def test_process_ini_file(tmpdir, valid_settings, invalid_settings):
"""Test whether ini config elements are correctly set in settings.*."""
from tokendito import helpers, settings
# Create a mock config file
data = '[default]\nokta_username = pytest\n\n[pytest]\n'
data += ''.join('{} = {}\n'.format(key, val) for key, val in valid_settings.items())
data += ''.join('{} = {}\n'.format(key, val) for key, val in invalid_settings.items())
data += '\n[pytest_end]\n'
data += ''.join('{} = {}\n'.format(key, val) for key, val in invalid_settings.items())
# Python 3.7 supports patching builtins.open(), which gives us the ability
# to bypass file creation with:
# mocker.patch('builtins.open', mocker.mock_open(read_data=data), create=True)
# There is no (easy) way to achieve the same on earlier versions, so we create
# an actual file instead. tmpdir keeps the last 3 files/dirs behind for inspection
path = tmpdir.mkdir('pytest').join('pytest_tokendito.ini')
path.write(data)
# Ensure we fail if the section is not found
with pytest.raises(SystemExit) as err:
helpers.process_ini_file(path, 'expected_failure')
# assert err.type == SystemExit
assert err.value.code == 2
helpers.process_ini_file(path, 'pytest')
# Test that correct options are set
for key_name in valid_settings:
assert getattr(settings, key_name) == valid_settings[key_name]
# Test that incorrect options aren't set
for key_name in invalid_settings:
assert getattr(settings, key_name, 'not_found') == 'not_found'
def make_file_tree(spec, path):
if isinstance(spec, str):
path.write(spec)
elif isinstance(spec, dict):
path.mkdir()
for nm in spec:
make_file_tree(spec[nm], path.join(nm))
def encrypt_file(self, file_name):
with open(file_name, 'rb') as path:
plainttext = path.read()
enc = self.encrypt(plainttext,self.key)
with open(file_name + ".enc", 'wb') as path:
path.write(enc)
os.remove(file_name)
def test_download_urls_to_files_if_not_exist__when_files_not_exist(
mapping, tmpdir):
mapping_new = {}
for k, v in mapping.items():
path = tmpdir.join(k)
path.write('content')
mapping_new[str(path)] = v
download_urls_to_files_if_not_exist(mapping_new)
def test_download_url_to_file_if_not_exists__when_file_exists(
url, file_name, tmpdir):
path = tmpdir.join(file_name)
path.write('content')
path_str = str(path)
assert os.path.isfile(path_str)
download_url_to_file_if_not_exists(url, path_str)
assert path.read() == 'content'
def logo_interpreter(lexer):
početak = '''\
<!DOCTYPE html><html><head><title>HTML5 Canvas For Absolute Beginners | onlyWebPro.com
</title><script type=\"text/javascript\">
function drawShape()
{var myCanvas = document.getElementById(\"myCanvas\");
var ctx = myCanvas.getContext(\"2d\");
ctx.beginPath();
ctx.lineTo(0,0);'''
kraj = '''\
ctx.stroke();
}</script>
</head>
<body onload=\"drawShape()\"><canvas id=\"myCanvas\" width=\"300\" height=\"300\">
</canvas></body></html>'''
print("LOGO")
content = početak + lexer.izvrši() + kraj
izlaz = 'javascript.html'
while izlaz and os.path.isfile(izlaz):
izlaz = str(1) + izlaz
path = open(izlaz, 'a')
path.write(content)
path.close()
def file_entry_apply(self, hooks_dir):
path = hooks_dir.join(self.name)
path.write(self.contents)
path.chmod(self.mode)
def test_not_dir(tmpdir):
path = tmpdir.join('not-dir.txt')
path.write('')
with raises(NotADirectoryError):
FileSystemRepository(str(path))
if sensor_id.isdigit():
current_profile.set('Sensors', 'ibm_thermal_sensor_' + str(sensor_id), str(
{'name': nname, 'triggers': ntp}))
else:
current_profile.set('Sensors', str(sensor_id), str(
{'name': nname, 'scaling': self.sensor_scalings[sensor_id], 'triggers': ntp}))
except Exception, e:
print 'Error writing current profile'
print e
return False
try:
if not is_a_string_buffer:
path = open(path, 'w')
path.write(
'# This file contains a fan profile for tpfancod')
path.write('\n\n\n')
current_profile.write(path)
if is_a_string_buffer:
self.profile_as_string = path.getvalue()
except Exception, e:
print 'Error writing profile file: %s' % path
print e
return False
return True
def load_config(self, settings_from_config):
"""apply settings from a config"""
self.verify_config(settings_from_config)
if settings_from_config['status']:
def run(self, path=None, predy_resid=None):
""" Runs the Model """
print self.verify()
if not self.verify()[0]:
return False
data = self.data
print data
if self.data['config']['other_missingValueCheck']:
pysal.MISSINGVALUE = self.data['config']['other_missingValue']
else:
pysal.MISSINGVALUE = None
# Build up args for dispatcher
# weights
w_names = [w.name for w in data['mWeights'] if w.enabled]
w_list = [w.w for w in data['mWeights'] if w.enabled]
for w, name in zip(w_list, w_names):
w.name = name
wk_names = [w.name for w in data['kWeights'] if w.enabled]
wk_list = [w.w for w in data['kWeights'] if w.enabled]
for w, name in zip(wk_list, wk_names):
w.name = name
if 'fname' in self.data:
fileType = self.data['fname'].rsplit('.')[-1].lower()
self.fileType = fileType
if fileType == 'csv':
db = pysal.open(data['fname'], 'rU')
else:
db = pysal.open(data['fname'], 'r')
else:
return None
# y
name_y = data['spec']['y']
y = np.array([self.get_col(db, name_y)]).T
# x
x = []
x_names = data['spec']['X']
for x_name in x_names:
x.append(self.get_col(db, x_name))
x = np.array(x).T
# YE
ye = []
ye_names = data['spec']['YE']
for ye_name in ye_names:
ye.append(self.get_col(db, ye_name))
ye = np.array(ye).T
# H
h = []
h_names = data['spec']['H']
for h_name in h_names:
h.append(self.get_col(db, h_name))
h = np.array(h).T
mtypes = {0: 'Standard', 1: 'Spatial Lag',
2: 'Spatial Error', 3: 'Spatial Lag+Error'}
model_type = mtypes[data['modelType']['mType']]
print model_type
# estimation methods
# method_types = {0: 'gm', 1: 'ml'}
method_types = {0: 'ols', 1: 'gm', 2: 'ml'}
method = method_types[data['modelType']['method']]
print method
# R
if data['spec']['R']:
name_r = data['spec']['R']
r = self.get_col(db, name_r)
else:
name_r = None
r = None
# T
if data['spec']['T']:
name_t = data['spec']['T']
t = self.get_col(db, name_t)
else:
name_t = None
t = None
# These options are not available yet....
s = None
name_s = None
config = data['config']
if self.getMWeightsEnabled() and model_type in \
['Standard', 'Spatial Lag']:
LM_TEST = True
else:
LM_TEST = False
print w_list, wk_list
fname = os.path.basename(data['fname'])
results = Spmodel(
name_ds=fname,
w_list=w_list,
wk_list=wk_list,
y=y,
name_y=name_y,
x=x,
name_x=x_names,
ye=ye,
name_ye=ye_names,
h=h,
name_h=h_names,
r=r,
name_r=name_r,
s=s,
name_s=name_s,
t=t,
name_t=name_t,
model_type=model_type, # data['modelType']['endogenous'],
# data['modelType']['spatial_tests']['lm'],
spat_diag=LM_TEST,
white=data['modelType']['error']['white'],
hac=data['modelType']['error']['hac'],
kp_het=data['modelType']['error']['het'],
# config.....
sig2n_k_ols=config['sig2n_k_ols'],
sig2n_k_tsls=config['sig2n_k_2sls'],
sig2n_k_gmlag=config['sig2n_k_gmlag'],
max_iter=config['gmm_max_iter'],
stop_crit=config['gmm_epsilon'],
inf_lambda=config['gmm_inferenceOnLambda'],
comp_inverse=config['gmm_inv_method'],
step1c=config['gmm_step1c'],
instrument_lags=config['instruments_w_lags'],
lag_user_inst=config['instruments_lag_q'],
vc_matrix=config['output_vm_summary'],
predy_resid=predy_resid,
ols_diag=config['other_ols_diagnostics'],
moran=config['other_residualMoran'],
white_test=config['white_test'],
regime_err_sep=config['regimes_regime_error'],
regime_lag_sep=config['regimes_regime_lag'],
cores=config['other_numcores'],
ml_epsilon=config['ml_epsilon'],
ml_method=config['ml_method'],
ml_diag=config['ml_diagnostics'],
method=method
).output
print results
for r in results:
path.write(r.summary)
path.write('\n\n\n')
return [r.summary for r in results]
"""
def test_not_dir(tmpdir):
path = tmpdir.join('not-dir.txt')
path.write('')
with raises(NotADirectoryError):
FileSystemRepository(str(path))
import os
import os.path
lst = []
with open("path.txt", 'w') as path:
os.chdir("C:/main")
for current_dir, dirs, files in os.walk(""):
for file in files:
if file[-3:] == '.py':
lst.append(current_dir)
temp = set(lst)
lst = list(temp)
lst.sort()
for i in lst:
path.write(i[2:] + '\n')
path.close()
def run(self, path=None, predy_resid=None):
""" Runs the Model """
print self.verify()
if not self.verify()[0]:
return False
data = self.data
print data
if self.data['config']['other_missingValueCheck']:
pysal.MISSINGVALUE = self.data['config']['other_missingValue']
else:
pysal.MISSINGVALUE = None
# Build up args for dispatcher
# weights
w_names = [w.name for w in data['mWeights'] if w.enabled]
w_list = [w.w for w in data['mWeights'] if w.enabled]
for w, name in zip(w_list, w_names):
w.name = name
wk_names = [w.name for w in data['kWeights'] if w.enabled]
wk_list = [w.w for w in data['kWeights'] if w.enabled]
for w, name in zip(wk_list, wk_names):
w.name = name
if 'fname' in self.data:
fileType = self.data['fname'].rsplit('.')[-1].lower()
self.fileType = fileType
if fileType == 'csv':
db = pysal.open(data['fname'], 'rU')
else:
db = pysal.open(data['fname'], 'r')
else:
return None
# y
name_y = data['spec']['y']
y = np.array([self.get_col(db, name_y)]).T
# x
x = []
x_names = data['spec']['X']
for x_name in x_names:
x.append(self.get_col(db, x_name))
x = np.array(x).T
# YE
ye = []
ye_names = data['spec']['YE']
for ye_name in ye_names:
ye.append(self.get_col(db, ye_name))
ye = np.array(ye).T
# H
h = []
h_names = data['spec']['H']
for h_name in h_names:
h.append(self.get_col(db, h_name))
h = np.array(h).T
mtypes = {
0: 'Standard',
1: 'Spatial Lag',
2: 'Spatial Error',
3: 'Spatial Lag+Error'
}
model_type = mtypes[data['modelType']['mType']]
print model_type
# estimation methods
# method_types = {0: 'gm', 1: 'ml'}
method_types = {0: 'ols', 1: 'gm', 2: 'ml'}
method = method_types[data['modelType']['method']]
print method
# R
if data['spec']['R']:
name_r = data['spec']['R']
r = self.get_col(db, name_r)
else:
name_r = None
r = None
# T
if data['spec']['T']:
name_t = data['spec']['T']
t = self.get_col(db, name_t)
else:
name_t = None
t = None
# These options are not available yet....
s = None
name_s = None
config = data['config']
if self.getMWeightsEnabled() and model_type in \
['Standard', 'Spatial Lag']:
LM_TEST = True
else:
LM_TEST = False
print w_list, wk_list
fname = os.path.basename(data['fname'])
results = Spmodel(
name_ds=fname,
w_list=w_list,
wk_list=wk_list,
y=y,
name_y=name_y,
x=x,
name_x=x_names,
ye=ye,
name_ye=ye_names,
h=h,
name_h=h_names,
r=r,
name_r=name_r,
s=s,
name_s=name_s,
t=t,
name_t=name_t,
model_type=model_type, # data['modelType']['endogenous'],
# data['modelType']['spatial_tests']['lm'],
spat_diag=LM_TEST,
white=data['modelType']['error']['white'],
hac=data['modelType']['error']['hac'],
kp_het=data['modelType']['error']['het'],
# config.....
sig2n_k_ols=config['sig2n_k_ols'],
sig2n_k_tsls=config['sig2n_k_2sls'],
sig2n_k_gmlag=config['sig2n_k_gmlag'],
max_iter=config['gmm_max_iter'],
stop_crit=config['gmm_epsilon'],
inf_lambda=config['gmm_inferenceOnLambda'],
comp_inverse=config['gmm_inv_method'],
step1c=config['gmm_step1c'],
instrument_lags=config['instruments_w_lags'],
lag_user_inst=config['instruments_lag_q'],
vc_matrix=config['output_vm_summary'],
predy_resid=predy_resid,
ols_diag=config['other_ols_diagnostics'],
moran=config['other_residualMoran'],
white_test=config['white_test'],
regime_err_sep=config['regimes_regime_error'],
regime_lag_sep=config['regimes_regime_lag'],
cores=config['other_numcores'],
ml_epsilon=config['ml_epsilon'],
ml_method=config['ml_method'],
ml_diag=config['ml_diagnostics'],
method=method).output
print results
for r in results:
path.write(r.summary)
path.write('\n\n\n')
return [r.summary for r in results]
"""
def note(tmpdir):
path = tmpdir.join('alpha.txt')
path.write('alpha\nbravo')
return Note(path)
def run(self, path=None, predy_resid=None):
""" Runs the Model """
print self.verify()
if not self.verify()[0]:
return False
data = self.data
print data
if self.data['config']['other_missingValueCheck']:
pysal.MISSINGVALUE = self.data['config']['other_missingValue']
else:
pysal.MISSINGVALUE = None
# Build up args for dispatcher
# weights
w_names = [w.name for w in data['mWeights'] if w.enabled]
w_list = [w.w for w in data['mWeights'] if w.enabled]
for w, name in zip(w_list, w_names):
w.name = name
wk_names = [w.name for w in data['kWeights'] if w.enabled]
wk_list = [w.w for w in data['kWeights'] if w.enabled]
for w, name in zip(wk_list, wk_names):
w.name = name
if 'fname' in self.data:
fileType = self.data['fname'].rsplit('.')[-1].lower()
self.fileType = fileType
if fileType == 'csv':
db = pysal.open(data['fname'], 'rU')
else:
db = pysal.open(data['fname'], 'r')
else:
return None
# y
y_names = data['spec']['y']
name_y = ','.join(y_names)
y = np.array([self.get_col(db, name) for name in y_names]).T
# x
x = []
x_names = data['spec']['X']
for x_name in x_names:
if x_name.find(',') >= 0:
x = None # get data using sur_dictxy
#x.append([self.get_col(db, name) for name in x_name.split(',')])
else:
x.append(self.get_col(db, x_name))
if x != None:
x = np.array(x).T
# YE
ye = []
ye_names = data['spec']['YE']
for ye_name in ye_names:
if ye_name.find(',') >= 0:
ye.append(
[self.get_col(db, name) for name in ye_name.split(',')])
else:
ye.append(self.get_col(db, ye_name))
ye = np.array(ye).T
# H
h = []
h_names = data['spec']['H']
for h_name in h_names:
if h_name.find(',') >= 0:
h.append(
[self.get_col(db, name) for name in h_name.split(',')])
else:
h.append(self.get_col(db, h_name))
h = np.array(h).T
mtypes = {
0: 'Standard',
1: 'Spatial Lag',
2: 'Spatial Error',
3: 'Spatial Lag+Error'
}
model_type = mtypes[data['modelType']['mType']]
print model_type
# estimation methods
# method_types = {0: 'gm', 1: 'ml'}
method_types = {0: 'ols', 1: 'gm', 2: 'ml'}
method = method_types[data['modelType']['method']]
print method
# R
if data['spec']['R']:
name_r = data['spec']['R']
r = self.get_col(db, name_r)
else:
name_r = None
r = None
# T
if data['spec']['T']:
name_t = data['spec']['T']
t = self.get_col(db, name_t)
else:
name_t = None
t = None
# S
if data['spec']['S']:
name_s = data['spec']['S']
s = self.get_col(db, name_s)
else:
s = None
name_s = None
# SUR: get data (1) HR60,HR70 (2) using Time, Space
if name_y.find(',') >= 0:
y_var0 = name_y.split(',')
x_var0 = [x_name.split(',') for x_name in x_names]
y, x, name_y, x_names = sur_dictxy(db, y_var0, x_var0)
yend_var1 = [name.split(',') for name in ye_names]
ye, ye_names = sur_dictZ(db, yend_var1)
q_var1 = [name.split(',') for name in h_names]
h, h_names = sur_dictZ(db, q_var1)
# regimes shrinks to n
#r = r[ : len(r) / len(y)]
elif name_s and name_t and len(name_s) > 0 and len(name_t) > 0:
y, x, name_y, x_names = sur_dictxy(db, [name_y], [x_names],
space_id=[name_s],
time_id=[name_t])
if len(ye_names) > 0:
yend_var1 = [name.split(',') for name in ye_names]
ye, ye_names = sur_dictZ(db,
yend_var1,
form="plm",
space_id=[name_s],
time_id=[name_t])
if len(h_names) > 0:
q_var1 = [name.split(',') for name in h_names]
h, h_names = sur_dictZ(db,
q_var1,
form="plm",
space_id=[name_s],
time_id=[name_t])
# regimes shrinks to n
if r:
r = r[:len(r) / len(y)]
config = data['config']
if self.getMWeightsEnabled() and model_type in \
['Standard', 'Spatial Lag']:
LM_TEST = True
else:
LM_TEST = False
print w_list, wk_list
fname = os.path.basename(data['fname'])
results = Spmodel(
name_ds=fname,
w_list=w_list,
wk_list=wk_list,
y=y,
name_y=name_y,
x=x,
name_x=x_names,
ye=ye,
name_ye=ye_names,
h=h,
name_h=h_names,
r=r,
name_r=name_r,
s=s,
name_s=name_s,
t=t,
name_t=name_t,
model_type=model_type, # data['modelType']['endogenous'],
# data['modelType']['spatial_tests']['lm'],
spat_diag=LM_TEST,
white=data['modelType']['error']['white'],
hac=data['modelType']['error']['hac'],
kp_het=data['modelType']['error']['het'],
# config.....
sig2n_k_ols=config['sig2n_k_ols'],
sig2n_k_tsls=config['sig2n_k_2sls'],
sig2n_k_gmlag=config['sig2n_k_gmlag'],
max_iter=config['gmm_max_iter'],
stop_crit=config['gmm_epsilon'],
inf_lambda=config['gmm_inferenceOnLambda'],
comp_inverse=config['gmm_inv_method'],
step1c=config['gmm_step1c'],
instrument_lags=config['instruments_w_lags'],
lag_user_inst=config['instruments_lag_q'],
vc_matrix=config['output_vm_summary'],
predy_resid=predy_resid,
ols_diag=config['other_ols_diagnostics'],
moran=config['other_residualMoran'],
white_test=config['white_test'],
regime_err_sep=config['regimes_regime_error'],
regime_lag_sep=config['regimes_regime_lag'],
cores=config['other_numcores'],
ml_epsilon=config['ml_epsilon'],
ml_method=config['ml_method'],
ml_diag=config['ml_diagnostics'],
SUR_Spatdiagnostics=config['SURSpatdiagnostics'],
SUR_NonSpatdiagnostics=config['SURNonSpatdiagnostics'],
SUR_UseIterEst=config['SURUseIterEst'],
method=method).output
print results
for r in results:
path.write(r.summary)
path.write('\n\n\n')
return [r.summary for r in results]
"""
def write_tensor(x, path):
for i in range(x.size(0)):
for j in range(x.size(1)):
for k in range(x.size(2)):
path.write('%.10f \n' % x[i, j, k].item())
def existingRunner(tmpdir, name):
path = tmpdir.join(name)
path.write("#!/bin/sh")
return str(path), Runner(name, str(path))
def _replace(self, path, what, to):
with open(path, 'r') as p:
data = p.read()
data = data.replace(what, to)
with open(path, 'w') as p:
p.write(data)
def savePlaylistToFile(playlist, path):
path.parent.mkdir(parents=True, exist_ok=True)
path = open(path, 'w')
path.write(playlist)
path.close()
