BNS", "NSBH"],
"labels": ["BBH", "BNS", "NSBH"],
"colors": ["black", "blue", "orange"],
"markers": ["+", "x", "^"],
}
database_res.plot_energy_vs_distance(f"{self.tmpdir}/eiso.png")
database_res.plot_energy_vs_distance(f"{self.tmpdir}/eiso.png", cat=cat)
database_res.plot_fnu_vs_distance(f"{self.tmpdir}/fnu.png")
database_res.plot_fnu_vs_distance(f"{self.tmpdir}/fnu.png", cat=cat)
database_res.plot_flux(f"{self.tmpdir}/flux.png")
database_res.plot_flux(f"{self.tmpdir}/flux.png", cat=cat)
database_res.plot_summary_observations(
f"{self.tmpdir}/obs.png", {s.shortname: "black" for s in self.det.samples}
)
#
jang.stacking.stack_events(database_res, self.pars)
with self.assertLogs(level="ERROR"):
jang.stacking.stack_events_listgw(
database_res, ["GW190412", "missing_ev"], self.pars
)
jang.stacking.stack_events_weightedevents(
database_res,
{"GW190412": 1},
self.pars,
outfile=f"{self.tmpdir}/stacking.png",
)
with self.assertLogs(level="ERROR"):
jang.stacking.stack_events_weightedevents(
database_res,
{"GW190412": 1, "missing_ev": 0.5},
self.pars,
outfile=f"{self.tmpdir}/stacking.png",
) | 0.361277 | 0.23456 |
from __future__ import division
import torch
from torch import nn
import torch.nn.functional as F
from inverse_warp import inverse_warp2
import math
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
# compute photometric loss (with ssim) and geometry consistency loss
def compute_photo_and_geometry_loss(tgt_img, ref_imgs, intrinsics, tgt_depth, ref_depths, poses, poses_inv, args):
photo_loss = 0
geometry_loss = 0
num_scales = min(len(tgt_depth), args.num_scales)
for ref_img, ref_depth, pose, pose_inv in zip(ref_imgs, ref_depths, poses, poses_inv):
for s in range(num_scales):
b, _, h, w = tgt_depth[s].size()
downscale = tgt_img.size(2)/h
tgt_img_scaled = F.interpolate(tgt_img, (h, w), mode='area')
ref_img_scaled = F.interpolate(ref_img, (h, w), mode='area')
intrinsic_scaled = torch.cat(
(intrinsics[:, 0:2]/downscale, intrinsics[:, 2:]), dim=1)
photo_loss1, geometry_loss1 = compute_pairwise_loss(
tgt_img_scaled, ref_img_scaled, tgt_depth[s], ref_depth[s], pose, intrinsic_scaled, args)
photo_loss2, geometry_loss2 = compute_pairwise_loss(
ref_img_scaled, tgt_img_scaled, ref_depth[s], tgt_depth[s], pose_inv, intrinsic_scaled, args)
photo_loss += (photo_loss1 + photo_loss2)
geometry_loss += (geometry_loss1 + geometry_loss2)
return photo_loss, geometry_loss
def compute_pairwise_loss(tgt_img, ref_img, tgt_depth, ref_depth, pose, intrinsic, args):
ref_img_warped, valid_mask, projected_depth, computed_depth = inverse_warp2(ref_img, tgt_depth, ref_depth,
pose, intrinsic, args.padding_mode)
diff_img = (tgt_img - ref_img_warped).abs()
diff_depth = ((computed_depth - projected_depth).abs() /
(computed_depth + projected_depth).abs()).clamp(0, 1)
if args.with_ssim:
ssim_map = (0.5*(1-ssim(tgt_img, ref_img_warped))).clamp(0, 1)
diff_img = (0.15 * diff_img + 0.85 * ssim_map)
if args.with_mask:
weight_mask = (1 - diff_depth)
diff_img = diff_img * weight_mask
# compute loss
reconstruction_loss = mean_on_mask(diff_img, valid_mask)
geometry_consistency_loss = mean_on_mask(diff_depth, valid_mask)
return reconstruction_loss, geometry_consistency_loss
# compute mean value given a binary mask
def mean_on_mask(diff, valid_mask):
mask = valid_mask.expand_as(diff)
mean_value = (diff * mask).sum() / mask.sum()
return mean_value
def edge_aware_smoothness_loss(pred_disp, img, max_scales):
def gradient_x(img):
gx = img[:, :, :-1, :] - img[:, :, 1:, :]
return gx
def gradient_y(img):
gy = img[:, :, :, :-1] - img[:, :, :, 1:]
return gy
def get_edge_smoothness(img, pred):
pred_gradients_x = gradient_x(pred)
pred_gradients_y = gradient_y(pred)
image_gradients_x = gradient_x(img)
image_gradients_y = gradient_y(img)
weights_x = torch.exp(-torch.mean(torch.abs(image_gradients_x),
1, keepdim=True))
weights_y = torch.exp(-torch.mean(torch.abs(image_gradients_y),
1, keepdim=True))
smoothness_x = torch.abs(pred_gradients_x) * weights_x
smoothness_y = torch.abs(pred_gradients_y) * weights_y
return torch.mean(smoothness_x) + torch.mean(smoothness_y)
loss = 0
weight = 1.
s = 0
for scaled_disp in pred_disp:
s += 1
if s > max_scales:
break
b, _, h, w = scaled_disp.size()
scaled_img = nn.functional.adaptive_avg_pool2d(img, (h, w))
loss += get_edge_smoothness(scaled_img, scaled_disp) * weight
weight /= 4.0
return loss
def compute_smooth_loss(tgt_depth, tgt_img, ref_depths, ref_imgs, max_scales=1):
loss = edge_aware_smoothness_loss(tgt_depth, tgt_img, max_scales)
for ref_depth, ref_img in zip(ref_depths, ref_imgs):
loss += edge_aware_smoothness_loss(ref_depth, ref_img, max_scales)
return loss
def create_gaussian_window(window_size, channel):
def _gaussian(window_size, sigma):
gauss = torch.Tensor(
[math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
_1D_window = _gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window@(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = _2D_window.expand(
channel, 1, window_size, window_size).contiguous()
return window
window_size = 5
gaussian_img_kernel = create_gaussian_window(window_size, 3).float().to(device)
def ssim(img1, img2):
params = {'weight': gaussian_img_kernel,
'groups': 3, 'padding': window_size//2}
mu1 = F.conv2d(img1, **params)
mu2 = F.conv2d(img2, **params)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1*mu2
sigma1_sq = F.conv2d(img1*img1, **params) - mu1_sq
sigma2_sq = F.conv2d(img2*img2, **params) - mu2_sq
sigma12 = F.conv2d(img1*img2, **params) - mu1_mu2
C1 = 0.01**2
C2 = 0.03**2
ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2)) / \
((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
return ssim_map
@torch.no_grad()
def compute_errors(gt, pred):
abs_diff, abs_rel, sq_rel, a1, a2, a3 = 0, 0, 0, 0, 0, 0
batch_size = gt.size(0)
'''
crop used by Garg ECCV16 to reprocude Eigen NIPS14 results
construct a mask of False values, with the same size as target
and then set to True values inside the crop
'''
crop_mask = gt[0] != gt[0]
y1, y2 = int(0.40810811 * gt.size(1)), int(0.99189189 * gt.size(1))
x1, x2 = int(0.03594771 * gt.size(2)), int(0.96405229 * gt.size(2))
crop_mask[y1:y2, x1:x2] = 1
max_depth = 80
for current_gt, current_pred in zip(gt, pred):
valid = (current_gt > 0) & (current_gt < max_depth)
valid = valid & crop_mask
valid_gt = current_gt[valid]
valid_pred = current_pred[valid].clamp(1e-3, max_depth)
valid_pred = valid_pred * \
torch.median(valid_gt)/torch.median(valid_pred)
thresh = torch.max((valid_gt / valid_pred), (valid_pred / valid_gt))
a1 += (thresh < 1.25).float().mean()
a2 += (thresh < 1.25 ** 2).float().mean()
a3 += (thresh < 1.25 ** 3).float().mean()
abs_diff += torch.mean(torch.abs(valid_gt - valid_pred))
abs_rel += torch.mean(torch.abs(valid_gt - valid_pred) / valid_gt)
sq_rel += torch.mean(((valid_gt - valid_pred)**2) / valid_gt)
return [metric.item() / batch_size for metric in [abs_diff, abs_rel, sq_rel, a1, a2, a3]] | loss_functions.py | from __future__ import division
import torch
from torch import nn
import torch.nn.functional as F
from inverse_warp import inverse_warp2
import math
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
# compute photometric loss (with ssim) and geometry consistency loss
def compute_photo_and_geometry_loss(tgt_img, ref_imgs, intrinsics, tgt_depth, ref_depths, poses, poses_inv, args):
photo_loss = 0
geometry_loss = 0
num_scales = min(len(tgt_depth), args.num_scales)
for ref_img, ref_depth, pose, pose_inv in zip(ref_imgs, ref_depths, poses, poses_inv):
for s in range(num_scales):
b, _, h, w = tgt_depth[s].size()
downscale = tgt_img.size(2)/h
tgt_img_scaled = F.interpolate(tgt_img, (h, w), mode='area')
ref_img_scaled = F.interpolate(ref_img, (h, w), mode='area')
intrinsic_scaled = torch.cat(
(intrinsics[:, 0:2]/downscale, intrinsics[:, 2:]), dim=1)
photo_loss1, geometry_loss1 = compute_pairwise_loss(
tgt_img_scaled, ref_img_scaled, tgt_depth[s], ref_depth[s], pose, intrinsic_scaled, args)
photo_loss2, geometry_loss2 = compute_pairwise_loss(
ref_img_scaled, tgt_img_scaled, ref_depth[s], tgt_depth[s], pose_inv, intrinsic_scaled, args)
photo_loss += (photo_loss1 + photo_loss2)
geometry_loss += (geometry_loss1 + geometry_loss2)
return photo_loss, geometry_loss
def compute_pairwise_loss(tgt_img, ref_img, tgt_depth, ref_depth, pose, intrinsic, args):
ref_img_warped, valid_mask, projected_depth, computed_depth = inverse_warp2(ref_img, tgt_depth, ref_depth,
pose, intrinsic, args.padding_mode)
diff_img = (tgt_img - ref_img_warped).abs()
diff_depth = ((computed_depth - projected_depth).abs() /
(computed_depth + projected_depth).abs()).clamp(0, 1)
if args.with_ssim:
ssim_map = (0.5*(1-ssim(tgt_img, ref_img_warped))).clamp(0, 1)
diff_img = (0.15 * diff_img + 0.85 * ssim_map)
if args.with_mask:
weight_mask = (1 - diff_depth)
diff_img = diff_img * weight_mask
# compute loss
reconstruction_loss = mean_on_mask(diff_img, valid_mask)
geometry_consistency_loss = mean_on_mask(diff_depth, valid_mask)
return reconstruction_loss, geometry_consistency_loss
# compute mean value given a binary mask
def mean_on_mask(diff, valid_mask):
mask = valid_mask.expand_as(diff)
mean_value = (diff * mask).sum() / mask.sum()
return mean_value
def edge_aware_smoothness_loss(pred_disp, img, max_scales):
def gradient_x(img):
gx = img[:, :, :-1, :] - img[:, :, 1:, :]
return gx
def gradient_y(img):
gy = img[:, :, :, :-1] - img[:, :, :, 1:]
return gy
def get_edge_smoothness(img, pred):
pred_gradients_x = gradient_x(pred)
pred_gradients_y = gradient_y(pred)
image_gradients_x = gradient_x(img)
image_gradients_y = gradient_y(img)
weights_x = torch.exp(-torch.mean(torch.abs(image_gradients_x),
1, keepdim=True))
weights_y = torch.exp(-torch.mean(torch.abs(image_gradients_y),
1, keepdim=True))
smoothness_x = torch.abs(pred_gradients_x) * weights_x
smoothness_y = torch.abs(pred_gradients_y) * weights_y
return torch.mean(smoothness_x) + torch.mean(smoothness_y)
loss = 0
weight = 1.
s = 0
for scaled_disp in pred_disp:
s += 1
if s > max_scales:
break
b, _, h, w = scaled_disp.size()
scaled_img = nn.functional.adaptive_avg_pool2d(img, (h, w))
loss += get_edge_smoothness(scaled_img, scaled_disp) * weight
weight /= 4.0
return loss
def compute_smooth_loss(tgt_depth, tgt_img, ref_depths, ref_imgs, max_scales=1):
loss = edge_aware_smoothness_loss(tgt_depth, tgt_img, max_scales)
for ref_depth, ref_img in zip(ref_depths, ref_imgs):
loss += edge_aware_smoothness_loss(ref_depth, ref_img, max_scales)
return loss
def create_gaussian_window(window_size, channel):
def _gaussian(window_size, sigma):
gauss = torch.Tensor(
[math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
_1D_window = _gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window@(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = _2D_window.expand(
channel, 1, window_size, window_size).contiguous()
return window
window_size = 5
gaussian_img_kernel = create_gaussian_window(window_size, 3).float().to(device)
def ssim(img1, img2):
params = {'weight': gaussian_img_kernel,
'groups': 3, 'padding': window_size//2}
mu1 = F.conv2d(img1, **params)
mu2 = F.conv2d(img2, **params)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1*mu2
sigma1_sq = F.conv2d(img1*img1, **params) - mu1_sq
sigma2_sq = F.conv2d(img2*img2, **params) - mu2_sq
sigma12 = F.conv2d(img1*img2, **params) - mu1_mu2
C1 = 0.01**2
C2 = 0.03**2
ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2)) / \
((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
return ssim_map
@torch.no_grad()
def compute_errors(gt, pred):
abs_diff, abs_rel, sq_rel, a1, a2, a3 = 0, 0, 0, 0, 0, 0
batch_size = gt.size(0)
'''
crop used by Garg ECCV16 to reprocude Eigen NIPS14 results
construct a mask of False values, with the same size as target
and then set to True values inside the crop
'''
crop_mask = gt[0] != gt[0]
y1, y2 = int(0.40810811 * gt.size(1)), int(0.99189189 * gt.size(1))
x1, x2 = int(0.03594771 * gt.size(2)), int(0.96405229 * gt.size(2))
crop_mask[y1:y2, x1:x2] = 1
max_depth = 80
for current_gt, current_pred in zip(gt, pred):
valid = (current_gt > 0) & (current_gt < max_depth)
valid = valid & crop_mask
valid_gt = current_gt[valid]
valid_pred = current_pred[valid].clamp(1e-3, max_depth)
valid_pred = valid_pred * \
torch.median(valid_gt)/torch.median(valid_pred)
thresh = torch.max((valid_gt / valid_pred), (valid_pred / valid_gt))
a1 += (thresh < 1.25).float().mean()
a2 += (thresh < 1.25 ** 2).float().mean()
a3 += (thresh < 1.25 ** 3).float().mean()
abs_diff += torch.mean(torch.abs(valid_gt - valid_pred))
abs_rel += torch.mean(torch.abs(valid_gt - valid_pred) / valid_gt)
sq_rel += torch.mean(((valid_gt - valid_pred)**2) / valid_gt)
return [metric.item() / batch_size for metric in [abs_diff, abs_rel, sq_rel, a1, a2, a3]] | 0.836421 | 0.572364 |
from tkinter import *
from tkinter import messagebox
from .utils import *
from .data import Tawqeetex
gui = None
def generate_callback():
'''
This function executes when the user clicks on the Generate button
The user's input are stored and passed to the Tawqeetex object in order
to produce the prayer time schedule.
'''
city = gui.entry_city.get()
country = gui.entry_country.get()
month = str(dict_month['en'].index(gui.var_month.get()) + 1)
year = gui.entry_year.get()
method = str(list_method.index(gui.var_method.get()) + 1)
lang = dict_lang[gui.var_lang.get()]
adj = str(gui.slider_adj.get())
data = Tawqeetex(city, country, month, year, method, lang, adj)
data.create_schedule()
messagebox.showinfo("Warning", "The prayer time schedule has been generated successfully")
# gui.root.exit()
class GUI:
'''This class gather all the widget being used in the GUI.'''
def __init__(self):
'''GUI initialization.'''
self.root = Tk()
self.root.title('tawqeeTeX')
# self.root.iconbitmap('')
# self.root.geometry("400x400")
self.var_month = StringVar()
self.var_method = StringVar()
self.var_lang = StringVar()
self.var_month.set(dict_month['en'][0])
self.var_method.set(list_method[0])
self.var_lang.set(list_lang[0])
self.label_city = Label(self.root, text="City")
self.entry_city = Entry(self.root)
self.label_country = Label(self.root, text="Country")
self.entry_country = Entry(self.root)
self.label_month = Label(self.root, text="Month")
self.menu_month = OptionMenu(self.root, self.var_month, *dict_month['en'])
self.label_year = Label(self.root, text="Year")
self.entry_year = Entry(self.root)
self.label_method = Label(self.root, text="Method")
self.menu_method = OptionMenu(self.root, self.var_method, *list_method)
self.label_adj = Label(self.root, text="Adjustment")
self.slider_adj = Scale(self.root, from_=-1, to=1, orient=HORIZONTAL)
self.label_lang = Label(self.root, text="Language")
self.menu_lang = OptionMenu(self.root, self.var_lang, *list_lang)
# state=DISABLED,
self.button_go = Button(self.root, text="Generate", command=generate_callback)
self.label_city.grid(row=0, column=0)
self.entry_city.grid(row=0, column=1)
self.label_country.grid(row=1, column=0)
self.entry_country.grid(row=1, column=1)
self.label_month.grid(row=2, column=0)
self.menu_month.grid(row=2, column=1)
self.label_year.grid(row=3, column=0)
self.entry_year.grid(row=3, column=1)
self.label_method.grid(row=4, column=0)
self.menu_method.grid(row=4, column=1)
self.label_adj.grid(row=5, column=0)
self.slider_adj.grid(row=5, column=1)
self.label_lang.grid(row=6, column=0)
self.menu_lang.grid(row=6, column=1)
self.button_go.grid(row=7, column=0, columnspan=2)
def run(self):
'''Run the GUI.'''
self.root.mainloop()
def gui_start():
global gui
gui = GUI()
gui.run() | tawqeetex/gui.py | from tkinter import *
from tkinter import messagebox
from .utils import *
from .data import Tawqeetex
gui = None
def generate_callback():
'''
This function executes when the user clicks on the Generate button
The user's input are stored and passed to the Tawqeetex object in order
to produce the prayer time schedule.
'''
city = gui.entry_city.get()
country = gui.entry_country.get()
month = str(dict_month['en'].index(gui.var_month.get()) + 1)
year = gui.entry_year.get()
method = str(list_method.index(gui.var_method.get()) + 1)
lang = dict_lang[gui.var_lang.get()]
adj = str(gui.slider_adj.get())
data = Tawqeetex(city, country, month, year, method, lang, adj)
data.create_schedule()
messagebox.showinfo("Warning", "The prayer time schedule has been generated successfully")
# gui.root.exit()
class GUI:
'''This class gather all the widget being used in the GUI.'''
def __init__(self):
'''GUI initialization.'''
self.root = Tk()
self.root.title('tawqeeTeX')
# self.root.iconbitmap('')
# self.root.geometry("400x400")
self.var_month = StringVar()
self.var_method = StringVar()
self.var_lang = StringVar()
self.var_month.set(dict_month['en'][0])
self.var_method.set(list_method[0])
self.var_lang.set(list_lang[0])
self.label_city = Label(self.root, text="City")
self.entry_city = Entry(self.root)
self.label_country = Label(self.root, text="Country")
self.entry_country = Entry(self.root)
self.label_month = Label(self.root, text="Month")
self.menu_month = OptionMenu(self.root, self.var_month, *dict_month['en'])
self.label_year = Label(self.root, text="Year")
self.entry_year = Entry(self.root)
self.label_method = Label(self.root, text="Method")
self.menu_method = OptionMenu(self.root, self.var_method, *list_method)
self.label_adj = Label(self.root, text="Adjustment")
self.slider_adj = Scale(self.root, from_=-1, to=1, orient=HORIZONTAL)
self.label_lang = Label(self.root, text="Language")
self.menu_lang = OptionMenu(self.root, self.var_lang, *list_lang)
# state=DISABLED,
self.button_go = Button(self.root, text="Generate", command=generate_callback)
self.label_city.grid(row=0, column=0)
self.entry_city.grid(row=0, column=1)
self.label_country.grid(row=1, column=0)
self.entry_country.grid(row=1, column=1)
self.label_month.grid(row=2, column=0)
self.menu_month.grid(row=2, column=1)
self.label_year.grid(row=3, column=0)
self.entry_year.grid(row=3, column=1)
self.label_method.grid(row=4, column=0)
self.menu_method.grid(row=4, column=1)
self.label_adj.grid(row=5, column=0)
self.slider_adj.grid(row=5, column=1)
self.label_lang.grid(row=6, column=0)
self.menu_lang.grid(row=6, column=1)
self.button_go.grid(row=7, column=0, columnspan=2)
def run(self):
'''Run the GUI.'''
self.root.mainloop()
def gui_start():
global gui
gui = GUI()
gui.run() | 0.386416 | 0.114542 |
import logging
import queue
import socket
import threading
import traceback
import numpy
import pyflac
import select
import sounddevice as sd
import config
logging.basicConfig(
format='%(asctime)s.%(msecs)03d %(levelname)s:\t%(message)s',
level=logging.INFO,
datefmt='%H:%M:%S')
class _AudioHandler:
# _AudioHandler class takes care of the audio IO.
# No user-threads required! All handled by callback functions.
# TO-DO:
# - switch to UDP
# - add headers to packets (problem since the send and recv funcs decide what they like and send
# packets of varying sizes.)
def __init__(self, outgoing_buffer, incoming_buffer, audio_devices=None):
if audio_devices:
sd.default.device = audio_devices
self._stream = self._stream = sd.RawStream(samplerate=config.SAMPLE_RATE,
blocksize=config.PACKET_SIZE,
channels=config.CHANNELS,
dtype=numpy.int16,
callback=self._audio_callback)
self._encoder = pyflac.StreamEncoder(write_callback=self._encoder_callback, sample_rate=config.SAMPLE_RATE,
blocksize=config.PACKET_SIZE)
self._decoder = pyflac.StreamDecoder(write_callback=self._decoder_callback)
self._outgoing_buffer = outgoing_buffer
self._incoming_buffer = incoming_buffer
self._is_muted = False
def _audio_callback(self, in_data, out_data, *_) -> None:
if not self._is_muted:
self._encoder.process(numpy.frombuffer(in_data, dtype=numpy.int16))
if self._incoming_buffer.qsize() > 0:
data = self._incoming_buffer.get(block=False)
out_data[:] = data.tobytes()
elif self._incoming_buffer.qsize() == 0:
out_data[:] = bytes(config.PACKET_SIZE * 2)
def _encoder_callback(self, buffer, *_):
self._outgoing_buffer.put(buffer) # buffer is a built-in bytes object.
def _decoder_callback(self, data, *_):
self._incoming_buffer.put(data)
def _toggle_mute(self):
self._outgoing_buffer.queue.clear() # this is for when muting, some packets haven't returned yet and will play
# once un-muted from the buffer. Since I haven't implemented a UDP system where each packet is timestamped and
# reconstructed chronologically (hence dropping older packets and making this issue moot),
# this will have to do.
self._is_muted = False if self._is_muted else True
def start(self):
self._incoming_buffer.queue.clear()
self._outgoing_buffer.queue.clear()
self._stream.start()
def stop(self):
self._stream.stop()
class Client:
def __init__(self, default_audio_devices=None):
# Client class handles the internet IO, and passes the audio data to the AudioHandler class.
"""
:param default_audio_devices: Query by name or index, the audio devices to be used. Use get_sound_devices() method to list all audio devices.
Client class that handles audio and internet IO.
"""
self._is_muted = threading.Event()
self._outgoing_buffer = queue.Queue()
self._incoming_buffer = queue.Queue()
self._audio_handler = _AudioHandler(self._outgoing_buffer, self._incoming_buffer,
audio_devices=default_audio_devices)
self._socket = None
self._is_connected = False
self._internet_io_flag = threading.Event()
self._internet_thread = None
def _internet_io(self, ):
while not self._internet_io_flag.is_set():
try:
readable, writable, exceptional = select.select([self._socket], [self._socket], [self._socket])
except ValueError:
logging.info("Disconnect!")
break
if readable:
try:
data = self._socket.recv(config.PACKET_SIZE)
# data, header = self._read_header(packed_data)
self._audio_handler._decoder.process(data) # messy but since the callback audio func only runs
# whenever it has enough samples of audio to send, the audio needs to be processed by the time it
# does a callback.
except ConnectionResetError:
logging.error("Disconnected!")
break
except TimeoutError:
logging.error(f"Timed out! {traceback.format_exc()}")
if writable and self._outgoing_buffer.qsize() > 0:
data = self._outgoing_buffer.get()
try:
self._socket.send(data)
except ConnectionResetError:
logging.info("Disconnected!")
break
if exceptional:
logging.info("Disconnected!")
break
@staticmethod
def _add_header(data, metadata):
return bytearray(f"{metadata:<{config.HEADER_SIZE}}", "utf-8") + bytearray(data)
@staticmethod
def _read_header(data):
return bytes(data[config.HEADER_SIZE:]), data[:config.HEADER_SIZE].decode("utf-8", errors="ignore")
@staticmethod
def get_sound_devices(*args):
return sd.query_devices(*args)
def connect(self, ip: str, port: int) -> bool:
"""
:param ip: IP/Hostname of the server.
:param port: Port of the server.
:return: Boolean if the client is successfully connected.
"""
self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Putting the socket here so if the
# socket closes, a new socket instance can be made on method call.
self._internet_io_flag.clear()
self._internet_thread = threading.Thread(target=Client._internet_io, args=(self,), daemon=True)
self._internet_thread.start()
self._is_connected = False
logging.info(f"Connecting to {ip}: {port}...")
try:
self._socket.connect((ip, int(port)))
self._socket.setblocking(False)
self._socket.settimeout(config.SOCKET_TIMEOUT)
self._is_connected = True
logging.info("Connected!")
except ConnectionRefusedError as error:
logging.info(error)
return self._is_connected
def disconnect(self):
if self._is_connected:
self._audio_handler.stop()
self._internet_io_flag.set()
self._socket.close()
self._is_connected = False
else:
logging.info("Not connected to a socket!")
def start_talking(self):
if self._is_connected:
self._audio_handler.start()
else:
logging.info("Not connected to a socket!")
def stop_talking(self):
self._audio_handler.stop()
def toggle_mute(self):
self._audio_handler._toggle_mute() | client.py | import logging
import queue
import socket
import threading
import traceback
import numpy
import pyflac
import select
import sounddevice as sd
import config
logging.basicConfig(
format='%(asctime)s.%(msecs)03d %(levelname)s:\t%(message)s',
level=logging.INFO,
datefmt='%H:%M:%S')
class _AudioHandler:
# _AudioHandler class takes care of the audio IO.
# No user-threads required! All handled by callback functions.
# TO-DO:
# - switch to UDP
# - add headers to packets (problem since the send and recv funcs decide what they like and send
# packets of varying sizes.)
def __init__(self, outgoing_buffer, incoming_buffer, audio_devices=None):
if audio_devices:
sd.default.device = audio_devices
self._stream = self._stream = sd.RawStream(samplerate=config.SAMPLE_RATE,
blocksize=config.PACKET_SIZE,
channels=config.CHANNELS,
dtype=numpy.int16,
callback=self._audio_callback)
self._encoder = pyflac.StreamEncoder(write_callback=self._encoder_callback, sample_rate=config.SAMPLE_RATE,
blocksize=config.PACKET_SIZE)
self._decoder = pyflac.StreamDecoder(write_callback=self._decoder_callback)
self._outgoing_buffer = outgoing_buffer
self._incoming_buffer = incoming_buffer
self._is_muted = False
def _audio_callback(self, in_data, out_data, *_) -> None:
if not self._is_muted:
self._encoder.process(numpy.frombuffer(in_data, dtype=numpy.int16))
if self._incoming_buffer.qsize() > 0:
data = self._incoming_buffer.get(block=False)
out_data[:] = data.tobytes()
elif self._incoming_buffer.qsize() == 0:
out_data[:] = bytes(config.PACKET_SIZE * 2)
def _encoder_callback(self, buffer, *_):
self._outgoing_buffer.put(buffer) # buffer is a built-in bytes object.
def _decoder_callback(self, data, *_):
self._incoming_buffer.put(data)
def _toggle_mute(self):
self._outgoing_buffer.queue.clear() # this is for when muting, some packets haven't returned yet and will play
# once un-muted from the buffer. Since I haven't implemented a UDP system where each packet is timestamped and
# reconstructed chronologically (hence dropping older packets and making this issue moot),
# this will have to do.
self._is_muted = False if self._is_muted else True
def start(self):
self._incoming_buffer.queue.clear()
self._outgoing_buffer.queue.clear()
self._stream.start()
def stop(self):
self._stream.stop()
class Client:
def __init__(self, default_audio_devices=None):
# Client class handles the internet IO, and passes the audio data to the AudioHandler class.
"""
:param default_audio_devices: Query by name or index, the audio devices to be used. Use get_sound_devices() method to list all audio devices.
Client class that handles audio and internet IO.
"""
self._is_muted = threading.Event()
self._outgoing_buffer = queue.Queue()
self._incoming_buffer = queue.Queue()
self._audio_handler = _AudioHandler(self._outgoing_buffer, self._incoming_buffer,
audio_devices=default_audio_devices)
self._socket = None
self._is_connected = False
self._internet_io_flag = threading.Event()
self._internet_thread = None
def _internet_io(self, ):
while not self._internet_io_flag.is_set():
try:
readable, writable, exceptional = select.select([self._socket], [self._socket], [self._socket])
except ValueError:
logging.info("Disconnect!")
break
if readable:
try:
data = self._socket.recv(config.PACKET_SIZE)
# data, header = self._read_header(packed_data)
self._audio_handler._decoder.process(data) # messy but since the callback audio func only runs
# whenever it has enough samples of audio to send, the audio needs to be processed by the time it
# does a callback.
except ConnectionResetError:
logging.error("Disconnected!")
break
except TimeoutError:
logging.error(f"Timed out! {traceback.format_exc()}")
if writable and self._outgoing_buffer.qsize() > 0:
data = self._outgoing_buffer.get()
try:
self._socket.send(data)
except ConnectionResetError:
logging.info("Disconnected!")
break
if exceptional:
logging.info("Disconnected!")
break
@staticmethod
def _add_header(data, metadata):
return bytearray(f"{metadata:<{config.HEADER_SIZE}}", "utf-8") + bytearray(data)
@staticmethod
def _read_header(data):
return bytes(data[config.HEADER_SIZE:]), data[:config.HEADER_SIZE].decode("utf-8", errors="ignore")
@staticmethod
def get_sound_devices(*args):
return sd.query_devices(*args)
def connect(self, ip: str, port: int) -> bool:
"""
:param ip: IP/Hostname of the server.
:param port: Port of the server.
:return: Boolean if the client is successfully connected.
"""
self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Putting the socket here so if the
# socket closes, a new socket instance can be made on method call.
self._internet_io_flag.clear()
self._internet_thread = threading.Thread(target=Client._internet_io, args=(self,), daemon=True)
self._internet_thread.start()
self._is_connected = False
logging.info(f"Connecting to {ip}: {port}...")
try:
self._socket.connect((ip, int(port)))
self._socket.setblocking(False)
self._socket.settimeout(config.SOCKET_TIMEOUT)
self._is_connected = True
logging.info("Connected!")
except ConnectionRefusedError as error:
logging.info(error)
return self._is_connected
def disconnect(self):
if self._is_connected:
self._audio_handler.stop()
self._internet_io_flag.set()
self._socket.close()
self._is_connected = False
else:
logging.info("Not connected to a socket!")
def start_talking(self):
if self._is_connected:
self._audio_handler.start()
else:
logging.info("Not connected to a socket!")
def stop_talking(self):
self._audio_handler.stop()
def toggle_mute(self):
self._audio_handler._toggle_mute() | 0.545528 | 0.057785 |
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers import Layer
from tensorflow.python.keras.utils import conv_utils
class ExtendRGB(Layer):
"""
Extend the RGB channels
Input:
(batch, ..., 3)
Output:
(batch, ..., k*6)
Usage:
```python
x = ExtendRGB(4)(x) # got (batch, ..., 24)
```
"""
def __init__(self, k, data_format=None, dilation_rate=1, trainable=False, **kwargs):
super(ExtendRGB, self).__init__(trainable=trainable, **kwargs)
self.k = k
self.data_format = data_format
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate')
if self.data_format == 'channels_First':
self.axis = 1
else:
self.axis = -1
def build(self, input_shape):
self.kernel = self._color_weight()
self._convolution_op = K.conv2d
self.built = True
def call(self, inputs, **kwargs):
assert inputs.shape[self.axis] != 3, f"Input Tensor must have 3 channels(RGB), but got {inputs.shape[self.axis]}"
x = self._convolution_op(
inputs,
self.kernel,
padding='same',
data_format=self.data_format,
dilation_rate=self.dilation_rate
)
return x
def _color_weight(self):
_weight = []
for i in range(3):
i_ = i + 1 if i + 1 <= 2 else 0
for j in range(self.k + 1):
_t = [0, 0, 0]
_t[i] = 1. / (1. + j / self.k)
_t[i_] = j / self.k / (1. + j / self.k)
_weight.append(_t)
for j in range(1, self.k):
_t = [0, 0, 0]
_t[i_] = 1. / (1. + (self.k - j) / self.k)
_t[i] = (self.k - j) / self.k / (1. + (self.k - j) / self.k)
_weight.append(_t)
_weight = K.variable(_weight)
_weight = K.transpose(_weight)
_weight = K.reshape(_weight, (1, 1, 3, 6 * self.k))
return _weight
def compute_output_shape(self, input_shape):
input_shape[self.axis] = 6 * self.k
return input_shape
def get_config(self):
config = {
'k': self.k,
}
base_config = super(ExtendRGB, self).get_config()
return dict(list(base_config.items()) + list(config.items())) | models/advance/extendrgb.py |
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers import Layer
from tensorflow.python.keras.utils import conv_utils
class ExtendRGB(Layer):
"""
Extend the RGB channels
Input:
(batch, ..., 3)
Output:
(batch, ..., k*6)
Usage:
```python
x = ExtendRGB(4)(x) # got (batch, ..., 24)
```
"""
def __init__(self, k, data_format=None, dilation_rate=1, trainable=False, **kwargs):
super(ExtendRGB, self).__init__(trainable=trainable, **kwargs)
self.k = k
self.data_format = data_format
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate')
if self.data_format == 'channels_First':
self.axis = 1
else:
self.axis = -1
def build(self, input_shape):
self.kernel = self._color_weight()
self._convolution_op = K.conv2d
self.built = True
def call(self, inputs, **kwargs):
assert inputs.shape[self.axis] != 3, f"Input Tensor must have 3 channels(RGB), but got {inputs.shape[self.axis]}"
x = self._convolution_op(
inputs,
self.kernel,
padding='same',
data_format=self.data_format,
dilation_rate=self.dilation_rate
)
return x
def _color_weight(self):
_weight = []
for i in range(3):
i_ = i + 1 if i + 1 <= 2 else 0
for j in range(self.k + 1):
_t = [0, 0, 0]
_t[i] = 1. / (1. + j / self.k)
_t[i_] = j / self.k / (1. + j / self.k)
_weight.append(_t)
for j in range(1, self.k):
_t = [0, 0, 0]
_t[i_] = 1. / (1. + (self.k - j) / self.k)
_t[i] = (self.k - j) / self.k / (1. + (self.k - j) / self.k)
_weight.append(_t)
_weight = K.variable(_weight)
_weight = K.transpose(_weight)
_weight = K.reshape(_weight, (1, 1, 3, 6 * self.k))
return _weight
def compute_output_shape(self, input_shape):
input_shape[self.axis] = 6 * self.k
return input_shape
def get_config(self):
config = {
'k': self.k,
}
base_config = super(ExtendRGB, self).get_config()
return dict(list(base_config.items()) + list(config.items())) | 0.928498 | 0.796451 |
"""Test class for the connection module."""
# Third Party Imports
import pytest
# RAMSTK Package Imports
from ramstk.analyses.milhdbk217f import connection
ATTRIBUTES = {
"category_id": 8,
"subcategory_id": 1,
"environment_active_id": 2,
"type_id": 2,
"specification_id": 1,
"n_circuit_planes": 3,
"contact_gauge": 20,
"current_operating": 0.005,
"n_active_pins": 15,
"n_cycles": 0.1,
"temperature_active": 40.0,
"insert_id": 2,
"n_wave_soldered": 45,
"n_hand_soldered": 4,
"lambda_b": 0.0,
"piQ": 1.0,
"piE": 1.0,
"piC": 0.0,
"piK": 0.0,
"piP": 0.0,
"hazard_rate_active": 0.0,
}
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize(
"subcategory_id",
[1, 2, 3, 4, 5],
)
@pytest.mark.parametrize(
"environment_active_id",
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
)
@pytest.mark.parametrize("type_id", [1, 2])
def test_get_part_count_lambda_b(subcategory_id, environment_active_id, type_id):
"""get_part_count_lambda_b() should return a float value for the base hazard rates on success."""
_lambda_b = connection.get_part_count_lambda_b(
subcategory_id=subcategory_id,
environment_active_id=environment_active_id,
type_id=type_id,
)
assert isinstance(_lambda_b, float)
# Verify a sampling of base hazard rates.
@pytest.mark.unit
@pytest.mark.calculation
def test_get_part_count_lambda_b_no_subcategory():
"""get_part_count_lambda_b() should raise a KeyError when passed an unknown subcategory ID."""
with pytest.raises(KeyError):
_lambda_b = connection.get_part_count_lambda_b(
subcategory_id=88, environment_active_id=12, type_id=2
)
@pytest.mark.unit
@pytest.mark.calculation
def test_get_part_count_lambda_b_no_environment():
"""get_part_count_lambda_b_list() should raise an IndexError when passed an unknown active environment ID."""
with pytest.raises(IndexError):
_lambda_b = connection.get_part_count_lambda_b(
subcategory_id=3, environment_active_id=22, type_id=-1
)
@pytest.mark.unit
@pytest.mark.calculation
def test_get_part_count_lambda_b_no_type():
"""get_part_count_lambda_b() should raise a KeyError when passed an unknown type ID."""
with pytest.raises(KeyError):
_lambda_b = connection.get_part_count_lambda_b(
subcategory_id=1, environment_active_id=2, type_id=22
)
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_part_count():
"""calculate_part_count() should return a list of base hazard rates on success."""
_lst_lambda_b = connection.calculate_part_count(**ATTRIBUTES)
assert isinstance(_lst_lambda_b, float)
assert _lst_lambda_b == 0.015
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("contact_gauge", [12, 16, 20, 22, 26])
def test_calculate_insert_temperature(contact_gauge):
"""calculate_insert_temperature() should return a float value for the temperature rise on success."""
_dic_factors = {12: 0.1, 16: 0.274, 20: 0.64, 22: 0.989, 26: 2.1}
_temperature_rise = connection.calculate_insert_temperature(contact_gauge, 0.05)
assert isinstance(_temperature_rise, float)
assert _temperature_rise == _dic_factors[contact_gauge] * 0.05 ** 1.85
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_insert_temperature_no_gauge():
"""calculate_insert_temperature() should raise a KeyError when passed an unknown contact gauge."""
with pytest.raises(KeyError):
_temperature_rise = connection.calculate_insert_temperature(0, 0.05)
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_insert_temperature_string_current():
"""calculate_insert_temperature() should raise a TypeError when passed a string for the operating current."""
with pytest.raises(TypeError):
_temperature_rise = connection.calculate_insert_temperature(12, "0.05")
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_active_pins_factor():
"""calculate_active_pins_factor() should return a float value for piP on success."""
_pi_p = connection.calculate_active_pins_factor(15)
assert isinstance(_pi_p, float)
assert _pi_p == pytest.approx(3.2787411)
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("n_circuit_planes", [1, 2])
def test_calculate_complexity_factor_less_than_three_planes(n_circuit_planes):
"""calculate_complexity_factor() should return 1.0 for piC when there are less than three planes in the PCB/PWA."""
_pi_c = connection.calculate_complexity_factor(n_circuit_planes)
assert isinstance(_pi_c, float)
assert _pi_c == 1.0
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("n_cycles", [0.01, 0.1, 1, 10, 100])
def test_get_mate_unmate_factor(n_cycles):
"""get_mate_unmate_factor() should return a float value for piK on success."""
_pi_k = connection.get_mate_unmate_factor(n_cycles)
assert isinstance(_pi_k, float)
if n_cycles == 0.01:
assert _pi_k == 1.0
elif n_cycles == 0.1:
assert _pi_k == 1.5
elif n_cycles == 1:
assert _pi_k == 2.0
elif n_cycles == 10:
assert _pi_k == 3.0
elif n_cycles == 100:
assert _pi_k == 4.0
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("subcategory_id", [1, 3, 5])
def test_calculate_part_stress_lambda_b(subcategory_id):
"""calculate_part_stress_lamba_b() should return a float value for the part stress base hazard rate on success."""
if subcategory_id == 1:
_factor_key = 2
else:
_factor_key = 5
_lambda_b = connection.calculate_part_stress_lambda_b(
subcategory_id, 4, 325, _factor_key
)
assert isinstance(_lambda_b, float)
if subcategory_id == 1:
assert _lambda_b == pytest.approx(0.00097886687)
elif subcategory_id == 3:
assert _lambda_b == 0.00042
elif subcategory_id == 5:
assert _lambda_b == 5e-05
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_part_stress_lambda_no_type():
"""calculate_part_stress_lamba_b() should raise an IndexError when passed an unknown type ID."""
with pytest.raises(IndexError):
_lambda_b = connection.calculate_part_stress_lambda_b(4, 26, 325, 5)
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_part_stress_lambda_zero_contact_temperature():
"""calculate_part_stress_lamba_b() should raise a ZeroDivisionError when passed a contact temperature=0.0."""
with pytest.raises(ZeroDivisionError):
_lambda_b = connection.calculate_part_stress_lambda_b(1, 4, 0.0, 2)
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("subcategory_id", [1, 3, 4, 5])
def test_calculate_part_stress(subcategory_id):
"""calculate_part_stress() should return a dict of updated attributes on success."""
ATTRIBUTES["subcategory_id"] = subcategory_id
_attributes = connection.calculate_part_stress(**ATTRIBUTES)
assert isinstance(_attributes, dict)
if subcategory_id == 1:
assert _attributes["lambda_b"] == pytest.approx(0.00073120394)
assert _attributes["piK"] == 1.5
assert _attributes["piP"] == pytest.approx(3.27874110)
assert _attributes["hazard_rate_active"] == pytest.approx(0.0035961426)
elif subcategory_id == 3:
assert _attributes["lambda_b"] == 0.00042
assert _attributes["piP"] == pytest.approx(3.27874110)
assert _attributes["hazard_rate_active"] == pytest.approx(0.0013770713)
elif subcategory_id == 4:
assert _attributes["lambda_b"] == 0.00026
assert _attributes["piC"] == pytest.approx(1.29867281)
assert _attributes["hazard_rate_active"] == pytest.approx(0.030065092)
elif subcategory_id == 5:
assert _attributes["lambda_b"] == 0.00014
assert _attributes["hazard_rate_active"] == 0.00014 | tests/analyses/milhdbk217f/models/test_connection.py | """Test class for the connection module."""
# Third Party Imports
import pytest
# RAMSTK Package Imports
from ramstk.analyses.milhdbk217f import connection
ATTRIBUTES = {
"category_id": 8,
"subcategory_id": 1,
"environment_active_id": 2,
"type_id": 2,
"specification_id": 1,
"n_circuit_planes": 3,
"contact_gauge": 20,
"current_operating": 0.005,
"n_active_pins": 15,
"n_cycles": 0.1,
"temperature_active": 40.0,
"insert_id": 2,
"n_wave_soldered": 45,
"n_hand_soldered": 4,
"lambda_b": 0.0,
"piQ": 1.0,
"piE": 1.0,
"piC": 0.0,
"piK": 0.0,
"piP": 0.0,
"hazard_rate_active": 0.0,
}
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize(
"subcategory_id",
[1, 2, 3, 4, 5],
)
@pytest.mark.parametrize(
"environment_active_id",
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
)
@pytest.mark.parametrize("type_id", [1, 2])
def test_get_part_count_lambda_b(subcategory_id, environment_active_id, type_id):
"""get_part_count_lambda_b() should return a float value for the base hazard rates on success."""
_lambda_b = connection.get_part_count_lambda_b(
subcategory_id=subcategory_id,
environment_active_id=environment_active_id,
type_id=type_id,
)
assert isinstance(_lambda_b, float)
# Verify a sampling of base hazard rates.
@pytest.mark.unit
@pytest.mark.calculation
def test_get_part_count_lambda_b_no_subcategory():
"""get_part_count_lambda_b() should raise a KeyError when passed an unknown subcategory ID."""
with pytest.raises(KeyError):
_lambda_b = connection.get_part_count_lambda_b(
subcategory_id=88, environment_active_id=12, type_id=2
)
@pytest.mark.unit
@pytest.mark.calculation
def test_get_part_count_lambda_b_no_environment():
"""get_part_count_lambda_b_list() should raise an IndexError when passed an unknown active environment ID."""
with pytest.raises(IndexError):
_lambda_b = connection.get_part_count_lambda_b(
subcategory_id=3, environment_active_id=22, type_id=-1
)
@pytest.mark.unit
@pytest.mark.calculation
def test_get_part_count_lambda_b_no_type():
"""get_part_count_lambda_b() should raise a KeyError when passed an unknown type ID."""
with pytest.raises(KeyError):
_lambda_b = connection.get_part_count_lambda_b(
subcategory_id=1, environment_active_id=2, type_id=22
)
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_part_count():
"""calculate_part_count() should return a list of base hazard rates on success."""
_lst_lambda_b = connection.calculate_part_count(**ATTRIBUTES)
assert isinstance(_lst_lambda_b, float)
assert _lst_lambda_b == 0.015
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("contact_gauge", [12, 16, 20, 22, 26])
def test_calculate_insert_temperature(contact_gauge):
"""calculate_insert_temperature() should return a float value for the temperature rise on success."""
_dic_factors = {12: 0.1, 16: 0.274, 20: 0.64, 22: 0.989, 26: 2.1}
_temperature_rise = connection.calculate_insert_temperature(contact_gauge, 0.05)
assert isinstance(_temperature_rise, float)
assert _temperature_rise == _dic_factors[contact_gauge] * 0.05 ** 1.85
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_insert_temperature_no_gauge():
"""calculate_insert_temperature() should raise a KeyError when passed an unknown contact gauge."""
with pytest.raises(KeyError):
_temperature_rise = connection.calculate_insert_temperature(0, 0.05)
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_insert_temperature_string_current():
"""calculate_insert_temperature() should raise a TypeError when passed a string for the operating current."""
with pytest.raises(TypeError):
_temperature_rise = connection.calculate_insert_temperature(12, "0.05")
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_active_pins_factor():
"""calculate_active_pins_factor() should return a float value for piP on success."""
_pi_p = connection.calculate_active_pins_factor(15)
assert isinstance(_pi_p, float)
assert _pi_p == pytest.approx(3.2787411)
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("n_circuit_planes", [1, 2])
def test_calculate_complexity_factor_less_than_three_planes(n_circuit_planes):
"""calculate_complexity_factor() should return 1.0 for piC when there are less than three planes in the PCB/PWA."""
_pi_c = connection.calculate_complexity_factor(n_circuit_planes)
assert isinstance(_pi_c, float)
assert _pi_c == 1.0
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("n_cycles", [0.01, 0.1, 1, 10, 100])
def test_get_mate_unmate_factor(n_cycles):
"""get_mate_unmate_factor() should return a float value for piK on success."""
_pi_k = connection.get_mate_unmate_factor(n_cycles)
assert isinstance(_pi_k, float)
if n_cycles == 0.01:
assert _pi_k == 1.0
elif n_cycles == 0.1:
assert _pi_k == 1.5
elif n_cycles == 1:
assert _pi_k == 2.0
elif n_cycles == 10:
assert _pi_k == 3.0
elif n_cycles == 100:
assert _pi_k == 4.0
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("subcategory_id", [1, 3, 5])
def test_calculate_part_stress_lambda_b(subcategory_id):
"""calculate_part_stress_lamba_b() should return a float value for the part stress base hazard rate on success."""
if subcategory_id == 1:
_factor_key = 2
else:
_factor_key = 5
_lambda_b = connection.calculate_part_stress_lambda_b(
subcategory_id, 4, 325, _factor_key
)
assert isinstance(_lambda_b, float)
if subcategory_id == 1:
assert _lambda_b == pytest.approx(0.00097886687)
elif subcategory_id == 3:
assert _lambda_b == 0.00042
elif subcategory_id == 5:
assert _lambda_b == 5e-05
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_part_stress_lambda_no_type():
"""calculate_part_stress_lamba_b() should raise an IndexError when passed an unknown type ID."""
with pytest.raises(IndexError):
_lambda_b = connection.calculate_part_stress_lambda_b(4, 26, 325, 5)
@pytest.mark.unit
@pytest.mark.calculation
def test_calculate_part_stress_lambda_zero_contact_temperature():
"""calculate_part_stress_lamba_b() should raise a ZeroDivisionError when passed a contact temperature=0.0."""
with pytest.raises(ZeroDivisionError):
_lambda_b = connection.calculate_part_stress_lambda_b(1, 4, 0.0, 2)
@pytest.mark.unit
@pytest.mark.calculation
@pytest.mark.parametrize("subcategory_id", [1, 3, 4, 5])
def test_calculate_part_stress(subcategory_id):
"""calculate_part_stress() should return a dict of updated attributes on success."""
ATTRIBUTES["subcategory_id"] = subcategory_id
_attributes = connection.calculate_part_stress(**ATTRIBUTES)
assert isinstance(_attributes, dict)
if subcategory_id == 1:
assert _attributes["lambda_b"] == pytest.approx(0.00073120394)
assert _attributes["piK"] == 1.5
assert _attributes["piP"] == pytest.approx(3.27874110)
assert _attributes["hazard_rate_active"] == pytest.approx(0.0035961426)
elif subcategory_id == 3:
assert _attributes["lambda_b"] == 0.00042
assert _attributes["piP"] == pytest.approx(3.27874110)
assert _attributes["hazard_rate_active"] == pytest.approx(0.0013770713)
elif subcategory_id == 4:
assert _attributes["lambda_b"] == 0.00026
assert _attributes["piC"] == pytest.approx(1.29867281)
assert _attributes["hazard_rate_active"] == pytest.approx(0.030065092)
elif subcategory_id == 5:
assert _attributes["lambda_b"] == 0.00014
assert _attributes["hazard_rate_active"] == 0.00014 | 0.857082 | 0.52902 |
#Psudocode: Import Values, Check that thsoe values are valid, Generate the trendlines, plot them.
#The trendline generator must be smart and be able to create different types of trendlines, and their equations.
import math
import numpy as np
import matplotlib.pyplot as plt
def cfit(x,y,filename,flag,fitType):
"""
'x' is the x-points
'y' is the y-points
'filename' is an external file loaction that can be loaded.
'flag' determines wether to use the points given (1) or load from an external file (2).
'fitType' is a string for the curve fit. It can be one of the following:
'line' - straight line curve fit
'log' - logrithmic curve fit
'power' - power law curve fit
'exp' - exponential curve fit
'second' - second order polynomial curve fit
'third' - third order polynomial curve fit
'fourth' - fourth order polynomial curve fit
'all' - all the curve fits listed above
'best' - first and second options for best curve fits
'temp' is a temporary variable that does various things throught the program, being constantly overwritten.
Example Input: cfit(x,y,'data.txt',2,'all')
Example Input: cfit(np.array([0.5,0.9,1.7,2.4]),np.array([8.7,9.3,10.6,12.1]),'dummy',1,'line')
"""
temp = check(x,y,filename,flag,fitType)
x,y,fitType = temp[0],temp[1],temp[2]
if temp[3] == 1:
if (fitType == 'all') or (fitType =='best'):
temp = [(lineGenerator(x,y,'line'))]
temp.append(lineGenerator(x,y,'log'))
temp.append(lineGenerator(x,y,'power'))
temp.append(lineGenerator(x,y,'exp'))
temp.append(lineGenerator(x,y,'second'))
temp.append(lineGenerator(x,y,'third'))
temp.append(lineGenerator(x,y,'fourth'))
if fitType == 'best':
temp = optimizer(np.transpose(np.array(temp)).tolist())
else:
temp = np.transpose(np.array(temp)).tolist()
temp[1] = (np.array(temp[1]).flatten().tolist())
else:
temp = lineGenerator(x,y,fitType)
showplots(x,y,temp,fitType)
eqn = temp[1]
r2 = temp[0][1]
return eqn,r2
else: print('failed the check') #program ends
#The Black Box
def check(x,y,filename,flag,fitType):
"""This checks that the inputs will work.
'x' is the flagged x list to use.
'y' is the flagged y list to use.
'n' is wether the other checks failed or not.
Example Input: optimizer(np.array([0.5,0.9,1.7,2.4]),np.array([8.7,9.3,10.6,12.1]),2,0)
"""
n = 1
if flag == 2:
data = np.loadtxt(filename)
x = data[:,0]
y = data[:,1]
if len(x) > len(y):
print('Your x-list is larger than your y-list.')
n = 0
elif len(y) > len(x):
print('Your y-list is larger than your x-list.')
n = 0
if type(x) != np.ndarray:
print("Your x-list is not an np.array. I'll fix that for you")
x = np.array(x)
if type(y) != np.ndarray:
print("Your y-list is not an np.array. I'll fix that for you")
y = np.array(x)
fitType = fitType.lower() #Gets rid of any accidental caps for you
if fitType not in ('line','log','power','exp','second','third','fourth','all','best'):
print('I do not understand the fitType you gave me. Please make sure you spelled it correctly.')
n = 0
return [x,y,fitType,n]
def lineGenerator(x,y,fitType):
"""This determines which type of trendline(s) should be computed."""
if (fitType == 'line') or (fitType == 'log') or (fitType == 'power') or (fitType == 'exp'):
solved = trendline(x,y,fitType)
eqn = equation(solved,fitType)
elif fitType == 'second':
solved = trendline(x,y,2)
eqn = equation(solved,fitType)
elif fitType == 'third':
solved = trendline(x,y,3)
eqn = equation(solved,fitType)
elif fitType == 'fourth':
solved = trendline(x,y,4)
eqn = equation(solved,fitType)
return [solved,[eqn]]
def optimizer(myList):
"""Finds the best two fits for the trendlines"""
eqns = np.transpose(np.array(myList[1])).tolist()
myList = np.transpose(np.array(myList[0])).tolist()
solvedMax,r2Max,eqnMax = [],[],[]
for i in range(2):
n = np.array(myList[1]).flatten().argmax()
solvedMax.append(myList[0][i])
r2Max.append(myList[1][i])
eqnMax.append(eqns[0][i])
myList[1][n] = [0]
myList[0][n] = [0]
return [[solvedMax,r2Max],eqnMax]
def trendline(x,y,fitType):
"""This creates various types of trendlines from lists of data in the form of numpy arrays.
It returns the a & b values as a list, as well as the r2 value.
Example Input: trendline(np.array([0.5,0.9,1.7,2.4]),np.array([8.7,9.3,10.6,12.1]),'line')
"""
if type(fitType)==str:
n = len(x)
if fitType == 'line':
sumx = np.sum(x)
sumy = np.sum(y)
sumxy = np.sum(x*y)
sumx2 = np.sum(x**2)
sumy2 = np.sum(y**2)
elif fitType == 'power':
sumx = np.sum(np.log(x))
sumy = np.sum(np.log(y))
sumxy = np.sum(np.log(x)*np.log(y))
sumx2 = np.sum(np.log(x)**2)
sumy2 = np.sum(np.log(y)**2)
elif fitType == 'exp':
sumx = np.sum(x)
sumy = np.sum(np.log(y))
sumxy = np.sum(x*np.log(y))
sumx2 = np.sum(x**2)
sumy2 = np.sum(np.log(y)**2)
elif fitType == 'log':
sumx = np.sum(np.log(x))
sumy = np.sum(y)
sumxy = np.sum(np.log(x)*y)
sumx2 = np.sum(np.log(x)**2)
sumy2 = np.sum(y**2)
A = np.array([[n,sumx],[sumx,sumx2]])
b = np.array([[sumy],[sumxy]])
solved = np.linalg.solve(A,b)
r2 = (solved[0,0]*sumy+solved[1,0]*sumxy-1/n*sumy**2)/(sumy2-1/n*sumy**2)
if fitType == 'power': solved[0,0] = np.exp(solved[0,0])
elif fitType == 'exp': solved[0,0] = np.exp(solved[0,0])
else:
#The fitType is the order that the polynomial is.
sumxList,sumyList,b,r2 = [],[],[],0
n = len(x)
A = [[n]]
for i in range(fitType*2): #Create a list that ranges from x^1 to x^n
sumxList.append(np.sum(x**(i+1)))
sumyList.append(np.sum(x**(i)*y))
for i in range(fitType): #Initialize the A
A.append([sumxList[i]])
for j in range(fitType+1):#Set up the A and b
for i in range(fitType):
A[j].append(sumxList[i+j])
b.append(sumyList[j])
A = np.array(A)
b = np.array(b).transpose()
solved = np.linalg.solve(A,b)
for i in range(fitType+1):
r2 += solved[i]*sumyList[i]
r2 = (r2-sumyList[0]**2/n)/(np.sum(y**2)-1/n*sumyList[0]**2)
return [solved.tolist(),[r2]]
def equation(solved,fitType):
"""This creates an equation for the trendline
Example Input: equation([[[7.7307334109429595], [1.7776484284051208]], 0.99394696088416035],'line')"""
if fitType == 'line': eqn = ('y='+str(solved[0][0][0])+'+'+str(solved[0][1][0])+'*x')
elif fitType == 'power': eqn = ('y='+str(solved[0][0][0])+'*x**('+str(solved[0][1][0])+')')
elif fitType == 'exp': eqn = ('y='+str(solved[0][0][0])+'*np.exp('+str(solved[0][1][0])+'*x)')
elif fitType == 'log': eqn = ('y='+str(solved[0][0][0])+'+'+str(solved[0][1][0])+'*np.log(x)')
else:
if fitType == 'second': n = 2
elif fitType == 'third': n = 3
elif fitType == 'fourth': n = 4
eqn = 'y='+str(solved[0][0])+'+'+str(solved[0][1])+'*x'
for i in range(n-1): eqn += '+'+str(solved[0][i+2])+'*x**'+str(i+2)
return eqn
def showplots(xlist,ylist,answer,fitType):
"""This creates a dynamic plot(s) for the trendlines.
Example Input: optimizer(np.array([0.5,0.9,1.7,2.4]),np.array([8.7,9.3,10.6,12.1]),2,0)
"""
myLegends = ['given']
h = 50 #step scalar
x = np.arange(xlist.min(),xlist.max(),(xlist.max()-xlist.min())/h)
plt.figure(1,figsize=(8,4))
plt.plot(xlist,ylist,'ko')
for j in range(len(answer[1])):
if j<7:
if j%2 == 0:
if j == 0: lnStl = 'Purple'
elif j == 2: lnStl = 'OrangeRed'
elif j == 4: lnStl = 'y-'
elif j == 6: lnStl = 'b-'
else:
if j == 1: lnStl = 'r-'
elif j == 3: lnStl = 'Orange'
elif j == 5: lnStl = 'g-'
else: lnStl = 'w--'
if fitType == 'best':
plt.figure(2,figsize=(16,8))
plt.subplot(2,1,j+1)
if 'e**' in answer[1][j]: myLegends.append('exp')
elif 'log' in answer[1][j]: myLegends.append('log')
elif '**4' in answer[1][j]: myLegends.append('fourth')
elif '**3' in answer[1][j]: myLegends.append('third')
elif '**2' in answer[1][j]: myLegends.append('second')
elif '**' in answer[1][j]: myLegends.append('power')
else: myLegends.append('line')
elif fitType == 'all':
plt.figure(2,figsize=(16,8))
plt.subplot(3,3,j+1)
if j == 0: myLegends.append('line')
elif j == 1: myLegends.append('log')
elif j == 2: myLegends.append('power')
elif j == 3: myLegends.append('exp')
elif j == 4: myLegends.append('second')
elif j == 5: myLegends.append('third')
elif j == 6: myLegends.append('fourth')
else: myLegends.append(fitType)
plt.plot(xlist,ylist,'ko')
plt.plot(x,eval(answer[1][j].strip('y=')),lnStl)
plt.figure(1)
plt.plot(x,eval(answer[1][j].strip('y=')),lnStl)
plt.figure(1)
plt.legend(myLegends)
if fitType == 'best':
plt.figure(2)
for j in range(len(answer[1])):
plt.subplot(2,1,j+1)
plt.legend(['given',myLegends[j+1]])
elif fitType == 'all':
plt.figure(2)
for j in range(len(answer[1])):
plt.subplot(3,3,j+1)
plt.legend(['given',myLegends[j+1]])
if fitType in ('all','best'):
plt.figure(1).canvas.set_window_title('All Lines')
plt.figure(2).canvas.set_window_title('Individual Lines')
else: plt.figure(1).canvas.set_window_title('Curve Fit')
plt.show() | CurveFitting/Curve_Fit.py |
#Psudocode: Import Values, Check that thsoe values are valid, Generate the trendlines, plot them.
#The trendline generator must be smart and be able to create different types of trendlines, and their equations.
import math
import numpy as np
import matplotlib.pyplot as plt
def cfit(x,y,filename,flag,fitType):
"""
'x' is the x-points
'y' is the y-points
'filename' is an external file loaction that can be loaded.
'flag' determines wether to use the points given (1) or load from an external file (2).
'fitType' is a string for the curve fit. It can be one of the following:
'line' - straight line curve fit
'log' - logrithmic curve fit
'power' - power law curve fit
'exp' - exponential curve fit
'second' - second order polynomial curve fit
'third' - third order polynomial curve fit
'fourth' - fourth order polynomial curve fit
'all' - all the curve fits listed above
'best' - first and second options for best curve fits
'temp' is a temporary variable that does various things throught the program, being constantly overwritten.
Example Input: cfit(x,y,'data.txt',2,'all')
Example Input: cfit(np.array([0.5,0.9,1.7,2.4]),np.array([8.7,9.3,10.6,12.1]),'dummy',1,'line')
"""
temp = check(x,y,filename,flag,fitType)
x,y,fitType = temp[0],temp[1],temp[2]
if temp[3] == 1:
if (fitType == 'all') or (fitType =='best'):
temp = [(lineGenerator(x,y,'line'))]
temp.append(lineGenerator(x,y,'log'))
temp.append(lineGenerator(x,y,'power'))
temp.append(lineGenerator(x,y,'exp'))
temp.append(lineGenerator(x,y,'second'))
temp.append(lineGenerator(x,y,'third'))
temp.append(lineGenerator(x,y,'fourth'))
if fitType == 'best':
temp = optimizer(np.transpose(np.array(temp)).tolist())
else:
temp = np.transpose(np.array(temp)).tolist()
temp[1] = (np.array(temp[1]).flatten().tolist())
else:
temp = lineGenerator(x,y,fitType)
showplots(x,y,temp,fitType)
eqn = temp[1]
r2 = temp[0][1]
return eqn,r2
else: print('failed the check') #program ends
#The Black Box
def check(x,y,filename,flag,fitType):
"""This checks that the inputs will work.
'x' is the flagged x list to use.
'y' is the flagged y list to use.
'n' is wether the other checks failed or not.
Example Input: optimizer(np.array([0.5,0.9,1.7,2.4]),np.array([8.7,9.3,10.6,12.1]),2,0)
"""
n = 1
if flag == 2:
data = np.loadtxt(filename)
x = data[:,0]
y = data[:,1]
if len(x) > len(y):
print('Your x-list is larger than your y-list.')
n = 0
elif len(y) > len(x):
print('Your y-list is larger than your x-list.')
n = 0
if type(x) != np.ndarray:
print("Your x-list is not an np.array. I'll fix that for you")
x = np.array(x)
if type(y) != np.ndarray:
print("Your y-list is not an np.array. I'll fix that for you")
y = np.array(x)
fitType = fitType.lower() #Gets rid of any accidental caps for you
if fitType not in ('line','log','power','exp','second','third','fourth','all','best'):
print('I do not understand the fitType you gave me. Please make sure you spelled it correctly.')
n = 0
return [x,y,fitType,n]
def lineGenerator(x,y,fitType):
"""This determines which type of trendline(s) should be computed."""
if (fitType == 'line') or (fitType == 'log') or (fitType == 'power') or (fitType == 'exp'):
solved = trendline(x,y,fitType)
eqn = equation(solved,fitType)
elif fitType == 'second':
solved = trendline(x,y,2)
eqn = equation(solved,fitType)
elif fitType == 'third':
solved = trendline(x,y,3)
eqn = equation(solved,fitType)
elif fitType == 'fourth':
solved = trendline(x,y,4)
eqn = equation(solved,fitType)
return [solved,[eqn]]
def optimizer(myList):
"""Finds the best two fits for the trendlines"""
eqns = np.transpose(np.array(myList[1])).tolist()
myList = np.transpose(np.array(myList[0])).tolist()
solvedMax,r2Max,eqnMax = [],[],[]
for i in range(2):
n = np.array(myList[1]).flatten().argmax()
solvedMax.append(myList[0][i])
r2Max.append(myList[1][i])
eqnMax.append(eqns[0][i])
myList[1][n] = [0]
myList[0][n] = [0]
return [[solvedMax,r2Max],eqnMax]
def trendline(x,y,fitType):
"""This creates various types of trendlines from lists of data in the form of numpy arrays.
It returns the a & b values as a list, as well as the r2 value.
Example Input: trendline(np.array([0.5,0.9,1.7,2.4]),np.array([8.7,9.3,10.6,12.1]),'line')
"""
if type(fitType)==str:
n = len(x)
if fitType == 'line':
sumx = np.sum(x)
sumy = np.sum(y)
sumxy = np.sum(x*y)
sumx2 = np.sum(x**2)
sumy2 = np.sum(y**2)
elif fitType == 'power':
sumx = np.sum(np.log(x))
sumy = np.sum(np.log(y))
sumxy = np.sum(np.log(x)*np.log(y))
sumx2 = np.sum(np.log(x)**2)
sumy2 = np.sum(np.log(y)**2)
elif fitType == 'exp':
sumx = np.sum(x)
sumy = np.sum(np.log(y))
sumxy = np.sum(x*np.log(y))
sumx2 = np.sum(x**2)
sumy2 = np.sum(np.log(y)**2)
elif fitType == 'log':
sumx = np.sum(np.log(x))
sumy = np.sum(y)
sumxy = np.sum(np.log(x)*y)
sumx2 = np.sum(np.log(x)**2)
sumy2 = np.sum(y**2)
A = np.array([[n,sumx],[sumx,sumx2]])
b = np.array([[sumy],[sumxy]])
solved = np.linalg.solve(A,b)
r2 = (solved[0,0]*sumy+solved[1,0]*sumxy-1/n*sumy**2)/(sumy2-1/n*sumy**2)
if fitType == 'power': solved[0,0] = np.exp(solved[0,0])
elif fitType == 'exp': solved[0,0] = np.exp(solved[0,0])
else:
#The fitType is the order that the polynomial is.
sumxList,sumyList,b,r2 = [],[],[],0
n = len(x)
A = [[n]]
for i in range(fitType*2): #Create a list that ranges from x^1 to x^n
sumxList.append(np.sum(x**(i+1)))
sumyList.append(np.sum(x**(i)*y))
for i in range(fitType): #Initialize the A
A.append([sumxList[i]])
for j in range(fitType+1):#Set up the A and b
for i in range(fitType):
A[j].append(sumxList[i+j])
b.append(sumyList[j])
A = np.array(A)
b = np.array(b).transpose()
solved = np.linalg.solve(A,b)
for i in range(fitType+1):
r2 += solved[i]*sumyList[i]
r2 = (r2-sumyList[0]**2/n)/(np.sum(y**2)-1/n*sumyList[0]**2)
return [solved.tolist(),[r2]]
def equation(solved,fitType):
"""This creates an equation for the trendline
Example Input: equation([[[7.7307334109429595], [1.7776484284051208]], 0.99394696088416035],'line')"""
if fitType == 'line': eqn = ('y='+str(solved[0][0][0])+'+'+str(solved[0][1][0])+'*x')
elif fitType == 'power': eqn = ('y='+str(solved[0][0][0])+'*x**('+str(solved[0][1][0])+')')
elif fitType == 'exp': eqn = ('y='+str(solved[0][0][0])+'*np.exp('+str(solved[0][1][0])+'*x)')
elif fitType == 'log': eqn = ('y='+str(solved[0][0][0])+'+'+str(solved[0][1][0])+'*np.log(x)')
else:
if fitType == 'second': n = 2
elif fitType == 'third': n = 3
elif fitType == 'fourth': n = 4
eqn = 'y='+str(solved[0][0])+'+'+str(solved[0][1])+'*x'
for i in range(n-1): eqn += '+'+str(solved[0][i+2])+'*x**'+str(i+2)
return eqn
def showplots(xlist,ylist,answer,fitType):
"""This creates a dynamic plot(s) for the trendlines.
Example Input: optimizer(np.array([0.5,0.9,1.7,2.4]),np.array([8.7,9.3,10.6,12.1]),2,0)
"""
myLegends = ['given']
h = 50 #step scalar
x = np.arange(xlist.min(),xlist.max(),(xlist.max()-xlist.min())/h)
plt.figure(1,figsize=(8,4))
plt.plot(xlist,ylist,'ko')
for j in range(len(answer[1])):
if j<7:
if j%2 == 0:
if j == 0: lnStl = 'Purple'
elif j == 2: lnStl = 'OrangeRed'
elif j == 4: lnStl = 'y-'
elif j == 6: lnStl = 'b-'
else:
if j == 1: lnStl = 'r-'
elif j == 3: lnStl = 'Orange'
elif j == 5: lnStl = 'g-'
else: lnStl = 'w--'
if fitType == 'best':
plt.figure(2,figsize=(16,8))
plt.subplot(2,1,j+1)
if 'e**' in answer[1][j]: myLegends.append('exp')
elif 'log' in answer[1][j]: myLegends.append('log')
elif '**4' in answer[1][j]: myLegends.append('fourth')
elif '**3' in answer[1][j]: myLegends.append('third')
elif '**2' in answer[1][j]: myLegends.append('second')
elif '**' in answer[1][j]: myLegends.append('power')
else: myLegends.append('line')
elif fitType == 'all':
plt.figure(2,figsize=(16,8))
plt.subplot(3,3,j+1)
if j == 0: myLegends.append('line')
elif j == 1: myLegends.append('log')
elif j == 2: myLegends.append('power')
elif j == 3: myLegends.append('exp')
elif j == 4: myLegends.append('second')
elif j == 5: myLegends.append('third')
elif j == 6: myLegends.append('fourth')
else: myLegends.append(fitType)
plt.plot(xlist,ylist,'ko')
plt.plot(x,eval(answer[1][j].strip('y=')),lnStl)
plt.figure(1)
plt.plot(x,eval(answer[1][j].strip('y=')),lnStl)
plt.figure(1)
plt.legend(myLegends)
if fitType == 'best':
plt.figure(2)
for j in range(len(answer[1])):
plt.subplot(2,1,j+1)
plt.legend(['given',myLegends[j+1]])
elif fitType == 'all':
plt.figure(2)
for j in range(len(answer[1])):
plt.subplot(3,3,j+1)
plt.legend(['given',myLegends[j+1]])
if fitType in ('all','best'):
plt.figure(1).canvas.set_window_title('All Lines')
plt.figure(2).canvas.set_window_title('Individual Lines')
else: plt.figure(1).canvas.set_window_title('Curve Fit')
plt.show() | 0.58948 | 0.687603 |
from . import deepclustering_loss, pit_loss, \
l41_loss, pit_l41_loss,\
deepclustering_L1_loss, dist2mean_rat_loss,\
dist2mean_rat_squared_loss, intravar2centervar_rat_loss,\
dist2mean_rat_fracbins_loss, crossentropy_multi_loss,\
dist2mean_closest_rat_loss,direct_loss, dist2mean_epsilon_closest_rat_loss,\
dc_pit_loss, \
deepclustering_full_crossentropy_multi_reshapedlogits_avtime_loss,\
deepclustering_2and3spk_loss, deepclustering_flat_loss, deepclusteringnoise_loss, \
deepattractornetnoise_hard_loss, deepattractornetnoise_hard_loss, deepattractornetnoise_soft_loss, \
deepattractornet_loss, noisefilter_loss, deepattractornet_noisefilter_loss, pit_noise_loss, dummy_loss, \
anchor_deepattractornet_softmax_loss, deepclustering_diar_loss, crossentropy_loss, ms_loss
def factory(loss_type):
"""gets a Loss computer class
Args:
loss_type: the loss type
Returns: a Loss computer class
"""
if loss_type == 'deepclustering':
return deepclustering_loss.DeepclusteringLoss
elif loss_type == 'deepclustering_flat':
return deepclustering_flat_loss.DeepclusteringFlatLoss
elif loss_type == 'deepclustering_2and3spk':
return deepclustering_2and3spk_loss.Deepclustering2and3SpkLoss
elif loss_type == 'pit':
return pit_loss.PITLoss
elif loss_type == 'deepattractornet':
return deepattractornet_loss.DeepattractornetLoss
elif loss_type == 'anchor_deepattractornet':
return anchor_deepattractornet_softmax_loss.AnchorDeepattractornetLoss
elif loss_type == 'anchor_deepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.AnchorDeepattractornetSoftmaxLoss
elif loss_type == 'anchor_normdeepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.AnchorNormDeepattractornetSoftmaxLoss
elif loss_type == 'weighted_anchor_normdeepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.WeightedAnchorNormDeepattractornetSoftmaxLoss
elif loss_type == 'time_anchor_deepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.TimeAnchorDeepattractornetSoftmaxLoss
elif loss_type == 'time_anchor_normdeepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.TimeAnchorNormDeepattractornetSoftmaxLoss
elif loss_type == 'time_anchor_read_heads_normdeepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.TimeAnchorReadHeadsNormDeepattractornetSoftmaxLoss
elif loss_type == 'time_anchor_read_heads_normdeepattractornet_softmax_framebased':
return anchor_deepattractornet_softmax_loss.TimeAnchorReadHeadsNormDeepattractornetSoftmaxFramebasedLoss
elif loss_type == 'l41':
return l41_loss.L41Loss
elif loss_type == 'pit_l41':
return pit_l41_loss.PITL41Loss
elif loss_type == 'deepclustering_l1':
return deepclustering_L1_loss.DeepclusteringL1Loss
elif loss_type == 'intravar2centervar_rat':
return intravar2centervar_rat_loss.IntraVar2CenterVarRatLoss
elif loss_type == 'dist2mean_rat':
return dist2mean_rat_loss.Dist2MeanRatLoss
elif loss_type == 'dist2mean_rat_squared':
return dist2mean_rat_squared_loss.Dist2MeanRatSquaredLoss
elif loss_type == 'dist2mean_rat_fracbins':
return dist2mean_rat_fracbins_loss.Dist2MeanRatFracBinsLoss
elif loss_type == 'dist2mean_closest_rat':
return dist2mean_closest_rat_loss.Dist2MeanClosestRatLoss
elif loss_type == 'dist2mean_epsilon_closest_rat':
return dist2mean_epsilon_closest_rat_loss.Dist2MeanEpsilonClosestRatLoss
elif loss_type == 'direct':
return direct_loss.DirectLoss
elif loss_type == 'crossentropy':
return crossentropy_loss.CrossEntropyLoss
elif loss_type == 'crossentropy_multi':
return crossentropy_multi_loss.CrossEntropyMultiLoss
elif loss_type == 'dc_pit':
return dc_pit_loss.DcPitLoss
elif loss_type == 'deepclustering_full_crossentropy_multi_reshapelogits_avtime':
return deepclustering_full_crossentropy_multi_reshapedlogits_avtime_loss.DeepclusteringFullCrossEntropyMultiReshapedLogitsAvTimeLoss
elif loss_type == 'deepclusteringnoise':
return deepclusteringnoise_loss.DeepclusteringnoiseLoss
elif loss_type == 'deepattractornetnoisehard':
return deepattractornetnoise_hard_loss.DeepattractornetnoisehardLoss
elif loss_type == 'deepattractornetnoisesoft':
return deepattractornetnoise_soft_loss.DeepattractornetnoisesoftLoss
elif loss_type == 'noisefilter':
return noisefilter_loss.NoisefilterLoss
elif loss_type == 'deepattractornet_noisefilter':
return deepattractornet_noisefilter_loss.DeepattractornetnoisefilterLoss
elif loss_type == 'pit_noise':
return pit_noise_loss.PITNoiseLoss
elif loss_type == 'deepclusteringnoise_rat_as_weight':
return deepclusteringnoise_loss.DeepclusteringnoiseRatAsWeightLoss
elif loss_type == 'deepclusteringnoise_alpha_as_weight':
return deepclusteringnoise_loss.DeepclusteringnoiseAlphaAsWeightLoss
elif loss_type == 'deepclusteringnoise_snr_target':
return deepclusteringnoise_loss.DeepclusteringnoiseSnrTargetLoss
elif loss_type == 'deepclusteringnoise_dconly':
return deepclusteringnoise_loss.DeepclusteringnoiseDConlyLoss
elif loss_type == 'deepclusteringnoise_noiseonly':
return deepclusteringnoise_loss.DeepclusteringnoiseNoiseonlyLoss
elif loss_type == 'deepclustering_diar':
return deepclustering_diar_loss.DeepclusteringDiarLoss
elif loss_type == 'msl':
return ms_loss.MsLoss
elif loss_type in ['dummy_loss', 'dummy']:
return dummy_loss.DummyLoss
else:
raise Exception('Undefined loss type: %s' % loss_type) | nabu/neuralnetworks/loss_computers/loss_computer_factory.py | from . import deepclustering_loss, pit_loss, \
l41_loss, pit_l41_loss,\
deepclustering_L1_loss, dist2mean_rat_loss,\
dist2mean_rat_squared_loss, intravar2centervar_rat_loss,\
dist2mean_rat_fracbins_loss, crossentropy_multi_loss,\
dist2mean_closest_rat_loss,direct_loss, dist2mean_epsilon_closest_rat_loss,\
dc_pit_loss, \
deepclustering_full_crossentropy_multi_reshapedlogits_avtime_loss,\
deepclustering_2and3spk_loss, deepclustering_flat_loss, deepclusteringnoise_loss, \
deepattractornetnoise_hard_loss, deepattractornetnoise_hard_loss, deepattractornetnoise_soft_loss, \
deepattractornet_loss, noisefilter_loss, deepattractornet_noisefilter_loss, pit_noise_loss, dummy_loss, \
anchor_deepattractornet_softmax_loss, deepclustering_diar_loss, crossentropy_loss, ms_loss
def factory(loss_type):
"""gets a Loss computer class
Args:
loss_type: the loss type
Returns: a Loss computer class
"""
if loss_type == 'deepclustering':
return deepclustering_loss.DeepclusteringLoss
elif loss_type == 'deepclustering_flat':
return deepclustering_flat_loss.DeepclusteringFlatLoss
elif loss_type == 'deepclustering_2and3spk':
return deepclustering_2and3spk_loss.Deepclustering2and3SpkLoss
elif loss_type == 'pit':
return pit_loss.PITLoss
elif loss_type == 'deepattractornet':
return deepattractornet_loss.DeepattractornetLoss
elif loss_type == 'anchor_deepattractornet':
return anchor_deepattractornet_softmax_loss.AnchorDeepattractornetLoss
elif loss_type == 'anchor_deepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.AnchorDeepattractornetSoftmaxLoss
elif loss_type == 'anchor_normdeepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.AnchorNormDeepattractornetSoftmaxLoss
elif loss_type == 'weighted_anchor_normdeepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.WeightedAnchorNormDeepattractornetSoftmaxLoss
elif loss_type == 'time_anchor_deepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.TimeAnchorDeepattractornetSoftmaxLoss
elif loss_type == 'time_anchor_normdeepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.TimeAnchorNormDeepattractornetSoftmaxLoss
elif loss_type == 'time_anchor_read_heads_normdeepattractornet_softmax':
return anchor_deepattractornet_softmax_loss.TimeAnchorReadHeadsNormDeepattractornetSoftmaxLoss
elif loss_type == 'time_anchor_read_heads_normdeepattractornet_softmax_framebased':
return anchor_deepattractornet_softmax_loss.TimeAnchorReadHeadsNormDeepattractornetSoftmaxFramebasedLoss
elif loss_type == 'l41':
return l41_loss.L41Loss
elif loss_type == 'pit_l41':
return pit_l41_loss.PITL41Loss
elif loss_type == 'deepclustering_l1':
return deepclustering_L1_loss.DeepclusteringL1Loss
elif loss_type == 'intravar2centervar_rat':
return intravar2centervar_rat_loss.IntraVar2CenterVarRatLoss
elif loss_type == 'dist2mean_rat':
return dist2mean_rat_loss.Dist2MeanRatLoss
elif loss_type == 'dist2mean_rat_squared':
return dist2mean_rat_squared_loss.Dist2MeanRatSquaredLoss
elif loss_type == 'dist2mean_rat_fracbins':
return dist2mean_rat_fracbins_loss.Dist2MeanRatFracBinsLoss
elif loss_type == 'dist2mean_closest_rat':
return dist2mean_closest_rat_loss.Dist2MeanClosestRatLoss
elif loss_type == 'dist2mean_epsilon_closest_rat':
return dist2mean_epsilon_closest_rat_loss.Dist2MeanEpsilonClosestRatLoss
elif loss_type == 'direct':
return direct_loss.DirectLoss
elif loss_type == 'crossentropy':
return crossentropy_loss.CrossEntropyLoss
elif loss_type == 'crossentropy_multi':
return crossentropy_multi_loss.CrossEntropyMultiLoss
elif loss_type == 'dc_pit':
return dc_pit_loss.DcPitLoss
elif loss_type == 'deepclustering_full_crossentropy_multi_reshapelogits_avtime':
return deepclustering_full_crossentropy_multi_reshapedlogits_avtime_loss.DeepclusteringFullCrossEntropyMultiReshapedLogitsAvTimeLoss
elif loss_type == 'deepclusteringnoise':
return deepclusteringnoise_loss.DeepclusteringnoiseLoss
elif loss_type == 'deepattractornetnoisehard':
return deepattractornetnoise_hard_loss.DeepattractornetnoisehardLoss
elif loss_type == 'deepattractornetnoisesoft':
return deepattractornetnoise_soft_loss.DeepattractornetnoisesoftLoss
elif loss_type == 'noisefilter':
return noisefilter_loss.NoisefilterLoss
elif loss_type == 'deepattractornet_noisefilter':
return deepattractornet_noisefilter_loss.DeepattractornetnoisefilterLoss
elif loss_type == 'pit_noise':
return pit_noise_loss.PITNoiseLoss
elif loss_type == 'deepclusteringnoise_rat_as_weight':
return deepclusteringnoise_loss.DeepclusteringnoiseRatAsWeightLoss
elif loss_type == 'deepclusteringnoise_alpha_as_weight':
return deepclusteringnoise_loss.DeepclusteringnoiseAlphaAsWeightLoss
elif loss_type == 'deepclusteringnoise_snr_target':
return deepclusteringnoise_loss.DeepclusteringnoiseSnrTargetLoss
elif loss_type == 'deepclusteringnoise_dconly':
return deepclusteringnoise_loss.DeepclusteringnoiseDConlyLoss
elif loss_type == 'deepclusteringnoise_noiseonly':
return deepclusteringnoise_loss.DeepclusteringnoiseNoiseonlyLoss
elif loss_type == 'deepclustering_diar':
return deepclustering_diar_loss.DeepclusteringDiarLoss
elif loss_type == 'msl':
return ms_loss.MsLoss
elif loss_type in ['dummy_loss', 'dummy']:
return dummy_loss.DummyLoss
else:
raise Exception('Undefined loss type: %s' % loss_type) | 0.848847 | 0.411939 |
from allennlp.models import load_archive
from allennlp.predictors import Predictor, SentenceTaggerPredictor
from allennlp_models.coref import CorefPredictor
from allennlp_models.nli import DecomposableAttentionPredictor
from allennlp_models.rc.bidaf import ReadingComprehensionPredictor
from allennlp_models.syntax import (
SemanticRoleLabelerPredictor,
OpenIePredictor,
ConstituencyParserPredictor,
BiaffineDependencyParserPredictor,
)
# flake8: noqa: E501
def _load_predictor(archive_file: str, predictor_name: str) -> Predictor:
"""
Helper to load the desired predictor from the given archive.
"""
archive = load_archive(archive_file)
return Predictor.from_archive(archive, predictor_name)
def bert_srl_shi_2019() -> SemanticRoleLabelerPredictor:
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz",
"semantic-role-labeling",
)
return predictor
def bidirectional_attention_flow_seo_2017() -> ReadingComprehensionPredictor:
"""
Reading Comprehension
Based on `BiDAF (Seo et al, 2017) <https://www.semanticscholar.org/paper/Bidirectional-Attention-Flow-for-Machine-Comprehen-Seo-Kembhavi/007ab5528b3bd310a80d553cccad4b78dc496b02>`_
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/bidaf-model-2020.03.19.tar.gz",
"reading-comprehension",
)
return predictor
def naqanet_dua_2019() -> ReadingComprehensionPredictor:
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/naqanet-2020.02.19.tar.gz",
"reading-comprehension",
)
return predictor
def open_information_extraction_stanovsky_2018() -> OpenIePredictor:
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/openie-model.2020.03.26.tar.gz",
"open-information-extraction",
)
return predictor
def decomposable_attention_with_elmo_parikh_2017() -> DecomposableAttentionPredictor:
"""
Textual Entailment
Based on `Parikh et al, 2017 <https://www.semanticscholar.org/paper/A-Decomposable-Attention-Model-for-Natural-Languag-Parikh-T%C3%A4ckstr%C3%B6m/07a9478e87a8304fc3267fa16e83e9f3bbd98b27>`_
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/decomposable-attention-elmo-2020.04.09.tar.gz",
"textual-entailment",
)
return predictor
def neural_coreference_resolution() -> CorefPredictor:
"""
Coreference Resolution
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/coref-spanbert-large-2020.02.27.tar.gz",
"coreference-resolution",
)
return predictor
def named_entity_recognition_with_elmo_peters_2018() -> SentenceTaggerPredictor:
"""
Named Entity Recognition
Based on `Deep contextualized word representations <https://arxiv.org/abs/1802.05365>`_
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/ner-model-2020.02.10.tar.gz",
"sentence-tagger",
)
return predictor
def fine_grained_named_entity_recognition_with_elmo_peters_2018() -> SentenceTaggerPredictor:
"""
Fine Grained Named Entity Recognition
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/fine-grained-ner-model-elmo-2018.12.21.tar.gz",
"sentence-tagger",
)
return predictor
def span_based_constituency_parsing_with_elmo_joshi_2018() -> ConstituencyParserPredictor:
"""
Constituency Parsing
Based on `Minimal Span Based Constituency Parser (Stern et al, 2017) <https://www.semanticscholar.org/paper/A-Minimal-Span-Based-Neural-Constituency-Parser-Stern-Andreas/593e4e749bd2dbcaf8dc25298d830b41d435e435>`_ but with ELMo embeddings
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/elmo-constituency-parser-2020.02.10.tar.gz",
"constituency-parser",
)
return predictor
def biaffine_parser_universal_dependencies_todzat_2017() -> BiaffineDependencyParserPredictor:
"""
Biaffine Dependency Parser (Stanford Dependencies)
Based on `Dozat and Manning, 2017 <https://arxiv.org/pdf/1611.01734.pdf>`_
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/biaffine-dependency-parser-ptb-2020.04.06.tar.gz",
"biaffine-dependency-parser",
)
return predictor
def esim_nli_with_elmo_chen_2017() -> DecomposableAttentionPredictor:
"""
ESIM
Based on `Enhanced LSTM for Natural Language Inference <https://arxiv.org/pdf/1609.06038.pdf>`_ and uses ELMo
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/decomposable-attention-elmo-2020.04.09.tar.gz",
"textual-entailment",
)
return predictor | allennlp_models/pretrained.py | from allennlp.models import load_archive
from allennlp.predictors import Predictor, SentenceTaggerPredictor
from allennlp_models.coref import CorefPredictor
from allennlp_models.nli import DecomposableAttentionPredictor
from allennlp_models.rc.bidaf import ReadingComprehensionPredictor
from allennlp_models.syntax import (
SemanticRoleLabelerPredictor,
OpenIePredictor,
ConstituencyParserPredictor,
BiaffineDependencyParserPredictor,
)
# flake8: noqa: E501
def _load_predictor(archive_file: str, predictor_name: str) -> Predictor:
"""
Helper to load the desired predictor from the given archive.
"""
archive = load_archive(archive_file)
return Predictor.from_archive(archive, predictor_name)
def bert_srl_shi_2019() -> SemanticRoleLabelerPredictor:
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz",
"semantic-role-labeling",
)
return predictor
def bidirectional_attention_flow_seo_2017() -> ReadingComprehensionPredictor:
"""
Reading Comprehension
Based on `BiDAF (Seo et al, 2017) <https://www.semanticscholar.org/paper/Bidirectional-Attention-Flow-for-Machine-Comprehen-Seo-Kembhavi/007ab5528b3bd310a80d553cccad4b78dc496b02>`_
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/bidaf-model-2020.03.19.tar.gz",
"reading-comprehension",
)
return predictor
def naqanet_dua_2019() -> ReadingComprehensionPredictor:
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/naqanet-2020.02.19.tar.gz",
"reading-comprehension",
)
return predictor
def open_information_extraction_stanovsky_2018() -> OpenIePredictor:
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/openie-model.2020.03.26.tar.gz",
"open-information-extraction",
)
return predictor
def decomposable_attention_with_elmo_parikh_2017() -> DecomposableAttentionPredictor:
"""
Textual Entailment
Based on `Parikh et al, 2017 <https://www.semanticscholar.org/paper/A-Decomposable-Attention-Model-for-Natural-Languag-Parikh-T%C3%A4ckstr%C3%B6m/07a9478e87a8304fc3267fa16e83e9f3bbd98b27>`_
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/decomposable-attention-elmo-2020.04.09.tar.gz",
"textual-entailment",
)
return predictor
def neural_coreference_resolution() -> CorefPredictor:
"""
Coreference Resolution
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/coref-spanbert-large-2020.02.27.tar.gz",
"coreference-resolution",
)
return predictor
def named_entity_recognition_with_elmo_peters_2018() -> SentenceTaggerPredictor:
"""
Named Entity Recognition
Based on `Deep contextualized word representations <https://arxiv.org/abs/1802.05365>`_
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/ner-model-2020.02.10.tar.gz",
"sentence-tagger",
)
return predictor
def fine_grained_named_entity_recognition_with_elmo_peters_2018() -> SentenceTaggerPredictor:
"""
Fine Grained Named Entity Recognition
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/fine-grained-ner-model-elmo-2018.12.21.tar.gz",
"sentence-tagger",
)
return predictor
def span_based_constituency_parsing_with_elmo_joshi_2018() -> ConstituencyParserPredictor:
"""
Constituency Parsing
Based on `Minimal Span Based Constituency Parser (Stern et al, 2017) <https://www.semanticscholar.org/paper/A-Minimal-Span-Based-Neural-Constituency-Parser-Stern-Andreas/593e4e749bd2dbcaf8dc25298d830b41d435e435>`_ but with ELMo embeddings
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/elmo-constituency-parser-2020.02.10.tar.gz",
"constituency-parser",
)
return predictor
def biaffine_parser_universal_dependencies_todzat_2017() -> BiaffineDependencyParserPredictor:
"""
Biaffine Dependency Parser (Stanford Dependencies)
Based on `Dozat and Manning, 2017 <https://arxiv.org/pdf/1611.01734.pdf>`_
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/biaffine-dependency-parser-ptb-2020.04.06.tar.gz",
"biaffine-dependency-parser",
)
return predictor
def esim_nli_with_elmo_chen_2017() -> DecomposableAttentionPredictor:
"""
ESIM
Based on `Enhanced LSTM for Natural Language Inference <https://arxiv.org/pdf/1609.06038.pdf>`_ and uses ELMo
"""
predictor = _load_predictor(
"https://storage.googleapis.com/allennlp-public-models/decomposable-attention-elmo-2020.04.09.tar.gz",
"textual-entailment",
)
return predictor | 0.891717 | 0.411998 |
import codecs
import csv
import json
import re
import sys
import urllib.request
import geopandas
import geopy.distance
import pandas
def getListOfSites(network=None,verified_only=False):
"""Get all site records on DEIMS-SDR and return a list of DEIMS.IDs.
'network' must be the ID of a network. If provided, only sites from
that network are returned. Defaults to None.
'verified_only' must be a boolean. If True, only verified members of
the network are returned. Ignored if 'network' not supplied.
Defaults to False.
"""
csv.field_size_limit(sys.maxsize)
# set API URL based on input
if network is not None:
if verified_only:
url = f"https://deims.org/api/sites?format=csv&network={network}&verified={str(verified_only).lower()}"
else:
url = f"https://deims.org/api/sites?format=csv&network={network}"
else:
url = "https://deims.org/api/sites?format=csv"
# connect to DEIMS REST-API
csv_stream = urllib.request.urlopen(url)
csvfile = csv.reader(codecs.iterdecode(csv_stream, 'utf-8'), delimiter=';')
next(csvfile) # ignore first row
# load all site records into one object
list_of_sites = []
for line in csvfile:
list_of_sites.append(line[2])
# returns list of UUIDs
return list_of_sites
def getSiteById(site_id):
"""Get complete record of a site and return as a dictionary.
'site_id' is the only, mandatory argument and must be a valid
DEIMS.ID.
"""
# make sure we have a well-formed DEIMS.ID suffix
deims_id_suffix = normaliseDeimsID(site_id)
# construct URL
site_json_url = "https://deims.org/api/sites/" + deims_id_suffix
# open URL and parse as json
with urllib.request.urlopen(site_json_url) as f:
parsed_site_json = json.loads(f.read().decode("utf-8"))
# optional processing steps
# ...
# returns entire site records as dict
return parsed_site_json
def normaliseDeimsID(deims_id):
"""Extract standardised DEIMS.ID suffix from input string.
'deims_id' is the only, mandatory argument and must be a valid
DEIMS.ID.
Returns a string of the form '00000000-0000-0000-0000-000000000000'
or raises a RuntimeError if no DEIMS.ID suffix is found.
"""
# extract ID from lowercased string via regex
# returns the first match only
normalised_deims_id = re.search(r"([a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12})",deims_id.lower())
if normalised_deims_id:
return normalised_deims_id.group(1)
else:
raise RuntimeError("no ID found")
def getSitesWithinRadius(lat, lon, distance):
"""Get all sites within a given distance of a point.
'lat' and 'lon' should be coordinates in degrees describing a point
to search from.
'distance' should be the number of metres from the point to search.
Returns a list of (DEIMS.ID, distance to input coordinates in
meters) tuples or None if no sites are found.
"""
# construct GeoDataFrame from input coordinates
gdf = geopandas.GeoDataFrame(
geometry=geopandas.points_from_xy(x=[lon], y=[lat], crs="EPSG:4326").to_crs(3857)
)
# construct query URL from bounding box centred on input point
bounding_box = gdf.geometry.buffer(distance).to_crs(4326).bounds
bounding_box_string = str(bounding_box['miny'][0]) + ',' + str(bounding_box['minx'][0]) + ',' + str(bounding_box['maxy'][0]) + ',' + str(bounding_box['maxx'][0])
query_string = 'https://deims.org/geoserver/deims/ows?service=WFS&version=1.0.0&request=GetFeature&typeName=deims%3Adeims_all_sites&outputFormat=application%2Fjson&srsName=EPSG:3857&bbox=' + bounding_box_string + ',urn:ogc:def:crs:EPSG:4326'
# open URL and parse as json
with urllib.request.urlopen(query_string) as f:
parsed_results_json = json.loads(f.read().decode("utf-8"))
if parsed_results_json['totalFeatures']>0:
# sites were returned but need distance checking still
results = [];
for site in parsed_results_json['features']:
current_distance = geopy.distance.geodesic((lat,lon),(site['properties']['field_coordinates_lat'],site['properties']['field_coordinates_lon']))
if (current_distance < distance):
results.append([normaliseDeimsID(site['properties']['deimsid']), round(current_distance.meters)])
else:
continue
return sorted(results, key=lambda x: x[1])
else:
# no sites returned
return None
def getSiteBoundaries(site_ids, filename=None):
"""Get all available boundaries for one or more sites.
'site_ids' can either be a string featuring the DEIMS.ID or a
list of ids as returned by other functions in this package.
If 'filename' is provided, output will be saved as a shapefile.
Otherwise, it is returned as a GeoDataFrame.
"""
# ensure input is a list
if isinstance(site_ids, str):
site_ids = [site_ids]
# initialise GeoDataFrame
list_of_boundaries = geopandas.GeoDataFrame(columns=['name', 'deimsid', 'field_elev', 'geometry'], geometry='geometry')
# get boundaries
for site_id in site_ids:
current_boundary = geopandas.read_file("https://deims.org/geoserver/deims/ows?service=WFS&version=2.0.0&request=GetFeature&typeName=deims:deims_sites_boundaries&srsName=EPSG:4326&CQL_FILTER=deimsid=%27https://deims.org/" + normaliseDeimsID(site_id) + "%27&outputFormat=SHAPE-ZIP")
list_of_boundaries = pandas.concat([list_of_boundaries, current_boundary])
# save file
if (filename):
list_of_boundaries.to_file(filename + ".shp")
else:
return list_of_boundaries | deims.py | import codecs
import csv
import json
import re
import sys
import urllib.request
import geopandas
import geopy.distance
import pandas
def getListOfSites(network=None,verified_only=False):
"""Get all site records on DEIMS-SDR and return a list of DEIMS.IDs.
'network' must be the ID of a network. If provided, only sites from
that network are returned. Defaults to None.
'verified_only' must be a boolean. If True, only verified members of
the network are returned. Ignored if 'network' not supplied.
Defaults to False.
"""
csv.field_size_limit(sys.maxsize)
# set API URL based on input
if network is not None:
if verified_only:
url = f"https://deims.org/api/sites?format=csv&network={network}&verified={str(verified_only).lower()}"
else:
url = f"https://deims.org/api/sites?format=csv&network={network}"
else:
url = "https://deims.org/api/sites?format=csv"
# connect to DEIMS REST-API
csv_stream = urllib.request.urlopen(url)
csvfile = csv.reader(codecs.iterdecode(csv_stream, 'utf-8'), delimiter=';')
next(csvfile) # ignore first row
# load all site records into one object
list_of_sites = []
for line in csvfile:
list_of_sites.append(line[2])
# returns list of UUIDs
return list_of_sites
def getSiteById(site_id):
"""Get complete record of a site and return as a dictionary.
'site_id' is the only, mandatory argument and must be a valid
DEIMS.ID.
"""
# make sure we have a well-formed DEIMS.ID suffix
deims_id_suffix = normaliseDeimsID(site_id)
# construct URL
site_json_url = "https://deims.org/api/sites/" + deims_id_suffix
# open URL and parse as json
with urllib.request.urlopen(site_json_url) as f:
parsed_site_json = json.loads(f.read().decode("utf-8"))
# optional processing steps
# ...
# returns entire site records as dict
return parsed_site_json
def normaliseDeimsID(deims_id):
"""Extract standardised DEIMS.ID suffix from input string.
'deims_id' is the only, mandatory argument and must be a valid
DEIMS.ID.
Returns a string of the form '00000000-0000-0000-0000-000000000000'
or raises a RuntimeError if no DEIMS.ID suffix is found.
"""
# extract ID from lowercased string via regex
# returns the first match only
normalised_deims_id = re.search(r"([a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12})",deims_id.lower())
if normalised_deims_id:
return normalised_deims_id.group(1)
else:
raise RuntimeError("no ID found")
def getSitesWithinRadius(lat, lon, distance):
"""Get all sites within a given distance of a point.
'lat' and 'lon' should be coordinates in degrees describing a point
to search from.
'distance' should be the number of metres from the point to search.
Returns a list of (DEIMS.ID, distance to input coordinates in
meters) tuples or None if no sites are found.
"""
# construct GeoDataFrame from input coordinates
gdf = geopandas.GeoDataFrame(
geometry=geopandas.points_from_xy(x=[lon], y=[lat], crs="EPSG:4326").to_crs(3857)
)
# construct query URL from bounding box centred on input point
bounding_box = gdf.geometry.buffer(distance).to_crs(4326).bounds
bounding_box_string = str(bounding_box['miny'][0]) + ',' + str(bounding_box['minx'][0]) + ',' + str(bounding_box['maxy'][0]) + ',' + str(bounding_box['maxx'][0])
query_string = 'https://deims.org/geoserver/deims/ows?service=WFS&version=1.0.0&request=GetFeature&typeName=deims%3Adeims_all_sites&outputFormat=application%2Fjson&srsName=EPSG:3857&bbox=' + bounding_box_string + ',urn:ogc:def:crs:EPSG:4326'
# open URL and parse as json
with urllib.request.urlopen(query_string) as f:
parsed_results_json = json.loads(f.read().decode("utf-8"))
if parsed_results_json['totalFeatures']>0:
# sites were returned but need distance checking still
results = [];
for site in parsed_results_json['features']:
current_distance = geopy.distance.geodesic((lat,lon),(site['properties']['field_coordinates_lat'],site['properties']['field_coordinates_lon']))
if (current_distance < distance):
results.append([normaliseDeimsID(site['properties']['deimsid']), round(current_distance.meters)])
else:
continue
return sorted(results, key=lambda x: x[1])
else:
# no sites returned
return None
def getSiteBoundaries(site_ids, filename=None):
"""Get all available boundaries for one or more sites.
'site_ids' can either be a string featuring the DEIMS.ID or a
list of ids as returned by other functions in this package.
If 'filename' is provided, output will be saved as a shapefile.
Otherwise, it is returned as a GeoDataFrame.
"""
# ensure input is a list
if isinstance(site_ids, str):
site_ids = [site_ids]
# initialise GeoDataFrame
list_of_boundaries = geopandas.GeoDataFrame(columns=['name', 'deimsid', 'field_elev', 'geometry'], geometry='geometry')
# get boundaries
for site_id in site_ids:
current_boundary = geopandas.read_file("https://deims.org/geoserver/deims/ows?service=WFS&version=2.0.0&request=GetFeature&typeName=deims:deims_sites_boundaries&srsName=EPSG:4326&CQL_FILTER=deimsid=%27https://deims.org/" + normaliseDeimsID(site_id) + "%27&outputFormat=SHAPE-ZIP")
list_of_boundaries = pandas.concat([list_of_boundaries, current_boundary])
# save file
if (filename):
list_of_boundaries.to_file(filename + ".shp")
else:
return list_of_boundaries | 0.496094 | 0.339965 |
import sys
def domination_count_and_set(population, element):
# gives the domination count and dominating set of the element of the population
domination_count = 0
dominated_set = []
for chromosome in population:
dominated_sum = 0 # will equal number of objectives if chromosome is dominated by element
dominating_sum = 0 # will equal number of objectives if element is dominated by chromosome
for i in range(len(population[element])):
if population[element][i] < population[chromosome][i]:
dominated_sum += 1
elif population[element][i] == population[chromosome][i]:
dominated_sum += 1
dominating_sum += 1
else:
dominating_sum += 1
if dominated_sum == len(population[element]) and population[element] != population[chromosome]:
dominated_set.append(chromosome)
if dominating_sum == len(population[element]) and population[element] != population[chromosome]:
domination_count += 1
return [domination_count, dominated_set]
# what should we do if two chromosomes have the exact same objectives? here neither dominates the other
def fast_nondominated_sort(population):
# gives the list of the domination fronts, in order, and a dictionary with the ranks of the chromosomes
domination_dict = {}
for chromosome in population:
domination_dict[chromosome] = domination_count_and_set(population, chromosome)
i = 0
domination_fronts = []
domination_fronts.append([])
rank_list = {}
for chromosome in domination_dict:
if domination_dict[chromosome][0] == 0:
domination_fronts[0].append(chromosome)
rank_list[chromosome] = 1
while domination_fronts[i] != []:
domination_fronts.append([])
for chromosome in domination_fronts[i]:
for chrom in domination_dict[chromosome][1]:
domination_dict[chrom][0] -= 1
if domination_dict[chrom][0] == 0:
domination_fronts[i + 1].append(chrom)
rank_list[chrom] = i + 2
i += 1
return [rank_list, domination_fronts[:len(domination_fronts) - 1]]
def crowding_distance(population):
# gives a dictionary in which the value of each chromosome is its crowding distance
l = len(population)
num_objectives = len(list(population.values())[0])
crowding_distances = {}
for chromosome in population:
crowding_distances[chromosome] = 0
for i in range(num_objectives):
ordered_by_objective = sorted(population, key = lambda x:population[x][i])
if population[ordered_by_objective[0]] == population[ordered_by_objective[l-1]]:
for j in range(1, l - 1):
crowding_distances[ordered_by_objective[j]] -= 1
else:
crowding_distances[ordered_by_objective[0]] -= float("inf")
crowding_distances[ordered_by_objective[l - 1]] -= float("inf")
for j in range(1, l - 1):
crowding_distance = (float(population[ordered_by_objective[j + 1]][i]) - float(population[ordered_by_objective[j - 1]][i]))/(float(population[ordered_by_objective[l-1]][i]) - float(population[ordered_by_objective[0]][i]))
crowding_distances[ordered_by_objective[j]] -= crowding_distance
return crowding_distances
def log_stdout(generation, population, objectives, fronts):
print("Generation %d" % generation)
for p in population:
print(p, population[p].genes)
print(objectives)
print(fronts)
sys.stdout.flush()
class log_file(object):
def __init__(self, filename):
self.filename = filename
f = open(self.filename, 'w')
f.close()
def log(self, generation, population, objectives, fronts):
log_stdout(generation, population, objectives, fronts)
f = open(self.filename, 'a')
f.write("Generation %d\n" % generation)
for p in population.values():
f.write(p.genes.__str__())
f.write("\n")
f.close()
def log_discard(generation, population, objectives, fronts):
pass
class NSGAII(object):
def __init__(self, context, initial_population):
self.context = context
def poppair(i):
key = i, 0
return key, initial_population[i]
self.population = dict([poppair(i) for i in range(len(initial_population))])
def get_objectives(self, population):
objectives = {}
for chromosome in population:
objectives[chromosome] = population[chromosome].getObjectives()
return objectives
def new_population(self, parents, parents_objectives, children):
# combines the two new populations and then selects the best chromosome with respect to the crowded comparison order
children_objectives = self.get_objectives(children)
combined_population_hyperparameters = dict(list(parents.items()) + list(children.items()))
combined_population_objectives = dict(list(parents_objectives.items()) + list(children_objectives.items()))
fns = fast_nondominated_sort(combined_population_objectives)
fronts = fns[1]
new_pop_hyperparameters = {}
new_pop_objectives = {}
spaces_remaining = len(parents)
front = 0
while spaces_remaining > 0:
if spaces_remaining >= len(fronts[front]):
for chromosome in fronts[front]:
new_pop_objectives[chromosome] = combined_population_objectives[chromosome]
new_pop_hyperparameters[chromosome] = combined_population_hyperparameters[chromosome]
spaces_remaining -= 1
front += 1
else:
crowding_distances = crowding_distance(combined_population_objectives)
front_with_crowding = dict((key, crowding_distances[key]) for key in fronts[front])
sorted_by_crowding = sorted(front_with_crowding, key = lambda x:front_with_crowding[x])
for i in range(spaces_remaining):
new_pop_objectives[sorted_by_crowding[i]] = combined_population_objectives[sorted_by_crowding[i]]
new_pop_hyperparameters[sorted_by_crowding[i]] = combined_population_hyperparameters[sorted_by_crowding[i]]
spaces_remaining = 0
return new_pop_hyperparameters, new_pop_objectives, fronts
def make_children(self, parents, generation):
children = {}
keys = list(parents.keys())
while len(keys) > 1:
choice1, choice2 = self.context.r.choice(len(keys), size=2, replace=False)
key1 = keys[choice1]
key2 = keys[choice2]
keys.remove(key1)
keys.remove(key2)
child0, child1 = parents[key1].crossover(parents[key2])
child0.mutate()
child1.mutate()
new_key1 = key1[0], generation + 1
new_key2 = key2[0], generation + 1
children[new_key1] = child0
children[new_key2] = child1
return children
def evolve(self, num_generations=100, log_out=log_discard):
objectives = self.get_objectives(self.population)
for generation in range(num_generations):
self.population, objectives, self.fronts = self.new_population(self.population, objectives, self.make_children(self.population, generation))
log_out(generation, self.population, objectives, self.fronts)
return self.population.values() | evolutionary/NSGAII.py |
import sys
def domination_count_and_set(population, element):
# gives the domination count and dominating set of the element of the population
domination_count = 0
dominated_set = []
for chromosome in population:
dominated_sum = 0 # will equal number of objectives if chromosome is dominated by element
dominating_sum = 0 # will equal number of objectives if element is dominated by chromosome
for i in range(len(population[element])):
if population[element][i] < population[chromosome][i]:
dominated_sum += 1
elif population[element][i] == population[chromosome][i]:
dominated_sum += 1
dominating_sum += 1
else:
dominating_sum += 1
if dominated_sum == len(population[element]) and population[element] != population[chromosome]:
dominated_set.append(chromosome)
if dominating_sum == len(population[element]) and population[element] != population[chromosome]:
domination_count += 1
return [domination_count, dominated_set]
# what should we do if two chromosomes have the exact same objectives? here neither dominates the other
def fast_nondominated_sort(population):
# gives the list of the domination fronts, in order, and a dictionary with the ranks of the chromosomes
domination_dict = {}
for chromosome in population:
domination_dict[chromosome] = domination_count_and_set(population, chromosome)
i = 0
domination_fronts = []
domination_fronts.append([])
rank_list = {}
for chromosome in domination_dict:
if domination_dict[chromosome][0] == 0:
domination_fronts[0].append(chromosome)
rank_list[chromosome] = 1
while domination_fronts[i] != []:
domination_fronts.append([])
for chromosome in domination_fronts[i]:
for chrom in domination_dict[chromosome][1]:
domination_dict[chrom][0] -= 1
if domination_dict[chrom][0] == 0:
domination_fronts[i + 1].append(chrom)
rank_list[chrom] = i + 2
i += 1
return [rank_list, domination_fronts[:len(domination_fronts) - 1]]
def crowding_distance(population):
# gives a dictionary in which the value of each chromosome is its crowding distance
l = len(population)
num_objectives = len(list(population.values())[0])
crowding_distances = {}
for chromosome in population:
crowding_distances[chromosome] = 0
for i in range(num_objectives):
ordered_by_objective = sorted(population, key = lambda x:population[x][i])
if population[ordered_by_objective[0]] == population[ordered_by_objective[l-1]]:
for j in range(1, l - 1):
crowding_distances[ordered_by_objective[j]] -= 1
else:
crowding_distances[ordered_by_objective[0]] -= float("inf")
crowding_distances[ordered_by_objective[l - 1]] -= float("inf")
for j in range(1, l - 1):
crowding_distance = (float(population[ordered_by_objective[j + 1]][i]) - float(population[ordered_by_objective[j - 1]][i]))/(float(population[ordered_by_objective[l-1]][i]) - float(population[ordered_by_objective[0]][i]))
crowding_distances[ordered_by_objective[j]] -= crowding_distance
return crowding_distances
def log_stdout(generation, population, objectives, fronts):
print("Generation %d" % generation)
for p in population:
print(p, population[p].genes)
print(objectives)
print(fronts)
sys.stdout.flush()
class log_file(object):
def __init__(self, filename):
self.filename = filename
f = open(self.filename, 'w')
f.close()
def log(self, generation, population, objectives, fronts):
log_stdout(generation, population, objectives, fronts)
f = open(self.filename, 'a')
f.write("Generation %d\n" % generation)
for p in population.values():
f.write(p.genes.__str__())
f.write("\n")
f.close()
def log_discard(generation, population, objectives, fronts):
pass
class NSGAII(object):
def __init__(self, context, initial_population):
self.context = context
def poppair(i):
key = i, 0
return key, initial_population[i]
self.population = dict([poppair(i) for i in range(len(initial_population))])
def get_objectives(self, population):
objectives = {}
for chromosome in population:
objectives[chromosome] = population[chromosome].getObjectives()
return objectives
def new_population(self, parents, parents_objectives, children):
# combines the two new populations and then selects the best chromosome with respect to the crowded comparison order
children_objectives = self.get_objectives(children)
combined_population_hyperparameters = dict(list(parents.items()) + list(children.items()))
combined_population_objectives = dict(list(parents_objectives.items()) + list(children_objectives.items()))
fns = fast_nondominated_sort(combined_population_objectives)
fronts = fns[1]
new_pop_hyperparameters = {}
new_pop_objectives = {}
spaces_remaining = len(parents)
front = 0
while spaces_remaining > 0:
if spaces_remaining >= len(fronts[front]):
for chromosome in fronts[front]:
new_pop_objectives[chromosome] = combined_population_objectives[chromosome]
new_pop_hyperparameters[chromosome] = combined_population_hyperparameters[chromosome]
spaces_remaining -= 1
front += 1
else:
crowding_distances = crowding_distance(combined_population_objectives)
front_with_crowding = dict((key, crowding_distances[key]) for key in fronts[front])
sorted_by_crowding = sorted(front_with_crowding, key = lambda x:front_with_crowding[x])
for i in range(spaces_remaining):
new_pop_objectives[sorted_by_crowding[i]] = combined_population_objectives[sorted_by_crowding[i]]
new_pop_hyperparameters[sorted_by_crowding[i]] = combined_population_hyperparameters[sorted_by_crowding[i]]
spaces_remaining = 0
return new_pop_hyperparameters, new_pop_objectives, fronts
def make_children(self, parents, generation):
children = {}
keys = list(parents.keys())
while len(keys) > 1:
choice1, choice2 = self.context.r.choice(len(keys), size=2, replace=False)
key1 = keys[choice1]
key2 = keys[choice2]
keys.remove(key1)
keys.remove(key2)
child0, child1 = parents[key1].crossover(parents[key2])
child0.mutate()
child1.mutate()
new_key1 = key1[0], generation + 1
new_key2 = key2[0], generation + 1
children[new_key1] = child0
children[new_key2] = child1
return children
def evolve(self, num_generations=100, log_out=log_discard):
objectives = self.get_objectives(self.population)
for generation in range(num_generations):
self.population, objectives, self.fronts = self.new_population(self.population, objectives, self.make_children(self.population, generation))
log_out(generation, self.population, objectives, self.fronts)
return self.population.values() | 0.341802 | 0.515071 |
import falcon
import git
import os
from sqlalchemy.exc import SQLAlchemyError
from lockfile import LockFile
from db import session
import model
import util
class WaveDiff(object):
def on_post(self, req, resp, id):
try:
user = req.context['user']
if (not user.is_logged_in()) or (not user.is_org()):
resp.status = falcon.HTTP_400
return
# Kontrola zamku
lock = util.lock.git_locked()
if lock:
req.context['result'] = ('GIT uzamcen zamkem ' + lock +
'\nNekdo momentalne provadi akci s '
'gitem, opakujte prosim akci za 20 '
'sekund.')
resp.status = falcon.HTTP_409
return
pullLock = LockFile(util.admin.waveDiff.LOCKFILE)
pullLock.acquire(60) # Timeout zamku je 1 minuta
# Fetch
repo = git.Repo(util.git.GIT_SEMINAR_PATH)
repo.remotes.origin.fetch()
# Ulohy ve vlne
tasks = session.query(model.Task).\
filter(model.Task.wave == id).all()
# Diffujeme adresare uloh task.git_commit oproti HEAD
for task in tasks:
if ((not task.git_branch) or (not task.git_path) or
(not task.git_commit)):
task.deploy_status = 'default'
continue
# Checkout && pull vetve ve ktere je uloha
repo.git.checkout(task.git_branch)
repo.remotes.origin.pull()
# Kontrola existence adresare ulohy
if os.path.isdir(util.git.GIT_SEMINAR_PATH + task.git_path):
hcommit = repo.head.commit
diff = hcommit.diff(task.git_commit, paths=[task.git_path])
if len(diff) > 0:
task.deploy_status = 'diff'
else:
task.deploy_status = 'default'
session.commit()
req.context['result'] = {}
except SQLAlchemyError:
session.rollback()
req.context['result'] = 'Nastala vyjimka backendu'
raise
finally:
pullLock.release()
session.close() | endpoint/admin/waveDiff.py | import falcon
import git
import os
from sqlalchemy.exc import SQLAlchemyError
from lockfile import LockFile
from db import session
import model
import util
class WaveDiff(object):
def on_post(self, req, resp, id):
try:
user = req.context['user']
if (not user.is_logged_in()) or (not user.is_org()):
resp.status = falcon.HTTP_400
return
# Kontrola zamku
lock = util.lock.git_locked()
if lock:
req.context['result'] = ('GIT uzamcen zamkem ' + lock +
'\nNekdo momentalne provadi akci s '
'gitem, opakujte prosim akci za 20 '
'sekund.')
resp.status = falcon.HTTP_409
return
pullLock = LockFile(util.admin.waveDiff.LOCKFILE)
pullLock.acquire(60) # Timeout zamku je 1 minuta
# Fetch
repo = git.Repo(util.git.GIT_SEMINAR_PATH)
repo.remotes.origin.fetch()
# Ulohy ve vlne
tasks = session.query(model.Task).\
filter(model.Task.wave == id).all()
# Diffujeme adresare uloh task.git_commit oproti HEAD
for task in tasks:
if ((not task.git_branch) or (not task.git_path) or
(not task.git_commit)):
task.deploy_status = 'default'
continue
# Checkout && pull vetve ve ktere je uloha
repo.git.checkout(task.git_branch)
repo.remotes.origin.pull()
# Kontrola existence adresare ulohy
if os.path.isdir(util.git.GIT_SEMINAR_PATH + task.git_path):
hcommit = repo.head.commit
diff = hcommit.diff(task.git_commit, paths=[task.git_path])
if len(diff) > 0:
task.deploy_status = 'diff'
else:
task.deploy_status = 'default'
session.commit()
req.context['result'] = {}
except SQLAlchemyError:
session.rollback()
req.context['result'] = 'Nastala vyjimka backendu'
raise
finally:
pullLock.release()
session.close() | 0.25945 | 0.074433 |
from common import *
from tables import *
from scrape import Scraper
from utils.logger import *
class Miner:
""" Data Mining class.
"""
def __init__(self, read=False):
log_path = '.'.join(['datamining', __name__])
self.logger = logging.getLogger(log_path)
self.read = read
self.scraper = Scraper()
self.coin_df = None
self.coinlist = []
self.bases = []
self.exchanges = []
if self.read:
#self.logger.info("Reading datasets from file.")
pass
else:
self._import()
def _import(self):
""" Imports data into dataset. This will be used for an initial
creation of a dataset, and well thus call on the Scraper
class to get data from the APIs.
"""
pass
def _create_dataset(self):
data_file = tables.open_file('data/dataset.hdf5', mode='w')
def get_coinlist(self):
""" Gets Coin List from CryptoCompare. Will use this list to create
columns for class CoinPair.
"""
url = 'https://www.cryptocompare.com/api/data/coinlist'
df = self.scraper.get_api_data(url)
df = df.transpose()
if df is not None:
coinlist = df[['Symbol', 'CoinName', 'Id']]
coinlist['Id'] = coinlist['Id'].astype(np.int64)
coinlist.loc[:, 'pricedata'] = False
coinlist.loc[:, 'twitter'] = []
coinlist.loc[:, 'reddit'] = []
coinlist.reset_index(drop=True, inplace=True)
if self.read:
old_cl = pd.read_csv('data/coinlist.csv', encoding='utf-8')
if len(old_cl) < len(coinlist):
old_cl.to_csv('data/coinlistold.csv', index=False,
encoding='utf-8')
diff = pd.concat([old_cl, coinlist])
diff = diff.reset_index(drop=True)
diff_gp = diff.groupby(list(diff.columns)[0])
idx = [x[0] for x in diff_gp.groups.values() if len(x) == 1]
diff_coins = diff.reindex(idx)['Symbol']
coinlist = old_cl.append(diff.iloc[idx], ignore_index=True)
coinlist.to_csv('data/coinlist.csv', index=False,
encoding='utf-8')
coinlist_logger = create_logger('datamining', 'output/coinlist.log')
coinlist_logger.info('Added %d coins.' % len(diff_coins))
coinlist_logger.info('Added: %s' % diff_coins.values)
else:
coinlist.to_csv('data/coinlist.csv', index=False,
encoding='utf-8')
self.coinlist = coinlist
def get_exchange_coins(self, exchanges):
""" Gets coins that are traded on EXCHANGES. By taking an EXCHANGES
list, narrowing the exchanges list is possible. The coins will
be in a nested list in order of exchanges given, and fed into
MAKE_DATASET to create the datasets.
Args:
exchanges (list): List of exchanges to consider
Returns:
coins (list): list of lists with traded coins for each
given exchange.
"""
url = 'https://min-api.cryptocompare.com/data/all/exchanges'
df = self.scraper.get_tradepair_data(url)
coinlist = pd.read_csv('data/coinlist.csv', encoding='utf-8')
#dtype={'pricedata':'boolean'})
coins = []
data_f = {}
for ex in exchanges:
ex_df = df[ex]
ex_df.dropna(inplace=True)
ex_coins = ex_df.index.values
ex_coins = [s.encode('utf-8') for s in ex_coins]
coins.append(ex_coins)
data_f[ex] = ex_coins
for c in ex_coins:
coinlist.loc[coinlist.Symbol == c, 'pricedata'] = True
with open('data/exchange_coins.txt', 'w') as f:
json.dump(data_f, f, ensure_ascii=False)
coinlist.to_csv('data/coinlist.csv', index=False, encoding='utf-8')
return coins
def get_bases(self, coin):
""" Gets unique base coins/currencies that COIN is traded against.
Also gets the exchanges that said pairs are traded on.
"""
url = 'https://min-api.cryptocompare.com/data/all/exchanges'
df = self.scraper.get_tradepair_data(url)
self.coin_df = df.loc[coin]
arr = self.coin_df.as_matrix()
idx = np.where(self.coin_df.notnull())
self.exchanges = self.coin_df.iloc[idx].index.values
self.exchanges = [s.encode('utf-8') for s in self.exchanges]
arr = arr[idx]
self.coin_df = self.coin_df.iloc[idx]
self.coin_df = self.coin_df.reindex(self.exchanges)
self.coin_df.to_csv('data/exchanges/{}.csv'.format(coin),
encoding='utf-8')
def make_dataset(self, exchange, coin):
""" Makes Dataset for COIN, for EXCHANGE.
Args:
exchange (str): String of exchange COIN is traded on.
coin (str): String of symbol for COIN
"""
coinpath = 'data/exchanges/{}/{}.hdf5'.format(exchange, coin)
if os.path.exists(coinpath):
data_file = tables.open_file(coinpath)
bases = data_file.root.bases[:]
n_bases = len(bases)
shape = [0, n_bases, 6]
data_file.close()
base_dict = self.check_volume(exchange, bases, coin)
self.import_price(coin, base_dict, exchange, shape)
else:
self.get_bases(coin)
bases = self.coin_df[exchange]
bases = [s.encode('utf-8') for s in bases]
bases = self.check_volume(exchange, bases, coin)
n_bases = len(bases)
if n_bases == 0:
pass
else:
data_file = tables.open_file(coinpath, mode='w')
shape = [0, n_bases, 6]
filters = tables.Filters(complevel=5, complib='blosc')
data = data_file.create_earray(data_file.root, 'dataset',
tables.Atom.from_dtype(np.dtype(np.float32)),
shape=shape,
filters=filters)
time_store = data_file.create_earray(data_file.root, 'time',
tables.Atom.from_dtype(np.dtype(np.int32)),
shape=(0,),
filters=filters)
base_store = data_file.create_array(data_file.root, 'bases',
bases.keys())
data_file.close()
self.import_price(coin, bases, exchange, shape)
def check_volume(self, exchange, bases, coin):
""" Checks volume for BASES that COIN is traded with on EXCHANGE.
If the volume is 0 for all responses, will return bases with
that BASE omitted.
Args:
exchange (str): Exchange
bases (list): List of bases
coin (str): Coin that is traded.
Returns:
new_bases (dict): Dictionary of bases that COIN is traded
against that has volume. Keys = Base, Values = API data
(OHLCV)
"""
new_bases = {}
for base in bases:
url = 'https://min-api.cryptocompare.com/data/histohour?' +\
'fsym=' + coin +\
'&tsym=' + base +\
'&limit=2000' +\
'&aggregate=3' +\
'&e=' + exchange
data = self.scraper.get_api_data(url)
if data['volumefrom'].sum() == 0 and data['volumeto'].sum() == 0:
self.logger.info('%s for %s on %s has no volume'
% (base, coin, exchange))
else:
new_bases[base] = data
return new_bases
def import_price(self, coin, bases, exchange, shape):
""" Imports price data for COIN.
Args:
coin (str): coin to import price data for
bases (dict): bases coin and dataframes that it's traded against
exchange (str): exchange coin is traded on
shape (tuple): shape of dataset
"""
coinpath = 'data/exchanges/{}/{}.hdf5'.format(exchange, coin)
data_file = tables.open_file(coinpath, mode='a')
shape[0] = 1
add_data = np.empty(shape, dtype=np.float32)
add_data.fill(np.nan)
time_data = data_file.root.time[:].tolist()
base_keys = bases.keys()
for b in base_keys:
"""
url = 'https://min-api.cryptocompare.com/data/histohour?' +\
'fsym=' + coin +\
'&tsym=' + b +\
'&limit=2000' +\
'&aggregate=3' +\
'&e=' + exchange
apidata = self.scraper.get_api_data(url)
"""
apidata = bases[b]
p_idx = base_keys.index(b)
for i, row in apidata.iterrows():
time = row['time']
if time in time_data:
d_idx = time_data.index(time)
add_data[0] = data_file.root.dataset[d_idx]
if (~np.any(np.isnan(add_data[0, p_idx, :]))):
self.logger.debug('Data for %d for pair %s exists'
% (time, b))
continue
add_data[0, p_idx, 0] = row['open']
add_data[0, p_idx, 1] = row['high']
add_data[0, p_idx, 2] = row['low']
add_data[0, p_idx, 3] = row['close']
add_data[0, p_idx, 4] = row['volumefrom']
add_data[0, p_idx, 5] = row['volumeto']
if time in time_data:
data_file.root.dataset[d_idx] = add_data
else:
data_file.root.time.append([time])
time_data.append(time)
data_file.root.dataset.append(add_data)
data_file.close()
def import_supply(self, read=False):
""" Imports historical circulating supplies. Will do this
based on coins wtih a .csv file in the folder.
The method will pull data via api request from coincap.io,
and store the timestamp, market cap, and price from the json.
"""
coinlist = []
for f in listdir('data/exchanges'):
if f.endswith('.csv'):
coinlist.append(f.split(".")[0])
for c in coinlist:
hdf5_path = 'data/exchanges/{}_supply.hdf5'.format(c)
if read:#os.path.exists(hdf5_path):
hdf5_file = tables.open_file(hdf5_path, mode='a')
timedata = hdf5_file.root.dataset[:, 0]
else:
hdf5_file = tables.open_file(hdf5_path, mode='w')
shape = (0, 3)
filters = tables.Filters(complevel=5, complib='blosc')
data = hdf5_file.create_earray(hdf5_file.root, 'dataset',
tables.Atom.from_dtype(np.dtype(np.int32)),
shape=shape,
filters=filters)
timedata = []
apidata = self.scraper.get_coincapio_data(c)
if apidata is None:
self.logger.info('{} has no supply data'.format(c))
hdf5_file.close()
continue
else:
for a in apidata:
if a[0] in timedata:
continue
else:
timedata.append(a[0])
hdf5_file.root.dataset.append([a])
hdf5_file.close() | datamining/dataminer.py | from common import *
from tables import *
from scrape import Scraper
from utils.logger import *
class Miner:
""" Data Mining class.
"""
def __init__(self, read=False):
log_path = '.'.join(['datamining', __name__])
self.logger = logging.getLogger(log_path)
self.read = read
self.scraper = Scraper()
self.coin_df = None
self.coinlist = []
self.bases = []
self.exchanges = []
if self.read:
#self.logger.info("Reading datasets from file.")
pass
else:
self._import()
def _import(self):
""" Imports data into dataset. This will be used for an initial
creation of a dataset, and well thus call on the Scraper
class to get data from the APIs.
"""
pass
def _create_dataset(self):
data_file = tables.open_file('data/dataset.hdf5', mode='w')
def get_coinlist(self):
""" Gets Coin List from CryptoCompare. Will use this list to create
columns for class CoinPair.
"""
url = 'https://www.cryptocompare.com/api/data/coinlist'
df = self.scraper.get_api_data(url)
df = df.transpose()
if df is not None:
coinlist = df[['Symbol', 'CoinName', 'Id']]
coinlist['Id'] = coinlist['Id'].astype(np.int64)
coinlist.loc[:, 'pricedata'] = False
coinlist.loc[:, 'twitter'] = []
coinlist.loc[:, 'reddit'] = []
coinlist.reset_index(drop=True, inplace=True)
if self.read:
old_cl = pd.read_csv('data/coinlist.csv', encoding='utf-8')
if len(old_cl) < len(coinlist):
old_cl.to_csv('data/coinlistold.csv', index=False,
encoding='utf-8')
diff = pd.concat([old_cl, coinlist])
diff = diff.reset_index(drop=True)
diff_gp = diff.groupby(list(diff.columns)[0])
idx = [x[0] for x in diff_gp.groups.values() if len(x) == 1]
diff_coins = diff.reindex(idx)['Symbol']
coinlist = old_cl.append(diff.iloc[idx], ignore_index=True)
coinlist.to_csv('data/coinlist.csv', index=False,
encoding='utf-8')
coinlist_logger = create_logger('datamining', 'output/coinlist.log')
coinlist_logger.info('Added %d coins.' % len(diff_coins))
coinlist_logger.info('Added: %s' % diff_coins.values)
else:
coinlist.to_csv('data/coinlist.csv', index=False,
encoding='utf-8')
self.coinlist = coinlist
def get_exchange_coins(self, exchanges):
""" Gets coins that are traded on EXCHANGES. By taking an EXCHANGES
list, narrowing the exchanges list is possible. The coins will
be in a nested list in order of exchanges given, and fed into
MAKE_DATASET to create the datasets.
Args:
exchanges (list): List of exchanges to consider
Returns:
coins (list): list of lists with traded coins for each
given exchange.
"""
url = 'https://min-api.cryptocompare.com/data/all/exchanges'
df = self.scraper.get_tradepair_data(url)
coinlist = pd.read_csv('data/coinlist.csv', encoding='utf-8')
#dtype={'pricedata':'boolean'})
coins = []
data_f = {}
for ex in exchanges:
ex_df = df[ex]
ex_df.dropna(inplace=True)
ex_coins = ex_df.index.values
ex_coins = [s.encode('utf-8') for s in ex_coins]
coins.append(ex_coins)
data_f[ex] = ex_coins
for c in ex_coins:
coinlist.loc[coinlist.Symbol == c, 'pricedata'] = True
with open('data/exchange_coins.txt', 'w') as f:
json.dump(data_f, f, ensure_ascii=False)
coinlist.to_csv('data/coinlist.csv', index=False, encoding='utf-8')
return coins
def get_bases(self, coin):
""" Gets unique base coins/currencies that COIN is traded against.
Also gets the exchanges that said pairs are traded on.
"""
url = 'https://min-api.cryptocompare.com/data/all/exchanges'
df = self.scraper.get_tradepair_data(url)
self.coin_df = df.loc[coin]
arr = self.coin_df.as_matrix()
idx = np.where(self.coin_df.notnull())
self.exchanges = self.coin_df.iloc[idx].index.values
self.exchanges = [s.encode('utf-8') for s in self.exchanges]
arr = arr[idx]
self.coin_df = self.coin_df.iloc[idx]
self.coin_df = self.coin_df.reindex(self.exchanges)
self.coin_df.to_csv('data/exchanges/{}.csv'.format(coin),
encoding='utf-8')
def make_dataset(self, exchange, coin):
""" Makes Dataset for COIN, for EXCHANGE.
Args:
exchange (str): String of exchange COIN is traded on.
coin (str): String of symbol for COIN
"""
coinpath = 'data/exchanges/{}/{}.hdf5'.format(exchange, coin)
if os.path.exists(coinpath):
data_file = tables.open_file(coinpath)
bases = data_file.root.bases[:]
n_bases = len(bases)
shape = [0, n_bases, 6]
data_file.close()
base_dict = self.check_volume(exchange, bases, coin)
self.import_price(coin, base_dict, exchange, shape)
else:
self.get_bases(coin)
bases = self.coin_df[exchange]
bases = [s.encode('utf-8') for s in bases]
bases = self.check_volume(exchange, bases, coin)
n_bases = len(bases)
if n_bases == 0:
pass
else:
data_file = tables.open_file(coinpath, mode='w')
shape = [0, n_bases, 6]
filters = tables.Filters(complevel=5, complib='blosc')
data = data_file.create_earray(data_file.root, 'dataset',
tables.Atom.from_dtype(np.dtype(np.float32)),
shape=shape,
filters=filters)
time_store = data_file.create_earray(data_file.root, 'time',
tables.Atom.from_dtype(np.dtype(np.int32)),
shape=(0,),
filters=filters)
base_store = data_file.create_array(data_file.root, 'bases',
bases.keys())
data_file.close()
self.import_price(coin, bases, exchange, shape)
def check_volume(self, exchange, bases, coin):
""" Checks volume for BASES that COIN is traded with on EXCHANGE.
If the volume is 0 for all responses, will return bases with
that BASE omitted.
Args:
exchange (str): Exchange
bases (list): List of bases
coin (str): Coin that is traded.
Returns:
new_bases (dict): Dictionary of bases that COIN is traded
against that has volume. Keys = Base, Values = API data
(OHLCV)
"""
new_bases = {}
for base in bases:
url = 'https://min-api.cryptocompare.com/data/histohour?' +\
'fsym=' + coin +\
'&tsym=' + base +\
'&limit=2000' +\
'&aggregate=3' +\
'&e=' + exchange
data = self.scraper.get_api_data(url)
if data['volumefrom'].sum() == 0 and data['volumeto'].sum() == 0:
self.logger.info('%s for %s on %s has no volume'
% (base, coin, exchange))
else:
new_bases[base] = data
return new_bases
def import_price(self, coin, bases, exchange, shape):
""" Imports price data for COIN.
Args:
coin (str): coin to import price data for
bases (dict): bases coin and dataframes that it's traded against
exchange (str): exchange coin is traded on
shape (tuple): shape of dataset
"""
coinpath = 'data/exchanges/{}/{}.hdf5'.format(exchange, coin)
data_file = tables.open_file(coinpath, mode='a')
shape[0] = 1
add_data = np.empty(shape, dtype=np.float32)
add_data.fill(np.nan)
time_data = data_file.root.time[:].tolist()
base_keys = bases.keys()
for b in base_keys:
"""
url = 'https://min-api.cryptocompare.com/data/histohour?' +\
'fsym=' + coin +\
'&tsym=' + b +\
'&limit=2000' +\
'&aggregate=3' +\
'&e=' + exchange
apidata = self.scraper.get_api_data(url)
"""
apidata = bases[b]
p_idx = base_keys.index(b)
for i, row in apidata.iterrows():
time = row['time']
if time in time_data:
d_idx = time_data.index(time)
add_data[0] = data_file.root.dataset[d_idx]
if (~np.any(np.isnan(add_data[0, p_idx, :]))):
self.logger.debug('Data for %d for pair %s exists'
% (time, b))
continue
add_data[0, p_idx, 0] = row['open']
add_data[0, p_idx, 1] = row['high']
add_data[0, p_idx, 2] = row['low']
add_data[0, p_idx, 3] = row['close']
add_data[0, p_idx, 4] = row['volumefrom']
add_data[0, p_idx, 5] = row['volumeto']
if time in time_data:
data_file.root.dataset[d_idx] = add_data
else:
data_file.root.time.append([time])
time_data.append(time)
data_file.root.dataset.append(add_data)
data_file.close()
def import_supply(self, read=False):
""" Imports historical circulating supplies. Will do this
based on coins wtih a .csv file in the folder.
The method will pull data via api request from coincap.io,
and store the timestamp, market cap, and price from the json.
"""
coinlist = []
for f in listdir('data/exchanges'):
if f.endswith('.csv'):
coinlist.append(f.split(".")[0])
for c in coinlist:
hdf5_path = 'data/exchanges/{}_supply.hdf5'.format(c)
if read:#os.path.exists(hdf5_path):
hdf5_file = tables.open_file(hdf5_path, mode='a')
timedata = hdf5_file.root.dataset[:, 0]
else:
hdf5_file = tables.open_file(hdf5_path, mode='w')
shape = (0, 3)
filters = tables.Filters(complevel=5, complib='blosc')
data = hdf5_file.create_earray(hdf5_file.root, 'dataset',
tables.Atom.from_dtype(np.dtype(np.int32)),
shape=shape,
filters=filters)
timedata = []
apidata = self.scraper.get_coincapio_data(c)
if apidata is None:
self.logger.info('{} has no supply data'.format(c))
hdf5_file.close()
continue
else:
for a in apidata:
if a[0] in timedata:
continue
else:
timedata.append(a[0])
hdf5_file.root.dataset.append([a])
hdf5_file.close() | 0.624752 | 0.261229 |
from typing import List
import pytest
from fwordle.game.wordle_guess import WordleGuess, WordleLetterState
@pytest.fixture
def word_guess() -> WordleGuess:
wg = WordleGuess(1)
wg.append("a", "p1")
wg.append("r", "p1")
wg.append("i", "p1")
wg.append("s", "p1")
wg.append("e", "p1")
return wg
@pytest.fixture
def word_guess_multi_letter() -> WordleGuess:
wg = WordleGuess(1)
wg.append("g", "p1")
wg.append("r", "p1")
wg.append("e", "p1")
wg.append("e", "p1")
wg.append("t", "p1")
return wg
def verify_word(word_guess: WordleGuess, states: List[WordleLetterState]):
if len(word_guess.letters) != len(states):
raise AssertionError("test case was not written correctly")
for i, lg in enumerate(word_guess.letters):
assert lg.state == states[i]
def test_wordle_guess_verify_simple(word_guess: WordleGuess):
assert word_guess.verify("arise")
assert not word_guess.verify("raise")
verify_word(
word_guess,
[
WordleLetterState.PARTIAL,
WordleLetterState.PARTIAL,
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
],
)
assert not word_guess.verify("ccccc")
verify_word(
word_guess,
[
WordleLetterState.INCORRECT,
WordleLetterState.INCORRECT,
WordleLetterState.INCORRECT,
WordleLetterState.INCORRECT,
WordleLetterState.INCORRECT,
],
)
def test_wordle_guess_verify_double_letters(word_guess_multi_letter):
assert not word_guess_multi_letter.verify("eeeer")
verify_word(
word_guess_multi_letter,
[
WordleLetterState.INCORRECT,
WordleLetterState.PARTIAL,
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
WordleLetterState.INCORRECT,
],
)
assert not word_guess_multi_letter.verify("grtee")
verify_word(
word_guess_multi_letter,
[
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
WordleLetterState.PARTIAL,
WordleLetterState.CORRECT,
WordleLetterState.PARTIAL,
],
)
assert not word_guess_multi_letter.verify("grtet")
verify_word(
word_guess_multi_letter,
[
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
WordleLetterState.INCORRECT,
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
],
) | backend/tests/unit/game/wordle_guess_test.py | from typing import List
import pytest
from fwordle.game.wordle_guess import WordleGuess, WordleLetterState
@pytest.fixture
def word_guess() -> WordleGuess:
wg = WordleGuess(1)
wg.append("a", "p1")
wg.append("r", "p1")
wg.append("i", "p1")
wg.append("s", "p1")
wg.append("e", "p1")
return wg
@pytest.fixture
def word_guess_multi_letter() -> WordleGuess:
wg = WordleGuess(1)
wg.append("g", "p1")
wg.append("r", "p1")
wg.append("e", "p1")
wg.append("e", "p1")
wg.append("t", "p1")
return wg
def verify_word(word_guess: WordleGuess, states: List[WordleLetterState]):
if len(word_guess.letters) != len(states):
raise AssertionError("test case was not written correctly")
for i, lg in enumerate(word_guess.letters):
assert lg.state == states[i]
def test_wordle_guess_verify_simple(word_guess: WordleGuess):
assert word_guess.verify("arise")
assert not word_guess.verify("raise")
verify_word(
word_guess,
[
WordleLetterState.PARTIAL,
WordleLetterState.PARTIAL,
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
],
)
assert not word_guess.verify("ccccc")
verify_word(
word_guess,
[
WordleLetterState.INCORRECT,
WordleLetterState.INCORRECT,
WordleLetterState.INCORRECT,
WordleLetterState.INCORRECT,
WordleLetterState.INCORRECT,
],
)
def test_wordle_guess_verify_double_letters(word_guess_multi_letter):
assert not word_guess_multi_letter.verify("eeeer")
verify_word(
word_guess_multi_letter,
[
WordleLetterState.INCORRECT,
WordleLetterState.PARTIAL,
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
WordleLetterState.INCORRECT,
],
)
assert not word_guess_multi_letter.verify("grtee")
verify_word(
word_guess_multi_letter,
[
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
WordleLetterState.PARTIAL,
WordleLetterState.CORRECT,
WordleLetterState.PARTIAL,
],
)
assert not word_guess_multi_letter.verify("grtet")
verify_word(
word_guess_multi_letter,
[
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
WordleLetterState.INCORRECT,
WordleLetterState.CORRECT,
WordleLetterState.CORRECT,
],
) | 0.564699 | 0.38194 |
import json
from pprint import pformat
import click
import jira_scraper.jira_worker as scraper
import jira_template_commentor.util as jira_template_commentor_util
import logger
from common_util import project_dir_path
import os
import configparser
#constant
THIS_MODULE_DIRECTORY = os.path.join(project_dir_path, 'jira_template_commentor')
@click.command()
@click.option('--open-tickets-filter', help='If this argument is given, it picks up the tasks that satisfy this '
'filter instead of default filters.')
def post_template_comment_on_new_tickets(open_tickets_filter):
"""This creates a template comment on each ticket which needs to be filled by a developer before the ticket is
marked as completed."""
try:
jira_obj = scraper.connect_to_jira()
# read jql filters config
jql_filters = configparser.ConfigParser()
jql_filters_config_file = os.path.join(os.path.join(THIS_MODULE_DIRECTORY, 'settings'), 'jira_jql_filters.config')
jql_filters.read(jql_filters_config_file)
default_filters = dict(jql_filters['FILTERS_FOR_TRAINING_MODEL'])
logger.logger.info("Loaded default filters")
# finalize which filter to be used
if open_tickets_filter is not None:
filter_to_use = {'custom': open_tickets_filter}
else:
filter_to_use = default_filters
logger.logger.info("Filter I'm using : " + pformat(filter_to_use))
# crawl filter and post templated comments
for filter_name, filter_query in filter_to_use.items():
# scrape every ticket in filter
jira_tickets_corpus = scraper.filter_crawler(jira_obj, filter_query)
# find jira tickets on which already commented the template comment
already_commented_tickets_file = os.path.join(os.path.join(THIS_MODULE_DIRECTORY, 'data'),
filter_name+'_already_commented_template_tickets.json')
jira_template_commentor_util.create_already_commented_tickets_file_if_not_exists(already_commented_tickets_file)
with open(already_commented_tickets_file, 'r') as data_file:
tickets_already_commented = json.load(data_file)
# Get only those tickets which do not have templated comment
new_tickets_corpus = [ticket for ticket in jira_tickets_corpus if
ticket['jiraid'] not in tickets_already_commented]
if not new_tickets_corpus:
logger.logger.info("No new tickets to comment on from " + filter_name + ". Moving on to next filter.")
continue
# post template comment.
templated_comment = jira_template_commentor_util.get_template_comment()
for ticket in new_tickets_corpus:
scraper.comment_on_task(jira_obj, ticket['jiraid'], templated_comment)
tickets_already_commented.append(ticket['jiraid'])
# update the already commented file with new jira IDs
with open(already_commented_tickets_file, 'w') as outfile:
json.dump(tickets_already_commented, outfile)
logger.logger.info("Done for " + str(filter_name))
logger.logger.info("Execution completed.")
except Exception as e:
logger.logger.exception(e)
logger.sentry_client.captureException() | jira_template_commentor/commands.py | import json
from pprint import pformat
import click
import jira_scraper.jira_worker as scraper
import jira_template_commentor.util as jira_template_commentor_util
import logger
from common_util import project_dir_path
import os
import configparser
#constant
THIS_MODULE_DIRECTORY = os.path.join(project_dir_path, 'jira_template_commentor')
@click.command()
@click.option('--open-tickets-filter', help='If this argument is given, it picks up the tasks that satisfy this '
'filter instead of default filters.')
def post_template_comment_on_new_tickets(open_tickets_filter):
"""This creates a template comment on each ticket which needs to be filled by a developer before the ticket is
marked as completed."""
try:
jira_obj = scraper.connect_to_jira()
# read jql filters config
jql_filters = configparser.ConfigParser()
jql_filters_config_file = os.path.join(os.path.join(THIS_MODULE_DIRECTORY, 'settings'), 'jira_jql_filters.config')
jql_filters.read(jql_filters_config_file)
default_filters = dict(jql_filters['FILTERS_FOR_TRAINING_MODEL'])
logger.logger.info("Loaded default filters")
# finalize which filter to be used
if open_tickets_filter is not None:
filter_to_use = {'custom': open_tickets_filter}
else:
filter_to_use = default_filters
logger.logger.info("Filter I'm using : " + pformat(filter_to_use))
# crawl filter and post templated comments
for filter_name, filter_query in filter_to_use.items():
# scrape every ticket in filter
jira_tickets_corpus = scraper.filter_crawler(jira_obj, filter_query)
# find jira tickets on which already commented the template comment
already_commented_tickets_file = os.path.join(os.path.join(THIS_MODULE_DIRECTORY, 'data'),
filter_name+'_already_commented_template_tickets.json')
jira_template_commentor_util.create_already_commented_tickets_file_if_not_exists(already_commented_tickets_file)
with open(already_commented_tickets_file, 'r') as data_file:
tickets_already_commented = json.load(data_file)
# Get only those tickets which do not have templated comment
new_tickets_corpus = [ticket for ticket in jira_tickets_corpus if
ticket['jiraid'] not in tickets_already_commented]
if not new_tickets_corpus:
logger.logger.info("No new tickets to comment on from " + filter_name + ". Moving on to next filter.")
continue
# post template comment.
templated_comment = jira_template_commentor_util.get_template_comment()
for ticket in new_tickets_corpus:
scraper.comment_on_task(jira_obj, ticket['jiraid'], templated_comment)
tickets_already_commented.append(ticket['jiraid'])
# update the already commented file with new jira IDs
with open(already_commented_tickets_file, 'w') as outfile:
json.dump(tickets_already_commented, outfile)
logger.logger.info("Done for " + str(filter_name))
logger.logger.info("Execution completed.")
except Exception as e:
logger.logger.exception(e)
logger.sentry_client.captureException() | 0.316264 | 0.211396 |
import json
import sys
from pathlib import Path
from django.conf import settings
from django.contrib import messages
from django.core.exceptions import ObjectDoesNotExist
from django.db.models import F
from django.forms import formset_factory
from django.http import HttpResponse, HttpResponseRedirect
from django.shortcuts import get_object_or_404, render, redirect
from django.urls import reverse_lazy
from django.views import View
from django.views.generic import DetailView, ListView, TemplateView, View
from django.views.generic.edit import CreateView, DeleteView, UpdateView, FormView
from experiments import forms as forms
from experiments import models as models
from experiments.utils.repo import find_new_experiments, get_latest_commit
sys.path.append(Path(settings.ROOT_DIR, "expfactory_deploy_local"))
from expfactory_deploy_local.utils import generate_experiment_context
# Repo Views
class RepoOriginListView(ListView):
model = models.RepoOrigin
queryset = models.RepoOrigin.objects.prefetch_related("experimentrepo_set")
class RepoOriginCreate(CreateView):
model = models.RepoOrigin
form_class = forms.RepoOriginForm
success_url = reverse_lazy("experiments:experiment-repo-list")
def form_valid(self, form):
response = super().form_valid(form)
self.object.clone()
return response
# Experiment Views
def experiment_instances_from_latest(experiment_repos):
for experiment_repo in experiment_repos:
latest = get_latest_commit(experiment_repo.location)
ExperimentInstance.get_or_create(
experiment_repo_id=experiment_repo.id, commit=latest
)
class ExperimentRepoList(ListView):
model = models.ExperimentRepo
queryset = models.ExperimentRepo.objects.prefetch_related("origin")
class ExperimentRepoDetail(DetailView):
model = models.ExperimentRepo
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
batteries = models.Battery.objects.filter(batteryexperiments__experiment_instance__experiment_repo_id=self.get_object())
results = models.Result.objects.filter(battery_experiment__experiment_instance__experiment_repo_id=self.get_object())
batt_results = [(batt, list(results.filter(battery_experiment__battery=batt))) for batt in batteries]
context['batt_results'] = batt_results
return context
def add_new_experiments(request):
created_repos, created_experiments, errors = find_new_experiments()
for repo in created_repos:
messages.info(request, f"Tracking previously unseen repository {repo.url}")
for experiment in created_experiments:
messages.info(request, f"Added new experiment {experiment.name}")
for error in errors:
messages.error(request, error)
return redirect('/experiments')
class ExperimentRepoUpdate(UpdateView):
model = models.ExperimentRepo
fields = ["name"]
class ExperimentRepoDelete(DeleteView):
model = models.ExperimentRepo
success_url = reverse_lazy("experiment-repo-list")
# Battery Views
# Inject list of experiment repos into a context and the id attribute used by the form
def add_experiment_repos(context):
context[
"experiment_repo_list"
] = models.ExperimentRepo.objects.all().prefetch_related("origin")
context["exp_repo_select_id"] = "place_holder"
class BatteryList(ListView):
model = models.Battery
def get_queryset(self):
return models.Battery.objects.filter(status='template').prefetch_related("children")
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
return context
class BatteryDetail(DetailView):
model = models.Battery
queryset = models.Battery.objects.prefetch_related("assignment_set", "experiment_instances")
"""
View used for battery creation. Handles creating expdeirmentinstance
objects and order entries in the battery <-> experiment instance pivot table
as needed.
"""
class BatteryComplex(TemplateView):
template_name = "experiments/battery_form.html"
battery = None
battery_kwargs = {}
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
qs = models.ExperimentInstance.objects.none()
ordering = None
if self.battery:
qs = models.ExperimentInstance.objects.filter(
batteryexperiments__battery=self.battery
).order_by("batteryexperiments__order")
ordering = qs.annotate(exp_order=F("batteryexperiments__order"))
context["form"] = forms.BatteryForm(**self.battery_kwargs)
add_experiment_repos(context)
context["exp_instance_formset"] = forms.ExpInstanceFormset(
queryset=qs, form_kwargs={"ordering": ordering}
)
return context
def get_object(self):
battery_id = self.kwargs.get("pk")
if battery_id is not None:
battery = get_object_or_404(models.Battery, pk=battery_id)
self.battery = battery
self.battery_kwargs = {"instance": battery}
def get(self, request, *args, **kwargs):
self.get_object()
if self.battery.status in ['published', 'inactive']:
return redirect("experiments:battery-detail", pk=self.battery.id)
return self.render_to_response(self.get_context_data())
def post(self, request, *args, **kwargs):
self.get_object()
form = forms.BatteryForm(self.request.POST, **self.battery_kwargs)
battery = form.save()
ordering = models.ExperimentInstance.objects.filter(
batteryexperiments__battery=self.battery
).order_by("batteryexperiments__order")
exp_instance_formset = forms.ExpInstanceFormset(
self.request.POST, form_kwargs={"battery_id": battery.id}
)
valid = exp_instance_formset.is_valid()
if valid:
ei = exp_instance_formset.save()
battery.batteryexperiments_set.exclude(experiment_instance__in=ei).delete()
elif not valid:
print(exp_instance_formset.errors)
if form.is_valid():
return HttpResponseRedirect("/battery/")
else:
print(form.errors)
return HttpResponseRedirect(reverse_lazy("battery-list"))
class BatteryClone(View):
def get(self, request, *args, **kwargs):
pk = self.kwargs.get("pk")
batt = get_object_or_404(models.Battery, pk=pk)
return redirect('experiments:battery-list')
"""
class BatteryDeploymentDelete(DeleteView):
model = models.Battery
success_url = reverse_lazy('battery-list')
"""
def jspsych_context(exp_instance):
deploy_static_fs = exp_instance.deploy_static()
deploy_static_url = deploy_static_fs.replace(
settings.DEPLOYMENT_DIR, settings.STATIC_DEPLOYMENT_URL
)
location = exp_instance.experiment_repo_id.location
exp_fs_path = Path(deploy_static_fs, Path(location).stem)
exp_url_path = Path(deploy_static_url, Path(location).stem)
# default js/css location for poldracklab style experiments
static_url_path = Path(settings.STATIC_NON_REPO_URL, "default")
return generate_experiment_context(
exp_fs_path, static_url_path, exp_url_path
)
class Preview(View):
def get(self, request, *args, **kwargs):
exp_id = self.kwargs.get("exp_id")
experiment = get_object_or_404(models.ExperimentRepo, id=exp_id)
commit = experiment.get_latest_commit()
exp_instance, created = models.ExperimentInstance.objects.get_or_create(
experiment_repo_id=experiment, commit=commit
)
# Could embed commit or instance id in kwargs, default to latest for now
# default template for poldracklab style experiments
template = "experiments/jspsych_deploy.html"
context = jspsych_context(exp_instance)
return render(request, template, context)
class Serve(TemplateView):
subjectg = None
battery = None
experiment = None
assignment = None
def get_template_names(self):
"""
Return a list of template names to be used for the request. Must return
a list. May not be called if render_to_response() is overridden.
"""
return ["experiments/jspsych_deploy.html"]
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
return context
def set_objects(self):
subject_id = self.kwargs.get("subject_id")
battery_id = self.kwargs.get("battery_id")
""" we might accept the uuid assocaited with the subject instead of its id """
if subject_id is not None:
self.subject = get_object_or_404(
models.Subject, id=subject_id
)
else:
self.subject = None
self.battery = get_object_or_404(
models.Battery, id=battery_id
)
try:
self.assignment = models.Assignment.objects.get(
subject=self.subject, battery=self.battery
)
except ObjectDoesNotExist:
# make new assignment for testing, in future 404.
assignment = models.Assignment(
subject_id=subject_id, battery_id=battery_id,
)
assignment.save()
self.assignment = assignment
def get(self, request, *args, **kwargs):
self.set_objects()
if self.assignment.consent_accepted is not True:
# display instructions and consent
pass
self.experiment = self.assignment.get_next_experiment()
exp_context = jspsych_context(self.experiment)
exp_context["post_url"] = reverse_lazy("experiments:push-results", args=[self.assignment.id, self.experiment.id])
exp_context["next_page"] = reverse_lazy("serve-battery", args=[self.subject.id, self.battery.id])
context = {**self.get_context_data(), **exp_context}
return self.render_to_response(context)
def post(self, request, *args, **kwargs):
return
class Results(View):
# If more frameworks are added this would dispatch to their respective
# versions of this function.
# expfactory-docker purges keys and process survey data at this step
def process_exp_data(self, post_data, assignment):
data = json.loads(post_data)
finished = data.get("status") == "finished"
if finished:
assignment.status = "completed"
elif assignment.status == "not-started":
assignment.status = "started"
return data, finished
def post(self, request, *args, **kwargs):
assignment_id = self.kwargs.get("assignment_id")
experiment_id = self.kwargs.get("experiment_id")
exp_instance = get_object_or_404(models.ExperimentInstance, id=experiment_id)
assignment = get_object_or_404(models.Assignment, id=assignment_id)
batt_exp = get_object_or_404(models.BatteryExperiments, battery=assignment.battery, experiment_instance=exp_instance)
data, finished = self.process_exp_data(request.body, assignment)
if finished:
models.Result(assignment=assignment, battery_experiment=batt_exp, subject=assignment.subject, data=data, status="completed").save()
assignment.save()
return HttpResponse('recieved')
class SubjectList(ListView):
model = models.Subject
queryset = models.Subject.objects.prefetch_related("assignment_set")
class CreateSubjects(FormView):
template_name = 'experiments/create_subjects.html'
form_class = forms.SubjectCount
success_url = reverse_lazy('experiments:subject-list')
def form_valid(self, form):
new_subjects = [models.Subject() for i in range(form.cleaned_data['count'])]
models.Subject.objects.bulk_create(new_subjects)
return super().form_valid(form) | expfactory_deploy/experiments/views.py | import json
import sys
from pathlib import Path
from django.conf import settings
from django.contrib import messages
from django.core.exceptions import ObjectDoesNotExist
from django.db.models import F
from django.forms import formset_factory
from django.http import HttpResponse, HttpResponseRedirect
from django.shortcuts import get_object_or_404, render, redirect
from django.urls import reverse_lazy
from django.views import View
from django.views.generic import DetailView, ListView, TemplateView, View
from django.views.generic.edit import CreateView, DeleteView, UpdateView, FormView
from experiments import forms as forms
from experiments import models as models
from experiments.utils.repo import find_new_experiments, get_latest_commit
sys.path.append(Path(settings.ROOT_DIR, "expfactory_deploy_local"))
from expfactory_deploy_local.utils import generate_experiment_context
# Repo Views
class RepoOriginListView(ListView):
model = models.RepoOrigin
queryset = models.RepoOrigin.objects.prefetch_related("experimentrepo_set")
class RepoOriginCreate(CreateView):
model = models.RepoOrigin
form_class = forms.RepoOriginForm
success_url = reverse_lazy("experiments:experiment-repo-list")
def form_valid(self, form):
response = super().form_valid(form)
self.object.clone()
return response
# Experiment Views
def experiment_instances_from_latest(experiment_repos):
for experiment_repo in experiment_repos:
latest = get_latest_commit(experiment_repo.location)
ExperimentInstance.get_or_create(
experiment_repo_id=experiment_repo.id, commit=latest
)
class ExperimentRepoList(ListView):
model = models.ExperimentRepo
queryset = models.ExperimentRepo.objects.prefetch_related("origin")
class ExperimentRepoDetail(DetailView):
model = models.ExperimentRepo
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
batteries = models.Battery.objects.filter(batteryexperiments__experiment_instance__experiment_repo_id=self.get_object())
results = models.Result.objects.filter(battery_experiment__experiment_instance__experiment_repo_id=self.get_object())
batt_results = [(batt, list(results.filter(battery_experiment__battery=batt))) for batt in batteries]
context['batt_results'] = batt_results
return context
def add_new_experiments(request):
created_repos, created_experiments, errors = find_new_experiments()
for repo in created_repos:
messages.info(request, f"Tracking previously unseen repository {repo.url}")
for experiment in created_experiments:
messages.info(request, f"Added new experiment {experiment.name}")
for error in errors:
messages.error(request, error)
return redirect('/experiments')
class ExperimentRepoUpdate(UpdateView):
model = models.ExperimentRepo
fields = ["name"]
class ExperimentRepoDelete(DeleteView):
model = models.ExperimentRepo
success_url = reverse_lazy("experiment-repo-list")
# Battery Views
# Inject list of experiment repos into a context and the id attribute used by the form
def add_experiment_repos(context):
context[
"experiment_repo_list"
] = models.ExperimentRepo.objects.all().prefetch_related("origin")
context["exp_repo_select_id"] = "place_holder"
class BatteryList(ListView):
model = models.Battery
def get_queryset(self):
return models.Battery.objects.filter(status='template').prefetch_related("children")
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
return context
class BatteryDetail(DetailView):
model = models.Battery
queryset = models.Battery.objects.prefetch_related("assignment_set", "experiment_instances")
"""
View used for battery creation. Handles creating expdeirmentinstance
objects and order entries in the battery <-> experiment instance pivot table
as needed.
"""
class BatteryComplex(TemplateView):
template_name = "experiments/battery_form.html"
battery = None
battery_kwargs = {}
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
qs = models.ExperimentInstance.objects.none()
ordering = None
if self.battery:
qs = models.ExperimentInstance.objects.filter(
batteryexperiments__battery=self.battery
).order_by("batteryexperiments__order")
ordering = qs.annotate(exp_order=F("batteryexperiments__order"))
context["form"] = forms.BatteryForm(**self.battery_kwargs)
add_experiment_repos(context)
context["exp_instance_formset"] = forms.ExpInstanceFormset(
queryset=qs, form_kwargs={"ordering": ordering}
)
return context
def get_object(self):
battery_id = self.kwargs.get("pk")
if battery_id is not None:
battery = get_object_or_404(models.Battery, pk=battery_id)
self.battery = battery
self.battery_kwargs = {"instance": battery}
def get(self, request, *args, **kwargs):
self.get_object()
if self.battery.status in ['published', 'inactive']:
return redirect("experiments:battery-detail", pk=self.battery.id)
return self.render_to_response(self.get_context_data())
def post(self, request, *args, **kwargs):
self.get_object()
form = forms.BatteryForm(self.request.POST, **self.battery_kwargs)
battery = form.save()
ordering = models.ExperimentInstance.objects.filter(
batteryexperiments__battery=self.battery
).order_by("batteryexperiments__order")
exp_instance_formset = forms.ExpInstanceFormset(
self.request.POST, form_kwargs={"battery_id": battery.id}
)
valid = exp_instance_formset.is_valid()
if valid:
ei = exp_instance_formset.save()
battery.batteryexperiments_set.exclude(experiment_instance__in=ei).delete()
elif not valid:
print(exp_instance_formset.errors)
if form.is_valid():
return HttpResponseRedirect("/battery/")
else:
print(form.errors)
return HttpResponseRedirect(reverse_lazy("battery-list"))
class BatteryClone(View):
def get(self, request, *args, **kwargs):
pk = self.kwargs.get("pk")
batt = get_object_or_404(models.Battery, pk=pk)
return redirect('experiments:battery-list')
"""
class BatteryDeploymentDelete(DeleteView):
model = models.Battery
success_url = reverse_lazy('battery-list')
"""
def jspsych_context(exp_instance):
deploy_static_fs = exp_instance.deploy_static()
deploy_static_url = deploy_static_fs.replace(
settings.DEPLOYMENT_DIR, settings.STATIC_DEPLOYMENT_URL
)
location = exp_instance.experiment_repo_id.location
exp_fs_path = Path(deploy_static_fs, Path(location).stem)
exp_url_path = Path(deploy_static_url, Path(location).stem)
# default js/css location for poldracklab style experiments
static_url_path = Path(settings.STATIC_NON_REPO_URL, "default")
return generate_experiment_context(
exp_fs_path, static_url_path, exp_url_path
)
class Preview(View):
def get(self, request, *args, **kwargs):
exp_id = self.kwargs.get("exp_id")
experiment = get_object_or_404(models.ExperimentRepo, id=exp_id)
commit = experiment.get_latest_commit()
exp_instance, created = models.ExperimentInstance.objects.get_or_create(
experiment_repo_id=experiment, commit=commit
)
# Could embed commit or instance id in kwargs, default to latest for now
# default template for poldracklab style experiments
template = "experiments/jspsych_deploy.html"
context = jspsych_context(exp_instance)
return render(request, template, context)
class Serve(TemplateView):
subjectg = None
battery = None
experiment = None
assignment = None
def get_template_names(self):
"""
Return a list of template names to be used for the request. Must return
a list. May not be called if render_to_response() is overridden.
"""
return ["experiments/jspsych_deploy.html"]
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
return context
def set_objects(self):
subject_id = self.kwargs.get("subject_id")
battery_id = self.kwargs.get("battery_id")
""" we might accept the uuid assocaited with the subject instead of its id """
if subject_id is not None:
self.subject = get_object_or_404(
models.Subject, id=subject_id
)
else:
self.subject = None
self.battery = get_object_or_404(
models.Battery, id=battery_id
)
try:
self.assignment = models.Assignment.objects.get(
subject=self.subject, battery=self.battery
)
except ObjectDoesNotExist:
# make new assignment for testing, in future 404.
assignment = models.Assignment(
subject_id=subject_id, battery_id=battery_id,
)
assignment.save()
self.assignment = assignment
def get(self, request, *args, **kwargs):
self.set_objects()
if self.assignment.consent_accepted is not True:
# display instructions and consent
pass
self.experiment = self.assignment.get_next_experiment()
exp_context = jspsych_context(self.experiment)
exp_context["post_url"] = reverse_lazy("experiments:push-results", args=[self.assignment.id, self.experiment.id])
exp_context["next_page"] = reverse_lazy("serve-battery", args=[self.subject.id, self.battery.id])
context = {**self.get_context_data(), **exp_context}
return self.render_to_response(context)
def post(self, request, *args, **kwargs):
return
class Results(View):
# If more frameworks are added this would dispatch to their respective
# versions of this function.
# expfactory-docker purges keys and process survey data at this step
def process_exp_data(self, post_data, assignment):
data = json.loads(post_data)
finished = data.get("status") == "finished"
if finished:
assignment.status = "completed"
elif assignment.status == "not-started":
assignment.status = "started"
return data, finished
def post(self, request, *args, **kwargs):
assignment_id = self.kwargs.get("assignment_id")
experiment_id = self.kwargs.get("experiment_id")
exp_instance = get_object_or_404(models.ExperimentInstance, id=experiment_id)
assignment = get_object_or_404(models.Assignment, id=assignment_id)
batt_exp = get_object_or_404(models.BatteryExperiments, battery=assignment.battery, experiment_instance=exp_instance)
data, finished = self.process_exp_data(request.body, assignment)
if finished:
models.Result(assignment=assignment, battery_experiment=batt_exp, subject=assignment.subject, data=data, status="completed").save()
assignment.save()
return HttpResponse('recieved')
class SubjectList(ListView):
model = models.Subject
queryset = models.Subject.objects.prefetch_related("assignment_set")
class CreateSubjects(FormView):
template_name = 'experiments/create_subjects.html'
form_class = forms.SubjectCount
success_url = reverse_lazy('experiments:subject-list')
def form_valid(self, form):
new_subjects = [models.Subject() for i in range(form.cleaned_data['count'])]
models.Subject.objects.bulk_create(new_subjects)
return super().form_valid(form) | 0.372962 | 0.076649 |
import sys
import func_timeout
from sss.read_id_list import ReadIDList
from sss.se05x import Se05x
import sss.sss_api as apis
from .cli import se05x, pass_context, session_open, session_close, \
log_traceback, TIME_OUT
@se05x.command('reset', short_help='Reset SE05X')
@pass_context
def reset(cli_ctx):
"""
Resets the SE05X Secure Module to the initial state.
This command uses ``Se05x_API_DeleteAll_Iterative`` API of the SE05X
MW to iterately delete objects provisioned inside the SE. Because of this,
some objects are purposefully skipped from deletion.
It does not use the low level SE05X API ``Se05x_API_DeleteAll``
For more information, see documentation/implementation of the
``Se05x_API_DeleteAll_Iterative`` API.
"""
try:
session_open(cli_ctx)
se05x_obj = Se05x(cli_ctx.session)
func_timeout.func_timeout(TIME_OUT, se05x_obj.debug_reset, None)
status = apis.kStatus_SSS_Success
except func_timeout.FunctionTimedOut as timeout_exc:
log_traceback(cli_ctx, timeout_exc.getMsg())
status = apis.kStatus_SSS_Fail
except Exception as exc: # pylint: disable=broad-except
log_traceback(cli_ctx, exc)
status = apis.kStatus_SSS_Fail
session_status = session_close(cli_ctx)
if status == apis.kStatus_SSS_Success and session_status == apis.kStatus_SSS_Success:
ret_value = 0
else:
ret_value = 1
sys.exit(ret_value)
@se05x.command('uid', short_help='Get SE05X Unique ID (18 bytes)')
@pass_context
def uid(cli_ctx):
""" Get 18 bytes Unique ID from the SE05X Secure Module."""
try:
session_open(cli_ctx)
se05x_obj = Se05x(cli_ctx.session)
unique_id = func_timeout.func_timeout(TIME_OUT, se05x_obj.get_unique_id, None)
cli_ctx.log("Unique ID: %s" % (unique_id,))
status = apis.kStatus_SSS_Success
except func_timeout.FunctionTimedOut as timeout_exc:
log_traceback(cli_ctx, timeout_exc.getMsg())
status = apis.kStatus_SSS_Fail
except Exception as exc: # pylint: disable=broad-except
log_traceback(cli_ctx, exc)
status = apis.kStatus_SSS_Fail
session_status = session_close(cli_ctx)
if status == apis.kStatus_SSS_Success and session_status == apis.kStatus_SSS_Success:
ret_value = 0
else:
ret_value = 1
sys.exit(ret_value)
@se05x.command('certuid', short_help='Get SE05X Cert Unique ID (10 bytes)')
@pass_context
def certuid(cli_ctx):
""" Get 10 bytes Cert Unique ID from the SE05X Secure Module.
The cert uid is a subset of the Secure Module Unique Identifier"""
try:
session_open(cli_ctx)
se05x_obj = Se05x(cli_ctx.session)
cert_uid = func_timeout.func_timeout(TIME_OUT, se05x_obj.get_cert_unique_id, None)
cli_ctx.log("Cert UID: %s" % (cert_uid,))
status = apis.kStatus_SSS_Success
except func_timeout.FunctionTimedOut as timeout_exc:
log_traceback(cli_ctx, timeout_exc.getMsg())
status = apis.kStatus_SSS_Fail
except Exception as exc: # pylint: disable=broad-except
log_traceback(cli_ctx, exc)
status = apis.kStatus_SSS_Fail
session_status = session_close(cli_ctx)
if status == apis.kStatus_SSS_Success and session_status == apis.kStatus_SSS_Success:
ret_value = 0
else:
ret_value = 1
sys.exit(ret_value)
@se05x.command('readidlist', short_help='Read contents of SE050')
@pass_context
def readidlist(cli_ctx):
""" Read contents of SE050"""
try:
session_open(cli_ctx)
read_id_list_obj = ReadIDList(cli_ctx.session)
func_timeout.func_timeout(TIME_OUT, read_id_list_obj.do_read_id_list, None)
status = apis.kStatus_SSS_Success
except func_timeout.FunctionTimedOut as timeout_exc:
log_traceback(cli_ctx, timeout_exc.getMsg())
status = apis.kStatus_SSS_Fail
except Exception as exc: # pylint: disable=broad-except
log_traceback(cli_ctx, exc)
status = apis.kStatus_SSS_Fail
session_status = session_close(cli_ctx)
if status == apis.kStatus_SSS_Success and session_status == apis.kStatus_SSS_Success:
ret_value = 0
else:
ret_value = 1
sys.exit(ret_value) | src/cli/cli_se05x.py | import sys
import func_timeout
from sss.read_id_list import ReadIDList
from sss.se05x import Se05x
import sss.sss_api as apis
from .cli import se05x, pass_context, session_open, session_close, \
log_traceback, TIME_OUT
@se05x.command('reset', short_help='Reset SE05X')
@pass_context
def reset(cli_ctx):
"""
Resets the SE05X Secure Module to the initial state.
This command uses ``Se05x_API_DeleteAll_Iterative`` API of the SE05X
MW to iterately delete objects provisioned inside the SE. Because of this,
some objects are purposefully skipped from deletion.
It does not use the low level SE05X API ``Se05x_API_DeleteAll``
For more information, see documentation/implementation of the
``Se05x_API_DeleteAll_Iterative`` API.
"""
try:
session_open(cli_ctx)
se05x_obj = Se05x(cli_ctx.session)
func_timeout.func_timeout(TIME_OUT, se05x_obj.debug_reset, None)
status = apis.kStatus_SSS_Success
except func_timeout.FunctionTimedOut as timeout_exc:
log_traceback(cli_ctx, timeout_exc.getMsg())
status = apis.kStatus_SSS_Fail
except Exception as exc: # pylint: disable=broad-except
log_traceback(cli_ctx, exc)
status = apis.kStatus_SSS_Fail
session_status = session_close(cli_ctx)
if status == apis.kStatus_SSS_Success and session_status == apis.kStatus_SSS_Success:
ret_value = 0
else:
ret_value = 1
sys.exit(ret_value)
@se05x.command('uid', short_help='Get SE05X Unique ID (18 bytes)')
@pass_context
def uid(cli_ctx):
""" Get 18 bytes Unique ID from the SE05X Secure Module."""
try:
session_open(cli_ctx)
se05x_obj = Se05x(cli_ctx.session)
unique_id = func_timeout.func_timeout(TIME_OUT, se05x_obj.get_unique_id, None)
cli_ctx.log("Unique ID: %s" % (unique_id,))
status = apis.kStatus_SSS_Success
except func_timeout.FunctionTimedOut as timeout_exc:
log_traceback(cli_ctx, timeout_exc.getMsg())
status = apis.kStatus_SSS_Fail
except Exception as exc: # pylint: disable=broad-except
log_traceback(cli_ctx, exc)
status = apis.kStatus_SSS_Fail
session_status = session_close(cli_ctx)
if status == apis.kStatus_SSS_Success and session_status == apis.kStatus_SSS_Success:
ret_value = 0
else:
ret_value = 1
sys.exit(ret_value)
@se05x.command('certuid', short_help='Get SE05X Cert Unique ID (10 bytes)')
@pass_context
def certuid(cli_ctx):
""" Get 10 bytes Cert Unique ID from the SE05X Secure Module.
The cert uid is a subset of the Secure Module Unique Identifier"""
try:
session_open(cli_ctx)
se05x_obj = Se05x(cli_ctx.session)
cert_uid = func_timeout.func_timeout(TIME_OUT, se05x_obj.get_cert_unique_id, None)
cli_ctx.log("Cert UID: %s" % (cert_uid,))
status = apis.kStatus_SSS_Success
except func_timeout.FunctionTimedOut as timeout_exc:
log_traceback(cli_ctx, timeout_exc.getMsg())
status = apis.kStatus_SSS_Fail
except Exception as exc: # pylint: disable=broad-except
log_traceback(cli_ctx, exc)
status = apis.kStatus_SSS_Fail
session_status = session_close(cli_ctx)
if status == apis.kStatus_SSS_Success and session_status == apis.kStatus_SSS_Success:
ret_value = 0
else:
ret_value = 1
sys.exit(ret_value)
@se05x.command('readidlist', short_help='Read contents of SE050')
@pass_context
def readidlist(cli_ctx):
""" Read contents of SE050"""
try:
session_open(cli_ctx)
read_id_list_obj = ReadIDList(cli_ctx.session)
func_timeout.func_timeout(TIME_OUT, read_id_list_obj.do_read_id_list, None)
status = apis.kStatus_SSS_Success
except func_timeout.FunctionTimedOut as timeout_exc:
log_traceback(cli_ctx, timeout_exc.getMsg())
status = apis.kStatus_SSS_Fail
except Exception as exc: # pylint: disable=broad-except
log_traceback(cli_ctx, exc)
status = apis.kStatus_SSS_Fail
session_status = session_close(cli_ctx)
if status == apis.kStatus_SSS_Success and session_status == apis.kStatus_SSS_Success:
ret_value = 0
else:
ret_value = 1
sys.exit(ret_value) | 0.293202 | 0.166574 |
import logging
from bson import ObjectId
from .db import (
get_mailbox_collection,
get_message_collection,
get_message_fs,
get_message_fs_files_collection,
get_message_fs_chunks_collection,
)
def process_message(peer, mailfrom, rcpttos, data):
mailboxes = get_mailbox_collection()
messages = get_message_collection()
logging.debug("Processing message from %s", mailfrom)
for rcptto in rcpttos:
if mailboxes.find({"name":rcptto}).count() == 0:
logging.debug("Skipping recipient: %s", rcptto)
continue
message_file_id = get_message_fs().put(data)
assert get_message_fs().get(message_file_id)
_associate_file_with_mailbox(message_file_id, rcptto)
messages.save({
"mailbox_name":rcptto,
"mail_from" : mailfrom,
"sent_from" : peer,
"recipients" : rcpttos,
"file_id":message_file_id,
})
def _associate_file_with_mailbox(message_file_id, mailbox_name):
get_message_fs_files_collection().update({"_id":message_file_id}, {"$set" : {"mailbox_name" : mailbox_name}}, multi=True)
get_message_fs_chunks_collection().update({"file_id":message_file_id}, {"$set" : {"mailbox_name" : mailbox_name}}, multi=True)
def delete_messages_by_mailbox(mailbox_name):
for collection in (get_message_collection(), get_message_fs_chunks_collection(), get_message_fs_files_collection()):
collection.remove({"mailbox_name" : mailbox_name})
def delete_all_messages():
for collection in (get_message_fs_chunks_collection(), get_message_fs_files_collection()):
collection.remove()
def get_messages(mailbox_name, include_read=True):
returned = []
returned_ids = []
criteria = {"mailbox_name" : mailbox_name}
if not include_read:
criteria.update({"read" : {"$ne" : True}})
for message in get_message_collection().find(criteria):
message_dict = dict(message)
message_dict["message"] = get_message_fs().get(ObjectId(message_dict.pop("file_id"))).read()
returned_ids.append(message_dict.pop("_id"))
returned.append(message_dict)
get_message_collection().update({"_id" : {"$in" : returned_ids}}, {"$set" : {"read" : True}}, multi=True)
return returned | www/flask_app/messages.py | import logging
from bson import ObjectId
from .db import (
get_mailbox_collection,
get_message_collection,
get_message_fs,
get_message_fs_files_collection,
get_message_fs_chunks_collection,
)
def process_message(peer, mailfrom, rcpttos, data):
mailboxes = get_mailbox_collection()
messages = get_message_collection()
logging.debug("Processing message from %s", mailfrom)
for rcptto in rcpttos:
if mailboxes.find({"name":rcptto}).count() == 0:
logging.debug("Skipping recipient: %s", rcptto)
continue
message_file_id = get_message_fs().put(data)
assert get_message_fs().get(message_file_id)
_associate_file_with_mailbox(message_file_id, rcptto)
messages.save({
"mailbox_name":rcptto,
"mail_from" : mailfrom,
"sent_from" : peer,
"recipients" : rcpttos,
"file_id":message_file_id,
})
def _associate_file_with_mailbox(message_file_id, mailbox_name):
get_message_fs_files_collection().update({"_id":message_file_id}, {"$set" : {"mailbox_name" : mailbox_name}}, multi=True)
get_message_fs_chunks_collection().update({"file_id":message_file_id}, {"$set" : {"mailbox_name" : mailbox_name}}, multi=True)
def delete_messages_by_mailbox(mailbox_name):
for collection in (get_message_collection(), get_message_fs_chunks_collection(), get_message_fs_files_collection()):
collection.remove({"mailbox_name" : mailbox_name})
def delete_all_messages():
for collection in (get_message_fs_chunks_collection(), get_message_fs_files_collection()):
collection.remove()
def get_messages(mailbox_name, include_read=True):
returned = []
returned_ids = []
criteria = {"mailbox_name" : mailbox_name}
if not include_read:
criteria.update({"read" : {"$ne" : True}})
for message in get_message_collection().find(criteria):
message_dict = dict(message)
message_dict["message"] = get_message_fs().get(ObjectId(message_dict.pop("file_id"))).read()
returned_ids.append(message_dict.pop("_id"))
returned.append(message_dict)
get_message_collection().update({"_id" : {"$in" : returned_ids}}, {"$set" : {"read" : True}}, multi=True)
return returned | 0.157882 | 0.09886 |
from __future__ import print_function
import bluetooth_raspi
import bluez_service_consts
import dbus
import dbus.service
import dbus.mainloop.glib
import logging
import os
import raspi_bluez_client
# Libraries needed on raspberry pi. ImportError on
# Fizz can be ignored.
try:
from bluetooth import *
except ImportError:
pass
try:
from gi.repository import GLib
except ImportError:
pass
from raspi_bluez_client import BluezKeyboardClient
from bluez_service_consts import *
KEYBOARD_PROFILE_SDP_PATH = (os.path.dirname(os.path.abspath(__file__)) +
"/keyboard_sdp_record.xml")
KEYBOARD_SERVICE_UUID = "00001124-0000-1000-8000-00805f9b34fb"
P_CTRL = 17
P_INTR = 19
class BluezServiceException(Exception):
"""Exception class for BluezPeripheral class."""
def __init__(self, message):
super(BluezServiceException, self).__init__()
self.message = message
class BluezKeyboardProfile(dbus.service.Object):
"""Implementation of org.bluez.Profile1 interface for a keyboard."""
fd = -1
@dbus.service.method("org.bluez.Profile1",
in_signature="", out_signature="")
def Release(self):
print("Release")
@dbus.service.method("org.bluez.Profile1",
in_signature="", out_signature="")
def Cancel(self):
print("Cancel")
@dbus.service.method("org.bluez.Profile1",
in_signature="oha{sv}", out_signature="")
def NewConnection(self, path, fd, properties):
self.fd = fd.take()
print("NewConnection(%s, %d)" % (path, self.fd))
for key in properties.keys():
if key == "Version" or key == "Features":
print(" %s = 0x%04x" % (key, properties[key]))
else:
print(" %s = %s" % (key, properties[key]))
@dbus.service.method("org.bluez.Profile1",
in_signature="o", out_signature="")
def RequestDisconnection(self, path):
print("RequestDisconnection(%s)" % (path))
if self.fd > 0:
os.close(self.fd)
self.fd = -1
def __init__(self, bus, path):
dbus.service.Object.__init__(self, bus, path)
class BluezKeyboardService(dbus.service.Object):
"""Bluez Keyboard Service implementation."""
def __init__(self, adapter_address):
self._bus_name = dbus.service.BusName(BLUEZ_KEYBOARD_SERVICE_NAME,
bus=dbus.SystemBus())
super(BluezKeyboardService, self).__init__(self._bus_name,
BLUEZ_KEYBOARD_SERVICE_PATH)
# Init keyboard profile
self._init_bluez_profile(KEYBOARD_PROFILE_SDP_PATH,
BLUEZ_KEYBOARD_PROFILE_PATH,
KEYBOARD_SERVICE_UUID)
self._listen(adapter_address)
def _init_bluez_profile(self, profile_sdp_path,
profile_dbus_path,
profile_uuid):
"""Register a Bluetooth profile with bluez.
profile_sdp_path: Relative path of XML file for profile SDP
profile_uuid: Service Class/ Profile UUID
www.bluetooth.com/specifications/assigned-numbers/service-discovery/
"""
logging.debug("Configuring Bluez Profile from %s" %
KEYBOARD_PROFILE_SDP_PATH)
try:
with open(profile_sdp_path, "r") as prfd:
prf_content = prfd.read()
except IOError as e:
raise BluezServiceException("I/O error ({0}): {1}".format(e.errno,
e.strerror))
except:
raise BluezServiceException("Unknown error in _init_bluez_profile()")
else:
opts = {
"ServiceRecord":prf_content,
"Role":"server",
"RequireAuthentication":False,
"RequireAuthorization":False
}
self._profile = BluezKeyboardProfile(dbus.SystemBus(), profile_dbus_path)
manager = dbus.Interface(dbus.SystemBus().get_object("org.bluez",
"/org/bluez"),
"org.bluez.ProfileManager1")
manager.RegisterProfile(profile_dbus_path, profile_uuid, opts)
def _listen(self, dev_addr):
self._scontrol = BluetoothSocket(L2CAP)
self._sinterrupt = BluetoothSocket(L2CAP)
self._scch = GLib.IOChannel(self._scontrol.fileno())
self._sich = GLib.IOChannel(self._sinterrupt.fileno())
self._scontrol.bind((dev_addr, P_CTRL))
self._sinterrupt.bind((dev_addr, P_INTR))
# Start listening on server sockets. Add watch to process connection
# asynchronously.
self._scontrol.listen(1)
self._sinterrupt.listen(1)
GLib.io_add_watch(self._scch, GLib.IO_IN, self.on_connect)
GLib.io_add_watch(self._sich, GLib.IO_IN, self.on_connect)
def on_connect(self, fd, cond):
if fd == self._scch:
self._ccontrol, cinfo = self._scontrol.accept()
elif fd == self._sich:
self._cinterrupt, cinfo = self._sinterrupt.accept()
self.connected()
logging.debug("Bluez keyboard service connected")
@dbus.service.method("org.chromium.autotest.btkbservice", in_signature="yay")
def send_keys(self, modifier, keys):
report = ""
report += chr(0xA1)
report += chr(0x01)
report += chr(modifier)
report += chr(0x00)
count = 0
for key_code in keys:
if count < 6:
report += chr(key_code)
count += 1
self._cinterrupt.send(report)
@dbus.service.signal("org.chromium.autotest.btkbservice", signature="")
def connected(self):
pass
if __name__ == "__main__":
adapter = bluetooth_raspi.BluezPeripheral()
service = BluezKeyboardService(adapter.GetLocalBluetoothAddress())
client = BluezKeyboardClient()
client.register_connected_handler() | chameleond/utils/raspi_bluez_service.py | from __future__ import print_function
import bluetooth_raspi
import bluez_service_consts
import dbus
import dbus.service
import dbus.mainloop.glib
import logging
import os
import raspi_bluez_client
# Libraries needed on raspberry pi. ImportError on
# Fizz can be ignored.
try:
from bluetooth import *
except ImportError:
pass
try:
from gi.repository import GLib
except ImportError:
pass
from raspi_bluez_client import BluezKeyboardClient
from bluez_service_consts import *
KEYBOARD_PROFILE_SDP_PATH = (os.path.dirname(os.path.abspath(__file__)) +
"/keyboard_sdp_record.xml")
KEYBOARD_SERVICE_UUID = "00001124-0000-1000-8000-00805f9b34fb"
P_CTRL = 17
P_INTR = 19
class BluezServiceException(Exception):
"""Exception class for BluezPeripheral class."""
def __init__(self, message):
super(BluezServiceException, self).__init__()
self.message = message
class BluezKeyboardProfile(dbus.service.Object):
"""Implementation of org.bluez.Profile1 interface for a keyboard."""
fd = -1
@dbus.service.method("org.bluez.Profile1",
in_signature="", out_signature="")
def Release(self):
print("Release")
@dbus.service.method("org.bluez.Profile1",
in_signature="", out_signature="")
def Cancel(self):
print("Cancel")
@dbus.service.method("org.bluez.Profile1",
in_signature="oha{sv}", out_signature="")
def NewConnection(self, path, fd, properties):
self.fd = fd.take()
print("NewConnection(%s, %d)" % (path, self.fd))
for key in properties.keys():
if key == "Version" or key == "Features":
print(" %s = 0x%04x" % (key, properties[key]))
else:
print(" %s = %s" % (key, properties[key]))
@dbus.service.method("org.bluez.Profile1",
in_signature="o", out_signature="")
def RequestDisconnection(self, path):
print("RequestDisconnection(%s)" % (path))
if self.fd > 0:
os.close(self.fd)
self.fd = -1
def __init__(self, bus, path):
dbus.service.Object.__init__(self, bus, path)
class BluezKeyboardService(dbus.service.Object):
"""Bluez Keyboard Service implementation."""
def __init__(self, adapter_address):
self._bus_name = dbus.service.BusName(BLUEZ_KEYBOARD_SERVICE_NAME,
bus=dbus.SystemBus())
super(BluezKeyboardService, self).__init__(self._bus_name,
BLUEZ_KEYBOARD_SERVICE_PATH)
# Init keyboard profile
self._init_bluez_profile(KEYBOARD_PROFILE_SDP_PATH,
BLUEZ_KEYBOARD_PROFILE_PATH,
KEYBOARD_SERVICE_UUID)
self._listen(adapter_address)
def _init_bluez_profile(self, profile_sdp_path,
profile_dbus_path,
profile_uuid):
"""Register a Bluetooth profile with bluez.
profile_sdp_path: Relative path of XML file for profile SDP
profile_uuid: Service Class/ Profile UUID
www.bluetooth.com/specifications/assigned-numbers/service-discovery/
"""
logging.debug("Configuring Bluez Profile from %s" %
KEYBOARD_PROFILE_SDP_PATH)
try:
with open(profile_sdp_path, "r") as prfd:
prf_content = prfd.read()
except IOError as e:
raise BluezServiceException("I/O error ({0}): {1}".format(e.errno,
e.strerror))
except:
raise BluezServiceException("Unknown error in _init_bluez_profile()")
else:
opts = {
"ServiceRecord":prf_content,
"Role":"server",
"RequireAuthentication":False,
"RequireAuthorization":False
}
self._profile = BluezKeyboardProfile(dbus.SystemBus(), profile_dbus_path)
manager = dbus.Interface(dbus.SystemBus().get_object("org.bluez",
"/org/bluez"),
"org.bluez.ProfileManager1")
manager.RegisterProfile(profile_dbus_path, profile_uuid, opts)
def _listen(self, dev_addr):
self._scontrol = BluetoothSocket(L2CAP)
self._sinterrupt = BluetoothSocket(L2CAP)
self._scch = GLib.IOChannel(self._scontrol.fileno())
self._sich = GLib.IOChannel(self._sinterrupt.fileno())
self._scontrol.bind((dev_addr, P_CTRL))
self._sinterrupt.bind((dev_addr, P_INTR))
# Start listening on server sockets. Add watch to process connection
# asynchronously.
self._scontrol.listen(1)
self._sinterrupt.listen(1)
GLib.io_add_watch(self._scch, GLib.IO_IN, self.on_connect)
GLib.io_add_watch(self._sich, GLib.IO_IN, self.on_connect)
def on_connect(self, fd, cond):
if fd == self._scch:
self._ccontrol, cinfo = self._scontrol.accept()
elif fd == self._sich:
self._cinterrupt, cinfo = self._sinterrupt.accept()
self.connected()
logging.debug("Bluez keyboard service connected")
@dbus.service.method("org.chromium.autotest.btkbservice", in_signature="yay")
def send_keys(self, modifier, keys):
report = ""
report += chr(0xA1)
report += chr(0x01)
report += chr(modifier)
report += chr(0x00)
count = 0
for key_code in keys:
if count < 6:
report += chr(key_code)
count += 1
self._cinterrupt.send(report)
@dbus.service.signal("org.chromium.autotest.btkbservice", signature="")
def connected(self):
pass
if __name__ == "__main__":
adapter = bluetooth_raspi.BluezPeripheral()
service = BluezKeyboardService(adapter.GetLocalBluetoothAddress())
client = BluezKeyboardClient()
client.register_connected_handler() | 0.503174 | 0.06134 |
import pandas as pd
import numpy as np
from ast import literal_eval
import time
# Create a functions that label sentences with question marks, exlamation points and quotes
def question_mark_finder(sentence):
"""
Returns 1 if sentence contains question mark, 0 otherwise
"""
if "?" in sentence:
return 1
else:
return 0
def exclamation_mark_finder(sentence):
"""
Returns 1 if sentence contains question mark, 0 otherwise
"""
if "!" in sentence:
return 1
else:
return 0
def quotes_finder(sentence):
"""
Returns 1 if sentence contains question mark, 0 otherwise
"""
if "'" or "`" in sentence:
return 1
else:
return 0
# Apply these functions to the dataset
if __name__ == "__main__":
start_time = time.time()
# Load Data
train = pd.read_csv(
"data/interim/cleaned/train_cleaned_step_1.csv.gz",
compression="gzip",
nrows=6000000,
)
test = pd.read_csv(
"data/interim/cleaned/test_cleaned_step_1.csv.gz", compression="gzip"
)
val = pd.read_csv(
"data/interim/cleaned/val_cleaned_step_1.csv.gz", compression="gzip"
)
# Create question mark column
train["question_mark"] = train["tokens"].apply(lambda x: question_mark_finder(x))
test["question_mark"] = test["tokens"].apply(lambda x: question_mark_finder(x))
val["question_mark"] = val["tokens"].apply(lambda x: question_mark_finder(x))
# Create exclamation mark column
train["exclamation_mark"] = train["tokens"].apply(
lambda x: exclamation_mark_finder(x)
)
test["exclamation_mark"] = test["tokens"].apply(
lambda x: exclamation_mark_finder(x)
)
val["exclamation_mark"] = val["tokens"].apply(lambda x: exclamation_mark_finder(x))
# Create quote finder column
train["quote_marks"] = train["tokens"].apply(lambda x: exclamation_mark_finder(x))
test["quote_marks"] = test["tokens"].apply(lambda x: exclamation_mark_finder(x))
val["quote_marks"] = val["tokens"].apply(lambda x: exclamation_mark_finder(x))
train.to_csv(
"src/features/cropped/propernouns_verbs_sent_len/train_pn_verb_counts_punctuation.csv.gz",
compression="gzip",
index=False,
)
test.to_csv(
"src/features/cropped/propernouns_verbs_sent_len/test_pn_verb_counts_punctuation.csv.gz",
compression="gzip",
index=False,
)
val.to_csv(
"src/features/cropped/propernouns_verbs_sent_len/val_pn_verb_counts_punctuation.csv.gz",
compression="gzip",
index=False,
)
print(time.time() - start_time) | src/features/build_punctuation_feature.py | import pandas as pd
import numpy as np
from ast import literal_eval
import time
# Create a functions that label sentences with question marks, exlamation points and quotes
def question_mark_finder(sentence):
"""
Returns 1 if sentence contains question mark, 0 otherwise
"""
if "?" in sentence:
return 1
else:
return 0
def exclamation_mark_finder(sentence):
"""
Returns 1 if sentence contains question mark, 0 otherwise
"""
if "!" in sentence:
return 1
else:
return 0
def quotes_finder(sentence):
"""
Returns 1 if sentence contains question mark, 0 otherwise
"""
if "'" or "`" in sentence:
return 1
else:
return 0
# Apply these functions to the dataset
if __name__ == "__main__":
start_time = time.time()
# Load Data
train = pd.read_csv(
"data/interim/cleaned/train_cleaned_step_1.csv.gz",
compression="gzip",
nrows=6000000,
)
test = pd.read_csv(
"data/interim/cleaned/test_cleaned_step_1.csv.gz", compression="gzip"
)
val = pd.read_csv(
"data/interim/cleaned/val_cleaned_step_1.csv.gz", compression="gzip"
)
# Create question mark column
train["question_mark"] = train["tokens"].apply(lambda x: question_mark_finder(x))
test["question_mark"] = test["tokens"].apply(lambda x: question_mark_finder(x))
val["question_mark"] = val["tokens"].apply(lambda x: question_mark_finder(x))
# Create exclamation mark column
train["exclamation_mark"] = train["tokens"].apply(
lambda x: exclamation_mark_finder(x)
)
test["exclamation_mark"] = test["tokens"].apply(
lambda x: exclamation_mark_finder(x)
)
val["exclamation_mark"] = val["tokens"].apply(lambda x: exclamation_mark_finder(x))
# Create quote finder column
train["quote_marks"] = train["tokens"].apply(lambda x: exclamation_mark_finder(x))
test["quote_marks"] = test["tokens"].apply(lambda x: exclamation_mark_finder(x))
val["quote_marks"] = val["tokens"].apply(lambda x: exclamation_mark_finder(x))
train.to_csv(
"src/features/cropped/propernouns_verbs_sent_len/train_pn_verb_counts_punctuation.csv.gz",
compression="gzip",
index=False,
)
test.to_csv(
"src/features/cropped/propernouns_verbs_sent_len/test_pn_verb_counts_punctuation.csv.gz",
compression="gzip",
index=False,
)
val.to_csv(
"src/features/cropped/propernouns_verbs_sent_len/val_pn_verb_counts_punctuation.csv.gz",
compression="gzip",
index=False,
)
print(time.time() - start_time) | 0.634656 | 0.41478 |
class DynamicArray():
def __init__(self):
self.size = 0
self.capacity = 1
self.array = self._create_array(self.capacity)
def _create_array(self,length):
return [None] * length
def len(self):
return self.size
def get_capacity(self):
return self.capacity
def is_empty(self):
return self.size == 0
def at(self,index):
if not 0 <= index < self.size:
raise IndexError("Index out of range")
return self.array[index]
def push(self,item):
if self.size == self.capacity:
self._resize(self.capacity * 2)
self.array[self.size] = item
self.size += 1
def _resize(self,new_capacity):
new_array = self._create_array(new_capacity)
for i in range(self.size):
new_array[i] = self.array[i]
self.capacity = new_capacity
self.array = new_array
def insert(self,index,item):
if not 0 <= index < self.size:
raise IndexError("Index out of range")
if self.size == self.capacity:
self._resize(self.capacity * 2)
new_arr = self._create_array(self.capacity)
for i in range(0, index):
new_arr[i] = self.array[i]
new_arr[index] = item
for i in range(index , self.size):
new_arr[i + 1] = self.array[i]
self.size += 1
self.array = new_arr
def delete(self,index):
if not 0 <= index < self.size:
raise IndexError("Index out of range")
new_arr = self._create_array(self.capacity)
1
for i in range(index):
new_arr[i] = self.array[i]
for i in range(index + 1 , self.size):
new_arr[i - 1] = self.array[i]
self.size -= 1
self.array = new_arr
def remove(self,item):
for i in range(self.size):
if self.array[i] == item:
self.delete(i)
def prepend(self,item):
self.insert(0,item)
def find(self,item):
for i in range(self.size):
if self.array[i] == item:
return i
return -1 | array.py | class DynamicArray():
def __init__(self):
self.size = 0
self.capacity = 1
self.array = self._create_array(self.capacity)
def _create_array(self,length):
return [None] * length
def len(self):
return self.size
def get_capacity(self):
return self.capacity
def is_empty(self):
return self.size == 0
def at(self,index):
if not 0 <= index < self.size:
raise IndexError("Index out of range")
return self.array[index]
def push(self,item):
if self.size == self.capacity:
self._resize(self.capacity * 2)
self.array[self.size] = item
self.size += 1
def _resize(self,new_capacity):
new_array = self._create_array(new_capacity)
for i in range(self.size):
new_array[i] = self.array[i]
self.capacity = new_capacity
self.array = new_array
def insert(self,index,item):
if not 0 <= index < self.size:
raise IndexError("Index out of range")
if self.size == self.capacity:
self._resize(self.capacity * 2)
new_arr = self._create_array(self.capacity)
for i in range(0, index):
new_arr[i] = self.array[i]
new_arr[index] = item
for i in range(index , self.size):
new_arr[i + 1] = self.array[i]
self.size += 1
self.array = new_arr
def delete(self,index):
if not 0 <= index < self.size:
raise IndexError("Index out of range")
new_arr = self._create_array(self.capacity)
1
for i in range(index):
new_arr[i] = self.array[i]
for i in range(index + 1 , self.size):
new_arr[i - 1] = self.array[i]
self.size -= 1
self.array = new_arr
def remove(self,item):
for i in range(self.size):
if self.array[i] == item:
self.delete(i)
def prepend(self,item):
self.insert(0,item)
def find(self,item):
for i in range(self.size):
if self.array[i] == item:
return i
return -1 | 0.530966 | 0.259194 |
import json
import os
import shutil
import h5py
import numpy as np
from ..preprocessings.h5_to_memmap import h5_to_memmaps
from ..utils.utils import standardize_signals_durations
def generate_fake_hypno(transition_kernel, hypnogram_length, s_0=0):
generated_hypno = []
s_0 = np.array(s_0)
for i in range(hypnogram_length):
generated_hypno += [s_0 - 1]
s_0 = np.random.choice(range(5), p=transition_kernel[s_0])
return generated_hypno
def generate_fake_signal(hypno, sampling_fs, class_fs=None, random_noise=0.3):
if class_fs is None:
class_fs = [50, 1, 2.5, 5, 10]
fake_signal = []
epoch_length = sampling_fs * 30
for sleep_stage in hypno:
x = 2 * np.arange(0, epoch_length) * np.pi * class_fs[sleep_stage + 1] / sampling_fs
x = x + np.random.normal(scale=2)
x = np.cos(x)
x = x + np.random.normal(scale=random_noise, size=x.shape)
fake_signal += [x]
return np.concatenate(fake_signal)
def generate_fake_record(record_description, file_name='/tmp/fake.h5', build_hypno=True):
with h5py.File(file_name, 'w') as fake_h5:
description = []
hypno_duration = int(3.4 * 60 * 60 // 30)
transition_kernel_hypno = np.abs(np.random.normal(size=(5, 5)) + 30 * np.identity(5))
transition_kernel_hypno = transition_kernel_hypno / np.sum(transition_kernel_hypno, 1,
keepdims=True)
hypno = generate_fake_hypno(transition_kernel_hypno, hypno_duration)
fake_h5['hypnogram'] = hypno
for signal in record_description:
group_name = signal['path'].split('/')[0]
signal_name = signal['path'].split('/')[1]
signal_description = {"fs": signal['fs'], "unit": '',
"path": 'signals/' + signal['path'], 'name': signal_name,
'domain': group_name, "default": True}
data = generate_fake_signal(hypno, signal['fs'])
fake_h5.create_dataset('signals/' + signal['path'], data=data)
description += [signal_description]
fake_h5['signals/' + group_name].attrs.create('fs', signal['fs'])
fake_h5['signals/' + group_name].attrs.create('unit', b'')
# Add description
fake_h5.attrs.create('description', json.dumps(description), dtype=np.dtype('S32768'))
# Add event (nothing for now)
fake_h5.create_group('events')
fake_h5.attrs.create('events_description', json.dumps([]), dtype=np.dtype('S32768'))
# add duration
duration = standardize_signals_durations(fake_h5)
fake_h5.attrs.create('duration', duration)
if not build_hypno:
del fake_h5['hypnogram']
return file_name
def generate_memmaps(n_memmaps, record_description, memmaps_description, erase=True):
dataset_size = n_memmaps
# generate fake h5
if not os.path.exists('/tmp/fake_dataset/'):
os.makedirs('/tmp/fake_dataset/')
else:
shutil.rmtree('/tmp/fake_dataset/')
os.makedirs('/tmp/fake_dataset/')
if erase:
if not os.path.exists('/tmp/fake_memmmaps/'):
os.makedirs('/tmp/fake_memmmaps/')
else:
shutil.rmtree('/tmp/fake_memmmaps/')
os.makedirs('/tmp/fake_memmmaps/')
for i in range(dataset_size):
generate_fake_record(record_description, '/tmp/fake_dataset/record_' + str(i) + '.h5')
# generate fake_memmaps
records = ['/tmp/fake_dataset/record_' + str(i) + '.h5' for i in range(dataset_size)]
memmaps_dir = h5_to_memmaps(
records,
'/tmp/fake_memmmaps/',
memmaps_description,
parallel=False,
force=True)[0]
shutil.rmtree('/tmp/fake_dataset/')
# build dataset
memmaps = [os.path.join(memmaps_dir, filename) for filename in os.listdir(memmaps_dir) if
'.' not in filename]
return memmaps | dreem_learning_open/test/utils.py | import json
import os
import shutil
import h5py
import numpy as np
from ..preprocessings.h5_to_memmap import h5_to_memmaps
from ..utils.utils import standardize_signals_durations
def generate_fake_hypno(transition_kernel, hypnogram_length, s_0=0):
generated_hypno = []
s_0 = np.array(s_0)
for i in range(hypnogram_length):
generated_hypno += [s_0 - 1]
s_0 = np.random.choice(range(5), p=transition_kernel[s_0])
return generated_hypno
def generate_fake_signal(hypno, sampling_fs, class_fs=None, random_noise=0.3):
if class_fs is None:
class_fs = [50, 1, 2.5, 5, 10]
fake_signal = []
epoch_length = sampling_fs * 30
for sleep_stage in hypno:
x = 2 * np.arange(0, epoch_length) * np.pi * class_fs[sleep_stage + 1] / sampling_fs
x = x + np.random.normal(scale=2)
x = np.cos(x)
x = x + np.random.normal(scale=random_noise, size=x.shape)
fake_signal += [x]
return np.concatenate(fake_signal)
def generate_fake_record(record_description, file_name='/tmp/fake.h5', build_hypno=True):
with h5py.File(file_name, 'w') as fake_h5:
description = []
hypno_duration = int(3.4 * 60 * 60 // 30)
transition_kernel_hypno = np.abs(np.random.normal(size=(5, 5)) + 30 * np.identity(5))
transition_kernel_hypno = transition_kernel_hypno / np.sum(transition_kernel_hypno, 1,
keepdims=True)
hypno = generate_fake_hypno(transition_kernel_hypno, hypno_duration)
fake_h5['hypnogram'] = hypno
for signal in record_description:
group_name = signal['path'].split('/')[0]
signal_name = signal['path'].split('/')[1]
signal_description = {"fs": signal['fs'], "unit": '',
"path": 'signals/' + signal['path'], 'name': signal_name,
'domain': group_name, "default": True}
data = generate_fake_signal(hypno, signal['fs'])
fake_h5.create_dataset('signals/' + signal['path'], data=data)
description += [signal_description]
fake_h5['signals/' + group_name].attrs.create('fs', signal['fs'])
fake_h5['signals/' + group_name].attrs.create('unit', b'')
# Add description
fake_h5.attrs.create('description', json.dumps(description), dtype=np.dtype('S32768'))
# Add event (nothing for now)
fake_h5.create_group('events')
fake_h5.attrs.create('events_description', json.dumps([]), dtype=np.dtype('S32768'))
# add duration
duration = standardize_signals_durations(fake_h5)
fake_h5.attrs.create('duration', duration)
if not build_hypno:
del fake_h5['hypnogram']
return file_name
def generate_memmaps(n_memmaps, record_description, memmaps_description, erase=True):
dataset_size = n_memmaps
# generate fake h5
if not os.path.exists('/tmp/fake_dataset/'):
os.makedirs('/tmp/fake_dataset/')
else:
shutil.rmtree('/tmp/fake_dataset/')
os.makedirs('/tmp/fake_dataset/')
if erase:
if not os.path.exists('/tmp/fake_memmmaps/'):
os.makedirs('/tmp/fake_memmmaps/')
else:
shutil.rmtree('/tmp/fake_memmmaps/')
os.makedirs('/tmp/fake_memmmaps/')
for i in range(dataset_size):
generate_fake_record(record_description, '/tmp/fake_dataset/record_' + str(i) + '.h5')
# generate fake_memmaps
records = ['/tmp/fake_dataset/record_' + str(i) + '.h5' for i in range(dataset_size)]
memmaps_dir = h5_to_memmaps(
records,
'/tmp/fake_memmmaps/',
memmaps_description,
parallel=False,
force=True)[0]
shutil.rmtree('/tmp/fake_dataset/')
# build dataset
memmaps = [os.path.join(memmaps_dir, filename) for filename in os.listdir(memmaps_dir) if
'.' not in filename]
return memmaps | 0.33112 | 0.220689 |
import sys
import os
import re
import shutil
import argparse
from string import Template
KEYWORDS = ["CENTER_X", "CENTER_Y", "CENTER_Z", "RADIUS", "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8"]
COORD = "{:>11.3f}{:>8.3f}{:>8.3f}"
CENTER = "{:.3f}"
DIR=os.path.dirname(os.path.abspath(__file__))
BOX=os.path.join(DIR,"box_template.pdb")
def parse_args():
parser = argparse.ArgumentParser(description="Create pdb-box from center and radius")
parser.add_argument("-cf", "--cfile", type=str, help='control file with the box info', default=None)
parser.add_argument("-c", "--center", nargs='+', type=float, help='center of the box', default=None)
parser.add_argument("-r", "--radius", type= int, help='radius of the box', default=None)
parser.add_argument("-f", "--file", help='output file', default='box.pdb')
args = parser.parse_args()
if any([args.cfile, args.center, args.radius]):
return args.cfile, args.center, args.radius, args.file
else:
raise ValueError("Non arguments were passed. Either use a PELE control file or define a center and radius.")
class TemplateBuilder(object):
def __init__(self, file, keywords):
self.file = file
self.keywords = keywords
self.fill_in()
def fill_in(self):
"""
Fill the control file in
"""
with open(os.path.join(self.file), 'r') as infile:
confile_data = infile.read()
confile_template = Template(confile_data)
confile_text = confile_template.safe_substitute(self.keywords)
with open(os.path.join(self.file), 'w') as outfile:
outfile.write(confile_text)
def parse(control_file):
with open(control_file, "r") as f:
for line in f:
try:
if re.search('"radius"\s*:\s*(.*),', line):
result = re.search('"radius"\s*:\s*(.*),', line)
radius = float(result.group(1))
elif re.search('"radius"\s*:\s*(.*)}', line):
result = re.search('"radius"\s*:\s*(.*)}', line)
radius = float(result.group(1))
if re.search('"fixedCenter"\s*:\s*\[(.*)\]', line):
result = re.search('"fixedCenter"\s*:\s*\[(.*)\]', line)
center = [float(coord) for coord in result.group(1).split(",")]
except ValueError:
raise ValueError("No Box found. Check your {} file.".format(control_file))
return center, radius
def build_box(control_file, center, radius, file):
if control_file:
center, radius = parse(control_file)
cx, cy, cz = center
v1 = COORD.format(cx-radius,cy-radius, cz-radius)
v2 = COORD.format(cx+radius,cy-radius, cz-radius)
v3 = COORD.format(cx+radius,cy-radius, cz+radius)
v4 = COORD.format(cx-radius,cy-radius, cz+radius)
v5 = COORD.format(cx-radius,cy+radius, cz-radius)
v6 = COORD.format(cx+radius,cy+radius, cz-radius)
v7 = COORD.format(cx+radius,cy+radius, cz+radius)
v8 = COORD.format(cx-radius,cy+radius, cz+radius)
cx = CENTER.format(cx)
cy = CENTER.format(cy)
cz = CENTER.format(cz)
values = [cx, cy, cz, radius, v1, v2, v3, v4,
v5, v6, v7, v8]
replace = {keyword : value for keyword, value in zip(KEYWORDS, values)}
shutil.copy(BOX, file)
TemplateBuilder(file, replace)
return file
if __name__ == "__main__":
control_file, center, radius, box_file = parse_args()
box = build_box(control_file, center, radius, box_file)
print("Box created in {}".format(os.path.abspath(box))) | Analysis_tools/box.py | import sys
import os
import re
import shutil
import argparse
from string import Template
KEYWORDS = ["CENTER_X", "CENTER_Y", "CENTER_Z", "RADIUS", "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8"]
COORD = "{:>11.3f}{:>8.3f}{:>8.3f}"
CENTER = "{:.3f}"
DIR=os.path.dirname(os.path.abspath(__file__))
BOX=os.path.join(DIR,"box_template.pdb")
def parse_args():
parser = argparse.ArgumentParser(description="Create pdb-box from center and radius")
parser.add_argument("-cf", "--cfile", type=str, help='control file with the box info', default=None)
parser.add_argument("-c", "--center", nargs='+', type=float, help='center of the box', default=None)
parser.add_argument("-r", "--radius", type= int, help='radius of the box', default=None)
parser.add_argument("-f", "--file", help='output file', default='box.pdb')
args = parser.parse_args()
if any([args.cfile, args.center, args.radius]):
return args.cfile, args.center, args.radius, args.file
else:
raise ValueError("Non arguments were passed. Either use a PELE control file or define a center and radius.")
class TemplateBuilder(object):
def __init__(self, file, keywords):
self.file = file
self.keywords = keywords
self.fill_in()
def fill_in(self):
"""
Fill the control file in
"""
with open(os.path.join(self.file), 'r') as infile:
confile_data = infile.read()
confile_template = Template(confile_data)
confile_text = confile_template.safe_substitute(self.keywords)
with open(os.path.join(self.file), 'w') as outfile:
outfile.write(confile_text)
def parse(control_file):
with open(control_file, "r") as f:
for line in f:
try:
if re.search('"radius"\s*:\s*(.*),', line):
result = re.search('"radius"\s*:\s*(.*),', line)
radius = float(result.group(1))
elif re.search('"radius"\s*:\s*(.*)}', line):
result = re.search('"radius"\s*:\s*(.*)}', line)
radius = float(result.group(1))
if re.search('"fixedCenter"\s*:\s*\[(.*)\]', line):
result = re.search('"fixedCenter"\s*:\s*\[(.*)\]', line)
center = [float(coord) for coord in result.group(1).split(",")]
except ValueError:
raise ValueError("No Box found. Check your {} file.".format(control_file))
return center, radius
def build_box(control_file, center, radius, file):
if control_file:
center, radius = parse(control_file)
cx, cy, cz = center
v1 = COORD.format(cx-radius,cy-radius, cz-radius)
v2 = COORD.format(cx+radius,cy-radius, cz-radius)
v3 = COORD.format(cx+radius,cy-radius, cz+radius)
v4 = COORD.format(cx-radius,cy-radius, cz+radius)
v5 = COORD.format(cx-radius,cy+radius, cz-radius)
v6 = COORD.format(cx+radius,cy+radius, cz-radius)
v7 = COORD.format(cx+radius,cy+radius, cz+radius)
v8 = COORD.format(cx-radius,cy+radius, cz+radius)
cx = CENTER.format(cx)
cy = CENTER.format(cy)
cz = CENTER.format(cz)
values = [cx, cy, cz, radius, v1, v2, v3, v4,
v5, v6, v7, v8]
replace = {keyword : value for keyword, value in zip(KEYWORDS, values)}
shutil.copy(BOX, file)
TemplateBuilder(file, replace)
return file
if __name__ == "__main__":
control_file, center, radius, box_file = parse_args()
box = build_box(control_file, center, radius, box_file)
print("Box created in {}".format(os.path.abspath(box))) | 0.367043 | 0.185043 |
from watchdog.observers import Observer
from optimus.watchers.templates import TemplatesWatchEventHandler
from optimus.watchers.assets import AssetsWatchEventHandler
def watcher_interface(settings, views, build_env):
"""
Initialize observer for views and possible assets according to settings and build
environment.
Commonly before using this function you will use ``builder_interface`` first since
it will perform a first (required) build and init the builder environment as
expected in ``build_env`` argument.
Once this interface returns the observer object, you may use it like so: ::
observer.start()
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
observer.stop()
observer.join()
Arguments:
settings (optimus.conf.model.SettingsModel): Settings object which defines
everything required for building.
views (object): Module which defines page views to build, in fact the module
object require only a ``PAGES`` attribute that is a list of Page view.
build_env (dict): A dictionnary with initialized builder (``builder`` item),
asset manager (``assets_env`` item) and the list of builded pages
(``builded`` item).
Returns:
watchdog.observers.Observer: The initialized and configured observer for
setted watchers.
"""
# Perform a first scanning of page templates
build_env["builder"].scan_bulk(views.PAGES)
# Init templates events watchers
templates_event_handler = TemplatesWatchEventHandler(
settings, build_env["builder"], **settings.WATCHER_TEMPLATES_PATTERNS
)
# Init assets events watchers
if build_env["assets_env"] is not None:
assets_event_handler = AssetsWatchEventHandler(
settings,
build_env["assets_env"],
build_env["builder"],
**settings.WATCHER_ASSETS_PATTERNS,
)
# Initialize observer to use
observer = Observer()
# Register views events watcher
observer.schedule(
templates_event_handler,
settings.TEMPLATES_DIR,
recursive=True,
)
# Register assets events watcher
# NOTE: This observe a single handler for every assets for simplicity. However the
# drawback is it watch on the whole sources directory, but it has been
# configured to care only about assets (css and js files) so it may not be
# a performance leak except on very huge source directory. The only issue is
# that it may trigger some event for css or js files which are in templates.
if build_env["assets_env"] is not None:
observer.schedule(
assets_event_handler,
settings.SOURCES_DIR,
recursive=True,
)
return observer | optimus/interfaces/watch.py | from watchdog.observers import Observer
from optimus.watchers.templates import TemplatesWatchEventHandler
from optimus.watchers.assets import AssetsWatchEventHandler
def watcher_interface(settings, views, build_env):
"""
Initialize observer for views and possible assets according to settings and build
environment.
Commonly before using this function you will use ``builder_interface`` first since
it will perform a first (required) build and init the builder environment as
expected in ``build_env`` argument.
Once this interface returns the observer object, you may use it like so: ::
observer.start()
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
observer.stop()
observer.join()
Arguments:
settings (optimus.conf.model.SettingsModel): Settings object which defines
everything required for building.
views (object): Module which defines page views to build, in fact the module
object require only a ``PAGES`` attribute that is a list of Page view.
build_env (dict): A dictionnary with initialized builder (``builder`` item),
asset manager (``assets_env`` item) and the list of builded pages
(``builded`` item).
Returns:
watchdog.observers.Observer: The initialized and configured observer for
setted watchers.
"""
# Perform a first scanning of page templates
build_env["builder"].scan_bulk(views.PAGES)
# Init templates events watchers
templates_event_handler = TemplatesWatchEventHandler(
settings, build_env["builder"], **settings.WATCHER_TEMPLATES_PATTERNS
)
# Init assets events watchers
if build_env["assets_env"] is not None:
assets_event_handler = AssetsWatchEventHandler(
settings,
build_env["assets_env"],
build_env["builder"],
**settings.WATCHER_ASSETS_PATTERNS,
)
# Initialize observer to use
observer = Observer()
# Register views events watcher
observer.schedule(
templates_event_handler,
settings.TEMPLATES_DIR,
recursive=True,
)
# Register assets events watcher
# NOTE: This observe a single handler for every assets for simplicity. However the
# drawback is it watch on the whole sources directory, but it has been
# configured to care only about assets (css and js files) so it may not be
# a performance leak except on very huge source directory. The only issue is
# that it may trigger some event for css or js files which are in templates.
if build_env["assets_env"] is not None:
observer.schedule(
assets_event_handler,
settings.SOURCES_DIR,
recursive=True,
)
return observer | 0.827967 | 0.254636 |
import errno
import glob
import os
import subprocess
import sys
CONFIGS = {}
script_dir = os.path.dirname(__file__)
for config_file in sorted(glob.glob(os.path.join(script_dir, "*build_configs.py"))):
with open(config_file) as f:
config_file_content = f.read()
exec(config_file_content, globals(), CONFIGS)
DEFAULT_CONFIG_NAME = CONFIGS.pop("DEFAULT")
RELEASE_CONFIG_NAME = CONFIGS.pop("RELEASE")
DEBUG_CONFIG_NAME = CONFIGS.pop("DEBUG")
CMAKE = "cmake"
if os.name == "posix":
MAKE = "make"
CMAKE_GENERATOR = "Unix Makefiles"
elif os.name == "nt":
MAKE = "nmake"
CMAKE_GENERATOR = "NMake Makefiles"
else:
print("Unsupported OS: {}".format(os.name))
sys.exit(1)
# TODO: Print usage
def get_project_root_path():
import __main__
return os.path.dirname(__main__.__file__)
def get_builds_path():
return os.path.join(get_project_root_path(), "builds")
def get_src_path():
return os.path.join(get_project_root_path(), "src")
def get_build_path(config_name):
return os.path.join(get_builds_path(), config_name)
def build(config_name, cmake_parameters, make_parameters):
print("Building configuration {}".format(config_name))
build_path = get_build_path(config_name)
rel_src_path = os.path.relpath(get_src_path(), build_path)
try:
os.makedirs(build_path)
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(build_path):
pass
else:
raise
try:
subprocess.check_call(
[CMAKE, "-G", CMAKE_GENERATOR] + cmake_parameters + [rel_src_path],
cwd=build_path,
)
subprocess.check_call([MAKE] + make_parameters, cwd=build_path)
print("Built configuration {} successfully".format(config_name))
# Run unit tests for debug build
if config_name == DEBUG_CONFIG_NAME:
# run parser unit tests
exitcode = subprocess.call(
"./builds/debug/rddl_parser/rddl-parser -dt-exit", shell=True
)
# run search unit tests
subprocess.call("./prost.py --debug -dt-exit", shell=True)
except subprocess.CalledProcessError as sCPE:
print("Building configuration {} failed due to CalledProcessError".format(config_name))
def main():
config_names = set()
make_parameters = []
for arg in sys.argv[1:]:
if arg == "--help" or arg == "-h":
print_usage()
sys.exit(0)
elif arg == "--release":
config_names.add(RELEASE_CONFIG_NAME)
elif arg == "--debug":
config_names.add(DEBUG_CONFIG_NAME)
elif arg == "--all":
config_names |= set(CONFIGS.keys())
elif arg in CONFIGS:
config_names.add(arg)
else:
make_parameters.append(arg)
if not config_names:
config_names.add(DEFAULT_CONFIG_NAME)
for config_name in config_names:
build(config_name, CONFIGS[config_name], make_parameters)
if __name__ == "__main__":
main() | build.py |
import errno
import glob
import os
import subprocess
import sys
CONFIGS = {}
script_dir = os.path.dirname(__file__)
for config_file in sorted(glob.glob(os.path.join(script_dir, "*build_configs.py"))):
with open(config_file) as f:
config_file_content = f.read()
exec(config_file_content, globals(), CONFIGS)
DEFAULT_CONFIG_NAME = CONFIGS.pop("DEFAULT")
RELEASE_CONFIG_NAME = CONFIGS.pop("RELEASE")
DEBUG_CONFIG_NAME = CONFIGS.pop("DEBUG")
CMAKE = "cmake"
if os.name == "posix":
MAKE = "make"
CMAKE_GENERATOR = "Unix Makefiles"
elif os.name == "nt":
MAKE = "nmake"
CMAKE_GENERATOR = "NMake Makefiles"
else:
print("Unsupported OS: {}".format(os.name))
sys.exit(1)
# TODO: Print usage
def get_project_root_path():
import __main__
return os.path.dirname(__main__.__file__)
def get_builds_path():
return os.path.join(get_project_root_path(), "builds")
def get_src_path():
return os.path.join(get_project_root_path(), "src")
def get_build_path(config_name):
return os.path.join(get_builds_path(), config_name)
def build(config_name, cmake_parameters, make_parameters):
print("Building configuration {}".format(config_name))
build_path = get_build_path(config_name)
rel_src_path = os.path.relpath(get_src_path(), build_path)
try:
os.makedirs(build_path)
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(build_path):
pass
else:
raise
try:
subprocess.check_call(
[CMAKE, "-G", CMAKE_GENERATOR] + cmake_parameters + [rel_src_path],
cwd=build_path,
)
subprocess.check_call([MAKE] + make_parameters, cwd=build_path)
print("Built configuration {} successfully".format(config_name))
# Run unit tests for debug build
if config_name == DEBUG_CONFIG_NAME:
# run parser unit tests
exitcode = subprocess.call(
"./builds/debug/rddl_parser/rddl-parser -dt-exit", shell=True
)
# run search unit tests
subprocess.call("./prost.py --debug -dt-exit", shell=True)
except subprocess.CalledProcessError as sCPE:
print("Building configuration {} failed due to CalledProcessError".format(config_name))
def main():
config_names = set()
make_parameters = []
for arg in sys.argv[1:]:
if arg == "--help" or arg == "-h":
print_usage()
sys.exit(0)
elif arg == "--release":
config_names.add(RELEASE_CONFIG_NAME)
elif arg == "--debug":
config_names.add(DEBUG_CONFIG_NAME)
elif arg == "--all":
config_names |= set(CONFIGS.keys())
elif arg in CONFIGS:
config_names.add(arg)
else:
make_parameters.append(arg)
if not config_names:
config_names.add(DEFAULT_CONFIG_NAME)
for config_name in config_names:
build(config_name, CONFIGS[config_name], make_parameters)
if __name__ == "__main__":
main() | 0.116211 | 0.050682 |
import json
import re
import traceback
from functools import wraps
from flask import request
from flask_login import current_user
from flask_socketio import disconnect
from flask_socketio import emit
from flask_socketio import join_room
import flask_together.models as models
import flask_together.youtube as youtube
from extensions import fm
from extensions import pipe
from extensions import socketio
# DECORATORS
def login_required(event):
@wraps(event)
def inner(*args, **kwargs):
if not current_user.is_authenticated:
disconnect()
else:
return event(*args, **kwargs)
return inner
def room_exists(event):
@wraps(event)
def inner(room_id, *args, **kwargs):
if type(room_id) is int:
room = models.Room.query.get(room_id)
# TODO: check if room is private and current_user is in it
if room and room.public:
return event(str(room_id), room=room, *args, **kwargs)
disconnect()
return inner
# USER HANDLERS
# handle when a user joins
@socketio.on('user:connected')
@login_required
@room_exists
def on_connect(room_id, room=None):
join_room(room_id)
# add to active users
pipe.sadd(
'room:' + room_id,
current_user.name
).execute()
# notify active users in room that a user has joined
emit('server:user-joined',
{'online_users': room.online_users, 'sid': request.sid},
room=room_id,
include_self=False,
callback=time_state_sync
)
# wait for proper time/state sync from user
pipe.set(f'time-state-sync:{request.sid}', 'waiting').execute()
# sync new user w/ room
emit('server:sync', {
'history': room.recent_history,
'most_recent': room.most_recent_video,
'online_users': room.online_users
}, room=request.sid)
"""
This is the path that time and state data takes:
New User -------> Room ----------> Online User
^ |
| |
------------- Server <--------------| callback function
Initialize a request to an online user to get the currently playing video's time
If there are no online users, the video will play at 0:00 by default.
"""
def time_state_sync(time, state, sid):
# ignore data saying video is unplayed
if time != 0 and state != -1:
if pipe.get(f'time-state-sync:{sid}').execute()[0]:
pipe.delete(f'time-state-sync:{sid}').execute()
emit('server:time-state-sync', {
'time': time,
'state': state,
}, room=sid)
# Handle when a user disconnects
@socketio.on('disconnected')
@login_required
def on_disconnect():
# disconnect from all rooms
for room in models.Room.query.with_parent(current_user):
# remove from active users
pipe.srem(
'room:' + str(room.id),
current_user.name
)
# notify room that user disconnected
emit('server:disconnected', {
'user_name': current_user.name,
}, room=str(room.id))
# clear lastfm cache for user
if fm.enabled:
pipe.set('lastfm:'+current_user.name, '')
pipe.execute()
# TODO: split search and play-new into different events
# process new video being played.
@socketio.on('user:play-new')
@login_required
@room_exists
def play_new(room_id, url, room=None):
# play new types: direct link, channel, query
# TODO: playlists with auto-play?
# extract unique_id from Youtube url
yt_regex = r'(https?://)?(www\.)?youtube\.(com|nl|ca)/watch\?v=([-\w]+)'
user_input = re.findall(yt_regex, url)
# check if user wants to play a specific video link
if user_input:
# create video wrapper to parse video data
wrapper = youtube.VideoWrapper(user_input[0][3])
if not wrapper:
return # do nothing if can't connect to youtube api
# create video object
video = models.Video(
watch_id=wrapper.watch_id,
title=wrapper.title,
thumbnail=wrapper.thumbnail,
user_id=current_user.id,
room_id=room_id
)
# save video object to database
models.db.session.add(video)
models.db.session.commit()
emit('server:play-new', {
'most_recent': room.most_recent_video,
'video': wrapper.return_as_dict()
}, room=room_id)
elif '/channel/' in url:
# channel URL entered into search bar
results = youtube.check_channel(url)
emit('server:serve-list', {'results': results,
'append': False, 'page': 1}, room=request.sid)
else:
# standard Youtube search query
results = youtube.search(url, (0, 10))
emit('server:serve-list', {'results': results,
'append': False, 'page': 1}, room=request.sid)
# Handles loading more results for a Youtube search
@socketio.on('user:search-load-more')
@login_required
@room_exists
def search_load_more(room_id, url, page, room=None):
if page != 0:
results = youtube.search(url, (page * 10, ((page)+1) * 10))
else:
results = youtube.search(url, (0, 10))
emit('server:serve-list', {'results': results,
'append': True, 'page': page + 1}, room=request.sid)
# This is for managing cache for LastFM scrobbling
@socketio.on('user:play-callback')
def play_new_handler(d):
# Scrobbling
if fm.enabled:
get_cache = pipe.get(current_user.name).execute()
d = json.loads(d['data'])
scrobbleable = False
# Checks if the video played can be scrobbled
if get_cache != [b''] and get_cache != [None]:
# Send scrobble to API then clear from cache
fm.scrobble(current_user.name)
pipe.set(current_user.name, '').execute()
elif len(d['title'].split(' - ')) == 2:
# Check if song
title = d['title'].split(' - ')
track = re.sub(r'\([^)]*\)', '', title[1])
artist = title[0]
scrobbleable = True
elif len(d['title'].split('- ')) == 2:
# Check if song
title = d['title'].split('- ')
track = re.sub(r'\([^)]*\)', '', title[1])
artist = title[0]
scrobbleable = True
elif ' - Topic' in d['author']:
# Youtube "Topic" music videos
track = d['title']
artist = d['author'].rstrip(' - Topic')
scrobbleable = True
if scrobbleable:
emit('server:play-new-artist', {
'artist': fm.get_artist(artist),
}, broadcast=True)
# Handle scrobbling after playing video
if fm.enabled:
if current_user.lastfm_connected:
duration = d['duration']
fm.update_now_playing(artist, track, current_user, duration)
else:
# Denote that nothing is being scrobbled anymore
pipe.set(current_user.name, '').execute()
# VIDEO CONTROLS
@socketio.on('user:play')
@login_required
@room_exists
def control_play(room_id, time, room=None):
emit('server:play', {'time': time}, room=room_id)
@socketio.on('user:pause')
@login_required
@room_exists
def control_pause(room_id, time, room=None):
emit('server:pause', {'time': time}, room=room_id)
@socketio.on('user:rate')
@login_required
@room_exists
def control_playback_rate(room_id, rate, room=None):
emit('server:rate', {'rate': rate}, room=room_id)
@socketio.on('user:skip')
@login_required
@room_exists
def control_skip(room_id, time, room=None):
emit('server:skip', {'time': time}, room=room_id)
# Error handling
@socketio.on_error()
def error_handler(e):
print(e.args, type(e).__name__)
traceback.print_exc() | flask_together/events.py | import json
import re
import traceback
from functools import wraps
from flask import request
from flask_login import current_user
from flask_socketio import disconnect
from flask_socketio import emit
from flask_socketio import join_room
import flask_together.models as models
import flask_together.youtube as youtube
from extensions import fm
from extensions import pipe
from extensions import socketio
# DECORATORS
def login_required(event):
@wraps(event)
def inner(*args, **kwargs):
if not current_user.is_authenticated:
disconnect()
else:
return event(*args, **kwargs)
return inner
def room_exists(event):
@wraps(event)
def inner(room_id, *args, **kwargs):
if type(room_id) is int:
room = models.Room.query.get(room_id)
# TODO: check if room is private and current_user is in it
if room and room.public:
return event(str(room_id), room=room, *args, **kwargs)
disconnect()
return inner
# USER HANDLERS
# handle when a user joins
@socketio.on('user:connected')
@login_required
@room_exists
def on_connect(room_id, room=None):
join_room(room_id)
# add to active users
pipe.sadd(
'room:' + room_id,
current_user.name
).execute()
# notify active users in room that a user has joined
emit('server:user-joined',
{'online_users': room.online_users, 'sid': request.sid},
room=room_id,
include_self=False,
callback=time_state_sync
)
# wait for proper time/state sync from user
pipe.set(f'time-state-sync:{request.sid}', 'waiting').execute()
# sync new user w/ room
emit('server:sync', {
'history': room.recent_history,
'most_recent': room.most_recent_video,
'online_users': room.online_users
}, room=request.sid)
"""
This is the path that time and state data takes:
New User -------> Room ----------> Online User
^ |
| |
------------- Server <--------------| callback function
Initialize a request to an online user to get the currently playing video's time
If there are no online users, the video will play at 0:00 by default.
"""
def time_state_sync(time, state, sid):
# ignore data saying video is unplayed
if time != 0 and state != -1:
if pipe.get(f'time-state-sync:{sid}').execute()[0]:
pipe.delete(f'time-state-sync:{sid}').execute()
emit('server:time-state-sync', {
'time': time,
'state': state,
}, room=sid)
# Handle when a user disconnects
@socketio.on('disconnected')
@login_required
def on_disconnect():
# disconnect from all rooms
for room in models.Room.query.with_parent(current_user):
# remove from active users
pipe.srem(
'room:' + str(room.id),
current_user.name
)
# notify room that user disconnected
emit('server:disconnected', {
'user_name': current_user.name,
}, room=str(room.id))
# clear lastfm cache for user
if fm.enabled:
pipe.set('lastfm:'+current_user.name, '')
pipe.execute()
# TODO: split search and play-new into different events
# process new video being played.
@socketio.on('user:play-new')
@login_required
@room_exists
def play_new(room_id, url, room=None):
# play new types: direct link, channel, query
# TODO: playlists with auto-play?
# extract unique_id from Youtube url
yt_regex = r'(https?://)?(www\.)?youtube\.(com|nl|ca)/watch\?v=([-\w]+)'
user_input = re.findall(yt_regex, url)
# check if user wants to play a specific video link
if user_input:
# create video wrapper to parse video data
wrapper = youtube.VideoWrapper(user_input[0][3])
if not wrapper:
return # do nothing if can't connect to youtube api
# create video object
video = models.Video(
watch_id=wrapper.watch_id,
title=wrapper.title,
thumbnail=wrapper.thumbnail,
user_id=current_user.id,
room_id=room_id
)
# save video object to database
models.db.session.add(video)
models.db.session.commit()
emit('server:play-new', {
'most_recent': room.most_recent_video,
'video': wrapper.return_as_dict()
}, room=room_id)
elif '/channel/' in url:
# channel URL entered into search bar
results = youtube.check_channel(url)
emit('server:serve-list', {'results': results,
'append': False, 'page': 1}, room=request.sid)
else:
# standard Youtube search query
results = youtube.search(url, (0, 10))
emit('server:serve-list', {'results': results,
'append': False, 'page': 1}, room=request.sid)
# Handles loading more results for a Youtube search
@socketio.on('user:search-load-more')
@login_required
@room_exists
def search_load_more(room_id, url, page, room=None):
if page != 0:
results = youtube.search(url, (page * 10, ((page)+1) * 10))
else:
results = youtube.search(url, (0, 10))
emit('server:serve-list', {'results': results,
'append': True, 'page': page + 1}, room=request.sid)
# This is for managing cache for LastFM scrobbling
@socketio.on('user:play-callback')
def play_new_handler(d):
# Scrobbling
if fm.enabled:
get_cache = pipe.get(current_user.name).execute()
d = json.loads(d['data'])
scrobbleable = False
# Checks if the video played can be scrobbled
if get_cache != [b''] and get_cache != [None]:
# Send scrobble to API then clear from cache
fm.scrobble(current_user.name)
pipe.set(current_user.name, '').execute()
elif len(d['title'].split(' - ')) == 2:
# Check if song
title = d['title'].split(' - ')
track = re.sub(r'\([^)]*\)', '', title[1])
artist = title[0]
scrobbleable = True
elif len(d['title'].split('- ')) == 2:
# Check if song
title = d['title'].split('- ')
track = re.sub(r'\([^)]*\)', '', title[1])
artist = title[0]
scrobbleable = True
elif ' - Topic' in d['author']:
# Youtube "Topic" music videos
track = d['title']
artist = d['author'].rstrip(' - Topic')
scrobbleable = True
if scrobbleable:
emit('server:play-new-artist', {
'artist': fm.get_artist(artist),
}, broadcast=True)
# Handle scrobbling after playing video
if fm.enabled:
if current_user.lastfm_connected:
duration = d['duration']
fm.update_now_playing(artist, track, current_user, duration)
else:
# Denote that nothing is being scrobbled anymore
pipe.set(current_user.name, '').execute()
# VIDEO CONTROLS
@socketio.on('user:play')
@login_required
@room_exists
def control_play(room_id, time, room=None):
emit('server:play', {'time': time}, room=room_id)
@socketio.on('user:pause')
@login_required
@room_exists
def control_pause(room_id, time, room=None):
emit('server:pause', {'time': time}, room=room_id)
@socketio.on('user:rate')
@login_required
@room_exists
def control_playback_rate(room_id, rate, room=None):
emit('server:rate', {'rate': rate}, room=room_id)
@socketio.on('user:skip')
@login_required
@room_exists
def control_skip(room_id, time, room=None):
emit('server:skip', {'time': time}, room=room_id)
# Error handling
@socketio.on_error()
def error_handler(e):
print(e.args, type(e).__name__)
traceback.print_exc() | 0.17989 | 0.069479 |
from django.core.exceptions import PermissionDenied
from django.utils.dateparse import parse_datetime
from django.utils.translation import gettext_lazy as _
from django.views.generic import RedirectView
from apps.ota.models import DeviceVersionAttribute
from apps.property.views import *
from apps.report.views import BaseGeneratedUserReportScheduleView
from apps.staff.views import FILTER_LOGS_LIMIT
from apps.streamer.models import StreamerReport
from apps.streamfilter.models import StreamFilter
from apps.utils.data_mask.mask_utils import clear_data_mask, set_data_mask
from apps.utils.timezone_utils import convert_to_utc
from apps.utils.views.basic import LoginRequiredAccessMixin
from apps.verticals.utils import get_device_detail_vertical_helper
from .claim_utils import *
from .data_utils import StreamDataCountHelper
from .forms import *
from .mixins import DeviceAccessMixin, DeviceCanResetAccessMixin, DeviceWriteAccessMixin
from .tasks import schedule_reset
from .worker.device_data_trim import DeviceDataTrimAction, get_streams_to_trim
from .worker.device_move import DeviceMoveAction
UPLOAD_EVENT_LIMIT = 100
logger = logging.getLogger(__name__)
class DeviceDetailView(DeviceAccessMixin, DetailView):
model = Device
template_name = 'device/detail.html'
def _get_filter_list_for_device(self, streams):
filters = {}
for stream in streams:
elements = stream.slug.split('--')
filter_stream_key = '--'.join(['f', ] + elements[1:])
try:
# If 2 filters (project and device) are defined for one stream, take the filter of the device first
filters[stream.slug] = {'filter': StreamFilter.objects.get(slug=filter_stream_key),
'is_project_filter': False}
except StreamFilter.DoesNotExist:
# Check for project-wide filter
filter_project_key = '--'.join(['f', elements[1], '', elements[3]])
try:
filters[stream.slug] = {'filter': StreamFilter.objects.get(slug=filter_project_key),
'is_project_filter': True}
except StreamFilter.DoesNotExist:
pass
return filters
def get_context_data(self, **kwargs):
context = super(DeviceDetailView, self).get_context_data(**kwargs)
context.update(self.get_basic_context())
context['api_key'] = getattr(settings, 'GOOGLE_API_KEY')
streams = self.object.streamids.filter(
enabled=True,
block__isnull=True,
project=self.object.project
).select_related('variable')
streams.select_related('variable')
context['stream_count'] = len(streams)
context['data_counter'] = StreamDataCountHelper(self.object)
# Get last streamer report upload (date & username of the uploader)
context['last_streamerreport_update'] = StreamerReport.objects.filter(streamer__device=self.object).order_by('-sent_timestamp')[:UPLOAD_EVENT_LIMIT].first()
# Get all archives but make sure only to include the ones for the current Org
context['data_block_count'] = self.object.data_blocks.filter(org=self.object.org).count()
filter_logs = []
for stream in streams:
try:
filter_logs += DynamoFilterLogModel.target_index.query(stream.slug, limit=FILTER_LOGS_LIMIT)
except Exception as e:
logger.error(str(e))
for log in filter_logs:
try:
log.timestamp = convert_to_utc(log.timestamp)
except Exception:
pass
context['filter_logs'] = filter_logs
context['filters'] = self._get_filter_list_for_device(streams)
context['generated_user_reports'] = GeneratedUserReport.objects.filter(
source_ref=self.object.slug
).order_by('-created_on')
# To show any application specific actions
vertical_helper = get_device_detail_vertical_helper(self.object)
context['vertical_action_menus'] = vertical_helper.action_menus(self.request.user)
context['device_versions'] = DeviceVersionAttribute.objects.current_device_version_qs(self.object)
return context
class DeviceListView(LoginRequiredAccessMixin, ListView):
model = Device
template_name = 'device/list.html'
def get_queryset(self):
project = Project.objects.get_from_request(self.request)
return project.devices.all()
def get_context_data(self, **kwargs):
context = super(DeviceListView, self).get_context_data(**kwargs)
return context
class DeviceUpdateView(DeviceWriteAccessMixin, UpdateView):
model = Device
form_class = DeviceForm
template_name = 'org/form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
self.object.save()
return HttpResponseRedirect(self.get_success_url())
def get_context_data(self, **kwargs):
context = super(DeviceUpdateView, self).get_context_data(**kwargs)
context['title'] = _('Edit IOTile Device')
return context
class DevicePropertyView(DeviceAccessMixin, DetailView):
model = Device
template_name = 'device/property-detail.html'
def get_context_data(self, **kwargs):
context = super(DevicePropertyView, self).get_context_data(**kwargs)
context.update(self.get_basic_context())
context['properties'] = self.object.get_properties_qs()
self.kwargs['target_slug'] = self.object.slug
return context
class DeviceUploadEventsView(DeviceAccessMixin, DetailView):
model = Device
template_name = 'device/upload-events-detail.html'
def get_context_data(self, **kwargs):
context = super(DeviceUploadEventsView, self).get_context_data(**kwargs)
context.update(self.get_basic_context())
context['reports'] = StreamerReport.objects.filter(streamer__device=self.object).order_by('-sent_timestamp')[:UPLOAD_EVENT_LIMIT]
return context
class DeviceMoveView(DeviceCanResetAccessMixin, UpdateView):
model = Device
form_class = DeviceMoveForm
template_name = 'org/form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
dst_project = form.cleaned_data['dst_project']
move_data = form.cleaned_data['move_data']
# 1. Set Device as busy
self.object.set_state('B0')
self.object.save()
args = {
'user': self.request.user.slug,
'device_slug': self.object.slug,
'project_slug': dst_project.slug,
'move_data': move_data
}
DeviceMoveAction.schedule(args=args)
messages.info(
self.request,
'Task has been scheduled to move device {} to project {}. You will receive an email when it is done.'.format(
self.object.slug,
dst_project.slug
))
device = Device.objects.get(id=self.object.id)
return HttpResponseRedirect(device.get_absolute_url())
def get_context_data(self, **kwargs):
context = super(DeviceMoveView, self).get_context_data(**kwargs)
context['title'] = _('Move IOTile Device')
return context
def get_form_kwargs(self):
kwargs = super(DeviceMoveView, self).get_form_kwargs()
project = self.object.project
org = project.org
kwargs['project_qs'] = Project.objects.user_project_qs(self.request.user)
# Only allow move between projects in same org and make sure they move to another project
kwargs['project_qs'] = kwargs['project_qs'].filter(org=org).exclude(id=project.id)
return kwargs
class DeviceResetView(DeviceCanResetAccessMixin, UpdateView):
model = Device
form_class = DeviceResetForm
template_name = 'project/form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
schedule_reset(self.object, self.request.user)
messages.info(
self.request,
'Task has been scheduled to reset device {}. You will receive an email when it is done.'.format(
self.object.slug
)
)
return HttpResponseRedirect(self.get_success_url())
def get_context_data(self, **kwargs):
context = super(DeviceResetView, self).get_context_data(**kwargs)
context['title'] = _('Reset/Clear Device Data')
return context
class DeviceTrimView(DeviceCanResetAccessMixin, UpdateView):
model = Device
form_class = DeviceTrimForm
template_name = 'project/utc_form.html'
def form_valid(self, form):
assert self.object.project
org = self.object.project.org
base_url = reverse('org:project:device:trim-confirm', kwargs={'org_slug': org.slug,
'project_id': str(self.object.project.id),
'pk': self.object.id})
start_str = str_utc(form.cleaned_data['start']) if form.cleaned_data['start'] else None
end_str = str_utc(form.cleaned_data['end']) if form.cleaned_data['end'] else None
args = []
if start_str:
args.append('start={}'.format(start_str))
if end_str:
args.append('end={}'.format(end_str))
confirm_url = '{base}?{args}'.format(base=base_url, args='&'.join(args))
return HttpResponseRedirect(confirm_url)
def get_context_data(self, **kwargs):
context = super(DeviceTrimView, self).get_context_data(**kwargs)
context['title'] = _('Trim Device Data')
return context
def get_form_kwargs(self):
kwargs = super(DeviceTrimView, self).get_form_kwargs()
kwargs['start'] = self.request.GET.get('start', None)
kwargs['end'] = self.request.GET.get('end', None)
return kwargs
class DeviceTrimByMaskView(DeviceCanResetAccessMixin, RedirectView, DetailView):
def get_redirect_url(self, *args, **kwargs):
device = get_object_or_404(Device, pk=kwargs['pk'])
assert device.project
org = device.project.org
base_url = reverse('org:project:device:trim-confirm', kwargs={'org_slug': org.slug,
'project_id': str(device.project.id),
'pk': device.id})
data_mask_range = get_data_mask_date_range(device)
if data_mask_range is None:
url = device.get_absolute_url()
messages.add_message(self.request, messages.INFO, "Data is not masked, operation can't be performed.")
return url
start_str = end_str = None
if 'start' in data_mask_range and data_mask_range['start'] is not None:
start_str = str_utc(parse_datetime(data_mask_range['start']))
if 'end' in data_mask_range and data_mask_range['end'] is not None:
end_str = str_utc(parse_datetime(data_mask_range['end']))
args = []
if start_str:
args.append('start={}'.format(start_str))
if end_str:
args.append('end={}'.format(end_str))
confirm_url = '{base}?{args}'.format(base=base_url, args='&'.join(args))
return confirm_url
class DeviceTrimConfirmView(DeviceCanResetAccessMixin, UpdateView):
model = StreamId
form_class = DeviceTrimConfirmForm
template_name = 'device/trim-confirmation-form.html'
def _get_date(self, type):
'''
:param type: 'start' or 'end'
:return: datetime or None
'''
if type in self.request.GET and self.request.GET[type]:
return parse_datetime(self.request.GET[type])
else:
return None
def get_success_url(self):
assert self.object.project
org = self.object.project.org
return reverse('org:project:device:detail', args=(org.slug, str(self.object.project.id), self.object.pk))
def form_valid(self, form):
self.object = form.save(commit=False)
start = self._get_date('start')
end = self._get_date('end')
start_str = str_utc(start) if start else None
end_str = str_utc(end) if end else None
args = {
'username': self.request.user.username,
'device_slug': self.object.slug,
'start': start_str,
'end': end_str
}
DeviceDataTrimAction.schedule(args=args)
messages.info(self.request,
'Task has been scheduled to data trim device {}. You will receive an email when it is done.'.format(
self.object.slug))
return HttpResponseRedirect(self.get_success_url())
def get_context_data(self, **kwargs):
context = super(DeviceTrimConfirmView, self).get_context_data(**kwargs)
context['start'] = self._get_date('start')
context['end'] = self._get_date('end')
# We don't want to delete system information that may be useful regardless of trimming,
# For example, we want to keep the trip start and trip ended around even after the trim
stream_qs = get_streams_to_trim(self.object)
stream_slugs = [s.slug for s in stream_qs]
if context['start']:
data_qs = DataManager.filter_qs('data', stream_slug__in=stream_slugs, device_slug=self.object.slug)
event_qs = DataManager.filter_qs('event', stream_slug__in=stream_slugs, device_slug=self.object.slug)
logger.info('First Data: {}'.format(data_qs.order_by('timestamp').first()))
data0_qs = data_qs.filter(timestamp__lt=context['start'])
first_data = None
if data0_qs.exists():
context['data0_qs'] = data0_qs
msg = '{0} data row(s) before {1} deleted'.format(data0_qs.count(), context['start'])
logger.info(msg)
first_data = data0_qs.first()
context['oldest'] = convert_to_utc(first_data.timestamp)
logger.info('First Event: {}'.format(event_qs.order_by('timestamp').first()))
event0_qs = event_qs.filter(timestamp__lt=context['start'])
if event0_qs.exists():
context['event0_qs'] = event0_qs
msg = '{0} event row(s) before {1} deleted'.format(event0_qs.count(), context['start'])
logger.info(msg)
older_event = event0_qs.first()
if older_event and (not first_data or (older_event.timestamp < context['oldest'])):
context['oldest'] = older_event
if context['end']:
data_qs = DataManager.filter_qs('data', stream_slug__in=stream_slugs, device_slug=self.object.slug)
event_qs = DataManager.filter_qs('event', stream_slug__in=stream_slugs, device_slug=self.object.slug)
logger.info('Last Data: {}'.format(data_qs.order_by('timestamp').last()))
data1_qs = data_qs.filter(timestamp__gt=context['end'])
last_data = None
if data1_qs.exists():
context['data1_qs'] = data1_qs
msg = '{0} data row(s) after {1} deleted'.format(data1_qs.count(), context['end'])
logger.info(msg)
last_data = data1_qs.last()
context['newest'] = convert_to_utc(last_data.timestamp) if last_data else None
logger.info('Last Event: {}'.format(event_qs.order_by('timestamp').last()))
event1_qs = event_qs.filter(timestamp__gt=context['end'])
if event1_qs.exists():
context['event1_qs'] = event1_qs
msg = '{0} event row(s) after {1} deleted'.format(event1_qs.count(), context['end'])
logger.info(msg)
newest_event = event1_qs.last()
if newest_event and (not last_data or (newest_event.timestamp > context['newest'])):
context['newest'] = newest_event
return context
def get_form_kwargs(self):
kwargs = super(DeviceTrimConfirmView, self).get_form_kwargs()
kwargs['start'] = self.request.GET.get('start', None)
kwargs['end'] = self.request.GET.get('end', None)
return kwargs
class DeviceMaskView(DeviceCanResetAccessMixin, UpdateView):
model = Device
form_class = DeviceMaskForm
template_name = 'project/utc_form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
start = form.cleaned_data.get('start', None)
end = form.cleaned_data.get('end', None)
if start or end:
start_str = str_utc(start) if start else None
end_str = str_utc(end) if end else None
set_data_mask(self.object, start_str, end_str, [], [], user=self.request.user)
messages.info(self.request, 'Device Mask Configuration Set {}'.format(self.object.slug))
else:
clear_data_mask(self.object, self.request.user)
return HttpResponseRedirect(self.get_success_url())
def get_context_data(self, **kwargs):
context = super(DeviceMaskView, self).get_context_data(**kwargs)
context['title'] = _('Mask Device Data')
return context
def get_form_kwargs(self):
kwargs = super(DeviceMaskView, self).get_form_kwargs()
kwargs['start'] = self.request.GET.get('start', None)
kwargs['end'] = self.request.GET.get('end', None)
kwargs['event_list'] = self.request.GET.get('events', None)
kwargs['data_list'] = self.request.GET.get('data', None)
return kwargs
class DeviceHealthStatusView(DeviceAccessMixin, DetailView):
model = Device
template_name = 'device/device-health-status.html'
def get_context_data(self, **kwargs):
context = super(DeviceHealthStatusView, self).get_context_data(**kwargs)
context.update(self.get_basic_context())
context['status'] = DeviceStatus.get_or_create(self.object)
return context
class SeviceStatusSettingsView(DeviceWriteAccessMixin, UpdateView):
model = DeviceStatus
form_class = DeviceHealthForm
template_name = 'project/form.html'
def get_object(self, queryset=None):
device = super(SeviceStatusSettingsView, self).get_object(queryset=queryset)
if device:
return device.get_or_create_status()
raise PermissionDenied('User has no write permission to Device {}'.format(device.slug))
def get_context_data(self, **kwargs):
context = super(SeviceStatusSettingsView, self).get_context_data(**kwargs)
org = self.object.device.org
context.update(org.permissions(self.request.user))
return context
def get_form_kwargs(self):
kwargs = super(SeviceStatusSettingsView, self).get_form_kwargs()
org = self.object.device.org
kwargs['org'] = org
return kwargs
def form_valid(self, form):
self.object = form.save(commit=False)
recipients = form.cleaned_data['recipients']
self.object.notification_recipients = recipients
extras = form.cleaned_data['extras']
extra_emails = extras.split('\n')
for extra_email in extra_emails:
extra_email = extra_email.strip()
if extra_email:
self.object.notification_recipients.append('email:{}'.format(extra_email))
self.object.save()
return HttpResponseRedirect(self.object.get_absolute_url())
class DeviceFilterLogsClearView(DeviceAccessMixin, UpdateView):
model = Device
form_class = DeviceFilterLogsClearForm
template_name = 'project/form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
for stream in self.object.streamids.all():
try:
qs = DynamoFilterLogModel.target_index.query(stream.slug)
with DynamoFilterLogModel.batch_write() as batch:
for item in qs:
batch.delete(item)
except Exception:
pass
return HttpResponseRedirect(self.object.get_absolute_url())
class DeviceGeneratedUserReportScheduleView(BaseGeneratedUserReportScheduleView):
template_name = "project/form.html"
def get_source_ref_object(self):
return get_object_or_404(Device, pk=self.kwargs['pk']) | server/apps/physicaldevice/views.py | from django.core.exceptions import PermissionDenied
from django.utils.dateparse import parse_datetime
from django.utils.translation import gettext_lazy as _
from django.views.generic import RedirectView
from apps.ota.models import DeviceVersionAttribute
from apps.property.views import *
from apps.report.views import BaseGeneratedUserReportScheduleView
from apps.staff.views import FILTER_LOGS_LIMIT
from apps.streamer.models import StreamerReport
from apps.streamfilter.models import StreamFilter
from apps.utils.data_mask.mask_utils import clear_data_mask, set_data_mask
from apps.utils.timezone_utils import convert_to_utc
from apps.utils.views.basic import LoginRequiredAccessMixin
from apps.verticals.utils import get_device_detail_vertical_helper
from .claim_utils import *
from .data_utils import StreamDataCountHelper
from .forms import *
from .mixins import DeviceAccessMixin, DeviceCanResetAccessMixin, DeviceWriteAccessMixin
from .tasks import schedule_reset
from .worker.device_data_trim import DeviceDataTrimAction, get_streams_to_trim
from .worker.device_move import DeviceMoveAction
UPLOAD_EVENT_LIMIT = 100
logger = logging.getLogger(__name__)
class DeviceDetailView(DeviceAccessMixin, DetailView):
model = Device
template_name = 'device/detail.html'
def _get_filter_list_for_device(self, streams):
filters = {}
for stream in streams:
elements = stream.slug.split('--')
filter_stream_key = '--'.join(['f', ] + elements[1:])
try:
# If 2 filters (project and device) are defined for one stream, take the filter of the device first
filters[stream.slug] = {'filter': StreamFilter.objects.get(slug=filter_stream_key),
'is_project_filter': False}
except StreamFilter.DoesNotExist:
# Check for project-wide filter
filter_project_key = '--'.join(['f', elements[1], '', elements[3]])
try:
filters[stream.slug] = {'filter': StreamFilter.objects.get(slug=filter_project_key),
'is_project_filter': True}
except StreamFilter.DoesNotExist:
pass
return filters
def get_context_data(self, **kwargs):
context = super(DeviceDetailView, self).get_context_data(**kwargs)
context.update(self.get_basic_context())
context['api_key'] = getattr(settings, 'GOOGLE_API_KEY')
streams = self.object.streamids.filter(
enabled=True,
block__isnull=True,
project=self.object.project
).select_related('variable')
streams.select_related('variable')
context['stream_count'] = len(streams)
context['data_counter'] = StreamDataCountHelper(self.object)
# Get last streamer report upload (date & username of the uploader)
context['last_streamerreport_update'] = StreamerReport.objects.filter(streamer__device=self.object).order_by('-sent_timestamp')[:UPLOAD_EVENT_LIMIT].first()
# Get all archives but make sure only to include the ones for the current Org
context['data_block_count'] = self.object.data_blocks.filter(org=self.object.org).count()
filter_logs = []
for stream in streams:
try:
filter_logs += DynamoFilterLogModel.target_index.query(stream.slug, limit=FILTER_LOGS_LIMIT)
except Exception as e:
logger.error(str(e))
for log in filter_logs:
try:
log.timestamp = convert_to_utc(log.timestamp)
except Exception:
pass
context['filter_logs'] = filter_logs
context['filters'] = self._get_filter_list_for_device(streams)
context['generated_user_reports'] = GeneratedUserReport.objects.filter(
source_ref=self.object.slug
).order_by('-created_on')
# To show any application specific actions
vertical_helper = get_device_detail_vertical_helper(self.object)
context['vertical_action_menus'] = vertical_helper.action_menus(self.request.user)
context['device_versions'] = DeviceVersionAttribute.objects.current_device_version_qs(self.object)
return context
class DeviceListView(LoginRequiredAccessMixin, ListView):
model = Device
template_name = 'device/list.html'
def get_queryset(self):
project = Project.objects.get_from_request(self.request)
return project.devices.all()
def get_context_data(self, **kwargs):
context = super(DeviceListView, self).get_context_data(**kwargs)
return context
class DeviceUpdateView(DeviceWriteAccessMixin, UpdateView):
model = Device
form_class = DeviceForm
template_name = 'org/form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
self.object.save()
return HttpResponseRedirect(self.get_success_url())
def get_context_data(self, **kwargs):
context = super(DeviceUpdateView, self).get_context_data(**kwargs)
context['title'] = _('Edit IOTile Device')
return context
class DevicePropertyView(DeviceAccessMixin, DetailView):
model = Device
template_name = 'device/property-detail.html'
def get_context_data(self, **kwargs):
context = super(DevicePropertyView, self).get_context_data(**kwargs)
context.update(self.get_basic_context())
context['properties'] = self.object.get_properties_qs()
self.kwargs['target_slug'] = self.object.slug
return context
class DeviceUploadEventsView(DeviceAccessMixin, DetailView):
model = Device
template_name = 'device/upload-events-detail.html'
def get_context_data(self, **kwargs):
context = super(DeviceUploadEventsView, self).get_context_data(**kwargs)
context.update(self.get_basic_context())
context['reports'] = StreamerReport.objects.filter(streamer__device=self.object).order_by('-sent_timestamp')[:UPLOAD_EVENT_LIMIT]
return context
class DeviceMoveView(DeviceCanResetAccessMixin, UpdateView):
model = Device
form_class = DeviceMoveForm
template_name = 'org/form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
dst_project = form.cleaned_data['dst_project']
move_data = form.cleaned_data['move_data']
# 1. Set Device as busy
self.object.set_state('B0')
self.object.save()
args = {
'user': self.request.user.slug,
'device_slug': self.object.slug,
'project_slug': dst_project.slug,
'move_data': move_data
}
DeviceMoveAction.schedule(args=args)
messages.info(
self.request,
'Task has been scheduled to move device {} to project {}. You will receive an email when it is done.'.format(
self.object.slug,
dst_project.slug
))
device = Device.objects.get(id=self.object.id)
return HttpResponseRedirect(device.get_absolute_url())
def get_context_data(self, **kwargs):
context = super(DeviceMoveView, self).get_context_data(**kwargs)
context['title'] = _('Move IOTile Device')
return context
def get_form_kwargs(self):
kwargs = super(DeviceMoveView, self).get_form_kwargs()
project = self.object.project
org = project.org
kwargs['project_qs'] = Project.objects.user_project_qs(self.request.user)
# Only allow move between projects in same org and make sure they move to another project
kwargs['project_qs'] = kwargs['project_qs'].filter(org=org).exclude(id=project.id)
return kwargs
class DeviceResetView(DeviceCanResetAccessMixin, UpdateView):
model = Device
form_class = DeviceResetForm
template_name = 'project/form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
schedule_reset(self.object, self.request.user)
messages.info(
self.request,
'Task has been scheduled to reset device {}. You will receive an email when it is done.'.format(
self.object.slug
)
)
return HttpResponseRedirect(self.get_success_url())
def get_context_data(self, **kwargs):
context = super(DeviceResetView, self).get_context_data(**kwargs)
context['title'] = _('Reset/Clear Device Data')
return context
class DeviceTrimView(DeviceCanResetAccessMixin, UpdateView):
model = Device
form_class = DeviceTrimForm
template_name = 'project/utc_form.html'
def form_valid(self, form):
assert self.object.project
org = self.object.project.org
base_url = reverse('org:project:device:trim-confirm', kwargs={'org_slug': org.slug,
'project_id': str(self.object.project.id),
'pk': self.object.id})
start_str = str_utc(form.cleaned_data['start']) if form.cleaned_data['start'] else None
end_str = str_utc(form.cleaned_data['end']) if form.cleaned_data['end'] else None
args = []
if start_str:
args.append('start={}'.format(start_str))
if end_str:
args.append('end={}'.format(end_str))
confirm_url = '{base}?{args}'.format(base=base_url, args='&'.join(args))
return HttpResponseRedirect(confirm_url)
def get_context_data(self, **kwargs):
context = super(DeviceTrimView, self).get_context_data(**kwargs)
context['title'] = _('Trim Device Data')
return context
def get_form_kwargs(self):
kwargs = super(DeviceTrimView, self).get_form_kwargs()
kwargs['start'] = self.request.GET.get('start', None)
kwargs['end'] = self.request.GET.get('end', None)
return kwargs
class DeviceTrimByMaskView(DeviceCanResetAccessMixin, RedirectView, DetailView):
def get_redirect_url(self, *args, **kwargs):
device = get_object_or_404(Device, pk=kwargs['pk'])
assert device.project
org = device.project.org
base_url = reverse('org:project:device:trim-confirm', kwargs={'org_slug': org.slug,
'project_id': str(device.project.id),
'pk': device.id})
data_mask_range = get_data_mask_date_range(device)
if data_mask_range is None:
url = device.get_absolute_url()
messages.add_message(self.request, messages.INFO, "Data is not masked, operation can't be performed.")
return url
start_str = end_str = None
if 'start' in data_mask_range and data_mask_range['start'] is not None:
start_str = str_utc(parse_datetime(data_mask_range['start']))
if 'end' in data_mask_range and data_mask_range['end'] is not None:
end_str = str_utc(parse_datetime(data_mask_range['end']))
args = []
if start_str:
args.append('start={}'.format(start_str))
if end_str:
args.append('end={}'.format(end_str))
confirm_url = '{base}?{args}'.format(base=base_url, args='&'.join(args))
return confirm_url
class DeviceTrimConfirmView(DeviceCanResetAccessMixin, UpdateView):
model = StreamId
form_class = DeviceTrimConfirmForm
template_name = 'device/trim-confirmation-form.html'
def _get_date(self, type):
'''
:param type: 'start' or 'end'
:return: datetime or None
'''
if type in self.request.GET and self.request.GET[type]:
return parse_datetime(self.request.GET[type])
else:
return None
def get_success_url(self):
assert self.object.project
org = self.object.project.org
return reverse('org:project:device:detail', args=(org.slug, str(self.object.project.id), self.object.pk))
def form_valid(self, form):
self.object = form.save(commit=False)
start = self._get_date('start')
end = self._get_date('end')
start_str = str_utc(start) if start else None
end_str = str_utc(end) if end else None
args = {
'username': self.request.user.username,
'device_slug': self.object.slug,
'start': start_str,
'end': end_str
}
DeviceDataTrimAction.schedule(args=args)
messages.info(self.request,
'Task has been scheduled to data trim device {}. You will receive an email when it is done.'.format(
self.object.slug))
return HttpResponseRedirect(self.get_success_url())
def get_context_data(self, **kwargs):
context = super(DeviceTrimConfirmView, self).get_context_data(**kwargs)
context['start'] = self._get_date('start')
context['end'] = self._get_date('end')
# We don't want to delete system information that may be useful regardless of trimming,
# For example, we want to keep the trip start and trip ended around even after the trim
stream_qs = get_streams_to_trim(self.object)
stream_slugs = [s.slug for s in stream_qs]
if context['start']:
data_qs = DataManager.filter_qs('data', stream_slug__in=stream_slugs, device_slug=self.object.slug)
event_qs = DataManager.filter_qs('event', stream_slug__in=stream_slugs, device_slug=self.object.slug)
logger.info('First Data: {}'.format(data_qs.order_by('timestamp').first()))
data0_qs = data_qs.filter(timestamp__lt=context['start'])
first_data = None
if data0_qs.exists():
context['data0_qs'] = data0_qs
msg = '{0} data row(s) before {1} deleted'.format(data0_qs.count(), context['start'])
logger.info(msg)
first_data = data0_qs.first()
context['oldest'] = convert_to_utc(first_data.timestamp)
logger.info('First Event: {}'.format(event_qs.order_by('timestamp').first()))
event0_qs = event_qs.filter(timestamp__lt=context['start'])
if event0_qs.exists():
context['event0_qs'] = event0_qs
msg = '{0} event row(s) before {1} deleted'.format(event0_qs.count(), context['start'])
logger.info(msg)
older_event = event0_qs.first()
if older_event and (not first_data or (older_event.timestamp < context['oldest'])):
context['oldest'] = older_event
if context['end']:
data_qs = DataManager.filter_qs('data', stream_slug__in=stream_slugs, device_slug=self.object.slug)
event_qs = DataManager.filter_qs('event', stream_slug__in=stream_slugs, device_slug=self.object.slug)
logger.info('Last Data: {}'.format(data_qs.order_by('timestamp').last()))
data1_qs = data_qs.filter(timestamp__gt=context['end'])
last_data = None
if data1_qs.exists():
context['data1_qs'] = data1_qs
msg = '{0} data row(s) after {1} deleted'.format(data1_qs.count(), context['end'])
logger.info(msg)
last_data = data1_qs.last()
context['newest'] = convert_to_utc(last_data.timestamp) if last_data else None
logger.info('Last Event: {}'.format(event_qs.order_by('timestamp').last()))
event1_qs = event_qs.filter(timestamp__gt=context['end'])
if event1_qs.exists():
context['event1_qs'] = event1_qs
msg = '{0} event row(s) after {1} deleted'.format(event1_qs.count(), context['end'])
logger.info(msg)
newest_event = event1_qs.last()
if newest_event and (not last_data or (newest_event.timestamp > context['newest'])):
context['newest'] = newest_event
return context
def get_form_kwargs(self):
kwargs = super(DeviceTrimConfirmView, self).get_form_kwargs()
kwargs['start'] = self.request.GET.get('start', None)
kwargs['end'] = self.request.GET.get('end', None)
return kwargs
class DeviceMaskView(DeviceCanResetAccessMixin, UpdateView):
model = Device
form_class = DeviceMaskForm
template_name = 'project/utc_form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
start = form.cleaned_data.get('start', None)
end = form.cleaned_data.get('end', None)
if start or end:
start_str = str_utc(start) if start else None
end_str = str_utc(end) if end else None
set_data_mask(self.object, start_str, end_str, [], [], user=self.request.user)
messages.info(self.request, 'Device Mask Configuration Set {}'.format(self.object.slug))
else:
clear_data_mask(self.object, self.request.user)
return HttpResponseRedirect(self.get_success_url())
def get_context_data(self, **kwargs):
context = super(DeviceMaskView, self).get_context_data(**kwargs)
context['title'] = _('Mask Device Data')
return context
def get_form_kwargs(self):
kwargs = super(DeviceMaskView, self).get_form_kwargs()
kwargs['start'] = self.request.GET.get('start', None)
kwargs['end'] = self.request.GET.get('end', None)
kwargs['event_list'] = self.request.GET.get('events', None)
kwargs['data_list'] = self.request.GET.get('data', None)
return kwargs
class DeviceHealthStatusView(DeviceAccessMixin, DetailView):
model = Device
template_name = 'device/device-health-status.html'
def get_context_data(self, **kwargs):
context = super(DeviceHealthStatusView, self).get_context_data(**kwargs)
context.update(self.get_basic_context())
context['status'] = DeviceStatus.get_or_create(self.object)
return context
class SeviceStatusSettingsView(DeviceWriteAccessMixin, UpdateView):
model = DeviceStatus
form_class = DeviceHealthForm
template_name = 'project/form.html'
def get_object(self, queryset=None):
device = super(SeviceStatusSettingsView, self).get_object(queryset=queryset)
if device:
return device.get_or_create_status()
raise PermissionDenied('User has no write permission to Device {}'.format(device.slug))
def get_context_data(self, **kwargs):
context = super(SeviceStatusSettingsView, self).get_context_data(**kwargs)
org = self.object.device.org
context.update(org.permissions(self.request.user))
return context
def get_form_kwargs(self):
kwargs = super(SeviceStatusSettingsView, self).get_form_kwargs()
org = self.object.device.org
kwargs['org'] = org
return kwargs
def form_valid(self, form):
self.object = form.save(commit=False)
recipients = form.cleaned_data['recipients']
self.object.notification_recipients = recipients
extras = form.cleaned_data['extras']
extra_emails = extras.split('\n')
for extra_email in extra_emails:
extra_email = extra_email.strip()
if extra_email:
self.object.notification_recipients.append('email:{}'.format(extra_email))
self.object.save()
return HttpResponseRedirect(self.object.get_absolute_url())
class DeviceFilterLogsClearView(DeviceAccessMixin, UpdateView):
model = Device
form_class = DeviceFilterLogsClearForm
template_name = 'project/form.html'
def form_valid(self, form):
self.object = form.save(commit=False)
for stream in self.object.streamids.all():
try:
qs = DynamoFilterLogModel.target_index.query(stream.slug)
with DynamoFilterLogModel.batch_write() as batch:
for item in qs:
batch.delete(item)
except Exception:
pass
return HttpResponseRedirect(self.object.get_absolute_url())
class DeviceGeneratedUserReportScheduleView(BaseGeneratedUserReportScheduleView):
template_name = "project/form.html"
def get_source_ref_object(self):
return get_object_or_404(Device, pk=self.kwargs['pk']) | 0.540196 | 0.076822 |
Here is an abstract class for neural network models based on Tensorflow.
If you use something different, ex. Pytorch, then write similar to this class, inherit it from
Trainable and Inferable interfaces and make a pull-request to deeppavlov.
"""
from abc import abstractmethod
from warnings import warn
import tensorflow as tf
from overrides import overrides
from deeppavlov.core.models.trainable import Trainable
from deeppavlov.core.models.inferable import Inferable
from deeppavlov.core.common.attributes import check_attr_true
from .tf_backend import TfModelMeta
class TFModel(Trainable, Inferable, metaclass=TfModelMeta):
def __init__(self, **kwargs):
self._saver = tf.train.Saver
super().__init__(**kwargs)
@abstractmethod
def _add_placeholders(self):
"""
Add all needed placeholders for a computational graph.
"""
pass
@abstractmethod
def run_sess(self, *args, **kwargs):
"""
1. Call _build_graph()
2. Define all comuptations.
3. Run tf.sess.
3. Reset state if needed.
:return:
"""
pass
@abstractmethod
def _train_step(self, features, *args):
"""
Define a single training step. Feed dict to tf session.
:param features: input features
:param args: any other inputs, including target vector, you need to pass for training
:return: metric to return, usually loss
"""
pass
@abstractmethod
def _forward(self, features, *args):
"""
Pass an instance to get a prediction.
:param features: input features
:param args: any other inputs you need to pass for training
:return: prediction
"""
pass
@check_attr_true('train_now')
def train(self, features, *args, **kwargs):
"""
Just a wrapper for a private method.
"""
return self._train_step(features, *args, **kwargs)
def infer(self, instance, *args):
"""
Just a wrapper for a private method.
"""
return self._forward(instance, *args)
def save(self):
save_path = str(self.save_path)
saver = tf.train.Saver()
print('\n:: saving model to {}'.format(save_path))
saver.save(self.sess, save_path)
print('model saved')
def get_checkpoint_state(self):
if self.load_path:
if self.load_path.parent.is_dir():
return tf.train.get_checkpoint_state(self.load_path.parent)
else:
warn('Provided `load_path` is incorrect!')
else:
warn('No `load_path` is provided for {}".format(self.__class__.__name__)')
@overrides
def load(self):
"""
Load session from checkpoint
"""
ckpt = self.get_checkpoint_state()
if ckpt and ckpt.model_checkpoint_path:
print('\n:: restoring checkpoint from', ckpt.model_checkpoint_path, '\n')
self._saver().restore(self.sess, ckpt.model_checkpoint_path)
print('session restored')
else:
print('\n:: <ERR> checkpoint not found! \n')
class SimpleTFModel(Trainable, Inferable, metaclass=TfModelMeta):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs) | deeppavlov/core/models/tf_model.py | Here is an abstract class for neural network models based on Tensorflow.
If you use something different, ex. Pytorch, then write similar to this class, inherit it from
Trainable and Inferable interfaces and make a pull-request to deeppavlov.
"""
from abc import abstractmethod
from warnings import warn
import tensorflow as tf
from overrides import overrides
from deeppavlov.core.models.trainable import Trainable
from deeppavlov.core.models.inferable import Inferable
from deeppavlov.core.common.attributes import check_attr_true
from .tf_backend import TfModelMeta
class TFModel(Trainable, Inferable, metaclass=TfModelMeta):
def __init__(self, **kwargs):
self._saver = tf.train.Saver
super().__init__(**kwargs)
@abstractmethod
def _add_placeholders(self):
"""
Add all needed placeholders for a computational graph.
"""
pass
@abstractmethod
def run_sess(self, *args, **kwargs):
"""
1. Call _build_graph()
2. Define all comuptations.
3. Run tf.sess.
3. Reset state if needed.
:return:
"""
pass
@abstractmethod
def _train_step(self, features, *args):
"""
Define a single training step. Feed dict to tf session.
:param features: input features
:param args: any other inputs, including target vector, you need to pass for training
:return: metric to return, usually loss
"""
pass
@abstractmethod
def _forward(self, features, *args):
"""
Pass an instance to get a prediction.
:param features: input features
:param args: any other inputs you need to pass for training
:return: prediction
"""
pass
@check_attr_true('train_now')
def train(self, features, *args, **kwargs):
"""
Just a wrapper for a private method.
"""
return self._train_step(features, *args, **kwargs)
def infer(self, instance, *args):
"""
Just a wrapper for a private method.
"""
return self._forward(instance, *args)
def save(self):
save_path = str(self.save_path)
saver = tf.train.Saver()
print('\n:: saving model to {}'.format(save_path))
saver.save(self.sess, save_path)
print('model saved')
def get_checkpoint_state(self):
if self.load_path:
if self.load_path.parent.is_dir():
return tf.train.get_checkpoint_state(self.load_path.parent)
else:
warn('Provided `load_path` is incorrect!')
else:
warn('No `load_path` is provided for {}".format(self.__class__.__name__)')
@overrides
def load(self):
"""
Load session from checkpoint
"""
ckpt = self.get_checkpoint_state()
if ckpt and ckpt.model_checkpoint_path:
print('\n:: restoring checkpoint from', ckpt.model_checkpoint_path, '\n')
self._saver().restore(self.sess, ckpt.model_checkpoint_path)
print('session restored')
else:
print('\n:: <ERR> checkpoint not found! \n')
class SimpleTFModel(Trainable, Inferable, metaclass=TfModelMeta):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs) | 0.912048 | 0.463809 |
from functools import lru_cache
from pathlib import Path
import re
from typing import Iterable, Set, Tuple, Union
import pandas as pd
from rapidfuzz import process, fuzz
DATADIR = Path(__file__).parent / 'data'
MASTER_PLAYERS = DATADIR / 'master_players.csv'
LEGAL_CHARS = re.compile(r'\W')
SUFFIXES = {'II', 'The Second', 'III', 'The Third', 'IV', 'The Fourth', 'Jr', 'Junior', 'Sr', 'Senior', 'Esq', 'JD', 'MD', 'PhD'}
LOWER_SUFFIXES = set([s.lower() for s in SUFFIXES])
KNOWN_DUPS = {'<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>'}
def _explode(s: pd.Series) -> pd.DataFrame:
"""Explodes series into individual columns
Args:
s (pd.Series): pandas series
Returns:
pd.DataFrame
"""
return pd.DataFrame(s.tolist(), index=s.index)
@lru_cache(maxsize=None)
def master_player_records() -> pd.DataFrame:
"""The master player records
Returns:
pd.DataFrame
"""
return pd.read_csv(MASTER_PLAYERS)
def match_name(match: str, match_from: Iterable[str]) -> Tuple[str, int]:
"""Finds best match from list of names
Args:
match (str): the name to match
match_from (List[str]): the names to match from
Returns:
Tuple[str, int]: the match and the confidence
"""
return process.extractOne(match, match_from, scorer=fuzz.WRatio)
def rearrange_name(s: str) -> str:
"""Converts last, first to first last
Args:
s (str): the original string
Returns:
str: the standardized string
"""
return ' '.join(reversed([i.strip() for i in s.split(', ')]))
def remove_chars(s: str, keep: Union[str, re.Pattern] = LEGAL_CHARS) -> str:
"""Removes all but legal characters from string"""
return ' '.join([re.sub(keep, '', i) for i in s.split()])
def remove_suffixes(s: str, remove: Set[str] = LOWER_SUFFIXES) -> str:
"""Removes suffixes from string"""
s = s.split()
if s[-1].lower() in remove:
s = s[:-1]
return ' '.join(s)
def standardize_defense_name(s: str) -> str:
"""Standardizes DST name
Args:
s (str): the original string
Returns:
str: the standardized string
"""
return standardize_player_name(s).split()[-1] + ' defense'
def standardize_player_name(s: str) -> str:
"""Standardizes player name
Args:
s (str): the original string
Returns:
str: the standardized string
"""
return re.sub(r'\s+', ' ' , remove_suffixes(remove_chars(s))).lower()
def standardize_positions(s: str) -> str:
"""Standardizes position names
Args:
s (str): the position string
Returns:
str: the standardized string
"""
mapping = {'Def': 'DST', 'Defense': 'DST', 'DEF': 'DST', 'def': 'DST', 'dst': 'DST', 'PK': 'K', 'Kicker': 'K'}
std = mapping[s] if s in mapping else s
return std | nflnames/players.py |
from functools import lru_cache
from pathlib import Path
import re
from typing import Iterable, Set, Tuple, Union
import pandas as pd
from rapidfuzz import process, fuzz
DATADIR = Path(__file__).parent / 'data'
MASTER_PLAYERS = DATADIR / 'master_players.csv'
LEGAL_CHARS = re.compile(r'\W')
SUFFIXES = {'II', 'The Second', 'III', 'The Third', 'IV', 'The Fourth', 'Jr', 'Junior', 'Sr', 'Senior', 'Esq', 'JD', 'MD', 'PhD'}
LOWER_SUFFIXES = set([s.lower() for s in SUFFIXES])
KNOWN_DUPS = {'<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>'}
def _explode(s: pd.Series) -> pd.DataFrame:
"""Explodes series into individual columns
Args:
s (pd.Series): pandas series
Returns:
pd.DataFrame
"""
return pd.DataFrame(s.tolist(), index=s.index)
@lru_cache(maxsize=None)
def master_player_records() -> pd.DataFrame:
"""The master player records
Returns:
pd.DataFrame
"""
return pd.read_csv(MASTER_PLAYERS)
def match_name(match: str, match_from: Iterable[str]) -> Tuple[str, int]:
"""Finds best match from list of names
Args:
match (str): the name to match
match_from (List[str]): the names to match from
Returns:
Tuple[str, int]: the match and the confidence
"""
return process.extractOne(match, match_from, scorer=fuzz.WRatio)
def rearrange_name(s: str) -> str:
"""Converts last, first to first last
Args:
s (str): the original string
Returns:
str: the standardized string
"""
return ' '.join(reversed([i.strip() for i in s.split(', ')]))
def remove_chars(s: str, keep: Union[str, re.Pattern] = LEGAL_CHARS) -> str:
"""Removes all but legal characters from string"""
return ' '.join([re.sub(keep, '', i) for i in s.split()])
def remove_suffixes(s: str, remove: Set[str] = LOWER_SUFFIXES) -> str:
"""Removes suffixes from string"""
s = s.split()
if s[-1].lower() in remove:
s = s[:-1]
return ' '.join(s)
def standardize_defense_name(s: str) -> str:
"""Standardizes DST name
Args:
s (str): the original string
Returns:
str: the standardized string
"""
return standardize_player_name(s).split()[-1] + ' defense'
def standardize_player_name(s: str) -> str:
"""Standardizes player name
Args:
s (str): the original string
Returns:
str: the standardized string
"""
return re.sub(r'\s+', ' ' , remove_suffixes(remove_chars(s))).lower()
def standardize_positions(s: str) -> str:
"""Standardizes position names
Args:
s (str): the position string
Returns:
str: the standardized string
"""
mapping = {'Def': 'DST', 'Defense': 'DST', 'DEF': 'DST', 'def': 'DST', 'dst': 'DST', 'PK': 'K', 'Kicker': 'K'}
std = mapping[s] if s in mapping else s
return std | 0.89783 | 0.346375 |
import pathlib
import numpy as np
import torch
import torch.utils.data
import torchvision
import torchvision.models
import torchvision.transforms
from sampler import ImbalancedDatasetSampler
import augmentations
from PIL import Image, ImageEnhance
import transforms
import cv2
import random
import torchvision.transforms.functional as F
RGBD = True
from nyu import NYU
class RGBDCompose(object):
"""Composes several transforms together.
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
Example:
>>> transforms.Compose([
>>> transforms.CenterCrop(10),
>>> transforms.ToTensor(),
>>> ])
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, depth):
for t in self.transforms:
img, depth = t(img, depth)
return img, depth
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
class RGBDRandomCrop(torchvision.transforms.RandomCrop):
def __call__(self, img, depth):
if not (img.size == depth.size):
print(img.size)
print(depth.size)
assert img.size == depth.size
if self.padding > 0:
img = F.pad(img, self.padding)
depth = F.pad(depth, self.padding)
if self.pad_if_needed and img.size[0] < self.size[1]:
img = F.pad(img, (int((1 + self.size[1] - img.size[0]) / 2), 0))
depth = F.pad(depth, (int((1 + self.size[1] - depth.size[0]) / 2), 0))
# pad the height if needed
if self.pad_if_needed and img.size[1] < self.size[0]:
img = F.pad(img, (0, int((1 + self.size[0] - img.size[1]) / 2)))
depth = F.pad(depth, (0, int((1 + self.size[0] - depth.size[1]) / 2)))
i, j, h, w = self.get_params(img, self.size)
return F.crop(img, i, j, h, w), F.crop(depth, i, j, h, w)
class Resize(object):
def __init__(self, size):
self.size = size
def __call__(self, img):
pass
class RGBDRandomHorizontalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, rgb, depth):
"""
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Randomly flipped image.
"""
if random.random() < self.p:
return F.hflip(rgb), F.hflip(depth)
return rgb, depth
def __repr__(self):
return self.__class__.__name__ + '(p={})'.format(self.p)
class RGBDNormalize(object):
def __init__(self, mean, std, mean_d, std_d):
self.mean = np.array(mean)
self.std = np.array(std)
self.mean_d = np.array(mean_d)
self.std_d = np.array(std_d)
def __call__(self, image, depth):
image = np.asarray(image).astype(np.float32) / 255.
depth = np.asarray(depth).astype(np.float32) / 255.
image = (image - self.mean) / self.std
return image, depth
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
class RGBDToTensor(object):
def __call__(self, data, depth):
if isinstance(data, tuple):
return tuple([self._to_tensor(image) for image in data]), tuple([self._to_tensor(image) for image in depth])
else:
return self._to_tensor(data), self._to_tensor(depth)
def _to_tensor(self, data):
if len(data.shape) == 3:
return torch.from_numpy(data.transpose(2, 0, 1).astype(np.float32))
else:
return torch.from_numpy(data[None, :, :].astype(np.float32))
class Dataset:
def __init__(self, config):
self.config = config
#dataset_rootdir = pathlib.Path('~/.torchvision/datasets').expanduser()
dataset_rootdir = pathlib.Path('/backup')
self.dataset_dir = dataset_rootdir / config['dataset']
self._train_transforms = []
if RGBD:
self.train_transform = self._get_train_transformRGBD()
self.test_transform = self._get_test_transformRGBD()
else:
self.train_transform = self._get_train_transform()
self.test_transform = self._get_test_transform()
def get_datasets(self):
train_dataset = NYU(
self.dataset_dir,
train=True,
transform=self.train_transform,
download=True)
test_dataset = NYU(
self.dataset_dir,
train=False,
transform=self.test_transform,
download=True)
return train_dataset, test_dataset
def _add_random_crop(self):
transform = torchvision.transforms.RandomCrop(
self.size, padding=self.config['random_crop_padding'])
self._train_transforms.append(transform)
def _add_random_cropRGBD(self):
transform = RGBDRandomCrop(
self.size, padding=self.config['random_crop_padding'])
self._train_transforms.append(transform)
def _add_horizontal_flip(self):
self._train_transforms.append(
torchvision.transforms.RandomHorizontalFlip())
def _add_horizontal_flipRGBD(self):
self._train_transforms.append(
RGBDRandomHorizontalFlip())
def _add_normalization(self):
self._train_transforms.append(
transforms.Normalize(self.mean, self.std))
def _add_normalizationRGBD(self):
self._train_transforms.append(
RGBDNormalize(self.mean, self.std, self.mean_d, self.std_d))
def _add_to_tensor(self):
self._train_transforms.append(transforms.ToTensor())
def _add_to_tensorRGBD(self):
self._train_transforms.append(RGBDToTensor())
def _add_random_erasing(self):
transform = augmentations.random_erasing.RandomErasing(
self.config['random_erasing_prob'],
self.config['random_erasing_area_ratio_range'],
self.config['random_erasing_min_aspect_ratio'],
self.config['random_erasing_max_attempt'])
self._train_transforms.append(transform)
def _add_random_erasingRGBD(self):
transform = augmentations.random_erasing.RGBDRandomErasing(
self.config['random_erasing_prob'],
self.config['random_erasing_area_ratio_range'],
self.config['random_erasing_min_aspect_ratio'],
self.config['random_erasing_max_attempt'])
self._train_transforms.append(transform)
def _add_cutout(self):
transform = augmentations.cutout.Cutout(self.config['cutout_size'],
self.config['cutout_prob'],
self.config['cutout_inside'])
self._train_transforms.append(transform)
def _add_cutoutRGBD(self):
transform = augmentations.cutout.RGBDCutout(self.config['cutout_size'],
self.config['cutout_prob'],
self.config['cutout_inside'])
self._train_transforms.append(transform)
def _add_dual_cutout(self):
transform = augmentations.cutout.DualCutout(
self.config['cutout_size'], self.config['cutout_prob'],
self.config['cutout_inside'])
self._train_transforms.append(transform)
def _get_train_transform(self):
if self.config['use_random_crop']:
self._add_random_crop()
if self.config['use_horizontal_flip']:
self._add_horizontal_flip()
self._add_normalization()
if self.config['use_random_erasing']:
self._add_random_erasing()
if self.config['use_cutout']:
self._add_cutout()
elif self.config['use_dual_cutout']:
self._add_dual_cutout()
self._add_to_tensor()
return torchvision.transforms.Compose(self._train_transforms)
def _get_train_transformRGBD(self):
if self.config['use_random_crop']:
self._add_random_cropRGBD()
if self.config['use_horizontal_flip']:
self._add_horizontal_flipRGBD()
self._add_normalizationRGBD()
if self.config['use_random_erasing']:
self._add_random_erasingRGBD()
if self.config['use_cutout']:
self._add_cutoutRGBD()
self._add_to_tensorRGBD()
return RGBDCompose(self._train_transforms)
def _get_test_transform(self):
transform = torchvision.transforms.Compose([
transforms.Normalize(self.mean, self.std),
transforms.ToTensor(),
])
return transform
def _get_test_transformRGBD(self):
transform = RGBDCompose([
RGBDNormalize(self.mean, self.std, self.mean_d, self.std_d),
RGBDToTensor(),
])
return transform
class CIFAR(Dataset):
def __init__(self, config):
self.size = 32
if config['dataset'] == 'CIFAR10':
self.mean = np.array([0.4914, 0.4822, 0.4465])
self.std = np.array([0.2470, 0.2435, 0.2616])
elif config['dataset'] == 'CIFAR100':
self.mean = np.array([0.5071, 0.4865, 0.4409])
self.std = np.array([0.2673, 0.2564, 0.2762])
super(CIFAR, self).__init__(config)
class MNIST(Dataset):
def __init__(self, config):
self.size = 28
if config['dataset'] == 'MNIST':
self.mean = np.array([0.1307])
self.std = np.array([0.3081])
elif config['dataset'] == 'FashionMNIST':
self.mean = np.array([0.2860])
self.std = np.array([0.3530])
elif config['dataset'] == 'KMNIST':
self.mean = np.array([0.1904])
self.std = np.array([0.3475])
super(MNIST, self).__init__(config)
class NYUv2(Dataset):
def __init__(self, config):
self.size = (224,224)
self.mean = np.array([0.4487, 0.3479, 0.3301])
self.std = np.array([0.2474, 0.2269, 0.2211])
self.mean_hha = np.array([0.5241,0.3707,0.4655])
self.std_hha = np.array([0.2272,0.2465,0.1844])
self.mean_d = np.array([0.4281])
self.std_d = np.array([0.2720])
super(NYUv2, self).__init__(config)
def worker_init_fn(worker_id):
np.random.seed(np.random.get_state()[1][0] + worker_id)
def get_loader(config):
batch_size = config['batch_size']
num_workers = config['num_workers']
use_gpu = config['use_gpu']
dataset_name = config['dataset']
assert dataset_name in [
'CIFAR10', 'CIFAR100', 'MNIST', 'FashionMNIST', 'KMNIST', 'NYU'
]
if dataset_name in ['CIFAR10', 'CIFAR100']:
dataset = CIFAR(config)
train_dataset, test_dataset = dataset.get_datasets()
elif dataset_name in ['MNIST', 'FashionMNIST', 'KMNIST']:
dataset = MNIST(config)
train_dataset, test_dataset = dataset.get_datasets()
else:
dataset = NYUv2(config)
train_dataset, test_dataset = dataset.get_datasets()
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
#shuffle=True,
sampler=ImbalancedDatasetSampler(train_dataset),
num_workers=num_workers,
pin_memory=use_gpu,
drop_last=True,
worker_init_fn=worker_init_fn,
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False,
pin_memory=use_gpu,
drop_last=False,
)
return train_loader, test_loader | dataloader.py | import pathlib
import numpy as np
import torch
import torch.utils.data
import torchvision
import torchvision.models
import torchvision.transforms
from sampler import ImbalancedDatasetSampler
import augmentations
from PIL import Image, ImageEnhance
import transforms
import cv2
import random
import torchvision.transforms.functional as F
RGBD = True
from nyu import NYU
class RGBDCompose(object):
"""Composes several transforms together.
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
Example:
>>> transforms.Compose([
>>> transforms.CenterCrop(10),
>>> transforms.ToTensor(),
>>> ])
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, depth):
for t in self.transforms:
img, depth = t(img, depth)
return img, depth
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
class RGBDRandomCrop(torchvision.transforms.RandomCrop):
def __call__(self, img, depth):
if not (img.size == depth.size):
print(img.size)
print(depth.size)
assert img.size == depth.size
if self.padding > 0:
img = F.pad(img, self.padding)
depth = F.pad(depth, self.padding)
if self.pad_if_needed and img.size[0] < self.size[1]:
img = F.pad(img, (int((1 + self.size[1] - img.size[0]) / 2), 0))
depth = F.pad(depth, (int((1 + self.size[1] - depth.size[0]) / 2), 0))
# pad the height if needed
if self.pad_if_needed and img.size[1] < self.size[0]:
img = F.pad(img, (0, int((1 + self.size[0] - img.size[1]) / 2)))
depth = F.pad(depth, (0, int((1 + self.size[0] - depth.size[1]) / 2)))
i, j, h, w = self.get_params(img, self.size)
return F.crop(img, i, j, h, w), F.crop(depth, i, j, h, w)
class Resize(object):
def __init__(self, size):
self.size = size
def __call__(self, img):
pass
class RGBDRandomHorizontalFlip(object):
def __init__(self, p=0.5):
self.p = p
def __call__(self, rgb, depth):
"""
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Randomly flipped image.
"""
if random.random() < self.p:
return F.hflip(rgb), F.hflip(depth)
return rgb, depth
def __repr__(self):
return self.__class__.__name__ + '(p={})'.format(self.p)
class RGBDNormalize(object):
def __init__(self, mean, std, mean_d, std_d):
self.mean = np.array(mean)
self.std = np.array(std)
self.mean_d = np.array(mean_d)
self.std_d = np.array(std_d)
def __call__(self, image, depth):
image = np.asarray(image).astype(np.float32) / 255.
depth = np.asarray(depth).astype(np.float32) / 255.
image = (image - self.mean) / self.std
return image, depth
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
class RGBDToTensor(object):
def __call__(self, data, depth):
if isinstance(data, tuple):
return tuple([self._to_tensor(image) for image in data]), tuple([self._to_tensor(image) for image in depth])
else:
return self._to_tensor(data), self._to_tensor(depth)
def _to_tensor(self, data):
if len(data.shape) == 3:
return torch.from_numpy(data.transpose(2, 0, 1).astype(np.float32))
else:
return torch.from_numpy(data[None, :, :].astype(np.float32))
class Dataset:
def __init__(self, config):
self.config = config
#dataset_rootdir = pathlib.Path('~/.torchvision/datasets').expanduser()
dataset_rootdir = pathlib.Path('/backup')
self.dataset_dir = dataset_rootdir / config['dataset']
self._train_transforms = []
if RGBD:
self.train_transform = self._get_train_transformRGBD()
self.test_transform = self._get_test_transformRGBD()
else:
self.train_transform = self._get_train_transform()
self.test_transform = self._get_test_transform()
def get_datasets(self):
train_dataset = NYU(
self.dataset_dir,
train=True,
transform=self.train_transform,
download=True)
test_dataset = NYU(
self.dataset_dir,
train=False,
transform=self.test_transform,
download=True)
return train_dataset, test_dataset
def _add_random_crop(self):
transform = torchvision.transforms.RandomCrop(
self.size, padding=self.config['random_crop_padding'])
self._train_transforms.append(transform)
def _add_random_cropRGBD(self):
transform = RGBDRandomCrop(
self.size, padding=self.config['random_crop_padding'])
self._train_transforms.append(transform)
def _add_horizontal_flip(self):
self._train_transforms.append(
torchvision.transforms.RandomHorizontalFlip())
def _add_horizontal_flipRGBD(self):
self._train_transforms.append(
RGBDRandomHorizontalFlip())
def _add_normalization(self):
self._train_transforms.append(
transforms.Normalize(self.mean, self.std))
def _add_normalizationRGBD(self):
self._train_transforms.append(
RGBDNormalize(self.mean, self.std, self.mean_d, self.std_d))
def _add_to_tensor(self):
self._train_transforms.append(transforms.ToTensor())
def _add_to_tensorRGBD(self):
self._train_transforms.append(RGBDToTensor())
def _add_random_erasing(self):
transform = augmentations.random_erasing.RandomErasing(
self.config['random_erasing_prob'],
self.config['random_erasing_area_ratio_range'],
self.config['random_erasing_min_aspect_ratio'],
self.config['random_erasing_max_attempt'])
self._train_transforms.append(transform)
def _add_random_erasingRGBD(self):
transform = augmentations.random_erasing.RGBDRandomErasing(
self.config['random_erasing_prob'],
self.config['random_erasing_area_ratio_range'],
self.config['random_erasing_min_aspect_ratio'],
self.config['random_erasing_max_attempt'])
self._train_transforms.append(transform)
def _add_cutout(self):
transform = augmentations.cutout.Cutout(self.config['cutout_size'],
self.config['cutout_prob'],
self.config['cutout_inside'])
self._train_transforms.append(transform)
def _add_cutoutRGBD(self):
transform = augmentations.cutout.RGBDCutout(self.config['cutout_size'],
self.config['cutout_prob'],
self.config['cutout_inside'])
self._train_transforms.append(transform)
def _add_dual_cutout(self):
transform = augmentations.cutout.DualCutout(
self.config['cutout_size'], self.config['cutout_prob'],
self.config['cutout_inside'])
self._train_transforms.append(transform)
def _get_train_transform(self):
if self.config['use_random_crop']:
self._add_random_crop()
if self.config['use_horizontal_flip']:
self._add_horizontal_flip()
self._add_normalization()
if self.config['use_random_erasing']:
self._add_random_erasing()
if self.config['use_cutout']:
self._add_cutout()
elif self.config['use_dual_cutout']:
self._add_dual_cutout()
self._add_to_tensor()
return torchvision.transforms.Compose(self._train_transforms)
def _get_train_transformRGBD(self):
if self.config['use_random_crop']:
self._add_random_cropRGBD()
if self.config['use_horizontal_flip']:
self._add_horizontal_flipRGBD()
self._add_normalizationRGBD()
if self.config['use_random_erasing']:
self._add_random_erasingRGBD()
if self.config['use_cutout']:
self._add_cutoutRGBD()
self._add_to_tensorRGBD()
return RGBDCompose(self._train_transforms)
def _get_test_transform(self):
transform = torchvision.transforms.Compose([
transforms.Normalize(self.mean, self.std),
transforms.ToTensor(),
])
return transform
def _get_test_transformRGBD(self):
transform = RGBDCompose([
RGBDNormalize(self.mean, self.std, self.mean_d, self.std_d),
RGBDToTensor(),
])
return transform
class CIFAR(Dataset):
def __init__(self, config):
self.size = 32
if config['dataset'] == 'CIFAR10':
self.mean = np.array([0.4914, 0.4822, 0.4465])
self.std = np.array([0.2470, 0.2435, 0.2616])
elif config['dataset'] == 'CIFAR100':
self.mean = np.array([0.5071, 0.4865, 0.4409])
self.std = np.array([0.2673, 0.2564, 0.2762])
super(CIFAR, self).__init__(config)
class MNIST(Dataset):
def __init__(self, config):
self.size = 28
if config['dataset'] == 'MNIST':
self.mean = np.array([0.1307])
self.std = np.array([0.3081])
elif config['dataset'] == 'FashionMNIST':
self.mean = np.array([0.2860])
self.std = np.array([0.3530])
elif config['dataset'] == 'KMNIST':
self.mean = np.array([0.1904])
self.std = np.array([0.3475])
super(MNIST, self).__init__(config)
class NYUv2(Dataset):
def __init__(self, config):
self.size = (224,224)
self.mean = np.array([0.4487, 0.3479, 0.3301])
self.std = np.array([0.2474, 0.2269, 0.2211])
self.mean_hha = np.array([0.5241,0.3707,0.4655])
self.std_hha = np.array([0.2272,0.2465,0.1844])
self.mean_d = np.array([0.4281])
self.std_d = np.array([0.2720])
super(NYUv2, self).__init__(config)
def worker_init_fn(worker_id):
np.random.seed(np.random.get_state()[1][0] + worker_id)
def get_loader(config):
batch_size = config['batch_size']
num_workers = config['num_workers']
use_gpu = config['use_gpu']
dataset_name = config['dataset']
assert dataset_name in [
'CIFAR10', 'CIFAR100', 'MNIST', 'FashionMNIST', 'KMNIST', 'NYU'
]
if dataset_name in ['CIFAR10', 'CIFAR100']:
dataset = CIFAR(config)
train_dataset, test_dataset = dataset.get_datasets()
elif dataset_name in ['MNIST', 'FashionMNIST', 'KMNIST']:
dataset = MNIST(config)
train_dataset, test_dataset = dataset.get_datasets()
else:
dataset = NYUv2(config)
train_dataset, test_dataset = dataset.get_datasets()
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
#shuffle=True,
sampler=ImbalancedDatasetSampler(train_dataset),
num_workers=num_workers,
pin_memory=use_gpu,
drop_last=True,
worker_init_fn=worker_init_fn,
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False,
pin_memory=use_gpu,
drop_last=False,
)
return train_loader, test_loader | 0.895188 | 0.44565 |
import numpy as np
def summary_stats(df, pct_vars=None, int_vars=None, float_vars=None, count=False):
"""
Generates a transposed df.describe() table where pct_vars are formatted with two
decimal percentages, int_vars are formatted with zero decimal places, and float_
vars are formatted with two decimal places.
"""
stats = ['mean', 'std', 'min', '25%','50%','75%','max']
all_vars = _if_lists_exist_then_combine(pct_vars, int_vars, float_vars)
summ = df[all_vars].replace([np.inf, -np.inf], np.nan).dropna().describe().T
apply_formatting(summ, stats, pct_vars=pct_vars, int_vars=int_vars, float_vars=float_vars)
# formats = _if_vars_list_exists_then_add_to_format_dict(pct_vars, int_vars, float_vars)
# _apply_formatting_from_format_dict(summ, formats, stats)
if count:
#Format count column individually
summ['count'] = summ['count'].apply(lambda x: '{0:.0f}'.format(x))
else:
summ.drop('count', axis=1, inplace=True)
return summ
def summ_vars_single_stat_by_groups(df, groupvar, pct_vars=None, int_vars=None, float_vars=None, stat='mean'):
"""
Generates a table where groups are columns and rows are variables, where the values
are a single summary stat
"""
all_vars = _if_lists_exist_then_combine(pct_vars, float_vars, int_vars)
group = df.groupby(groupvar)[all_vars]
func = getattr(group, stat) #gets .mean(), .median(), etc.
summ = func().T #applies .mean(), .median(), etc.
apply_formatting(summ, summ.columns, pct_vars=pct_vars,
float_vars=float_vars, int_vars=int_vars)
return summ
def apply_formatting(df, cols, pct_vars=None, int_vars=None, float_vars=None):
"""
Applies percentage, integer, and float formatting to a summary table. Variables must
be in rows, with statistics in columns.
Note: inplace
"""
formats = _if_vars_list_exists_then_add_to_format_dict(pct_vars, int_vars, float_vars)
_apply_formatting_from_format_dict(df, formats, cols)
def _if_lists_exist_then_combine(*args):
out = []
for a in args:
if a:
out += a
return out
def _if_vars_list_exists_then_add_to_format_dict(pct_vars, int_vars, float_vars):
formats = {
0: '{:.2%}',
1: '{0:.0f}',
2: '{0:.2f}'
}
for i, var_list in enumerate([pct_vars, int_vars, float_vars]):
if var_list:
formats[tuple(var_list)] = formats.pop(i) #replaces int key with tuple of vars
return formats
def _apply_formatting_from_format_dict(df, formats, cols):
"""
Note: inplace
"""
for k in formats: #apply appropriate format to stats columns but not count column
if not isinstance(k, int): #skip empty lists - these are marked by an integer
df.loc[list(k), cols] = df.loc[list(k), cols].applymap(
lambda x: formats[k].format(x)) | dero/summ.py | import numpy as np
def summary_stats(df, pct_vars=None, int_vars=None, float_vars=None, count=False):
"""
Generates a transposed df.describe() table where pct_vars are formatted with two
decimal percentages, int_vars are formatted with zero decimal places, and float_
vars are formatted with two decimal places.
"""
stats = ['mean', 'std', 'min', '25%','50%','75%','max']
all_vars = _if_lists_exist_then_combine(pct_vars, int_vars, float_vars)
summ = df[all_vars].replace([np.inf, -np.inf], np.nan).dropna().describe().T
apply_formatting(summ, stats, pct_vars=pct_vars, int_vars=int_vars, float_vars=float_vars)
# formats = _if_vars_list_exists_then_add_to_format_dict(pct_vars, int_vars, float_vars)
# _apply_formatting_from_format_dict(summ, formats, stats)
if count:
#Format count column individually
summ['count'] = summ['count'].apply(lambda x: '{0:.0f}'.format(x))
else:
summ.drop('count', axis=1, inplace=True)
return summ
def summ_vars_single_stat_by_groups(df, groupvar, pct_vars=None, int_vars=None, float_vars=None, stat='mean'):
"""
Generates a table where groups are columns and rows are variables, where the values
are a single summary stat
"""
all_vars = _if_lists_exist_then_combine(pct_vars, float_vars, int_vars)
group = df.groupby(groupvar)[all_vars]
func = getattr(group, stat) #gets .mean(), .median(), etc.
summ = func().T #applies .mean(), .median(), etc.
apply_formatting(summ, summ.columns, pct_vars=pct_vars,
float_vars=float_vars, int_vars=int_vars)
return summ
def apply_formatting(df, cols, pct_vars=None, int_vars=None, float_vars=None):
"""
Applies percentage, integer, and float formatting to a summary table. Variables must
be in rows, with statistics in columns.
Note: inplace
"""
formats = _if_vars_list_exists_then_add_to_format_dict(pct_vars, int_vars, float_vars)
_apply_formatting_from_format_dict(df, formats, cols)
def _if_lists_exist_then_combine(*args):
out = []
for a in args:
if a:
out += a
return out
def _if_vars_list_exists_then_add_to_format_dict(pct_vars, int_vars, float_vars):
formats = {
0: '{:.2%}',
1: '{0:.0f}',
2: '{0:.2f}'
}
for i, var_list in enumerate([pct_vars, int_vars, float_vars]):
if var_list:
formats[tuple(var_list)] = formats.pop(i) #replaces int key with tuple of vars
return formats
def _apply_formatting_from_format_dict(df, formats, cols):
"""
Note: inplace
"""
for k in formats: #apply appropriate format to stats columns but not count column
if not isinstance(k, int): #skip empty lists - these are marked by an integer
df.loc[list(k), cols] = df.loc[list(k), cols].applymap(
lambda x: formats[k].format(x)) | 0.621771 | 0.321766 |
import pytest
from sayn.utils.task_query import get_query
tasks = {
"task1": {"group": "group1", "tags": list()},
"task2": {"group": "group1", "tags": ["tag1"]},
"task3": {"group": "group2", "tags": ["tag1"]},
"task4": {"group": "group2", "tags": list()},
"task5": {"group": "group3", "tags": ["tag1", "tag2"]},
"task6": {"group": "group3", "tags": list()},
"task7": {"group": "group3", "tags": list()},
}
def test_simple01():
assert get_query(tasks, include=["task1"]).value == [
{
"operation": "include",
"task": "task1",
"upstream": False,
"downstream": False,
}
]
def test_simple02():
assert get_query(tasks, include=["task_undefined"]).is_err
def test_simple03():
assert get_query(tasks, include=["task2+"]).value == [
{
"operation": "include",
"task": "task2",
"upstream": False,
"downstream": True,
}
]
def test_simple04():
assert get_query(tasks, include=["+task4+"]).value == [
{"operation": "include", "task": "task4", "upstream": True, "downstream": True}
]
def test_group01():
assert get_query(tasks, include=["group:group1"]).value == [
{
"operation": "include",
"task": "task1",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task2",
"upstream": False,
"downstream": False,
},
]
def test_tag01():
assert get_query(tasks, include=["tag:tag1"]).value == [
{
"operation": "include",
"task": "task2",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task3",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task5",
"upstream": False,
"downstream": False,
},
]
def test_error_identifier01():
assert get_query(tasks, include=["+_task_undefined"]).is_err
def test_error_identifier02():
assert get_query(tasks, include=["tag:tag_undefined"]).is_err
def test_error_identifier03():
assert get_query(tasks, include=["group:group_undefined"]).is_err
def test_full_query02():
assert get_query(tasks, include=["tag:tag1"], exclude=["group:group2"]).value == [
{
"operation": "include",
"task": "task2",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task3",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task5",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task3",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task4",
"upstream": False,
"downstream": False,
},
]
def test_full_query03():
assert get_query(
tasks,
include=["task1", "task2+", "tag:tag1", "+task2"],
exclude=["group:group3", "+task3"],
).value == [
{
"operation": "include",
"task": "task1",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task2",
"upstream": True,
"downstream": True,
},
{
"operation": "include",
"task": "task3",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task5",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task5",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task6",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task7",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task3",
"upstream": True,
"downstream": False,
},
] | tests/test_task_query.py | import pytest
from sayn.utils.task_query import get_query
tasks = {
"task1": {"group": "group1", "tags": list()},
"task2": {"group": "group1", "tags": ["tag1"]},
"task3": {"group": "group2", "tags": ["tag1"]},
"task4": {"group": "group2", "tags": list()},
"task5": {"group": "group3", "tags": ["tag1", "tag2"]},
"task6": {"group": "group3", "tags": list()},
"task7": {"group": "group3", "tags": list()},
}
def test_simple01():
assert get_query(tasks, include=["task1"]).value == [
{
"operation": "include",
"task": "task1",
"upstream": False,
"downstream": False,
}
]
def test_simple02():
assert get_query(tasks, include=["task_undefined"]).is_err
def test_simple03():
assert get_query(tasks, include=["task2+"]).value == [
{
"operation": "include",
"task": "task2",
"upstream": False,
"downstream": True,
}
]
def test_simple04():
assert get_query(tasks, include=["+task4+"]).value == [
{"operation": "include", "task": "task4", "upstream": True, "downstream": True}
]
def test_group01():
assert get_query(tasks, include=["group:group1"]).value == [
{
"operation": "include",
"task": "task1",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task2",
"upstream": False,
"downstream": False,
},
]
def test_tag01():
assert get_query(tasks, include=["tag:tag1"]).value == [
{
"operation": "include",
"task": "task2",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task3",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task5",
"upstream": False,
"downstream": False,
},
]
def test_error_identifier01():
assert get_query(tasks, include=["+_task_undefined"]).is_err
def test_error_identifier02():
assert get_query(tasks, include=["tag:tag_undefined"]).is_err
def test_error_identifier03():
assert get_query(tasks, include=["group:group_undefined"]).is_err
def test_full_query02():
assert get_query(tasks, include=["tag:tag1"], exclude=["group:group2"]).value == [
{
"operation": "include",
"task": "task2",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task3",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task5",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task3",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task4",
"upstream": False,
"downstream": False,
},
]
def test_full_query03():
assert get_query(
tasks,
include=["task1", "task2+", "tag:tag1", "+task2"],
exclude=["group:group3", "+task3"],
).value == [
{
"operation": "include",
"task": "task1",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task2",
"upstream": True,
"downstream": True,
},
{
"operation": "include",
"task": "task3",
"upstream": False,
"downstream": False,
},
{
"operation": "include",
"task": "task5",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task5",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task6",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task7",
"upstream": False,
"downstream": False,
},
{
"operation": "exclude",
"task": "task3",
"upstream": True,
"downstream": False,
},
] | 0.374676 | 0.567008 |
import asyncio
from collections import OrderedDict
import concurrent
from contextlib import contextmanager
from functools import wraps
import functools
import itertools
import json
import logging
import math
from os import access
import os
from os.path import realpath, basename, dirname
from pathlib import Path
from threading import Lock
import threading
import time
import traceback
from uuid import uuid5
import uuid
from asgiref.sync import async_to_sync
from channels.generic.websocket import AsyncWebsocketConsumer
from django.core.cache import caches
from server import settings
from server.beakercache import cache
import numpy as np
from plato.backend.adapters.branch_training_trace.adapter import BranchTrainingTraceAdapter
from plato.backend.adapters.pevent_trace.adapter import PeventTraceAdapter
from plato.backend.adapters.sparta_statistics.adapter import SpartaStatisticsAdapter
from plato.backend.common import logtime, synchronized, synchronizedFine, countactive
from plato.backend.datasources.branch_training_trace.datasource import BranchTrainingDatasource
from plato.backend.datasources.pevent_trace.datasource import PeventDataSource
from plato.backend.datasources.sparta_statistics.datasource import SpartaDataSource
from plato.backend.processors.branch_training_heatmap.adapter import BranchTrainingHeatMapAdapter
from plato.backend.processors.branch_training_heatmap.generator import BranchTrainingHeatMapGenerator
from plato.backend.processors.branch_training_line_plot.generator import BranchTrainingLinePlotGenerator
from plato.backend.processors.branch_training_profile.generator import BranchTrainingProfileHtmlGenerator
from plato.backend.processors.branch_training_table.generator import BranchTrainingListHtmlGenerator
from plato.backend.processors.general_line_plot.generator import GeneralTraceLinePlotGenerator
from plato.backend.processors.pevent_trace_generator.generator import PeventTraceGenerator
from plato.backend.processors.sparta_statistics.generator import SpartaStatsGenerator
from plato.backend.units import Units
from .models import DataId, ProcessorId, LongFunctionCall
logger = logging.getLogger("plato.backend.bpEndpoint")
class bpEndpoint(AsyncWebsocketConsumer):
'''
this is the endpoint for most of the data operations that happen in plato
handles branch prediction, SPARTA data sources, etc.
'''
# this is the cache that will speed up complex lookups and hold references
# to useful data structures that are used for doing data processing
beakerCache = cache.get_cache('branchPredictor', expire = 3600)
def __init__(self, *args, **kwargs):
'''
ctor
'''
super().__init__(*args, **kwargs)
self.callback = None
self.sendLock = Lock()
self.loop = asyncio.get_event_loop()
# the thread pool, whenever a request is received via an async function
# call, it is immediately launched onto a concurrent thread to avoid
# keeping the async thread busy, when the processing is done then it
# will come back to the async thread to send the result back to the client
self.threadPoolExecutor = concurrent.futures.ThreadPoolExecutor(max_workers = settings.WORKER_THREADS,
thread_name_prefix = "wsEndpointWorker")
async def connect(self):
'''
look at the call that this wants to use and cache that
'''
path = self.scope.get('path', '')
if path == '/ws/getData':
self.callback = self.getData
await self.accept()
elif path == '/ws/sources':
self.callback = self.sources
await self.accept()
else:
logger.error(f"error: not recognized: {path}")
await self.close()
async def disconnect(self, close_code):
'''
when the client disconnects from the websocket
'''
await self.close()
async def receive(self, text_data = None, bytes_data = None):
'''
capture and route the actual request
'''
# go run this on a separate thread
# TODO ensure that only text_data is received, clients should be sending
# json as text, not binary
blockingTask = asyncio.get_event_loop().run_in_executor(self.threadPoolExecutor,
self.callback,
text_data)
def sendMessage(self, text_data = None, bytes_data = None, close = False):
with self.sendLock:
loop = self.loop
if text_data:
future = asyncio.run_coroutine_threadsafe(self.send(json.dumps(text_data), None, close), loop)
future.result()
if bytes_data is not None:
future = asyncio.run_coroutine_threadsafe(self.send(None, bytes_data if len(bytes_data) else b'0', close), loop)
future.result()
@logtime(logger)
@cache.cache('getSourceInformation', expire = 360)
def getSourceInformation(self, path):
'''
go look at the file type and get info about it, determine what can read
it and then go cache the reader for it
'''
isHdf5File = path.endswith("hdf5")
if isHdf5File:
if BranchTrainingDatasource.can_read(path):
return BranchTrainingDatasource.get_source_information(path), "branch-predictor-training-trace"
elif PeventDataSource.can_read(path):
return PeventDataSource.get_source_information(path), "pevent-trace"
else:
raise ValueError("unknown hdf5 type, pevent and branch training data sources cannot read this")
else:
return SpartaDataSource.get_source_information(path), "sparta-statistics"
@logtime(logger)
def loadFile(self, jsonData, returnValue):
'''
load a single file and return data
'''
returnValue['waitEstimate'] = 12345
file = jsonData['file']
if not access(file, os.R_OK):
raise ValueError("cannot read file")
currentFile = realpath(file)
# use the django ORM to lookup or create a persistent UUID for this file
dataIdObj, created = DataId.objects.get_or_create(path = file,
defaults = {'uuid': str(uuid5(uuid.NAMESPACE_URL,
file))})
returnValue["result"] = "complete"
logger.debug(f"{currentFile} created? {created}: {dataIdObj}")
statVals, typeId = self.getSourceInformation(file)
newDict = {"name": str(basename(file)),
"directory": dirname(file),
"typeId": typeId,
"dataId": dataIdObj.uuid}
for key, value in statVals.items():
newDict[key] = value
returnValue["sources"] = [newDict]
@logtime(logger)
def getProcessor(self, jsonData, returnValue):
'''
get the processor ID for a given processor type + data ID
this may take a long time so it should be run in a separate thread,
rather than the main event loop
'''
dataId = jsonData['dataId']
processor = jsonData['processor']
kwargs = json.dumps(OrderedDict(sorted(jsonData['kwargs'].items(), key = lambda kv: kv[0])))
# TODO need a better UUID?
processorUuid = str(uuid.uuid5(uuid.NAMESPACE_DNS, name = str(time.time())))
processorIdObj, created = ProcessorId.objects.get_or_create(dataUuid = dataId,
processor = processor,
kwargs = kwargs,
defaults = {'uuid': processorUuid, })
logger.debug(f"{dataId} created? {created}: {processorIdObj}")
returnValue['processorId'] = processorIdObj.uuid
if processor == "shp-heatmap-generator":
bpEndpoint.getShpHeatmapGenerator(processorIdObj.uuid)
elif processor == "shp-line-plot-generator":
bpEndpoint.getShpLinePlotGenerator(processorIdObj.uuid)
elif processor == "shp-branch-list-generator":
bpEndpoint.getShpTableHtmlGenerator(processorIdObj.uuid)
elif processor == "simdb-line-plot-generator":
bpEndpoint.getSimDbLinePlotGenerator(processorIdObj.uuid)
elif processor == "simdb-get-all-data-generator":
bpEndpoint.getSimDbStatsGenerator(processorIdObj.uuid)
elif processor == "shp-branch-profile-generator":
bpEndpoint.getShpBranchProfileHtmlGenerator(processorIdObj.uuid)
elif processor == "pevent-trace-generator":
bpEndpoint.getPeventTraceGenerator(processorIdObj.uuid)
else:
raise ValueError(f"unknown processor type {processor}")
returnValue['result'] = 'complete'
@logtime(logger)
def loadDirectory(self, jsonData, returnValue):
'''
go scan a directory and return metadata + generated UUIDs
'''
returnValue['waitEstimate'] = 12345
directory = Path(jsonData['directory']).resolve()
returnValue['directory'] = str(directory)
returnValue["result"] = "in-progress"
hdf5GlobPattern = jsonData.get('globPattern', "*hdf5")
dbGlobPattern = jsonData.get('globPattern', "*db")
self.sendMessage(returnValue)
# detect errors
if not directory.is_dir():
raise ValueError(f"cannot read directory: {str(directory)}")
if not access(directory, os.R_OK):
raise ValueError("cannot access directory")
returnValue['subDirectories'] = [str(x) for x in Path(directory).iterdir() if x.is_dir()]
dbFiles = list(filter(os.path.isfile, directory.glob(dbGlobPattern)))
hdf5Files = list(filter(os.path.isfile, directory.glob(hdf5GlobPattern)))
returnValue['result'] = 'partial'
returnValue['sources'] = []
for i, currentFile in enumerate(hdf5Files + dbFiles):
# need the canonical path of the file to generate a UUID
relativePath = currentFile.relative_to(directory)
currentFile = realpath(currentFile)
isHdf5File = currentFile.endswith("hdf5")
if isHdf5File:
# two options, branch training or p-events
if BranchTrainingDatasource.can_read(currentFile):
typeId = "branch-predictor-training-trace"
elif PeventDataSource.can_read(currentFile):
typeId = "pevent-trace"
else:
raise ValueError("unknown hdf5 type, pevent and branch training data sources cannot read this")
else:
typeId = "sparta-statistics"
dataIdObj, created = DataId.objects.get_or_create(path = currentFile,
defaults = {'uuid': str(uuid5(uuid.NAMESPACE_URL,
currentFile))})
logger.debug(f"{currentFile} created? {created}: {dataIdObj}")
newDict = {"name": str(relativePath),
"typeId": typeId,
"dataId": dataIdObj.uuid}
returnValue['sources'].append(newDict)
if i + 1 != len(hdf5Files + dbFiles):
self.sendMessage(returnValue)
returnValue['result'] = 'complete'
@logtime(logger)
def sources(self, value):
'''
load sources and return progress
this may take a while and should be run in its own thread when possible
'''
returnValue = {}
try:
jsonData = json.loads(value)
command = returnValue["command"] = jsonData['command']
returnValue["result"] = "error"
returnValue["reqSeqNum"] = jsonData["reqSeqNum"]
if command == 'getProcessor':
# cache a processor
self.getProcessor(jsonData, returnValue)
elif command == 'loadFile':
# populate with just a single file
self.loadFile(jsonData, returnValue)
elif command == 'loadDirectory':
# then populate the cache with all files in this directory
self.loadDirectory(jsonData, returnValue)
else:
raise ValueError(f"unknown command {command}")
self.sendMessage(returnValue)
except Exception as e:
logger.exception("problem in sources()")
returnValue["result"] = "error"
returnValue["stackTrace"] = traceback.format_exc()
returnValue["errorMessage"] = "error in sources(): {}".format(e)
self.sendMessage(returnValue)
@countactive
@logtime(logger)
def getData(self, value):
'''
get a branch predictor heatmap or line-plot in byte format
'''
logger.debug(f"getData() with request ${json.loads(value)}")
start = time.time()
responseValue = {"reqSeqNum": -1,
"result": "error",
"duration": 0,
"processorSpecific": {}}
try:
jsonData = json.loads(value)
first = jsonData['first']
last = jsonData['last']
processorId = jsonData['processorId']
kwargs = jsonData['kwargs']
responseValue["reqSeqNum"] = jsonData['reqSeqNum']
processor = self.lookupProcessorId(processorId)[0]
if processor.processor == "shp-heatmap-generator":
bphmg = bpEndpoint.getShpHeatmapGenerator(processor.uuid)
logger.debug(f"calling generate_2d_heatmap_with_profiles() with {[first,last]}, {kwargs}")
heatMap, tableMeans, rowMeans, tableStds, tableMins, tableMaxs, tableMedians, = \
bphmg.generate_2d_heatmap_with_profiles_and_stats(first, last, **kwargs)
responseValue["processorSpecific"]["numRows"] = heatMap.shape[0]
responseValue["processorSpecific"]["numCols"] = heatMap.shape[1]
responseValue["processorSpecific"]["zMin"] = int(round(np.nanmin(heatMap)))
responseValue["processorSpecific"]["zMax"] = int(round(np.nanmax(heatMap)))
responseValue["processorSpecific"]["zBlobOffset"] = 0
resultBin = heatMap.tobytes()
responseValue["processorSpecific"]["tableMeansBlobOffset"] = len(resultBin)
resultBin += tableMeans.tobytes()
responseValue["processorSpecific"]["rowMeansBlobOffset"] = len(resultBin)
resultBin += rowMeans.tobytes()
responseValue["processorSpecific"]["tableStdsBlobOffset"] = len(resultBin)
resultBin += tableStds.tobytes()
responseValue["processorSpecific"]["tableMinsBlobOffset"] = len(resultBin)
resultBin += tableMins.tobytes()
responseValue["processorSpecific"]["tableMaxsBlobOffset"] = len(resultBin)
resultBin += tableMaxs.tobytes()
responseValue["processorSpecific"]["tableMediansBlobOffset"] = len(resultBin)
resultBin += tableMedians.tobytes()
elif processor.processor == "shp-line-plot-generator":
logger.debug(f"calling generate_lines() with {[first,last]}, {kwargs}")
resultBin = self.getShpLinePlot(jsonData, responseValue)
elif processor.processor == "shp-branch-list-generator":
bptg = bpEndpoint.getShpTableHtmlGenerator(processor.uuid)
logger.debug(f"calling generate_table() with {[first,last]}, {kwargs}")
result = bptg.generate_table(first, last, **kwargs)
responseValue["processorSpecific"] = {"stringLength": len(result),
"stringBlobOffset": 0}
resultBin = result.encode('utf-8')
elif processor.processor == "shp-branch-profile-generator":
sbpg = bpEndpoint.getShpBranchProfileHtmlGenerator(processor.uuid)
logger.debug(f"calling generate_table() with {[first,last]}, {kwargs}")
result = sbpg.generate_table(first, last, **kwargs)
responseValue["processorSpecific"] = {"stringLength": len(result),
"stringBlobOffset": 0}
resultBin = result.encode('utf-8')
elif processor.processor == "simdb-line-plot-generator":
sdblp = bpEndpoint.getSimDbLinePlotGenerator(processor.uuid)
result = sdblp.generate_lines(first, last, **kwargs)
if result.shape[1] == 0:
yExtents = [-1, 1]
else:
yExtents = [np.nanmin(result[1:]), np.nanmax(result[1:])] # Skip the first row (units)
responseValue["processorSpecific"]["numSeries"] = result.shape[0]
responseValue["processorSpecific"]["pointsPerSeries"] = result.shape[1]
responseValue["processorSpecific"]["seriesBlobOffset"] = 0
responseValue["processorSpecific"]["yExtents"] = yExtents
resultBin = result.astype(dtype = 'f4', copy = False).tobytes()
elif processor.processor == "simdb-get-all-data-generator":
resultBin = self.simdbGetAllData(processor.uuid, jsonData, responseValue)
elif processor.processor == "pevent-trace-generator":
peventTraceGenerator = bpEndpoint.getPeventTraceGenerator(processor.uuid)
histograms = peventTraceGenerator.generate_histograms(first, last, **kwargs)
psData = responseValue["processorSpecific"] = {}
psData["numSeries"] = len(histograms.keys())
psData["series"] = list(histograms.keys())
psData["histLengths"] = [len(v[0]) for v in histograms.values()]
psData["edgesLengths"] = [len(v[1]) for v in histograms.values()]
resultBin = next(itertools.islice(histograms.values(), 0, 1))[1].astype(dtype = 'f4').tobytes()
for v in histograms.values():
resultBin += v[0].astype(dtype = 'f4').tobytes()
else:
raise ValueError(f"{processorId} isn't in the database, can't do a lookup")
responseValue["result"] = "complete"
responseValue["durationMs"] = math.ceil((time.time() - start) * 1000)
# send the metadata back in json format
self.sendMessage(responseValue, resultBin)
except Exception as e:
# something went wrong, need to let the client know
logger.exception(f"problem in getData(), {value}")
responseValue["stackTrace"] = traceback.format_exc()
responseValue["errorMessage"] = str(e)
responseValue["durationMs"] = math.ceil((time.time() - start) * 1000)
self.sendMessage(responseValue)
def getShpLinePlot(self, jsonData, responseValue):
'''
find the shp generator then tell it to generate a line plot
'''
processorId = jsonData['processorId']
if True:
processor = self.lookupProcessorId(processorId)[0]
else:
# TODO this will saturate the db connections quickly, need to either
# find the connection leak or add db connection pooling
processor = ProcessorId.objects.get(uuid = processorId)
bplpg = bpEndpoint.getShpLinePlotGenerator(processor.uuid)
first = jsonData['first']
last = jsonData['last']
kwargs = jsonData['kwargs']
result, modalities, downsamplingLevel, yExtents = bplpg.generate_lines_and_more(first, last, **kwargs)
responseValue["processorSpecific"]["numSeries"] = result.shape[0]
responseValue["processorSpecific"]["pointsPerSeries"] = result.shape[1]
responseValue["processorSpecific"]["downsampling"] = downsamplingLevel
responseValue["processorSpecific"]["seriesBlobOffset"] = 0
responseValue["processorSpecific"]["yExtents"] = yExtents
blob = result.astype(dtype = 'f4', copy = False).tobytes()
responseValue["processorSpecific"]["modalitiesOffset"] = len(blob)
blob += modalities.astype(dtype = 'i2', copy = False).tobytes()
if downsamplingLevel <= 1:
# Get some data that only makes sense at no-downsampling. User can view individual points' data
otherKwargs = {"units": kwargs["units"],
"stat_cols": ['pc', 'tgt', 'trn_idx', 'instructions'],
"branch_predictor_filtering": kwargs.get("branch_predictor_filtering", {}),
"point_cache": kwargs.get("point_cache", True)}
points = bplpg.generate_simple_int_points(first, last, **otherKwargs)
pcs = points[0]
tgts = points[1]
indices = points[2]
instructions = points[3]
# Since these arrays are 8B, they must be aligned to 8B for javascript arrays to be happy.
misalignment = len(blob) % 8
if misalignment > 0:
blob += bytes([0] * (8 - misalignment))
responseValue["processorSpecific"]["indicesOffset"] = len(blob)
blob += indices.tobytes()
misalignment = len(blob) % 8
if misalignment > 0:
blob += bytes([0] * (8 - misalignment))
responseValue["processorSpecific"]["addressesOffset"] = len(blob)
blob += pcs.tobytes()
misalignment = len(blob) % 8
if misalignment > 0:
blob += bytes([0] * (8 - misalignment))
responseValue["processorSpecific"]["targetsOffset"] = len(blob)
blob += tgts.tobytes()
misalignment = len(blob) % 8
if misalignment > 0:
blob += bytes([0] * (8 - misalignment))
responseValue["processorSpecific"]["instructionNumsOffset"] = len(blob)
blob += instructions.tobytes()
return blob
def simdbGetAllData(self, processorUuid, jsonData, responseValue):
first = jsonData['first']
last = jsonData['last']
statsGen = bpEndpoint.getSimDbStatsGenerator(processorUuid)
kwargs = jsonData['kwargs']
whichChanged = kwargs["whichChanged"]
psData = responseValue["processorSpecific"]
if whichChanged:
del kwargs["whichChanged"]
# so x or y is updated
a = self.threadPoolExecutor.submit(functools.partial(statsGen.generate_lines,
first,
last,
**kwargs))
blockingTasks = [a]
else:
a = None
blockingTasks = []
psData["pointsPerSeries"] = 0
psData["stat_cols"] = kwargs['stat_cols']
# TODO this is redundant, should either be able to pass this to other calls and save them
# the trouble or get it as a byproduct of others calls calculating this
b = self.threadPoolExecutor.submit(functools.partial(statsGen.adapter.range_to_rows,
first,
last,
kwargs["units"]))
c = self.threadPoolExecutor.submit(functools.partial(statsGen.generate_histogram,
first,
last,
kwargs["units"],
kwargs["stat_cols"][0],
-1))
d = self.threadPoolExecutor.submit(functools.partial(statsGen.generate_histogram,
first,
last,
kwargs["units"],
kwargs["stat_cols"][1],
-1))
e = self.threadPoolExecutor.submit(functools.partial(statsGen.generate_regression_line,
first,
last,
**kwargs))
blockingTasks.extend([b, c, d, e])
results = []
for futureTask in blockingTasks:
result = futureTask.result()
results.append((futureTask, result))
x = y = indices = None
for futureTask, result in results:
if futureTask == a:
indices, x, y = result
psData["xMin"] = min(x)
psData["xMax"] = max(x)
psData["yMin"] = min(y)
psData["yMax"] = max(y)
psData["pointsPerSeries"] = len(x)
elif futureTask == b:
firstRow, lastRow, _ = result
psData["firstRow"] = firstRow
psData["lastRow"] = lastRow
psData["first"] = first
psData["last"] = last
elif futureTask == c:
hEdges, hHist = result
psData["hHistLength"] = len(hHist)
psData["hEdgesLength"] = len(hEdges)
elif futureTask == d:
vEdges, vHist = result
psData["vEdgesLength"] = len(vEdges)
psData["vHistLength"] = len(vHist)
elif futureTask == e:
regX, regY = result
psData["regLength"] = len(regX)
if x is not None:
return indices.astype(dtype = 'f4').tobytes() + \
x.astype(dtype = 'f4').tobytes() + y.astype(dtype = 'f4').tobytes() + \
hEdges.tobytes() + hHist.tobytes() + \
vEdges.tobytes() + vHist.tobytes() + \
regX.tobytes() + regY.tobytes()
else:
return hEdges.tobytes() + hHist.tobytes() + \
vEdges.tobytes() + vHist.tobytes() + \
regX.tobytes() + regY.tobytes()
@staticmethod
@logtime(logger)
@cache.cache('lookupProcessorId', expire = 3600)
def lookupProcessorId(processorId):
logger.debug(f"lookup in ProcessorId for {processorId}")
procId = ProcessorId.objects.get(uuid = processorId)
dataUuid = procId.dataUuid
logger.debug(f"returned {dataUuid}")
path = DataId.objects.filter(uuid = dataUuid)[0].path
logger.debug(f"returned {path}")
return procId, path
endpointLock = Lock()
endpointLockDict = {}
@staticmethod
# TODO need to make a mutex per path, or globally
@synchronized(endpointLock)
# @synchronizedFine(endpointLockDict, endpointLock)
@cache.cache('dataSource', expire = 3600)
@logtime(logger)
def getDataSource(path):
logger.debug(f"open data source: {path}")
if path.endswith("hdf5"):
if BranchTrainingDatasource.can_read(path):
return BranchTrainingDatasource(path)
elif PeventDataSource.can_read(path):
return PeventDataSource(path)
else:
raise ValueError("unknown type of file, can't choose data source")
else:
return SpartaDataSource(path)
@staticmethod
@cache.cache('shpHtmlTable', expire = 3600)
@logtime(logger)
def getShpTableHtmlGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
branch_hm_data = bpEndpoint.getDataSource(path)
bptta = BranchTrainingTraceAdapter(branch_hm_data)
bptg = BranchTrainingListHtmlGenerator(bptta, **json.loads(procIdObj.kwargs))
return bptg
@staticmethod
@cache.cache('shpHtmlTable', expire = 3600)
@logtime(logger)
def getShpBranchProfileHtmlGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
branch_hm_data = bpEndpoint.getDataSource(path)
bptta = BranchTrainingTraceAdapter(branch_hm_data)
bptg = BranchTrainingProfileHtmlGenerator(bptta, **json.loads(procIdObj.kwargs))
return bptg
@staticmethod
@cache.cache('shpLinePlot', expire = 3600)
@logtime(logger)
def getShpLinePlotGenerator(processorId):
logger.info(f"getShpLinePlotGenerator for pid={processorId}")
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
branchHeatMapDataSource = bpEndpoint.getDataSource(path)
logger.debug(f'List of stats are: {branchHeatMapDataSource.stats}')
# constructor adapter
branchTrainingTraceAdapter = BranchTrainingTraceAdapter(branchHeatMapDataSource)
# construct generator
linePlotGenerator = BranchTrainingLinePlotGenerator(branchTrainingTraceAdapter, **json.loads(procIdObj.kwargs))
return linePlotGenerator
@staticmethod
@cache.cache('simDbLinePlot', expire = 3600)
@logtime(logger)
def getSimDbLinePlotGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
simDbDataSource = bpEndpoint.getDataSource(path)
logger.debug(f'List of stats are: {simDbDataSource.stats}')
# constructor adapter
spartaStatsAdapter = SpartaStatisticsAdapter(simDbDataSource)
# construct generator
linePlotGenerator = GeneralTraceLinePlotGenerator(spartaStatsAdapter, **json.loads(procIdObj.kwargs))
return linePlotGenerator
@staticmethod
@cache.cache('peventTraceGenerator', expire = 3600)
@logtime(logger)
def getPeventTraceGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
peventDataSource = bpEndpoint.getDataSource(path)
# construct adapter
peventTraceAdapter = PeventTraceAdapter(peventDataSource)
# construct generator
peventTraceGenerator = PeventTraceGenerator(peventTraceAdapter, **json.loads(procIdObj.kwargs))
return peventTraceGenerator
@staticmethod
@cache.cache('simDbStatsAdapter', expire = 3600)
@logtime(logger)
def getSimDbStatsGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
simDbDataSource = bpEndpoint.getDataSource(path)
logger.debug(f'List of stats are: {simDbDataSource.stats}')
# constructor adapter
spartaStatsAdapter = SpartaStatisticsAdapter(simDbDataSource)
# construct generator
generator = SpartaStatsGenerator(spartaStatsAdapter, **json.loads(procIdObj.kwargs))
return generator
@staticmethod
@logtime(logger)
@cache.cache('shpHeatMap', expire = 3600)
def getShpHeatmapGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
branchHeatMapDataSource = bpEndpoint.getDataSource(path)
# constructor adapter
branchTrainingHeatMapAdapter = BranchTrainingHeatMapAdapter(branchHeatMapDataSource)
heatMapGenerator = BranchTrainingHeatMapGenerator(branchTrainingHeatMapAdapter, **json.loads(procIdObj.kwargs))
return heatMapGenerator | helios/plato/django/server/bpEndpoint.py | import asyncio
from collections import OrderedDict
import concurrent
from contextlib import contextmanager
from functools import wraps
import functools
import itertools
import json
import logging
import math
from os import access
import os
from os.path import realpath, basename, dirname
from pathlib import Path
from threading import Lock
import threading
import time
import traceback
from uuid import uuid5
import uuid
from asgiref.sync import async_to_sync
from channels.generic.websocket import AsyncWebsocketConsumer
from django.core.cache import caches
from server import settings
from server.beakercache import cache
import numpy as np
from plato.backend.adapters.branch_training_trace.adapter import BranchTrainingTraceAdapter
from plato.backend.adapters.pevent_trace.adapter import PeventTraceAdapter
from plato.backend.adapters.sparta_statistics.adapter import SpartaStatisticsAdapter
from plato.backend.common import logtime, synchronized, synchronizedFine, countactive
from plato.backend.datasources.branch_training_trace.datasource import BranchTrainingDatasource
from plato.backend.datasources.pevent_trace.datasource import PeventDataSource
from plato.backend.datasources.sparta_statistics.datasource import SpartaDataSource
from plato.backend.processors.branch_training_heatmap.adapter import BranchTrainingHeatMapAdapter
from plato.backend.processors.branch_training_heatmap.generator import BranchTrainingHeatMapGenerator
from plato.backend.processors.branch_training_line_plot.generator import BranchTrainingLinePlotGenerator
from plato.backend.processors.branch_training_profile.generator import BranchTrainingProfileHtmlGenerator
from plato.backend.processors.branch_training_table.generator import BranchTrainingListHtmlGenerator
from plato.backend.processors.general_line_plot.generator import GeneralTraceLinePlotGenerator
from plato.backend.processors.pevent_trace_generator.generator import PeventTraceGenerator
from plato.backend.processors.sparta_statistics.generator import SpartaStatsGenerator
from plato.backend.units import Units
from .models import DataId, ProcessorId, LongFunctionCall
logger = logging.getLogger("plato.backend.bpEndpoint")
class bpEndpoint(AsyncWebsocketConsumer):
'''
this is the endpoint for most of the data operations that happen in plato
handles branch prediction, SPARTA data sources, etc.
'''
# this is the cache that will speed up complex lookups and hold references
# to useful data structures that are used for doing data processing
beakerCache = cache.get_cache('branchPredictor', expire = 3600)
def __init__(self, *args, **kwargs):
'''
ctor
'''
super().__init__(*args, **kwargs)
self.callback = None
self.sendLock = Lock()
self.loop = asyncio.get_event_loop()
# the thread pool, whenever a request is received via an async function
# call, it is immediately launched onto a concurrent thread to avoid
# keeping the async thread busy, when the processing is done then it
# will come back to the async thread to send the result back to the client
self.threadPoolExecutor = concurrent.futures.ThreadPoolExecutor(max_workers = settings.WORKER_THREADS,
thread_name_prefix = "wsEndpointWorker")
async def connect(self):
'''
look at the call that this wants to use and cache that
'''
path = self.scope.get('path', '')
if path == '/ws/getData':
self.callback = self.getData
await self.accept()
elif path == '/ws/sources':
self.callback = self.sources
await self.accept()
else:
logger.error(f"error: not recognized: {path}")
await self.close()
async def disconnect(self, close_code):
'''
when the client disconnects from the websocket
'''
await self.close()
async def receive(self, text_data = None, bytes_data = None):
'''
capture and route the actual request
'''
# go run this on a separate thread
# TODO ensure that only text_data is received, clients should be sending
# json as text, not binary
blockingTask = asyncio.get_event_loop().run_in_executor(self.threadPoolExecutor,
self.callback,
text_data)
def sendMessage(self, text_data = None, bytes_data = None, close = False):
with self.sendLock:
loop = self.loop
if text_data:
future = asyncio.run_coroutine_threadsafe(self.send(json.dumps(text_data), None, close), loop)
future.result()
if bytes_data is not None:
future = asyncio.run_coroutine_threadsafe(self.send(None, bytes_data if len(bytes_data) else b'0', close), loop)
future.result()
@logtime(logger)
@cache.cache('getSourceInformation', expire = 360)
def getSourceInformation(self, path):
'''
go look at the file type and get info about it, determine what can read
it and then go cache the reader for it
'''
isHdf5File = path.endswith("hdf5")
if isHdf5File:
if BranchTrainingDatasource.can_read(path):
return BranchTrainingDatasource.get_source_information(path), "branch-predictor-training-trace"
elif PeventDataSource.can_read(path):
return PeventDataSource.get_source_information(path), "pevent-trace"
else:
raise ValueError("unknown hdf5 type, pevent and branch training data sources cannot read this")
else:
return SpartaDataSource.get_source_information(path), "sparta-statistics"
@logtime(logger)
def loadFile(self, jsonData, returnValue):
'''
load a single file and return data
'''
returnValue['waitEstimate'] = 12345
file = jsonData['file']
if not access(file, os.R_OK):
raise ValueError("cannot read file")
currentFile = realpath(file)
# use the django ORM to lookup or create a persistent UUID for this file
dataIdObj, created = DataId.objects.get_or_create(path = file,
defaults = {'uuid': str(uuid5(uuid.NAMESPACE_URL,
file))})
returnValue["result"] = "complete"
logger.debug(f"{currentFile} created? {created}: {dataIdObj}")
statVals, typeId = self.getSourceInformation(file)
newDict = {"name": str(basename(file)),
"directory": dirname(file),
"typeId": typeId,
"dataId": dataIdObj.uuid}
for key, value in statVals.items():
newDict[key] = value
returnValue["sources"] = [newDict]
@logtime(logger)
def getProcessor(self, jsonData, returnValue):
'''
get the processor ID for a given processor type + data ID
this may take a long time so it should be run in a separate thread,
rather than the main event loop
'''
dataId = jsonData['dataId']
processor = jsonData['processor']
kwargs = json.dumps(OrderedDict(sorted(jsonData['kwargs'].items(), key = lambda kv: kv[0])))
# TODO need a better UUID?
processorUuid = str(uuid.uuid5(uuid.NAMESPACE_DNS, name = str(time.time())))
processorIdObj, created = ProcessorId.objects.get_or_create(dataUuid = dataId,
processor = processor,
kwargs = kwargs,
defaults = {'uuid': processorUuid, })
logger.debug(f"{dataId} created? {created}: {processorIdObj}")
returnValue['processorId'] = processorIdObj.uuid
if processor == "shp-heatmap-generator":
bpEndpoint.getShpHeatmapGenerator(processorIdObj.uuid)
elif processor == "shp-line-plot-generator":
bpEndpoint.getShpLinePlotGenerator(processorIdObj.uuid)
elif processor == "shp-branch-list-generator":
bpEndpoint.getShpTableHtmlGenerator(processorIdObj.uuid)
elif processor == "simdb-line-plot-generator":
bpEndpoint.getSimDbLinePlotGenerator(processorIdObj.uuid)
elif processor == "simdb-get-all-data-generator":
bpEndpoint.getSimDbStatsGenerator(processorIdObj.uuid)
elif processor == "shp-branch-profile-generator":
bpEndpoint.getShpBranchProfileHtmlGenerator(processorIdObj.uuid)
elif processor == "pevent-trace-generator":
bpEndpoint.getPeventTraceGenerator(processorIdObj.uuid)
else:
raise ValueError(f"unknown processor type {processor}")
returnValue['result'] = 'complete'
@logtime(logger)
def loadDirectory(self, jsonData, returnValue):
'''
go scan a directory and return metadata + generated UUIDs
'''
returnValue['waitEstimate'] = 12345
directory = Path(jsonData['directory']).resolve()
returnValue['directory'] = str(directory)
returnValue["result"] = "in-progress"
hdf5GlobPattern = jsonData.get('globPattern', "*hdf5")
dbGlobPattern = jsonData.get('globPattern', "*db")
self.sendMessage(returnValue)
# detect errors
if not directory.is_dir():
raise ValueError(f"cannot read directory: {str(directory)}")
if not access(directory, os.R_OK):
raise ValueError("cannot access directory")
returnValue['subDirectories'] = [str(x) for x in Path(directory).iterdir() if x.is_dir()]
dbFiles = list(filter(os.path.isfile, directory.glob(dbGlobPattern)))
hdf5Files = list(filter(os.path.isfile, directory.glob(hdf5GlobPattern)))
returnValue['result'] = 'partial'
returnValue['sources'] = []
for i, currentFile in enumerate(hdf5Files + dbFiles):
# need the canonical path of the file to generate a UUID
relativePath = currentFile.relative_to(directory)
currentFile = realpath(currentFile)
isHdf5File = currentFile.endswith("hdf5")
if isHdf5File:
# two options, branch training or p-events
if BranchTrainingDatasource.can_read(currentFile):
typeId = "branch-predictor-training-trace"
elif PeventDataSource.can_read(currentFile):
typeId = "pevent-trace"
else:
raise ValueError("unknown hdf5 type, pevent and branch training data sources cannot read this")
else:
typeId = "sparta-statistics"
dataIdObj, created = DataId.objects.get_or_create(path = currentFile,
defaults = {'uuid': str(uuid5(uuid.NAMESPACE_URL,
currentFile))})
logger.debug(f"{currentFile} created? {created}: {dataIdObj}")
newDict = {"name": str(relativePath),
"typeId": typeId,
"dataId": dataIdObj.uuid}
returnValue['sources'].append(newDict)
if i + 1 != len(hdf5Files + dbFiles):
self.sendMessage(returnValue)
returnValue['result'] = 'complete'
@logtime(logger)
def sources(self, value):
'''
load sources and return progress
this may take a while and should be run in its own thread when possible
'''
returnValue = {}
try:
jsonData = json.loads(value)
command = returnValue["command"] = jsonData['command']
returnValue["result"] = "error"
returnValue["reqSeqNum"] = jsonData["reqSeqNum"]
if command == 'getProcessor':
# cache a processor
self.getProcessor(jsonData, returnValue)
elif command == 'loadFile':
# populate with just a single file
self.loadFile(jsonData, returnValue)
elif command == 'loadDirectory':
# then populate the cache with all files in this directory
self.loadDirectory(jsonData, returnValue)
else:
raise ValueError(f"unknown command {command}")
self.sendMessage(returnValue)
except Exception as e:
logger.exception("problem in sources()")
returnValue["result"] = "error"
returnValue["stackTrace"] = traceback.format_exc()
returnValue["errorMessage"] = "error in sources(): {}".format(e)
self.sendMessage(returnValue)
@countactive
@logtime(logger)
def getData(self, value):
'''
get a branch predictor heatmap or line-plot in byte format
'''
logger.debug(f"getData() with request ${json.loads(value)}")
start = time.time()
responseValue = {"reqSeqNum": -1,
"result": "error",
"duration": 0,
"processorSpecific": {}}
try:
jsonData = json.loads(value)
first = jsonData['first']
last = jsonData['last']
processorId = jsonData['processorId']
kwargs = jsonData['kwargs']
responseValue["reqSeqNum"] = jsonData['reqSeqNum']
processor = self.lookupProcessorId(processorId)[0]
if processor.processor == "shp-heatmap-generator":
bphmg = bpEndpoint.getShpHeatmapGenerator(processor.uuid)
logger.debug(f"calling generate_2d_heatmap_with_profiles() with {[first,last]}, {kwargs}")
heatMap, tableMeans, rowMeans, tableStds, tableMins, tableMaxs, tableMedians, = \
bphmg.generate_2d_heatmap_with_profiles_and_stats(first, last, **kwargs)
responseValue["processorSpecific"]["numRows"] = heatMap.shape[0]
responseValue["processorSpecific"]["numCols"] = heatMap.shape[1]
responseValue["processorSpecific"]["zMin"] = int(round(np.nanmin(heatMap)))
responseValue["processorSpecific"]["zMax"] = int(round(np.nanmax(heatMap)))
responseValue["processorSpecific"]["zBlobOffset"] = 0
resultBin = heatMap.tobytes()
responseValue["processorSpecific"]["tableMeansBlobOffset"] = len(resultBin)
resultBin += tableMeans.tobytes()
responseValue["processorSpecific"]["rowMeansBlobOffset"] = len(resultBin)
resultBin += rowMeans.tobytes()
responseValue["processorSpecific"]["tableStdsBlobOffset"] = len(resultBin)
resultBin += tableStds.tobytes()
responseValue["processorSpecific"]["tableMinsBlobOffset"] = len(resultBin)
resultBin += tableMins.tobytes()
responseValue["processorSpecific"]["tableMaxsBlobOffset"] = len(resultBin)
resultBin += tableMaxs.tobytes()
responseValue["processorSpecific"]["tableMediansBlobOffset"] = len(resultBin)
resultBin += tableMedians.tobytes()
elif processor.processor == "shp-line-plot-generator":
logger.debug(f"calling generate_lines() with {[first,last]}, {kwargs}")
resultBin = self.getShpLinePlot(jsonData, responseValue)
elif processor.processor == "shp-branch-list-generator":
bptg = bpEndpoint.getShpTableHtmlGenerator(processor.uuid)
logger.debug(f"calling generate_table() with {[first,last]}, {kwargs}")
result = bptg.generate_table(first, last, **kwargs)
responseValue["processorSpecific"] = {"stringLength": len(result),
"stringBlobOffset": 0}
resultBin = result.encode('utf-8')
elif processor.processor == "shp-branch-profile-generator":
sbpg = bpEndpoint.getShpBranchProfileHtmlGenerator(processor.uuid)
logger.debug(f"calling generate_table() with {[first,last]}, {kwargs}")
result = sbpg.generate_table(first, last, **kwargs)
responseValue["processorSpecific"] = {"stringLength": len(result),
"stringBlobOffset": 0}
resultBin = result.encode('utf-8')
elif processor.processor == "simdb-line-plot-generator":
sdblp = bpEndpoint.getSimDbLinePlotGenerator(processor.uuid)
result = sdblp.generate_lines(first, last, **kwargs)
if result.shape[1] == 0:
yExtents = [-1, 1]
else:
yExtents = [np.nanmin(result[1:]), np.nanmax(result[1:])] # Skip the first row (units)
responseValue["processorSpecific"]["numSeries"] = result.shape[0]
responseValue["processorSpecific"]["pointsPerSeries"] = result.shape[1]
responseValue["processorSpecific"]["seriesBlobOffset"] = 0
responseValue["processorSpecific"]["yExtents"] = yExtents
resultBin = result.astype(dtype = 'f4', copy = False).tobytes()
elif processor.processor == "simdb-get-all-data-generator":
resultBin = self.simdbGetAllData(processor.uuid, jsonData, responseValue)
elif processor.processor == "pevent-trace-generator":
peventTraceGenerator = bpEndpoint.getPeventTraceGenerator(processor.uuid)
histograms = peventTraceGenerator.generate_histograms(first, last, **kwargs)
psData = responseValue["processorSpecific"] = {}
psData["numSeries"] = len(histograms.keys())
psData["series"] = list(histograms.keys())
psData["histLengths"] = [len(v[0]) for v in histograms.values()]
psData["edgesLengths"] = [len(v[1]) for v in histograms.values()]
resultBin = next(itertools.islice(histograms.values(), 0, 1))[1].astype(dtype = 'f4').tobytes()
for v in histograms.values():
resultBin += v[0].astype(dtype = 'f4').tobytes()
else:
raise ValueError(f"{processorId} isn't in the database, can't do a lookup")
responseValue["result"] = "complete"
responseValue["durationMs"] = math.ceil((time.time() - start) * 1000)
# send the metadata back in json format
self.sendMessage(responseValue, resultBin)
except Exception as e:
# something went wrong, need to let the client know
logger.exception(f"problem in getData(), {value}")
responseValue["stackTrace"] = traceback.format_exc()
responseValue["errorMessage"] = str(e)
responseValue["durationMs"] = math.ceil((time.time() - start) * 1000)
self.sendMessage(responseValue)
def getShpLinePlot(self, jsonData, responseValue):
'''
find the shp generator then tell it to generate a line plot
'''
processorId = jsonData['processorId']
if True:
processor = self.lookupProcessorId(processorId)[0]
else:
# TODO this will saturate the db connections quickly, need to either
# find the connection leak or add db connection pooling
processor = ProcessorId.objects.get(uuid = processorId)
bplpg = bpEndpoint.getShpLinePlotGenerator(processor.uuid)
first = jsonData['first']
last = jsonData['last']
kwargs = jsonData['kwargs']
result, modalities, downsamplingLevel, yExtents = bplpg.generate_lines_and_more(first, last, **kwargs)
responseValue["processorSpecific"]["numSeries"] = result.shape[0]
responseValue["processorSpecific"]["pointsPerSeries"] = result.shape[1]
responseValue["processorSpecific"]["downsampling"] = downsamplingLevel
responseValue["processorSpecific"]["seriesBlobOffset"] = 0
responseValue["processorSpecific"]["yExtents"] = yExtents
blob = result.astype(dtype = 'f4', copy = False).tobytes()
responseValue["processorSpecific"]["modalitiesOffset"] = len(blob)
blob += modalities.astype(dtype = 'i2', copy = False).tobytes()
if downsamplingLevel <= 1:
# Get some data that only makes sense at no-downsampling. User can view individual points' data
otherKwargs = {"units": kwargs["units"],
"stat_cols": ['pc', 'tgt', 'trn_idx', 'instructions'],
"branch_predictor_filtering": kwargs.get("branch_predictor_filtering", {}),
"point_cache": kwargs.get("point_cache", True)}
points = bplpg.generate_simple_int_points(first, last, **otherKwargs)
pcs = points[0]
tgts = points[1]
indices = points[2]
instructions = points[3]
# Since these arrays are 8B, they must be aligned to 8B for javascript arrays to be happy.
misalignment = len(blob) % 8
if misalignment > 0:
blob += bytes([0] * (8 - misalignment))
responseValue["processorSpecific"]["indicesOffset"] = len(blob)
blob += indices.tobytes()
misalignment = len(blob) % 8
if misalignment > 0:
blob += bytes([0] * (8 - misalignment))
responseValue["processorSpecific"]["addressesOffset"] = len(blob)
blob += pcs.tobytes()
misalignment = len(blob) % 8
if misalignment > 0:
blob += bytes([0] * (8 - misalignment))
responseValue["processorSpecific"]["targetsOffset"] = len(blob)
blob += tgts.tobytes()
misalignment = len(blob) % 8
if misalignment > 0:
blob += bytes([0] * (8 - misalignment))
responseValue["processorSpecific"]["instructionNumsOffset"] = len(blob)
blob += instructions.tobytes()
return blob
def simdbGetAllData(self, processorUuid, jsonData, responseValue):
first = jsonData['first']
last = jsonData['last']
statsGen = bpEndpoint.getSimDbStatsGenerator(processorUuid)
kwargs = jsonData['kwargs']
whichChanged = kwargs["whichChanged"]
psData = responseValue["processorSpecific"]
if whichChanged:
del kwargs["whichChanged"]
# so x or y is updated
a = self.threadPoolExecutor.submit(functools.partial(statsGen.generate_lines,
first,
last,
**kwargs))
blockingTasks = [a]
else:
a = None
blockingTasks = []
psData["pointsPerSeries"] = 0
psData["stat_cols"] = kwargs['stat_cols']
# TODO this is redundant, should either be able to pass this to other calls and save them
# the trouble or get it as a byproduct of others calls calculating this
b = self.threadPoolExecutor.submit(functools.partial(statsGen.adapter.range_to_rows,
first,
last,
kwargs["units"]))
c = self.threadPoolExecutor.submit(functools.partial(statsGen.generate_histogram,
first,
last,
kwargs["units"],
kwargs["stat_cols"][0],
-1))
d = self.threadPoolExecutor.submit(functools.partial(statsGen.generate_histogram,
first,
last,
kwargs["units"],
kwargs["stat_cols"][1],
-1))
e = self.threadPoolExecutor.submit(functools.partial(statsGen.generate_regression_line,
first,
last,
**kwargs))
blockingTasks.extend([b, c, d, e])
results = []
for futureTask in blockingTasks:
result = futureTask.result()
results.append((futureTask, result))
x = y = indices = None
for futureTask, result in results:
if futureTask == a:
indices, x, y = result
psData["xMin"] = min(x)
psData["xMax"] = max(x)
psData["yMin"] = min(y)
psData["yMax"] = max(y)
psData["pointsPerSeries"] = len(x)
elif futureTask == b:
firstRow, lastRow, _ = result
psData["firstRow"] = firstRow
psData["lastRow"] = lastRow
psData["first"] = first
psData["last"] = last
elif futureTask == c:
hEdges, hHist = result
psData["hHistLength"] = len(hHist)
psData["hEdgesLength"] = len(hEdges)
elif futureTask == d:
vEdges, vHist = result
psData["vEdgesLength"] = len(vEdges)
psData["vHistLength"] = len(vHist)
elif futureTask == e:
regX, regY = result
psData["regLength"] = len(regX)
if x is not None:
return indices.astype(dtype = 'f4').tobytes() + \
x.astype(dtype = 'f4').tobytes() + y.astype(dtype = 'f4').tobytes() + \
hEdges.tobytes() + hHist.tobytes() + \
vEdges.tobytes() + vHist.tobytes() + \
regX.tobytes() + regY.tobytes()
else:
return hEdges.tobytes() + hHist.tobytes() + \
vEdges.tobytes() + vHist.tobytes() + \
regX.tobytes() + regY.tobytes()
@staticmethod
@logtime(logger)
@cache.cache('lookupProcessorId', expire = 3600)
def lookupProcessorId(processorId):
logger.debug(f"lookup in ProcessorId for {processorId}")
procId = ProcessorId.objects.get(uuid = processorId)
dataUuid = procId.dataUuid
logger.debug(f"returned {dataUuid}")
path = DataId.objects.filter(uuid = dataUuid)[0].path
logger.debug(f"returned {path}")
return procId, path
endpointLock = Lock()
endpointLockDict = {}
@staticmethod
# TODO need to make a mutex per path, or globally
@synchronized(endpointLock)
# @synchronizedFine(endpointLockDict, endpointLock)
@cache.cache('dataSource', expire = 3600)
@logtime(logger)
def getDataSource(path):
logger.debug(f"open data source: {path}")
if path.endswith("hdf5"):
if BranchTrainingDatasource.can_read(path):
return BranchTrainingDatasource(path)
elif PeventDataSource.can_read(path):
return PeventDataSource(path)
else:
raise ValueError("unknown type of file, can't choose data source")
else:
return SpartaDataSource(path)
@staticmethod
@cache.cache('shpHtmlTable', expire = 3600)
@logtime(logger)
def getShpTableHtmlGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
branch_hm_data = bpEndpoint.getDataSource(path)
bptta = BranchTrainingTraceAdapter(branch_hm_data)
bptg = BranchTrainingListHtmlGenerator(bptta, **json.loads(procIdObj.kwargs))
return bptg
@staticmethod
@cache.cache('shpHtmlTable', expire = 3600)
@logtime(logger)
def getShpBranchProfileHtmlGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
branch_hm_data = bpEndpoint.getDataSource(path)
bptta = BranchTrainingTraceAdapter(branch_hm_data)
bptg = BranchTrainingProfileHtmlGenerator(bptta, **json.loads(procIdObj.kwargs))
return bptg
@staticmethod
@cache.cache('shpLinePlot', expire = 3600)
@logtime(logger)
def getShpLinePlotGenerator(processorId):
logger.info(f"getShpLinePlotGenerator for pid={processorId}")
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
branchHeatMapDataSource = bpEndpoint.getDataSource(path)
logger.debug(f'List of stats are: {branchHeatMapDataSource.stats}')
# constructor adapter
branchTrainingTraceAdapter = BranchTrainingTraceAdapter(branchHeatMapDataSource)
# construct generator
linePlotGenerator = BranchTrainingLinePlotGenerator(branchTrainingTraceAdapter, **json.loads(procIdObj.kwargs))
return linePlotGenerator
@staticmethod
@cache.cache('simDbLinePlot', expire = 3600)
@logtime(logger)
def getSimDbLinePlotGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
simDbDataSource = bpEndpoint.getDataSource(path)
logger.debug(f'List of stats are: {simDbDataSource.stats}')
# constructor adapter
spartaStatsAdapter = SpartaStatisticsAdapter(simDbDataSource)
# construct generator
linePlotGenerator = GeneralTraceLinePlotGenerator(spartaStatsAdapter, **json.loads(procIdObj.kwargs))
return linePlotGenerator
@staticmethod
@cache.cache('peventTraceGenerator', expire = 3600)
@logtime(logger)
def getPeventTraceGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
peventDataSource = bpEndpoint.getDataSource(path)
# construct adapter
peventTraceAdapter = PeventTraceAdapter(peventDataSource)
# construct generator
peventTraceGenerator = PeventTraceGenerator(peventTraceAdapter, **json.loads(procIdObj.kwargs))
return peventTraceGenerator
@staticmethod
@cache.cache('simDbStatsAdapter', expire = 3600)
@logtime(logger)
def getSimDbStatsGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
simDbDataSource = bpEndpoint.getDataSource(path)
logger.debug(f'List of stats are: {simDbDataSource.stats}')
# constructor adapter
spartaStatsAdapter = SpartaStatisticsAdapter(simDbDataSource)
# construct generator
generator = SpartaStatsGenerator(spartaStatsAdapter, **json.loads(procIdObj.kwargs))
return generator
@staticmethod
@logtime(logger)
@cache.cache('shpHeatMap', expire = 3600)
def getShpHeatmapGenerator(processorId):
procIdObj, path = bpEndpoint.lookupProcessorId(processorId)
# load source data
branchHeatMapDataSource = bpEndpoint.getDataSource(path)
# constructor adapter
branchTrainingHeatMapAdapter = BranchTrainingHeatMapAdapter(branchHeatMapDataSource)
heatMapGenerator = BranchTrainingHeatMapGenerator(branchTrainingHeatMapAdapter, **json.loads(procIdObj.kwargs))
return heatMapGenerator | 0.322099 | 0.176246 |
from typing import Union, List
import requests
from nexus.core.data.program import Manifest
from nexus.core.data.store import Patch, Signal, ProgramInstance
from nexus.core.database import Instance
from nexus.core.exc import NexusError
from nexus.core.handlers.api import APIHandler
from nexus.core.handlers.container import ContainerHandler
class SynapserHandler(APIHandler, ContainerHandler):
class Meta:
label = 'synapser'
def __init__(self, **kw):
super(SynapserHandler, self).__init__(**kw)
self.endpoints = {'index': self.url_format,
'repair': f"{self.url_format}/repair",
'patches': f"{self.url_format}/patches",
'coverage': f"{self.url_format}/coverage",
'stream': f"{self.url_format}/stream" + "/{rid}"
}
def is_running(self, instance: Instance) -> bool:
try:
return self.get(endpoint_url=self.endpoints['index'].format(ip=instance.ip, port=instance.port)).ok
except (requests.exceptions.ConnectionError, NexusError) as ce:
self.app.log.warning(str(ce))
return False
def repair(self, instance: Instance, signals: List[Signal], program_instance: ProgramInstance, manifest: dict,
args: dict, iid: int):
return self.post(endpoint_url=self.endpoints['repair'].format(ip=instance.ip, port=instance.port),
json_data={'signals': {signal.arg: signal.command.to_json() for signal in signals},
'args': args, 'iid': iid,
'manifest': manifest, 'working_dir': str(program_instance.working_dir),
'build_dir': str(
program_instance.build_dir) if program_instance.build_dir else None,
'timeout': self.get_timeout()})
def coverage(self, instance: Instance, signals: List[Signal], program_instance: ProgramInstance,
manifest: Manifest, iid: int):
return self.post(endpoint_url=self.endpoints['coverage'].format(ip=instance.ip, port=instance.port),
json_data={'signals': {signal.arg: signal.command.to_json() for signal in signals}, 'iid': iid,
'manifest': manifest.to_str(), 'working_dir': str(program_instance.working_dir),
'build_dir': str(
program_instance.build_dir) if program_instance.build_dir else None,
'timeout': self.get_timeout(), })
def stream(self, instance: Instance, rid: int):
return self.get(endpoint_url=self.endpoints['stream'].format(ip=instance.ip, port=instance.port, rid=rid))
def get_timeout(self):
if self.app.pargs.timeout:
return self.app.pargs.timeout
return self.app.get_config('tool_timeout')
def get_patches(self, instance: Instance, fixes: bool = False) -> Union[List[Patch], None]:
response = self.get(endpoint_url=self.endpoints['patches'].format(ip=instance.ip, port=instance.port),
json_data={'fixes': fixes})
patches = response.json()['patches']
if patches:
return [Patch(is_fix=fixes, **patch) for patch in patches]
return None | nexus/core/handlers/synapser.py | from typing import Union, List
import requests
from nexus.core.data.program import Manifest
from nexus.core.data.store import Patch, Signal, ProgramInstance
from nexus.core.database import Instance
from nexus.core.exc import NexusError
from nexus.core.handlers.api import APIHandler
from nexus.core.handlers.container import ContainerHandler
class SynapserHandler(APIHandler, ContainerHandler):
class Meta:
label = 'synapser'
def __init__(self, **kw):
super(SynapserHandler, self).__init__(**kw)
self.endpoints = {'index': self.url_format,
'repair': f"{self.url_format}/repair",
'patches': f"{self.url_format}/patches",
'coverage': f"{self.url_format}/coverage",
'stream': f"{self.url_format}/stream" + "/{rid}"
}
def is_running(self, instance: Instance) -> bool:
try:
return self.get(endpoint_url=self.endpoints['index'].format(ip=instance.ip, port=instance.port)).ok
except (requests.exceptions.ConnectionError, NexusError) as ce:
self.app.log.warning(str(ce))
return False
def repair(self, instance: Instance, signals: List[Signal], program_instance: ProgramInstance, manifest: dict,
args: dict, iid: int):
return self.post(endpoint_url=self.endpoints['repair'].format(ip=instance.ip, port=instance.port),
json_data={'signals': {signal.arg: signal.command.to_json() for signal in signals},
'args': args, 'iid': iid,
'manifest': manifest, 'working_dir': str(program_instance.working_dir),
'build_dir': str(
program_instance.build_dir) if program_instance.build_dir else None,
'timeout': self.get_timeout()})
def coverage(self, instance: Instance, signals: List[Signal], program_instance: ProgramInstance,
manifest: Manifest, iid: int):
return self.post(endpoint_url=self.endpoints['coverage'].format(ip=instance.ip, port=instance.port),
json_data={'signals': {signal.arg: signal.command.to_json() for signal in signals}, 'iid': iid,
'manifest': manifest.to_str(), 'working_dir': str(program_instance.working_dir),
'build_dir': str(
program_instance.build_dir) if program_instance.build_dir else None,
'timeout': self.get_timeout(), })
def stream(self, instance: Instance, rid: int):
return self.get(endpoint_url=self.endpoints['stream'].format(ip=instance.ip, port=instance.port, rid=rid))
def get_timeout(self):
if self.app.pargs.timeout:
return self.app.pargs.timeout
return self.app.get_config('tool_timeout')
def get_patches(self, instance: Instance, fixes: bool = False) -> Union[List[Patch], None]:
response = self.get(endpoint_url=self.endpoints['patches'].format(ip=instance.ip, port=instance.port),
json_data={'fixes': fixes})
patches = response.json()['patches']
if patches:
return [Patch(is_fix=fixes, **patch) for patch in patches]
return None | 0.732305 | 0.143397 |
from django.db import models
from django.contrib.auth.models import User
from django.db.models.deletion import CASCADE
# Create your models here.
class Customer(models.Model):
user = models.OneToOneField(User, null=True, blank=True, on_delete=models.CASCADE)
name = models.CharField(max_length=200, null=True)
email = models.CharField(max_length=200)
def __str__(self):
return self.name
class Author(models.Model):
user = models.OneToOneField(User, null=True, blank=True, on_delete=CASCADE)
name = models.CharField(max_length=200, null=True)
email = models.CharField(max_length=200, null=True)
def __str__(self):
return self.name
class Category(models.Model):
name = models.CharField(max_length=200, null=True)
description = models.TextField(null=True)
def __str__(self):
return self.name
class Book(models.Model):
name = models.CharField(max_length=200, null=True)
ISBN = models.CharField(max_length=200, null=True)
price = models.FloatField()
description = models.TextField(null=True)
image = models.ImageField(null=True, blank=True)
authors = models.ManyToManyField(Author)
category = models.ForeignKey(Category, on_delete=models.SET_NULL, null=True)
def __str__(self):
return self.name
@property
def imageURL(self):
try:
url = self.image.url
except:
url = ''
return url
class Cart(models.Model):
user = models.ForeignKey(User, on_delete=models.SET_NULL, null=True, blank=True)
def __str__(self):
return self.id
class CartItem(models.Model):
product = models.ForeignKey(Book, on_delete=models.SET_NULL, blank=True, null=True)
cart = models.ForeignKey(Cart, on_delete=models.SET_NULL, blank=True, null=True)
quantity = models.IntegerField(default=0, null=True, blank=True)
date_added = models.DateTimeField(auto_now_add=True)
def __str__(self) :
return (self.product.name)
class Order(models.Model):
name = models.CharField(max_length=200, null=False)
address = models.CharField(max_length=400, null=True)
phone = models.CharField(max_length=20, null=False)
email = models.CharField(max_length=100, null=True)
notes = models.CharField(max_length=400, null=True)
amount = models.DecimalField(max_digits=7, decimal_places=2, null=False) | shop/models.py | from django.db import models
from django.contrib.auth.models import User
from django.db.models.deletion import CASCADE
# Create your models here.
class Customer(models.Model):
user = models.OneToOneField(User, null=True, blank=True, on_delete=models.CASCADE)
name = models.CharField(max_length=200, null=True)
email = models.CharField(max_length=200)
def __str__(self):
return self.name
class Author(models.Model):
user = models.OneToOneField(User, null=True, blank=True, on_delete=CASCADE)
name = models.CharField(max_length=200, null=True)
email = models.CharField(max_length=200, null=True)
def __str__(self):
return self.name
class Category(models.Model):
name = models.CharField(max_length=200, null=True)
description = models.TextField(null=True)
def __str__(self):
return self.name
class Book(models.Model):
name = models.CharField(max_length=200, null=True)
ISBN = models.CharField(max_length=200, null=True)
price = models.FloatField()
description = models.TextField(null=True)
image = models.ImageField(null=True, blank=True)
authors = models.ManyToManyField(Author)
category = models.ForeignKey(Category, on_delete=models.SET_NULL, null=True)
def __str__(self):
return self.name
@property
def imageURL(self):
try:
url = self.image.url
except:
url = ''
return url
class Cart(models.Model):
user = models.ForeignKey(User, on_delete=models.SET_NULL, null=True, blank=True)
def __str__(self):
return self.id
class CartItem(models.Model):
product = models.ForeignKey(Book, on_delete=models.SET_NULL, blank=True, null=True)
cart = models.ForeignKey(Cart, on_delete=models.SET_NULL, blank=True, null=True)
quantity = models.IntegerField(default=0, null=True, blank=True)
date_added = models.DateTimeField(auto_now_add=True)
def __str__(self) :
return (self.product.name)
class Order(models.Model):
name = models.CharField(max_length=200, null=False)
address = models.CharField(max_length=400, null=True)
phone = models.CharField(max_length=20, null=False)
email = models.CharField(max_length=100, null=True)
notes = models.CharField(max_length=400, null=True)
amount = models.DecimalField(max_digits=7, decimal_places=2, null=False) | 0.587233 | 0.111676 |
# Copyright Contributors to the Open Shading Language project.
# SPDX-License-Identifier: BSD-3-Clause
# https://github.com/AcademySoftwareFoundation/OpenShadingLanguage
def run_varying_option_test (option) :
global command
command += testshade("-g 512 512 -center"+
" -layer src vary_"+option+" -layer dst test_gabor_options"+
" --connect src "+option+" dst "+option+
" -od uint8 -o Cout out_v_"+option+".tif")
global outputs
outputs.append ("out_v_"+option+".tif")
return
run_varying_option_test ("anisotropic")
run_varying_option_test ("bandwidth")
run_varying_option_test ("direction")
run_varying_option_test ("do_filter")
run_varying_option_test ("impulses")
def run_fixed_and_varying_option_test (fixedoption, fixedval, varyoption) :
global command
command += testshade("-g 512 512 -center"+
" -layer src vary_"+varyoption+
" --param "+fixedoption+" "+fixedval+
" -layer dst test_gabor_options"+
" --connect src "+varyoption+" dst "+varyoption+
" -od uint8 -o Cout out_"+fixedoption+"_"+fixedval+"_v_"+varyoption+".tif")
global outputs
outputs.append ("out_"+fixedoption+"_"+fixedval+"_v_"+varyoption+".tif")
return
run_fixed_and_varying_option_test ("do_filter", "0", "anisotropic")
run_fixed_and_varying_option_test ("anisotropic", "0", "direction")
run_fixed_and_varying_option_test ("anisotropic", "1", "direction")
run_fixed_and_varying_option_test ("anisotropic", "2", "direction")
run_fixed_and_varying_option_test ("bandwidth", "10", "impulses")
def run_varying_option2_test (option1, option2) :
global command
command += testshade("-g 512 512 -center"+
" -layer src1 vary_"+option1+
" -layer src2 vary_"+option2+
" -layer dst test_gabor_options"+
" --connect src1 "+option1+" dst "+option1+
" --connect src2 "+option2+" dst "+option2+
" -od uint8 -o Cout out_v_"+option1+"_v_"+option2+".tif")
global outputs
outputs.append ("out_v_"+option1+"_v_"+option2+".tif")
return
run_varying_option2_test ("anisotropic","bandwidth")
run_varying_option2_test ("bandwidth","direction")
run_varying_option2_test ("direction","do_filter")
run_varying_option2_test ("do_filter","impulses")
# vary all options
command += testshade("-g 512 512 -center"+
" -layer src1 vary_anisotropic"+
" -layer src2 vary_bandwidth"+
" -layer src3 vary_direction"+
" -layer src4 vary_do_filter"+
" -layer src5 vary_impulses"+
" -layer dst test_gabor_options"+
" --connect src1 anisotropic dst anisotropic"+
" --connect src2 bandwidth dst bandwidth"+
" --connect src3 direction dst direction"+
" --connect src4 do_filter dst do_filter"+
" --connect src5 impulses dst impulses"+
" -od uint8 -o Cout out_v_all_options.tif")
outputs.append ("out_v_all_options.tif")
# expect a few LSB failures
failthresh = 0.008
failpercent = 3 | testsuite/noise-gabor-reg/run.py |
# Copyright Contributors to the Open Shading Language project.
# SPDX-License-Identifier: BSD-3-Clause
# https://github.com/AcademySoftwareFoundation/OpenShadingLanguage
def run_varying_option_test (option) :
global command
command += testshade("-g 512 512 -center"+
" -layer src vary_"+option+" -layer dst test_gabor_options"+
" --connect src "+option+" dst "+option+
" -od uint8 -o Cout out_v_"+option+".tif")
global outputs
outputs.append ("out_v_"+option+".tif")
return
run_varying_option_test ("anisotropic")
run_varying_option_test ("bandwidth")
run_varying_option_test ("direction")
run_varying_option_test ("do_filter")
run_varying_option_test ("impulses")
def run_fixed_and_varying_option_test (fixedoption, fixedval, varyoption) :
global command
command += testshade("-g 512 512 -center"+
" -layer src vary_"+varyoption+
" --param "+fixedoption+" "+fixedval+
" -layer dst test_gabor_options"+
" --connect src "+varyoption+" dst "+varyoption+
" -od uint8 -o Cout out_"+fixedoption+"_"+fixedval+"_v_"+varyoption+".tif")
global outputs
outputs.append ("out_"+fixedoption+"_"+fixedval+"_v_"+varyoption+".tif")
return
run_fixed_and_varying_option_test ("do_filter", "0", "anisotropic")
run_fixed_and_varying_option_test ("anisotropic", "0", "direction")
run_fixed_and_varying_option_test ("anisotropic", "1", "direction")
run_fixed_and_varying_option_test ("anisotropic", "2", "direction")
run_fixed_and_varying_option_test ("bandwidth", "10", "impulses")
def run_varying_option2_test (option1, option2) :
global command
command += testshade("-g 512 512 -center"+
" -layer src1 vary_"+option1+
" -layer src2 vary_"+option2+
" -layer dst test_gabor_options"+
" --connect src1 "+option1+" dst "+option1+
" --connect src2 "+option2+" dst "+option2+
" -od uint8 -o Cout out_v_"+option1+"_v_"+option2+".tif")
global outputs
outputs.append ("out_v_"+option1+"_v_"+option2+".tif")
return
run_varying_option2_test ("anisotropic","bandwidth")
run_varying_option2_test ("bandwidth","direction")
run_varying_option2_test ("direction","do_filter")
run_varying_option2_test ("do_filter","impulses")
# vary all options
command += testshade("-g 512 512 -center"+
" -layer src1 vary_anisotropic"+
" -layer src2 vary_bandwidth"+
" -layer src3 vary_direction"+
" -layer src4 vary_do_filter"+
" -layer src5 vary_impulses"+
" -layer dst test_gabor_options"+
" --connect src1 anisotropic dst anisotropic"+
" --connect src2 bandwidth dst bandwidth"+
" --connect src3 direction dst direction"+
" --connect src4 do_filter dst do_filter"+
" --connect src5 impulses dst impulses"+
" -od uint8 -o Cout out_v_all_options.tif")
outputs.append ("out_v_all_options.tif")
# expect a few LSB failures
failthresh = 0.008
failpercent = 3 | 0.512205 | 0.177312 |
import dash
import dash_core_components as dcc
import dash_html_components as html
import os
from datetime import datetime
from utilities.WordWorks import WordWorks as wordings
from sqlalchemy import create_engine
import pandas as pd
time_start = datetime.now()
print(f'Starting At: {time_start}\n\n')
engine = create_engine('mysql+pymysql://gappi:92cf6cc2050f9830996b42433da09d03a4baa26e5524b3b8075c2f076451650a@192.168.1.172:3306/stocks?charset=utf8')
conn = engine.connect()
conn.execute('TRUNCATE stock_word;')
read = pd.read_sql('SELECT title, description FROM stock_scrape;', conn)
results = wordings.do_dicts([
read.get('title').values,
read.get('description').values
])
# pprint.pprint(results)
class StockWord:
def __init__(self, name, count):
self.name = name
self.count = count
def __get_insert_sql__(self):
return f'(\'{self.name}\', {self.count}),'
insert_prefix = f'INSERT INTO stock_word (name, count) VALUES '
insert_suffix = f';'
stock_words = [StockWord(key, results[key]) for key in results]
def create_inserts(stock_words):
count = 0
inserts = []
insert = []
for word in stock_words:
insert.append(word.__get_insert_sql__())
count += 1
if count > 50:
last = insert[len(insert)-1]
last = last[:-1]
last = f'{last}{insert_suffix}'
insert[len(insert)-1] = last
inserts.append(''.join(insert))
insert = []
count = 0
if count > 0:
last = insert[len(insert)-1]
last = last[:-1]
last = f'{last}{insert_suffix}'
insert[len(insert)-1] = last
inserts.append(''.join(insert))
return inserts
inserts = create_inserts(stock_words)
if len(inserts) > 0:
for insert in inserts:
query_string = ''
try:
query_string = ''.join([insert_prefix, insert])
conn.execute(query_string)
except Exception as error:
print(f'ERROR INSERTING {query_string}')
print(error)
print('\n')
time_end = datetime.now()
print(f'Started: {time_start}\n'
f'Ended: {time_end}\n'
f'Duration: {time_end-time_start}')
# pprint.pprint(dir(read.get('description')))
# wordings.do_dicts(read)
# conn.execute()
# pprint.pprint(dir(read))
# print(type(read))
# print(read)
# print(dir(read))
# print(read.T)
# print(read._AXIS_NAMES)
# print(read.axes)
# print(read.blocks)
# print(read.columns)
# print(read.count)
# print(read.describe)
# print(read.div)
# print(read.hist)
# print(read.head)
# print(read.to_json)
# print(read.xs)
conn.close() | app_sql.py | import dash
import dash_core_components as dcc
import dash_html_components as html
import os
from datetime import datetime
from utilities.WordWorks import WordWorks as wordings
from sqlalchemy import create_engine
import pandas as pd
time_start = datetime.now()
print(f'Starting At: {time_start}\n\n')
engine = create_engine('mysql+pymysql://gappi:92cf6cc2050f9830996b42433da09d03a4baa26e5524b3b8075c2f076451650a@192.168.1.172:3306/stocks?charset=utf8')
conn = engine.connect()
conn.execute('TRUNCATE stock_word;')
read = pd.read_sql('SELECT title, description FROM stock_scrape;', conn)
results = wordings.do_dicts([
read.get('title').values,
read.get('description').values
])
# pprint.pprint(results)
class StockWord:
def __init__(self, name, count):
self.name = name
self.count = count
def __get_insert_sql__(self):
return f'(\'{self.name}\', {self.count}),'
insert_prefix = f'INSERT INTO stock_word (name, count) VALUES '
insert_suffix = f';'
stock_words = [StockWord(key, results[key]) for key in results]
def create_inserts(stock_words):
count = 0
inserts = []
insert = []
for word in stock_words:
insert.append(word.__get_insert_sql__())
count += 1
if count > 50:
last = insert[len(insert)-1]
last = last[:-1]
last = f'{last}{insert_suffix}'
insert[len(insert)-1] = last
inserts.append(''.join(insert))
insert = []
count = 0
if count > 0:
last = insert[len(insert)-1]
last = last[:-1]
last = f'{last}{insert_suffix}'
insert[len(insert)-1] = last
inserts.append(''.join(insert))
return inserts
inserts = create_inserts(stock_words)
if len(inserts) > 0:
for insert in inserts:
query_string = ''
try:
query_string = ''.join([insert_prefix, insert])
conn.execute(query_string)
except Exception as error:
print(f'ERROR INSERTING {query_string}')
print(error)
print('\n')
time_end = datetime.now()
print(f'Started: {time_start}\n'
f'Ended: {time_end}\n'
f'Duration: {time_end-time_start}')
# pprint.pprint(dir(read.get('description')))
# wordings.do_dicts(read)
# conn.execute()
# pprint.pprint(dir(read))
# print(type(read))
# print(read)
# print(dir(read))
# print(read.T)
# print(read._AXIS_NAMES)
# print(read.axes)
# print(read.blocks)
# print(read.columns)
# print(read.count)
# print(read.describe)
# print(read.div)
# print(read.hist)
# print(read.head)
# print(read.to_json)
# print(read.xs)
conn.close() | 0.047958 | 0.096748 |
import argparse
import os
import shutil
import sys
import urllib.request
from zipfile import ZipFile
DATASET_URLS = {
'train': 'http://images.cocodataset.org/zips/train2017.zip',
'val': 'http://images.cocodataset.org/zips/val2017.zip',
'test': 'http://images.cocodataset.org/zips/test2017.zip'
}
parser = argparse.ArgumentParser(description='Generate COCO dataset')
parser.add_argument('-f', '--force', action='store_true',
help='Remove existing dataset directory')
parser.add_argument('--dest-path', default='../../resources/data/COCO',
help='Path to output folder')
parser.add_argument('--from-zip', default=None,
help='Zipfile of the dataset to extract from')
parser.add_argument('fold', default='train', choices=['train', 'val', 'test'],
help='Dataset fold to download')
def download_and_extract_coco(dest_path, fold, from_zip_file=None):
"""Download and extract COCO dataset
Parameters
----------
dest_path : string
Destination file path
fold : string
Dataset fold to use
from_zip_file : string
If not None, use a local zipfile instead of downloading it from remote
"""
dest_path = os.path.join(dest_path, 'images')
if not os.path.exists(dest_path):
os.makedirs(dest_path)
zip_file = from_zip_file
if zip_file is None:
url = DATASET_URLS[fold]
print('Downloading dataset {} fold from {}'.format(fold, url))
data = urllib.request.urlopen(url)
zip_file = os.path.join(dest_path, FILENAME)
with open(zip_file, 'wb') as f:
f.write(data.read())
print('Extracting data')
with ZipFile(zip_file) as myzip:
for img in (img for img in myzip.infolist() if '.jpg' in img.filename):
myzip.extract(img, dest_path)
if from_zip_file is None:
os.remove(zip_file)
def main(argv):
args = parser.parse_args(argv)
if args.force and os.path.isdir(args.dest_path):
shutil.rmtree(args.dest_path)
if not os.path.isdir(args.dest_path):
download_and_extract_coco(args.dest_path, args.fold, args.from_zip)
if __name__ == '__main__':
main(sys.argv[1:]) | data/coco/download_coco.py | import argparse
import os
import shutil
import sys
import urllib.request
from zipfile import ZipFile
DATASET_URLS = {
'train': 'http://images.cocodataset.org/zips/train2017.zip',
'val': 'http://images.cocodataset.org/zips/val2017.zip',
'test': 'http://images.cocodataset.org/zips/test2017.zip'
}
parser = argparse.ArgumentParser(description='Generate COCO dataset')
parser.add_argument('-f', '--force', action='store_true',
help='Remove existing dataset directory')
parser.add_argument('--dest-path', default='../../resources/data/COCO',
help='Path to output folder')
parser.add_argument('--from-zip', default=None,
help='Zipfile of the dataset to extract from')
parser.add_argument('fold', default='train', choices=['train', 'val', 'test'],
help='Dataset fold to download')
def download_and_extract_coco(dest_path, fold, from_zip_file=None):
"""Download and extract COCO dataset
Parameters
----------
dest_path : string
Destination file path
fold : string
Dataset fold to use
from_zip_file : string
If not None, use a local zipfile instead of downloading it from remote
"""
dest_path = os.path.join(dest_path, 'images')
if not os.path.exists(dest_path):
os.makedirs(dest_path)
zip_file = from_zip_file
if zip_file is None:
url = DATASET_URLS[fold]
print('Downloading dataset {} fold from {}'.format(fold, url))
data = urllib.request.urlopen(url)
zip_file = os.path.join(dest_path, FILENAME)
with open(zip_file, 'wb') as f:
f.write(data.read())
print('Extracting data')
with ZipFile(zip_file) as myzip:
for img in (img for img in myzip.infolist() if '.jpg' in img.filename):
myzip.extract(img, dest_path)
if from_zip_file is None:
os.remove(zip_file)
def main(argv):
args = parser.parse_args(argv)
if args.force and os.path.isdir(args.dest_path):
shutil.rmtree(args.dest_path)
if not os.path.isdir(args.dest_path):
download_and_extract_coco(args.dest_path, args.fold, args.from_zip)
if __name__ == '__main__':
main(sys.argv[1:]) | 0.325735 | 0.214897 |
# 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.
# Coded by <NAME> for project 301060 (https://301060.exactas.uba.ar/)
# The data provided here is based on the work of <NAME>, <NAME>,
# <NAME> and <NAME>.
accumulated = {'ARG': {'BEL': [0.0071201329124175325,
0.01353150847439597,
0.017735705111119555,
0.01962846487528751,
0.020275310188803252,
0.03275710479838004,
0.050133697087757,
0.062229151669246606,
0.06784206737011222,
0.10026774667674178,
0.14540931699108386,
0.17683134752396573,
0.24000884190984886,
0.32796170415015397,
0.41002414935259446,
0.4633203822759146,
0.5775640224859575,
0.6387860292421373,
0.6533674744737203,
0.6553209865835445,
0.6554989100828535,
0.7296955251559486,
0.8092179504032382,
0.8608645432893203,
0.8892747167677233,
0.9261772297104369,
0.9501439165475021,
0.9552188243773564,
0.9651066653659011,
0.9779501694836301,
0.9862915049790749,
0.9868763490282665,
0.9884236499131996,
0.9915310840934048,
0.9958156790753416,
1.0],
'BRA': [0.0008929590028715159,
0.0019196827511384473,
0.0026723657866933996,
0.003047827629658679,
0.0031894701333307404,
0.006240757295654328,
0.010903324086370448,
0.01446567792614047,
0.016280181317275825,
0.028838906627776436,
0.04802946210531313,
0.06269167544429294,
0.10145931014255466,
0.16069879711404025,
0.24048022200335534,
0.32257287231476095,
0.44448411908239177,
0.48974502865243263,
0.4972133030074715,
0.4979064730372883,
0.4979493998096058,
0.6233923505190876,
0.7165365385870331,
0.8123790642151473,
0.8354329430813779,
0.8828764180183728,
0.9316943051764934,
0.9345473083632898,
0.9433542678758308,
0.9614784527615424,
0.9801276856383202,
0.980350394791875,
0.9812786229798174,
0.9842044291095868,
0.9904796858333647,
1.0],
'COL': [0.003204866147478108,
0.005775423388880829,
0.007136665393213132,
0.0076260634876152995,
0.007759004965470113,
0.01647025846348095,
0.02604178620330918,
0.03130016510912457,
0.03322605402109467,
0.06221814713874735,
0.09407333578348842,
0.11157384944895707,
0.1839407134887803,
0.26345412068783736,
0.3838769083701238,
0.48407226701654016,
0.6163873297192443,
0.6600701810372445,
0.6664797695864517,
0.6670087892949517,
0.6670377868870903,
0.7771278737965344,
0.8498187496472502,
0.910299730999386,
0.9262986387432794,
0.9529217776059934,
0.9750730248739168,
0.9768336657108995,
0.9812283831104274,
0.9885414555690567,
0.9946261504337263,
0.9947476850629487,
0.9951567020545518,
0.9961967514261885,
0.9979922515169245,
1.0],
'CRC': [0.02075520969849545,
0.029581532226591913,
0.03231270330437273,
0.032884883344409,
0.03297535093452647,
0.06890879627614252,
0.0918497465269553,
0.09917282942934472,
0.10073125203488807,
0.18279485617521157,
0.23518663330895653,
0.2519108458267153,
0.39247143764742876,
0.4822093827158145,
0.6427131787649164,
0.7343514829740753,
0.8368217461292937,
0.8654673952432651,
0.8690264688185598,
0.8692752043289154,
0.8692866834010937,
0.9277912258390668,
0.9605012149158592,
0.9791767104709945,
0.9852727806267905,
0.9922337814899265,
0.9962081068977887,
0.9967761607613522,
0.9977491376136972,
0.9988601636576716,
0.9994944947146822,
0.9995274706635107,
0.9996034930084369,
0.9997357631264917,
0.9998915736936538,
1.0],
'CRO': [0.00815701314244515,
0.013661683464660268,
0.016257510759024964,
0.017090015787989115,
0.01729188849308093,
0.034141820436635224,
0.05068654540633515,
0.058809068548861004,
0.06146753450791829,
0.10721844582415763,
0.1521406595978379,
0.1741949229149047,
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('KSA', 'FRA',1.48855993378956e-05,3.55530420306222e-05,5.02471281562042e-05,4.8370748480799e-05,3.5229891884974e-05,9.9972964065253e-05,0.000295123162952298,0.000435606176756544,0.000428640795999069,0.00075691105539391,0.00223442394481382,0.00329804296659339,0.00429801960310673,0.0126878816844113,0.0162704916489614,0.0307966136668581,0.0480309752054191,0.0187275020245671,0.00324530697192067,0.00031634009421554,2.06176514311654e-05,0.090912519385249,0.0708944335843364,0.134188230407095,0.0184280476338861,0.0697608254083063,0.132042548899737,0.00239506067280158,0.0136000361587716,0.0514840077944083,0.0974483827701124,0.00019684950510622,0.00151018044157206,0.0087655726909473,0.03465523385083,0.130612303435969),
('KSA', 'AUS',0.000904095481729793,0.00117690070474476,0.00100135567499817,0.000582505084448992,0.000256769430673397,0.00274833279884672,0.00492191246451803,0.00440725779617075,0.00263094497270245,0.0107309896537836,0.019217829716887,0.0172083372989296,0.0314247256549317,0.0562776636657584,0.0613496309503655,0.0598856018517915,0.109869340930944,0.0503930481757312,0.0102726435799883,0.00117792147115873,9.10182665386592e-05,0.107247452100118,0.0983809673310516,0.096033233584535,0.0300825009311427,0.0587292423951517,0.0573277454591185,0.00459924763306618,0.0134684777221119,0.0262941400599025,0.0256666646281298,0.000448867691464991,0.00178012204317495,0.00534950317747236,0.0109910690954055,0.017053223477166),
('KSA', 'PER',9.45364177498724e-05,0.000158038707051548,0.000158631910190464,0.000108189325784111,5.57690108561102e-05,0.000510291841462171,0.00106450371391973,0.00111031380954667,0.00077206353818508,0.00308744392028561,0.00644061937232944,0.00671778645543591,0.0140100857779664,0.0292259980097777,0.0423829568134188,0.0641079239883449,0.0884137492880697,0.0304837162742737,0.00467125414001046,0.000402644348293461,2.30875115766423e-05,0.133733518023207,0.0922185667411802,0.139488636741029,0.0211970396489576,0.0641247477176046,0.0969942817002904,0.00243613897806784,0.0110546189440165,0.0334421533685328,0.0505841778711789,0.000175971347428519,0.00107716553650874,0.00498353608387018,0.0156660511073823,0.0387581689229721),
('KSA', 'DEN',0.000129510007567426,0.000215545771126887,0.000218540633733418,0.000150810151264859,7.87134155963813e-05,0.000631632726786177,0.00133556944668547,0.00141201181578704,0.000995219616057272,0.00355278241538844,0.0075122574298157,0.00794222740004908,0.014987660972679,0.0316909831038038,0.0421509970061788,0.0592723091299455,0.0891270850312361,0.0335048414798131,0.00559787136009953,0.000526090897281079,3.30063054559699e-05,0.125329612845171,0.0942283439345546,0.132502951939026,0.0236150116456451,0.0664143849391157,0.0933912418301616,0.00295913496798413,0.0124833177175294,0.0351078322826985,0.0493682815530401,0.000234042433230451,0.00133328277583751,0.0057447315338851,0.0168470122989076,0.0393019036826893),
('KSA', 'ARG',2.01310496674589e-05,4.61190196146527e-05,6.27891021256798e-05,5.82336516705574e-05,4.08639914291378e-05,0.000128917534012193,0.000366692059204354,0.000521508060613429,0.000494457861772629,0.000935557465626366,0.0026610925829693,0.00378459581335985,0.00509202129599249,0.0144837068816668,0.0184764569656741,0.0335210167201263,0.0525542945221312,0.020598673182999,0.00358829190740866,0.000351608051054635,2.30418361568871e-05,0.0953469265597658,0.0747425190297609,0.135602038570254,0.0195302367626657,0.0708656853730785,0.12856847114614,0.00255162759401982,0.0138879144526863,0.0503924549431815,0.0914247968567154,0.000210876792935541,0.00155077942940506,0.00862875789199411,0.0327063500850218,0.115437747115433),
('KSA', 'ICE',0.000586551779944689,0.000824520988244431,0.000746763315412371,0.000462257494247652,0.000216799151187009,0.00195526597870231,0.00372485611740331,0.0035479963458898,0.00225302269826966,0.00822990268808007,0.0156782778953044,0.0149338580952124,0.0259803393731452,0.0494935354577699,0.0546768744440047,0.0575350567216925,0.104161527112753,0.0471435345190601,0.00948318937820481,0.00107302350060171,8.17603220878128e-05,0.109606473149591,0.09921583704983,0.104402251781934,0.0299367425987199,0.063003315423379,0.06629675458591,0.0045164591867118,0.0142576585511372,0.0300059285319353,0.0315744602746078,0.000434600378771989,0.00185758633131692,0.00601562207137153,0.0133147129201228,0.0227414941917101),
('KSA', 'CRO',7.59321955237123e-05,0.000135364303141648,0.000145032707047454,0.000105681141850487,5.82250685654145e-05,0.000414182691103772,0.000924010910627106,0.00103069995595738,0.000766471756983589,0.00255421702000612,0.00569826901338036,0.0063562080853985,0.0118136719473407,0.0263554272266227,0.0364267780918763,0.0561599377428748,0.0812654442564649,0.0293985031662472,0.00472674317177339,0.000427485431762599,2.57680918694625e-05,0.125288662054576,0.0906485939209841,0.139754863255856,0.0218619611306676,0.0674100999511264,0.103927583354963,0.00263625349167266,0.0121931040615248,0.0375967351963162,0.0579636854688322,0.000200263139391502,0.00125020007275233,0.00590118170570881,0.0189438478708524,0.0494460617179709),
('KSA', 'NGA',0.000308757758404001,0.000405109559960444,0.000325369151271497,0.000177400579214857,7.30780300515019e-05,0.0013814822589338,0.00230171706493033,0.00191746995400444,0.00106491253291698,0.0070112780730492,0.0116816399800632,0.00973151479673555,0.0266876501126186,0.0444648632219949,0.0677223811039826,0.0859259036115834,0.112833703984773,0.0370419286263027,0.00540462814015322,0.000443568372634208,2.41720043255949e-05,0.14316298119889,0.0939973207334158,0.119263448647579,0.0205721158525515,0.0522036471552449,0.066235792075109,0.00225119156242263,0.00856891027976104,0.0217444025668422,0.0275892165720097,0.000154484654434276,0.000792778861381954,0.00307377388640656,0.00809033580624198,0.0153609672111756),
('KSA', 'BRA',3.31314993332401e-06,9.51961198991297e-06,1.58560467186127e-05,1.79708649316919e-05,1.54034671844172e-05,2.82793322027352e-05,9.81826809698106e-05,0.000170439647819676,0.000197249136418994,0.000264360083348599,0.000917828665065066,0.00159329927526214,0.00185346280015724,0.00643501570308118,0.00866324524250443,0.0202463783263976,0.0300778192960926,0.0111708276787072,0.00184391900665606,0.000171206739375582,1.06065837306941e-05,0.0702931628570251,0.0522134135814254,0.122025002812526,0.0129279550908344,0.0604263788981791,0.141219057503341,0.00160047017965979,0.0112211038186455,0.0524484086026878,0.12257419632744,0.000124983005449822,0.00118313399311127,0.00847017425992694,0.0412618933377709,0.215071884563759),
('KSA', 'SWI',8.46509035656936e-05,0.000150215003899503,0.00016089916033431,0.000117266525073567,6.46341584428427e-05,0.000447296554799299,0.000998592156683115,0.00111468139502714,0.000829510896318101,0.00269442557431056,0.00601532074504843,0.00671461925146489,0.012173013235184,0.0271763227537239,0.0366639065574936,0.0552140221194914,0.0818523843495812,0.0303356491998181,0.00499680882677853,0.000462971973547902,2.86189558717471e-05,0.123265625088911,0.0913679617474124,0.137595611995781,0.0225748376804297,0.0679931684353704,0.10239433433199,0.00278885118235758,0.0125996140615182,0.0379487858664931,0.0571489865367917,0.000217302810600917,0.00132550248425588,0.00611453609039598,0.0191922677323744,0.0490633117508024),
('KSA', 'CRC',0.000337709814191046,0.000392589851462272,0.000276011363646902,0.00013138082709009,4.71970161203114e-05,0.00159765711938916,0.00231750070576585,0.00168083922890746,0.00081272050429304,0.00828704232054318,0.0120208686792026,0.0087185076540772,0.0322386463226527,0.046764155273973,0.0836108771996687,0.108422337528266,0.121282761218514,0.0339171532917564,0.00421557863205633,0.000294725369954477,1.36066447694494e-05,0.157273322725597,0.0879640823146708,0.114067352747774,0.0163996445664692,0.0425324517115905,0.0551539224300472,0.00152873954248976,0.00594717530373984,0.0154239895505941,0.0200010460013878,8.87783001038092e-05,0.000464853982949318,0.00183697738608809,0.00491482658167421,0.00901989638563606),
('KSA', 'SRB',0.000230645006683659,0.000371629669149729,0.000374146705559096,0.000257062512224965,0.000133732200699417,0.000947617825174236,0.0020005323629572,0.00211167921757026,0.00148600082674211,0.00470827117797942,0.00993971262981951,0.0104919495318654,0.0175449033063375,0.037039348497723,0.043586222400616,0.0541399046203275,0.0920156271600842,0.0390972042758486,0.00738324530959204,0.000784280504836471,5.59250784005373e-05,0.114295688032743,0.0971278901374115,0.120645800860659,0.027512927812997,0.0683494556573712,0.084899144874943,0.0038967241838172,0.0145207543134505,0.0360734291822598,0.0448080128923511,0.000350684529404337,0.00176766506586738,0.00674658152833422,0.0175724712685512,0.0366608084452574),
('KSA', 'GER',9.25656165665694e-06,2.54962240741151e-05,4.20885400735845e-05,4.75040462429947e-05,4.06290084501707e-05,5.94383624574108e-05,0.000206539704395302,0.000358847785571691,0.000415648034314494,0.000449645885591535,0.00156245435528583,0.00271465134073232,0.00255114812307788,0.00886487038714572,0.00964960346862239,0.0182495963796311,0.0335309750393814,0.0154020705951957,0.00314434013247986,0.000361079186027863,2.79438896022008e-05,0.0634147053476339,0.0582576419200848,0.110178460133389,0.0178399884983746,0.0674789571553404,0.127618065986621,0.0027315316852227,0.0154978443152352,0.0586199017248901,0.110863575873508,0.000267003365248327,0.0020515949731856,0.0119455292631558,0.0475551891954284,0.204812928181547),
('KSA', 'MEX',0.00011388993825642,0.000195738106752434,0.000204831468228383,0.000145943903079626,7.86614918484999e-05,0.000562023237707338,0.00122748516856573,0.00134044265250954,0.000975863253140175,0.0032068603506052,0.00700393374140017,0.00764846025251241,0.0137235695318868,0.0299729209220768,0.0391528036991048,0.0558507039272411,0.0855115636222904,0.0327311340724324,0.00556819890023379,0.000532832803615156,3.40645259963474e-05,0.121980562590801,0.0933806371738356,0.133205641144219,0.0238287784375496,0.0679825669543987,0.0969757938247162,0.00304030203161285,0.0130107930396475,0.0371192804224427,0.0529499229939818,0.000245069883210716,0.00141658124263087,0.00619412511305048,0.0184410838986095,0.0443517809375777),
('KSA', 'SWE',9.04426036006911e-05,0.000168261773646896,0.000191362745545442,0.000148423156394243,8.71414409620588e-05,0.000443794434759074,0.00105690223136063,0.00125851322049756,0.000999055213226379,0.00256211166039394,0.00610170231705474,0.00726564180271259,0.0110936770149398,0.0264197364201947,0.0320229784450226,0.046218722030098,0.0762631405951079,0.0314594733365936,0.00576774022092968,0.000594815753314174,4.11206100918285e-05,0.110070488988367,0.0908108317190775,0.131067152162369,0.0249737152779803,0.0720890598276744,0.104046043790292,0.00343398713005401,0.0148688071184305,0.0429202589200567,0.0619467523888075,0.000299525574559277,0.00175355749021226,0.0077711431982594,0.0234867082870573,0.0600175693397711),
('KSA', 'KOR',0.000556626094784477,0.000719374790037828,0.000587029619970727,0.000326201944756857,0.000137126775052802,0.00205179844683334,0.00349534461130106,0.00297725002438891,0.00169063305500023,0.00907796080099746,0.0154647750203439,0.0131725214325433,0.0301233434130556,0.0513166711066079,0.0666387410324705,0.0737089795362495,0.113522536646433,0.0437102996396215,0.00748002350126025,0.000720020178834175,4.63162282901398e-05,0.125567053052486,0.0966957518027034,0.106954979647572,0.024820917561291,0.0549087355568341,0.0607344437006686,0.00318565157775205,0.0105709287112612,0.0233849666420871,0.0258660653093309,0.000258387310012078,0.00115820219170449,0.00392741477808756,0.00906882219826348,0.0153618753012223),
('KSA', 'BEL',4.06194533918646e-05,9.71077845324604e-05,0.000144677191604745,0.000147989665297138,0.000114909811441546,0.000188969276161056,0.000597726529147529,0.000945330931315655,0.000996721773948628,0.00111498108935695,0.00352678377218554,0.00557776445469799,0.00493406727886835,0.0156068910729424,0.0145563125956633,0.021471761977196,0.046042903808219,0.024682988211964,0.0058809873852502,0.000788179775267168,7.18144366488814e-05,0.0679170816655552,0.0728188879278568,0.107413867265868,0.026024824727508,0.0767775270522536,0.113253186562843,0.00465052077400935,0.0205797054315701,0.0607135266824553,0.0895574607342664,0.000536148079849161,0.00322171169676325,0.0146954536974707,0.0460219077774596,0.146667893736182),
('KSA', 'TUN',0.000539264023074218,0.000680267897994562,0.000538509868147844,0.000289991212400256,0.0001180868352951,0.00205249492745817,0.00338484679231452,0.00279103925036994,0.00153426936285427,0.00924064136271976,0.0152390901712318,0.0125656791629116,0.0312020696076048,0.0514565097394268,0.0702382089137637,0.0790557494014933,0.115832479271506,0.0424294354166557,0.00690751180247865,0.000632555367363942,3.86278361769901e-05,0.130373817833038,0.0955118550264936,0.107502189942184,0.0233239940406966,0.0525040674139945,0.0590953052509642,0.00284785955554707,0.00961611955241044,0.0216466094254631,0.0243640741499043,0.000219188840252822,0.000999122519405522,0.00344385241128835,0.00807509791464607,0.0137002316193552),
('KSA', 'PAN',0.00149771886227677,0.00145006452502258,0.000901506332618838,0.000380658441464417,0.000121462433338417,0.00480064954801496,0.0061974483973864,0.00400033019012489,0.0017214226031876,0.018382268088361,0.0237307799215726,0.0153177484132842,0.0527909468332412,0.0681510211432667,0.101071462061661,0.0967537149461527,0.130479253757348,0.0439901343078906,0.00659153547216194,0.000555572097413415,3.11233490796419e-05,0.124905213271016,0.0842217739498417,0.0806238412187104,0.0189298402671349,0.0362423246250301,0.0346940617482787,0.00212735279568351,0.00610941847832057,0.0116968513540957,0.0111971648435367,0.000150104597589533,0.000581352439844175,0.00170147210453022,0.00338216971642023,0.00451896891094668),
('KSA', 'ENG',1.28969897708897e-05,2.84972170085205e-05,3.651160048719e-05,3.17452193199495e-05,2.08504208861336e-05,9.93779451623232e-05,0.000263939302065713,0.000350500078569481,0.000310299386047545,0.00082035527021667,0.00217879326356818,0.00289334405331257,0.00507896471601184,0.0134892948346932,0.020963179834415,0.0432622525792123,0.0556764083372417,0.017913205280166,0.00256149124711032,0.000206032141364548,1.09954490851577e-05,0.114900833710104,0.0739358835305671,0.152583381587724,0.0158586458047748,0.0654557892256502,0.135082793193564,0.00170077528496245,0.0105298008994417,0.0434612410634962,0.0896920793098504,0.000114282793066072,0.000953696901616856,0.00601213785885039,0.0257197373508107,0.0973477644258117),
('KSA', 'POL',0.00020619459041188,0.000387291611895809,0.000468995985457438,0.000390268802656149,0.000246610683006532,0.000727641814964728,0.00187410819181367,0.00241346871660568,0.00207203663462541,0.00339383684021211,0.00874113786908857,0.011256800907618,0.0118720450032197,0.0305775401255534,0.0276865504922474,0.0322836157524622,0.0713092486075704,0.0393776287015532,0.00966430752086684,0.0013341800700657,0.000125449735747146,0.0831494115631424,0.0918317530817022,0.107079465576429,0.0338068973891847,0.0788403658592979,0.0919309929164999,0.00622282746832689,0.0217681907615309,0.0507651472413179,0.0591941747172433,0.000740631563434371,0.00352001485912831,0.0127039892154215,0.0315064457121594,0.0701813767032672),
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('TUN', 'COL',0.00015989231402061,0.000227971217983027,0.000195061932418985,0.000113180090089991,4.95938052206605e-05,0.000832722440421581,0.00147482107551172,0.00130601572576354,0.000771021020453771,0.00478004273578549,0.0084658553995664,0.00749686892020957,0.0205790135885806,0.0364471539139925,0.0590644371033912,0.0847612961458049,0.104608057168745,0.03227547867424,0.00442586058578187,0.000341385675295387,1.7445413378233e-05,0.150119343343547,0.0926348083658551,0.132936955135349,0.0190541905523365,0.0546879972934665,0.078480821312125,0.00195964229866411,0.00843664120219194,0.0242142540751932,0.0347490243075452,0.000126048597747593,0.000731124283180262,0.00320261167298048,0.00951310666144934,0.0207433302663243),
('TUN', 'JPN',0.00306904951365818,0.00315990027974586,0.00223680012411807,0.00108372084343399,0.000398049847153434,0.00702137521347358,0.0104849093277428,0.00782847521095527,0.00389671302585852,0.0219006639236009,0.0327039174627335,0.0244181231477806,0.0512334548836798,0.0765061134991863,0.0799022017011216,0.0623065714695772,0.119316702846994,0.0571226888372004,0.0121543999275644,0.00145472508982141,0.000117597859871063,0.0930414245812473,0.0890868791796882,0.0694686490038599,0.0284334713549946,0.0443440124900408,0.0345788141582557,0.00453749362835255,0.0106148140487497,0.0165545543447875,0.0129090000209096,0.000463505686264255,0.00146944263395097,0.00353166899738102,0.00580956743331933,0.00683556763372846),
('PAN', 'ENG',2.23777855011489e-05,4.17052301893845e-05,4.47958051698894e-05,3.25708908187221e-05,1.78718730797019e-05,0.000171739726940876,0.000380470030653895,0.00042144425988173,0.00031122077058562,0.00137529250686195,0.00304680571912158,0.00337492753132689,0.00826001144197312,0.018299125441203,0.0330731044152947,0.066212392279913,0.0732697394764035,0.0202698261537088,0.00249225733259467,0.000172368645033429,7.86954536431148e-06,0.146686100933879,0.0811604295673161,0.162483271380846,0.014968505958449,0.0599339315668658,0.119987798482794,0.00138032888577978,0.00829025995076654,0.0331942195126236,0.0664548648411416,7.92654764284629e-05,0.000640704305217058,0.00390822317200242,0.0161378819261683,0.0532895998226203),
('PAN', 'POL',0.000343029405432512,0.000546773213620006,0.000557085084850624,0.00038840036963521,0.000205271594168296,0.00122664066663972,0.0026352996430967,0.00283082258634483,0.00202723474889644,0.00555012824993319,0.0119238269152263,0.012808501164454,0.0188343198797726,0.0404634199786497,0.0426094184205403,0.0481982505799566,0.0915415477491164,0.043465555613903,0.00917254184953669,0.00108882151789306,8.72570170637328e-05,0.103548525664975,0.0983333647222955,0.111231186177626,0.0311269541036019,0.0704193950205618,0.0796559017396454,0.00492654387706546,0.0167181908424373,0.0378220394145954,0.0427829386253931,0.000498846774373709,0.00229380376366817,0.00799533141959692,0.0190704298956562,0.0369890492422174),
('PAN', 'SEN',0.000492746717820627,0.000544886540695813,0.000368984813903937,0.000169270265897704,5.8618405444147e-05,0.00212879539815654,0.00297781677521301,0.00208272545929469,0.000971124298154978,0.0102592540195709,0.0143509229431373,0.0100372302375489,0.0370816377407243,0.0518708012110147,0.0893533360165185,0.107654612143958,0.124989871333641,0.036279142214338,0.00468011668381897,0.000339608755774167,1.6292718299643e-05,0.150590193049522,0.0874196120283194,0.105324824413318,0.0169160828967322,0.0407616419135394,0.0491104075816683,0.00163666855714492,0.0059156689581822,0.0142546227294172,0.0171742427267647,9.87915771880904e-05,0.000480786775350314,0.0017663072351752,0.00439591973778256,0.00744418289236164),
('PAN', 'COL',3.84014800220745e-05,6.65904750302618e-05,6.71760396952427e-05,4.58919874648177e-05,2.36635071683907e-05,0.000266476212753711,0.000554904752815389,0.000577761297181337,0.000401039535556373,0.00195404232320054,0.00406905877692193,0.00423666343698385,0.0107465916790516,0.0223784882622286,0.0394018189353922,0.0722323589554322,0.0820495622137516,0.0233002589034302,0.0029407811588726,0.000208779183371977,9.7925686240495e-06,0.150415224222967,0.0854291862833425,0.156610832078772,0.0161733315377165,0.0592986781170243,0.108707758145757,0.00153095601403321,0.00841975578832684,0.0308705962745904,0.0565927170755943,9.03347061837832e-05,0.000668787142682985,0.00373715379123382,0.0141421100006404,0.0416777945105515),
('PAN', 'JPN',0.000850860752178634,0.00109551865394287,0.000915672132881195,0.000522754207172638,0.000226052939646034,0.00267929059480571,0.0047041725917641,0.00412968265107303,0.00241690094852268,0.0106757630506439,0.0187440033702834,0.0164549203967208,0.0319035709306175,0.0560147914683722,0.0635606612408876,0.063315132750575,0.111596823839658,0.049174070044837,0.00963025880556853,0.00106087029352946,7.85913153840217e-05,0.11116573644777,0.0979682310407459,0.0975897895425547,0.0287791742310287,0.0573360063069119,0.0571145230373195,0.00422708902988885,0.0126322806206307,0.0251669667628706,0.0250697492822088,0.000395359976144636,0.00159905213029002,0.00489877649775301,0.0102506156075699,0.0160404439269428),
('ENG', 'POL',0.0184621956964289,0.0115289455049573,0.00546187420657279,0.00176858462028305,0.000433898107222962,0.0273033674482566,0.0272092136546924,0.01355769227202,0.00450364649944206,0.0600483030200366,0.0598412305577426,0.0298174360853297,0.0990481861555975,0.0987066252660891,0.108918350586753,0.059886039083556,0.108542753110877,0.0491831211129631,0.00990487089939128,0.00112202899569766,8.5598010203284e-05,0.059679526177339,0.0540842254285918,0.029736862708371,0.016337838828896,0.0179659065598649,0.00987810572432994,0.00246767865753581,0.00407037472752969,0.00447598809024212,0.00246101043823896,0.000237745368974215,0.000530975478529967,0.000898492849123713,0.0010391705449285,0.000780881365050145),
('ENG', 'SEN',0.0187083344220117,0.0081627339805564,0.00256783519978617,0.000546830083827601,8.7879826334736e-05,0.0335675681833608,0.0217806613035131,0.00706630287048185,0.00152834772588918,0.0786322322619764,0.0510213313363058,0.0165528573744173,0.138147361094055,0.0896383338086211,0.161805516035438,0.094757600913091,0.104989170575513,0.0290813766703632,0.00358016382666506,0.000247921028935957,1.13335725599457e-05,0.0614844423685287,0.0340616507033051,0.019947405891144,0.00628991662465288,0.0073670839400177,0.00431435971397012,0.000580756515438723,0.00102031924743963,0.0011950520157997,0.00069985415057625,3.33934358359637e-05,7.89580166368221e-05,0.000140890884302531,0.000170185794369637,0.000121604275538285),
('ENG', 'COL',0.00267052224537788,0.00194441536999925,0.000911184895212354,0.000288958103169717,6.91442790451802e-05,0.00818934580230116,0.00791036165770345,0.00382044079382275,0.00123009696991464,0.0291892941662231,0.0281949106756534,0.0136172012841607,0.0780295747509126,0.0753713631317269,0.139060449152708,0.123913327608688,0.134323115867387,0.0364018540307898,0.0043844359701759,0.000297047900435947,1.32766638805767e-05,0.119692007061026,0.0648735839926352,0.0578072465277421,0.0117205874293225,0.0208878512725216,0.0186126477624936,0.00105876815518615,0.00283032636686991,0.00504406767667382,0.00449464397899153,5.95138127780459e-05,0.000214071041133352,0.000581019993306905,0.00106717374579176,0.00122577239531734),
('ENG', 'JPN',0.0336653016534908,0.0164963929979039,0.00641019716749126,0.00169947673530822,0.000341127206288074,0.0425667920703919,0.03466741681627,0.0141169880352453,0.00383240459369388,0.082442071036456,0.0671428007797968,0.0273413539948673,0.119753475882217,0.09753010414176,0.115967448865044,0.056150558876027,0.0944466728964608,0.0397154284826693,0.00742248490868817,0.000780300564102094,5.50388057944189e-05,0.045730362433765,0.0384598182874222,0.018621952213697,0.0107817326314577,0.0104408662746867,0.00505538823360488,0.00151126245146947,0.00219522543840172,0.00212582300346322,0.00102930736930227,0.000134519392573044,0.000264272701231036,0.000393076434497778,0.000398899190043957,0.000253158067481132),
('POL', 'SEN',0.0114679168062028,0.00846796637814816,0.00461149491085485,0.00171855011668212,0.000485489289020544,0.0189695072671213,0.0217844139462314,0.0125085139086161,0.00478822215394381,0.0458219762822281,0.0526215512669948,0.0302150614444244,0.0830142876476399,0.0953328718612597,0.100262981692446,0.0605478031717205,0.115141119161137,0.0547397123727156,0.0115662362170226,0.00137468796631343,0.000110311901063894,0.069532560295535,0.0661135201541584,0.0399252878551202,0.0209542001070971,0.0253080601093065,0.0152832821874059,0.00332064077895459,0.00601590440143311,0.00726588795683705,0.00438779643745114,0.000336687705590155,0.000826527698379096,0.00153809991944227,0.00195870731818608,0.00167219432145355),
('POL', 'COL',0.00126431892537162,0.00155021296911538,0.001258195007329,0.000698425229643447,0.000293824299865103,0.00356193089423025,0.00608935463952059,0.00520507570569588,0.00296613951240854,0.0130917261894836,0.022381165154102,0.0191310353732255,0.0360885639790291,0.0616957686785292,0.0663209944570263,0.0609400017734822,0.11338009278863,0.0527364828792641,0.0109019201914227,0.00126770254236316,9.94098493268571e-05,0.10418093263203,0.0969153549792435,0.0890520709568016,0.0300521595569626,0.0552277200211447,0.0507467866486036,0.00465938701989257,0.0128440347847729,0.0236038530173097,0.0216887405943213,0.000461556552951484,0.00172343275128294,0.00487707676399098,0.00943930917007925,0.0135924479708831),
('POL', 'JPN',0.0194188259873626,0.0157813906254145,0.0104950442778384,0.0048399431923703,0.00170147180288478,0.0214067158877229,0.0308559735763712,0.0222381403653737,0.0106848005878246,0.0427528486282354,0.0616246216610927,0.0444133445691004,0.0640385268138123,0.0923061296090719,0.0639479090955819,0.0319287098029775,0.0921755118191253,0.0665257461979046,0.0213393621212177,0.00385030948156546,0.000482474744352444,0.0460225394299421,0.0664316089398513,0.0331688024453631,0.031963748784841,0.0319185184816925,0.0159366760908426,0.00768972220216342,0.0115182612909865,0.0115019623751916,0.00574284326158883,0.00122452874917547,0.00250685216512328,0.00391216320272281,0.00423372054298345,0.00337030974281601),
('SEN', 'COL',0.000200160137633604,0.000259976125914611,0.000202138048931571,0.000106436688680065,4.23017078458978e-05,0.00103892076626562,0.00166752366202697,0.00133823254559382,0.000715978384342531,0.00586465469604854,0.00941308595691141,0.0075542544126173,0.0248292573076915,0.0398522923167234,0.0700799450724048,0.0988994282126574,0.112482077975818,0.0319825354261721,0.00404166143400596,0.000287295945985011,1.34927810310097e-05,0.158738897190722,0.0902698899998137,0.127392230353167,0.0171112293726555,0.0482960082010002,0.0681571252816741,0.00162177095066933,0.00686611067748617,0.0193794221541305,0.0273489622195767,9.58180895519285e-05,0.000546097976042108,0.00234918320672911,0.00684371462819793,0.0141045476792737),
('SEN', 'JPN',0.00368475384996407,0.00346040731601935,0.00222920610205188,0.00098089747271618,0.000326932073479013,0.0084508523601196,0.011436510234931,0.00773849552566493,0.00349082670445099,0.0259217382902355,0.0350798017324829,0.023736689179789,0.0596333204646667,0.0807015731401829,0.0914581902860837,0.0701336141038935,0.12377006303069,0.0546065845114155,0.0107075940264164,0.00118103102600112,8.76084458535156e-05,0.0949115854037386,0.0837488061741803,0.0642217655467371,0.0246329693955506,0.0377789930987391,0.0289703668412055,0.00362263779605494,0.00833392877440378,0.0127815462520103,0.00980137513858018,0.000339276792118488,0.0010563719582828,0.00249138070209681,0.00401341175910841,0.00447601916078209),
('COL', 'JPN',0.0254736621106176,0.0139354215558308,0.00590624749150027,0.00170894492765462,0.000374465009609126,0.0348979039850724,0.0310383548328686,0.013802827114533,0.00409209911155764,0.0719063100061187,0.0639537997940173,0.0284404004860298,0.111120944847599,0.0988314747217687,0.114481046079474,0.0589713754208319,0.101819964069264,0.0439505819945818,0.00843167393139975,0.00090988289934348,6.59631529938957e-05,0.0524494100298124,0.0452795700166344,0.0233243721453341,0.0130299493024226,0.013423951970078,0.00691493429147325,0.00187479230435858,0.00289722407165109,0.0029848310136684,0.00153754351748836,0.000171560878445957,0.000358708860180815,0.000567979546015193,0.000613971460457921,0.000421440740167786)
] | estimations_data.py |
# 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.
# Coded by <NAME> for project 301060 (https://301060.exactas.uba.ar/)
# The data provided here is based on the work of <NAME>, <NAME>,
# <NAME> and <NAME>.
accumulated = {'ARG': {'BEL': [0.0071201329124175325,
0.01353150847439597,
0.017735705111119555,
0.01962846487528751,
0.020275310188803252,
0.03275710479838004,
0.050133697087757,
0.062229151669246606,
0.06784206737011222,
0.10026774667674178,
0.14540931699108386,
0.17683134752396573,
0.24000884190984886,
0.32796170415015397,
0.41002414935259446,
0.4633203822759146,
0.5775640224859575,
0.6387860292421373,
0.6533674744737203,
0.6553209865835445,
0.6554989100828535,
0.7296955251559486,
0.8092179504032382,
0.8608645432893203,
0.8892747167677233,
0.9261772297104369,
0.9501439165475021,
0.9552188243773564,
0.9651066653659011,
0.9779501694836301,
0.9862915049790749,
0.9868763490282665,
0.9884236499131996,
0.9915310840934048,
0.9958156790753416,
1.0],
'BRA': [0.0008929590028715159,
0.0019196827511384473,
0.0026723657866933996,
0.003047827629658679,
0.0031894701333307404,
0.006240757295654328,
0.010903324086370448,
0.01446567792614047,
0.016280181317275825,
0.028838906627776436,
0.04802946210531313,
0.06269167544429294,
0.10145931014255466,
0.16069879711404025,
0.24048022200335534,
0.32257287231476095,
0.44448411908239177,
0.48974502865243263,
0.4972133030074715,
0.4979064730372883,
0.4979493998096058,
0.6233923505190876,
0.7165365385870331,
0.8123790642151473,
0.8354329430813779,
0.8828764180183728,
0.9316943051764934,
0.9345473083632898,
0.9433542678758308,
0.9614784527615424,
0.9801276856383202,
0.980350394791875,
0.9812786229798174,
0.9842044291095868,
0.9904796858333647,
1.0],
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prob_ivan = [
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('KSA', 'MAR',8.0664097612042e-05,0.000137584917535734,0.000140357101270082,9.72676707047222e-05,5.09421934360866e-05,0.000450146059845246,0.00095394289102777,0.00101079085270153,0.000714017689119257,0.00279966692776387,0.00593301286232309,0.00628657667728469,0.0130593194169734,0.0276751171025812,0.0406109793378781,0.0631446245444461,0.0860621884602589,0.0293243499981368,0.00444080685889554,0.000378283939016125,2.14260627940423e-05,0.133815157043695,0.0911908602467614,0.141789553614534,0.0207145766749203,0.0644167753676894,0.100159443610433,0.00235272309999067,0.0109745063449424,0.0341277700764868,0.0530641039234672,0.00016785701123051,0.00105606878658799,0.00502134344288341,0.0162187809995,0.0414838346268935),
('KSA', 'IRN',4.40945012914024e-05,5.92815150352852e-05,4.52988478022193e-05,2.33308456268765e-05,9.05228525999337e-06,0.000349584885903483,0.000546178591179943,0.000426664689310493,0.000222201748712683,0.0027536237291249,0.00430216061866111,0.00336076890117455,0.0162673872942261,0.0254155686724739,0.0640677922165853,0.126162914955755,0.1000971663933,0.0198541756971173,0.00175024731497466,8.67899635849245e-05,2.81605055509957e-06,0.197112306419087,0.0781940689176295,0.153980515413258,0.0103398117296224,0.0407225141610284,0.0801911680289234,0.000683630536714097,0.00403863555839639,0.0159058402627256,0.0313219342034168,2.7850449722519e-05,0.000220840358053607,0.00131919531241795,0.00531147594843879,0.0147772901402103),
('KSA', 'FRA',1.48855993378956e-05,3.55530420306222e-05,5.02471281562042e-05,4.8370748480799e-05,3.5229891884974e-05,9.9972964065253e-05,0.000295123162952298,0.000435606176756544,0.000428640795999069,0.00075691105539391,0.00223442394481382,0.00329804296659339,0.00429801960310673,0.0126878816844113,0.0162704916489614,0.0307966136668581,0.0480309752054191,0.0187275020245671,0.00324530697192067,0.00031634009421554,2.06176514311654e-05,0.090912519385249,0.0708944335843364,0.134188230407095,0.0184280476338861,0.0697608254083063,0.132042548899737,0.00239506067280158,0.0136000361587716,0.0514840077944083,0.0974483827701124,0.00019684950510622,0.00151018044157206,0.0087655726909473,0.03465523385083,0.130612303435969),
('KSA', 'AUS',0.000904095481729793,0.00117690070474476,0.00100135567499817,0.000582505084448992,0.000256769430673397,0.00274833279884672,0.00492191246451803,0.00440725779617075,0.00263094497270245,0.0107309896537836,0.019217829716887,0.0172083372989296,0.0314247256549317,0.0562776636657584,0.0613496309503655,0.0598856018517915,0.109869340930944,0.0503930481757312,0.0102726435799883,0.00117792147115873,9.10182665386592e-05,0.107247452100118,0.0983809673310516,0.096033233584535,0.0300825009311427,0.0587292423951517,0.0573277454591185,0.00459924763306618,0.0134684777221119,0.0262941400599025,0.0256666646281298,0.000448867691464991,0.00178012204317495,0.00534950317747236,0.0109910690954055,0.017053223477166),
('KSA', 'PER',9.45364177498724e-05,0.000158038707051548,0.000158631910190464,0.000108189325784111,5.57690108561102e-05,0.000510291841462171,0.00106450371391973,0.00111031380954667,0.00077206353818508,0.00308744392028561,0.00644061937232944,0.00671778645543591,0.0140100857779664,0.0292259980097777,0.0423829568134188,0.0641079239883449,0.0884137492880697,0.0304837162742737,0.00467125414001046,0.000402644348293461,2.30875115766423e-05,0.133733518023207,0.0922185667411802,0.139488636741029,0.0211970396489576,0.0641247477176046,0.0969942817002904,0.00243613897806784,0.0110546189440165,0.0334421533685328,0.0505841778711789,0.000175971347428519,0.00107716553650874,0.00498353608387018,0.0156660511073823,0.0387581689229721),
('KSA', 'DEN',0.000129510007567426,0.000215545771126887,0.000218540633733418,0.000150810151264859,7.87134155963813e-05,0.000631632726786177,0.00133556944668547,0.00141201181578704,0.000995219616057272,0.00355278241538844,0.0075122574298157,0.00794222740004908,0.014987660972679,0.0316909831038038,0.0421509970061788,0.0592723091299455,0.0891270850312361,0.0335048414798131,0.00559787136009953,0.000526090897281079,3.30063054559699e-05,0.125329612845171,0.0942283439345546,0.132502951939026,0.0236150116456451,0.0664143849391157,0.0933912418301616,0.00295913496798413,0.0124833177175294,0.0351078322826985,0.0493682815530401,0.000234042433230451,0.00133328277583751,0.0057447315338851,0.0168470122989076,0.0393019036826893),
('KSA', 'ARG',2.01310496674589e-05,4.61190196146527e-05,6.27891021256798e-05,5.82336516705574e-05,4.08639914291378e-05,0.000128917534012193,0.000366692059204354,0.000521508060613429,0.000494457861772629,0.000935557465626366,0.0026610925829693,0.00378459581335985,0.00509202129599249,0.0144837068816668,0.0184764569656741,0.0335210167201263,0.0525542945221312,0.020598673182999,0.00358829190740866,0.000351608051054635,2.30418361568871e-05,0.0953469265597658,0.0747425190297609,0.135602038570254,0.0195302367626657,0.0708656853730785,0.12856847114614,0.00255162759401982,0.0138879144526863,0.0503924549431815,0.0914247968567154,0.000210876792935541,0.00155077942940506,0.00862875789199411,0.0327063500850218,0.115437747115433),
('KSA', 'ICE',0.000586551779944689,0.000824520988244431,0.000746763315412371,0.000462257494247652,0.000216799151187009,0.00195526597870231,0.00372485611740331,0.0035479963458898,0.00225302269826966,0.00822990268808007,0.0156782778953044,0.0149338580952124,0.0259803393731452,0.0494935354577699,0.0546768744440047,0.0575350567216925,0.104161527112753,0.0471435345190601,0.00948318937820481,0.00107302350060171,8.17603220878128e-05,0.109606473149591,0.09921583704983,0.104402251781934,0.0299367425987199,0.063003315423379,0.06629675458591,0.0045164591867118,0.0142576585511372,0.0300059285319353,0.0315744602746078,0.000434600378771989,0.00185758633131692,0.00601562207137153,0.0133147129201228,0.0227414941917101),
('KSA', 'CRO',7.59321955237123e-05,0.000135364303141648,0.000145032707047454,0.000105681141850487,5.82250685654145e-05,0.000414182691103772,0.000924010910627106,0.00103069995595738,0.000766471756983589,0.00255421702000612,0.00569826901338036,0.0063562080853985,0.0118136719473407,0.0263554272266227,0.0364267780918763,0.0561599377428748,0.0812654442564649,0.0293985031662472,0.00472674317177339,0.000427485431762599,2.57680918694625e-05,0.125288662054576,0.0906485939209841,0.139754863255856,0.0218619611306676,0.0674100999511264,0.103927583354963,0.00263625349167266,0.0121931040615248,0.0375967351963162,0.0579636854688322,0.000200263139391502,0.00125020007275233,0.00590118170570881,0.0189438478708524,0.0494460617179709),
('KSA', 'NGA',0.000308757758404001,0.000405109559960444,0.000325369151271497,0.000177400579214857,7.30780300515019e-05,0.0013814822589338,0.00230171706493033,0.00191746995400444,0.00106491253291698,0.0070112780730492,0.0116816399800632,0.00973151479673555,0.0266876501126186,0.0444648632219949,0.0677223811039826,0.0859259036115834,0.112833703984773,0.0370419286263027,0.00540462814015322,0.000443568372634208,2.41720043255949e-05,0.14316298119889,0.0939973207334158,0.119263448647579,0.0205721158525515,0.0522036471552449,0.066235792075109,0.00225119156242263,0.00856891027976104,0.0217444025668422,0.0275892165720097,0.000154484654434276,0.000792778861381954,0.00307377388640656,0.00809033580624198,0.0153609672111756),
('KSA', 'BRA',3.31314993332401e-06,9.51961198991297e-06,1.58560467186127e-05,1.79708649316919e-05,1.54034671844172e-05,2.82793322027352e-05,9.81826809698106e-05,0.000170439647819676,0.000197249136418994,0.000264360083348599,0.000917828665065066,0.00159329927526214,0.00185346280015724,0.00643501570308118,0.00866324524250443,0.0202463783263976,0.0300778192960926,0.0111708276787072,0.00184391900665606,0.000171206739375582,1.06065837306941e-05,0.0702931628570251,0.0522134135814254,0.122025002812526,0.0129279550908344,0.0604263788981791,0.141219057503341,0.00160047017965979,0.0112211038186455,0.0524484086026878,0.12257419632744,0.000124983005449822,0.00118313399311127,0.00847017425992694,0.0412618933377709,0.215071884563759),
('KSA', 'SWI',8.46509035656936e-05,0.000150215003899503,0.00016089916033431,0.000117266525073567,6.46341584428427e-05,0.000447296554799299,0.000998592156683115,0.00111468139502714,0.000829510896318101,0.00269442557431056,0.00601532074504843,0.00671461925146489,0.012173013235184,0.0271763227537239,0.0366639065574936,0.0552140221194914,0.0818523843495812,0.0303356491998181,0.00499680882677853,0.000462971973547902,2.86189558717471e-05,0.123265625088911,0.0913679617474124,0.137595611995781,0.0225748376804297,0.0679931684353704,0.10239433433199,0.00278885118235758,0.0125996140615182,0.0379487858664931,0.0571489865367917,0.000217302810600917,0.00132550248425588,0.00611453609039598,0.0191922677323744,0.0490633117508024),
('KSA', 'CRC',0.000337709814191046,0.000392589851462272,0.000276011363646902,0.00013138082709009,4.71970161203114e-05,0.00159765711938916,0.00231750070576585,0.00168083922890746,0.00081272050429304,0.00828704232054318,0.0120208686792026,0.0087185076540772,0.0322386463226527,0.046764155273973,0.0836108771996687,0.108422337528266,0.121282761218514,0.0339171532917564,0.00421557863205633,0.000294725369954477,1.36066447694494e-05,0.157273322725597,0.0879640823146708,0.114067352747774,0.0163996445664692,0.0425324517115905,0.0551539224300472,0.00152873954248976,0.00594717530373984,0.0154239895505941,0.0200010460013878,8.87783001038092e-05,0.000464853982949318,0.00183697738608809,0.00491482658167421,0.00901989638563606),
('KSA', 'SRB',0.000230645006683659,0.000371629669149729,0.000374146705559096,0.000257062512224965,0.000133732200699417,0.000947617825174236,0.0020005323629572,0.00211167921757026,0.00148600082674211,0.00470827117797942,0.00993971262981951,0.0104919495318654,0.0175449033063375,0.037039348497723,0.043586222400616,0.0541399046203275,0.0920156271600842,0.0390972042758486,0.00738324530959204,0.000784280504836471,5.59250784005373e-05,0.114295688032743,0.0971278901374115,0.120645800860659,0.027512927812997,0.0683494556573712,0.084899144874943,0.0038967241838172,0.0145207543134505,0.0360734291822598,0.0448080128923511,0.000350684529404337,0.00176766506586738,0.00674658152833422,0.0175724712685512,0.0366608084452574),
('KSA', 'GER',9.25656165665694e-06,2.54962240741151e-05,4.20885400735845e-05,4.75040462429947e-05,4.06290084501707e-05,5.94383624574108e-05,0.000206539704395302,0.000358847785571691,0.000415648034314494,0.000449645885591535,0.00156245435528583,0.00271465134073232,0.00255114812307788,0.00886487038714572,0.00964960346862239,0.0182495963796311,0.0335309750393814,0.0154020705951957,0.00314434013247986,0.000361079186027863,2.79438896022008e-05,0.0634147053476339,0.0582576419200848,0.110178460133389,0.0178399884983746,0.0674789571553404,0.127618065986621,0.0027315316852227,0.0154978443152352,0.0586199017248901,0.110863575873508,0.000267003365248327,0.0020515949731856,0.0119455292631558,0.0475551891954284,0.204812928181547),
('KSA', 'MEX',0.00011388993825642,0.000195738106752434,0.000204831468228383,0.000145943903079626,7.86614918484999e-05,0.000562023237707338,0.00122748516856573,0.00134044265250954,0.000975863253140175,0.0032068603506052,0.00700393374140017,0.00764846025251241,0.0137235695318868,0.0299729209220768,0.0391528036991048,0.0558507039272411,0.0855115636222904,0.0327311340724324,0.00556819890023379,0.000532832803615156,3.40645259963474e-05,0.121980562590801,0.0933806371738356,0.133205641144219,0.0238287784375496,0.0679825669543987,0.0969757938247162,0.00304030203161285,0.0130107930396475,0.0371192804224427,0.0529499229939818,0.000245069883210716,0.00141658124263087,0.00619412511305048,0.0184410838986095,0.0443517809375777),
('KSA', 'SWE',9.04426036006911e-05,0.000168261773646896,0.000191362745545442,0.000148423156394243,8.71414409620588e-05,0.000443794434759074,0.00105690223136063,0.00125851322049756,0.000999055213226379,0.00256211166039394,0.00610170231705474,0.00726564180271259,0.0110936770149398,0.0264197364201947,0.0320229784450226,0.046218722030098,0.0762631405951079,0.0314594733365936,0.00576774022092968,0.000594815753314174,4.11206100918285e-05,0.110070488988367,0.0908108317190775,0.131067152162369,0.0249737152779803,0.0720890598276744,0.104046043790292,0.00343398713005401,0.0148688071184305,0.0429202589200567,0.0619467523888075,0.000299525574559277,0.00175355749021226,0.0077711431982594,0.0234867082870573,0.0600175693397711),
('KSA', 'KOR',0.000556626094784477,0.000719374790037828,0.000587029619970727,0.000326201944756857,0.000137126775052802,0.00205179844683334,0.00349534461130106,0.00297725002438891,0.00169063305500023,0.00907796080099746,0.0154647750203439,0.0131725214325433,0.0301233434130556,0.0513166711066079,0.0666387410324705,0.0737089795362495,0.113522536646433,0.0437102996396215,0.00748002350126025,0.000720020178834175,4.63162282901398e-05,0.125567053052486,0.0966957518027034,0.106954979647572,0.024820917561291,0.0549087355568341,0.0607344437006686,0.00318565157775205,0.0105709287112612,0.0233849666420871,0.0258660653093309,0.000258387310012078,0.00115820219170449,0.00392741477808756,0.00906882219826348,0.0153618753012223),
('KSA', 'BEL',4.06194533918646e-05,9.71077845324604e-05,0.000144677191604745,0.000147989665297138,0.000114909811441546,0.000188969276161056,0.000597726529147529,0.000945330931315655,0.000996721773948628,0.00111498108935695,0.00352678377218554,0.00557776445469799,0.00493406727886835,0.0156068910729424,0.0145563125956633,0.021471761977196,0.046042903808219,0.024682988211964,0.0058809873852502,0.000788179775267168,7.18144366488814e-05,0.0679170816655552,0.0728188879278568,0.107413867265868,0.026024824727508,0.0767775270522536,0.113253186562843,0.00465052077400935,0.0205797054315701,0.0607135266824553,0.0895574607342664,0.000536148079849161,0.00322171169676325,0.0146954536974707,0.0460219077774596,0.146667893736182),
('KSA', 'TUN',0.000539264023074218,0.000680267897994562,0.000538509868147844,0.000289991212400256,0.0001180868352951,0.00205249492745817,0.00338484679231452,0.00279103925036994,0.00153426936285427,0.00924064136271976,0.0152390901712318,0.0125656791629116,0.0312020696076048,0.0514565097394268,0.0702382089137637,0.0790557494014933,0.115832479271506,0.0424294354166557,0.00690751180247865,0.000632555367363942,3.86278361769901e-05,0.130373817833038,0.0955118550264936,0.107502189942184,0.0233239940406966,0.0525040674139945,0.0590953052509642,0.00284785955554707,0.00961611955241044,0.0216466094254631,0.0243640741499043,0.000219188840252822,0.000999122519405522,0.00344385241128835,0.00807509791464607,0.0137002316193552),
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('TUN', 'ENG',9.6003018745251e-05,0.000147482572057539,0.000134371909111705,8.29834888854739e-05,3.86951806213476e-05,0.000554483997409941,0.00104476297107405,0.00098427517369855,0.000618192926294952,0.00347591636353222,0.00654934808602597,0.00617016577296803,0.0163422136022511,0.0307921233376761,0.0512225492004659,0.0802752188428358,0.0965139173333082,0.0290093766584978,0.00387529111456007,0.00029120095502746,1.44836886590434e-05,0.151255178749288,0.090926129062471,0.142498079907265,0.0182198961486777,0.0571079016354593,0.0894986558263668,0.00182546325842444,0.00858251677319363,0.0269007858040834,0.0421585122406872,0.000114263084383248,0.000723585440625805,0.00345983734605725,0.0112133909155524,0.0272543421539926),
('TUN', 'POL',0.00134959967239687,0.00173075711984186,0.00149293510150475,0.00088319433208123,0.000396430399723058,0.00352411457033159,0.00643934558331085,0.00588306235705092,0.00358322361065706,0.0124822153674301,0.02280779946057,0.0208374755971174,0.0331584779742803,0.0605879560473261,0.0587227309258861,0.0519981515748215,0.107299564324884,0.0553538739148415,0.0126915762598698,0.00163683775532932,0.000143187561232901,0.0950122537168675,0.0980301522321972,0.0868043197974907,0.033714636343311,0.0597076717389746,0.0528703027994613,0.00579758716006755,0.0154010454468428,0.0272748178747466,0.0241514672711098,0.000641416792538213,0.00231208088938148,0.0063235514332222,0.0118612272955041,0.017072639095023),
('TUN', 'SEN',0.00173856287332313,0.00155666064434846,0.000894046790606878,0.00034833240128544,0.00010250713995965,0.00555028161525414,0.00660325605628305,0.00392799803391979,0.00155773337910537,0.0209388522396881,0.024911277021212,0.0148186661743407,0.0592450388342252,0.0704847408844333,0.111753168584728,0.105399295320505,0.132954476622919,0.0419283943035893,0.00587666560274597,0.000463314885743002,2.42183465785723e-05,0.125395175127744,0.0790890006875837,0.0745922916159127,0.0166276201706282,0.0313644699481162,0.0295812017905001,0.00174789003613044,0.00494553435322613,0.00932869899645279,0.00879830355537317,0.000115020459194757,0.000438566883695662,0.00126305769869099,0.00246761765097453,0.00316734021735619),
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('ENG', 'COL',0.00267052224537788,0.00194441536999925,0.000911184895212354,0.000288958103169717,6.91442790451802e-05,0.00818934580230116,0.00791036165770345,0.00382044079382275,0.00123009696991464,0.0291892941662231,0.0281949106756534,0.0136172012841607,0.0780295747509126,0.0753713631317269,0.139060449152708,0.123913327608688,0.134323115867387,0.0364018540307898,0.0043844359701759,0.000297047900435947,1.32766638805767e-05,0.119692007061026,0.0648735839926352,0.0578072465277421,0.0117205874293225,0.0208878512725216,0.0186126477624936,0.00105876815518615,0.00283032636686991,0.00504406767667382,0.00449464397899153,5.95138127780459e-05,0.000214071041133352,0.000581019993306905,0.00106717374579176,0.00122577239531734),
('ENG', 'JPN',0.0336653016534908,0.0164963929979039,0.00641019716749126,0.00169947673530822,0.000341127206288074,0.0425667920703919,0.03466741681627,0.0141169880352453,0.00383240459369388,0.082442071036456,0.0671428007797968,0.0273413539948673,0.119753475882217,0.09753010414176,0.115967448865044,0.056150558876027,0.0944466728964608,0.0397154284826693,0.00742248490868817,0.000780300564102094,5.50388057944189e-05,0.045730362433765,0.0384598182874222,0.018621952213697,0.0107817326314577,0.0104408662746867,0.00505538823360488,0.00151126245146947,0.00219522543840172,0.00212582300346322,0.00102930736930227,0.000134519392573044,0.000264272701231036,0.000393076434497778,0.000398899190043957,0.000253158067481132),
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] | 0.456894 | 0.14259 |
import pytest
from addons.osfstorage.models import Region
from admin.institutional_storage_quota_control import views
from django.test import RequestFactory
from django.urls import reverse
from nose import tools as nt
from osf.models import UserQuota
from admin_tests.utilities import setup_view
from osf_tests.factories import (
AuthUserFactory,
InstitutionFactory,
RegionFactory
)
from tests.base import AdminTestCase
from api.base import settings as api_settings
pytestmark = pytest.mark.django_db
class TestUpdateQuotaUserListByInstitutionStorageID(AdminTestCase):
def setUp(self):
super(TestUpdateQuotaUserListByInstitutionStorageID, self).setUp()
self.user1 = AuthUserFactory(fullname='fullname1')
self.institution = InstitutionFactory()
self.user1.affiliated_institutions.add(self.institution)
self.user1.save()
self.view = views.UpdateQuotaUserListByInstitutionStorageID.as_view()
def test_post_create_quota(self):
max_quota = 50
request = RequestFactory().post(
reverse(
'institutional_storage_quota_control'
':update_quota_institution_user_list',
kwargs={'institution_id': self.institution.id}),
{'maxQuota': max_quota})
request.user = self.user1
response = self.view(
request,
institution_id=self.institution.id
)
nt.assert_equal(response.status_code, 302)
user_quota = UserQuota.objects.filter(
user=self.user1, storage_type=UserQuota.CUSTOM_STORAGE
).first()
nt.assert_is_not_none(user_quota)
nt.assert_equal(user_quota.max_quota, max_quota)
def test_post_update_quota(self):
UserQuota.objects.create(user=self.user1, max_quota=100)
max_quota = 150
request = RequestFactory().post(
reverse(
'institutional_storage_quota_control:'
'update_quota_institution_user_list',
kwargs={'institution_id': self.institution.id}),
{'maxQuota': max_quota})
request.user = self.user1
response = self.view(
request,
institution_id=self.institution.id
)
nt.assert_equal(response.status_code, 302)
user_quota = UserQuota.objects.filter(
user=self.user1, storage_type=UserQuota.CUSTOM_STORAGE
).first()
nt.assert_is_not_none(user_quota)
nt.assert_equal(user_quota.max_quota, max_quota)
class TestUserListByInstitutionStorageID(AdminTestCase):
def setUp(self):
super(TestUserListByInstitutionStorageID, self).setUp()
self.user = AuthUserFactory(fullname='fullname')
self.institution = InstitutionFactory()
self.region = RegionFactory(_id=self.institution._id, name='Storage')
self.user.affiliated_institutions.add(self.institution)
self.user.save()
self.view = views.UserListByInstitutionStorageID()
def test_get_user_list(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:institution_user_list',
kwargs={'institution_id': self.institution.id}
)
)
request.user = self.user
view = setup_view(self.view, request,
institution_id=self.institution.id)
user_list = view.get_userlist()
nt.assert_equal(len(user_list), 1)
nt.assert_equal(user_list[0]['fullname'], self.user.fullname)
nt.assert_equal(user_list[0]['quota'], api_settings.DEFAULT_MAX_QUOTA)
def test_get_institution(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:institution_user_list',
kwargs={'institution_id': self.institution.id}
)
)
request.user = self.user
view = setup_view(self.view, request,
institution_id=self.institution.id)
institution = view.get_institution()
nt.assert_equal(institution.storage_name, self.region.name)
def test_get_context_data_has_storage_name(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:institution_user_list',
kwargs={'institution_id': self.institution.id}
)
)
request.user = self.user
view = setup_view(self.view, request,
institution_id=self.institution.id)
view.object_list = view.get_queryset()
res = view.get_context_data()
nt.assert_is_instance(res, dict)
nt.assert_equal(res['institution_storage_name'], self.region.name)
class TestInstitutionStorageListByAdmin(AdminTestCase):
def setUp(self):
super(TestInstitutionStorageListByAdmin, self).setUp()
self.user = AuthUserFactory(fullname='fullname')
self.user.is_registered = True
self.user.is_active = True
self.user.is_staff = True
self.user.is_superuser = False
self.institution = InstitutionFactory()
self.region = RegionFactory(_id=self.institution._id, name='Storage')
self.user.affiliated_institutions.add(self.institution)
self.user.save()
self.view = views.InstitutionStorageList.as_view()
def test_get_redirect_to_user_list(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
response = self.view(
request,
institution_id=self.institution.id
)
nt.assert_equal(response.status_code, 302)
nt.assert_equal(response.url,
'/institutional_storage_quota_control'
'/user_list_by_institution_id/{}/'.format(
self.institution.id
))
def test_get_render_response(self):
inst1 = InstitutionFactory()
inst2 = InstitutionFactory()
region1 = RegionFactory(_id=inst1._id, name='Storage1')
region2 = RegionFactory(_id=inst2._id, name='Storage2')
self.user.affiliated_institutions.add(inst1)
self.user.save()
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
response = self.view(
request,
)
nt.assert_equal(response.status_code, 200)
nt.assert_is_not_none(Region.objects.filter(id=region1.id))
nt.assert_is_not_none(Region.objects.filter(id=region2.id))
nt.assert_is_instance(
response.context_data['view'],
views.InstitutionStorageList
)
def test_get_query_set(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
view = views.InstitutionStorageList()
view = setup_view(view, request)
query_set = view.get_queryset()
nt.assert_equal(query_set.exists(), True)
nt.assert_equal(query_set.first().id, self.region.id)
def test_get_context_data(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
view = views.InstitutionStorageList()
view = setup_view(view, request)
view.object_list = view.get_queryset()
res = view.get_context_data()
nt.assert_is_instance(res, dict)
nt.assert_is_instance(res['view'], views.InstitutionStorageList)
class TestInstitutionStorageListBySuperUser(AdminTestCase):
def setUp(self):
super(TestInstitutionStorageListBySuperUser, self).setUp()
self.user = AuthUserFactory(fullname='fullname')
self.user.is_registered = True
self.user.is_active = True
self.user.is_superuser = True
self.institution = InstitutionFactory()
self.institution_1 = InstitutionFactory()
self.region = RegionFactory(_id=self.institution._id, name='Storage')
self.region_1 = RegionFactory(_id=self.institution_1._id,
name='Storage_1')
self.user.affiliated_institutions.add(self.institution)
self.user.save()
self.view = views.InstitutionStorageList.as_view()
def test_get_render_response(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
response = self.view(
request,
)
nt.assert_equal(response.status_code, 200)
nt.assert_is_instance(
response.context_data['view'],
views.InstitutionStorageList
)
def test_get_query_set(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
view = views.InstitutionStorageList()
view = setup_view(view, request)
query_set = view.get_queryset()
nt.assert_equal(query_set.exists(), True)
nt.assert_equal(len(query_set), 2) | admin_tests/institutional_storage_quota_control/test_views.py | import pytest
from addons.osfstorage.models import Region
from admin.institutional_storage_quota_control import views
from django.test import RequestFactory
from django.urls import reverse
from nose import tools as nt
from osf.models import UserQuota
from admin_tests.utilities import setup_view
from osf_tests.factories import (
AuthUserFactory,
InstitutionFactory,
RegionFactory
)
from tests.base import AdminTestCase
from api.base import settings as api_settings
pytestmark = pytest.mark.django_db
class TestUpdateQuotaUserListByInstitutionStorageID(AdminTestCase):
def setUp(self):
super(TestUpdateQuotaUserListByInstitutionStorageID, self).setUp()
self.user1 = AuthUserFactory(fullname='fullname1')
self.institution = InstitutionFactory()
self.user1.affiliated_institutions.add(self.institution)
self.user1.save()
self.view = views.UpdateQuotaUserListByInstitutionStorageID.as_view()
def test_post_create_quota(self):
max_quota = 50
request = RequestFactory().post(
reverse(
'institutional_storage_quota_control'
':update_quota_institution_user_list',
kwargs={'institution_id': self.institution.id}),
{'maxQuota': max_quota})
request.user = self.user1
response = self.view(
request,
institution_id=self.institution.id
)
nt.assert_equal(response.status_code, 302)
user_quota = UserQuota.objects.filter(
user=self.user1, storage_type=UserQuota.CUSTOM_STORAGE
).first()
nt.assert_is_not_none(user_quota)
nt.assert_equal(user_quota.max_quota, max_quota)
def test_post_update_quota(self):
UserQuota.objects.create(user=self.user1, max_quota=100)
max_quota = 150
request = RequestFactory().post(
reverse(
'institutional_storage_quota_control:'
'update_quota_institution_user_list',
kwargs={'institution_id': self.institution.id}),
{'maxQuota': max_quota})
request.user = self.user1
response = self.view(
request,
institution_id=self.institution.id
)
nt.assert_equal(response.status_code, 302)
user_quota = UserQuota.objects.filter(
user=self.user1, storage_type=UserQuota.CUSTOM_STORAGE
).first()
nt.assert_is_not_none(user_quota)
nt.assert_equal(user_quota.max_quota, max_quota)
class TestUserListByInstitutionStorageID(AdminTestCase):
def setUp(self):
super(TestUserListByInstitutionStorageID, self).setUp()
self.user = AuthUserFactory(fullname='fullname')
self.institution = InstitutionFactory()
self.region = RegionFactory(_id=self.institution._id, name='Storage')
self.user.affiliated_institutions.add(self.institution)
self.user.save()
self.view = views.UserListByInstitutionStorageID()
def test_get_user_list(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:institution_user_list',
kwargs={'institution_id': self.institution.id}
)
)
request.user = self.user
view = setup_view(self.view, request,
institution_id=self.institution.id)
user_list = view.get_userlist()
nt.assert_equal(len(user_list), 1)
nt.assert_equal(user_list[0]['fullname'], self.user.fullname)
nt.assert_equal(user_list[0]['quota'], api_settings.DEFAULT_MAX_QUOTA)
def test_get_institution(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:institution_user_list',
kwargs={'institution_id': self.institution.id}
)
)
request.user = self.user
view = setup_view(self.view, request,
institution_id=self.institution.id)
institution = view.get_institution()
nt.assert_equal(institution.storage_name, self.region.name)
def test_get_context_data_has_storage_name(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:institution_user_list',
kwargs={'institution_id': self.institution.id}
)
)
request.user = self.user
view = setup_view(self.view, request,
institution_id=self.institution.id)
view.object_list = view.get_queryset()
res = view.get_context_data()
nt.assert_is_instance(res, dict)
nt.assert_equal(res['institution_storage_name'], self.region.name)
class TestInstitutionStorageListByAdmin(AdminTestCase):
def setUp(self):
super(TestInstitutionStorageListByAdmin, self).setUp()
self.user = AuthUserFactory(fullname='fullname')
self.user.is_registered = True
self.user.is_active = True
self.user.is_staff = True
self.user.is_superuser = False
self.institution = InstitutionFactory()
self.region = RegionFactory(_id=self.institution._id, name='Storage')
self.user.affiliated_institutions.add(self.institution)
self.user.save()
self.view = views.InstitutionStorageList.as_view()
def test_get_redirect_to_user_list(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
response = self.view(
request,
institution_id=self.institution.id
)
nt.assert_equal(response.status_code, 302)
nt.assert_equal(response.url,
'/institutional_storage_quota_control'
'/user_list_by_institution_id/{}/'.format(
self.institution.id
))
def test_get_render_response(self):
inst1 = InstitutionFactory()
inst2 = InstitutionFactory()
region1 = RegionFactory(_id=inst1._id, name='Storage1')
region2 = RegionFactory(_id=inst2._id, name='Storage2')
self.user.affiliated_institutions.add(inst1)
self.user.save()
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
response = self.view(
request,
)
nt.assert_equal(response.status_code, 200)
nt.assert_is_not_none(Region.objects.filter(id=region1.id))
nt.assert_is_not_none(Region.objects.filter(id=region2.id))
nt.assert_is_instance(
response.context_data['view'],
views.InstitutionStorageList
)
def test_get_query_set(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
view = views.InstitutionStorageList()
view = setup_view(view, request)
query_set = view.get_queryset()
nt.assert_equal(query_set.exists(), True)
nt.assert_equal(query_set.first().id, self.region.id)
def test_get_context_data(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
view = views.InstitutionStorageList()
view = setup_view(view, request)
view.object_list = view.get_queryset()
res = view.get_context_data()
nt.assert_is_instance(res, dict)
nt.assert_is_instance(res['view'], views.InstitutionStorageList)
class TestInstitutionStorageListBySuperUser(AdminTestCase):
def setUp(self):
super(TestInstitutionStorageListBySuperUser, self).setUp()
self.user = AuthUserFactory(fullname='fullname')
self.user.is_registered = True
self.user.is_active = True
self.user.is_superuser = True
self.institution = InstitutionFactory()
self.institution_1 = InstitutionFactory()
self.region = RegionFactory(_id=self.institution._id, name='Storage')
self.region_1 = RegionFactory(_id=self.institution_1._id,
name='Storage_1')
self.user.affiliated_institutions.add(self.institution)
self.user.save()
self.view = views.InstitutionStorageList.as_view()
def test_get_render_response(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
response = self.view(
request,
)
nt.assert_equal(response.status_code, 200)
nt.assert_is_instance(
response.context_data['view'],
views.InstitutionStorageList
)
def test_get_query_set(self):
request = RequestFactory().get(
reverse(
'institutional_storage_quota_control:list_institution_storage'
)
)
request.user = self.user
view = views.InstitutionStorageList()
view = setup_view(view, request)
query_set = view.get_queryset()
nt.assert_equal(query_set.exists(), True)
nt.assert_equal(len(query_set), 2) | 0.477554 | 0.234472 |
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
from cs3.storage.registry.v1beta1 import registry_api_pb2 as cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2
class RegistryAPIStub(object):
"""Storage Registry API
The Storage Registry API is meant to as registry to obtain
information of available storage providers.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
RFC 2119.
The following are global requirements that apply to all methods:
Any method MUST return CODE_OK on a succesful operation.
Any method MAY return NOT_IMPLEMENTED.
Any method MAY return INTERNAL.
Any method MAY return UNKNOWN.
Any method MAY return UNAUTHENTICATED.
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.GetStorageProviders = channel.unary_unary(
'/cs3.storage.registry.v1beta1.RegistryAPI/GetStorageProviders',
request_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersRequest.SerializeToString,
response_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersResponse.FromString,
)
self.ListStorageProviders = channel.unary_unary(
'/cs3.storage.registry.v1beta1.RegistryAPI/ListStorageProviders',
request_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersRequest.SerializeToString,
response_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersResponse.FromString,
)
self.GetHome = channel.unary_unary(
'/cs3.storage.registry.v1beta1.RegistryAPI/GetHome',
request_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeRequest.SerializeToString,
response_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeResponse.FromString,
)
class RegistryAPIServicer(object):
"""Storage Registry API
The Storage Registry API is meant to as registry to obtain
information of available storage providers.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
RFC 2119.
The following are global requirements that apply to all methods:
Any method MUST return CODE_OK on a succesful operation.
Any method MAY return NOT_IMPLEMENTED.
Any method MAY return INTERNAL.
Any method MAY return UNKNOWN.
Any method MAY return UNAUTHENTICATED.
"""
def GetStorageProviders(self, request, context):
"""Returns the storage provider that is reponsible for the given
resource reference.
MUST return CODE_NOT_FOUND if the reference does not exist.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def ListStorageProviders(self, request, context):
"""Returns a list of the available storage providers known by this registry.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def GetHome(self, request, context):
"""Gets the user home storage provider.
TODO(labkode): missing methods for adding and removing a storage provider
with different visibility levels (system-wide, user-wide, group-wide)...
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_RegistryAPIServicer_to_server(servicer, server):
rpc_method_handlers = {
'GetStorageProviders': grpc.unary_unary_rpc_method_handler(
servicer.GetStorageProviders,
request_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersRequest.FromString,
response_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersResponse.SerializeToString,
),
'ListStorageProviders': grpc.unary_unary_rpc_method_handler(
servicer.ListStorageProviders,
request_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersRequest.FromString,
response_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersResponse.SerializeToString,
),
'GetHome': grpc.unary_unary_rpc_method_handler(
servicer.GetHome,
request_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeRequest.FromString,
response_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeResponse.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'cs3.storage.registry.v1beta1.RegistryAPI', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class RegistryAPI(object):
"""Storage Registry API
The Storage Registry API is meant to as registry to obtain
information of available storage providers.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
RFC 2119.
The following are global requirements that apply to all methods:
Any method MUST return CODE_OK on a succesful operation.
Any method MAY return NOT_IMPLEMENTED.
Any method MAY return INTERNAL.
Any method MAY return UNKNOWN.
Any method MAY return UNAUTHENTICATED.
"""
@staticmethod
def GetStorageProviders(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/cs3.storage.registry.v1beta1.RegistryAPI/GetStorageProviders',
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersRequest.SerializeToString,
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def ListStorageProviders(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/cs3.storage.registry.v1beta1.RegistryAPI/ListStorageProviders',
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersRequest.SerializeToString,
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def GetHome(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/cs3.storage.registry.v1beta1.RegistryAPI/GetHome',
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeRequest.SerializeToString,
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) | cs3/storage/registry/v1beta1/registry_api_pb2_grpc.py | """Client and server classes corresponding to protobuf-defined services."""
import grpc
from cs3.storage.registry.v1beta1 import registry_api_pb2 as cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2
class RegistryAPIStub(object):
"""Storage Registry API
The Storage Registry API is meant to as registry to obtain
information of available storage providers.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
RFC 2119.
The following are global requirements that apply to all methods:
Any method MUST return CODE_OK on a succesful operation.
Any method MAY return NOT_IMPLEMENTED.
Any method MAY return INTERNAL.
Any method MAY return UNKNOWN.
Any method MAY return UNAUTHENTICATED.
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.GetStorageProviders = channel.unary_unary(
'/cs3.storage.registry.v1beta1.RegistryAPI/GetStorageProviders',
request_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersRequest.SerializeToString,
response_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersResponse.FromString,
)
self.ListStorageProviders = channel.unary_unary(
'/cs3.storage.registry.v1beta1.RegistryAPI/ListStorageProviders',
request_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersRequest.SerializeToString,
response_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersResponse.FromString,
)
self.GetHome = channel.unary_unary(
'/cs3.storage.registry.v1beta1.RegistryAPI/GetHome',
request_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeRequest.SerializeToString,
response_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeResponse.FromString,
)
class RegistryAPIServicer(object):
"""Storage Registry API
The Storage Registry API is meant to as registry to obtain
information of available storage providers.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
RFC 2119.
The following are global requirements that apply to all methods:
Any method MUST return CODE_OK on a succesful operation.
Any method MAY return NOT_IMPLEMENTED.
Any method MAY return INTERNAL.
Any method MAY return UNKNOWN.
Any method MAY return UNAUTHENTICATED.
"""
def GetStorageProviders(self, request, context):
"""Returns the storage provider that is reponsible for the given
resource reference.
MUST return CODE_NOT_FOUND if the reference does not exist.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def ListStorageProviders(self, request, context):
"""Returns a list of the available storage providers known by this registry.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def GetHome(self, request, context):
"""Gets the user home storage provider.
TODO(labkode): missing methods for adding and removing a storage provider
with different visibility levels (system-wide, user-wide, group-wide)...
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_RegistryAPIServicer_to_server(servicer, server):
rpc_method_handlers = {
'GetStorageProviders': grpc.unary_unary_rpc_method_handler(
servicer.GetStorageProviders,
request_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersRequest.FromString,
response_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersResponse.SerializeToString,
),
'ListStorageProviders': grpc.unary_unary_rpc_method_handler(
servicer.ListStorageProviders,
request_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersRequest.FromString,
response_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersResponse.SerializeToString,
),
'GetHome': grpc.unary_unary_rpc_method_handler(
servicer.GetHome,
request_deserializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeRequest.FromString,
response_serializer=cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeResponse.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'cs3.storage.registry.v1beta1.RegistryAPI', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class RegistryAPI(object):
"""Storage Registry API
The Storage Registry API is meant to as registry to obtain
information of available storage providers.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
RFC 2119.
The following are global requirements that apply to all methods:
Any method MUST return CODE_OK on a succesful operation.
Any method MAY return NOT_IMPLEMENTED.
Any method MAY return INTERNAL.
Any method MAY return UNKNOWN.
Any method MAY return UNAUTHENTICATED.
"""
@staticmethod
def GetStorageProviders(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/cs3.storage.registry.v1beta1.RegistryAPI/GetStorageProviders',
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersRequest.SerializeToString,
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetStorageProvidersResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def ListStorageProviders(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/cs3.storage.registry.v1beta1.RegistryAPI/ListStorageProviders',
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersRequest.SerializeToString,
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.ListStorageProvidersResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def GetHome(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/cs3.storage.registry.v1beta1.RegistryAPI/GetHome',
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeRequest.SerializeToString,
cs3_dot_storage_dot_registry_dot_v1beta1_dot_registry__api__pb2.GetHomeResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) | 0.695545 | 0.151028 |
import unittest
from selenium import webdriver
import settings
class SearchText(unittest.TestCase):
def setUp(self):
self.driver = webdriver.Chrome()
self.driver.implicitly_wait(30)
self.driver.maximize_window()
#navigate to application home page
self.driver.get("https://my.wealthsimple.com/app/login?locale=en-ca")
def get_login_credentials(self):
# TODO: To check if .env information is filled out?
# Get the Email Textbox
self.email_textbox = self.driver.find_element_by_id(
'input--b3695a77-df8b-4e64-b32e-d4bc8756bed1')
# Input information
self.email_textbox.send_keys(settings.WS_USERNAME)
# Get the password Textbox
self.password_textbox = self.driver.find_element_by_xpath(
'input--244423b7-be19-4fc6-8ca4-1256f097365a')
self.password_textbox.send_keys(<PASSWORD>.WS_PASSWORD)
# Get the login button
self.login_button = self.driver.find_element_by_xpath(
'/html/body/div[1]/ws-card-loading-indicator/div/div/div/div/ng-transclude/div/layout/div/main/login-wizard/wizard/div/div/ng-transclude/form/ws-micro-app-loader/login-form/span/div/div/div/div/div/div[5]/button'
)
# Click the login button
self.login_button.click()
self.driver.implicitly_wait(10)
# Two-step verification page. Stop here?
self.TFA_code_textbox = self.driver.find_element_by_id(
'input--84218225-36ab-40a5-8215-0c7ef2208962')
self.assertNotEqual(0, self.TFA_code_textbox.size)
def get_login_credentials_fail(self):
# Just click the login button
self.login_button = self.driver.find_element_by_xpath(
'/html/body/div[1]/ws-card-loading-indicator/div/div/div/div/ng-transclude/div/layout/div/main/login-wizard/wizard/div/div/ng-transclude/form/ws-micro-app-loader/login-form/span/div/div/div/div/div/div[5]/button'
)
self.login_button.click()
self.driver.implicitly_wait(10)
# Check if login failed
self.driver.fail_text = self.driver.find_element_by_xpath(
'/html/body/div[1]/ws-card-loading-indicator/div/div/div/div/ng-transclude/div/layout/div/main/login-wizard/wizard/div/div/ng-transclude/form/ws-micro-app-loader/login-form/span/div/div/div/div/div/div[2]/div/p'
)
self.assertEqual('The login credentials you entered are invalid',
self.driver.fail_text.text)
def tearDown(self):
# Close the browser window
self.driver.quit()
test = SearchText()
test.setUp()
test.get_login_credentials_fail()
test.tearDown() | test.py | import unittest
from selenium import webdriver
import settings
class SearchText(unittest.TestCase):
def setUp(self):
self.driver = webdriver.Chrome()
self.driver.implicitly_wait(30)
self.driver.maximize_window()
#navigate to application home page
self.driver.get("https://my.wealthsimple.com/app/login?locale=en-ca")
def get_login_credentials(self):
# TODO: To check if .env information is filled out?
# Get the Email Textbox
self.email_textbox = self.driver.find_element_by_id(
'input--b3695a77-df8b-4e64-b32e-d4bc8756bed1')
# Input information
self.email_textbox.send_keys(settings.WS_USERNAME)
# Get the password Textbox
self.password_textbox = self.driver.find_element_by_xpath(
'input--244423b7-be19-4fc6-8ca4-1256f097365a')
self.password_textbox.send_keys(<PASSWORD>.WS_PASSWORD)
# Get the login button
self.login_button = self.driver.find_element_by_xpath(
'/html/body/div[1]/ws-card-loading-indicator/div/div/div/div/ng-transclude/div/layout/div/main/login-wizard/wizard/div/div/ng-transclude/form/ws-micro-app-loader/login-form/span/div/div/div/div/div/div[5]/button'
)
# Click the login button
self.login_button.click()
self.driver.implicitly_wait(10)
# Two-step verification page. Stop here?
self.TFA_code_textbox = self.driver.find_element_by_id(
'input--84218225-36ab-40a5-8215-0c7ef2208962')
self.assertNotEqual(0, self.TFA_code_textbox.size)
def get_login_credentials_fail(self):
# Just click the login button
self.login_button = self.driver.find_element_by_xpath(
'/html/body/div[1]/ws-card-loading-indicator/div/div/div/div/ng-transclude/div/layout/div/main/login-wizard/wizard/div/div/ng-transclude/form/ws-micro-app-loader/login-form/span/div/div/div/div/div/div[5]/button'
)
self.login_button.click()
self.driver.implicitly_wait(10)
# Check if login failed
self.driver.fail_text = self.driver.find_element_by_xpath(
'/html/body/div[1]/ws-card-loading-indicator/div/div/div/div/ng-transclude/div/layout/div/main/login-wizard/wizard/div/div/ng-transclude/form/ws-micro-app-loader/login-form/span/div/div/div/div/div/div[2]/div/p'
)
self.assertEqual('The login credentials you entered are invalid',
self.driver.fail_text.text)
def tearDown(self):
# Close the browser window
self.driver.quit()
test = SearchText()
test.setUp()
test.get_login_credentials_fail()
test.tearDown() | 0.148726 | 0.12768 |
import os
PURPLE_DESCRIPTION = "A collection of various utilities called Purple Tools. Call 'purple.py <positional argument> -h' for additional help, where <positional argument> is one of the arguments from the positional argument list below."
PARSER_IDENTIFIER_NAME = "which"
TEST_COMMAND_DESCRIPTION = "test command will run all unit tests or exploratory tests of the entire Purple Tools for you."
TEST_ARGUMENTS_KIND_HELP = "specify the kind of tests you want to run. unit: run all unit tests, exploratory: run all exploratory tests."
KIND_OF_TEST = {
'unit' : 'unit',
'u' : 'unit',
'exploratory' : 'exploratory',
'e' : 'exploratory'
}
PROJECT_DIR = os.environ['PT_PROJECT_DIR']
CALCULATE_COMMAND_DESCRIPTION = "Calculate command performs various calculations, works mostly with numbers and formulas. Run 'purple.py c <positional argument> -h' for detailed help on subcommands, where <positional arguments> is one of the positional arguments from the list below."
CALCULATE_ARGUMENTS_SUBCOMMANDS = "Choose which type of calculation to perform.\n\t=> progresssion | pr: perform various calculations on arithmetic progressions."
PROGRESSION_SUM_INVALID_ARGUMENT_MESSAGE = "First argument must be <= second argument."
BINARY_SEARCH_BOUNDS_MIXED_UP = "Upper bound in binary search must be greater than lower bound"
BINARY_SEARCH_INTS_OF_WRONG_TYPE = "Lower bound, upper bound and step of a binary search have to be ints"
BINARY_SEARCH_CONDITION_NOT_CALLABLE = "Exit condition of a binary search has to be a function that returns values of the BinarySearch enum"
BINARY_SEARCH_INTS_DONT_MAKE_PROGRESSION = "Specified values of lower bound, upper bound and step in a binary search don't create a valid progression"
BINARY_SEARCH_STEP_MUST_BE_POSITIVE = "Value of step in progression can only be > 0."
BINARY_SEARCH_ELEMENT_NOT_IN_PROGRESSION = "You're trying to look for an element that is not in the specified progression."
LIMITER_PROGRESSION_LIMITER_IS_INCORRECT = "Progression limiter can only be positive."
LIMITER_PROGRESSION_RESULT = "The progression is: 1 .. {}\nLimiter-to-sum remainder: {}\n"
SUM_PROGRESSION_RESULT = "sum({} .. {}) = {}\n"
PROGRESSIONS_COMMAND_DESCRIPTION = "Progression command performs various calculations on arithmetic progressions."
PROGRESSIONS_ERROR_NO_ARGUMENT = "No arguments provided, expected progression calculation type. Please provide one of the arguments from the 'optional arguments' section."
PROGRESSIONS_ARGUMENTS_BY_LIMITER = "Calculates arithmetic progression parameters via a progression sum limiter. Expects the limiter (an integer number) to be the only argument. Calculates a progression with first element and step equal to 1. Determines progression's last element and the sum of elements such that the sum <= the specified limiter."
PROGRESSIONS_ARGUMENTS_BY_BOUNDS = "Calculates the sum of an arithmetic progression with step=1 specified via its bounds (lower and upper bound / first and last element). Expects <lower_bound> (an integer number) to be the first argument, <upper_bound> (also an integer) to be the second argument, where <lower bound> <= <upper bound>" | scripts/utils/constants.py | import os
PURPLE_DESCRIPTION = "A collection of various utilities called Purple Tools. Call 'purple.py <positional argument> -h' for additional help, where <positional argument> is one of the arguments from the positional argument list below."
PARSER_IDENTIFIER_NAME = "which"
TEST_COMMAND_DESCRIPTION = "test command will run all unit tests or exploratory tests of the entire Purple Tools for you."
TEST_ARGUMENTS_KIND_HELP = "specify the kind of tests you want to run. unit: run all unit tests, exploratory: run all exploratory tests."
KIND_OF_TEST = {
'unit' : 'unit',
'u' : 'unit',
'exploratory' : 'exploratory',
'e' : 'exploratory'
}
PROJECT_DIR = os.environ['PT_PROJECT_DIR']
CALCULATE_COMMAND_DESCRIPTION = "Calculate command performs various calculations, works mostly with numbers and formulas. Run 'purple.py c <positional argument> -h' for detailed help on subcommands, where <positional arguments> is one of the positional arguments from the list below."
CALCULATE_ARGUMENTS_SUBCOMMANDS = "Choose which type of calculation to perform.\n\t=> progresssion | pr: perform various calculations on arithmetic progressions."
PROGRESSION_SUM_INVALID_ARGUMENT_MESSAGE = "First argument must be <= second argument."
BINARY_SEARCH_BOUNDS_MIXED_UP = "Upper bound in binary search must be greater than lower bound"
BINARY_SEARCH_INTS_OF_WRONG_TYPE = "Lower bound, upper bound and step of a binary search have to be ints"
BINARY_SEARCH_CONDITION_NOT_CALLABLE = "Exit condition of a binary search has to be a function that returns values of the BinarySearch enum"
BINARY_SEARCH_INTS_DONT_MAKE_PROGRESSION = "Specified values of lower bound, upper bound and step in a binary search don't create a valid progression"
BINARY_SEARCH_STEP_MUST_BE_POSITIVE = "Value of step in progression can only be > 0."
BINARY_SEARCH_ELEMENT_NOT_IN_PROGRESSION = "You're trying to look for an element that is not in the specified progression."
LIMITER_PROGRESSION_LIMITER_IS_INCORRECT = "Progression limiter can only be positive."
LIMITER_PROGRESSION_RESULT = "The progression is: 1 .. {}\nLimiter-to-sum remainder: {}\n"
SUM_PROGRESSION_RESULT = "sum({} .. {}) = {}\n"
PROGRESSIONS_COMMAND_DESCRIPTION = "Progression command performs various calculations on arithmetic progressions."
PROGRESSIONS_ERROR_NO_ARGUMENT = "No arguments provided, expected progression calculation type. Please provide one of the arguments from the 'optional arguments' section."
PROGRESSIONS_ARGUMENTS_BY_LIMITER = "Calculates arithmetic progression parameters via a progression sum limiter. Expects the limiter (an integer number) to be the only argument. Calculates a progression with first element and step equal to 1. Determines progression's last element and the sum of elements such that the sum <= the specified limiter."
PROGRESSIONS_ARGUMENTS_BY_BOUNDS = "Calculates the sum of an arithmetic progression with step=1 specified via its bounds (lower and upper bound / first and last element). Expects <lower_bound> (an integer number) to be the first argument, <upper_bound> (also an integer) to be the second argument, where <lower bound> <= <upper bound>" | 0.503906 | 0.323166 |
import autodisc as ad
import ipywidgets
class ExperimentRepetitionLoaderWidget(ipywidgets.Box):
@staticmethod
def get_default_gui_config():
default_config = ad.Config()
default_config.box_layout = ad.Config()
default_config.box_layout.display = 'stretch'
default_config.box_layout.align_items = 'stretch'
default_config.experiment_selection = ad.Config()
default_config.experiment_selection.description = 'Experiment:'
default_config.experiment_selection.layout = ad.Config()
default_config.experiment_selection.layout.flex = '1 1 0%'
default_config.experiment_selection.layout.width = 'auto'
default_config.repetition_selection = ad.Config()
default_config.repetition_selection.description = 'Repetition:'
default_config.repetition_selection.layout = ad.Config()
default_config.repetition_selection.layout.flex = '1 1 0%'
default_config.repetition_selection.layout.width = 'auto'
default_config.load_button = ad.Config()
default_config.load_button.layout = ad.Config()
default_config.load_button.layout.width = '150px'
default_config.load_button.description = 'Load'
default_config.load_button.disabled = False
default_config.load_button.button_style = '' # 'success', 'info', 'warning', 'danger' or ''
default_config.load_button.tooltip = 'Load the data for the selected experiment and repetition.'
return default_config
def __init__(self, experiment_definitions, repetition_ids, load_function, config=None, **kwargs):
self.config = ad.config.set_default_config(kwargs, config, ExperimentRepetitionLoaderWidget.get_default_gui_config())
self.data = []
self.experiment_definitions = experiment_definitions
self.repetition_ids = repetition_ids
self.load_function = load_function
self.experiment_ids = [exp_def['id'] for exp_def in self.experiment_definitions]
# create Experiment selection
self.exp_selection_widget = ipywidgets.Dropdown(
options=self.experiment_ids,
value=self.experiment_ids[0],
**self.config.experiment_selection
)
# create Repetition selection
self.rep_selection_widget = ipywidgets.Dropdown(
options=list(repetition_ids),
value=list(repetition_ids)[0],
**self.config.repetition_selection
)
# create Load button
self.load_btn = ipywidgets.Button(
**self.config.load_button
)
self.load_btn.on_click(self.load_data)
return super().__init__([self.exp_selection_widget, self.rep_selection_widget, self.load_btn], layout=self.config.box_layout)
def load_data(self, btn):
self.data = self.load_function(
self.exp_selection_widget.value,
self.rep_selection_widget.value
) | autodisc/autodisc/gui/jupyter/experimentrepetitionloaderwidget.py | import autodisc as ad
import ipywidgets
class ExperimentRepetitionLoaderWidget(ipywidgets.Box):
@staticmethod
def get_default_gui_config():
default_config = ad.Config()
default_config.box_layout = ad.Config()
default_config.box_layout.display = 'stretch'
default_config.box_layout.align_items = 'stretch'
default_config.experiment_selection = ad.Config()
default_config.experiment_selection.description = 'Experiment:'
default_config.experiment_selection.layout = ad.Config()
default_config.experiment_selection.layout.flex = '1 1 0%'
default_config.experiment_selection.layout.width = 'auto'
default_config.repetition_selection = ad.Config()
default_config.repetition_selection.description = 'Repetition:'
default_config.repetition_selection.layout = ad.Config()
default_config.repetition_selection.layout.flex = '1 1 0%'
default_config.repetition_selection.layout.width = 'auto'
default_config.load_button = ad.Config()
default_config.load_button.layout = ad.Config()
default_config.load_button.layout.width = '150px'
default_config.load_button.description = 'Load'
default_config.load_button.disabled = False
default_config.load_button.button_style = '' # 'success', 'info', 'warning', 'danger' or ''
default_config.load_button.tooltip = 'Load the data for the selected experiment and repetition.'
return default_config
def __init__(self, experiment_definitions, repetition_ids, load_function, config=None, **kwargs):
self.config = ad.config.set_default_config(kwargs, config, ExperimentRepetitionLoaderWidget.get_default_gui_config())
self.data = []
self.experiment_definitions = experiment_definitions
self.repetition_ids = repetition_ids
self.load_function = load_function
self.experiment_ids = [exp_def['id'] for exp_def in self.experiment_definitions]
# create Experiment selection
self.exp_selection_widget = ipywidgets.Dropdown(
options=self.experiment_ids,
value=self.experiment_ids[0],
**self.config.experiment_selection
)
# create Repetition selection
self.rep_selection_widget = ipywidgets.Dropdown(
options=list(repetition_ids),
value=list(repetition_ids)[0],
**self.config.repetition_selection
)
# create Load button
self.load_btn = ipywidgets.Button(
**self.config.load_button
)
self.load_btn.on_click(self.load_data)
return super().__init__([self.exp_selection_widget, self.rep_selection_widget, self.load_btn], layout=self.config.box_layout)
def load_data(self, btn):
self.data = self.load_function(
self.exp_selection_widget.value,
self.rep_selection_widget.value
) | 0.580233 | 0.068694 |
from __future__ import absolute_import
import pprint
import json
import bigml.api
from bigml.constants import RENAMED_RESOURCES
from bigml.resourcehandler import get_resource_type
INDENT = 4 * " "
def sort_lists(dict_structure):
"""Sorts the lists in a dict_structure
"""
if dict_structure is not None and isinstance(dict_structure, dict):
for key, value in dict_structure.items():
if value is not None:
if isinstance(value, list):
dict_structure[key] = sorted(value)
elif isinstance(value, dict):
sort_lists(dict_structure[key])
return dict_structure
def resource_alias(resource_id, alias):
"""Returns the alias if found
"""
if isinstance(resource_id, basestring):
return alias.get(resource_id, '"%s"' % resource_id)
elif isinstance(resource_id, list):
alias_names = []
for resource_id_id in resource_id:
alias_names.append(
alias.get(resource_id_id, '"%s"' % resource_id_id))
return repr(alias_names)
def to_whizzml(args):
"""Transforming dict syntax to whizzml argument syntax
"""
whizzml_args = []
if isinstance(args, list):
return "[%s]" % " ".join([to_whizzml(arg) for arg in args])
if isinstance(args, dict):
for arg, value in args.items():
whizzml_args.append("%s %s" % (json.dumps(arg), to_whizzml(value)))
return "{%s}" % " ".join(whizzml_args)
return json.dumps(args)
class RESTCall(object):
"""Object to store the REST call definition
"""
def __init__(self, action, origins=None, args=None,
resource_id=None, resource_type=None, suffix=None, name=None):
"""Constructor for the REST call definition
resource_id: ID for the generated resource
action: ["create" | "update" | "get"]
origins: list of resouce IDs for the origin resources
args: dict for the rest of arguments
"""
self.action = action
self.origins = [resource_id] if origins is None and \
action == "update" else origins
self.args = args or {}
input_data = self.args.get("input_data")
if input_data:
del self.args["input_data"]
self.input_data = input_data
self.resource_id = resource_id
self.resource_type = resource_type
self.suffix = suffix
if resource_id and not resource_type:
self.resource_type = bigml.api.get_resource_type(self.resource_id)
else:
self.resource_type = resource_type
self.name = name
def reify(self, language=None, alias=None):
"""REST call command line
language: computing language to write the output in
alias: list of aliases for the related resources (e.g
{"source/55c4de8d1fa89c2dc70012d0": "source1"}
will cause the references to
"source/55c4de8d1fa89c2dc70012d0" to be renamed
as "source1")
"""
out = ""
if not language:
out += "\n\n"
out += "resource ID: %s\n" % self.resource_id
out += "action: %s\n" % self.action
out += "origins: %s\n" % self.origins
out += "args: %s\n" % self.args
out += "input data: %s\n" % self.input_data
return out
else:
try:
reify_handler = getattr(self, "reify_%s" % language)
except AttributeError:
reify_handler = self.reify
return reify_handler(
alias=alias)
def reify_python(self, alias=None):
"""REST call command line in python. See ``reify`` method.
"""
resource_type = get_resource_type(self.resource_id)
resource_name = resource_alias(self.resource_id, alias)
resource_method_suffix = RENAMED_RESOURCES.get(
resource_type, resource_type)
origin_names = [resource_alias(resource_id, alias) for resource_id
in self.origins]
arguments = ", ".join(origin_names)
if self.suffix:
arguments = "%s%s" % (arguments, self.suffix)
if self.input_data:
arguments = "%s, \\\n%s%s" % ( \
arguments, INDENT,
pprint.pformat(self.input_data).replace("\n", "\n%s" % INDENT))
if self.args:
sort_lists(self.args)
arguments = "%s, \\\n%s%s" % (arguments, \
INDENT, \
pprint.pformat(self.args).replace( \
"\n", "\n%s" % INDENT))
out = "%s = api.%s_%s(%s)\napi.ok(%s)\n\n" % (
resource_name,
self.action,
resource_method_suffix,
arguments,
resource_name)
return out
def reify_whizzml(self, alias=None):
""" Whizzml command for the REST call
"""
name = self.name if self.name else resource_alias( \
self.resource_id, alias)
args = self.origin_attributes()
arguments = to_whizzml(args)
for resource_id, name in alias.items():
arguments = arguments.replace('"%s"' % resource_id, name)
command = "(%s-and-wait-%s %s)" % ( \
self.action, self.resource_type, arguments)
return command
def origin_attributes(self):
""" Add as arguments the origin attributes
"""
args = {}
args.update(self.args)
origin_types = []
if isinstance(self.origins, basestring):
origin_types.append(bigml.api.get_resource_type(self.origins))
else:
for origin in self.origins:
origin_types.append(bigml.api.get_resource_type(origin))
if self.resource_type == "source":
args.update({"remote": self.origins[0]})
return args
if self.resource_type == "dataset" and origin_types[0] == "source":
args.update({"source": self.origins[0]})
return args
if self.resource_type == "dataset" and origin_types[0] == "cluster":
args.update({"cluster": self.origins[0]})
return args
if (self.resource_type == "dataset" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"origin_dataset": self.origins[0]})
return args
if self.resource_type == "dataset":
args.update({"origin_datasets": self.origins})
return args
if self.resource_type == "model" and origin_types[0] == "cluster":
args.update({"cluster": self.origins[0]})
return args
if (self.resource_type == "model" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "model":
args.update({"datasets": self.origins})
return args
if (self.resource_type == "ensemble" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "ensemble":
args.update({"datasets": self.origins})
return args
if (self.resource_type == "cluster" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "cluster":
args.update({"datasets": self.origins})
return args
if (self.resource_type == "anomaly" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "anomaly":
args.update({"datasets": self.origins})
return args
if self.resource_type == "evaluation":
args.update({origin_types[0]: self.origins[0],
"dataset": self.origins[1]})
return args
if (self.resource_type == "correlation" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if (self.resource_type == "statisticaltest" and
len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "batchprediction":
args.update({origin_types[0]: self.origins[0],
"dataset": self.origins[1]})
return args
if self.resource_type == "batchcentroid":
args.update({origin_types[0]: self.origins[0],
"dataset": self.origins[1]})
return args
if self.resource_type == "batchanomalyscore":
args.update({origin_types[0]: self.origins[0],
"dataset": self.origins[1]})
return args
if self.resource_type == "prediction":
args.update({origin_types[0]: self.origins[0],
"input_data": self.input_data})
return args
if self.resource_type == "centroid":
args.update({origin_types[0]: self.origins[0],
"input_data": self.input_data})
return args
if self.resource_type == "anomalyscore":
args.update({origin_types[0]: self.origins[0],
"input_data": self.input_data})
return args | bigmler/reify/restcall.py | from __future__ import absolute_import
import pprint
import json
import bigml.api
from bigml.constants import RENAMED_RESOURCES
from bigml.resourcehandler import get_resource_type
INDENT = 4 * " "
def sort_lists(dict_structure):
"""Sorts the lists in a dict_structure
"""
if dict_structure is not None and isinstance(dict_structure, dict):
for key, value in dict_structure.items():
if value is not None:
if isinstance(value, list):
dict_structure[key] = sorted(value)
elif isinstance(value, dict):
sort_lists(dict_structure[key])
return dict_structure
def resource_alias(resource_id, alias):
"""Returns the alias if found
"""
if isinstance(resource_id, basestring):
return alias.get(resource_id, '"%s"' % resource_id)
elif isinstance(resource_id, list):
alias_names = []
for resource_id_id in resource_id:
alias_names.append(
alias.get(resource_id_id, '"%s"' % resource_id_id))
return repr(alias_names)
def to_whizzml(args):
"""Transforming dict syntax to whizzml argument syntax
"""
whizzml_args = []
if isinstance(args, list):
return "[%s]" % " ".join([to_whizzml(arg) for arg in args])
if isinstance(args, dict):
for arg, value in args.items():
whizzml_args.append("%s %s" % (json.dumps(arg), to_whizzml(value)))
return "{%s}" % " ".join(whizzml_args)
return json.dumps(args)
class RESTCall(object):
"""Object to store the REST call definition
"""
def __init__(self, action, origins=None, args=None,
resource_id=None, resource_type=None, suffix=None, name=None):
"""Constructor for the REST call definition
resource_id: ID for the generated resource
action: ["create" | "update" | "get"]
origins: list of resouce IDs for the origin resources
args: dict for the rest of arguments
"""
self.action = action
self.origins = [resource_id] if origins is None and \
action == "update" else origins
self.args = args or {}
input_data = self.args.get("input_data")
if input_data:
del self.args["input_data"]
self.input_data = input_data
self.resource_id = resource_id
self.resource_type = resource_type
self.suffix = suffix
if resource_id and not resource_type:
self.resource_type = bigml.api.get_resource_type(self.resource_id)
else:
self.resource_type = resource_type
self.name = name
def reify(self, language=None, alias=None):
"""REST call command line
language: computing language to write the output in
alias: list of aliases for the related resources (e.g
{"source/55c4de8d1fa89c2dc70012d0": "source1"}
will cause the references to
"source/55c4de8d1fa89c2dc70012d0" to be renamed
as "source1")
"""
out = ""
if not language:
out += "\n\n"
out += "resource ID: %s\n" % self.resource_id
out += "action: %s\n" % self.action
out += "origins: %s\n" % self.origins
out += "args: %s\n" % self.args
out += "input data: %s\n" % self.input_data
return out
else:
try:
reify_handler = getattr(self, "reify_%s" % language)
except AttributeError:
reify_handler = self.reify
return reify_handler(
alias=alias)
def reify_python(self, alias=None):
"""REST call command line in python. See ``reify`` method.
"""
resource_type = get_resource_type(self.resource_id)
resource_name = resource_alias(self.resource_id, alias)
resource_method_suffix = RENAMED_RESOURCES.get(
resource_type, resource_type)
origin_names = [resource_alias(resource_id, alias) for resource_id
in self.origins]
arguments = ", ".join(origin_names)
if self.suffix:
arguments = "%s%s" % (arguments, self.suffix)
if self.input_data:
arguments = "%s, \\\n%s%s" % ( \
arguments, INDENT,
pprint.pformat(self.input_data).replace("\n", "\n%s" % INDENT))
if self.args:
sort_lists(self.args)
arguments = "%s, \\\n%s%s" % (arguments, \
INDENT, \
pprint.pformat(self.args).replace( \
"\n", "\n%s" % INDENT))
out = "%s = api.%s_%s(%s)\napi.ok(%s)\n\n" % (
resource_name,
self.action,
resource_method_suffix,
arguments,
resource_name)
return out
def reify_whizzml(self, alias=None):
""" Whizzml command for the REST call
"""
name = self.name if self.name else resource_alias( \
self.resource_id, alias)
args = self.origin_attributes()
arguments = to_whizzml(args)
for resource_id, name in alias.items():
arguments = arguments.replace('"%s"' % resource_id, name)
command = "(%s-and-wait-%s %s)" % ( \
self.action, self.resource_type, arguments)
return command
def origin_attributes(self):
""" Add as arguments the origin attributes
"""
args = {}
args.update(self.args)
origin_types = []
if isinstance(self.origins, basestring):
origin_types.append(bigml.api.get_resource_type(self.origins))
else:
for origin in self.origins:
origin_types.append(bigml.api.get_resource_type(origin))
if self.resource_type == "source":
args.update({"remote": self.origins[0]})
return args
if self.resource_type == "dataset" and origin_types[0] == "source":
args.update({"source": self.origins[0]})
return args
if self.resource_type == "dataset" and origin_types[0] == "cluster":
args.update({"cluster": self.origins[0]})
return args
if (self.resource_type == "dataset" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"origin_dataset": self.origins[0]})
return args
if self.resource_type == "dataset":
args.update({"origin_datasets": self.origins})
return args
if self.resource_type == "model" and origin_types[0] == "cluster":
args.update({"cluster": self.origins[0]})
return args
if (self.resource_type == "model" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "model":
args.update({"datasets": self.origins})
return args
if (self.resource_type == "ensemble" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "ensemble":
args.update({"datasets": self.origins})
return args
if (self.resource_type == "cluster" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "cluster":
args.update({"datasets": self.origins})
return args
if (self.resource_type == "anomaly" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "anomaly":
args.update({"datasets": self.origins})
return args
if self.resource_type == "evaluation":
args.update({origin_types[0]: self.origins[0],
"dataset": self.origins[1]})
return args
if (self.resource_type == "correlation" and len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if (self.resource_type == "statisticaltest" and
len(origin_types) < 2 and
origin_types[0] == "dataset"):
args.update({"dataset": self.origins[0]})
return args
if self.resource_type == "batchprediction":
args.update({origin_types[0]: self.origins[0],
"dataset": self.origins[1]})
return args
if self.resource_type == "batchcentroid":
args.update({origin_types[0]: self.origins[0],
"dataset": self.origins[1]})
return args
if self.resource_type == "batchanomalyscore":
args.update({origin_types[0]: self.origins[0],
"dataset": self.origins[1]})
return args
if self.resource_type == "prediction":
args.update({origin_types[0]: self.origins[0],
"input_data": self.input_data})
return args
if self.resource_type == "centroid":
args.update({origin_types[0]: self.origins[0],
"input_data": self.input_data})
return args
if self.resource_type == "anomalyscore":
args.update({origin_types[0]: self.origins[0],
"input_data": self.input_data})
return args | 0.630344 | 0.137214 |
custom_component_base_connections_template = '''
add_connection pll_using_AD1939_MCLK.outclk0 component_name_0.clock
add_connection clock_name.clk_reset component_name_0.reset
add_connection axi_master_name component_name_0.avalon_slave
set_connection_parameter_value axi_master_name/component_name_0.avalon_slave arbitrationPriority {1}
set_connection_parameter_value axi_master_name/component_name_0.avalon_slave baseAddress {component_base_address}
set_connection_parameter_value axi_master_name/component_name_0.avalon_slave defaultConnection {0}
'''
custom_component_initial_audio_in_connection_template = '''
add_connection audio_in component_name_0.avalon_streaming_sink
'''
custom_component_audio_in_connection_template = '''
add_connection last_component_name_0.avalon_streaming_source component_name_0.avalon_streaming_sink
'''
custom_component_final_audio_out_connection_template = '''
add_connection component_name_0.avalon_streaming_source audio_out
'''
custom_component_instantiation_template = "add_instance component_name_0 component_name 1.0\n"
quartus_project_template = """
set project project_name
set revision project_revision
set target_system target_name
load_package flow
if [project_exists $project] {
project_open -force -current_revision $project
create_revision -set_current $revision
} else {
project_new $project -revision ${revision} -overwrite
}
source ${target_system}_proj.tcl
#Add files here
files_list
# Add post-compile hook
set_global_assignment -name POST_FLOW_SCRIPT_FILE quartus_sh:gen_rbf.tcl
project_close
"""
quartus_compile_template = """
load_package flow
project_open -force -revision project_revision project_name
# compile the project
execute_flow -compile
project_close
"""
class QuartusTemplates:
"""Generate templates for Quartus workflow."""
def __init__(self, num_custom_components, baseAddress=40):
"""Initialize QuartusTemplates.
Parameters
----------
num_custom_components : [type]
[description]
baseAddress : [type]
[description]
"""
self.custom_components_added = 0
self.num_custom_components = num_custom_components
self.de10_components_base_address = baseAddress # Format is 0x0020
self.last_component_added = ""
def add_custom_component_instantiaion(self, component_name):
"""Generate a string representing a component being instantiated in _hw.tcl.
Parameters
----------
component_name : str
Name of Platform Designer component to instantiate
Returns
-------
str
String that instantiates the given component in _hw.tcl
"""
return custom_component_instantiation_template.replace("component_name", component_name)
def add_custom_component_connections(self, component_name, target):
"""Generate _hw.tcl section for connecting a custom component to the system being made.
Connects to initial audio source on first call.
Connects to the last passed in component on future calls.
Once the number of custom_compoents has been reached, the component also gets connnected to audio out
Parameters
----------
component_name : str
Name of Platform Designer component to instantiate
target : Target
target configuration tuple
Returns
-------
str
Returns _hw.tcl section for connecting a custom component to the system being generated.
"""
# Increment address by 0x10 each time a custom_component is added
component_base_address = "0x00" + \
str(self.de10_components_base_address +
self.custom_components_added * 10)
built_string = custom_component_base_connections_template \
.replace("component_name", component_name) \
.replace("component_base_address", component_base_address) \
.replace("axi_master_name", target.axi_master_name) \
.replace("clock_name", target.clock_name)
if(self.custom_components_added == 0):
built_string += custom_component_initial_audio_in_connection_template.replace(
"component_name", component_name).replace("audio_in", target.audio_in)
else:
built_string += custom_component_audio_in_connection_template.replace(
"last_component_name", self.last_component_added).replace("component_name", component_name)
if((self.custom_components_added + 1) == self.num_custom_components):
built_string += custom_component_final_audio_out_connection_template.replace(
"component_name", component_name).replace("audio_out", target.audio_out)
self.custom_components_added = 0
return built_string
self.last_component_added = component_name
self.custom_components_added += 1
return built_string
def add_quartus_project(self, proj_name, project_revision, target):
"""Generate a tcl file that can be run to make a Quartus project.
Parameters
----------
proj_name : str
Name of the project
project_revision : str
Name of the project revision
target : Target
NamedTuple that provides information about the target configuration
Returns
-------
str
tcl file that describes the project
"""
files = self.add_files_list(target.files_list)
quartus_project = quartus_project_template.replace("project_name", proj_name).replace(
"project_revision", project_revision).replace("files_list", files).replace("target_name", target.name)
if(target.name == "reflex"):
quartus_project.replace("rbf", "jic")
return quartus_project
def add_quartus_compile_project(self, project_name, project_revision):
"""Generate a tcl file that can be run to compile the given Quartus project.
Parameters
----------
project_name : str
Name of the project
project_revision : str
Name of the project revision
Returns
-------
str
tcl file that has the commands to compile the project
"""
return quartus_compile_template.replace("project_name", project_name).replace("project_revision", project_revision)
def add_files_list(self, files):
"""Generate tcl commands to add a list of files to an open Quartus project.
Parameters
----------
files : list of str
List of files to be added to a Quartus project
Returns
-------
str
List of tcl commands to add the given files to an open Quartus project
"""
files_list = ''
base_string = '\nset_global_assignment -name '
tcl_file_type = ''
for file in files:
if file.endswith('vhd'):
tcl_file_type = 'VHDL_FILE '
elif file.endswith('qip'):
tcl_file_type = 'QIP_FILE '
elif file.endswith('qsys'):
tcl_file_type = 'QSYS_FILE '
elif file.endswith('sdc'):
tcl_file_type = 'SDC_FILE '
else:
tcl_file_type = 'SOURCE_FILE '
files_list += base_string + tcl_file_type + file
return files_list | quartus/quartus_templates.py |
custom_component_base_connections_template = '''
add_connection pll_using_AD1939_MCLK.outclk0 component_name_0.clock
add_connection clock_name.clk_reset component_name_0.reset
add_connection axi_master_name component_name_0.avalon_slave
set_connection_parameter_value axi_master_name/component_name_0.avalon_slave arbitrationPriority {1}
set_connection_parameter_value axi_master_name/component_name_0.avalon_slave baseAddress {component_base_address}
set_connection_parameter_value axi_master_name/component_name_0.avalon_slave defaultConnection {0}
'''
custom_component_initial_audio_in_connection_template = '''
add_connection audio_in component_name_0.avalon_streaming_sink
'''
custom_component_audio_in_connection_template = '''
add_connection last_component_name_0.avalon_streaming_source component_name_0.avalon_streaming_sink
'''
custom_component_final_audio_out_connection_template = '''
add_connection component_name_0.avalon_streaming_source audio_out
'''
custom_component_instantiation_template = "add_instance component_name_0 component_name 1.0\n"
quartus_project_template = """
set project project_name
set revision project_revision
set target_system target_name
load_package flow
if [project_exists $project] {
project_open -force -current_revision $project
create_revision -set_current $revision
} else {
project_new $project -revision ${revision} -overwrite
}
source ${target_system}_proj.tcl
#Add files here
files_list
# Add post-compile hook
set_global_assignment -name POST_FLOW_SCRIPT_FILE quartus_sh:gen_rbf.tcl
project_close
"""
quartus_compile_template = """
load_package flow
project_open -force -revision project_revision project_name
# compile the project
execute_flow -compile
project_close
"""
class QuartusTemplates:
"""Generate templates for Quartus workflow."""
def __init__(self, num_custom_components, baseAddress=40):
"""Initialize QuartusTemplates.
Parameters
----------
num_custom_components : [type]
[description]
baseAddress : [type]
[description]
"""
self.custom_components_added = 0
self.num_custom_components = num_custom_components
self.de10_components_base_address = baseAddress # Format is 0x0020
self.last_component_added = ""
def add_custom_component_instantiaion(self, component_name):
"""Generate a string representing a component being instantiated in _hw.tcl.
Parameters
----------
component_name : str
Name of Platform Designer component to instantiate
Returns
-------
str
String that instantiates the given component in _hw.tcl
"""
return custom_component_instantiation_template.replace("component_name", component_name)
def add_custom_component_connections(self, component_name, target):
"""Generate _hw.tcl section for connecting a custom component to the system being made.
Connects to initial audio source on first call.
Connects to the last passed in component on future calls.
Once the number of custom_compoents has been reached, the component also gets connnected to audio out
Parameters
----------
component_name : str
Name of Platform Designer component to instantiate
target : Target
target configuration tuple
Returns
-------
str
Returns _hw.tcl section for connecting a custom component to the system being generated.
"""
# Increment address by 0x10 each time a custom_component is added
component_base_address = "0x00" + \
str(self.de10_components_base_address +
self.custom_components_added * 10)
built_string = custom_component_base_connections_template \
.replace("component_name", component_name) \
.replace("component_base_address", component_base_address) \
.replace("axi_master_name", target.axi_master_name) \
.replace("clock_name", target.clock_name)
if(self.custom_components_added == 0):
built_string += custom_component_initial_audio_in_connection_template.replace(
"component_name", component_name).replace("audio_in", target.audio_in)
else:
built_string += custom_component_audio_in_connection_template.replace(
"last_component_name", self.last_component_added).replace("component_name", component_name)
if((self.custom_components_added + 1) == self.num_custom_components):
built_string += custom_component_final_audio_out_connection_template.replace(
"component_name", component_name).replace("audio_out", target.audio_out)
self.custom_components_added = 0
return built_string
self.last_component_added = component_name
self.custom_components_added += 1
return built_string
def add_quartus_project(self, proj_name, project_revision, target):
"""Generate a tcl file that can be run to make a Quartus project.
Parameters
----------
proj_name : str
Name of the project
project_revision : str
Name of the project revision
target : Target
NamedTuple that provides information about the target configuration
Returns
-------
str
tcl file that describes the project
"""
files = self.add_files_list(target.files_list)
quartus_project = quartus_project_template.replace("project_name", proj_name).replace(
"project_revision", project_revision).replace("files_list", files).replace("target_name", target.name)
if(target.name == "reflex"):
quartus_project.replace("rbf", "jic")
return quartus_project
def add_quartus_compile_project(self, project_name, project_revision):
"""Generate a tcl file that can be run to compile the given Quartus project.
Parameters
----------
project_name : str
Name of the project
project_revision : str
Name of the project revision
Returns
-------
str
tcl file that has the commands to compile the project
"""
return quartus_compile_template.replace("project_name", project_name).replace("project_revision", project_revision)
def add_files_list(self, files):
"""Generate tcl commands to add a list of files to an open Quartus project.
Parameters
----------
files : list of str
List of files to be added to a Quartus project
Returns
-------
str
List of tcl commands to add the given files to an open Quartus project
"""
files_list = ''
base_string = '\nset_global_assignment -name '
tcl_file_type = ''
for file in files:
if file.endswith('vhd'):
tcl_file_type = 'VHDL_FILE '
elif file.endswith('qip'):
tcl_file_type = 'QIP_FILE '
elif file.endswith('qsys'):
tcl_file_type = 'QSYS_FILE '
elif file.endswith('sdc'):
tcl_file_type = 'SDC_FILE '
else:
tcl_file_type = 'SOURCE_FILE '
files_list += base_string + tcl_file_type + file
return files_list | 0.695441 | 0.205575 |
from io import BytesIO
from behave import given, when, then, use_step_matcher
from PIL import Image, ImageColor
from light_character.light_character import save_characteristic_as_image
use_step_matcher('re')
@when(u'I request an image with the characteristic (?P<characteristic>.+)')
def request_image(context, characteristic):
buffer = BytesIO()
try:
save_characteristic_as_image(characteristic, (1, 1), buffer)
buffer.seek(0)
context.image = Image.open(buffer)
except ValueError as e:
context.exception = e
@then(u'the image is a fixed (white|red|green|yellow) light')
def fixed_light(context, colour):
assert_frame_colour(context.image, colour)
try:
context.image.seek(1)
except EOFError:
return
assert False
@then(u'the first frame is (?P<colour>white|red|green|yellow|transparent) for (?P<duration>[.?\d]+) seconds?')
def first_frame(context, colour, duration):
context.image.seek(0)
assert_frame_colour(context.image, colour)
assert_frame_duration(context.image, duration)
@then(u'the next frame is (?P<colour>white|red|green|yellow|transparent) for (?P<duration>[.?\d]+) seconds?')
def next_frame(context, colour, duration):
context.image.seek(context.image.tell() + 1)
assert_frame_colour(context.image, colour)
assert_frame_duration(context.image, duration)
@then(u'the request fails')
def step_impl(context):
assert isinstance(context.exception, ValueError)
def assert_frame_duration(frame, duration):
duration = round(float(duration), 2) * 1000
try:
assert frame.info['duration'] == duration
except AssertionError:
print(frame.info['duration'], duration)
raise
def assert_frame_colour(frame, colour):
# the HTML colour 'green' is a dull mid-green (#008000)
# The appropriate colour for a light (#00ff00) is called 'lime'
colour = 'lime' if colour.lower() == 'green' else colour.replace(' ', '').lower()
rgba = frame.convert('RGBA').getpixel((0, 0))
assert rgba[:3] == ImageColor.getrgb(colour) if colour != 'transparent' else (0,0,0)
if colour == 'transparent':
assert rgba[3] == 0
else:
assert rgba[3] != 0 | features/steps/__init__.py | from io import BytesIO
from behave import given, when, then, use_step_matcher
from PIL import Image, ImageColor
from light_character.light_character import save_characteristic_as_image
use_step_matcher('re')
@when(u'I request an image with the characteristic (?P<characteristic>.+)')
def request_image(context, characteristic):
buffer = BytesIO()
try:
save_characteristic_as_image(characteristic, (1, 1), buffer)
buffer.seek(0)
context.image = Image.open(buffer)
except ValueError as e:
context.exception = e
@then(u'the image is a fixed (white|red|green|yellow) light')
def fixed_light(context, colour):
assert_frame_colour(context.image, colour)
try:
context.image.seek(1)
except EOFError:
return
assert False
@then(u'the first frame is (?P<colour>white|red|green|yellow|transparent) for (?P<duration>[.?\d]+) seconds?')
def first_frame(context, colour, duration):
context.image.seek(0)
assert_frame_colour(context.image, colour)
assert_frame_duration(context.image, duration)
@then(u'the next frame is (?P<colour>white|red|green|yellow|transparent) for (?P<duration>[.?\d]+) seconds?')
def next_frame(context, colour, duration):
context.image.seek(context.image.tell() + 1)
assert_frame_colour(context.image, colour)
assert_frame_duration(context.image, duration)
@then(u'the request fails')
def step_impl(context):
assert isinstance(context.exception, ValueError)
def assert_frame_duration(frame, duration):
duration = round(float(duration), 2) * 1000
try:
assert frame.info['duration'] == duration
except AssertionError:
print(frame.info['duration'], duration)
raise
def assert_frame_colour(frame, colour):
# the HTML colour 'green' is a dull mid-green (#008000)
# The appropriate colour for a light (#00ff00) is called 'lime'
colour = 'lime' if colour.lower() == 'green' else colour.replace(' ', '').lower()
rgba = frame.convert('RGBA').getpixel((0, 0))
assert rgba[:3] == ImageColor.getrgb(colour) if colour != 'transparent' else (0,0,0)
if colour == 'transparent':
assert rgba[3] == 0
else:
assert rgba[3] != 0 | 0.734024 | 0.375678 |
import pandas as pd
import geopandas as gpd
import folium
from shapely.geometry import Point
from folium import plugins
from auth import spreadsheet_service
def create():
range_name = 'Sheet1!A1:G1000'
spreadsheet_id = '1zB9WAxgGIbhyWlLpj6g1Fhnke1Rrf5EQmduipE1pq34'
result = spreadsheet_service.spreadsheets().values().get(
spreadsheetId=spreadsheet_id, range=range_name).execute()
rows = result.get('values', [])
df = pd.DataFrame(rows)
new_header = df.iloc[0] #grab the first row for the header
df = df[1:] #take the data less the header row
df.columns = new_header #set the header row as the df header
pdt = df['country'].value_counts()
map = folium.Map(location = [9.0820, 8.6753], tiles = "OpenStreetMap",
zoom_start = 6, min_zoom = 2, max_zoom = 10)
geometry = gpd.points_from_xy(df.long, df.lat)
geo_df = gpd.GeoDataFrame(df, geometry=geometry)
geo_df_list = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]
i = 1
for coordinates in geo_df_list:
if geo_df.country[i] == "United States":
type_color = "green"
elif geo_df.country[i] == "Nigeria":
type_color = "blue"
else:
type_color = "purple"
#now place the markers with the popup labels and data
map.add_child(folium.Marker(location = coordinates,
popup =
"Continent: " + str(geo_df.continent[i]) + '<br>' +
"Country: " + str(geo_df.country[i]) + '<br>' +
"Total: " + str(pdt[geo_df.country[i]]) + '<br>' +
"State: " + str(geo_df.state[i]),
icon = folium.Icon(color = "%s" % type_color)))
i = i + 1
map.save('./static/map/locations.html')
map2 = folium.Map(location = [15, 30], tiles = "Cartodb dark_matter",
zoom_start = 2)
heat_data = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]
heat_data
plugins.HeatMap(heat_data).add_to(map2)
map2.save('./static/map/locationsHeatMap.html') | map.py | import pandas as pd
import geopandas as gpd
import folium
from shapely.geometry import Point
from folium import plugins
from auth import spreadsheet_service
def create():
range_name = 'Sheet1!A1:G1000'
spreadsheet_id = '1zB9WAxgGIbhyWlLpj6g1Fhnke1Rrf5EQmduipE1pq34'
result = spreadsheet_service.spreadsheets().values().get(
spreadsheetId=spreadsheet_id, range=range_name).execute()
rows = result.get('values', [])
df = pd.DataFrame(rows)
new_header = df.iloc[0] #grab the first row for the header
df = df[1:] #take the data less the header row
df.columns = new_header #set the header row as the df header
pdt = df['country'].value_counts()
map = folium.Map(location = [9.0820, 8.6753], tiles = "OpenStreetMap",
zoom_start = 6, min_zoom = 2, max_zoom = 10)
geometry = gpd.points_from_xy(df.long, df.lat)
geo_df = gpd.GeoDataFrame(df, geometry=geometry)
geo_df_list = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]
i = 1
for coordinates in geo_df_list:
if geo_df.country[i] == "United States":
type_color = "green"
elif geo_df.country[i] == "Nigeria":
type_color = "blue"
else:
type_color = "purple"
#now place the markers with the popup labels and data
map.add_child(folium.Marker(location = coordinates,
popup =
"Continent: " + str(geo_df.continent[i]) + '<br>' +
"Country: " + str(geo_df.country[i]) + '<br>' +
"Total: " + str(pdt[geo_df.country[i]]) + '<br>' +
"State: " + str(geo_df.state[i]),
icon = folium.Icon(color = "%s" % type_color)))
i = i + 1
map.save('./static/map/locations.html')
map2 = folium.Map(location = [15, 30], tiles = "Cartodb dark_matter",
zoom_start = 2)
heat_data = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]
heat_data
plugins.HeatMap(heat_data).add_to(map2)
map2.save('./static/map/locationsHeatMap.html') | 0.213131 | 0.485783 |
from conans import ConanFile, CMake, tools
import os
import shutil
class gRPCConan(ConanFile):
name = "gRPC"
version = "1.1.0-dev" # Nov 8
folder = "grpc-31606bdb34474d8728350ad45baf0e91b590b041"
url = "https://github.com/a_teammate/conan-grpc.git"
license = "BSD-3Clause"
requires = "zlib/1.2.8@lasote/stable", "OpenSSL/1.0.2i@lasote/stable", "Protobuf/3.1.0@a_teammate/stable"
settings = "os", "compiler", "build_type", "arch"
options = {
"shared": [True, False],
"enable_mobile": [True, False], # Enables iOS and Android support
"non_cpp_plugins":[True, False] # Enables plugins such as --java-out and --py-out (if False, only --cpp-out is possible)
}
default_options = '''shared=False
enable_mobile=False
non_cpp_plugins=False
'''
generators = "cmake"
short_paths = True # Otherwise some folders go out of the 260 chars path length scope rapidly (on windows)
def source(self):
tools.download("https://github.com/grpc/grpc/archive/31606bdb34474d8728350ad45baf0e91b590b041.zip", "grpc.zip")
tools.unzip("grpc.zip")
os.unlink("grpc.zip")
cmake_name = "{}/CMakeLists.txt".format(self.folder)
# tell grpc to use our deps and flags
tools.replace_in_file(cmake_name, "project(${PACKAGE_NAME} C CXX)", '''project(${PACKAGE_NAME} C CXX)
include(${CMAKE_BINARY_DIR}/conanbuildinfo.cmake)
conan_basic_setup()''')
tools.replace_in_file(cmake_name, "\"module\" CACHE STRING ", '''\"package\" CACHE STRING ''') # tell grpc to use the find_package version
# skip installing the headers, TODO: use these!
tools.replace_in_file(cmake_name, ''' install(FILES ${_hdr}
DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}/${_path}"
)''', ''' # install(FILES ${_hdr} # COMMENTED BY CONAN
# DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}/${_path}"
# )''')
# Add some CMake Variables (effectively commenting out stuff we do not support)
tools.replace_in_file(cmake_name, "add_library(grpc_cronet", '''if(CONAN_ENABLE_MOBILE)
add_library(grpc_cronet''')
tools.replace_in_file(cmake_name, "add_library(grpc_unsecure", '''endif(CONAN_ENABLE_MOBILE)
add_library(grpc_unsecure''')
tools.replace_in_file(cmake_name, "add_library(grpc++_cronet", '''if(CONAN_ENABLE_MOBILE)
add_library(grpc++_cronet''')
tools.replace_in_file(cmake_name, "add_library(grpc++_reflection", '''endif(CONAN_ENABLE_MOBILE)
if(CONAN_ENABLE_REFLECTION_LIBS)
add_library(grpc++_reflection''')
tools.replace_in_file(cmake_name, "add_library(grpc++_unsecure", '''endif(CONAN_ENABLE_REFLECTION_LIBS)
add_library(grpc++_unsecure''')
tools.replace_in_file(cmake_name, "add_library(grpc_csharp_ext", '''if(CONAN_ADDITIONAL_PLUGINS)
add_library(grpc_csharp_ext''')
tools.replace_in_file(cmake_name, "add_executable(gen_hpack_tables", '''endif(CONAN_ADDITIONAL_PLUGINS)
if(CONAN_ADDITIONAL_BINS)
add_executable(gen_hpack_tables''')
tools.replace_in_file(cmake_name, "add_executable(grpc_cpp_plugin", '''endif(CONAN_ADDITIONAL_BINS)
add_executable(grpc_cpp_plugin''')
tools.replace_in_file(cmake_name, "add_executable(grpc_csharp_plugin", '''if(CONAN_ADDITIONAL_PLUGINS)
add_executable(grpc_csharp_plugin''')
tools.replace_in_file(cmake_name, '''install(TARGETS grpc_ruby_plugin EXPORT gRPCTargets
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
)
endif()''', '''install(TARGETS grpc_ruby_plugin EXPORT gRPCTargets
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
)
endif()
endif(CONAN_ADDITIONAL_PLUGINS)''')
def build(self):
tmp_install_dir = "{}/install".format(os.getcwd())
os.mkdir(tmp_install_dir)
args = ["-DgRPC_INSTALL=ON", '-DCMAKE_INSTALL_PREFIX="{}"'.format(tmp_install_dir)] # We need the generated cmake/ files (bc they depend on the list of targets, which is dynamic)
if self.options.non_cpp_plugins:
args += ["-DCONAN_ADDITIONAL_PLUGINS=ON"]
if self.options.enable_mobile:
args += ["-DCONAN_ENABLE_MOBILE=ON"]
cmake = CMake(self.settings)
self.run('cmake {0}/{1} {2} {3}'.format(self.conanfile_directory, self.folder, cmake.command_line, ' '.join(args)))
self.run("cmake --build . --target install {}".format(cmake.build_config))
# Patch the generated findGRPC .cmake files:
cmake_find_folder = "{}/lib/cmake/gRPC".format(tmp_install_dir)
cmake_find_file = "{}/gRPCTargets.cmake".format(cmake_find_folder)
tools.replace_in_file(cmake_find_file, 'get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)', '''get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
set(_IMPORT_PREFIX ${CONAN_GRPC_ROOT}) # NOTE: ADDED by conan''')
def package(self):
cmake_folder = "{}/install/lib/cmake/gRPC".format(os.getcwd()) # Is there a better way maybe to obtain the build dir?
cmake_files = ["gRPCConfig.cmake", "gRPCConfigVersion.cmake", "gRPCTargets.cmake"]
for file in cmake_files:
self.copy(file, dst='.', src=cmake_folder)
# Copy the build_type specific file only for our used one:
self.copy("gRPCTargets-{}.cmake".format("debug" if self.settings.build_type == "Debug" else "release"), dst=".", src=cmake_folder)
self.copy('*', dst='include', src='{}/include'.format(self.folder))
self.copy("*.lib", dst="lib", src="", keep_path=False)
self.copy("*.a", dst="lib", src="", keep_path=False)
self.copy("*", dst="bin", src="bin")
self.copy("*.dll", dst="bin", keep_path=False)
self.copy("*.so", dst="lib", keep_path=False)
def package_info(self):
self.cpp_info.libs = ["gpr", "grpc", "grpc++", "grpc_unsecure", "grpc++_unsecure"]
if self.settings.compiler == "Visual Studio":
self.cpp_info.libs += ["wsock32", "ws2_32"] | conanfile.py | from conans import ConanFile, CMake, tools
import os
import shutil
class gRPCConan(ConanFile):
name = "gRPC"
version = "1.1.0-dev" # Nov 8
folder = "grpc-31606bdb34474d8728350ad45baf0e91b590b041"
url = "https://github.com/a_teammate/conan-grpc.git"
license = "BSD-3Clause"
requires = "zlib/1.2.8@lasote/stable", "OpenSSL/1.0.2i@lasote/stable", "Protobuf/3.1.0@a_teammate/stable"
settings = "os", "compiler", "build_type", "arch"
options = {
"shared": [True, False],
"enable_mobile": [True, False], # Enables iOS and Android support
"non_cpp_plugins":[True, False] # Enables plugins such as --java-out and --py-out (if False, only --cpp-out is possible)
}
default_options = '''shared=False
enable_mobile=False
non_cpp_plugins=False
'''
generators = "cmake"
short_paths = True # Otherwise some folders go out of the 260 chars path length scope rapidly (on windows)
def source(self):
tools.download("https://github.com/grpc/grpc/archive/31606bdb34474d8728350ad45baf0e91b590b041.zip", "grpc.zip")
tools.unzip("grpc.zip")
os.unlink("grpc.zip")
cmake_name = "{}/CMakeLists.txt".format(self.folder)
# tell grpc to use our deps and flags
tools.replace_in_file(cmake_name, "project(${PACKAGE_NAME} C CXX)", '''project(${PACKAGE_NAME} C CXX)
include(${CMAKE_BINARY_DIR}/conanbuildinfo.cmake)
conan_basic_setup()''')
tools.replace_in_file(cmake_name, "\"module\" CACHE STRING ", '''\"package\" CACHE STRING ''') # tell grpc to use the find_package version
# skip installing the headers, TODO: use these!
tools.replace_in_file(cmake_name, ''' install(FILES ${_hdr}
DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}/${_path}"
)''', ''' # install(FILES ${_hdr} # COMMENTED BY CONAN
# DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}/${_path}"
# )''')
# Add some CMake Variables (effectively commenting out stuff we do not support)
tools.replace_in_file(cmake_name, "add_library(grpc_cronet", '''if(CONAN_ENABLE_MOBILE)
add_library(grpc_cronet''')
tools.replace_in_file(cmake_name, "add_library(grpc_unsecure", '''endif(CONAN_ENABLE_MOBILE)
add_library(grpc_unsecure''')
tools.replace_in_file(cmake_name, "add_library(grpc++_cronet", '''if(CONAN_ENABLE_MOBILE)
add_library(grpc++_cronet''')
tools.replace_in_file(cmake_name, "add_library(grpc++_reflection", '''endif(CONAN_ENABLE_MOBILE)
if(CONAN_ENABLE_REFLECTION_LIBS)
add_library(grpc++_reflection''')
tools.replace_in_file(cmake_name, "add_library(grpc++_unsecure", '''endif(CONAN_ENABLE_REFLECTION_LIBS)
add_library(grpc++_unsecure''')
tools.replace_in_file(cmake_name, "add_library(grpc_csharp_ext", '''if(CONAN_ADDITIONAL_PLUGINS)
add_library(grpc_csharp_ext''')
tools.replace_in_file(cmake_name, "add_executable(gen_hpack_tables", '''endif(CONAN_ADDITIONAL_PLUGINS)
if(CONAN_ADDITIONAL_BINS)
add_executable(gen_hpack_tables''')
tools.replace_in_file(cmake_name, "add_executable(grpc_cpp_plugin", '''endif(CONAN_ADDITIONAL_BINS)
add_executable(grpc_cpp_plugin''')
tools.replace_in_file(cmake_name, "add_executable(grpc_csharp_plugin", '''if(CONAN_ADDITIONAL_PLUGINS)
add_executable(grpc_csharp_plugin''')
tools.replace_in_file(cmake_name, '''install(TARGETS grpc_ruby_plugin EXPORT gRPCTargets
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
)
endif()''', '''install(TARGETS grpc_ruby_plugin EXPORT gRPCTargets
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
)
endif()
endif(CONAN_ADDITIONAL_PLUGINS)''')
def build(self):
tmp_install_dir = "{}/install".format(os.getcwd())
os.mkdir(tmp_install_dir)
args = ["-DgRPC_INSTALL=ON", '-DCMAKE_INSTALL_PREFIX="{}"'.format(tmp_install_dir)] # We need the generated cmake/ files (bc they depend on the list of targets, which is dynamic)
if self.options.non_cpp_plugins:
args += ["-DCONAN_ADDITIONAL_PLUGINS=ON"]
if self.options.enable_mobile:
args += ["-DCONAN_ENABLE_MOBILE=ON"]
cmake = CMake(self.settings)
self.run('cmake {0}/{1} {2} {3}'.format(self.conanfile_directory, self.folder, cmake.command_line, ' '.join(args)))
self.run("cmake --build . --target install {}".format(cmake.build_config))
# Patch the generated findGRPC .cmake files:
cmake_find_folder = "{}/lib/cmake/gRPC".format(tmp_install_dir)
cmake_find_file = "{}/gRPCTargets.cmake".format(cmake_find_folder)
tools.replace_in_file(cmake_find_file, 'get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)', '''get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
set(_IMPORT_PREFIX ${CONAN_GRPC_ROOT}) # NOTE: ADDED by conan''')
def package(self):
cmake_folder = "{}/install/lib/cmake/gRPC".format(os.getcwd()) # Is there a better way maybe to obtain the build dir?
cmake_files = ["gRPCConfig.cmake", "gRPCConfigVersion.cmake", "gRPCTargets.cmake"]
for file in cmake_files:
self.copy(file, dst='.', src=cmake_folder)
# Copy the build_type specific file only for our used one:
self.copy("gRPCTargets-{}.cmake".format("debug" if self.settings.build_type == "Debug" else "release"), dst=".", src=cmake_folder)
self.copy('*', dst='include', src='{}/include'.format(self.folder))
self.copy("*.lib", dst="lib", src="", keep_path=False)
self.copy("*.a", dst="lib", src="", keep_path=False)
self.copy("*", dst="bin", src="bin")
self.copy("*.dll", dst="bin", keep_path=False)
self.copy("*.so", dst="lib", keep_path=False)
def package_info(self):
self.cpp_info.libs = ["gpr", "grpc", "grpc++", "grpc_unsecure", "grpc++_unsecure"]
if self.settings.compiler == "Visual Studio":
self.cpp_info.libs += ["wsock32", "ws2_32"] | 0.223377 | 0.071332 |
from typing import Optional
from botocore.client import BaseClient
from typing import Dict
from typing import Union
from botocore.paginate import Paginator
from datetime import datetime
from botocore.waiter import Waiter
from typing import List
class Client(BaseClient):
def allocate_static_ip(self, staticIpName: str) -> Dict:
pass
def attach_disk(self, diskName: str, instanceName: str, diskPath: str) -> Dict:
pass
def attach_instances_to_load_balancer(self, loadBalancerName: str, instanceNames: List) -> Dict:
pass
def attach_load_balancer_tls_certificate(self, loadBalancerName: str, certificateName: str) -> Dict:
pass
def attach_static_ip(self, staticIpName: str, instanceName: str) -> Dict:
pass
def can_paginate(self, operation_name: str = None):
pass
def close_instance_public_ports(self, portInfo: Dict, instanceName: str) -> Dict:
pass
def copy_snapshot(self, sourceSnapshotName: str, targetSnapshotName: str, sourceRegion: str) -> Dict:
pass
def create_cloud_formation_stack(self, instances: List) -> Dict:
pass
def create_disk(self, diskName: str, availabilityZone: str, sizeInGb: int, tags: List = None) -> Dict:
pass
def create_disk_from_snapshot(self, diskName: str, diskSnapshotName: str, availabilityZone: str, sizeInGb: int, tags: List = None) -> Dict:
pass
def create_disk_snapshot(self, diskSnapshotName: str, diskName: str = None, instanceName: str = None, tags: List = None) -> Dict:
pass
def create_domain(self, domainName: str, tags: List = None) -> Dict:
pass
def create_domain_entry(self, domainName: str, domainEntry: Dict) -> Dict:
pass
def create_instance_snapshot(self, instanceSnapshotName: str, instanceName: str, tags: List = None) -> Dict:
pass
def create_instances(self, instanceNames: List, availabilityZone: str, blueprintId: str, bundleId: str, customImageName: str = None, userData: str = None, keyPairName: str = None, tags: List = None) -> Dict:
pass
def create_instances_from_snapshot(self, instanceNames: List, availabilityZone: str, instanceSnapshotName: str, bundleId: str, attachedDiskMapping: Dict = None, userData: str = None, keyPairName: str = None, tags: List = None) -> Dict:
pass
def create_key_pair(self, keyPairName: str, tags: List = None) -> Dict:
pass
def create_load_balancer(self, loadBalancerName: str, instancePort: int, healthCheckPath: str = None, certificateName: str = None, certificateDomainName: str = None, certificateAlternativeNames: List = None, tags: List = None) -> Dict:
pass
def create_load_balancer_tls_certificate(self, loadBalancerName: str, certificateName: str, certificateDomainName: str, certificateAlternativeNames: List = None, tags: List = None) -> Dict:
pass
def create_relational_database(self, relationalDatabaseName: str, relationalDatabaseBlueprintId: str, relationalDatabaseBundleId: str, masterDatabaseName: str, masterUsername: str, availabilityZone: str = None, masterUserPassword: str = None, preferredBackupWindow: str = None, preferredMaintenanceWindow: str = None, publiclyAccessible: bool = None, tags: List = None) -> Dict:
pass
def create_relational_database_from_snapshot(self, relationalDatabaseName: str, availabilityZone: str = None, publiclyAccessible: bool = None, relationalDatabaseSnapshotName: str = None, relationalDatabaseBundleId: str = None, sourceRelationalDatabaseName: str = None, restoreTime: datetime = None, useLatestRestorableTime: bool = None, tags: List = None) -> Dict:
pass
def create_relational_database_snapshot(self, relationalDatabaseName: str, relationalDatabaseSnapshotName: str, tags: List = None) -> Dict:
pass
def delete_disk(self, diskName: str) -> Dict:
pass
def delete_disk_snapshot(self, diskSnapshotName: str) -> Dict:
pass
def delete_domain(self, domainName: str) -> Dict:
pass
def delete_domain_entry(self, domainName: str, domainEntry: Dict) -> Dict:
pass
def delete_instance(self, instanceName: str) -> Dict:
pass
def delete_instance_snapshot(self, instanceSnapshotName: str) -> Dict:
pass
def delete_key_pair(self, keyPairName: str) -> Dict:
pass
def delete_known_host_keys(self, instanceName: str) -> Dict:
pass
def delete_load_balancer(self, loadBalancerName: str) -> Dict:
pass
def delete_load_balancer_tls_certificate(self, loadBalancerName: str, certificateName: str, force: bool = None) -> Dict:
pass
def delete_relational_database(self, relationalDatabaseName: str, skipFinalSnapshot: bool = None, finalRelationalDatabaseSnapshotName: str = None) -> Dict:
pass
def delete_relational_database_snapshot(self, relationalDatabaseSnapshotName: str) -> Dict:
pass
def detach_disk(self, diskName: str) -> Dict:
pass
def detach_instances_from_load_balancer(self, loadBalancerName: str, instanceNames: List) -> Dict:
pass
def detach_static_ip(self, staticIpName: str) -> Dict:
pass
def download_default_key_pair(self) -> Dict:
pass
def export_snapshot(self, sourceSnapshotName: str) -> Dict:
pass
def generate_presigned_url(self, ClientMethod: str = None, Params: Dict = None, ExpiresIn: int = None, HttpMethod: str = None):
pass
def get_active_names(self, pageToken: str = None) -> Dict:
pass
def get_blueprints(self, includeInactive: bool = None, pageToken: str = None) -> Dict:
pass
def get_bundles(self, includeInactive: bool = None, pageToken: str = None) -> Dict:
pass
def get_cloud_formation_stack_records(self, pageToken: str = None) -> Dict:
pass
def get_disk(self, diskName: str) -> Dict:
pass
def get_disk_snapshot(self, diskSnapshotName: str) -> Dict:
pass
def get_disk_snapshots(self, pageToken: str = None) -> Dict:
pass
def get_disks(self, pageToken: str = None) -> Dict:
pass
def get_domain(self, domainName: str) -> Dict:
pass
def get_domains(self, pageToken: str = None) -> Dict:
pass
def get_export_snapshot_records(self, pageToken: str = None) -> Dict:
pass
def get_instance(self, instanceName: str) -> Dict:
pass
def get_instance_access_details(self, instanceName: str, protocol: str = None) -> Dict:
pass
def get_instance_metric_data(self, instanceName: str, metricName: str, period: int, startTime: datetime, endTime: datetime, unit: str, statistics: List) -> Dict:
pass
def get_instance_port_states(self, instanceName: str) -> Dict:
pass
def get_instance_snapshot(self, instanceSnapshotName: str) -> Dict:
pass
def get_instance_snapshots(self, pageToken: str = None) -> Dict:
pass
def get_instance_state(self, instanceName: str) -> Dict:
pass
def get_instances(self, pageToken: str = None) -> Dict:
pass
def get_key_pair(self, keyPairName: str) -> Dict:
pass
def get_key_pairs(self, pageToken: str = None) -> Dict:
pass
def get_load_balancer(self, loadBalancerName: str) -> Dict:
pass
def get_load_balancer_metric_data(self, loadBalancerName: str, metricName: str, period: int, startTime: datetime, endTime: datetime, unit: str, statistics: List) -> Dict:
pass
def get_load_balancer_tls_certificates(self, loadBalancerName: str) -> Dict:
pass
def get_load_balancers(self, pageToken: str = None) -> Dict:
pass
def get_operation(self, operationId: str) -> Dict:
pass
def get_operations(self, pageToken: str = None) -> Dict:
pass
def get_operations_for_resource(self, resourceName: str, pageToken: str = None) -> Dict:
pass
def get_paginator(self, operation_name: str = None) -> Paginator:
pass
def get_regions(self, includeAvailabilityZones: bool = None, includeRelationalDatabaseAvailabilityZones: bool = None) -> Dict:
pass
def get_relational_database(self, relationalDatabaseName: str) -> Dict:
pass
def get_relational_database_blueprints(self, pageToken: str = None) -> Dict:
pass
def get_relational_database_bundles(self, pageToken: str = None) -> Dict:
pass
def get_relational_database_events(self, relationalDatabaseName: str, durationInMinutes: int = None, pageToken: str = None) -> Dict:
pass
def get_relational_database_log_events(self, relationalDatabaseName: str, logStreamName: str, startTime: datetime = None, endTime: datetime = None, startFromHead: bool = None, pageToken: str = None) -> Dict:
pass
def get_relational_database_log_streams(self, relationalDatabaseName: str) -> Dict:
pass
def get_relational_database_master_user_password(self, relationalDatabaseName: str, passwordVersion: str = None) -> Dict:
pass
def get_relational_database_metric_data(self, relationalDatabaseName: str, metricName: str, period: int, startTime: datetime, endTime: datetime, unit: str, statistics: List) -> Dict:
pass
def get_relational_database_parameters(self, relationalDatabaseName: str, pageToken: str = None) -> Dict:
pass
def get_relational_database_snapshot(self, relationalDatabaseSnapshotName: str) -> Dict:
pass
def get_relational_database_snapshots(self, pageToken: str = None) -> Dict:
pass
def get_relational_databases(self, pageToken: str = None) -> Dict:
pass
def get_static_ip(self, staticIpName: str) -> Dict:
pass
def get_static_ips(self, pageToken: str = None) -> Dict:
pass
def get_waiter(self, waiter_name: str = None) -> Waiter:
pass
def import_key_pair(self, keyPairName: str, publicKeyBase64: str) -> Dict:
pass
def is_vpc_peered(self) -> Dict:
pass
def open_instance_public_ports(self, portInfo: Dict, instanceName: str) -> Dict:
pass
def peer_vpc(self) -> Dict:
pass
def put_instance_public_ports(self, portInfos: List, instanceName: str) -> Dict:
pass
def reboot_instance(self, instanceName: str) -> Dict:
pass
def reboot_relational_database(self, relationalDatabaseName: str) -> Dict:
pass
def release_static_ip(self, staticIpName: str) -> Dict:
pass
def start_instance(self, instanceName: str) -> Dict:
pass
def start_relational_database(self, relationalDatabaseName: str) -> Dict:
pass
def stop_instance(self, instanceName: str, force: bool = None) -> Dict:
pass
def stop_relational_database(self, relationalDatabaseName: str, relationalDatabaseSnapshotName: str = None) -> Dict:
pass
def tag_resource(self, resourceName: str, tags: List) -> Dict:
pass
def unpeer_vpc(self) -> Dict:
pass
def untag_resource(self, resourceName: str, tagKeys: List) -> Dict:
pass
def update_domain_entry(self, domainName: str, domainEntry: Dict) -> Dict:
pass
def update_load_balancer_attribute(self, loadBalancerName: str, attributeName: str, attributeValue: str) -> Dict:
pass
def update_relational_database(self, relationalDatabaseName: str, masterUserPassword: str = None, rotateMasterUserPassword: bool = None, preferredBackupWindow: str = None, preferredMaintenanceWindow: str = None, enableBackupRetention: bool = None, disableBackupRetention: bool = None, publiclyAccessible: bool = None, applyImmediately: bool = None) -> Dict:
pass
def update_relational_database_parameters(self, relationalDatabaseName: str, parameters: List) -> Dict:
pass | boto3_type_annotations/boto3_type_annotations/lightsail/client.py | from typing import Optional
from botocore.client import BaseClient
from typing import Dict
from typing import Union
from botocore.paginate import Paginator
from datetime import datetime
from botocore.waiter import Waiter
from typing import List
class Client(BaseClient):
def allocate_static_ip(self, staticIpName: str) -> Dict:
pass
def attach_disk(self, diskName: str, instanceName: str, diskPath: str) -> Dict:
pass
def attach_instances_to_load_balancer(self, loadBalancerName: str, instanceNames: List) -> Dict:
pass
def attach_load_balancer_tls_certificate(self, loadBalancerName: str, certificateName: str) -> Dict:
pass
def attach_static_ip(self, staticIpName: str, instanceName: str) -> Dict:
pass
def can_paginate(self, operation_name: str = None):
pass
def close_instance_public_ports(self, portInfo: Dict, instanceName: str) -> Dict:
pass
def copy_snapshot(self, sourceSnapshotName: str, targetSnapshotName: str, sourceRegion: str) -> Dict:
pass
def create_cloud_formation_stack(self, instances: List) -> Dict:
pass
def create_disk(self, diskName: str, availabilityZone: str, sizeInGb: int, tags: List = None) -> Dict:
pass
def create_disk_from_snapshot(self, diskName: str, diskSnapshotName: str, availabilityZone: str, sizeInGb: int, tags: List = None) -> Dict:
pass
def create_disk_snapshot(self, diskSnapshotName: str, diskName: str = None, instanceName: str = None, tags: List = None) -> Dict:
pass
def create_domain(self, domainName: str, tags: List = None) -> Dict:
pass
def create_domain_entry(self, domainName: str, domainEntry: Dict) -> Dict:
pass
def create_instance_snapshot(self, instanceSnapshotName: str, instanceName: str, tags: List = None) -> Dict:
pass
def create_instances(self, instanceNames: List, availabilityZone: str, blueprintId: str, bundleId: str, customImageName: str = None, userData: str = None, keyPairName: str = None, tags: List = None) -> Dict:
pass
def create_instances_from_snapshot(self, instanceNames: List, availabilityZone: str, instanceSnapshotName: str, bundleId: str, attachedDiskMapping: Dict = None, userData: str = None, keyPairName: str = None, tags: List = None) -> Dict:
pass
def create_key_pair(self, keyPairName: str, tags: List = None) -> Dict:
pass
def create_load_balancer(self, loadBalancerName: str, instancePort: int, healthCheckPath: str = None, certificateName: str = None, certificateDomainName: str = None, certificateAlternativeNames: List = None, tags: List = None) -> Dict:
pass
def create_load_balancer_tls_certificate(self, loadBalancerName: str, certificateName: str, certificateDomainName: str, certificateAlternativeNames: List = None, tags: List = None) -> Dict:
pass
def create_relational_database(self, relationalDatabaseName: str, relationalDatabaseBlueprintId: str, relationalDatabaseBundleId: str, masterDatabaseName: str, masterUsername: str, availabilityZone: str = None, masterUserPassword: str = None, preferredBackupWindow: str = None, preferredMaintenanceWindow: str = None, publiclyAccessible: bool = None, tags: List = None) -> Dict:
pass
def create_relational_database_from_snapshot(self, relationalDatabaseName: str, availabilityZone: str = None, publiclyAccessible: bool = None, relationalDatabaseSnapshotName: str = None, relationalDatabaseBundleId: str = None, sourceRelationalDatabaseName: str = None, restoreTime: datetime = None, useLatestRestorableTime: bool = None, tags: List = None) -> Dict:
pass
def create_relational_database_snapshot(self, relationalDatabaseName: str, relationalDatabaseSnapshotName: str, tags: List = None) -> Dict:
pass
def delete_disk(self, diskName: str) -> Dict:
pass
def delete_disk_snapshot(self, diskSnapshotName: str) -> Dict:
pass
def delete_domain(self, domainName: str) -> Dict:
pass
def delete_domain_entry(self, domainName: str, domainEntry: Dict) -> Dict:
pass
def delete_instance(self, instanceName: str) -> Dict:
pass
def delete_instance_snapshot(self, instanceSnapshotName: str) -> Dict:
pass
def delete_key_pair(self, keyPairName: str) -> Dict:
pass
def delete_known_host_keys(self, instanceName: str) -> Dict:
pass
def delete_load_balancer(self, loadBalancerName: str) -> Dict:
pass
def delete_load_balancer_tls_certificate(self, loadBalancerName: str, certificateName: str, force: bool = None) -> Dict:
pass
def delete_relational_database(self, relationalDatabaseName: str, skipFinalSnapshot: bool = None, finalRelationalDatabaseSnapshotName: str = None) -> Dict:
pass
def delete_relational_database_snapshot(self, relationalDatabaseSnapshotName: str) -> Dict:
pass
def detach_disk(self, diskName: str) -> Dict:
pass
def detach_instances_from_load_balancer(self, loadBalancerName: str, instanceNames: List) -> Dict:
pass
def detach_static_ip(self, staticIpName: str) -> Dict:
pass
def download_default_key_pair(self) -> Dict:
pass
def export_snapshot(self, sourceSnapshotName: str) -> Dict:
pass
def generate_presigned_url(self, ClientMethod: str = None, Params: Dict = None, ExpiresIn: int = None, HttpMethod: str = None):
pass
def get_active_names(self, pageToken: str = None) -> Dict:
pass
def get_blueprints(self, includeInactive: bool = None, pageToken: str = None) -> Dict:
pass
def get_bundles(self, includeInactive: bool = None, pageToken: str = None) -> Dict:
pass
def get_cloud_formation_stack_records(self, pageToken: str = None) -> Dict:
pass
def get_disk(self, diskName: str) -> Dict:
pass
def get_disk_snapshot(self, diskSnapshotName: str) -> Dict:
pass
def get_disk_snapshots(self, pageToken: str = None) -> Dict:
pass
def get_disks(self, pageToken: str = None) -> Dict:
pass
def get_domain(self, domainName: str) -> Dict:
pass
def get_domains(self, pageToken: str = None) -> Dict:
pass
def get_export_snapshot_records(self, pageToken: str = None) -> Dict:
pass
def get_instance(self, instanceName: str) -> Dict:
pass
def get_instance_access_details(self, instanceName: str, protocol: str = None) -> Dict:
pass
def get_instance_metric_data(self, instanceName: str, metricName: str, period: int, startTime: datetime, endTime: datetime, unit: str, statistics: List) -> Dict:
pass
def get_instance_port_states(self, instanceName: str) -> Dict:
pass
def get_instance_snapshot(self, instanceSnapshotName: str) -> Dict:
pass
def get_instance_snapshots(self, pageToken: str = None) -> Dict:
pass
def get_instance_state(self, instanceName: str) -> Dict:
pass
def get_instances(self, pageToken: str = None) -> Dict:
pass
def get_key_pair(self, keyPairName: str) -> Dict:
pass
def get_key_pairs(self, pageToken: str = None) -> Dict:
pass
def get_load_balancer(self, loadBalancerName: str) -> Dict:
pass
def get_load_balancer_metric_data(self, loadBalancerName: str, metricName: str, period: int, startTime: datetime, endTime: datetime, unit: str, statistics: List) -> Dict:
pass
def get_load_balancer_tls_certificates(self, loadBalancerName: str) -> Dict:
pass
def get_load_balancers(self, pageToken: str = None) -> Dict:
pass
def get_operation(self, operationId: str) -> Dict:
pass
def get_operations(self, pageToken: str = None) -> Dict:
pass
def get_operations_for_resource(self, resourceName: str, pageToken: str = None) -> Dict:
pass
def get_paginator(self, operation_name: str = None) -> Paginator:
pass
def get_regions(self, includeAvailabilityZones: bool = None, includeRelationalDatabaseAvailabilityZones: bool = None) -> Dict:
pass
def get_relational_database(self, relationalDatabaseName: str) -> Dict:
pass
def get_relational_database_blueprints(self, pageToken: str = None) -> Dict:
pass
def get_relational_database_bundles(self, pageToken: str = None) -> Dict:
pass
def get_relational_database_events(self, relationalDatabaseName: str, durationInMinutes: int = None, pageToken: str = None) -> Dict:
pass
def get_relational_database_log_events(self, relationalDatabaseName: str, logStreamName: str, startTime: datetime = None, endTime: datetime = None, startFromHead: bool = None, pageToken: str = None) -> Dict:
pass
def get_relational_database_log_streams(self, relationalDatabaseName: str) -> Dict:
pass
def get_relational_database_master_user_password(self, relationalDatabaseName: str, passwordVersion: str = None) -> Dict:
pass
def get_relational_database_metric_data(self, relationalDatabaseName: str, metricName: str, period: int, startTime: datetime, endTime: datetime, unit: str, statistics: List) -> Dict:
pass
def get_relational_database_parameters(self, relationalDatabaseName: str, pageToken: str = None) -> Dict:
pass
def get_relational_database_snapshot(self, relationalDatabaseSnapshotName: str) -> Dict:
pass
def get_relational_database_snapshots(self, pageToken: str = None) -> Dict:
pass
def get_relational_databases(self, pageToken: str = None) -> Dict:
pass
def get_static_ip(self, staticIpName: str) -> Dict:
pass
def get_static_ips(self, pageToken: str = None) -> Dict:
pass
def get_waiter(self, waiter_name: str = None) -> Waiter:
pass
def import_key_pair(self, keyPairName: str, publicKeyBase64: str) -> Dict:
pass
def is_vpc_peered(self) -> Dict:
pass
def open_instance_public_ports(self, portInfo: Dict, instanceName: str) -> Dict:
pass
def peer_vpc(self) -> Dict:
pass
def put_instance_public_ports(self, portInfos: List, instanceName: str) -> Dict:
pass
def reboot_instance(self, instanceName: str) -> Dict:
pass
def reboot_relational_database(self, relationalDatabaseName: str) -> Dict:
pass
def release_static_ip(self, staticIpName: str) -> Dict:
pass
def start_instance(self, instanceName: str) -> Dict:
pass
def start_relational_database(self, relationalDatabaseName: str) -> Dict:
pass
def stop_instance(self, instanceName: str, force: bool = None) -> Dict:
pass
def stop_relational_database(self, relationalDatabaseName: str, relationalDatabaseSnapshotName: str = None) -> Dict:
pass
def tag_resource(self, resourceName: str, tags: List) -> Dict:
pass
def unpeer_vpc(self) -> Dict:
pass
def untag_resource(self, resourceName: str, tagKeys: List) -> Dict:
pass
def update_domain_entry(self, domainName: str, domainEntry: Dict) -> Dict:
pass
def update_load_balancer_attribute(self, loadBalancerName: str, attributeName: str, attributeValue: str) -> Dict:
pass
def update_relational_database(self, relationalDatabaseName: str, masterUserPassword: str = None, rotateMasterUserPassword: bool = None, preferredBackupWindow: str = None, preferredMaintenanceWindow: str = None, enableBackupRetention: bool = None, disableBackupRetention: bool = None, publiclyAccessible: bool = None, applyImmediately: bool = None) -> Dict:
pass
def update_relational_database_parameters(self, relationalDatabaseName: str, parameters: List) -> Dict:
pass | 0.786787 | 0.280561 |
from abc import abstractmethod
from typing import Optional
from PySDDP.dessem.script.templates.arquivo_entrada import ArquivoEntrada
class InfofcfTemplate(ArquivoEntrada):
"""
Classe que contem todos os elementos comuns a qualquer versao do arquivo Infofcf do Dessem.
Esta classe tem como intuito fornecer duck typing para a classe Dessem e ainda adicionar um nivel de especificacao
dentro da fabrica. Alem disso esta classe deve passar adiante a responsabilidade da implementacao dos metodos de
leitura e escrita
"""
def __init__(self):
super().__init__()
self.dir_base = None
self.const = None
self.mapfcf_sisgnl = None
self.mapfcf_durpat = None
self.fcffix_usit = None
self.mapfcf_tviag = None
self.mapfcf_cgtmin = None
self.mapfcf_sisgnl_df = None
self.mapfcf_durpat_df = None
self.fcffix_usit_df = None
self.mapfcf_tviag_df = None
self.mapfcf_cgtmin_df = None
self.infofcf = None
# O arquivo script.arq possuem tanto dados quanto nomes de arquivos,
# então resolvi criar duas estruturas para armazenar estas informações
# e ficar mais fácil na hora da escrita
# {chave: valor}
# chave -> mneumonico ou nome do registro
# valor -> dicionários contendo:
# tipo: 0 para dados ou 1 para arquivos
# descricao: Descricação de cada mneumomico usada na impressao do arquivo
self.dados = {
"infofcf":{
'descricao': 'Armazena todos os comentarios e os mneumos',
'valor': None
},
"mapfcf_sisgnl":{
'descricao':'Identificacao dos subsistemas onde ha usinas com despacho antecipado',
'cabecalho': "& Mnem Ind Num Nlag Npat\n"
"&XXXXX XXXXXX XXX XXX XXX XXX",
'formato': "{mneumo:>14} {ind:>3} {num:>3} {lag:>3} {patamares:>3}\n",
'valor': None
},
"mapfcf_durpat": {
'descricao': 'Duracao dos patamares de carga para os periodos futuros',
'cabecalho': "& Mnem Lag Pat Dur (h)\n"
"&XXXXX XXXXXX XXX XXX XXXXXXXXXX",
'formato': "{mneumo:>14} {lag:>3} {patamar:>3} {duracao:>9}\n",
'valor': None
},
"fcffix_usit":{
'descricao':'Valores de geracao termica sinalizada e/ou comandada para as semanas / meses alem do '
'horizonte de estudo do modelo dessem',
'cabecalho': "& TpEnt IdEnt IdVar lag Pat Valor Justificativa"
"XXXXXX XXXXXX XXX XXXXXX XXX XXX XXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
'formato':"{mneumo:>13} {num_ent:>3} {variavel:>6} {lag:>3} {patamar:>3} {valor:>10} {comentario:>20}\n",
'valor': None
},
"mapfcf_tviag": {
'descricao': 'Informacoes para tempos de viagem considerados pelo modelo DECOMP',
'cabecalho': "& Mnem Ind Num\n"
"&XXXXX XXXXXXX XXX XXX",
'formato':"{mneumo:>14} {ind:>3} {num:>3}\n",
'valor': None
},
"mapfcf_cgtmin": {
'descricao': 'Custo de geracao termica minima alem do horizonte de estudo',
'cabecalho': "& Mnem Custo\n"
"&XXXXX XXXXXXX XXXXXXXXXXXXXXX",
'formato': "{mneumo:>14} {custo:>15}\n",
'valor': None
},
}
# preenchido após o método de leitura ser chamado
# representa os nomes lidos no script.arq
self.nome_arquivos = list()
@abstractmethod
def ler(self, *args, **kwargs) -> None:
"""
Metodo abstrato da ArquivoEntrada sendo repassado para as classes filhas
:param args: conjunto de parametros obrigatorios
:param kwargs: conjunto de parametros opcionais
:return:
"""
@abstractmethod
def escrever(self, *args, **kwargs) -> None:
"""
Metodo abstrato da ArquivoEntrada sendo repassado para as classes filhas
:param args: conjunto de parametros obrigatorios
:param kwargs: conjunto de parametros opcionais
:return:
""" | PySDDP/dessem/script/templates/infofcf.py | from abc import abstractmethod
from typing import Optional
from PySDDP.dessem.script.templates.arquivo_entrada import ArquivoEntrada
class InfofcfTemplate(ArquivoEntrada):
"""
Classe que contem todos os elementos comuns a qualquer versao do arquivo Infofcf do Dessem.
Esta classe tem como intuito fornecer duck typing para a classe Dessem e ainda adicionar um nivel de especificacao
dentro da fabrica. Alem disso esta classe deve passar adiante a responsabilidade da implementacao dos metodos de
leitura e escrita
"""
def __init__(self):
super().__init__()
self.dir_base = None
self.const = None
self.mapfcf_sisgnl = None
self.mapfcf_durpat = None
self.fcffix_usit = None
self.mapfcf_tviag = None
self.mapfcf_cgtmin = None
self.mapfcf_sisgnl_df = None
self.mapfcf_durpat_df = None
self.fcffix_usit_df = None
self.mapfcf_tviag_df = None
self.mapfcf_cgtmin_df = None
self.infofcf = None
# O arquivo script.arq possuem tanto dados quanto nomes de arquivos,
# então resolvi criar duas estruturas para armazenar estas informações
# e ficar mais fácil na hora da escrita
# {chave: valor}
# chave -> mneumonico ou nome do registro
# valor -> dicionários contendo:
# tipo: 0 para dados ou 1 para arquivos
# descricao: Descricação de cada mneumomico usada na impressao do arquivo
self.dados = {
"infofcf":{
'descricao': 'Armazena todos os comentarios e os mneumos',
'valor': None
},
"mapfcf_sisgnl":{
'descricao':'Identificacao dos subsistemas onde ha usinas com despacho antecipado',
'cabecalho': "& Mnem Ind Num Nlag Npat\n"
"&XXXXX XXXXXX XXX XXX XXX XXX",
'formato': "{mneumo:>14} {ind:>3} {num:>3} {lag:>3} {patamares:>3}\n",
'valor': None
},
"mapfcf_durpat": {
'descricao': 'Duracao dos patamares de carga para os periodos futuros',
'cabecalho': "& Mnem Lag Pat Dur (h)\n"
"&XXXXX XXXXXX XXX XXX XXXXXXXXXX",
'formato': "{mneumo:>14} {lag:>3} {patamar:>3} {duracao:>9}\n",
'valor': None
},
"fcffix_usit":{
'descricao':'Valores de geracao termica sinalizada e/ou comandada para as semanas / meses alem do '
'horizonte de estudo do modelo dessem',
'cabecalho': "& TpEnt IdEnt IdVar lag Pat Valor Justificativa"
"XXXXXX XXXXXX XXX XXXXXX XXX XXX XXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
'formato':"{mneumo:>13} {num_ent:>3} {variavel:>6} {lag:>3} {patamar:>3} {valor:>10} {comentario:>20}\n",
'valor': None
},
"mapfcf_tviag": {
'descricao': 'Informacoes para tempos de viagem considerados pelo modelo DECOMP',
'cabecalho': "& Mnem Ind Num\n"
"&XXXXX XXXXXXX XXX XXX",
'formato':"{mneumo:>14} {ind:>3} {num:>3}\n",
'valor': None
},
"mapfcf_cgtmin": {
'descricao': 'Custo de geracao termica minima alem do horizonte de estudo',
'cabecalho': "& Mnem Custo\n"
"&XXXXX XXXXXXX XXXXXXXXXXXXXXX",
'formato': "{mneumo:>14} {custo:>15}\n",
'valor': None
},
}
# preenchido após o método de leitura ser chamado
# representa os nomes lidos no script.arq
self.nome_arquivos = list()
@abstractmethod
def ler(self, *args, **kwargs) -> None:
"""
Metodo abstrato da ArquivoEntrada sendo repassado para as classes filhas
:param args: conjunto de parametros obrigatorios
:param kwargs: conjunto de parametros opcionais
:return:
"""
@abstractmethod
def escrever(self, *args, **kwargs) -> None:
"""
Metodo abstrato da ArquivoEntrada sendo repassado para as classes filhas
:param args: conjunto de parametros obrigatorios
:param kwargs: conjunto de parametros opcionais
:return:
""" | 0.759493 | 0.326325 |
import encrypted_fields.fields
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("profiles", "0033_auto_20201113_1515"),
]
operations = [
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="email",
field=encrypted_fields.fields.EncryptedCharField(
blank=True, help_text="Email.", max_length=1024
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="first_name",
field=encrypted_fields.fields.EncryptedCharField(
blank=True, help_text="First name(s).", max_length=1024
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="given_name",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="The name the person is called with.",
max_length=1024,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="last_name",
field=encrypted_fields.fields.EncryptedCharField(
blank=True, help_text="Last name.", max_length=1024
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="municipality_of_residence",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Official municipality of residence in Finland as a free form text.",
max_length=1024,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="municipality_of_residence_number",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Official municipality of residence in Finland as an official number.",
max_length=4,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="national_identification_number",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Finnish national identification number.",
max_length=1024,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformationpermanentforeignaddress",
name="additional_address",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Additional address information, perhaps town, county, state, country etc.",
max_length=1024,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformationpermanentforeignaddress",
name="country_code",
field=encrypted_fields.fields.EncryptedCharField(
blank=True, help_text="An ISO 3166-1 country code.", max_length=3
),
),
migrations.AlterField(
model_name="verifiedpersonalinformationpermanentforeignaddress",
name="street_address",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Street address or whatever is the _first part_ of the address.",
max_length=1024,
),
),
] | profiles/migrations/0034_add_help_texts_to_fields__noop.py |
import encrypted_fields.fields
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("profiles", "0033_auto_20201113_1515"),
]
operations = [
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="email",
field=encrypted_fields.fields.EncryptedCharField(
blank=True, help_text="Email.", max_length=1024
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="first_name",
field=encrypted_fields.fields.EncryptedCharField(
blank=True, help_text="First name(s).", max_length=1024
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="given_name",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="The name the person is called with.",
max_length=1024,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="last_name",
field=encrypted_fields.fields.EncryptedCharField(
blank=True, help_text="Last name.", max_length=1024
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="municipality_of_residence",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Official municipality of residence in Finland as a free form text.",
max_length=1024,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="municipality_of_residence_number",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Official municipality of residence in Finland as an official number.",
max_length=4,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformation",
name="national_identification_number",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Finnish national identification number.",
max_length=1024,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformationpermanentforeignaddress",
name="additional_address",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Additional address information, perhaps town, county, state, country etc.",
max_length=1024,
),
),
migrations.AlterField(
model_name="verifiedpersonalinformationpermanentforeignaddress",
name="country_code",
field=encrypted_fields.fields.EncryptedCharField(
blank=True, help_text="An ISO 3166-1 country code.", max_length=3
),
),
migrations.AlterField(
model_name="verifiedpersonalinformationpermanentforeignaddress",
name="street_address",
field=encrypted_fields.fields.EncryptedCharField(
blank=True,
help_text="Street address or whatever is the _first part_ of the address.",
max_length=1024,
),
),
] | 0.573081 | 0.199133 |
from monitors.views import MonitorDashboard, AddIndicator, DomainMonitor, DeleteIndicator
from profiles.models import Profile
from django.test import TestCase, RequestFactory
from django.core.urlresolvers import reverse
from django.contrib.auth.models import AnonymousUser
from django.http import HttpResponseRedirect
import datetime
class TestMonitorDashboard(TestCase):
indicator = "twitter.com"
ip_indicator = "172.16.58.3"
def setUp(self):
self.factory = RequestFactory()
self.url = reverse("monitor_dashboard")
self.request = self.factory.get(self.url)
# We test each role, authenticated users first.
def test_monitor_dashboard_auth_get(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False)
response = MonitorDashboard.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("DomainPanel" in response.content.decode("utf-8"))
self.assertTrue("IpPanel" in response.content.decode("utf-8"))
self.assertTrue("AlertPanel" in response.content.decode("utf-8"))
# Admin users.
def test_monitor_dashboard_admin_get(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=True)
response = MonitorDashboard.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("DomainPanel" in response.content.decode("utf-8"))
self.assertTrue("IpPanel" in response.content.decode("utf-8"))
self.assertTrue("AlertPanel" in response.content.decode("utf-8"))
# Staff users.
def test_monitor_dashboard_staff_get(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False, is_staff=True)
response = MonitorDashboard.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("DomainPanel" in response.content.decode("utf-8"))
self.assertTrue("IpPanel" in response.content.decode("utf-8"))
self.assertTrue("AlertPanel" in response.content.decode("utf-8"))
# Anon users should be restricted.
def test_monitor_dashboard_anon_get(self):
# Spin up a user and run a request through the view.
self.request.user = AnonymousUser()
response = MonitorDashboard.as_view()(self.request)
# Anon users will be redirected, so we don't render().
self.assertNotEqual(response.status_code, 200)
self.assertTrue("DomainPanel" not in response.content.decode("utf-8"))
self.assertTrue("IpPanel" not in response.content.decode("utf-8"))
self.assertTrue("AlertPanel" not in response.content.decode("utf-8"))
class TestAddIndicator(TestCase):
indicator = "twitter.com"
ip_indicator = "172.16.58.3"
def setUp(self):
self.factory = RequestFactory()
self.url = reverse("add_indicators")
self.request = self.factory.get(self.url)
# We test each role, authenticated users first.
def test_add_indicator_auth(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False)
response = AddIndicator.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("New monitor submissions" in response.content.decode("utf-8"))
def test_add_indicator_admin(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=True)
response = AddIndicator.as_view()(self.request)
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("New monitor submissions" in response.content.decode("utf-8"))
# To test methods in the view, we must not use .as_view() because it will return a response.
# What we really need is just an instance of the class.
view = AddIndicator()
url = view.get_success_url()
self.assertEqual(url, "/monitors/")
def test_add_indicator_auth(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False)
response = AddIndicator.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("New monitor submissions" in response.content.decode("utf-8"))
# To test methods in the view, we must not use .as_view() because it will return a response.
# What we really need is just an instance of the class.
view = AddIndicator()
url = view.get_success_url()
self.assertEqual(url, "/monitors/")
def test_add_indicator_staff(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_staff=True)
response = AddIndicator.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("New monitor submissions" in response.content.decode("utf-8"))
# To test methods in the view, we must not use .as_view() because it will return a response.
# What we really need is just an instance of the class.
view = AddIndicator()
url = view.get_success_url()
self.assertEqual(url, "/monitors/")
# Anon users should be redirected to login page.
def test_add_indicator_anon(self):
# Spin up a user and run a request through the view.
self.request.user = AnonymousUser()
response = AddIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 302)
self.assertTrue(isinstance(response, HttpResponseRedirect))
# Ensure we are redirecting to the correct URL.
self.assertTrue("/profile/login/?next=/monitors/add_indicators" in response._headers['location'],)
# To test methods in the view, we must not use .as_view() because it will return a response.
# What we really need is just an instance of the class.
view = AddIndicator()
url = view.get_success_url()
self.assertEqual(url, "/monitors/")
class TestRemoveIndicator(TestCase):
indicator = "twitter.com"
ip_indicator = "172.16.58.3"
def setUp(self):
self.factory = RequestFactory()
self.url = reverse("delete_indicators")
self.request = self.factory.get(self.url)
def test_remove_indicator_anon(self):
# Spin up a user and run a request through the view.
self.request.user = AnonymousUser()
response = DeleteIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 302)
self.assertTrue(isinstance(response, HttpResponseRedirect))
# Ensure we are redirecting to the correct URL.
self.assertTrue("/profile/login/?next=/monitors/delete_indicators" in response._headers['location'])
def test_remove_indicator_admin(self):
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=True)
monitor = DomainMonitor(owner=self.request.user,
domain_name=self.indicator,
lookup_interval=24,
next_lookup= datetime.datetime.utcnow())
monitor.save()
response = DeleteIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 200)
self.assertFalse(isinstance(response, HttpResponseRedirect))
# There is a confirmation page.
self.assertTrue("Confirm Monitor Deletion" in response.content.decode("utf-8"))
def test_remove_indicator_auth(self):
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False)
monitor = DomainMonitor(owner=self.request.user,
domain_name=self.indicator,
lookup_interval=24,
next_lookup=datetime.datetime.utcnow())
monitor.save()
response = DeleteIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 200)
self.assertFalse(isinstance(response, HttpResponseRedirect))
# There is a confirmation page.
self.assertTrue("Confirm Monitor Deletion" in response.content.decode("utf-8"))
def test_remove_indicator_staff(self):
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_staff=True)
monitor = DomainMonitor(owner=self.request.user,
domain_name=self.indicator,
lookup_interval=24,
next_lookup=datetime.datetime.utcnow())
monitor.save()
response = DeleteIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 200)
self.assertFalse(isinstance(response, HttpResponseRedirect))
# There is a confirmation page.
self.assertTrue("Confirm Monitor Deletion" in response.content.decode("utf-8")) | unit_tests/test_monitor_views.py | from monitors.views import MonitorDashboard, AddIndicator, DomainMonitor, DeleteIndicator
from profiles.models import Profile
from django.test import TestCase, RequestFactory
from django.core.urlresolvers import reverse
from django.contrib.auth.models import AnonymousUser
from django.http import HttpResponseRedirect
import datetime
class TestMonitorDashboard(TestCase):
indicator = "twitter.com"
ip_indicator = "172.16.58.3"
def setUp(self):
self.factory = RequestFactory()
self.url = reverse("monitor_dashboard")
self.request = self.factory.get(self.url)
# We test each role, authenticated users first.
def test_monitor_dashboard_auth_get(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False)
response = MonitorDashboard.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("DomainPanel" in response.content.decode("utf-8"))
self.assertTrue("IpPanel" in response.content.decode("utf-8"))
self.assertTrue("AlertPanel" in response.content.decode("utf-8"))
# Admin users.
def test_monitor_dashboard_admin_get(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=True)
response = MonitorDashboard.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("DomainPanel" in response.content.decode("utf-8"))
self.assertTrue("IpPanel" in response.content.decode("utf-8"))
self.assertTrue("AlertPanel" in response.content.decode("utf-8"))
# Staff users.
def test_monitor_dashboard_staff_get(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False, is_staff=True)
response = MonitorDashboard.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("DomainPanel" in response.content.decode("utf-8"))
self.assertTrue("IpPanel" in response.content.decode("utf-8"))
self.assertTrue("AlertPanel" in response.content.decode("utf-8"))
# Anon users should be restricted.
def test_monitor_dashboard_anon_get(self):
# Spin up a user and run a request through the view.
self.request.user = AnonymousUser()
response = MonitorDashboard.as_view()(self.request)
# Anon users will be redirected, so we don't render().
self.assertNotEqual(response.status_code, 200)
self.assertTrue("DomainPanel" not in response.content.decode("utf-8"))
self.assertTrue("IpPanel" not in response.content.decode("utf-8"))
self.assertTrue("AlertPanel" not in response.content.decode("utf-8"))
class TestAddIndicator(TestCase):
indicator = "twitter.com"
ip_indicator = "172.16.58.3"
def setUp(self):
self.factory = RequestFactory()
self.url = reverse("add_indicators")
self.request = self.factory.get(self.url)
# We test each role, authenticated users first.
def test_add_indicator_auth(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False)
response = AddIndicator.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("New monitor submissions" in response.content.decode("utf-8"))
def test_add_indicator_admin(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=True)
response = AddIndicator.as_view()(self.request)
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("New monitor submissions" in response.content.decode("utf-8"))
# To test methods in the view, we must not use .as_view() because it will return a response.
# What we really need is just an instance of the class.
view = AddIndicator()
url = view.get_success_url()
self.assertEqual(url, "/monitors/")
def test_add_indicator_auth(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False)
response = AddIndicator.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("New monitor submissions" in response.content.decode("utf-8"))
# To test methods in the view, we must not use .as_view() because it will return a response.
# What we really need is just an instance of the class.
view = AddIndicator()
url = view.get_success_url()
self.assertEqual(url, "/monitors/")
def test_add_indicator_staff(self):
# Spin up a user and run a request through the view.
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_staff=True)
response = AddIndicator.as_view()(self.request)
# This view returns a template response, so we must manually render.
response = response.render()
self.assertEqual(response.status_code, 200)
self.assertTrue("New monitor submissions" in response.content.decode("utf-8"))
# To test methods in the view, we must not use .as_view() because it will return a response.
# What we really need is just an instance of the class.
view = AddIndicator()
url = view.get_success_url()
self.assertEqual(url, "/monitors/")
# Anon users should be redirected to login page.
def test_add_indicator_anon(self):
# Spin up a user and run a request through the view.
self.request.user = AnonymousUser()
response = AddIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 302)
self.assertTrue(isinstance(response, HttpResponseRedirect))
# Ensure we are redirecting to the correct URL.
self.assertTrue("/profile/login/?next=/monitors/add_indicators" in response._headers['location'],)
# To test methods in the view, we must not use .as_view() because it will return a response.
# What we really need is just an instance of the class.
view = AddIndicator()
url = view.get_success_url()
self.assertEqual(url, "/monitors/")
class TestRemoveIndicator(TestCase):
indicator = "twitter.com"
ip_indicator = "172.16.58.3"
def setUp(self):
self.factory = RequestFactory()
self.url = reverse("delete_indicators")
self.request = self.factory.get(self.url)
def test_remove_indicator_anon(self):
# Spin up a user and run a request through the view.
self.request.user = AnonymousUser()
response = DeleteIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 302)
self.assertTrue(isinstance(response, HttpResponseRedirect))
# Ensure we are redirecting to the correct URL.
self.assertTrue("/profile/login/?next=/monitors/delete_indicators" in response._headers['location'])
def test_remove_indicator_admin(self):
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=True)
monitor = DomainMonitor(owner=self.request.user,
domain_name=self.indicator,
lookup_interval=24,
next_lookup= datetime.datetime.utcnow())
monitor.save()
response = DeleteIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 200)
self.assertFalse(isinstance(response, HttpResponseRedirect))
# There is a confirmation page.
self.assertTrue("Confirm Monitor Deletion" in response.content.decode("utf-8"))
def test_remove_indicator_auth(self):
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_admin=False)
monitor = DomainMonitor(owner=self.request.user,
domain_name=self.indicator,
lookup_interval=24,
next_lookup=datetime.datetime.utcnow())
monitor.save()
response = DeleteIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 200)
self.assertFalse(isinstance(response, HttpResponseRedirect))
# There is a confirmation page.
self.assertTrue("Confirm Monitor Deletion" in response.content.decode("utf-8"))
def test_remove_indicator_staff(self):
self.request.user = Profile.objects.create_user(email='<EMAIL>', password='<PASSWORD>', is_staff=True)
monitor = DomainMonitor(owner=self.request.user,
domain_name=self.indicator,
lookup_interval=24,
next_lookup=datetime.datetime.utcnow())
monitor.save()
response = DeleteIndicator.as_view()(self.request)
self.assertEqual(response.status_code, 200)
self.assertFalse(isinstance(response, HttpResponseRedirect))
# There is a confirmation page.
self.assertTrue("Confirm Monitor Deletion" in response.content.decode("utf-8")) | 0.656878 | 0.294177 |
import six
import numpy as np
from monty.json import MSONable
from ruamel.yaml import safe_dump
from propnet import logger, ureg
from sympy.parsing.sympy_parser import parse_expr
import sympy as sp
# TODO: This could be split into separate classes
# or a base class + subclasses for symbols with
# units vs those without
# TODO: also symbols with branch points for things like
# sets of finite conditions (miller indices)
# TODO: I think object properties (e. g. structure)
# should have a "unitizer" metaclass that unitizes
# their attributes
class Symbol(MSONable):
"""
Class storing the complete description of a Symbol.
These classes are typically instantiated at runtime when all
.yaml files are read in from the symbols folder.
"""
def __init__(self, name, display_names=None, display_symbols=None,
units=None, shape=None, object_type=None, comment=None,
category='property', constraint=None, default_value=None):
"""
Parses and validates a series of inputs into a PropertyMetadata
tuple, a format that PropNet expects.
Parameters correspond exactly with those of a PropertyMetadata tuple.
Args:
name (str): string ASCII identifying the property uniquely
as an internal identifier.
units (str or tuple): units of the property as a Quantity
supported by the Pint package. Can be supplied as a
string (e. g. cm^2) or a tuple for Quantity.from_tuple
(e. g. [1.0, [['centimeter', 1.0]]])
display_names (list<str>): list of strings giving possible
human-readable names for the property.
display_symbols (list<str>): list of strings giving possible
human-readable symbols for the property.
shape (id): list giving the order of the tensor as the length,
and number of dimensions as individual integers in the list.
comment (str): any useful information on the property including
its definitions and possible citations.
category (str): 'property', for property of a material,
'condition', for other variables,
'object', for a value that is a python object.
object_type (class): class representing the object stored in
these symbols.
constraint (str): constraint associated with the symbol, must
be a string expression (e. g. inequality) using the symbol
name, e. g. bulk_modulus > 0.
default_value: default value for the symbol, e. g. 300 for
temperature or 1 for magnetic permeability
"""
# TODO: not sure object should be distinguished
# TODO: clean up for divergent logic
if category not in ('property', 'condition', 'object'):
raise ValueError('Unsupported category: {}'.format(category))
if not name.isidentifier():
raise ValueError(
"The canonical name ({}) is not valid.".format(name))
if not display_names:
display_names = [name]
self.object_type = None
self._object_class = None
self._object_module = None
if category in ('property', 'condition'):
if object_type is not None:
raise ValueError(
"Cannot define an object type for a {}.".format(category))
try:
np.zeros(shape)
except TypeError:
raise TypeError(
"Shape provided for ({}) is invalid.".format(name))
if units is None:
units = 'dimensionless'
logger.info("Units parsed from a string format automatically, "
"do these look correct? %s", units)
if isinstance(units, six.string_types):
units = 1 * ureg.parse_expression(units)
else:
units = ureg.Quantity.from_tuple(units)
else:
if units is not None:
raise ValueError("Cannot define units for generic objects.")
units = None # ureg.parse_expression("") # dimensionless
if object_type:
if isinstance(object_type, type):
self._object_module = object_type.__module__
self._object_class = object_type.__name__
else:
# Do not try to import the module for security reasons.
# We don't want malicious modules to be automatically imported.
modclass = object_type.rsplit('.', 1)
if len(modclass) == 1:
self._object_module = 'builtins'
self._object_class = modclass[0]
else:
self._object_module, self._object_class = modclass
if self._object_module == 'builtins':
self.object_type = self._object_class
else:
self.object_type = ".".join([self._object_module,
self._object_class])
self.name = name
self.category = category
self.units = units
self.display_names = display_names
self.display_symbols = display_symbols
# If a user enters [1] or [1, 1, ...] for shape, treat as a scalar
if shape and np.size(np.zeros(shape=shape)) == 1:
shape = 1
# If a user enters a 0 dimension, throw an error
if shape and np.size(np.zeros(shape=shape)) == 0:
raise ValueError("Symbol cannot have a shape with a 0-size dimension: {}".format(shape))
self.shape = shape
self.comment = comment
self.default_value = default_value
# TODO: This should explicity deal with only numerical symbols
# because it uses sympy to evaluate them until we make
# a class to evaluate them using either sympy or a custom func
# Note that symbol constraints are not constraint objects
# at the moment because using them would result in a circular
# dependence, this might be resolved with some reorganization
if constraint:
expr = parse_expr(constraint)
self.constraint = sp.lambdify(self.name, expr)
else:
self.constraint = None
@property
def object_class(self):
return self._object_class
@property
def object_module(self):
return self._object_module
@property
def dimension_as_string(self):
"""
Returns:
(str): shape of property (np.shape) as a human-readable string
"""
if isinstance(self.shape, int):
return 'scalar'
elif isinstance(self.shape, list) and len(self.shape) == 1:
return '{} vector'.format(self.shape)
elif isinstance(self.shape, list) and len(self.shape) == 2:
return '{} matrix'.format(self.shape)
else:
# technically might not always be true
return '{} tensor'.format(self.shape)
@property
def unit_as_string(self):
"""
Returns: unit of property as human-readable string
"""
if self.units.dimensionless:
return "dimensionless"
# self.units has both the units and (sometimes) a
# prefactor (its magnitude)
unit_str = '{:~P}'.format(self.units.units)
if self.units.magnitude != 1:
unit_str = '{} {}'.format(self.units.magnitude, unit_str)
return unit_str
@property
def compatible_units(self):
"""
Returns: list of compatible units as strings
"""
try:
compatible_units = [str(u) for u in self.units.compatible_units()]
return compatible_units
except KeyError:
logger.warning("Cannot find compatible units for %s", self.name)
return []
def is_correct_object_type(self, obj):
if self.category == 'object':
if not self.object_module and not self.object_class:
# If no type was specified, just accept any object.
# Leave it up to the model evaluation procedures to type check
return True
modname = obj.__class__.__module__
clsname = obj.__class__.__name__
return self.object_module == modname and self.object_class == clsname
else:
raise AttributeError("Object type not defined for symbol of category '{}'".format(self.category))
def __hash__(self):
return self.name.__hash__()
def __eq__(self, other):
if isinstance(other, Symbol):
return self.name == other.name
elif isinstance(other, str):
return self.name == other
def __str__(self):
return "Symbol: {}".format(self.name)
@property
def summary(self):
"""
Prints a full summary of the symbol
"""
to_return = self.name + ":\n"
for k, v in self.__dict__.items():
to_return += "\t" + k + ":\t" + str(v) + "\n"
return to_return
def __repr__(self):
return "{}<{}>".format(self.category, self.name)
# TODO: I don't think this is necessary, double check
def to_yaml(self):
"""
Method to serialize the symbol in a yaml format
"""
data = {
"name": self.name,
"category": self.category,
"display_names": self.display_names,
"display_symbols": self.display_symbols,
"comment": self.comment
}
if self.units:
data["units"] = self.units.to_tuple()
if self.shape:
data["shape"] = self.shape
if self.object_type:
data["object_type"] = self.object_type
return safe_dump(data)
def as_dict(self):
d = super().as_dict()
if self.units:
d['units'] = d['units'].to_tuple()
return d | propnet/core/symbols.py |
import six
import numpy as np
from monty.json import MSONable
from ruamel.yaml import safe_dump
from propnet import logger, ureg
from sympy.parsing.sympy_parser import parse_expr
import sympy as sp
# TODO: This could be split into separate classes
# or a base class + subclasses for symbols with
# units vs those without
# TODO: also symbols with branch points for things like
# sets of finite conditions (miller indices)
# TODO: I think object properties (e. g. structure)
# should have a "unitizer" metaclass that unitizes
# their attributes
class Symbol(MSONable):
"""
Class storing the complete description of a Symbol.
These classes are typically instantiated at runtime when all
.yaml files are read in from the symbols folder.
"""
def __init__(self, name, display_names=None, display_symbols=None,
units=None, shape=None, object_type=None, comment=None,
category='property', constraint=None, default_value=None):
"""
Parses and validates a series of inputs into a PropertyMetadata
tuple, a format that PropNet expects.
Parameters correspond exactly with those of a PropertyMetadata tuple.
Args:
name (str): string ASCII identifying the property uniquely
as an internal identifier.
units (str or tuple): units of the property as a Quantity
supported by the Pint package. Can be supplied as a
string (e. g. cm^2) or a tuple for Quantity.from_tuple
(e. g. [1.0, [['centimeter', 1.0]]])
display_names (list<str>): list of strings giving possible
human-readable names for the property.
display_symbols (list<str>): list of strings giving possible
human-readable symbols for the property.
shape (id): list giving the order of the tensor as the length,
and number of dimensions as individual integers in the list.
comment (str): any useful information on the property including
its definitions and possible citations.
category (str): 'property', for property of a material,
'condition', for other variables,
'object', for a value that is a python object.
object_type (class): class representing the object stored in
these symbols.
constraint (str): constraint associated with the symbol, must
be a string expression (e. g. inequality) using the symbol
name, e. g. bulk_modulus > 0.
default_value: default value for the symbol, e. g. 300 for
temperature or 1 for magnetic permeability
"""
# TODO: not sure object should be distinguished
# TODO: clean up for divergent logic
if category not in ('property', 'condition', 'object'):
raise ValueError('Unsupported category: {}'.format(category))
if not name.isidentifier():
raise ValueError(
"The canonical name ({}) is not valid.".format(name))
if not display_names:
display_names = [name]
self.object_type = None
self._object_class = None
self._object_module = None
if category in ('property', 'condition'):
if object_type is not None:
raise ValueError(
"Cannot define an object type for a {}.".format(category))
try:
np.zeros(shape)
except TypeError:
raise TypeError(
"Shape provided for ({}) is invalid.".format(name))
if units is None:
units = 'dimensionless'
logger.info("Units parsed from a string format automatically, "
"do these look correct? %s", units)
if isinstance(units, six.string_types):
units = 1 * ureg.parse_expression(units)
else:
units = ureg.Quantity.from_tuple(units)
else:
if units is not None:
raise ValueError("Cannot define units for generic objects.")
units = None # ureg.parse_expression("") # dimensionless
if object_type:
if isinstance(object_type, type):
self._object_module = object_type.__module__
self._object_class = object_type.__name__
else:
# Do not try to import the module for security reasons.
# We don't want malicious modules to be automatically imported.
modclass = object_type.rsplit('.', 1)
if len(modclass) == 1:
self._object_module = 'builtins'
self._object_class = modclass[0]
else:
self._object_module, self._object_class = modclass
if self._object_module == 'builtins':
self.object_type = self._object_class
else:
self.object_type = ".".join([self._object_module,
self._object_class])
self.name = name
self.category = category
self.units = units
self.display_names = display_names
self.display_symbols = display_symbols
# If a user enters [1] or [1, 1, ...] for shape, treat as a scalar
if shape and np.size(np.zeros(shape=shape)) == 1:
shape = 1
# If a user enters a 0 dimension, throw an error
if shape and np.size(np.zeros(shape=shape)) == 0:
raise ValueError("Symbol cannot have a shape with a 0-size dimension: {}".format(shape))
self.shape = shape
self.comment = comment
self.default_value = default_value
# TODO: This should explicity deal with only numerical symbols
# because it uses sympy to evaluate them until we make
# a class to evaluate them using either sympy or a custom func
# Note that symbol constraints are not constraint objects
# at the moment because using them would result in a circular
# dependence, this might be resolved with some reorganization
if constraint:
expr = parse_expr(constraint)
self.constraint = sp.lambdify(self.name, expr)
else:
self.constraint = None
@property
def object_class(self):
return self._object_class
@property
def object_module(self):
return self._object_module
@property
def dimension_as_string(self):
"""
Returns:
(str): shape of property (np.shape) as a human-readable string
"""
if isinstance(self.shape, int):
return 'scalar'
elif isinstance(self.shape, list) and len(self.shape) == 1:
return '{} vector'.format(self.shape)
elif isinstance(self.shape, list) and len(self.shape) == 2:
return '{} matrix'.format(self.shape)
else:
# technically might not always be true
return '{} tensor'.format(self.shape)
@property
def unit_as_string(self):
"""
Returns: unit of property as human-readable string
"""
if self.units.dimensionless:
return "dimensionless"
# self.units has both the units and (sometimes) a
# prefactor (its magnitude)
unit_str = '{:~P}'.format(self.units.units)
if self.units.magnitude != 1:
unit_str = '{} {}'.format(self.units.magnitude, unit_str)
return unit_str
@property
def compatible_units(self):
"""
Returns: list of compatible units as strings
"""
try:
compatible_units = [str(u) for u in self.units.compatible_units()]
return compatible_units
except KeyError:
logger.warning("Cannot find compatible units for %s", self.name)
return []
def is_correct_object_type(self, obj):
if self.category == 'object':
if not self.object_module and not self.object_class:
# If no type was specified, just accept any object.
# Leave it up to the model evaluation procedures to type check
return True
modname = obj.__class__.__module__
clsname = obj.__class__.__name__
return self.object_module == modname and self.object_class == clsname
else:
raise AttributeError("Object type not defined for symbol of category '{}'".format(self.category))
def __hash__(self):
return self.name.__hash__()
def __eq__(self, other):
if isinstance(other, Symbol):
return self.name == other.name
elif isinstance(other, str):
return self.name == other
def __str__(self):
return "Symbol: {}".format(self.name)
@property
def summary(self):
"""
Prints a full summary of the symbol
"""
to_return = self.name + ":\n"
for k, v in self.__dict__.items():
to_return += "\t" + k + ":\t" + str(v) + "\n"
return to_return
def __repr__(self):
return "{}<{}>".format(self.category, self.name)
# TODO: I don't think this is necessary, double check
def to_yaml(self):
"""
Method to serialize the symbol in a yaml format
"""
data = {
"name": self.name,
"category": self.category,
"display_names": self.display_names,
"display_symbols": self.display_symbols,
"comment": self.comment
}
if self.units:
data["units"] = self.units.to_tuple()
if self.shape:
data["shape"] = self.shape
if self.object_type:
data["object_type"] = self.object_type
return safe_dump(data)
def as_dict(self):
d = super().as_dict()
if self.units:
d['units'] = d['units'].to_tuple()
return d | 0.466603 | 0.412353 |
import os
from nacl import secret, utils
from hashlib import sha256
from random import randrange
from src.EncryptCore.sources.exceptions import EncryptionKeyError, CryptoError
from src.EncryptCore.sources.Utilities import generateKey, generateCipherMod, unbundleSizeAndRest, getModFromCipher
from src.models.User import User
from src.models.File import File
def encrypt(destination: str, file: File, user: User):
"""
Encrypt a File object and place it to it's destination path
:param destination: a destination path
:param file: the File object to encrypt and that store all file related infos
:param user: the User object that store all user related infos
"""
# Create a random number that will be used as a base to modify the generated key
base = randrange(10 ** 6, 10 ** 7 - 1)
# Create the temporary key from the files and user infos
tmpKey = generateKey(file.infos, user.infos)
# Create a cipher of the base for being saved in the file (see Utilities:generateCipherMod for more details)
sizeAndRest, cipherBase = generateCipherMod(tmpKey, base)
test = unbundleSizeAndRest(int.from_bytes(sizeAndRest, "little"))
# Create a sha256 hash of the temporary key on the random base and create the crypto box from it
key = sha256(str(tmpKey % base).encode())
box = secret.SecretBox(key.digest())
nonce = utils.random(secret.SecretBox.NONCE_SIZE)
# open the input file (decrypted) and the output file (encrypted)
readStream = file.openStream("rb")
writeStream = File(destination).openStream("wb")
# write the length of the cipher base and the base of the key
writeStream.write(sizeAndRest)
writeStream.write(cipherBase)
# Read every 65536 bytes of the input file, encrypt it and save it to the output file
print(os.fstat(readStream.fileno()).st_size)
try:
finished = False
while not finished:
chunk = readStream.read(16777214)
if len(chunk) == 0:
finished = True
else:
writeStream.write(box.encrypt(chunk, nonce))
except CryptoError:
raise EncryptionKeyError()
finally:
readStream.close()
writeStream.close()
def decrypt(destination: str, file: File, user: User):
"""
Decrypt a file and place it on it's destination path
:param destination: the destination path of the decrypted file
:param file: the encrypted file and all it's informations needed to generate the key
:param user: the user that contains all the infos used to generate the key
"""
# open the input file (encrypted)
readStream = file.openStream("rb")
# get the length and rest bundle of the cipher base saved in the file and get the cipher base
sizeAndRestBundle = int.from_bytes(readStream.read(4), 'little')
size, rest = unbundleSizeAndRest(sizeAndRestBundle)
cipherBase = int.from_bytes(readStream.read(size), 'big')
# generate the key using the file infos and the user infos
tmpKey = generateKey(file.infos, user.infos)
# generate the base using the temporary key and the cipher base from the file (see Utilities:getModFromCipher)
base = getModFromCipher(tmpKey, cipherBase, rest)
# create the sha256 hash from the temporary key on the base recovered and create the crypto box from it
key = sha256(str(tmpKey % base).encode())
box = secret.SecretBox(key.digest())
# open the output file (decrypted)
writeStream = File(destination).openStream("wb")
# read every 65536 bytes of the file plus 40 bytes of encryption infos and decrypt it using the box
print(os.fstat(readStream.fileno()).st_size)
try:
finished = False
while not finished:
chunk = readStream.read(16777214 + 40)
if len(chunk) == 0:
finished = True
else:
writeStream.write(box.decrypt(chunk))
except CryptoError:
return False
finally:
readStream.close()
writeStream.close()
return True
if __name__ == '__main__':
print("Tests of EncryptCore:FileEncryptor")
dec = File("./tests/test.txt")
encrypt("./tests/encFolder/test.txt", dec, User())
enc = File("./tests/encFolder/test.txt")
decrypt("./tests/decFolder/test.txt", enc, User()) | src/EncryptCore/sources/FileEncryptor.py | import os
from nacl import secret, utils
from hashlib import sha256
from random import randrange
from src.EncryptCore.sources.exceptions import EncryptionKeyError, CryptoError
from src.EncryptCore.sources.Utilities import generateKey, generateCipherMod, unbundleSizeAndRest, getModFromCipher
from src.models.User import User
from src.models.File import File
def encrypt(destination: str, file: File, user: User):
"""
Encrypt a File object and place it to it's destination path
:param destination: a destination path
:param file: the File object to encrypt and that store all file related infos
:param user: the User object that store all user related infos
"""
# Create a random number that will be used as a base to modify the generated key
base = randrange(10 ** 6, 10 ** 7 - 1)
# Create the temporary key from the files and user infos
tmpKey = generateKey(file.infos, user.infos)
# Create a cipher of the base for being saved in the file (see Utilities:generateCipherMod for more details)
sizeAndRest, cipherBase = generateCipherMod(tmpKey, base)
test = unbundleSizeAndRest(int.from_bytes(sizeAndRest, "little"))
# Create a sha256 hash of the temporary key on the random base and create the crypto box from it
key = sha256(str(tmpKey % base).encode())
box = secret.SecretBox(key.digest())
nonce = utils.random(secret.SecretBox.NONCE_SIZE)
# open the input file (decrypted) and the output file (encrypted)
readStream = file.openStream("rb")
writeStream = File(destination).openStream("wb")
# write the length of the cipher base and the base of the key
writeStream.write(sizeAndRest)
writeStream.write(cipherBase)
# Read every 65536 bytes of the input file, encrypt it and save it to the output file
print(os.fstat(readStream.fileno()).st_size)
try:
finished = False
while not finished:
chunk = readStream.read(16777214)
if len(chunk) == 0:
finished = True
else:
writeStream.write(box.encrypt(chunk, nonce))
except CryptoError:
raise EncryptionKeyError()
finally:
readStream.close()
writeStream.close()
def decrypt(destination: str, file: File, user: User):
"""
Decrypt a file and place it on it's destination path
:param destination: the destination path of the decrypted file
:param file: the encrypted file and all it's informations needed to generate the key
:param user: the user that contains all the infos used to generate the key
"""
# open the input file (encrypted)
readStream = file.openStream("rb")
# get the length and rest bundle of the cipher base saved in the file and get the cipher base
sizeAndRestBundle = int.from_bytes(readStream.read(4), 'little')
size, rest = unbundleSizeAndRest(sizeAndRestBundle)
cipherBase = int.from_bytes(readStream.read(size), 'big')
# generate the key using the file infos and the user infos
tmpKey = generateKey(file.infos, user.infos)
# generate the base using the temporary key and the cipher base from the file (see Utilities:getModFromCipher)
base = getModFromCipher(tmpKey, cipherBase, rest)
# create the sha256 hash from the temporary key on the base recovered and create the crypto box from it
key = sha256(str(tmpKey % base).encode())
box = secret.SecretBox(key.digest())
# open the output file (decrypted)
writeStream = File(destination).openStream("wb")
# read every 65536 bytes of the file plus 40 bytes of encryption infos and decrypt it using the box
print(os.fstat(readStream.fileno()).st_size)
try:
finished = False
while not finished:
chunk = readStream.read(16777214 + 40)
if len(chunk) == 0:
finished = True
else:
writeStream.write(box.decrypt(chunk))
except CryptoError:
return False
finally:
readStream.close()
writeStream.close()
return True
if __name__ == '__main__':
print("Tests of EncryptCore:FileEncryptor")
dec = File("./tests/test.txt")
encrypt("./tests/encFolder/test.txt", dec, User())
enc = File("./tests/encFolder/test.txt")
decrypt("./tests/decFolder/test.txt", enc, User()) | 0.482673 | 0.312285 |
from pathlib import Path
import depthai as dai
import cv2
import sys
# Importing from parent folder
sys.path.insert(0, str(Path(__file__).parent.parent.parent)) # move to parent path
from utils.compute import updateSpatialCalculatorConfig
from utils.draw import drawROI, displayFPS
from utils.OakRunner import OakRunner
def main():
# Set few parameters
nn_path = str(Path(__file__).parent) + "/../../../_models/coronamask.blob"
labels = ["background", "no mask", "mask", "no mask"]
# Init pipeline
runner = OakRunner()
# Configure middle camera and init output streams
runner.setMiddleCamera(frame_width=300, frame_height=300, stream_name="middle_cam", output_queue_size=4, block_output_queue=False)
middle_cam = runner.getMiddleCamera()
middle_cam.setInterleaved(False)
middle_cam.setFps(20)
# Configure stereo depth
runner.setMonoDepth()
stereo = runner.getStereo()
stereo.setConfidenceThreshold(255)
# Configure neural network model and init input / output streams
runner.addNeuralNetworkModel(stream_name="nn", path=nn_path, output_queue_size=4, handle_mono_depth=True)
middle_cam.preview.link(runner.neural_networks["nn"].input)
runner.labels = labels
slc = runner.getSpatialLocationCalculator()
slc.setWaitForConfigInput(True)
# Run the loop that call the process function
runner.run(process=process)
# Process function
def process(runner):
frame = runner.output_queues["middle_cam"].get().getCvFrame()
tensor = runner.output_queues["nn"].get().getLayerFp16("DetectionOutput")
keeped_roi = []
for i in range(100): # There is 100 detections, not all of them are relevant
if (tensor[i*7 + 2] >0.5): # 3rd value of each detection is the confidence
keeped_roi.append(tensor[i*7:i*7+7])
if(len(keeped_roi)>0):
# Set spatial location config (input ROIs into the calculator)
spatial_calculator_config = dai.SpatialLocationCalculatorConfig()
for id, label, confidence, left, top, right, bottom in keeped_roi:
updateSpatialCalculatorConfig(spatial_calculator_config, dai.Point2f(left, top), dai.Point2f(right, bottom))
# Draw infos
runner.input_queues["slc"].send(spatial_calculator_config)
spatial_data = runner.output_queues["slc"].get().getSpatialLocations()
for i in range(len(keeped_roi)):
drawROI(frame, (keeped_roi[i][3],keeped_roi[i][4]), (keeped_roi[i][5],keeped_roi[i][6]), label=runner.labels[int(keeped_roi[i][1])], confidence=keeped_roi[i][2], spatialCoordinates=spatial_data[i].spatialCoordinates)
displayFPS(frame, runner.getFPS())
cv2.imshow("output", frame)
if __name__ == "__main__":
main() | V-generalization/examples/mono_neural_inference/nn_coronamask_depth_v2.py | from pathlib import Path
import depthai as dai
import cv2
import sys
# Importing from parent folder
sys.path.insert(0, str(Path(__file__).parent.parent.parent)) # move to parent path
from utils.compute import updateSpatialCalculatorConfig
from utils.draw import drawROI, displayFPS
from utils.OakRunner import OakRunner
def main():
# Set few parameters
nn_path = str(Path(__file__).parent) + "/../../../_models/coronamask.blob"
labels = ["background", "no mask", "mask", "no mask"]
# Init pipeline
runner = OakRunner()
# Configure middle camera and init output streams
runner.setMiddleCamera(frame_width=300, frame_height=300, stream_name="middle_cam", output_queue_size=4, block_output_queue=False)
middle_cam = runner.getMiddleCamera()
middle_cam.setInterleaved(False)
middle_cam.setFps(20)
# Configure stereo depth
runner.setMonoDepth()
stereo = runner.getStereo()
stereo.setConfidenceThreshold(255)
# Configure neural network model and init input / output streams
runner.addNeuralNetworkModel(stream_name="nn", path=nn_path, output_queue_size=4, handle_mono_depth=True)
middle_cam.preview.link(runner.neural_networks["nn"].input)
runner.labels = labels
slc = runner.getSpatialLocationCalculator()
slc.setWaitForConfigInput(True)
# Run the loop that call the process function
runner.run(process=process)
# Process function
def process(runner):
frame = runner.output_queues["middle_cam"].get().getCvFrame()
tensor = runner.output_queues["nn"].get().getLayerFp16("DetectionOutput")
keeped_roi = []
for i in range(100): # There is 100 detections, not all of them are relevant
if (tensor[i*7 + 2] >0.5): # 3rd value of each detection is the confidence
keeped_roi.append(tensor[i*7:i*7+7])
if(len(keeped_roi)>0):
# Set spatial location config (input ROIs into the calculator)
spatial_calculator_config = dai.SpatialLocationCalculatorConfig()
for id, label, confidence, left, top, right, bottom in keeped_roi:
updateSpatialCalculatorConfig(spatial_calculator_config, dai.Point2f(left, top), dai.Point2f(right, bottom))
# Draw infos
runner.input_queues["slc"].send(spatial_calculator_config)
spatial_data = runner.output_queues["slc"].get().getSpatialLocations()
for i in range(len(keeped_roi)):
drawROI(frame, (keeped_roi[i][3],keeped_roi[i][4]), (keeped_roi[i][5],keeped_roi[i][6]), label=runner.labels[int(keeped_roi[i][1])], confidence=keeped_roi[i][2], spatialCoordinates=spatial_data[i].spatialCoordinates)
displayFPS(frame, runner.getFPS())
cv2.imshow("output", frame)
if __name__ == "__main__":
main() | 0.538255 | 0.24899 |
from django.contrib.gis.db import models
class CptCadastreScdb(models.Model):
"""This is an auto-generated Django model.
"""
objectid = models.AutoField(primary_key=True)
cad_pin = models.IntegerField(blank=True, null=True)
cad_usage_codes = models.CharField(max_length=12, blank=True, null=True)
cad_calc_area = models.FloatField(blank=True, null=True)
cad_cent_latitude = models.FloatField(blank=True, null=True)
cad_cent_longitude = models.FloatField(blank=True, null=True)
cad_unit_type = models.CharField(max_length=4, blank=True, null=True)
cad_level_type = models.CharField(max_length=4, blank=True, null=True)
cad_level_number = models.CharField(max_length=1, blank=True, null=True)
cad_address_si = models.CharField(max_length=50, blank=True, null=True)
cad_lot_number = models.CharField(max_length=6, blank=True, null=True)
cad_land_type = models.CharField(max_length=5, blank=True, null=True)
cad_house_number = models.CharField(max_length=7, blank=True, null=True)
cad_road_name = models.CharField(max_length=50, blank=True, null=True)
cad_road_type = models.CharField(max_length=4, blank=True, null=True)
cad_road_suffix = models.CharField(max_length=4, blank=True, null=True)
cad_locality = models.CharField(max_length=50, blank=True, null=True)
cad_postcode = models.FloatField(blank=True, null=True)
cad_pitype_1 = models.CharField(max_length=17, blank=True, null=True)
cad_pitype_2 = models.CharField(max_length=17, blank=True, null=True)
cad_pitype_3_1 = models.CharField(max_length=17, blank=True, null=True)
cad_pitype_3_2 = models.CharField(max_length=54, blank=True, null=True)
cad_land_name = models.CharField(max_length=17, blank=True, null=True)
cad_reg_number = models.CharField(max_length=50, blank=True, null=True)
cad_reg_number_formated = models.CharField(max_length=50, blank=True, null=True)
cad_owner_name = models.CharField(max_length=500, blank=True, null=True)
cad_owner_count = models.FloatField(blank=True, null=True)
cad_sale_date = models.CharField(max_length=10, blank=True, null=True)
cad_doc_number = models.CharField(max_length=15, blank=True, null=True)
cad_gprpfx = models.CharField(max_length=30, blank=True, null=True)
cad_gprsfx = models.CharField(max_length=30, blank=True, null=True)
cad_strata = models.CharField(max_length=1, blank=True, null=True)
cad_ownership = models.CharField(max_length=8, blank=True, null=True)
cad_legend = models.CharField(max_length=50, blank=True, null=True)
cad_unit_number = models.CharField(max_length=50, blank=True, null=True)
globalid = models.CharField(max_length=38, blank=True, null=True)
shape_length = models.FloatField(blank=True, null=True)
shape_area = models.FloatField(blank=True, null=True)
shape = models.MultiPolygonField(srid=4283, blank=True, null=True)
class Meta:
managed = False
db_table = 'cpt_cadastre_scdb'
def __str__(self):
return str(self.cad_pin) or 'NULL PIN' | cddp/models.py | from django.contrib.gis.db import models
class CptCadastreScdb(models.Model):
"""This is an auto-generated Django model.
"""
objectid = models.AutoField(primary_key=True)
cad_pin = models.IntegerField(blank=True, null=True)
cad_usage_codes = models.CharField(max_length=12, blank=True, null=True)
cad_calc_area = models.FloatField(blank=True, null=True)
cad_cent_latitude = models.FloatField(blank=True, null=True)
cad_cent_longitude = models.FloatField(blank=True, null=True)
cad_unit_type = models.CharField(max_length=4, blank=True, null=True)
cad_level_type = models.CharField(max_length=4, blank=True, null=True)
cad_level_number = models.CharField(max_length=1, blank=True, null=True)
cad_address_si = models.CharField(max_length=50, blank=True, null=True)
cad_lot_number = models.CharField(max_length=6, blank=True, null=True)
cad_land_type = models.CharField(max_length=5, blank=True, null=True)
cad_house_number = models.CharField(max_length=7, blank=True, null=True)
cad_road_name = models.CharField(max_length=50, blank=True, null=True)
cad_road_type = models.CharField(max_length=4, blank=True, null=True)
cad_road_suffix = models.CharField(max_length=4, blank=True, null=True)
cad_locality = models.CharField(max_length=50, blank=True, null=True)
cad_postcode = models.FloatField(blank=True, null=True)
cad_pitype_1 = models.CharField(max_length=17, blank=True, null=True)
cad_pitype_2 = models.CharField(max_length=17, blank=True, null=True)
cad_pitype_3_1 = models.CharField(max_length=17, blank=True, null=True)
cad_pitype_3_2 = models.CharField(max_length=54, blank=True, null=True)
cad_land_name = models.CharField(max_length=17, blank=True, null=True)
cad_reg_number = models.CharField(max_length=50, blank=True, null=True)
cad_reg_number_formated = models.CharField(max_length=50, blank=True, null=True)
cad_owner_name = models.CharField(max_length=500, blank=True, null=True)
cad_owner_count = models.FloatField(blank=True, null=True)
cad_sale_date = models.CharField(max_length=10, blank=True, null=True)
cad_doc_number = models.CharField(max_length=15, blank=True, null=True)
cad_gprpfx = models.CharField(max_length=30, blank=True, null=True)
cad_gprsfx = models.CharField(max_length=30, blank=True, null=True)
cad_strata = models.CharField(max_length=1, blank=True, null=True)
cad_ownership = models.CharField(max_length=8, blank=True, null=True)
cad_legend = models.CharField(max_length=50, blank=True, null=True)
cad_unit_number = models.CharField(max_length=50, blank=True, null=True)
globalid = models.CharField(max_length=38, blank=True, null=True)
shape_length = models.FloatField(blank=True, null=True)
shape_area = models.FloatField(blank=True, null=True)
shape = models.MultiPolygonField(srid=4283, blank=True, null=True)
class Meta:
managed = False
db_table = 'cpt_cadastre_scdb'
def __str__(self):
return str(self.cad_pin) or 'NULL PIN' | 0.598664 | 0.287668 |
import random
from tkinter import *
import pandas
BACKGROUND_COLOR = "#B1DDC6"
words_to_learn = {}
current_card = {}
try:
data = pandas.read_csv("data/words_to_learn.csv")
except FileNotFoundError:
original_data = pandas.read_csv("data/french_words.csv")
words_to_learn = original_data.to_dict(orient="records")
else:
words_to_learn = data.to_dict(orient="records")
def show_word():
global current_card, timer
window.after_cancel(timer)
canvas.itemconfig(card_bg, image=front_card)
current_card = random.choice(words_to_learn)
canvas.itemconfig(card_title, text="French", fill="black")
canvas.itemconfig(card_word, text=current_card['French'], fill="black")
timer = window.after(3000, func=flipped_card)
def known_word():
words_to_learn.remove(current_card)
data = pandas.DataFrame(words_to_learn)
data.to_csv("data/words_to_learn.csv", index=False)
show_word()
def flipped_card():
canvas.itemconfig(card_bg, image=back_card)
canvas.itemconfig(card_title, text="English", fill="white")
canvas.itemconfig(card_word, text=current_card['English'], fill="white")
# Window
window = Tk()
window.title("Flash Card")
window.config(padx=50, pady=50, bg=BACKGROUND_COLOR)
timer = window.after(3000, func=flipped_card)
# Card Canvas
canvas = Canvas(width=800, height=526, bg=BACKGROUND_COLOR, highlightthickness=0)
front_card = PhotoImage(file="images/card_front.png")
back_card = PhotoImage(file="images/card_back.png")
card_bg = canvas.create_image(400, 263, image=front_card)
card_title = canvas.create_text(400, 150, font=("Ariel", 40, "italic"))
card_word = canvas.create_text(400, 263, font=("Ariel", 60, "bold"))
canvas.grid(column=0, row=0, columnspan=2)
# Right Button
right_image = PhotoImage(file="images/right.png")
right_button = Button(image=right_image, highlightthickness=0, command=known_word)
right_button.grid(column=1, row=1)
# Wrong Button
wrong_image = PhotoImage(file="images/wrong.png")
wrong_button = Button(image=wrong_image, highlightthickness=0, command=show_word)
wrong_button.grid(column=0, row=1)
show_word() # to avoid blank card when running the program
window.mainloop() | day 31 (Flash Cards Game)/main.py | import random
from tkinter import *
import pandas
BACKGROUND_COLOR = "#B1DDC6"
words_to_learn = {}
current_card = {}
try:
data = pandas.read_csv("data/words_to_learn.csv")
except FileNotFoundError:
original_data = pandas.read_csv("data/french_words.csv")
words_to_learn = original_data.to_dict(orient="records")
else:
words_to_learn = data.to_dict(orient="records")
def show_word():
global current_card, timer
window.after_cancel(timer)
canvas.itemconfig(card_bg, image=front_card)
current_card = random.choice(words_to_learn)
canvas.itemconfig(card_title, text="French", fill="black")
canvas.itemconfig(card_word, text=current_card['French'], fill="black")
timer = window.after(3000, func=flipped_card)
def known_word():
words_to_learn.remove(current_card)
data = pandas.DataFrame(words_to_learn)
data.to_csv("data/words_to_learn.csv", index=False)
show_word()
def flipped_card():
canvas.itemconfig(card_bg, image=back_card)
canvas.itemconfig(card_title, text="English", fill="white")
canvas.itemconfig(card_word, text=current_card['English'], fill="white")
# Window
window = Tk()
window.title("Flash Card")
window.config(padx=50, pady=50, bg=BACKGROUND_COLOR)
timer = window.after(3000, func=flipped_card)
# Card Canvas
canvas = Canvas(width=800, height=526, bg=BACKGROUND_COLOR, highlightthickness=0)
front_card = PhotoImage(file="images/card_front.png")
back_card = PhotoImage(file="images/card_back.png")
card_bg = canvas.create_image(400, 263, image=front_card)
card_title = canvas.create_text(400, 150, font=("Ariel", 40, "italic"))
card_word = canvas.create_text(400, 263, font=("Ariel", 60, "bold"))
canvas.grid(column=0, row=0, columnspan=2)
# Right Button
right_image = PhotoImage(file="images/right.png")
right_button = Button(image=right_image, highlightthickness=0, command=known_word)
right_button.grid(column=1, row=1)
# Wrong Button
wrong_image = PhotoImage(file="images/wrong.png")
wrong_button = Button(image=wrong_image, highlightthickness=0, command=show_word)
wrong_button.grid(column=0, row=1)
show_word() # to avoid blank card when running the program
window.mainloop() | 0.216923 | 0.109539 |
from web3 import Web3
from wrkchain import constants
from wrkchain.documentation.sections.doc_section import DocSection
class SectionSetup(DocSection):
def __init__(self, section_number, title, network, oracle_addresses,
wrkchain_id, mainchain_rpc_host, mainchain_rpc_port,
mainchain_rpc_uri, mainchain_network_id, genesis_json,
build_dir):
path_to_md = 'setup.md'
DocSection.__init__(self, path_to_md, section_number, title)
self.__network = network
self.__oracle_addresses = oracle_addresses
self.__section_number = section_number
self.__sub_section_number = 1
self.__wrkchain_id = wrkchain_id
self.__mainchain_rpc_host = mainchain_rpc_host
self.__mainchain_rpc_port = mainchain_rpc_port
self.__mainchain_rpc_uri = mainchain_rpc_uri
self.__mainchain_network_id = mainchain_network_id
self.__genesis_json = genesis_json
self.__build_dir = build_dir
def generate(self):
d = {
'__FUND_ORACLE_ADDRESSES__': self.__fund(),
'__MAINCHAIN_NETWORK__': self.__network_title()
}
self.add_content(d, append=False)
return self.get_contents()
def __network_title(self):
if self.__network == 'eth':
network_title = 'Ethereum mainnet'
else:
network_title = f'UND {self.__network}'
return network_title
def __fund(self):
md_file = f'sub/fund/{self.__network}.md'
t = self.load_sub_section_template(md_file)
if self.__network == 'testnet':
fund_content = self.__fund_testnet(t)
elif self.__network == 'mainnet':
fund_content = self.__fund_mainnet(t)
elif self.__network == 'eth':
fund_content = self.__fund_eth(t)
else:
fund_content = ''
return fund_content
def __fund_testnet(self, t):
d = {
'__ORACLE_ADDRESSES__': '\n'.join(self.__oracle_addresses),
'__FAUCET_URL___': constants.TESTNET_FAUCET_URL
}
fund_content = t.substitute(d)
return fund_content
def __fund_mainnet(self, t):
d = {
'__ORACLE_ADDRESSES__': '\n'.join(self.__oracle_addresses),
'__MAINNET_UND_FUND_URL__': constants.MAINNET_UND_FUND_URL
}
fund_content = t.substitute(d)
return fund_content
def __fund_eth(self, t):
d = {
'__ORACLE_ADDRESSES__': '\n'.join(self.__oracle_addresses)
}
fund_content = t.substitute(d)
return fund_content
def __deply_contract(self):
if self.__network == 'eth':
deploy_content = self.__deploy_eth_mainnet()
else:
deploy_content = ''
return deploy_content
def __deploy_eth_mainnet(self):
md_file = f'sub/deploy_wrkchain_root_contract/ethereum.md'
t = self.load_sub_section_template(md_file)
d = {
'__SECTION_NUMBER__': self.__section_number,
'__SUB_SECTION_NUMBER__': self.__sub_section_number
}
self.__sub_section_number += 1
deploy_content = t.substitute(d)
return deploy_content
class SectionSetupBuilder:
def __init__(self):
self.__instance = None
def __call__(self, section_number, title, network, oracle_addresses,
wrkchain_id, mainchain_rpc_host, mainchain_rpc_port,
mainchain_rpc_uri, mainchain_network_id, genesis_json,
build_dir, **_ignored):
if not self.__instance:
self.__instance = SectionSetup(section_number, title, network,
oracle_addresses, wrkchain_id,
mainchain_rpc_host,
mainchain_rpc_port,
mainchain_rpc_uri,
mainchain_network_id,
genesis_json, build_dir)
return self.__instance | sdk/wrkchain/documentation/sections/section_setup.py | from web3 import Web3
from wrkchain import constants
from wrkchain.documentation.sections.doc_section import DocSection
class SectionSetup(DocSection):
def __init__(self, section_number, title, network, oracle_addresses,
wrkchain_id, mainchain_rpc_host, mainchain_rpc_port,
mainchain_rpc_uri, mainchain_network_id, genesis_json,
build_dir):
path_to_md = 'setup.md'
DocSection.__init__(self, path_to_md, section_number, title)
self.__network = network
self.__oracle_addresses = oracle_addresses
self.__section_number = section_number
self.__sub_section_number = 1
self.__wrkchain_id = wrkchain_id
self.__mainchain_rpc_host = mainchain_rpc_host
self.__mainchain_rpc_port = mainchain_rpc_port
self.__mainchain_rpc_uri = mainchain_rpc_uri
self.__mainchain_network_id = mainchain_network_id
self.__genesis_json = genesis_json
self.__build_dir = build_dir
def generate(self):
d = {
'__FUND_ORACLE_ADDRESSES__': self.__fund(),
'__MAINCHAIN_NETWORK__': self.__network_title()
}
self.add_content(d, append=False)
return self.get_contents()
def __network_title(self):
if self.__network == 'eth':
network_title = 'Ethereum mainnet'
else:
network_title = f'UND {self.__network}'
return network_title
def __fund(self):
md_file = f'sub/fund/{self.__network}.md'
t = self.load_sub_section_template(md_file)
if self.__network == 'testnet':
fund_content = self.__fund_testnet(t)
elif self.__network == 'mainnet':
fund_content = self.__fund_mainnet(t)
elif self.__network == 'eth':
fund_content = self.__fund_eth(t)
else:
fund_content = ''
return fund_content
def __fund_testnet(self, t):
d = {
'__ORACLE_ADDRESSES__': '\n'.join(self.__oracle_addresses),
'__FAUCET_URL___': constants.TESTNET_FAUCET_URL
}
fund_content = t.substitute(d)
return fund_content
def __fund_mainnet(self, t):
d = {
'__ORACLE_ADDRESSES__': '\n'.join(self.__oracle_addresses),
'__MAINNET_UND_FUND_URL__': constants.MAINNET_UND_FUND_URL
}
fund_content = t.substitute(d)
return fund_content
def __fund_eth(self, t):
d = {
'__ORACLE_ADDRESSES__': '\n'.join(self.__oracle_addresses)
}
fund_content = t.substitute(d)
return fund_content
def __deply_contract(self):
if self.__network == 'eth':
deploy_content = self.__deploy_eth_mainnet()
else:
deploy_content = ''
return deploy_content
def __deploy_eth_mainnet(self):
md_file = f'sub/deploy_wrkchain_root_contract/ethereum.md'
t = self.load_sub_section_template(md_file)
d = {
'__SECTION_NUMBER__': self.__section_number,
'__SUB_SECTION_NUMBER__': self.__sub_section_number
}
self.__sub_section_number += 1
deploy_content = t.substitute(d)
return deploy_content
class SectionSetupBuilder:
def __init__(self):
self.__instance = None
def __call__(self, section_number, title, network, oracle_addresses,
wrkchain_id, mainchain_rpc_host, mainchain_rpc_port,
mainchain_rpc_uri, mainchain_network_id, genesis_json,
build_dir, **_ignored):
if not self.__instance:
self.__instance = SectionSetup(section_number, title, network,
oracle_addresses, wrkchain_id,
mainchain_rpc_host,
mainchain_rpc_port,
mainchain_rpc_uri,
mainchain_network_id,
genesis_json, build_dir)
return self.__instance | 0.558447 | 0.077622 |
from vpos.vpos import Vpos
import time
class TestVpos:
# Payments
## Positives
def test_should_create_a_new_payment_request_transaction(self):
merchant = Vpos()
payment = merchant.new_payment('900000000', '123.45')
request_id = payment.get('location')[17:]
response = merchant.get_transaction(request_id)
while response.get('status_code') == 404 :
time.sleep(10)
response = merchant.get_transaction(request_id)
assert payment.get('status_code') == 202
assert response.get('data').get('status') == 'accepted'
assert response.get('data').get('amount') == '123.45'
assert response.get('data').get('mobile') == '900000000'
## Negatives
def test_should_not_create_a_payment_if_customer_format_is_invalid(self):
merchant = Vpos()
payment = merchant.new_payment('99256301', '123.45')
assert payment.get('status_code') == 400
def test_should_not_create_a_payment_if_amount_format_is_invalid(self):
merchant = Vpos()
payment = merchant.new_payment('992563019', '123.45.01')
assert payment.get('status_code') == 400
def test_should_not_create_a_payment_if_token_is_invalid(self):
merchant = Vpos(token='<KEY>')
payment = merchant.new_payment('925888553', '123.45')
assert payment.get('status_code') == 401
# Refunds
## Positives
def test_should_create_a_refund_request_transaction(self):
merchant = Vpos()
response = merchant.new_refund('<KEY>')
assert response.get('status_code') == 202
## Negatives
def test_should_not_create_a_refund_if_parent_transaction_id_is_blank(self):
merchant = Vpos()
response = merchant.new_refund(None)
assert response.get('status_code') == 400
def test_should_not_create_a_refund_if_token_is_invalid(self):
merchant = Vpos(token='<KEY>')
refund = merchant.new_refund('1jYQryG3Qo4nzaOKgJxzWDs25Hv')
assert refund.get('status_code') == 401
def test_should_not_create_a_refund_if_supervisor_card_is_invalid(self):
merchant = Vpos()
response = merchant.new_refund(
'1jYQryG3Qo4nzaOKgJxzWDs25Hv', supervisor_card='123123123123123')
assert response.get('status_code') == 400
# Transactions
## Positives
def test_should_get_a_single_transaction(self):
merchant = Vpos()
payment = merchant.new_payment('900000000', '123.45')
request_id = payment.get('location')[17:]
response = merchant.get_transaction(request_id)
while response.get('status_code') == 404 :
time.sleep(10)
response = merchant.get_transaction(request_id)
assert response.get('data').get('id') == request_id
## Negatives
def test_should_not_get_a_single_transaction_if_not_existent(self):
merchant = Vpos()
response = merchant.get_transaction('1jYQryG3Qo4nzaOKgJxzWDs25H')
assert response.get('status_code') == 404
def test_should_not_get_a_single_transaction_if_token_is_invalid(self):
merchant = Vpos(token='<KEY>')
transaction = merchant.get_transaction('1jYQryG3Qo4nzaOKgJxzWDs25Ht')
assert transaction.get('status_code') == 401
# Requests
## Positives
def test_should_get_a_running_single_request_status(self):
merchant = Vpos()
response = merchant.new_payment('925888553', '123.45')
refund_id = response.get('location')[17:]
response = merchant.get_request(refund_id)
assert response.get('status_code') == 200
## Negatives
def test_should_not_get_a_running_single_request_status_if_token_is_invalid(self):
initial_merchant = Vpos()
second_merchant = Vpos(token='<KEY>')
response = initial_merchant.new_payment('925888553', '123.45')
refund_id = response.get('location')[17:]
response = second_merchant.get_request(refund_id)
assert response.get('status_code') == 401 | vpos/test/test_vpos.py | from vpos.vpos import Vpos
import time
class TestVpos:
# Payments
## Positives
def test_should_create_a_new_payment_request_transaction(self):
merchant = Vpos()
payment = merchant.new_payment('900000000', '123.45')
request_id = payment.get('location')[17:]
response = merchant.get_transaction(request_id)
while response.get('status_code') == 404 :
time.sleep(10)
response = merchant.get_transaction(request_id)
assert payment.get('status_code') == 202
assert response.get('data').get('status') == 'accepted'
assert response.get('data').get('amount') == '123.45'
assert response.get('data').get('mobile') == '900000000'
## Negatives
def test_should_not_create_a_payment_if_customer_format_is_invalid(self):
merchant = Vpos()
payment = merchant.new_payment('99256301', '123.45')
assert payment.get('status_code') == 400
def test_should_not_create_a_payment_if_amount_format_is_invalid(self):
merchant = Vpos()
payment = merchant.new_payment('992563019', '123.45.01')
assert payment.get('status_code') == 400
def test_should_not_create_a_payment_if_token_is_invalid(self):
merchant = Vpos(token='<KEY>')
payment = merchant.new_payment('925888553', '123.45')
assert payment.get('status_code') == 401
# Refunds
## Positives
def test_should_create_a_refund_request_transaction(self):
merchant = Vpos()
response = merchant.new_refund('<KEY>')
assert response.get('status_code') == 202
## Negatives
def test_should_not_create_a_refund_if_parent_transaction_id_is_blank(self):
merchant = Vpos()
response = merchant.new_refund(None)
assert response.get('status_code') == 400
def test_should_not_create_a_refund_if_token_is_invalid(self):
merchant = Vpos(token='<KEY>')
refund = merchant.new_refund('1jYQryG3Qo4nzaOKgJxzWDs25Hv')
assert refund.get('status_code') == 401
def test_should_not_create_a_refund_if_supervisor_card_is_invalid(self):
merchant = Vpos()
response = merchant.new_refund(
'1jYQryG3Qo4nzaOKgJxzWDs25Hv', supervisor_card='123123123123123')
assert response.get('status_code') == 400
# Transactions
## Positives
def test_should_get_a_single_transaction(self):
merchant = Vpos()
payment = merchant.new_payment('900000000', '123.45')
request_id = payment.get('location')[17:]
response = merchant.get_transaction(request_id)
while response.get('status_code') == 404 :
time.sleep(10)
response = merchant.get_transaction(request_id)
assert response.get('data').get('id') == request_id
## Negatives
def test_should_not_get_a_single_transaction_if_not_existent(self):
merchant = Vpos()
response = merchant.get_transaction('1jYQryG3Qo4nzaOKgJxzWDs25H')
assert response.get('status_code') == 404
def test_should_not_get_a_single_transaction_if_token_is_invalid(self):
merchant = Vpos(token='<KEY>')
transaction = merchant.get_transaction('1jYQryG3Qo4nzaOKgJxzWDs25Ht')
assert transaction.get('status_code') == 401
# Requests
## Positives
def test_should_get_a_running_single_request_status(self):
merchant = Vpos()
response = merchant.new_payment('925888553', '123.45')
refund_id = response.get('location')[17:]
response = merchant.get_request(refund_id)
assert response.get('status_code') == 200
## Negatives
def test_should_not_get_a_running_single_request_status_if_token_is_invalid(self):
initial_merchant = Vpos()
second_merchant = Vpos(token='<KEY>')
response = initial_merchant.new_payment('925888553', '123.45')
refund_id = response.get('location')[17:]
response = second_merchant.get_request(refund_id)
assert response.get('status_code') == 401 | 0.481698 | 0.435781 |
import time
from typing import Optional, Set
from prometheus_client import Counter, Gauge, Histogram # type: ignore
from starlette.middleware.base import RequestResponseEndpoint
from starlette.requests import Request
from starlette.responses import Response
from starlette.types import ASGIApp
from fast_tools.base import NAMESPACE, BaseSearchRouteMiddleware, RouteTrie
class PrometheusMiddleware(BaseSearchRouteMiddleware):
def __init__(
self,
app: ASGIApp,
app_name: str = NAMESPACE.replace("-", "_"),
prefix: str = NAMESPACE.replace("-", "_"),
route_trie: Optional["RouteTrie"] = None,
block_url_set: Optional[Set[str]] = None,
) -> None:
super().__init__(app, route_trie=route_trie)
self._app_name: str = app_name
self._block_url_set = block_url_set or set()
self.request_count: "Counter" = Counter(
f"{prefix}_requests_total", "Count of requests", ["app_name", "method", "url_path"]
)
self.response_count: "Counter" = Counter(
f"{prefix}_responses_total",
"Count of responses",
["app_name", "method", "url_path", "status_code"],
)
self.request_time: "Histogram" = Histogram(
f"{prefix}_requests_time",
"Histogram of requests time by url",
["app_name", "method", "url_path"],
)
self.exception_count: "Counter" = Counter(
f"{prefix}_exceptions_total",
"count of exceptions",
["app_name", "method", "url_path", "exception_type"],
)
self.request_in_progress: "Gauge" = Gauge(
f"{prefix}_requests_in_progress",
"Gauge of current requests",
["app_name", "method", "url_path"],
)
async def dispatch(self, request: Request, call_next: RequestResponseEndpoint) -> Response:
url_path, is_match = self.search_route_url(request)
if url_path in self._block_url_set or not is_match:
return await call_next(request)
label_list: list = [self._app_name, request.method, url_path]
self.request_in_progress.labels(*label_list).inc()
self.request_count.labels(*label_list).inc()
status_code: int = 500
start_time: float = time.time()
try:
response = await call_next(request)
status_code = response.status_code
except Exception as e:
self.exception_count.labels(*label_list, type(e).__name__).inc()
raise e
finally:
self.request_time.labels(*label_list).observe(time.time() - start_time)
self.response_count.labels(*label_list, status_code).inc()
self.request_in_progress.labels(*label_list).dec()
return response | fast_tools/exporter/middleware.py | import time
from typing import Optional, Set
from prometheus_client import Counter, Gauge, Histogram # type: ignore
from starlette.middleware.base import RequestResponseEndpoint
from starlette.requests import Request
from starlette.responses import Response
from starlette.types import ASGIApp
from fast_tools.base import NAMESPACE, BaseSearchRouteMiddleware, RouteTrie
class PrometheusMiddleware(BaseSearchRouteMiddleware):
def __init__(
self,
app: ASGIApp,
app_name: str = NAMESPACE.replace("-", "_"),
prefix: str = NAMESPACE.replace("-", "_"),
route_trie: Optional["RouteTrie"] = None,
block_url_set: Optional[Set[str]] = None,
) -> None:
super().__init__(app, route_trie=route_trie)
self._app_name: str = app_name
self._block_url_set = block_url_set or set()
self.request_count: "Counter" = Counter(
f"{prefix}_requests_total", "Count of requests", ["app_name", "method", "url_path"]
)
self.response_count: "Counter" = Counter(
f"{prefix}_responses_total",
"Count of responses",
["app_name", "method", "url_path", "status_code"],
)
self.request_time: "Histogram" = Histogram(
f"{prefix}_requests_time",
"Histogram of requests time by url",
["app_name", "method", "url_path"],
)
self.exception_count: "Counter" = Counter(
f"{prefix}_exceptions_total",
"count of exceptions",
["app_name", "method", "url_path", "exception_type"],
)
self.request_in_progress: "Gauge" = Gauge(
f"{prefix}_requests_in_progress",
"Gauge of current requests",
["app_name", "method", "url_path"],
)
async def dispatch(self, request: Request, call_next: RequestResponseEndpoint) -> Response:
url_path, is_match = self.search_route_url(request)
if url_path in self._block_url_set or not is_match:
return await call_next(request)
label_list: list = [self._app_name, request.method, url_path]
self.request_in_progress.labels(*label_list).inc()
self.request_count.labels(*label_list).inc()
status_code: int = 500
start_time: float = time.time()
try:
response = await call_next(request)
status_code = response.status_code
except Exception as e:
self.exception_count.labels(*label_list, type(e).__name__).inc()
raise e
finally:
self.request_time.labels(*label_list).observe(time.time() - start_time)
self.response_count.labels(*label_list, status_code).inc()
self.request_in_progress.labels(*label_list).dec()
return response | 0.799677 | 0.082033 |
import random
import requests
import datetime
from bot.utils.answers import *
from bot.utils.decorators import run_async
from bot.utils.text_converter import convert_text
class Bot:
def __init__(self, token):
self.token = token
self.api_url = "https://api.telegram.org/bot{}/".format(token)
self.now = datetime.datetime.now()
self.today = self.now.day
self.start_message = 'Telegram Bot\nVersion 1.0.0\nStarted listening for updates...'
self.welcomed_users = []
self.serving_users = []
self.last_update = None
def get_updates(self, offset=None, timeout=30):
method = 'getUpdates'
params = {
'timeout': timeout,
'offset': offset
}
response = requests.get(self.api_url + method, params)
if not response.json()['ok']:
raise ValueError("Bot not found or invalid bot token.")
return response.json()['result']
def send_message(self, chat_id, username, text):
method = 'sendMessage'
params = {
'chat_id': chat_id,
'text': text
}
response = requests.post(self.api_url + method, params)
if response.json()['ok']:
print("Response sent, receiver: @{}, time: {}".format(username, datetime.datetime.now()))
else:
print("Response failed, receiver: @{}, time: {}".format(username, datetime.datetime.now()))
return response
def get_last_update(self):
get_result = self.get_updates()
if len(get_result) > 0:
last_update = get_result[-1]
else:
last_update = None
return last_update
@staticmethod
def check_received_message(string, data_list):
return any(word in string for word in data_list)
@staticmethod
def get_greeting(receiver, hour):
result = 'Good '
if 6 <= hour < 12:
result += 'morning'
elif 12 <= hour < 17:
result += 'afternoon'
elif 17 <= hour < 23:
result += 'evening'
else:
result += 'night'
return result + ', {}'.format(receiver)
@staticmethod
def parse_message(last_update):
if 'text' in last_update['message'].keys():
result = last_update['message']['text']
else:
result = None
return result
@staticmethod
def is_message(last_update):
return 'message' in last_update
@staticmethod
def is_command(text):
return len(text) > 0 and text[0] == '/'
@run_async
def control_datetime(self):
while True:
if self.today != self.now.day:
self.today = self.now.day
self.welcomed_users.clear()
self.now = datetime.datetime.now()
@run_async
def start_serve_user(self, chat_id):
last_update_id = self.last_update['update_id'] - 1
while True:
if self.last_update and last_update_id < self.last_update['update_id']:
last_update_id = self.last_update['update_id']
if self.is_message(self.last_update):
last_chat_id = self.last_update['message']['chat']['id']
if chat_id == last_chat_id:
last_chat_name = self.last_update['message']['chat']['first_name']
last_chat_text = self.parse_message(self.last_update)
if last_chat_text:
if self.is_command(last_chat_text):
if '/convert ' in last_chat_text:
message = convert_text(last_chat_text.replace('/convert ', ''))
else:
message = random.choice(INVALID_MESSAGE_ANSWER)
else:
print(last_chat_text)
if self.check_received_message(last_chat_text.lower(), GREETINGS):
if last_chat_id not in self.welcomed_users:
self.welcomed_users.append(last_chat_id)
data = {
'receiver': last_chat_name,
'hour': self.now.hour
}
message = self.get_greeting(**data)
else:
message = 'Hi again.'
else:
answers = RANDOM_ANSWERS
if '?' in last_chat_text and last_chat_text != '?':
answers.append(POSITIVE_ANSWERS + NEGATIVE_ANSWERS + NEUTRAL_ANSWERS)
elif last_chat_text == '?':
answers = ['What do you mean by sending me a question mark, {} ?'.format(last_chat_name)]
message = random.choice(answers)
else:
message = random.choice(INVALID_MESSAGE_ANSWER)
self.send_message(last_chat_id, self.last_update['message']['chat']['username'], message)
def listen(self):
new_offset = None
listening = True
self.control_datetime()
print(self.start_message)
while listening:
self.get_updates(new_offset)
self.last_update = self.get_last_update()
if self.last_update:
last_update_id = self.last_update['update_id']
if self.is_message(self.last_update):
last_chat_id = self.last_update['message']['chat']['id']
if last_chat_id not in self.serving_users:
self.serving_users.append(last_chat_id)
self.start_serve_user(last_chat_id)
new_offset = last_update_id + 1 | bot/telegram_bot.py | import random
import requests
import datetime
from bot.utils.answers import *
from bot.utils.decorators import run_async
from bot.utils.text_converter import convert_text
class Bot:
def __init__(self, token):
self.token = token
self.api_url = "https://api.telegram.org/bot{}/".format(token)
self.now = datetime.datetime.now()
self.today = self.now.day
self.start_message = 'Telegram Bot\nVersion 1.0.0\nStarted listening for updates...'
self.welcomed_users = []
self.serving_users = []
self.last_update = None
def get_updates(self, offset=None, timeout=30):
method = 'getUpdates'
params = {
'timeout': timeout,
'offset': offset
}
response = requests.get(self.api_url + method, params)
if not response.json()['ok']:
raise ValueError("Bot not found or invalid bot token.")
return response.json()['result']
def send_message(self, chat_id, username, text):
method = 'sendMessage'
params = {
'chat_id': chat_id,
'text': text
}
response = requests.post(self.api_url + method, params)
if response.json()['ok']:
print("Response sent, receiver: @{}, time: {}".format(username, datetime.datetime.now()))
else:
print("Response failed, receiver: @{}, time: {}".format(username, datetime.datetime.now()))
return response
def get_last_update(self):
get_result = self.get_updates()
if len(get_result) > 0:
last_update = get_result[-1]
else:
last_update = None
return last_update
@staticmethod
def check_received_message(string, data_list):
return any(word in string for word in data_list)
@staticmethod
def get_greeting(receiver, hour):
result = 'Good '
if 6 <= hour < 12:
result += 'morning'
elif 12 <= hour < 17:
result += 'afternoon'
elif 17 <= hour < 23:
result += 'evening'
else:
result += 'night'
return result + ', {}'.format(receiver)
@staticmethod
def parse_message(last_update):
if 'text' in last_update['message'].keys():
result = last_update['message']['text']
else:
result = None
return result
@staticmethod
def is_message(last_update):
return 'message' in last_update
@staticmethod
def is_command(text):
return len(text) > 0 and text[0] == '/'
@run_async
def control_datetime(self):
while True:
if self.today != self.now.day:
self.today = self.now.day
self.welcomed_users.clear()
self.now = datetime.datetime.now()
@run_async
def start_serve_user(self, chat_id):
last_update_id = self.last_update['update_id'] - 1
while True:
if self.last_update and last_update_id < self.last_update['update_id']:
last_update_id = self.last_update['update_id']
if self.is_message(self.last_update):
last_chat_id = self.last_update['message']['chat']['id']
if chat_id == last_chat_id:
last_chat_name = self.last_update['message']['chat']['first_name']
last_chat_text = self.parse_message(self.last_update)
if last_chat_text:
if self.is_command(last_chat_text):
if '/convert ' in last_chat_text:
message = convert_text(last_chat_text.replace('/convert ', ''))
else:
message = random.choice(INVALID_MESSAGE_ANSWER)
else:
print(last_chat_text)
if self.check_received_message(last_chat_text.lower(), GREETINGS):
if last_chat_id not in self.welcomed_users:
self.welcomed_users.append(last_chat_id)
data = {
'receiver': last_chat_name,
'hour': self.now.hour
}
message = self.get_greeting(**data)
else:
message = 'Hi again.'
else:
answers = RANDOM_ANSWERS
if '?' in last_chat_text and last_chat_text != '?':
answers.append(POSITIVE_ANSWERS + NEGATIVE_ANSWERS + NEUTRAL_ANSWERS)
elif last_chat_text == '?':
answers = ['What do you mean by sending me a question mark, {} ?'.format(last_chat_name)]
message = random.choice(answers)
else:
message = random.choice(INVALID_MESSAGE_ANSWER)
self.send_message(last_chat_id, self.last_update['message']['chat']['username'], message)
def listen(self):
new_offset = None
listening = True
self.control_datetime()
print(self.start_message)
while listening:
self.get_updates(new_offset)
self.last_update = self.get_last_update()
if self.last_update:
last_update_id = self.last_update['update_id']
if self.is_message(self.last_update):
last_chat_id = self.last_update['message']['chat']['id']
if last_chat_id not in self.serving_users:
self.serving_users.append(last_chat_id)
self.start_serve_user(last_chat_id)
new_offset = last_update_id + 1 | 0.163612 | 0.093969 |
import sys, os, argparse
import utils
import numpy as np
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Extract images with valid range (between -99 and 99 ).')
parser.add_argument('--root', dest='root', help='Path of AFLW2000')
args = parser.parse_args()
return args
def DB_AFLW2000Iterator(path):
extensions = ('.jpg')
f = open(os.path.join(path, 'filenames.txt'), 'w+')
counter = 0
for root, dirs, files in os.walk(path):
for file_ in files:
names = os.path.splitext(file_)
if names[1] in extensions:
#print( os.path.join(root, file_) )
names = os.path.splitext(file_)
mat_path = os.path.join(root, names[0]+'.mat')
# We get the pose in radians
pose = utils.get_ypr_from_mat(mat_path)
# And convert to degrees.
pitch = pose[0] * 180 / np.pi
yaw = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
if pitch >= -99.0 and pitch <= 99.0 and yaw >= -99.0 and yaw <= 99.0 and roll >= -99.0 and roll <= 99.0:
print(mat_path)
counter = counter +1
f.write(names[0]+os.linesep)
print counter
f.close()
def DB_300W_LP_Iterator(path):
extensions = ('.jpg')
subfolders = ['IBUG', 'AFW', 'LFPW', 'IBUG_Flip', 'AFW_Flip', 'HELEN', 'LFPW_Flip', 'HELEN_Flip']
f = open(os.path.join(path, 'filenames.txt'), 'w+')
counter = 0
for s in subfolders:
root = os.path.join(path, s)
files = os.listdir(root)
for file_ in files:
names = os.path.splitext(file_)
if names[1] in extensions:
names = os.path.splitext(file_)
mat_path = os.path.join(root, names[0]+'.mat')
# We get the pose in radians
pose = utils.get_ypr_from_mat(mat_path)
# And convert to degrees.
pitch = pose[0] * 180 / np.pi
yaw = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
if pitch >= -99.0 and pitch <= 99.0 and yaw >= -99.0 and yaw <= 99.0 and roll >= -99.0 and roll <= 99.0:
counter = counter +1
f.write(s + os.sep+ names[0]+os.linesep)
print counter
f.close()
if __name__ == '__main__':
args = parse_args()
#DB_AFLW2000Iterator(args.root)
DB_300W_LP_Iterator(args.root) | code/extract_valid_files.py | import sys, os, argparse
import utils
import numpy as np
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Extract images with valid range (between -99 and 99 ).')
parser.add_argument('--root', dest='root', help='Path of AFLW2000')
args = parser.parse_args()
return args
def DB_AFLW2000Iterator(path):
extensions = ('.jpg')
f = open(os.path.join(path, 'filenames.txt'), 'w+')
counter = 0
for root, dirs, files in os.walk(path):
for file_ in files:
names = os.path.splitext(file_)
if names[1] in extensions:
#print( os.path.join(root, file_) )
names = os.path.splitext(file_)
mat_path = os.path.join(root, names[0]+'.mat')
# We get the pose in radians
pose = utils.get_ypr_from_mat(mat_path)
# And convert to degrees.
pitch = pose[0] * 180 / np.pi
yaw = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
if pitch >= -99.0 and pitch <= 99.0 and yaw >= -99.0 and yaw <= 99.0 and roll >= -99.0 and roll <= 99.0:
print(mat_path)
counter = counter +1
f.write(names[0]+os.linesep)
print counter
f.close()
def DB_300W_LP_Iterator(path):
extensions = ('.jpg')
subfolders = ['IBUG', 'AFW', 'LFPW', 'IBUG_Flip', 'AFW_Flip', 'HELEN', 'LFPW_Flip', 'HELEN_Flip']
f = open(os.path.join(path, 'filenames.txt'), 'w+')
counter = 0
for s in subfolders:
root = os.path.join(path, s)
files = os.listdir(root)
for file_ in files:
names = os.path.splitext(file_)
if names[1] in extensions:
names = os.path.splitext(file_)
mat_path = os.path.join(root, names[0]+'.mat')
# We get the pose in radians
pose = utils.get_ypr_from_mat(mat_path)
# And convert to degrees.
pitch = pose[0] * 180 / np.pi
yaw = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
if pitch >= -99.0 and pitch <= 99.0 and yaw >= -99.0 and yaw <= 99.0 and roll >= -99.0 and roll <= 99.0:
counter = counter +1
f.write(s + os.sep+ names[0]+os.linesep)
print counter
f.close()
if __name__ == '__main__':
args = parse_args()
#DB_AFLW2000Iterator(args.root)
DB_300W_LP_Iterator(args.root) | 0.271735 | 0.221709 |
import json
import numpy as np
from typing import Dict, List, Any
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
def load_dataset(file_path: str = "dataset.json") -> Dict[str, Any]:
with open(file_path, "r") as f:
dataset = json.load(f)
return dataset
def make_label(target: List[str]) -> List[str]:
return list(set(target))
def make_text_vectorizer(
data: np.ndarray,
) -> tf.keras.layers.experimental.preprocessing.TextVectorization:
text_vectorizer = tf.keras.layers.experimental.preprocessing.TextVectorization(
output_mode="tf-idf", ngrams=2
)
text_vectorizer.adapt(data)
return text_vectorizer
def define_model(
text_vectorizer: tf.keras.layers.experimental.preprocessing.TextVectorization,
optimizer: str = "adam",
loss: str = "categorical_crossentropy",
metrics: List[str] = ["accuracy"],
) -> tf.keras.Model:
inputs = keras.Input(shape=(1,), dtype="string")
x = text_vectorizer(inputs)
x = layers.Dense(1)(x)
x = layers.Dense(256, activation="relu")(x)
x = layers.Dense(256, activation="relu")(x)
outputs = layers.Dense(6, activation="softmax")(x)
model = keras.Model(inputs, outputs)
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
return model
class TextModel(tf.keras.Model):
def __init__(self, model: tf.keras.Model, labels: List[str]):
super().__init__(self)
self.model = model
self.labels = labels
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="text")]
)
def serving_fn(self, text: str) -> tf.Tensor:
predictions = self.model(text)
def _convert_to_label(candidates):
max_prob = tf.math.reduce_max(candidates)
idx = tf.where(tf.equal(candidates, max_prob))
label = tf.squeeze(tf.gather(self.labels, idx))
return label
return tf.map_fn(_convert_to_label, predictions, dtype=tf.string)
def save(self, export_path="./saved_model/text/"):
signatures = {"serving_default": self.serving_fn}
tf.keras.backend.set_learning_phase(0)
tf.saved_model.save(self, export_path, signatures=signatures)
def main():
dataset = load_dataset(file_path="dataset.json")
data = dataset["data"]
target = dataset["target"]
labels = make_label(target=target)
label_dict = {t: i for i, t in enumerate(labels)}
target_int = [label_dict[t] for t in target]
train_data = np.array(data)
train_target = tf.keras.utils.to_categorical(target_int)
text_vectorizer = make_text_vectorizer(data=train_data)
model = define_model(text_vectorizer=text_vectorizer)
model.fit(train_data, train_target, epochs=40, batch_size=16)
text_model = TextModel(model=model, labels=labels)
version_number = 0
text_model.save(f"./saved_model/text/{version_number}/")
if __name__ == "__main__":
main() | text_classification/build_text_model.py | import json
import numpy as np
from typing import Dict, List, Any
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
def load_dataset(file_path: str = "dataset.json") -> Dict[str, Any]:
with open(file_path, "r") as f:
dataset = json.load(f)
return dataset
def make_label(target: List[str]) -> List[str]:
return list(set(target))
def make_text_vectorizer(
data: np.ndarray,
) -> tf.keras.layers.experimental.preprocessing.TextVectorization:
text_vectorizer = tf.keras.layers.experimental.preprocessing.TextVectorization(
output_mode="tf-idf", ngrams=2
)
text_vectorizer.adapt(data)
return text_vectorizer
def define_model(
text_vectorizer: tf.keras.layers.experimental.preprocessing.TextVectorization,
optimizer: str = "adam",
loss: str = "categorical_crossentropy",
metrics: List[str] = ["accuracy"],
) -> tf.keras.Model:
inputs = keras.Input(shape=(1,), dtype="string")
x = text_vectorizer(inputs)
x = layers.Dense(1)(x)
x = layers.Dense(256, activation="relu")(x)
x = layers.Dense(256, activation="relu")(x)
outputs = layers.Dense(6, activation="softmax")(x)
model = keras.Model(inputs, outputs)
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
return model
class TextModel(tf.keras.Model):
def __init__(self, model: tf.keras.Model, labels: List[str]):
super().__init__(self)
self.model = model
self.labels = labels
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="text")]
)
def serving_fn(self, text: str) -> tf.Tensor:
predictions = self.model(text)
def _convert_to_label(candidates):
max_prob = tf.math.reduce_max(candidates)
idx = tf.where(tf.equal(candidates, max_prob))
label = tf.squeeze(tf.gather(self.labels, idx))
return label
return tf.map_fn(_convert_to_label, predictions, dtype=tf.string)
def save(self, export_path="./saved_model/text/"):
signatures = {"serving_default": self.serving_fn}
tf.keras.backend.set_learning_phase(0)
tf.saved_model.save(self, export_path, signatures=signatures)
def main():
dataset = load_dataset(file_path="dataset.json")
data = dataset["data"]
target = dataset["target"]
labels = make_label(target=target)
label_dict = {t: i for i, t in enumerate(labels)}
target_int = [label_dict[t] for t in target]
train_data = np.array(data)
train_target = tf.keras.utils.to_categorical(target_int)
text_vectorizer = make_text_vectorizer(data=train_data)
model = define_model(text_vectorizer=text_vectorizer)
model.fit(train_data, train_target, epochs=40, batch_size=16)
text_model = TextModel(model=model, labels=labels)
version_number = 0
text_model.save(f"./saved_model/text/{version_number}/")
if __name__ == "__main__":
main() | 0.896754 | 0.510252 |
import os
import sys
import argparse
sys.path.append(os.path.join(os.environ["CONTECH_HOME"], "scripts"))
import util
import subprocess
import shutil
import tempfile
def main(arg):
# TODO: usage should be based on argparse
if (len(arg)) == 1:
print "Usage: {0} input\n".format(arg[0])
exit()
parser = argparse.ArgumentParser(description="Runs dynamic analysis backend on benchmark")
parser.add_argument("-b", "--bitcode", help="Bitcode file to analyze")
parser.add_argument("-t", "--taskgraph", help="Taskgraph file to analyze")
parser.add_argument("-o", default="output", help="Output directory")
parser.add_argument("-n", default="65", help="Number of contexts to analyze")
args = parser.parse_args()
DynAnalysisLib = "-load=lib/LLVMDynAnalysis.so"
ExecLatency = "-execution-units-latency={1,2,3,1,3,5,6,1,4,4,12,12,30,30,100,100}"
ROBSize = "-reorder-buffer-size=168"
LBSize = "-load-buffer-size=64"
SBSize = "-store-buffer-size=36"
CacheLineSize = "-cache-line-size=64"
ILP = "-instruction-fetch-bandwidth=4"
RSSize = "-reservation-station-size=56"
L1Size = "-l1-cache-size=32768"
L2Size = "-l2-cache-size=262144"
LLCSize = "-llc-cache-size=20971520"
ExecIssue = "-execution-units-parallel-issue={1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1}"
ExecThru = "-execution-units-throughput={3,1,1,1,1,1,1,1,8,8,32,32,32,32,8,8}"
FillBufSize = "-line-fill-buffer-size=10"
WordSize = "-memory-word-size=8"
AccessGran = "-mem-access-granularity={8,8,64,64,64,64,64,64}"
# Assume that the taskgraph is formatted according to regress / util script convention
benchName = args.taskgraph.split('/')[-1]
b = benchName.split('.')
if (b[0] == "splash2x"):
benchName = b[1]
else:
benchName = b[0]
try:
os.makedirs(args.o + "/" + benchName)
except os.error:
#path already exists
arg = arg
for ctid in range(int(args.n)):
filename = args.o + "/" + benchName + "/ct_{0}".format(ctid)
ofile = tempfile.NamedTemporaryFile(suffix=".bc")
with open(filename, "w") as of:
#Use bash to expand arguments per https://stackoverflow.com/questions/8945826/expand-shell-path-expression-in-python
util.pcall(["bash -c \" opt", DynAnalysisLib, "-EnginePass", args.bitcode, "-taskgraph-file=" + args.taskgraph,
"-o " + ofile.name, "-context-number={0}".format(ctid),
ExecLatency, ROBSize, LBSize, SBSize, CacheLineSize, ILP, RSSize, L1Size,
L2Size, LLCSize, ExecIssue, ExecThru, FillBufSize, WordSize, AccessGran, "\""],
outputFile=of, suppressOutput=False, silent=False)
ofile.close()
try:
os.unlink(ofile.name)
except os.error:
# we expect that there will be an error returned by the unlink
# The file should already be deleted, but we are calling just in case
arg = arg
if __name__ == "__main__":
main(sys.argv) | backend/DynamicAnalysis/run.py |
import os
import sys
import argparse
sys.path.append(os.path.join(os.environ["CONTECH_HOME"], "scripts"))
import util
import subprocess
import shutil
import tempfile
def main(arg):
# TODO: usage should be based on argparse
if (len(arg)) == 1:
print "Usage: {0} input\n".format(arg[0])
exit()
parser = argparse.ArgumentParser(description="Runs dynamic analysis backend on benchmark")
parser.add_argument("-b", "--bitcode", help="Bitcode file to analyze")
parser.add_argument("-t", "--taskgraph", help="Taskgraph file to analyze")
parser.add_argument("-o", default="output", help="Output directory")
parser.add_argument("-n", default="65", help="Number of contexts to analyze")
args = parser.parse_args()
DynAnalysisLib = "-load=lib/LLVMDynAnalysis.so"
ExecLatency = "-execution-units-latency={1,2,3,1,3,5,6,1,4,4,12,12,30,30,100,100}"
ROBSize = "-reorder-buffer-size=168"
LBSize = "-load-buffer-size=64"
SBSize = "-store-buffer-size=36"
CacheLineSize = "-cache-line-size=64"
ILP = "-instruction-fetch-bandwidth=4"
RSSize = "-reservation-station-size=56"
L1Size = "-l1-cache-size=32768"
L2Size = "-l2-cache-size=262144"
LLCSize = "-llc-cache-size=20971520"
ExecIssue = "-execution-units-parallel-issue={1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1}"
ExecThru = "-execution-units-throughput={3,1,1,1,1,1,1,1,8,8,32,32,32,32,8,8}"
FillBufSize = "-line-fill-buffer-size=10"
WordSize = "-memory-word-size=8"
AccessGran = "-mem-access-granularity={8,8,64,64,64,64,64,64}"
# Assume that the taskgraph is formatted according to regress / util script convention
benchName = args.taskgraph.split('/')[-1]
b = benchName.split('.')
if (b[0] == "splash2x"):
benchName = b[1]
else:
benchName = b[0]
try:
os.makedirs(args.o + "/" + benchName)
except os.error:
#path already exists
arg = arg
for ctid in range(int(args.n)):
filename = args.o + "/" + benchName + "/ct_{0}".format(ctid)
ofile = tempfile.NamedTemporaryFile(suffix=".bc")
with open(filename, "w") as of:
#Use bash to expand arguments per https://stackoverflow.com/questions/8945826/expand-shell-path-expression-in-python
util.pcall(["bash -c \" opt", DynAnalysisLib, "-EnginePass", args.bitcode, "-taskgraph-file=" + args.taskgraph,
"-o " + ofile.name, "-context-number={0}".format(ctid),
ExecLatency, ROBSize, LBSize, SBSize, CacheLineSize, ILP, RSSize, L1Size,
L2Size, LLCSize, ExecIssue, ExecThru, FillBufSize, WordSize, AccessGran, "\""],
outputFile=of, suppressOutput=False, silent=False)
ofile.close()
try:
os.unlink(ofile.name)
except os.error:
# we expect that there will be an error returned by the unlink
# The file should already be deleted, but we are calling just in case
arg = arg
if __name__ == "__main__":
main(sys.argv) | 0.191252 | 0.109277 |
import pyautogui
from time import time, sleep
from typeguard import typechecked
from defined import Coord, RGB, Ratio, Owner, Dict
from data import uData
from adb import adb
@typechecked
class Object():
def __init__(self,
coord_name: str,
rgb_kname: str,
ratio: Ratio = (0.0, 0.0),
rgb: RGB = (0, 0, 0),
tolerance: int = 0):
self.name = coord_name
self.coord = self.__calc_relative_pos(ratio)
self.rgb_kname = rgb_kname
self.rgb = rgb
self.__tolerance = tolerance
adb_use = uData.setting['adb']['use']
self.__click = adb.click if adb_use else pyautogui.click
self.__pixelMatchesColor = adb.pixelMatchesColor if adb_use else pyautogui.pixelMatchesColor
def __str__(self):
return str(self.__class__) + ": " + str(self.__dict__)
def found(self, adb_update_cache: bool = True) -> bool:
if adb_update_cache:
adb.set_update_cv2_IM_cache_flag()
while True:
try:
return self.__pixelMatchesColor(*self.coord, self.rgb, tolerance=self.__tolerance)
except WindowsError:
sleep(0.2)
def __calc_relative_pos(self, ratio: Ratio) -> Coord:
x0, y0, width, height = uData.setting['game_region']
ratioX, ratioY = ratio
return (int(round(x0 + ratioX * width)), int(round(y0 + ratioY * height)))
def click(self, times: int = 1, interval: float = uData.setting['sleep']['click']):
self.__click(*self.coord, times, interval)
sleep(interval)
def click_sec(self, sec: float = 0.0, interval: float = 1.0):
if sec <= 0:
return
duration = 0.0
t0 = round(time(), 1)
while duration < sec:
self.__click(*self.coord)
if sec - duration >= interval:
sleep(interval)
else:
sleep(sec - duration)
duration = round(time(), 1) - t0
self.__click(*self.coord)
@typechecked
def Load_Objects(obj_name: Owner) -> Dict:
objects = dict()
for key in uData.setting['ratio'].keys():
x, y, c_name, owner = uData.setting['ratio'][key].values()
if obj_name not in owner + ['all']:
continue
ratio = (x, y)
rgb, tolerance = uData.setting['color'][c_name].values() if c_name and c_name != "None" else [(0, 0, 0), 0]
objects[key] = Object(key, c_name, ratio, tuple(rgb), tolerance)
return objects
# Test
if __name__ == '__main__':
obj = Object("sk", "ivory", (0.64634, 0.78623), (250, 250, 235), 25)
print(obj)
print(obj.click_sec(8)) | src/object.py | import pyautogui
from time import time, sleep
from typeguard import typechecked
from defined import Coord, RGB, Ratio, Owner, Dict
from data import uData
from adb import adb
@typechecked
class Object():
def __init__(self,
coord_name: str,
rgb_kname: str,
ratio: Ratio = (0.0, 0.0),
rgb: RGB = (0, 0, 0),
tolerance: int = 0):
self.name = coord_name
self.coord = self.__calc_relative_pos(ratio)
self.rgb_kname = rgb_kname
self.rgb = rgb
self.__tolerance = tolerance
adb_use = uData.setting['adb']['use']
self.__click = adb.click if adb_use else pyautogui.click
self.__pixelMatchesColor = adb.pixelMatchesColor if adb_use else pyautogui.pixelMatchesColor
def __str__(self):
return str(self.__class__) + ": " + str(self.__dict__)
def found(self, adb_update_cache: bool = True) -> bool:
if adb_update_cache:
adb.set_update_cv2_IM_cache_flag()
while True:
try:
return self.__pixelMatchesColor(*self.coord, self.rgb, tolerance=self.__tolerance)
except WindowsError:
sleep(0.2)
def __calc_relative_pos(self, ratio: Ratio) -> Coord:
x0, y0, width, height = uData.setting['game_region']
ratioX, ratioY = ratio
return (int(round(x0 + ratioX * width)), int(round(y0 + ratioY * height)))
def click(self, times: int = 1, interval: float = uData.setting['sleep']['click']):
self.__click(*self.coord, times, interval)
sleep(interval)
def click_sec(self, sec: float = 0.0, interval: float = 1.0):
if sec <= 0:
return
duration = 0.0
t0 = round(time(), 1)
while duration < sec:
self.__click(*self.coord)
if sec - duration >= interval:
sleep(interval)
else:
sleep(sec - duration)
duration = round(time(), 1) - t0
self.__click(*self.coord)
@typechecked
def Load_Objects(obj_name: Owner) -> Dict:
objects = dict()
for key in uData.setting['ratio'].keys():
x, y, c_name, owner = uData.setting['ratio'][key].values()
if obj_name not in owner + ['all']:
continue
ratio = (x, y)
rgb, tolerance = uData.setting['color'][c_name].values() if c_name and c_name != "None" else [(0, 0, 0), 0]
objects[key] = Object(key, c_name, ratio, tuple(rgb), tolerance)
return objects
# Test
if __name__ == '__main__':
obj = Object("sk", "ivory", (0.64634, 0.78623), (250, 250, 235), 25)
print(obj)
print(obj.click_sec(8)) | 0.577614 | 0.173673 |
import csv
import os
# Custom Imports.
class Lookup:
"""
The Lookup class is a concrete class that can ingest environmental
parameters to generate and read lookup tables for models that may take large
processing loads to compute. The expected output lookup tables are in CSV
format, and can be indexed (if one knows the indexing parameters) to quickly
reach any expected output value.
"""
# Where all lookup files are located.
_fileRoot = "./External/"
def __init__(
self,
parameters=[(0.01, 81), (50, 21), (0.5, 161)],
header=["v_ref (V)", "irrad (G)", "temp (C)", "current (A)"],
fileName="model.csv",
):
"""
Sets up the initial lookup parameters.
The default parameters signify the following:
voltage[0]:
resolution: .01
num entries: 81 ([0, .8] inclusive, in increments of .01)
irradiance[1]:
resolution: 50
num entries: 21 ([0, 1000] inclusive, in increments of 50)
temperature[2]: resolution: .5
num entries: 161 ([0, 80] inclusive, in increments of .5)
Parameters
----------
parameters: List of tuples
A list of tuples, where each tuple represents an independent
variable.
The tuple is in the format (float, int), where the first entry is
the resolution and the second entry is the total number of entries.
header: List of Strings
The names of the columns (both independent and dependent variables)
prepended before the data. Dependent variables should be last.
fileName: String
Name of the file to access or write to. Don't abuse the file name.
"""
# Parameters used to access our lookup table.
self._parameters = parameters
self._multiplier = 1
for param in parameters:
self._multiplier *= param[1]
# File header we check against to make sure we're looking at a properly
# formatted file. Additionally also used for users to interpret the csv.
if not isinstance(header, list):
raise Exception("Header is not a list.")
self._header = header
# Name of the csv file containing the lookup table to perform operations on.
self._filename = fileName
self.data = []
def addLine(self, line):
"""
Writes a line of data to the internal buffer. Should be in the buffer
format expected.
Parameters
----------
line: List [independent var 1, ind. var 2, ..., dependent var 1, ...]
List of the parameters to append to the buffer. No sorting is done
and is expected to be inserted in order described in the File
Description.
"""
self.data.append(line)
def lookup(self, params):
"""
Searches the internal buffer for the matching indices given by the
parameter values. Indexing is interpolated from the values, a line in
the file corresponding to the indices are found, and a value pops out!
Dealing with problematic resolutions are not yet supported, but if they
were, we'd probably call the lookup subroutine a 2^N times (N being the
number of parameters that have over specified resolutions) and doing an
interpolation between all of those results.
Parameters
----------
params: List of floats
List of independent variables in column order to search.
Return
------
float: The resultant output given the input parameters.
Assumptions
-----------
params must match the number of parameters specified at initialization.
More specifically, the number of arguments should match and are in the
same order.
"""
idx = 1 # Start at one to ignore the header entry.
paramIndices = []
for count, param in enumerate(params):
paramIdx = int(round(param / self._parameters[count][0]))
paramIndices.append(paramIdx)
if paramIdx < 0 or paramIdx >= self._parameters[count][1]:
raise Exception(
"Parameters are out of bounds of the data: "
+ str(paramIdx)
+ " for "
+ str(param)
+ " with max num entries "
+ str(self._parameters[count][1] - 1)
)
multiplier = self._multiplier
for count, paramIdx in enumerate(paramIndices):
multiplier /= self._parameters[count][1]
idx += paramIdx * multiplier
resultStr = self.data[int(idx)][len(self._parameters) : :]
resultFloat = []
for result in resultStr:
resultFloat.append(float(result))
return resultFloat
def writeFile(self):
"""
Writes accumulated data into the file.
"""
with open(self._fileRoot + self._filename, "w", newline="\n") as csv_file:
writer = csv.writer(csv_file)
writer.writerow(self._header)
for line in self.data:
writer.writerow(line)
def readFile(self):
"""
Initializes an internal buffer with the contents of the file, parsed.
Repeat calls just repeat the operation.
"""
self.data = []
with open(self._fileRoot + self._filename, "r", newline="\n") as csv_file:
reader = csv.reader(csv_file)
for row in reader:
self.data.append(row) | ArraySimulation/PVSource/PVCell/Lookup.py | import csv
import os
# Custom Imports.
class Lookup:
"""
The Lookup class is a concrete class that can ingest environmental
parameters to generate and read lookup tables for models that may take large
processing loads to compute. The expected output lookup tables are in CSV
format, and can be indexed (if one knows the indexing parameters) to quickly
reach any expected output value.
"""
# Where all lookup files are located.
_fileRoot = "./External/"
def __init__(
self,
parameters=[(0.01, 81), (50, 21), (0.5, 161)],
header=["v_ref (V)", "irrad (G)", "temp (C)", "current (A)"],
fileName="model.csv",
):
"""
Sets up the initial lookup parameters.
The default parameters signify the following:
voltage[0]:
resolution: .01
num entries: 81 ([0, .8] inclusive, in increments of .01)
irradiance[1]:
resolution: 50
num entries: 21 ([0, 1000] inclusive, in increments of 50)
temperature[2]: resolution: .5
num entries: 161 ([0, 80] inclusive, in increments of .5)
Parameters
----------
parameters: List of tuples
A list of tuples, where each tuple represents an independent
variable.
The tuple is in the format (float, int), where the first entry is
the resolution and the second entry is the total number of entries.
header: List of Strings
The names of the columns (both independent and dependent variables)
prepended before the data. Dependent variables should be last.
fileName: String
Name of the file to access or write to. Don't abuse the file name.
"""
# Parameters used to access our lookup table.
self._parameters = parameters
self._multiplier = 1
for param in parameters:
self._multiplier *= param[1]
# File header we check against to make sure we're looking at a properly
# formatted file. Additionally also used for users to interpret the csv.
if not isinstance(header, list):
raise Exception("Header is not a list.")
self._header = header
# Name of the csv file containing the lookup table to perform operations on.
self._filename = fileName
self.data = []
def addLine(self, line):
"""
Writes a line of data to the internal buffer. Should be in the buffer
format expected.
Parameters
----------
line: List [independent var 1, ind. var 2, ..., dependent var 1, ...]
List of the parameters to append to the buffer. No sorting is done
and is expected to be inserted in order described in the File
Description.
"""
self.data.append(line)
def lookup(self, params):
"""
Searches the internal buffer for the matching indices given by the
parameter values. Indexing is interpolated from the values, a line in
the file corresponding to the indices are found, and a value pops out!
Dealing with problematic resolutions are not yet supported, but if they
were, we'd probably call the lookup subroutine a 2^N times (N being the
number of parameters that have over specified resolutions) and doing an
interpolation between all of those results.
Parameters
----------
params: List of floats
List of independent variables in column order to search.
Return
------
float: The resultant output given the input parameters.
Assumptions
-----------
params must match the number of parameters specified at initialization.
More specifically, the number of arguments should match and are in the
same order.
"""
idx = 1 # Start at one to ignore the header entry.
paramIndices = []
for count, param in enumerate(params):
paramIdx = int(round(param / self._parameters[count][0]))
paramIndices.append(paramIdx)
if paramIdx < 0 or paramIdx >= self._parameters[count][1]:
raise Exception(
"Parameters are out of bounds of the data: "
+ str(paramIdx)
+ " for "
+ str(param)
+ " with max num entries "
+ str(self._parameters[count][1] - 1)
)
multiplier = self._multiplier
for count, paramIdx in enumerate(paramIndices):
multiplier /= self._parameters[count][1]
idx += paramIdx * multiplier
resultStr = self.data[int(idx)][len(self._parameters) : :]
resultFloat = []
for result in resultStr:
resultFloat.append(float(result))
return resultFloat
def writeFile(self):
"""
Writes accumulated data into the file.
"""
with open(self._fileRoot + self._filename, "w", newline="\n") as csv_file:
writer = csv.writer(csv_file)
writer.writerow(self._header)
for line in self.data:
writer.writerow(line)
def readFile(self):
"""
Initializes an internal buffer with the contents of the file, parsed.
Repeat calls just repeat the operation.
"""
self.data = []
with open(self._fileRoot + self._filename, "r", newline="\n") as csv_file:
reader = csv.reader(csv_file)
for row in reader:
self.data.append(row) | 0.776877 | 0.71464 |