body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
febe45e759afd912319d24252aef2955773733766e747abcf7fafc2f46ed8524 | def has_selection(self):
' Returns True if the SEL tag is found in the Console widget '
return bool(self.tag_ranges(SEL)) | Returns True if the SEL tag is found in the Console widget | Troop/src/interface/console.py | has_selection | mathigatti/EP | 1 | python | def has_selection(self):
' '
return bool(self.tag_ranges(SEL)) | def has_selection(self):
' '
return bool(self.tag_ranges(SEL))<|docstring|>Returns True if the SEL tag is found in the Console widget<|endoftext|> |
ea034edca06b85494493526153960fd882a40ff991351460b7cef047042d15ef | def mouse_press_right(self, event):
' Displays popup menu'
self.popup.show(event)
return 'break' | Displays popup menu | Troop/src/interface/console.py | mouse_press_right | mathigatti/EP | 1 | python | def mouse_press_right(self, event):
' '
self.popup.show(event)
return 'break' | def mouse_press_right(self, event):
' '
self.popup.show(event)
return 'break'<|docstring|>Displays popup menu<|endoftext|> |
2e00694029e28922377f461b61b586499fd2bc117710da4709f6396e660708cf | def newer(source, target):
"\n Return true if 'source' exists and is more recently modified than\n 'target', or if 'source' exists and 'target' doesn't. Return false if\n both exist and 'target' is the same age or younger than 'source'.\n "
if (not os.path.exists(source)):
raise ValueError(("file '%s' does not exist" % os.path.abspath(source)))
if (not os.path.exists(target)):
return 1
mtime1 = os.stat(source)[ST_MTIME]
mtime2 = os.stat(target)[ST_MTIME]
return (mtime1 > mtime2) | Return true if 'source' exists and is more recently modified than
'target', or if 'source' exists and 'target' doesn't. Return false if
both exist and 'target' is the same age or younger than 'source'. | scipy/special/utils/makenpz.py | newer | bamford/scipy | 1 | python | def newer(source, target):
"\n Return true if 'source' exists and is more recently modified than\n 'target', or if 'source' exists and 'target' doesn't. Return false if\n both exist and 'target' is the same age or younger than 'source'.\n "
if (not os.path.exists(source)):
raise ValueError(("file '%s' does not exist" % os.path.abspath(source)))
if (not os.path.exists(target)):
return 1
mtime1 = os.stat(source)[ST_MTIME]
mtime2 = os.stat(target)[ST_MTIME]
return (mtime1 > mtime2) | def newer(source, target):
"\n Return true if 'source' exists and is more recently modified than\n 'target', or if 'source' exists and 'target' doesn't. Return false if\n both exist and 'target' is the same age or younger than 'source'.\n "
if (not os.path.exists(source)):
raise ValueError(("file '%s' does not exist" % os.path.abspath(source)))
if (not os.path.exists(target)):
return 1
mtime1 = os.stat(source)[ST_MTIME]
mtime2 = os.stat(target)[ST_MTIME]
return (mtime1 > mtime2)<|docstring|>Return true if 'source' exists and is more recently modified than
'target', or if 'source' exists and 'target' doesn't. Return false if
both exist and 'target' is the same age or younger than 'source'.<|endoftext|> |
7eaa299ae8f50bafbbbac0f0dbef692c7879a5427540bb5d0c7032c939ad95a1 | def getRegisterContext(self):
'\n return hexadecimal dump of registers as expected by GDB\n '
logging.debug('GDB getting register context')
resp = ''
reg_num_list = map((lambda reg: reg.reg_num), self._register_list)
vals = self._context.readCoreRegistersRaw(reg_num_list)
for (reg, regValue) in zip(self._register_list, vals):
resp += conversion.u32beToHex8le(regValue)
logging.debug('GDB reg: %s = 0x%X', reg.name, regValue)
return resp | return hexadecimal dump of registers as expected by GDB | pyOCD/gdbserver/context_facade.py | getRegisterContext | orenc17/pyOCD | 1 | python | def getRegisterContext(self):
'\n \n '
logging.debug('GDB getting register context')
resp =
reg_num_list = map((lambda reg: reg.reg_num), self._register_list)
vals = self._context.readCoreRegistersRaw(reg_num_list)
for (reg, regValue) in zip(self._register_list, vals):
resp += conversion.u32beToHex8le(regValue)
logging.debug('GDB reg: %s = 0x%X', reg.name, regValue)
return resp | def getRegisterContext(self):
'\n \n '
logging.debug('GDB getting register context')
resp =
reg_num_list = map((lambda reg: reg.reg_num), self._register_list)
vals = self._context.readCoreRegistersRaw(reg_num_list)
for (reg, regValue) in zip(self._register_list, vals):
resp += conversion.u32beToHex8le(regValue)
logging.debug('GDB reg: %s = 0x%X', reg.name, regValue)
return resp<|docstring|>return hexadecimal dump of registers as expected by GDB<|endoftext|> |
d573cae537851ef1fe8a208e1eaa5752908586927f564e1265aa35f8d210f078 | def setRegisterContext(self, data):
'\n Set registers from GDB hexadecimal string.\n '
logging.debug('GDB setting register context')
reg_num_list = []
reg_data_list = []
for reg in self._register_list:
regValue = conversion.hex8leToU32be(data)
reg_num_list.append(reg.reg_num)
reg_data_list.append(regValue)
logging.debug('GDB reg: %s = 0x%X', reg.name, regValue)
data = data[8:]
self._context.writeCoreRegistersRaw(reg_num_list, reg_data_list) | Set registers from GDB hexadecimal string. | pyOCD/gdbserver/context_facade.py | setRegisterContext | orenc17/pyOCD | 1 | python | def setRegisterContext(self, data):
'\n \n '
logging.debug('GDB setting register context')
reg_num_list = []
reg_data_list = []
for reg in self._register_list:
regValue = conversion.hex8leToU32be(data)
reg_num_list.append(reg.reg_num)
reg_data_list.append(regValue)
logging.debug('GDB reg: %s = 0x%X', reg.name, regValue)
data = data[8:]
self._context.writeCoreRegistersRaw(reg_num_list, reg_data_list) | def setRegisterContext(self, data):
'\n \n '
logging.debug('GDB setting register context')
reg_num_list = []
reg_data_list = []
for reg in self._register_list:
regValue = conversion.hex8leToU32be(data)
reg_num_list.append(reg.reg_num)
reg_data_list.append(regValue)
logging.debug('GDB reg: %s = 0x%X', reg.name, regValue)
data = data[8:]
self._context.writeCoreRegistersRaw(reg_num_list, reg_data_list)<|docstring|>Set registers from GDB hexadecimal string.<|endoftext|> |
e2e5c30f4e75c8fa630d3eeb16e953cbf6c0e2d9fa88e7d5ad8994b59641f13d | def setRegister(self, reg, data):
'\n Set single register from GDB hexadecimal string.\n reg parameter is the index of register in targetXML sent to GDB.\n '
if (reg < 0):
return
elif (reg < len(self._register_list)):
regName = self._register_list[reg].name
value = conversion.hex8leToU32be(data)
logging.debug('GDB: write reg %s: 0x%X', regName, value)
self._context.writeCoreRegisterRaw(regName, value) | Set single register from GDB hexadecimal string.
reg parameter is the index of register in targetXML sent to GDB. | pyOCD/gdbserver/context_facade.py | setRegister | orenc17/pyOCD | 1 | python | def setRegister(self, reg, data):
'\n Set single register from GDB hexadecimal string.\n reg parameter is the index of register in targetXML sent to GDB.\n '
if (reg < 0):
return
elif (reg < len(self._register_list)):
regName = self._register_list[reg].name
value = conversion.hex8leToU32be(data)
logging.debug('GDB: write reg %s: 0x%X', regName, value)
self._context.writeCoreRegisterRaw(regName, value) | def setRegister(self, reg, data):
'\n Set single register from GDB hexadecimal string.\n reg parameter is the index of register in targetXML sent to GDB.\n '
if (reg < 0):
return
elif (reg < len(self._register_list)):
regName = self._register_list[reg].name
value = conversion.hex8leToU32be(data)
logging.debug('GDB: write reg %s: 0x%X', regName, value)
self._context.writeCoreRegisterRaw(regName, value)<|docstring|>Set single register from GDB hexadecimal string.
reg parameter is the index of register in targetXML sent to GDB.<|endoftext|> |
4c0b7e506ee452dafd5218bf96730be728f6436cf609fe4b82b8312ea5b7659a | def getTResponse(self, forceSignal=None):
'\n Returns a GDB T response string. This includes:\n The signal encountered.\n The current value of the important registers (sp, lr, pc).\n '
if (forceSignal is not None):
response = ('T' + conversion.byteToHex2(forceSignal))
else:
response = ('T' + conversion.byteToHex2(self.getSignalValue()))
response += self.getRegIndexValuePairs([7, 13, 14, 15])
return response | Returns a GDB T response string. This includes:
The signal encountered.
The current value of the important registers (sp, lr, pc). | pyOCD/gdbserver/context_facade.py | getTResponse | orenc17/pyOCD | 1 | python | def getTResponse(self, forceSignal=None):
'\n Returns a GDB T response string. This includes:\n The signal encountered.\n The current value of the important registers (sp, lr, pc).\n '
if (forceSignal is not None):
response = ('T' + conversion.byteToHex2(forceSignal))
else:
response = ('T' + conversion.byteToHex2(self.getSignalValue()))
response += self.getRegIndexValuePairs([7, 13, 14, 15])
return response | def getTResponse(self, forceSignal=None):
'\n Returns a GDB T response string. This includes:\n The signal encountered.\n The current value of the important registers (sp, lr, pc).\n '
if (forceSignal is not None):
response = ('T' + conversion.byteToHex2(forceSignal))
else:
response = ('T' + conversion.byteToHex2(self.getSignalValue()))
response += self.getRegIndexValuePairs([7, 13, 14, 15])
return response<|docstring|>Returns a GDB T response string. This includes:
The signal encountered.
The current value of the important registers (sp, lr, pc).<|endoftext|> |
47c473d29f0775f64e5988dba8724c141c693ee611a5874a65df87f0a2b77e7b | def getRegIndexValuePairs(self, regIndexList):
'\n Returns a string like NN:MMMMMMMM;NN:MMMMMMMM;...\n for the T response string. NN is the index of the\n register to follow MMMMMMMM is the value of the register.\n '
str = ''
regList = self._context.readCoreRegistersRaw(regIndexList)
for (regIndex, reg) in zip(regIndexList, regList):
str += (((conversion.byteToHex2(regIndex) + ':') + conversion.u32beToHex8le(reg)) + ';')
return str | Returns a string like NN:MMMMMMMM;NN:MMMMMMMM;...
for the T response string. NN is the index of the
register to follow MMMMMMMM is the value of the register. | pyOCD/gdbserver/context_facade.py | getRegIndexValuePairs | orenc17/pyOCD | 1 | python | def getRegIndexValuePairs(self, regIndexList):
'\n Returns a string like NN:MMMMMMMM;NN:MMMMMMMM;...\n for the T response string. NN is the index of the\n register to follow MMMMMMMM is the value of the register.\n '
str =
regList = self._context.readCoreRegistersRaw(regIndexList)
for (regIndex, reg) in zip(regIndexList, regList):
str += (((conversion.byteToHex2(regIndex) + ':') + conversion.u32beToHex8le(reg)) + ';')
return str | def getRegIndexValuePairs(self, regIndexList):
'\n Returns a string like NN:MMMMMMMM;NN:MMMMMMMM;...\n for the T response string. NN is the index of the\n register to follow MMMMMMMM is the value of the register.\n '
str =
regList = self._context.readCoreRegistersRaw(regIndexList)
for (regIndex, reg) in zip(regIndexList, regList):
str += (((conversion.byteToHex2(regIndex) + ':') + conversion.u32beToHex8le(reg)) + ';')
return str<|docstring|>Returns a string like NN:MMMMMMMM;NN:MMMMMMMM;...
for the T response string. NN is the index of the
register to follow MMMMMMMM is the value of the register.<|endoftext|> |
c47c2e25753f5e0098c6d3bb31013e383d63ee6818dfe86b29fd8d6282fc215d | def merge_model(self, training, models_to_add: list):
'\n Will update this fields from training fields. If is necessary to add a new exercise, it will be added on\n models_to_add\n\n :param training: the training with the new fields\n :param models_to_add: list that will be updated with new exercise\n '
super().merge_model(training, models_to_add)
if training.exercises:
for exercise in training.exercises:
exercise.training = self
self.workout_plan_id = (training.workout_plan.id if training.workout_plan else None)
self.start_date = training.start_date
self.end_date = training.end_date
merge_lists(self.exercises, training.exercises, models_to_add) | Will update this fields from training fields. If is necessary to add a new exercise, it will be added on
models_to_add
:param training: the training with the new fields
:param models_to_add: list that will be updated with new exercise | workout_plan_server/adapters/mysql/models/training_model.py | merge_model | vitorsm/workout-plan-server | 0 | python | def merge_model(self, training, models_to_add: list):
'\n Will update this fields from training fields. If is necessary to add a new exercise, it will be added on\n models_to_add\n\n :param training: the training with the new fields\n :param models_to_add: list that will be updated with new exercise\n '
super().merge_model(training, models_to_add)
if training.exercises:
for exercise in training.exercises:
exercise.training = self
self.workout_plan_id = (training.workout_plan.id if training.workout_plan else None)
self.start_date = training.start_date
self.end_date = training.end_date
merge_lists(self.exercises, training.exercises, models_to_add) | def merge_model(self, training, models_to_add: list):
'\n Will update this fields from training fields. If is necessary to add a new exercise, it will be added on\n models_to_add\n\n :param training: the training with the new fields\n :param models_to_add: list that will be updated with new exercise\n '
super().merge_model(training, models_to_add)
if training.exercises:
for exercise in training.exercises:
exercise.training = self
self.workout_plan_id = (training.workout_plan.id if training.workout_plan else None)
self.start_date = training.start_date
self.end_date = training.end_date
merge_lists(self.exercises, training.exercises, models_to_add)<|docstring|>Will update this fields from training fields. If is necessary to add a new exercise, it will be added on
models_to_add
:param training: the training with the new fields
:param models_to_add: list that will be updated with new exercise<|endoftext|> |
e32f51e1298c459c7ef3cec2ca82c4ca848b6acff4c70a58c3bab0ea93c729b8 | @jit(nopython=True)
def crossCorr(t1, t2, binsize, nbins):
' \n\t\tFast crossCorr \n\t'
nt1 = len(t1)
nt2 = len(t2)
if ((np.floor((nbins / 2)) * 2) == nbins):
nbins = (nbins + 1)
m = ((- binsize) * ((nbins + 1) / 2))
B = np.zeros(nbins)
for j in range(nbins):
B[j] = (m + (j * binsize))
w = ((nbins / 2) * binsize)
C = np.zeros(nbins)
i2 = 1
for i1 in range(nt1):
lbound = (t1[i1] - w)
while ((i2 < nt2) and (t2[i2] < lbound)):
i2 = (i2 + 1)
while ((i2 > 1) and (t2[(i2 - 1)] > lbound)):
i2 = (i2 - 1)
rbound = lbound
l = i2
for j in range(nbins):
k = 0
rbound = (rbound + binsize)
while ((l < nt2) and (t2[l] < rbound)):
l = (l + 1)
k = (k + 1)
C[j] += k
C = (C / ((nt1 * binsize) / 1000))
return C | Fast crossCorr | python/functions.py | crossCorr | gviejo/ColdPlay | 0 | python | @jit(nopython=True)
def crossCorr(t1, t2, binsize, nbins):
' \n\t\t \n\t'
nt1 = len(t1)
nt2 = len(t2)
if ((np.floor((nbins / 2)) * 2) == nbins):
nbins = (nbins + 1)
m = ((- binsize) * ((nbins + 1) / 2))
B = np.zeros(nbins)
for j in range(nbins):
B[j] = (m + (j * binsize))
w = ((nbins / 2) * binsize)
C = np.zeros(nbins)
i2 = 1
for i1 in range(nt1):
lbound = (t1[i1] - w)
while ((i2 < nt2) and (t2[i2] < lbound)):
i2 = (i2 + 1)
while ((i2 > 1) and (t2[(i2 - 1)] > lbound)):
i2 = (i2 - 1)
rbound = lbound
l = i2
for j in range(nbins):
k = 0
rbound = (rbound + binsize)
while ((l < nt2) and (t2[l] < rbound)):
l = (l + 1)
k = (k + 1)
C[j] += k
C = (C / ((nt1 * binsize) / 1000))
return C | @jit(nopython=True)
def crossCorr(t1, t2, binsize, nbins):
' \n\t\t \n\t'
nt1 = len(t1)
nt2 = len(t2)
if ((np.floor((nbins / 2)) * 2) == nbins):
nbins = (nbins + 1)
m = ((- binsize) * ((nbins + 1) / 2))
B = np.zeros(nbins)
for j in range(nbins):
B[j] = (m + (j * binsize))
w = ((nbins / 2) * binsize)
C = np.zeros(nbins)
i2 = 1
for i1 in range(nt1):
lbound = (t1[i1] - w)
while ((i2 < nt2) and (t2[i2] < lbound)):
i2 = (i2 + 1)
while ((i2 > 1) and (t2[(i2 - 1)] > lbound)):
i2 = (i2 - 1)
rbound = lbound
l = i2
for j in range(nbins):
k = 0
rbound = (rbound + binsize)
while ((l < nt2) and (t2[l] < rbound)):
l = (l + 1)
k = (k + 1)
C[j] += k
C = (C / ((nt1 * binsize) / 1000))
return C<|docstring|>Fast crossCorr<|endoftext|> |
881e163586c8b3357fe7309fe525ec216a004832ecdb75e778d433307df73fbd | def crossCorr2(t1, t2, binsize, nbins):
'\n\t\tSlow crossCorr\n\t'
window = (np.arange(((- binsize) * (nbins / 2)), ((binsize * (nbins / 2)) + (2 * binsize)), binsize) - (binsize / 2.0))
allcount = np.zeros((nbins + 1))
for e in t1:
mwind = (window + e)
mwind = np.array((([(- 1.0)] + list(mwind)) + [(np.max([t1.max(), t2.max()]) + binsize)]))
index = np.digitize(t2, mwind)
count = np.array([np.sum((index == i)) for i in range(2, (mwind.shape[0] - 1))])
allcount += np.array(count)
allcount = (allcount / ((float(len(t1)) * binsize) / 1000))
return allcount | Slow crossCorr | python/functions.py | crossCorr2 | gviejo/ColdPlay | 0 | python | def crossCorr2(t1, t2, binsize, nbins):
'\n\t\t\n\t'
window = (np.arange(((- binsize) * (nbins / 2)), ((binsize * (nbins / 2)) + (2 * binsize)), binsize) - (binsize / 2.0))
allcount = np.zeros((nbins + 1))
for e in t1:
mwind = (window + e)
mwind = np.array((([(- 1.0)] + list(mwind)) + [(np.max([t1.max(), t2.max()]) + binsize)]))
index = np.digitize(t2, mwind)
count = np.array([np.sum((index == i)) for i in range(2, (mwind.shape[0] - 1))])
allcount += np.array(count)
allcount = (allcount / ((float(len(t1)) * binsize) / 1000))
return allcount | def crossCorr2(t1, t2, binsize, nbins):
'\n\t\t\n\t'
window = (np.arange(((- binsize) * (nbins / 2)), ((binsize * (nbins / 2)) + (2 * binsize)), binsize) - (binsize / 2.0))
allcount = np.zeros((nbins + 1))
for e in t1:
mwind = (window + e)
mwind = np.array((([(- 1.0)] + list(mwind)) + [(np.max([t1.max(), t2.max()]) + binsize)]))
index = np.digitize(t2, mwind)
count = np.array([np.sum((index == i)) for i in range(2, (mwind.shape[0] - 1))])
allcount += np.array(count)
allcount = (allcount / ((float(len(t1)) * binsize) / 1000))
return allcount<|docstring|>Slow crossCorr<|endoftext|> |
c052155119dbec84d736655db8acb586c374a88127cebc68f2c1b4a3d8501988 | def findHDCells(tuning_curves, z=50, p=0.0001, m=1):
'\n\t\tPeak firing rate larger than 1\n\t\tand Rayleigh test p<0.001 & z > 100\n\t'
cond1 = (tuning_curves.max() > m)
from pycircstat.tests import rayleigh
stat = pd.DataFrame(index=tuning_curves.columns, columns=['pval', 'z'])
for k in tuning_curves:
stat.loc[k] = rayleigh(tuning_curves[k].index.values, tuning_curves[k].values)
cond2 = np.logical_and((stat['pval'] < p), (stat['z'] > z))
tokeep = stat.index.values[np.where(np.logical_and(cond1, cond2))[0]]
return (tokeep, stat) | Peak firing rate larger than 1
and Rayleigh test p<0.001 & z > 100 | python/functions.py | findHDCells | gviejo/ColdPlay | 0 | python | def findHDCells(tuning_curves, z=50, p=0.0001, m=1):
'\n\t\tPeak firing rate larger than 1\n\t\tand Rayleigh test p<0.001 & z > 100\n\t'
cond1 = (tuning_curves.max() > m)
from pycircstat.tests import rayleigh
stat = pd.DataFrame(index=tuning_curves.columns, columns=['pval', 'z'])
for k in tuning_curves:
stat.loc[k] = rayleigh(tuning_curves[k].index.values, tuning_curves[k].values)
cond2 = np.logical_and((stat['pval'] < p), (stat['z'] > z))
tokeep = stat.index.values[np.where(np.logical_and(cond1, cond2))[0]]
return (tokeep, stat) | def findHDCells(tuning_curves, z=50, p=0.0001, m=1):
'\n\t\tPeak firing rate larger than 1\n\t\tand Rayleigh test p<0.001 & z > 100\n\t'
cond1 = (tuning_curves.max() > m)
from pycircstat.tests import rayleigh
stat = pd.DataFrame(index=tuning_curves.columns, columns=['pval', 'z'])
for k in tuning_curves:
stat.loc[k] = rayleigh(tuning_curves[k].index.values, tuning_curves[k].values)
cond2 = np.logical_and((stat['pval'] < p), (stat['z'] > z))
tokeep = stat.index.values[np.where(np.logical_and(cond1, cond2))[0]]
return (tokeep, stat)<|docstring|>Peak firing rate larger than 1
and Rayleigh test p<0.001 & z > 100<|endoftext|> |
0721d8d119c82449f0d22576ce222f77bc021d8688bb814698fc1348edb7766d | def decodeHD(tuning_curves, spikes, ep, bin_size=200, px=None):
'\n\t\tSee : Zhang, 1998, Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells\n\t\ttuning_curves: pd.DataFrame with angular position as index and columns as neuron\n\t\tspikes : dictionnary of spike times\n\t\tep : nts.IntervalSet, the epochs for decoding\n\t\tbin_size : in ms (default:200ms)\n\t\tpx : Occupancy. If None, px is uniform\n\t'
if (len(ep) == 1):
bins = np.arange(ep.as_units('ms').start.iloc[0], ep.as_units('ms').end.iloc[(- 1)], bin_size)
else:
print('TODO')
sys.exit()
order = tuning_curves.columns.values
w = scipy.signal.gaussian(51, 2)
spike_counts = pd.DataFrame(index=(bins[0:(- 1)] + (np.diff(bins) / 2)), columns=order)
for n in spike_counts:
spks = spikes[n].restrict(ep).as_units('ms').index.values
tmp = np.histogram(spks, bins)
spike_counts[n] = np.convolve(tmp[0], w, mode='same')
tcurves_array = tuning_curves.values
spike_counts_array = spike_counts.values
proba_angle = np.zeros((spike_counts.shape[0], tuning_curves.shape[0]))
part1 = np.exp(((- (bin_size / 1000)) * tcurves_array.sum(1)))
if (px is not None):
part2 = px
else:
part2 = np.ones(tuning_curves.shape[0])
for i in range(len(proba_angle)):
part3 = np.prod((tcurves_array ** spike_counts_array[i]), 1)
p = ((part1 * part2) * part3)
proba_angle[i] = (p / p.sum())
proba_angle = pd.DataFrame(index=spike_counts.index.values, columns=tuning_curves.index.values, data=proba_angle)
proba_angle = proba_angle.astype('float')
decoded = nts.Tsd(t=proba_angle.index.values, d=proba_angle.idxmax(1).values, time_units='ms')
return (decoded, proba_angle, spike_counts) | See : Zhang, 1998, Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells
tuning_curves: pd.DataFrame with angular position as index and columns as neuron
spikes : dictionnary of spike times
ep : nts.IntervalSet, the epochs for decoding
bin_size : in ms (default:200ms)
px : Occupancy. If None, px is uniform | python/functions.py | decodeHD | gviejo/ColdPlay | 0 | python | def decodeHD(tuning_curves, spikes, ep, bin_size=200, px=None):
'\n\t\tSee : Zhang, 1998, Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells\n\t\ttuning_curves: pd.DataFrame with angular position as index and columns as neuron\n\t\tspikes : dictionnary of spike times\n\t\tep : nts.IntervalSet, the epochs for decoding\n\t\tbin_size : in ms (default:200ms)\n\t\tpx : Occupancy. If None, px is uniform\n\t'
if (len(ep) == 1):
bins = np.arange(ep.as_units('ms').start.iloc[0], ep.as_units('ms').end.iloc[(- 1)], bin_size)
else:
print('TODO')
sys.exit()
order = tuning_curves.columns.values
w = scipy.signal.gaussian(51, 2)
spike_counts = pd.DataFrame(index=(bins[0:(- 1)] + (np.diff(bins) / 2)), columns=order)
for n in spike_counts:
spks = spikes[n].restrict(ep).as_units('ms').index.values
tmp = np.histogram(spks, bins)
spike_counts[n] = np.convolve(tmp[0], w, mode='same')
tcurves_array = tuning_curves.values
spike_counts_array = spike_counts.values
proba_angle = np.zeros((spike_counts.shape[0], tuning_curves.shape[0]))
part1 = np.exp(((- (bin_size / 1000)) * tcurves_array.sum(1)))
if (px is not None):
part2 = px
else:
part2 = np.ones(tuning_curves.shape[0])
for i in range(len(proba_angle)):
part3 = np.prod((tcurves_array ** spike_counts_array[i]), 1)
p = ((part1 * part2) * part3)
proba_angle[i] = (p / p.sum())
proba_angle = pd.DataFrame(index=spike_counts.index.values, columns=tuning_curves.index.values, data=proba_angle)
proba_angle = proba_angle.astype('float')
decoded = nts.Tsd(t=proba_angle.index.values, d=proba_angle.idxmax(1).values, time_units='ms')
return (decoded, proba_angle, spike_counts) | def decodeHD(tuning_curves, spikes, ep, bin_size=200, px=None):
'\n\t\tSee : Zhang, 1998, Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells\n\t\ttuning_curves: pd.DataFrame with angular position as index and columns as neuron\n\t\tspikes : dictionnary of spike times\n\t\tep : nts.IntervalSet, the epochs for decoding\n\t\tbin_size : in ms (default:200ms)\n\t\tpx : Occupancy. If None, px is uniform\n\t'
if (len(ep) == 1):
bins = np.arange(ep.as_units('ms').start.iloc[0], ep.as_units('ms').end.iloc[(- 1)], bin_size)
else:
print('TODO')
sys.exit()
order = tuning_curves.columns.values
w = scipy.signal.gaussian(51, 2)
spike_counts = pd.DataFrame(index=(bins[0:(- 1)] + (np.diff(bins) / 2)), columns=order)
for n in spike_counts:
spks = spikes[n].restrict(ep).as_units('ms').index.values
tmp = np.histogram(spks, bins)
spike_counts[n] = np.convolve(tmp[0], w, mode='same')
tcurves_array = tuning_curves.values
spike_counts_array = spike_counts.values
proba_angle = np.zeros((spike_counts.shape[0], tuning_curves.shape[0]))
part1 = np.exp(((- (bin_size / 1000)) * tcurves_array.sum(1)))
if (px is not None):
part2 = px
else:
part2 = np.ones(tuning_curves.shape[0])
for i in range(len(proba_angle)):
part3 = np.prod((tcurves_array ** spike_counts_array[i]), 1)
p = ((part1 * part2) * part3)
proba_angle[i] = (p / p.sum())
proba_angle = pd.DataFrame(index=spike_counts.index.values, columns=tuning_curves.index.values, data=proba_angle)
proba_angle = proba_angle.astype('float')
decoded = nts.Tsd(t=proba_angle.index.values, d=proba_angle.idxmax(1).values, time_units='ms')
return (decoded, proba_angle, spike_counts)<|docstring|>See : Zhang, 1998, Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells
tuning_curves: pd.DataFrame with angular position as index and columns as neuron
spikes : dictionnary of spike times
ep : nts.IntervalSet, the epochs for decoding
bin_size : in ms (default:200ms)
px : Occupancy. If None, px is uniform<|endoftext|> |
4c0c02c8609e8650d1d8ebc17332cd69b70cef8dd62840a31e0214191abf499f | def centerTuningCurves(tcurve):
'\n\tcenter tuning curves by peak\n\t'
peak = pd.Series(index=tcurve.columns, data=np.array([circmean(tcurve.index.values, tcurve[i].values) for i in tcurve.columns]))
new_tcurve = []
for p in tcurve.columns:
x = (tcurve[p].index.values - tcurve[p].index[tcurve[p].index.get_loc(peak[p], method='nearest')])
x[(x < (- np.pi))] += (2 * np.pi)
x[(x > np.pi)] -= (2 * np.pi)
tmp = pd.Series(index=x, data=tcurve[p].values).sort_index()
new_tcurve.append(tmp.values)
new_tcurve = pd.DataFrame(index=np.linspace((- np.pi), np.pi, (tcurve.shape[0] + 1))[0:(- 1)], data=np.array(new_tcurve).T, columns=tcurve.columns)
return new_tcurve | center tuning curves by peak | python/functions.py | centerTuningCurves | gviejo/ColdPlay | 0 | python | def centerTuningCurves(tcurve):
'\n\t\n\t'
peak = pd.Series(index=tcurve.columns, data=np.array([circmean(tcurve.index.values, tcurve[i].values) for i in tcurve.columns]))
new_tcurve = []
for p in tcurve.columns:
x = (tcurve[p].index.values - tcurve[p].index[tcurve[p].index.get_loc(peak[p], method='nearest')])
x[(x < (- np.pi))] += (2 * np.pi)
x[(x > np.pi)] -= (2 * np.pi)
tmp = pd.Series(index=x, data=tcurve[p].values).sort_index()
new_tcurve.append(tmp.values)
new_tcurve = pd.DataFrame(index=np.linspace((- np.pi), np.pi, (tcurve.shape[0] + 1))[0:(- 1)], data=np.array(new_tcurve).T, columns=tcurve.columns)
return new_tcurve | def centerTuningCurves(tcurve):
'\n\t\n\t'
peak = pd.Series(index=tcurve.columns, data=np.array([circmean(tcurve.index.values, tcurve[i].values) for i in tcurve.columns]))
new_tcurve = []
for p in tcurve.columns:
x = (tcurve[p].index.values - tcurve[p].index[tcurve[p].index.get_loc(peak[p], method='nearest')])
x[(x < (- np.pi))] += (2 * np.pi)
x[(x > np.pi)] -= (2 * np.pi)
tmp = pd.Series(index=x, data=tcurve[p].values).sort_index()
new_tcurve.append(tmp.values)
new_tcurve = pd.DataFrame(index=np.linspace((- np.pi), np.pi, (tcurve.shape[0] + 1))[0:(- 1)], data=np.array(new_tcurve).T, columns=tcurve.columns)
return new_tcurve<|docstring|>center tuning curves by peak<|endoftext|> |
47d7ddfad57a6c4d97f892f53f1fdf3bad88d4516c4ce082e46ef1315fb3b2b5 | def offsetTuningCurves(tcurve, diffs):
'\n\toffseting tuning curves synced by diff\n\t'
new_tcurve = []
for p in tcurve.columns:
x = (tcurve[p].index.values - tcurve[p].index[tcurve[p].index.get_loc(diffs[p], method='nearest')])
x[(x < (- np.pi))] += (2 * np.pi)
x[(x > np.pi)] -= (2 * np.pi)
tmp = pd.Series(index=x, data=tcurve[p].values).sort_index()
new_tcurve.append(tmp.values)
new_tcurve = pd.DataFrame(index=np.linspace((- np.pi), np.pi, (tcurve.shape[0] + 1))[0:(- 1)], data=np.array(new_tcurve).T, columns=tcurve.columns)
return new_tcurve | offseting tuning curves synced by diff | python/functions.py | offsetTuningCurves | gviejo/ColdPlay | 0 | python | def offsetTuningCurves(tcurve, diffs):
'\n\t\n\t'
new_tcurve = []
for p in tcurve.columns:
x = (tcurve[p].index.values - tcurve[p].index[tcurve[p].index.get_loc(diffs[p], method='nearest')])
x[(x < (- np.pi))] += (2 * np.pi)
x[(x > np.pi)] -= (2 * np.pi)
tmp = pd.Series(index=x, data=tcurve[p].values).sort_index()
new_tcurve.append(tmp.values)
new_tcurve = pd.DataFrame(index=np.linspace((- np.pi), np.pi, (tcurve.shape[0] + 1))[0:(- 1)], data=np.array(new_tcurve).T, columns=tcurve.columns)
return new_tcurve | def offsetTuningCurves(tcurve, diffs):
'\n\t\n\t'
new_tcurve = []
for p in tcurve.columns:
x = (tcurve[p].index.values - tcurve[p].index[tcurve[p].index.get_loc(diffs[p], method='nearest')])
x[(x < (- np.pi))] += (2 * np.pi)
x[(x > np.pi)] -= (2 * np.pi)
tmp = pd.Series(index=x, data=tcurve[p].values).sort_index()
new_tcurve.append(tmp.values)
new_tcurve = pd.DataFrame(index=np.linspace((- np.pi), np.pi, (tcurve.shape[0] + 1))[0:(- 1)], data=np.array(new_tcurve).T, columns=tcurve.columns)
return new_tcurve<|docstring|>offseting tuning curves synced by diff<|endoftext|> |
bd54abdfe10bf569574bf9061545dbf6974fdd89ed15dfeaad244218c84a8116 | def getPeaksandTroughs(lfp, min_points):
'\t \n\t\tAt 250Hz (1250/5), 2 troughs cannont be closer than 20 (min_points) points (if theta reaches 12Hz);\t\t\n\t'
import neuroseries as nts
import scipy.signal
if isinstance(lfp, nts.time_series.Tsd):
troughs = nts.Tsd(lfp.as_series().iloc[scipy.signal.argrelmin(lfp.values, order=min_points)[0]], time_units='us')
peaks = nts.Tsd(lfp.as_series().iloc[scipy.signal.argrelmax(lfp.values, order=min_points)[0]], time_units='us')
tmp = nts.Tsd(troughs.realign(peaks, align='next').as_series().drop_duplicates('first'))
peaks = peaks[tmp.index]
tmp = nts.Tsd(peaks.realign(troughs, align='prev').as_series().drop_duplicates('first'))
troughs = troughs[tmp.index]
return (peaks, troughs)
elif isinstance(lfp, nts.time_series.TsdFrame):
peaks = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
troughs = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
for i in lfp.keys():
(peaks[i], troughs[i]) = getPeaksandTroughs(lfp[i], min_points)
return (peaks, troughs) | At 250Hz (1250/5), 2 troughs cannont be closer than 20 (min_points) points (if theta reaches 12Hz); | python/functions.py | getPeaksandTroughs | gviejo/ColdPlay | 0 | python | def getPeaksandTroughs(lfp, min_points):
'\t \n\t\t\t\t\n\t'
import neuroseries as nts
import scipy.signal
if isinstance(lfp, nts.time_series.Tsd):
troughs = nts.Tsd(lfp.as_series().iloc[scipy.signal.argrelmin(lfp.values, order=min_points)[0]], time_units='us')
peaks = nts.Tsd(lfp.as_series().iloc[scipy.signal.argrelmax(lfp.values, order=min_points)[0]], time_units='us')
tmp = nts.Tsd(troughs.realign(peaks, align='next').as_series().drop_duplicates('first'))
peaks = peaks[tmp.index]
tmp = nts.Tsd(peaks.realign(troughs, align='prev').as_series().drop_duplicates('first'))
troughs = troughs[tmp.index]
return (peaks, troughs)
elif isinstance(lfp, nts.time_series.TsdFrame):
peaks = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
troughs = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
for i in lfp.keys():
(peaks[i], troughs[i]) = getPeaksandTroughs(lfp[i], min_points)
return (peaks, troughs) | def getPeaksandTroughs(lfp, min_points):
'\t \n\t\t\t\t\n\t'
import neuroseries as nts
import scipy.signal
if isinstance(lfp, nts.time_series.Tsd):
troughs = nts.Tsd(lfp.as_series().iloc[scipy.signal.argrelmin(lfp.values, order=min_points)[0]], time_units='us')
peaks = nts.Tsd(lfp.as_series().iloc[scipy.signal.argrelmax(lfp.values, order=min_points)[0]], time_units='us')
tmp = nts.Tsd(troughs.realign(peaks, align='next').as_series().drop_duplicates('first'))
peaks = peaks[tmp.index]
tmp = nts.Tsd(peaks.realign(troughs, align='prev').as_series().drop_duplicates('first'))
troughs = troughs[tmp.index]
return (peaks, troughs)
elif isinstance(lfp, nts.time_series.TsdFrame):
peaks = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
troughs = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
for i in lfp.keys():
(peaks[i], troughs[i]) = getPeaksandTroughs(lfp[i], min_points)
return (peaks, troughs)<|docstring|>At 250Hz (1250/5), 2 troughs cannont be closer than 20 (min_points) points (if theta reaches 12Hz);<|endoftext|> |
13606d90dcd479bfef47b4ad38e1a055012d2be819837099afd27aa490c73268 | def getPhase(lfp, fmin, fmax, nbins, fsamp, power=False):
' Continuous Wavelets Transform\n\t\treturn phase of lfp in a Tsd array\n\t'
import neuroseries as nts
from Wavelets import MyMorlet as Morlet
if isinstance(lfp, nts.time_series.TsdFrame):
allphase = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
allpwr = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
for i in lfp.keys():
(allphase[i], allpwr[i]) = getPhase(lfp[i], fmin, fmax, nbins, fsamp, power=True)
if power:
return (allphase, allpwr)
else:
return allphase
elif isinstance(lfp, nts.time_series.Tsd):
cw = Morlet(lfp.values, fmin, fmax, nbins, fsamp)
cwt = cw.getdata()
cwt = np.flip(cwt, axis=0)
wave = (np.abs(cwt) ** 2.0)
phases = np.arctan2(np.imag(cwt), np.real(cwt)).transpose()
cwt = None
index = np.argmax(wave, 0)
phase = np.zeros(len(index))
for i in range(len(index)):
phase[i] = phases[(i, index[i])]
phases = None
if power:
pwrs = cw.getpower()
pwr = np.zeros(len(index))
for i in range(len(index)):
pwr[i] = pwrs[(index[i], i)]
return (nts.Tsd(lfp.index.values, phase), nts.Tsd(lfp.index.values, pwr))
else:
return nts.Tsd(lfp.index.values, phase) | Continuous Wavelets Transform
return phase of lfp in a Tsd array | python/functions.py | getPhase | gviejo/ColdPlay | 0 | python | def getPhase(lfp, fmin, fmax, nbins, fsamp, power=False):
' Continuous Wavelets Transform\n\t\treturn phase of lfp in a Tsd array\n\t'
import neuroseries as nts
from Wavelets import MyMorlet as Morlet
if isinstance(lfp, nts.time_series.TsdFrame):
allphase = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
allpwr = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
for i in lfp.keys():
(allphase[i], allpwr[i]) = getPhase(lfp[i], fmin, fmax, nbins, fsamp, power=True)
if power:
return (allphase, allpwr)
else:
return allphase
elif isinstance(lfp, nts.time_series.Tsd):
cw = Morlet(lfp.values, fmin, fmax, nbins, fsamp)
cwt = cw.getdata()
cwt = np.flip(cwt, axis=0)
wave = (np.abs(cwt) ** 2.0)
phases = np.arctan2(np.imag(cwt), np.real(cwt)).transpose()
cwt = None
index = np.argmax(wave, 0)
phase = np.zeros(len(index))
for i in range(len(index)):
phase[i] = phases[(i, index[i])]
phases = None
if power:
pwrs = cw.getpower()
pwr = np.zeros(len(index))
for i in range(len(index)):
pwr[i] = pwrs[(index[i], i)]
return (nts.Tsd(lfp.index.values, phase), nts.Tsd(lfp.index.values, pwr))
else:
return nts.Tsd(lfp.index.values, phase) | def getPhase(lfp, fmin, fmax, nbins, fsamp, power=False):
' Continuous Wavelets Transform\n\t\treturn phase of lfp in a Tsd array\n\t'
import neuroseries as nts
from Wavelets import MyMorlet as Morlet
if isinstance(lfp, nts.time_series.TsdFrame):
allphase = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
allpwr = nts.TsdFrame(lfp.index.values, np.zeros(lfp.shape))
for i in lfp.keys():
(allphase[i], allpwr[i]) = getPhase(lfp[i], fmin, fmax, nbins, fsamp, power=True)
if power:
return (allphase, allpwr)
else:
return allphase
elif isinstance(lfp, nts.time_series.Tsd):
cw = Morlet(lfp.values, fmin, fmax, nbins, fsamp)
cwt = cw.getdata()
cwt = np.flip(cwt, axis=0)
wave = (np.abs(cwt) ** 2.0)
phases = np.arctan2(np.imag(cwt), np.real(cwt)).transpose()
cwt = None
index = np.argmax(wave, 0)
phase = np.zeros(len(index))
for i in range(len(index)):
phase[i] = phases[(i, index[i])]
phases = None
if power:
pwrs = cw.getpower()
pwr = np.zeros(len(index))
for i in range(len(index)):
pwr[i] = pwrs[(index[i], i)]
return (nts.Tsd(lfp.index.values, phase), nts.Tsd(lfp.index.values, pwr))
else:
return nts.Tsd(lfp.index.values, phase)<|docstring|>Continuous Wavelets Transform
return phase of lfp in a Tsd array<|endoftext|> |
f5894a33a29e86cf083d838e447f10c852df7f768e75811e010788a25d744a01 | def window_sumsquare(window, n_frames, hop_length=200, win_length=800, n_fft=800, dtype=np.float32, norm=None):
'\n # from librosa 0.6\n Compute the sum-square envelope of a window function at a given hop length.\n\n This is used to estimate modulation effects induced by windowing\n observations in short-time fourier transforms.\n\n Parameters\n ----------\n window : string, tuple, number, callable, or list-like\n Window specification, as in `get_window`\n\n n_frames : int > 0\n The number of analysis frames\n\n hop_length : int > 0\n The number of samples to advance between frames\n\n win_length : [optional]\n The length of the window function. By default, this matches `n_fft`.\n\n n_fft : int > 0\n The length of each analysis frame.\n\n dtype : np.dtype\n The data type of the output\n\n Returns\n -------\n wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`\n The sum-squared envelope of the window function\n '
if (win_length is None):
win_length = n_fft
n = (n_fft + (hop_length * (n_frames - 1)))
x = np.zeros(n, dtype=dtype)
win_sq = get_window(window, win_length, fftbins=True)
win_sq = (librosa_util.normalize(win_sq, norm=norm) ** 2)
win_sq = librosa_util.pad_center(win_sq, n_fft)
for i in range(n_frames):
sample = (i * hop_length)
x[sample:min(n, (sample + n_fft))] += win_sq[:max(0, min(n_fft, (n - sample)))]
return x | # from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing
observations in short-time fourier transforms.
Parameters
----------
window : string, tuple, number, callable, or list-like
Window specification, as in `get_window`
n_frames : int > 0
The number of analysis frames
hop_length : int > 0
The number of samples to advance between frames
win_length : [optional]
The length of the window function. By default, this matches `n_fft`.
n_fft : int > 0
The length of each analysis frame.
dtype : np.dtype
The data type of the output
Returns
-------
wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
The sum-squared envelope of the window function | uberduck_ml_dev/utils/utils.py | window_sumsquare | Cris140/uberduck-ml-dev | 167 | python | def window_sumsquare(window, n_frames, hop_length=200, win_length=800, n_fft=800, dtype=np.float32, norm=None):
'\n # from librosa 0.6\n Compute the sum-square envelope of a window function at a given hop length.\n\n This is used to estimate modulation effects induced by windowing\n observations in short-time fourier transforms.\n\n Parameters\n ----------\n window : string, tuple, number, callable, or list-like\n Window specification, as in `get_window`\n\n n_frames : int > 0\n The number of analysis frames\n\n hop_length : int > 0\n The number of samples to advance between frames\n\n win_length : [optional]\n The length of the window function. By default, this matches `n_fft`.\n\n n_fft : int > 0\n The length of each analysis frame.\n\n dtype : np.dtype\n The data type of the output\n\n Returns\n -------\n wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`\n The sum-squared envelope of the window function\n '
if (win_length is None):
win_length = n_fft
n = (n_fft + (hop_length * (n_frames - 1)))
x = np.zeros(n, dtype=dtype)
win_sq = get_window(window, win_length, fftbins=True)
win_sq = (librosa_util.normalize(win_sq, norm=norm) ** 2)
win_sq = librosa_util.pad_center(win_sq, n_fft)
for i in range(n_frames):
sample = (i * hop_length)
x[sample:min(n, (sample + n_fft))] += win_sq[:max(0, min(n_fft, (n - sample)))]
return x | def window_sumsquare(window, n_frames, hop_length=200, win_length=800, n_fft=800, dtype=np.float32, norm=None):
'\n # from librosa 0.6\n Compute the sum-square envelope of a window function at a given hop length.\n\n This is used to estimate modulation effects induced by windowing\n observations in short-time fourier transforms.\n\n Parameters\n ----------\n window : string, tuple, number, callable, or list-like\n Window specification, as in `get_window`\n\n n_frames : int > 0\n The number of analysis frames\n\n hop_length : int > 0\n The number of samples to advance between frames\n\n win_length : [optional]\n The length of the window function. By default, this matches `n_fft`.\n\n n_fft : int > 0\n The length of each analysis frame.\n\n dtype : np.dtype\n The data type of the output\n\n Returns\n -------\n wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`\n The sum-squared envelope of the window function\n '
if (win_length is None):
win_length = n_fft
n = (n_fft + (hop_length * (n_frames - 1)))
x = np.zeros(n, dtype=dtype)
win_sq = get_window(window, win_length, fftbins=True)
win_sq = (librosa_util.normalize(win_sq, norm=norm) ** 2)
win_sq = librosa_util.pad_center(win_sq, n_fft)
for i in range(n_frames):
sample = (i * hop_length)
x[sample:min(n, (sample + n_fft))] += win_sq[:max(0, min(n_fft, (n - sample)))]
return x<|docstring|># from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing
observations in short-time fourier transforms.
Parameters
----------
window : string, tuple, number, callable, or list-like
Window specification, as in `get_window`
n_frames : int > 0
The number of analysis frames
hop_length : int > 0
The number of samples to advance between frames
win_length : [optional]
The length of the window function. By default, this matches `n_fft`.
n_fft : int > 0
The length of each analysis frame.
dtype : np.dtype
The data type of the output
Returns
-------
wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
The sum-squared envelope of the window function<|endoftext|> |
2b3ec346caa42a07c83b9e18c0896681b179ef23c47a4e4c5ed42f3373d585da | def griffin_lim(magnitudes, stft_fn, n_iters=30):
'\n PARAMS\n ------\n magnitudes: spectrogram magnitudes\n stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods\n '
angles = np.angle(np.exp(((2j * np.pi) * np.random.rand(*magnitudes.size()))))
angles = angles.astype(np.float32)
angles = torch.autograd.Variable(torch.from_numpy(angles))
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
for i in range(n_iters):
(_, angles) = stft_fn.transform(signal)
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
return signal | PARAMS
------
magnitudes: spectrogram magnitudes
stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods | uberduck_ml_dev/utils/utils.py | griffin_lim | Cris140/uberduck-ml-dev | 167 | python | def griffin_lim(magnitudes, stft_fn, n_iters=30):
'\n PARAMS\n ------\n magnitudes: spectrogram magnitudes\n stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods\n '
angles = np.angle(np.exp(((2j * np.pi) * np.random.rand(*magnitudes.size()))))
angles = angles.astype(np.float32)
angles = torch.autograd.Variable(torch.from_numpy(angles))
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
for i in range(n_iters):
(_, angles) = stft_fn.transform(signal)
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
return signal | def griffin_lim(magnitudes, stft_fn, n_iters=30):
'\n PARAMS\n ------\n magnitudes: spectrogram magnitudes\n stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods\n '
angles = np.angle(np.exp(((2j * np.pi) * np.random.rand(*magnitudes.size()))))
angles = angles.astype(np.float32)
angles = torch.autograd.Variable(torch.from_numpy(angles))
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
for i in range(n_iters):
(_, angles) = stft_fn.transform(signal)
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
return signal<|docstring|>PARAMS
------
magnitudes: spectrogram magnitudes
stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods<|endoftext|> |
4d03ce31d2336c0c6159582e262b0a9f809ad01c71b07630d4090e5174374e3c | def dynamic_range_compression(x, C=1, clip_val=1e-05):
'\n PARAMS\n ------\n C: compression factor\n '
return torch.log((torch.clamp(x, min=clip_val) * C)) | PARAMS
------
C: compression factor | uberduck_ml_dev/utils/utils.py | dynamic_range_compression | Cris140/uberduck-ml-dev | 167 | python | def dynamic_range_compression(x, C=1, clip_val=1e-05):
'\n PARAMS\n ------\n C: compression factor\n '
return torch.log((torch.clamp(x, min=clip_val) * C)) | def dynamic_range_compression(x, C=1, clip_val=1e-05):
'\n PARAMS\n ------\n C: compression factor\n '
return torch.log((torch.clamp(x, min=clip_val) * C))<|docstring|>PARAMS
------
C: compression factor<|endoftext|> |
22b22b9c1ff123db5e96e9d8ac124d543df25750bb9beea6983e9210b57ba398 | def dynamic_range_decompression(x, C=1):
'\n PARAMS\n ------\n C: compression factor used to compress\n '
return (torch.exp(x) / C) | PARAMS
------
C: compression factor used to compress | uberduck_ml_dev/utils/utils.py | dynamic_range_decompression | Cris140/uberduck-ml-dev | 167 | python | def dynamic_range_decompression(x, C=1):
'\n PARAMS\n ------\n C: compression factor used to compress\n '
return (torch.exp(x) / C) | def dynamic_range_decompression(x, C=1):
'\n PARAMS\n ------\n C: compression factor used to compress\n '
return (torch.exp(x) / C)<|docstring|>PARAMS
------
C: compression factor used to compress<|endoftext|> |
4946cc4acc3a35bb9d0b518da3058f76eed4f476486b90828d3ebaaf578d6aa2 | def get_mask_from_lengths(lengths: torch.Tensor, max_len: int=0):
'Return a mask matrix. Unmasked entires are true.'
if (max_len == 0):
max_len = int(torch.max(lengths).item())
ids = torch.arange(0, max_len, device=lengths.device, dtype=torch.long)
mask = (ids < lengths.unsqueeze(1)).bool()
return mask | Return a mask matrix. Unmasked entires are true. | uberduck_ml_dev/utils/utils.py | get_mask_from_lengths | Cris140/uberduck-ml-dev | 167 | python | def get_mask_from_lengths(lengths: torch.Tensor, max_len: int=0):
if (max_len == 0):
max_len = int(torch.max(lengths).item())
ids = torch.arange(0, max_len, device=lengths.device, dtype=torch.long)
mask = (ids < lengths.unsqueeze(1)).bool()
return mask | def get_mask_from_lengths(lengths: torch.Tensor, max_len: int=0):
if (max_len == 0):
max_len = int(torch.max(lengths).item())
ids = torch.arange(0, max_len, device=lengths.device, dtype=torch.long)
mask = (ids < lengths.unsqueeze(1)).bool()
return mask<|docstring|>Return a mask matrix. Unmasked entires are true.<|endoftext|> |
ef7664adc09d98e00b52ff3e22732bc83df9c4e558cc300e59b071863cf776c1 | def convert_pad_shape(pad_shape):
'Reverse, then flatten a list of lists.'
l = pad_shape[::(- 1)]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape | Reverse, then flatten a list of lists. | uberduck_ml_dev/utils/utils.py | convert_pad_shape | Cris140/uberduck-ml-dev | 167 | python | def convert_pad_shape(pad_shape):
l = pad_shape[::(- 1)]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape | def convert_pad_shape(pad_shape):
l = pad_shape[::(- 1)]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape<|docstring|>Reverse, then flatten a list of lists.<|endoftext|> |
da70f01d105bad5a56e34ae30b6e180d32a1f6639b5518803fa9cfb7f442db18 | def sequence_mask(length, max_length=None):
'The same as get_mask_from_lengths'
if (max_length is None):
max_length = length.max()
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return (x.unsqueeze(0) < length.unsqueeze(1)) | The same as get_mask_from_lengths | uberduck_ml_dev/utils/utils.py | sequence_mask | Cris140/uberduck-ml-dev | 167 | python | def sequence_mask(length, max_length=None):
if (max_length is None):
max_length = length.max()
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return (x.unsqueeze(0) < length.unsqueeze(1)) | def sequence_mask(length, max_length=None):
if (max_length is None):
max_length = length.max()
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return (x.unsqueeze(0) < length.unsqueeze(1))<|docstring|>The same as get_mask_from_lengths<|endoftext|> |
bb415066c5c77df91c5194a18c9af0c7f08f443c3baf64abc8465586ace6df75 | def generate_path(duration, mask):
'\n duration: [b, 1, t_x]\n mask: [b, 1, t_y, t_x]\n '
device = duration.device
(b, _, t_y, t_x) = mask.shape
cum_duration = torch.cumsum(duration, (- 1))
cum_duration_flat = cum_duration.view((b * t_x))
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
path = path.view(b, t_x, t_y)
path = (path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[(:, :(- 1))])
path = (path.unsqueeze(1).transpose(2, 3) * mask)
return path | duration: [b, 1, t_x]
mask: [b, 1, t_y, t_x] | uberduck_ml_dev/utils/utils.py | generate_path | Cris140/uberduck-ml-dev | 167 | python | def generate_path(duration, mask):
'\n duration: [b, 1, t_x]\n mask: [b, 1, t_y, t_x]\n '
device = duration.device
(b, _, t_y, t_x) = mask.shape
cum_duration = torch.cumsum(duration, (- 1))
cum_duration_flat = cum_duration.view((b * t_x))
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
path = path.view(b, t_x, t_y)
path = (path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[(:, :(- 1))])
path = (path.unsqueeze(1).transpose(2, 3) * mask)
return path | def generate_path(duration, mask):
'\n duration: [b, 1, t_x]\n mask: [b, 1, t_y, t_x]\n '
device = duration.device
(b, _, t_y, t_x) = mask.shape
cum_duration = torch.cumsum(duration, (- 1))
cum_duration_flat = cum_duration.view((b * t_x))
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
path = path.view(b, t_x, t_y)
path = (path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[(:, :(- 1))])
path = (path.unsqueeze(1).transpose(2, 3) * mask)
return path<|docstring|>duration: [b, 1, t_x]
mask: [b, 1, t_y, t_x]<|endoftext|> |
654f258f5f960eb270d40135683c04e35d5bd7dc89dabb8a36729f921507b809 | def draw(self, surface):
'draw all sprites onto a surface in z order (lowest z first)'
spritedict = self.spritedict
items = sorted(spritedict.items(), key=(lambda a: a[0].z))
surface_blit = surface.blit
dirty = self.lostsprites
self.lostsprites = []
dirty_append = dirty.append
for (s, r) in items:
newrect = surface_blit(s.image, s.rect)
surface_blit(s.image, s.rect)
if (r != 0):
dirty_append(newrect.union(r))
else:
dirty_append(newrect)
spritedict[s] = newrect
return dirty | draw all sprites onto a surface in z order (lowest z first) | giftrun-0.1/gamesys.py | draw | olemb/giftrun | 4 | python | def draw(self, surface):
spritedict = self.spritedict
items = sorted(spritedict.items(), key=(lambda a: a[0].z))
surface_blit = surface.blit
dirty = self.lostsprites
self.lostsprites = []
dirty_append = dirty.append
for (s, r) in items:
newrect = surface_blit(s.image, s.rect)
surface_blit(s.image, s.rect)
if (r != 0):
dirty_append(newrect.union(r))
else:
dirty_append(newrect)
spritedict[s] = newrect
return dirty | def draw(self, surface):
spritedict = self.spritedict
items = sorted(spritedict.items(), key=(lambda a: a[0].z))
surface_blit = surface.blit
dirty = self.lostsprites
self.lostsprites = []
dirty_append = dirty.append
for (s, r) in items:
newrect = surface_blit(s.image, s.rect)
surface_blit(s.image, s.rect)
if (r != 0):
dirty_append(newrect.union(r))
else:
dirty_append(newrect)
spritedict[s] = newrect
return dirty<|docstring|>draw all sprites onto a surface in z order (lowest z first)<|endoftext|> |
d9487edf2795b57803767fa64d2afc91386d7b9c767ce7856de2dc0bc528bd3c | def init(self):
'Called at init time. Put initialization code for the game here.' | Called at init time. Put initialization code for the game here. | giftrun-0.1/gamesys.py | init | olemb/giftrun | 4 | python | def init(self):
| def init(self):
<|docstring|>Called at init time. Put initialization code for the game here.<|endoftext|> |
078ba0fb831e041d6c63e77033887b644d740b05c62c55bea7d758abeea291bd | def update(self, events):
'Handles events and updates the objects. Called every frame.'
pass | Handles events and updates the objects. Called every frame. | giftrun-0.1/gamesys.py | update | olemb/giftrun | 4 | python | def update(self, events):
pass | def update(self, events):
pass<|docstring|>Handles events and updates the objects. Called every frame.<|endoftext|> |
a009c36b602dd9bb5c22ab746fa8046f2e63c6c8726c65a04ad53a27865f2227 | def test_statement_find_by_account(session):
'Assert that the statement settings by id works.'
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
assert (statements.get('total') == 1) | Assert that the statement settings by id works. | pay-api/tests/unit/services/test_statement.py | test_statement_find_by_account | thorwolpert/sbc-pay | 4 | python | def test_statement_find_by_account(session):
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
assert (statements.get('total') == 1) | def test_statement_find_by_account(session):
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
assert (statements.get('total') == 1)<|docstring|>Assert that the statement settings by id works.<|endoftext|> |
a5d6d0e7926a2831701fa02123a5ea6349af0ba21a82771111191b30da21f4b7 | def test_get_statement_report(session):
'Assert that the get statement report works.'
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None) | Assert that the get statement report works. | pay-api/tests/unit/services/test_statement.py | test_get_statement_report | thorwolpert/sbc-pay | 4 | python | def test_get_statement_report(session):
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None) | def test_get_statement_report(session):
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None)<|docstring|>Assert that the get statement report works.<|endoftext|> |
1689205c764bd515dba3ee977588e7e51fa728dbf9ebd0786de35fef4bdbbe4e | def test_get_statement_report_for_empty_invoices(session):
'Assert that the get statement report works for statement with no invoices.'
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None) | Assert that the get statement report works for statement with no invoices. | pay-api/tests/unit/services/test_statement.py | test_get_statement_report_for_empty_invoices | thorwolpert/sbc-pay | 4 | python | def test_get_statement_report_for_empty_invoices(session):
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None) | def test_get_statement_report_for_empty_invoices(session):
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.DAILY.value, statement_settings_id=settings_model.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None)<|docstring|>Assert that the get statement report works for statement with no invoices.<|endoftext|> |
8fe8067819857cc003b8c83b959c57e930525d4ee1ba4d089c8c54b6ea836562 | def test_get_weekly_statement_report(session):
'Assert that the get statement report works.'
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.WEEKLY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.WEEKLY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None) | Assert that the get statement report works. | pay-api/tests/unit/services/test_statement.py | test_get_weekly_statement_report | thorwolpert/sbc-pay | 4 | python | def test_get_weekly_statement_report(session):
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.WEEKLY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.WEEKLY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None) | def test_get_weekly_statement_report(session):
bcol_account = factory_premium_payment_account()
bcol_account.save()
payment = factory_payment()
payment.save()
i = factory_invoice(payment_account=bcol_account)
i.save()
factory_invoice_reference(i.id).save()
factory_payment_line_item(invoice_id=i.id, fee_schedule_id=1).save()
settings_model = factory_statement_settings(payment_account_id=bcol_account.id, frequency=StatementFrequency.WEEKLY.value)
statement_model = factory_statement(payment_account_id=bcol_account.id, frequency=StatementFrequency.WEEKLY.value, statement_settings_id=settings_model.id)
factory_statement_invoices(statement_id=statement_model.id, invoice_id=i.id)
payment_account = PaymentAccount.find_by_id(bcol_account.id)
statements = StatementService.find_by_account_id(payment_account.auth_account_id, page=1, limit=10)
assert (statements is not None)
(report_response, report_name) = StatementService.get_statement_report(statement_id=statement_model.id, content_type='application/pdf', auth=get_auth_premium_user())
assert (report_response is not None)<|docstring|>Assert that the get statement report works.<|endoftext|> |
60cd1b430f12cee1e94262372524af674ed97bca0ef56a406c555fe4b386eaa9 | @property
def incenter(self):
'\n The intersection of angle bisectors, Point.\n\n '
n = len(self.vertices)
return (sum([(s * p) for (s, p) in zip(self.sides, self.vertices)]) / n) | The intersection of angle bisectors, Point. | src/geometry3/polygon.py | incenter | JnyJny/Geometry3 | 0 | python | @property
def incenter(self):
'\n \n\n '
n = len(self.vertices)
return (sum([(s * p) for (s, p) in zip(self.sides, self.vertices)]) / n) | @property
def incenter(self):
'\n \n\n '
n = len(self.vertices)
return (sum([(s * p) for (s, p) in zip(self.sides, self.vertices)]) / n)<|docstring|>The intersection of angle bisectors, Point.<|endoftext|> |
08afb3a977e75d5406819bd1ce80b8a9e9350837e93cb76bc442a494c887f476 | def search_sysout(captured, find_me):
'Search capsys message for find_me, return message'
for msg in captured.out.split('/n'):
if (find_me in msg):
return msg
return '' | Search capsys message for find_me, return message | tests/unit_tests/test_set_performance_config.py | search_sysout | flywheel-apps/bids-mriqc | 0 | python | def search_sysout(captured, find_me):
for msg in captured.out.split('/n'):
if (find_me in msg):
return msg
return | def search_sysout(captured, find_me):
for msg in captured.out.split('/n'):
if (find_me in msg):
return msg
return <|docstring|>Search capsys message for find_me, return message<|endoftext|> |
947eceb854f39e7d43069c271267731fb1d0d1d80acfc753f861d8e2f05a8b63 | def search_stdout_contains(captured, find_me, contains_me):
'Search stdout message for find_me, return true if it contains contains_me'
for msg in captured.out.split('/n'):
if (find_me in msg):
print(f"Found '{find_me}' in '{msg}'")
if (contains_me in msg):
print(f"Found '{contains_me}' in '{msg}'")
return True
return False | Search stdout message for find_me, return true if it contains contains_me | tests/unit_tests/test_set_performance_config.py | search_stdout_contains | flywheel-apps/bids-mriqc | 0 | python | def search_stdout_contains(captured, find_me, contains_me):
for msg in captured.out.split('/n'):
if (find_me in msg):
print(f"Found '{find_me}' in '{msg}'")
if (contains_me in msg):
print(f"Found '{contains_me}' in '{msg}'")
return True
return False | def search_stdout_contains(captured, find_me, contains_me):
for msg in captured.out.split('/n'):
if (find_me in msg):
print(f"Found '{find_me}' in '{msg}'")
if (contains_me in msg):
print(f"Found '{contains_me}' in '{msg}'")
return True
return False<|docstring|>Search stdout message for find_me, return true if it contains contains_me<|endoftext|> |
fabb47d62c472441a5550953f6da4c222fef965b89fcd8a3ffd8a361fcbb73a0 | def gpio_handler():
' Thread to handle buttons connected to GPIO pins. '
all_buttons = {}
dpad_bits = DpadBits()
def gpio_pressed(button):
' Called when button connected to GPIO pin is pressed/closed '
print('pressed', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.press(ns_button)
else:
Gamepad.dPad(dpad_bits.set_bit((255 - ns_button)))
else:
print('Invalid button')
def gpio_released(button):
' Called when button connected to GPIO pin is released/opened '
print('released', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.release(ns_button)
else:
Gamepad.dPad(dpad_bits.clear_bit((255 - ns_button)))
else:
print('Invalid button')
gpio_ns_map = ({'gpio_number': 4, 'ns_button': NSButton.LEFT_THROTTLE}, {'gpio_number': 17, 'ns_button': NSButton.LEFT_TRIGGER}, {'gpio_number': 27, 'ns_button': NSButton.MINUS}, {'gpio_number': 22, 'ns_button': NSButton.CAPTURE}, {'gpio_number': 5, 'ns_button': 255}, {'gpio_number': 6, 'ns_button': 254}, {'gpio_number': 13, 'ns_button': 253}, {'gpio_number': 19, 'ns_button': 252}, {'gpio_number': 26, 'ns_button': NSButton.LEFT_STICK}, {'gpio_number': 23, 'ns_button': NSButton.RIGHT_THROTTLE}, {'gpio_number': 24, 'ns_button': NSButton.RIGHT_TRIGGER}, {'gpio_number': 25, 'ns_button': NSButton.PLUS}, {'gpio_number': 8, 'ns_button': NSButton.HOME}, {'gpio_number': 7, 'ns_button': NSButton.A}, {'gpio_number': 12, 'ns_button': NSButton.B}, {'gpio_number': 16, 'ns_button': NSButton.X}, {'gpio_number': 20, 'ns_button': NSButton.Y}, {'gpio_number': 21, 'ns_button': NSButton.RIGHT_STICK})
for element in gpio_ns_map:
element['button'] = Button(element['gpio_number'])
all_buttons[element['button'].pin] = element['ns_button']
element['button'].when_pressed = gpio_pressed
element['button'].when_released = gpio_released
signal.pause() | Thread to handle buttons connected to GPIO pins. | NSGamepad/Code/gamepad_ns_gpio.py | gpio_handler | gdsports/RaspberryPi-Joystick | 70 | python | def gpio_handler():
' '
all_buttons = {}
dpad_bits = DpadBits()
def gpio_pressed(button):
' Called when button connected to GPIO pin is pressed/closed '
print('pressed', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.press(ns_button)
else:
Gamepad.dPad(dpad_bits.set_bit((255 - ns_button)))
else:
print('Invalid button')
def gpio_released(button):
' Called when button connected to GPIO pin is released/opened '
print('released', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.release(ns_button)
else:
Gamepad.dPad(dpad_bits.clear_bit((255 - ns_button)))
else:
print('Invalid button')
gpio_ns_map = ({'gpio_number': 4, 'ns_button': NSButton.LEFT_THROTTLE}, {'gpio_number': 17, 'ns_button': NSButton.LEFT_TRIGGER}, {'gpio_number': 27, 'ns_button': NSButton.MINUS}, {'gpio_number': 22, 'ns_button': NSButton.CAPTURE}, {'gpio_number': 5, 'ns_button': 255}, {'gpio_number': 6, 'ns_button': 254}, {'gpio_number': 13, 'ns_button': 253}, {'gpio_number': 19, 'ns_button': 252}, {'gpio_number': 26, 'ns_button': NSButton.LEFT_STICK}, {'gpio_number': 23, 'ns_button': NSButton.RIGHT_THROTTLE}, {'gpio_number': 24, 'ns_button': NSButton.RIGHT_TRIGGER}, {'gpio_number': 25, 'ns_button': NSButton.PLUS}, {'gpio_number': 8, 'ns_button': NSButton.HOME}, {'gpio_number': 7, 'ns_button': NSButton.A}, {'gpio_number': 12, 'ns_button': NSButton.B}, {'gpio_number': 16, 'ns_button': NSButton.X}, {'gpio_number': 20, 'ns_button': NSButton.Y}, {'gpio_number': 21, 'ns_button': NSButton.RIGHT_STICK})
for element in gpio_ns_map:
element['button'] = Button(element['gpio_number'])
all_buttons[element['button'].pin] = element['ns_button']
element['button'].when_pressed = gpio_pressed
element['button'].when_released = gpio_released
signal.pause() | def gpio_handler():
' '
all_buttons = {}
dpad_bits = DpadBits()
def gpio_pressed(button):
' Called when button connected to GPIO pin is pressed/closed '
print('pressed', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.press(ns_button)
else:
Gamepad.dPad(dpad_bits.set_bit((255 - ns_button)))
else:
print('Invalid button')
def gpio_released(button):
' Called when button connected to GPIO pin is released/opened '
print('released', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.release(ns_button)
else:
Gamepad.dPad(dpad_bits.clear_bit((255 - ns_button)))
else:
print('Invalid button')
gpio_ns_map = ({'gpio_number': 4, 'ns_button': NSButton.LEFT_THROTTLE}, {'gpio_number': 17, 'ns_button': NSButton.LEFT_TRIGGER}, {'gpio_number': 27, 'ns_button': NSButton.MINUS}, {'gpio_number': 22, 'ns_button': NSButton.CAPTURE}, {'gpio_number': 5, 'ns_button': 255}, {'gpio_number': 6, 'ns_button': 254}, {'gpio_number': 13, 'ns_button': 253}, {'gpio_number': 19, 'ns_button': 252}, {'gpio_number': 26, 'ns_button': NSButton.LEFT_STICK}, {'gpio_number': 23, 'ns_button': NSButton.RIGHT_THROTTLE}, {'gpio_number': 24, 'ns_button': NSButton.RIGHT_TRIGGER}, {'gpio_number': 25, 'ns_button': NSButton.PLUS}, {'gpio_number': 8, 'ns_button': NSButton.HOME}, {'gpio_number': 7, 'ns_button': NSButton.A}, {'gpio_number': 12, 'ns_button': NSButton.B}, {'gpio_number': 16, 'ns_button': NSButton.X}, {'gpio_number': 20, 'ns_button': NSButton.Y}, {'gpio_number': 21, 'ns_button': NSButton.RIGHT_STICK})
for element in gpio_ns_map:
element['button'] = Button(element['gpio_number'])
all_buttons[element['button'].pin] = element['ns_button']
element['button'].when_pressed = gpio_pressed
element['button'].when_released = gpio_released
signal.pause()<|docstring|>Thread to handle buttons connected to GPIO pins.<|endoftext|> |
3e05bf599a3b8cf887ff3e6b27df5589416b778b3284320f9b6f504d4364f801 | def main():
' main program '
threading.Thread(target=gpio_handler, args=(), daemon=True).start()
Gamepad.begin('/dev/hidg0')
while True:
' Read from keyboard and mouse input using evdev? '
pass | main program | NSGamepad/Code/gamepad_ns_gpio.py | main | gdsports/RaspberryPi-Joystick | 70 | python | def main():
' '
threading.Thread(target=gpio_handler, args=(), daemon=True).start()
Gamepad.begin('/dev/hidg0')
while True:
' Read from keyboard and mouse input using evdev? '
pass | def main():
' '
threading.Thread(target=gpio_handler, args=(), daemon=True).start()
Gamepad.begin('/dev/hidg0')
while True:
' Read from keyboard and mouse input using evdev? '
pass<|docstring|>main program<|endoftext|> |
01a75d51c4b26415e83346814a97d4fdf618cdacbf3b0438a2c390f563793270 | def set_bit(self, bit_num):
' Set bit in direction pad bit map. Update NSGadget direction pad. '
self.dpad_bits |= (1 << bit_num)
return BUTTONS_MAP_DPAD[self.dpad_bits] | Set bit in direction pad bit map. Update NSGadget direction pad. | NSGamepad/Code/gamepad_ns_gpio.py | set_bit | gdsports/RaspberryPi-Joystick | 70 | python | def set_bit(self, bit_num):
' '
self.dpad_bits |= (1 << bit_num)
return BUTTONS_MAP_DPAD[self.dpad_bits] | def set_bit(self, bit_num):
' '
self.dpad_bits |= (1 << bit_num)
return BUTTONS_MAP_DPAD[self.dpad_bits]<|docstring|>Set bit in direction pad bit map. Update NSGadget direction pad.<|endoftext|> |
5cfed180cf71a408e49266a517104f7ed9dde5c51f3090408242bc923d1d5f7c | def clear_bit(self, bit_num):
' Clear bit in direction pad bit map. Update NSGadget direction pad. '
self.dpad_bits &= (~ (1 << bit_num))
return BUTTONS_MAP_DPAD[self.dpad_bits] | Clear bit in direction pad bit map. Update NSGadget direction pad. | NSGamepad/Code/gamepad_ns_gpio.py | clear_bit | gdsports/RaspberryPi-Joystick | 70 | python | def clear_bit(self, bit_num):
' '
self.dpad_bits &= (~ (1 << bit_num))
return BUTTONS_MAP_DPAD[self.dpad_bits] | def clear_bit(self, bit_num):
' '
self.dpad_bits &= (~ (1 << bit_num))
return BUTTONS_MAP_DPAD[self.dpad_bits]<|docstring|>Clear bit in direction pad bit map. Update NSGadget direction pad.<|endoftext|> |
284ca9416c9e5d54f6d4a1c4803ebd1d30b33d45f25b7b160e9b92e270b6b94c | def gpio_pressed(button):
' Called when button connected to GPIO pin is pressed/closed '
print('pressed', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.press(ns_button)
else:
Gamepad.dPad(dpad_bits.set_bit((255 - ns_button)))
else:
print('Invalid button') | Called when button connected to GPIO pin is pressed/closed | NSGamepad/Code/gamepad_ns_gpio.py | gpio_pressed | gdsports/RaspberryPi-Joystick | 70 | python | def gpio_pressed(button):
' '
print('pressed', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.press(ns_button)
else:
Gamepad.dPad(dpad_bits.set_bit((255 - ns_button)))
else:
print('Invalid button') | def gpio_pressed(button):
' '
print('pressed', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.press(ns_button)
else:
Gamepad.dPad(dpad_bits.set_bit((255 - ns_button)))
else:
print('Invalid button')<|docstring|>Called when button connected to GPIO pin is pressed/closed<|endoftext|> |
41c7e2d268116c16586cb5e3b70846edc9b6169fcefae0a294dbc3c8ef11923b | def gpio_released(button):
' Called when button connected to GPIO pin is released/opened '
print('released', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.release(ns_button)
else:
Gamepad.dPad(dpad_bits.clear_bit((255 - ns_button)))
else:
print('Invalid button') | Called when button connected to GPIO pin is released/opened | NSGamepad/Code/gamepad_ns_gpio.py | gpio_released | gdsports/RaspberryPi-Joystick | 70 | python | def gpio_released(button):
' '
print('released', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.release(ns_button)
else:
Gamepad.dPad(dpad_bits.clear_bit((255 - ns_button)))
else:
print('Invalid button') | def gpio_released(button):
' '
print('released', button.pin)
if (button.pin in all_buttons):
ns_button = all_buttons[button.pin]
if (ns_button < 128):
Gamepad.release(ns_button)
else:
Gamepad.dPad(dpad_bits.clear_bit((255 - ns_button)))
else:
print('Invalid button')<|docstring|>Called when button connected to GPIO pin is released/opened<|endoftext|> |
d019913a2d22038d1df68932b2b61088ac5522c50cfbe6ea427f92baa6620dfb | def error(self, message: str) -> None:
' Raises an SnakeParseException with the given message.'
raise SnakeParseException(message) | Raises an SnakeParseException with the given message. | src/snakeparse/api.py | error | nh13/snakeparse | 47 | python | def error(self, message: str) -> None:
' '
raise SnakeParseException(message) | def error(self, message: str) -> None:
' '
raise SnakeParseException(message)<|docstring|>Raises an SnakeParseException with the given message.<|endoftext|> |
fcd681592981994a1c295d09227451ebf7308efd9c427ee56c672389dcdef986 | @abstractmethod
def parse_args(self, args: List[str]) -> Any:
'Parses the command line arguments.' | Parses the command line arguments. | src/snakeparse/api.py | parse_args | nh13/snakeparse | 47 | python | @abstractmethod
def parse_args(self, args: List[str]) -> Any:
| @abstractmethod
def parse_args(self, args: List[str]) -> Any:
<|docstring|>Parses the command line arguments.<|endoftext|> |
b76cb9320ee3c6ab551aa1d3661952a7053d231029678e8c10f72d92753bc029 | @abstractmethod
def parse_args_file(self, args_file: Path) -> Any:
'Parses command line arguments from an arguments file' | Parses command line arguments from an arguments file | src/snakeparse/api.py | parse_args_file | nh13/snakeparse | 47 | python | @abstractmethod
def parse_args_file(self, args_file: Path) -> Any:
| @abstractmethod
def parse_args_file(self, args_file: Path) -> Any:
<|docstring|>Parses command line arguments from an arguments file<|endoftext|> |
ba9e5499b80760f22443e37f1b93040cdff1f7b440075dbdcd909f021f83c7e1 | def parse_config(self, config: dict) -> Any:
'Parses arguments from a Snakemake config object. It is assumed the\n arguments are contained in an arguments file, whose path is stored in\n the config with key ``SnakeParse.ARGUMENT_FILE_NAME_KEY``.'
args_file = config[SnakeParse.ARGUMENT_FILE_NAME_KEY]
if (args_file is not None):
args_file = Path(config[SnakeParse.ARGUMENT_FILE_NAME_KEY])
return self.parse_args_file(args_file=args_file)
else:
try:
return self.parse_args([''])
except SnakeParseException:
return argparse.Namespace() | Parses arguments from a Snakemake config object. It is assumed the
arguments are contained in an arguments file, whose path is stored in
the config with key ``SnakeParse.ARGUMENT_FILE_NAME_KEY``. | src/snakeparse/api.py | parse_config | nh13/snakeparse | 47 | python | def parse_config(self, config: dict) -> Any:
'Parses arguments from a Snakemake config object. It is assumed the\n arguments are contained in an arguments file, whose path is stored in\n the config with key ``SnakeParse.ARGUMENT_FILE_NAME_KEY``.'
args_file = config[SnakeParse.ARGUMENT_FILE_NAME_KEY]
if (args_file is not None):
args_file = Path(config[SnakeParse.ARGUMENT_FILE_NAME_KEY])
return self.parse_args_file(args_file=args_file)
else:
try:
return self.parse_args([])
except SnakeParseException:
return argparse.Namespace() | def parse_config(self, config: dict) -> Any:
'Parses arguments from a Snakemake config object. It is assumed the\n arguments are contained in an arguments file, whose path is stored in\n the config with key ``SnakeParse.ARGUMENT_FILE_NAME_KEY``.'
args_file = config[SnakeParse.ARGUMENT_FILE_NAME_KEY]
if (args_file is not None):
args_file = Path(config[SnakeParse.ARGUMENT_FILE_NAME_KEY])
return self.parse_args_file(args_file=args_file)
else:
try:
return self.parse_args([])
except SnakeParseException:
return argparse.Namespace()<|docstring|>Parses arguments from a Snakemake config object. It is assumed the
arguments are contained in an arguments file, whose path is stored in
the config with key ``SnakeParse.ARGUMENT_FILE_NAME_KEY``.<|endoftext|> |
3af58ec9c714f8d7ca51209e5b41792104bb1ad906fa59a2e9be63e635e641f2 | @abstractmethod
def print_help(self, file: Optional[IO[str]]=None) -> None:
'Prints the help message' | Prints the help message | src/snakeparse/api.py | print_help | nh13/snakeparse | 47 | python | @abstractmethod
def print_help(self, file: Optional[IO[str]]=None) -> None:
| @abstractmethod
def print_help(self, file: Optional[IO[str]]=None) -> None:
<|docstring|>Prints the help message<|endoftext|> |
cff24f17a0d9e82bec98f8992108fb54d67e4c508ef635451919e053a4c461c5 | @property
def group(self) -> Optional[str]:
'The name of the workflow group to which this group belongs.'
return self._group | The name of the workflow group to which this group belongs. | src/snakeparse/api.py | group | nh13/snakeparse | 47 | python | @property
def group(self) -> Optional[str]:
return self._group | @property
def group(self) -> Optional[str]:
return self._group<|docstring|>The name of the workflow group to which this group belongs.<|endoftext|> |
9a042c8966465dd95e1b15250ccbf5ad80be3b2d806f4336147922fdc8909616 | @property
def description(self) -> Optional[str]:
'A short description of the workflow, used when listing the\n workflows.\n '
return self._description | A short description of the workflow, used when listing the
workflows. | src/snakeparse/api.py | description | nh13/snakeparse | 47 | python | @property
def description(self) -> Optional[str]:
'A short description of the workflow, used when listing the\n workflows.\n '
return self._description | @property
def description(self) -> Optional[str]:
'A short description of the workflow, used when listing the\n workflows.\n '
return self._description<|docstring|>A short description of the workflow, used when listing the
workflows.<|endoftext|> |
099c127e2b4ff7dab90c9a33c576cd35b1b665ed96d295b399c385c5f40b5c94 | def parse_args(self, args: List[str]) -> Any:
'Parses the command line arguments.'
return self.parser.parse_args(args=args) | Parses the command line arguments. | src/snakeparse/api.py | parse_args | nh13/snakeparse | 47 | python | def parse_args(self, args: List[str]) -> Any:
return self.parser.parse_args(args=args) | def parse_args(self, args: List[str]) -> Any:
return self.parser.parse_args(args=args)<|docstring|>Parses the command line arguments.<|endoftext|> |
fe1465269975df9e8e6c0198e3bf47b42f3973b0d6596d5c6b46a445323a67f5 | def parse_args_file(self, args_file: Path) -> Any:
'Parses command line arguments from an arguments file'
return self.parse_args(args=[('@' + str(args_file))]) | Parses command line arguments from an arguments file | src/snakeparse/api.py | parse_args_file | nh13/snakeparse | 47 | python | def parse_args_file(self, args_file: Path) -> Any:
return self.parse_args(args=[('@' + str(args_file))]) | def parse_args_file(self, args_file: Path) -> Any:
return self.parse_args(args=[('@' + str(args_file))])<|docstring|>Parses command line arguments from an arguments file<|endoftext|> |
e8a6980d8b9e2825e57a2ca07dc73a7eb7ebfa3755591754e8c31556067541f8 | def print_help(self, file: Optional[IO[str]]=None) -> None:
'Prints the help message'
self.parser.print_help(suppress=False, file=file) | Prints the help message | src/snakeparse/api.py | print_help | nh13/snakeparse | 47 | python | def print_help(self, file: Optional[IO[str]]=None) -> None:
self.parser.print_help(suppress=False, file=file) | def print_help(self, file: Optional[IO[str]]=None) -> None:
self.parser.print_help(suppress=False, file=file)<|docstring|>Prints the help message<|endoftext|> |
e0b78f6f386b80d792595db83ef5f7d98fe853985da0d5cb5394d392245c9997 | def add_workflow(self, workflow: SnakeParseWorkflow) -> 'SnakeParseWorkflow':
'Adds the workflow to the list of workflows. A workflow with the same\n name should not exist.'
if (workflow.name in self.workflows):
raise SnakeParseException(f"Multiple workflows with name '{workflow.name}'.")
self.workflows[workflow.name] = workflow
return workflow | Adds the workflow to the list of workflows. A workflow with the same
name should not exist. | src/snakeparse/api.py | add_workflow | nh13/snakeparse | 47 | python | def add_workflow(self, workflow: SnakeParseWorkflow) -> 'SnakeParseWorkflow':
'Adds the workflow to the list of workflows. A workflow with the same\n name should not exist.'
if (workflow.name in self.workflows):
raise SnakeParseException(f"Multiple workflows with name '{workflow.name}'.")
self.workflows[workflow.name] = workflow
return workflow | def add_workflow(self, workflow: SnakeParseWorkflow) -> 'SnakeParseWorkflow':
'Adds the workflow to the list of workflows. A workflow with the same\n name should not exist.'
if (workflow.name in self.workflows):
raise SnakeParseException(f"Multiple workflows with name '{workflow.name}'.")
self.workflows[workflow.name] = workflow
return workflow<|docstring|>Adds the workflow to the list of workflows. A workflow with the same
name should not exist.<|endoftext|> |
eaa8a6b7caef094ed5d8a09731d05ebc5b28af442f1398685724c2afc07dc744 | def add_snakefile(self, snakefile: Path) -> 'SnakeParseWorkflow':
'Adds a new workflow with the given snakefile. A workflow with the\n same name should not exist.'
name = snakefile.with_suffix('').name
if (self.name_transform is not None):
name = self.name_transform(name)
snakefile = snakefile
group = (snakefile.parent.name if self.parent_dir_is_group_name else None)
description = None
if (name in self.workflows):
raise SnakeParseException(f"Multiple workflows with name '{name}'.")
workflow = SnakeParseWorkflow(name=name, snakefile=snakefile, group=group, description=description)
return self.add_workflow(workflow=workflow) | Adds a new workflow with the given snakefile. A workflow with the
same name should not exist. | src/snakeparse/api.py | add_snakefile | nh13/snakeparse | 47 | python | def add_snakefile(self, snakefile: Path) -> 'SnakeParseWorkflow':
'Adds a new workflow with the given snakefile. A workflow with the\n same name should not exist.'
name = snakefile.with_suffix().name
if (self.name_transform is not None):
name = self.name_transform(name)
snakefile = snakefile
group = (snakefile.parent.name if self.parent_dir_is_group_name else None)
description = None
if (name in self.workflows):
raise SnakeParseException(f"Multiple workflows with name '{name}'.")
workflow = SnakeParseWorkflow(name=name, snakefile=snakefile, group=group, description=description)
return self.add_workflow(workflow=workflow) | def add_snakefile(self, snakefile: Path) -> 'SnakeParseWorkflow':
'Adds a new workflow with the given snakefile. A workflow with the\n same name should not exist.'
name = snakefile.with_suffix().name
if (self.name_transform is not None):
name = self.name_transform(name)
snakefile = snakefile
group = (snakefile.parent.name if self.parent_dir_is_group_name else None)
description = None
if (name in self.workflows):
raise SnakeParseException(f"Multiple workflows with name '{name}'.")
workflow = SnakeParseWorkflow(name=name, snakefile=snakefile, group=group, description=description)
return self.add_workflow(workflow=workflow)<|docstring|>Adds a new workflow with the given snakefile. A workflow with the
same name should not exist.<|endoftext|> |
8671695cba3c12769fb376e087fcc03a58477eed45b933bfc226244d13560bcf | def add_group(self, name: str, description: str, strict: bool=True) -> 'SnakeParseConfig':
'Adds a new group with the given name and description. If strict is\n ``True``, then no group with the same name should already exist. '
if (strict and (name in self.groups)):
raise SnakeParseException(f"Group '{name}' already defined")
self.groups[name] = description
return self | Adds a new group with the given name and description. If strict is
``True``, then no group with the same name should already exist. | src/snakeparse/api.py | add_group | nh13/snakeparse | 47 | python | def add_group(self, name: str, description: str, strict: bool=True) -> 'SnakeParseConfig':
'Adds a new group with the given name and description. If strict is\n ``True``, then no group with the same name should already exist. '
if (strict and (name in self.groups)):
raise SnakeParseException(f"Group '{name}' already defined")
self.groups[name] = description
return self | def add_group(self, name: str, description: str, strict: bool=True) -> 'SnakeParseConfig':
'Adds a new group with the given name and description. If strict is\n ``True``, then no group with the same name should already exist. '
if (strict and (name in self.groups)):
raise SnakeParseException(f"Group '{name}' already defined")
self.groups[name] = description
return self<|docstring|>Adds a new group with the given name and description. If strict is
``True``, then no group with the same name should already exist.<|endoftext|> |
001a88e781b5acf3dd827918a7886d2a5073cc7f86123e8fa2558d144e2138fa | @staticmethod
def name_transfrom_from(key: str) -> Callable[([str], str)]:
"Returns the built-in method to format the workflow's name. Should be\n either 'snake_to_camel' or 'camel_to_snake' for converting from Snake case\n to Camel case, or vice versa.\n "
if (key is None):
return None
elif (key == 'snake_to_camel'):
return SnakeParseConfig._snake_to_camel
elif (key == 'camel_to_snake'):
return SnakeParseConfig._camel_to_snake
else:
raise SnakeParseException(f"Unknown 'name_transform': {key}") | Returns the built-in method to format the workflow's name. Should be
either 'snake_to_camel' or 'camel_to_snake' for converting from Snake case
to Camel case, or vice versa. | src/snakeparse/api.py | name_transfrom_from | nh13/snakeparse | 47 | python | @staticmethod
def name_transfrom_from(key: str) -> Callable[([str], str)]:
"Returns the built-in method to format the workflow's name. Should be\n either 'snake_to_camel' or 'camel_to_snake' for converting from Snake case\n to Camel case, or vice versa.\n "
if (key is None):
return None
elif (key == 'snake_to_camel'):
return SnakeParseConfig._snake_to_camel
elif (key == 'camel_to_snake'):
return SnakeParseConfig._camel_to_snake
else:
raise SnakeParseException(f"Unknown 'name_transform': {key}") | @staticmethod
def name_transfrom_from(key: str) -> Callable[([str], str)]:
"Returns the built-in method to format the workflow's name. Should be\n either 'snake_to_camel' or 'camel_to_snake' for converting from Snake case\n to Camel case, or vice versa.\n "
if (key is None):
return None
elif (key == 'snake_to_camel'):
return SnakeParseConfig._snake_to_camel
elif (key == 'camel_to_snake'):
return SnakeParseConfig._camel_to_snake
else:
raise SnakeParseException(f"Unknown 'name_transform': {key}")<|docstring|>Returns the built-in method to format the workflow's name. Should be
either 'snake_to_camel' or 'camel_to_snake' for converting from Snake case
to Camel case, or vice versa.<|endoftext|> |
2b240dd16d41a4bf4b3069ecd0f4cae10a2441f1126989a0759868fc969fc7a3 | @staticmethod
def _snake_to_camel(snake_str: str) -> str:
'Converts a string in Snake case to Camel case.'
return ''.join([s.title() for s in snake_str.split('_')]) | Converts a string in Snake case to Camel case. | src/snakeparse/api.py | _snake_to_camel | nh13/snakeparse | 47 | python | @staticmethod
def _snake_to_camel(snake_str: str) -> str:
return .join([s.title() for s in snake_str.split('_')]) | @staticmethod
def _snake_to_camel(snake_str: str) -> str:
return .join([s.title() for s in snake_str.split('_')])<|docstring|>Converts a string in Snake case to Camel case.<|endoftext|> |
82bc97b63800ba1a4ac0d1e510734f1f8d66f4891ce41a47640f66e88ca37d91 | @staticmethod
def _camel_to_snake(camel_str: str) -> str:
'Converts a string in Camel case to Snake case.'
if (not camel_str):
return camel_str
first_char = camel_str[0].lower()
return (first_char + ''.join([(('_' + c.lower()) if c.isupper() else c) for c in camel_str[1:]])) | Converts a string in Camel case to Snake case. | src/snakeparse/api.py | _camel_to_snake | nh13/snakeparse | 47 | python | @staticmethod
def _camel_to_snake(camel_str: str) -> str:
if (not camel_str):
return camel_str
first_char = camel_str[0].lower()
return (first_char + .join([(('_' + c.lower()) if c.isupper() else c) for c in camel_str[1:]])) | @staticmethod
def _camel_to_snake(camel_str: str) -> str:
if (not camel_str):
return camel_str
first_char = camel_str[0].lower()
return (first_char + .join([(('_' + c.lower()) if c.isupper() else c) for c in camel_str[1:]]))<|docstring|>Converts a string in Camel case to Snake case.<|endoftext|> |
18f7d1aa8df698119e54742666f57c33a274f1b3a36d86c3f208b6d4a17260ee | @staticmethod
def parser_from(workflow: 'SnakeParseWorkflow') -> SnakeParser:
'Builds the SnakeParser for the given workflow'
parent_module_name = str(workflow.snakefile.resolve().parent)
sys.path.insert(0, parent_module_name)
exec_exception = None
from snakemake.workflow import Workflow
snakefile = str(workflow.snakefile)
global config
config = dict([(SnakeParse.ARGUMENT_FILE_NAME_KEY, None)])
globals_copy = dict(globals())
globals_copy['config'] = dict([(SnakeParse.ARGUMENT_FILE_NAME_KEY, None)])
globals_copy['workflow'] = Workflow(snakefile=snakefile)
(code, linemap, rulecount) = snakemake_parser.parse(snakefile)
code = compile(code, snakefile, 'exec')
try:
exec(code, globals_copy)
except Exception as e:
exec_exception = SnakeParseException(f'Could not compile {snakefile}', e)
def classes_predicate(obj: Any) -> bool:
return (inspect.isclass(obj) and (not inspect.isabstract(obj)) and (SnakeParser in inspect.getmro(obj)))
def methods_predicate(key: str, obj: Any) -> bool:
return ((key == 'snakeparser') and inspect.isfunction(obj))
classes = [obj for (key, obj) in globals_copy.items() if classes_predicate(obj)]
methods = [obj for (key, obj) in globals_copy.items() if methods_predicate(key, obj)]
if ((len(classes) + len(methods)) == 0):
raise SnakeParseException(f'Could not find either a concrete subclass of SnakeParser or a method named "snakeparser" in {workflow.snakefile}', exec_exception)
elif ((len(classes) + len(methods)) > 1):
raise SnakeParseException(f'Found {len(classes)} concrete subclasses of SnakeParser and {len(methods)} methods named snakeparser in {workflow.snakefile}', exec_exception)
elif ((len(classes) == 1) and (len(methods) == 0)):
parser_class = classes[0]
if issubclass(parser_class, SnakeArgumentParser):
return parser_class(usage=argparse.SUPPRESS)
else:
return parser_class()
else:
assert ((len(classes) == 0) and (len(methods) == 1)), f'Bug: {len(classes)} != 0 and {len(methods)} != 1'
parser_method = methods[0]
parser = parser_method()
if issubclass(parser.__class__, SnakeArgumentParser):
return parser_method(usage=argparse.SUPPRESS)
else:
return parser | Builds the SnakeParser for the given workflow | src/snakeparse/api.py | parser_from | nh13/snakeparse | 47 | python | @staticmethod
def parser_from(workflow: 'SnakeParseWorkflow') -> SnakeParser:
parent_module_name = str(workflow.snakefile.resolve().parent)
sys.path.insert(0, parent_module_name)
exec_exception = None
from snakemake.workflow import Workflow
snakefile = str(workflow.snakefile)
global config
config = dict([(SnakeParse.ARGUMENT_FILE_NAME_KEY, None)])
globals_copy = dict(globals())
globals_copy['config'] = dict([(SnakeParse.ARGUMENT_FILE_NAME_KEY, None)])
globals_copy['workflow'] = Workflow(snakefile=snakefile)
(code, linemap, rulecount) = snakemake_parser.parse(snakefile)
code = compile(code, snakefile, 'exec')
try:
exec(code, globals_copy)
except Exception as e:
exec_exception = SnakeParseException(f'Could not compile {snakefile}', e)
def classes_predicate(obj: Any) -> bool:
return (inspect.isclass(obj) and (not inspect.isabstract(obj)) and (SnakeParser in inspect.getmro(obj)))
def methods_predicate(key: str, obj: Any) -> bool:
return ((key == 'snakeparser') and inspect.isfunction(obj))
classes = [obj for (key, obj) in globals_copy.items() if classes_predicate(obj)]
methods = [obj for (key, obj) in globals_copy.items() if methods_predicate(key, obj)]
if ((len(classes) + len(methods)) == 0):
raise SnakeParseException(f'Could not find either a concrete subclass of SnakeParser or a method named "snakeparser" in {workflow.snakefile}', exec_exception)
elif ((len(classes) + len(methods)) > 1):
raise SnakeParseException(f'Found {len(classes)} concrete subclasses of SnakeParser and {len(methods)} methods named snakeparser in {workflow.snakefile}', exec_exception)
elif ((len(classes) == 1) and (len(methods) == 0)):
parser_class = classes[0]
if issubclass(parser_class, SnakeArgumentParser):
return parser_class(usage=argparse.SUPPRESS)
else:
return parser_class()
else:
assert ((len(classes) == 0) and (len(methods) == 1)), f'Bug: {len(classes)} != 0 and {len(methods)} != 1'
parser_method = methods[0]
parser = parser_method()
if issubclass(parser.__class__, SnakeArgumentParser):
return parser_method(usage=argparse.SUPPRESS)
else:
return parser | @staticmethod
def parser_from(workflow: 'SnakeParseWorkflow') -> SnakeParser:
parent_module_name = str(workflow.snakefile.resolve().parent)
sys.path.insert(0, parent_module_name)
exec_exception = None
from snakemake.workflow import Workflow
snakefile = str(workflow.snakefile)
global config
config = dict([(SnakeParse.ARGUMENT_FILE_NAME_KEY, None)])
globals_copy = dict(globals())
globals_copy['config'] = dict([(SnakeParse.ARGUMENT_FILE_NAME_KEY, None)])
globals_copy['workflow'] = Workflow(snakefile=snakefile)
(code, linemap, rulecount) = snakemake_parser.parse(snakefile)
code = compile(code, snakefile, 'exec')
try:
exec(code, globals_copy)
except Exception as e:
exec_exception = SnakeParseException(f'Could not compile {snakefile}', e)
def classes_predicate(obj: Any) -> bool:
return (inspect.isclass(obj) and (not inspect.isabstract(obj)) and (SnakeParser in inspect.getmro(obj)))
def methods_predicate(key: str, obj: Any) -> bool:
return ((key == 'snakeparser') and inspect.isfunction(obj))
classes = [obj for (key, obj) in globals_copy.items() if classes_predicate(obj)]
methods = [obj for (key, obj) in globals_copy.items() if methods_predicate(key, obj)]
if ((len(classes) + len(methods)) == 0):
raise SnakeParseException(f'Could not find either a concrete subclass of SnakeParser or a method named "snakeparser" in {workflow.snakefile}', exec_exception)
elif ((len(classes) + len(methods)) > 1):
raise SnakeParseException(f'Found {len(classes)} concrete subclasses of SnakeParser and {len(methods)} methods named snakeparser in {workflow.snakefile}', exec_exception)
elif ((len(classes) == 1) and (len(methods) == 0)):
parser_class = classes[0]
if issubclass(parser_class, SnakeArgumentParser):
return parser_class(usage=argparse.SUPPRESS)
else:
return parser_class()
else:
assert ((len(classes) == 0) and (len(methods) == 1)), f'Bug: {len(classes)} != 0 and {len(methods)} != 1'
parser_method = methods[0]
parser = parser_method()
if issubclass(parser.__class__, SnakeArgumentParser):
return parser_method(usage=argparse.SUPPRESS)
else:
return parser<|docstring|>Builds the SnakeParser for the given workflow<|endoftext|> |
b6dde4b09ac38818415161e85bbfaf8c2d0eda07e3c8b9666610acd8eef48be8 | @staticmethod
def config_parser(usage: str=argparse.SUPPRESS) -> argparse.ArgumentParser:
'Returns an :class:`~argparse.ArgumentParser` for the configuration options'
class _ConfigParser(_ArgumentParser):
def exit(self, status: int=0, message: str=None) -> None:
raise SnakeParseException(message)
parser = _ConfigParser(usage=usage, allow_abbrev=False)
parser.add_argument('--config', help='The path to the snakeparse configuration file (can be JSON, YAML, or HOCON).', type=Path)
parser.add_argument('--snakefile-globs', help='Optionally, or more glob strings specifying where SnakeMake (snakefile) files can be found', nargs='*', default=[])
parser.add_argument('--prog', help='The name of the tool-chain to use ont the command-line', default='snakeparse')
parser.add_argument('--snakemake', help='The path to the snakemake executable, otherwise it should be on the system path', type=Path)
parser.add_argument('--name-transform', help='Transform the name of the workflow from Snake case to Camel case("snake_to_camel") or vice versa ("camel_to_snake")', default='snake_to_camel')
parser.add_argument('--parent-dir-is-group-name', help='In the last resort if no group name is found, use the name of the parent directory of the snakefile as the group name', type=bool, default=False)
parser.add_argument('--extra-help', help='Produce help with extra debugging information', type=bool, default=False)
return parser | Returns an :class:`~argparse.ArgumentParser` for the configuration options | src/snakeparse/api.py | config_parser | nh13/snakeparse | 47 | python | @staticmethod
def config_parser(usage: str=argparse.SUPPRESS) -> argparse.ArgumentParser:
class _ConfigParser(_ArgumentParser):
def exit(self, status: int=0, message: str=None) -> None:
raise SnakeParseException(message)
parser = _ConfigParser(usage=usage, allow_abbrev=False)
parser.add_argument('--config', help='The path to the snakeparse configuration file (can be JSON, YAML, or HOCON).', type=Path)
parser.add_argument('--snakefile-globs', help='Optionally, or more glob strings specifying where SnakeMake (snakefile) files can be found', nargs='*', default=[])
parser.add_argument('--prog', help='The name of the tool-chain to use ont the command-line', default='snakeparse')
parser.add_argument('--snakemake', help='The path to the snakemake executable, otherwise it should be on the system path', type=Path)
parser.add_argument('--name-transform', help='Transform the name of the workflow from Snake case to Camel case("snake_to_camel") or vice versa ("camel_to_snake")', default='snake_to_camel')
parser.add_argument('--parent-dir-is-group-name', help='In the last resort if no group name is found, use the name of the parent directory of the snakefile as the group name', type=bool, default=False)
parser.add_argument('--extra-help', help='Produce help with extra debugging information', type=bool, default=False)
return parser | @staticmethod
def config_parser(usage: str=argparse.SUPPRESS) -> argparse.ArgumentParser:
class _ConfigParser(_ArgumentParser):
def exit(self, status: int=0, message: str=None) -> None:
raise SnakeParseException(message)
parser = _ConfigParser(usage=usage, allow_abbrev=False)
parser.add_argument('--config', help='The path to the snakeparse configuration file (can be JSON, YAML, or HOCON).', type=Path)
parser.add_argument('--snakefile-globs', help='Optionally, or more glob strings specifying where SnakeMake (snakefile) files can be found', nargs='*', default=[])
parser.add_argument('--prog', help='The name of the tool-chain to use ont the command-line', default='snakeparse')
parser.add_argument('--snakemake', help='The path to the snakemake executable, otherwise it should be on the system path', type=Path)
parser.add_argument('--name-transform', help='Transform the name of the workflow from Snake case to Camel case("snake_to_camel") or vice versa ("camel_to_snake")', default='snake_to_camel')
parser.add_argument('--parent-dir-is-group-name', help='In the last resort if no group name is found, use the name of the parent directory of the snakefile as the group name', type=bool, default=False)
parser.add_argument('--extra-help', help='Produce help with extra debugging information', type=bool, default=False)
return parser<|docstring|>Returns an :class:`~argparse.ArgumentParser` for the configuration options<|endoftext|> |
c7f419f871a3a6754d6794bcd28920f9d91a7a75808e196a941aef98bdadaf80 | def run(self) -> None:
'Execute the Snakemake workflow'
snakemake = (self.config.snakemake if self.config.snakemake else 'snakemake')
retcode = subprocess.call(([str(snakemake)] + self.snakemake_args))
self.snakeparse_args_file.unlink()
sys.exit(retcode) | Execute the Snakemake workflow | src/snakeparse/api.py | run | nh13/snakeparse | 47 | python | def run(self) -> None:
snakemake = (self.config.snakemake if self.config.snakemake else 'snakemake')
retcode = subprocess.call(([str(snakemake)] + self.snakemake_args))
self.snakeparse_args_file.unlink()
sys.exit(retcode) | def run(self) -> None:
snakemake = (self.config.snakemake if self.config.snakemake else 'snakemake')
retcode = subprocess.call(([str(snakemake)] + self.snakemake_args))
self.snakeparse_args_file.unlink()
sys.exit(retcode)<|docstring|>Execute the Snakemake workflow<|endoftext|> |
00570cfc7db567d8bc3eba7497d6bac2113990d279e8a817c68350bd1b1c57b7 | def _parse_workflow_args(self, workflow: 'SnakeParseWorkflow', args_file: Path) -> None:
'Dynamically loads the module containing the workflow parser and\n attempts to parse the arguments in the given arguments file.\n\n The module must have a single concrete class implementing SnakeParser.\n '
parser = self.config.parser_from(workflow=workflow)
try:
parser.parse_args_file(args_file=args_file)
except SnakeParseException as e:
self._print_workflow_help(workflow=workflow, parser=parser, message=str(e))
except SystemExit:
self._print_workflow_help(workflow=workflow, parser=parser, message=None) | Dynamically loads the module containing the workflow parser and
attempts to parse the arguments in the given arguments file.
The module must have a single concrete class implementing SnakeParser. | src/snakeparse/api.py | _parse_workflow_args | nh13/snakeparse | 47 | python | def _parse_workflow_args(self, workflow: 'SnakeParseWorkflow', args_file: Path) -> None:
'Dynamically loads the module containing the workflow parser and\n attempts to parse the arguments in the given arguments file.\n\n The module must have a single concrete class implementing SnakeParser.\n '
parser = self.config.parser_from(workflow=workflow)
try:
parser.parse_args_file(args_file=args_file)
except SnakeParseException as e:
self._print_workflow_help(workflow=workflow, parser=parser, message=str(e))
except SystemExit:
self._print_workflow_help(workflow=workflow, parser=parser, message=None) | def _parse_workflow_args(self, workflow: 'SnakeParseWorkflow', args_file: Path) -> None:
'Dynamically loads the module containing the workflow parser and\n attempts to parse the arguments in the given arguments file.\n\n The module must have a single concrete class implementing SnakeParser.\n '
parser = self.config.parser_from(workflow=workflow)
try:
parser.parse_args_file(args_file=args_file)
except SnakeParseException as e:
self._print_workflow_help(workflow=workflow, parser=parser, message=str(e))
except SystemExit:
self._print_workflow_help(workflow=workflow, parser=parser, message=None)<|docstring|>Dynamically loads the module containing the workflow parser and
attempts to parse the arguments in the given arguments file.
The module must have a single concrete class implementing SnakeParser.<|endoftext|> |
81572d71f550bcf624c055299056e16372cab9db7810d61fdf5de0d3ad74a128 | def _print_workflow_help(self, workflow: 'SnakeParseWorkflow', parser: 'SnakeParser', message: Optional[str]=None) -> None:
'Prints the help message with all available workflows and the workflow\n specific help.'
self._usage(exit=False)
self.file.write(f'''
{workflow.name} Arguments:
''')
self._print_line()
self.file.write('\n')
parser.print_help(file=self.file)
if message:
self.file.write(f'''
error: {message}''')
self.file.write('\n')
sys.exit(2) | Prints the help message with all available workflows and the workflow
specific help. | src/snakeparse/api.py | _print_workflow_help | nh13/snakeparse | 47 | python | def _print_workflow_help(self, workflow: 'SnakeParseWorkflow', parser: 'SnakeParser', message: Optional[str]=None) -> None:
'Prints the help message with all available workflows and the workflow\n specific help.'
self._usage(exit=False)
self.file.write(f'
{workflow.name} Arguments:
')
self._print_line()
self.file.write('\n')
parser.print_help(file=self.file)
if message:
self.file.write(f'
error: {message}')
self.file.write('\n')
sys.exit(2) | def _print_workflow_help(self, workflow: 'SnakeParseWorkflow', parser: 'SnakeParser', message: Optional[str]=None) -> None:
'Prints the help message with all available workflows and the workflow\n specific help.'
self._usage(exit=False)
self.file.write(f'
{workflow.name} Arguments:
')
self._print_line()
self.file.write('\n')
parser.print_help(file=self.file)
if message:
self.file.write(f'
error: {message}')
self.file.write('\n')
sys.exit(2)<|docstring|>Prints the help message with all available workflows and the workflow
specific help.<|endoftext|> |
3a1412927a166dc69ad58e6a9c6b8e70db769fcf5b954a8f36f4cea9c065d45e | @staticmethod
def _parse_known_args(parser: argparse.ArgumentParser, args: Sequence[str]) -> Tuple[(int, argparse.Namespace)]:
'Parses the args with the given parsers until an unknown argument is\n encountered.'
if (not args):
(namespace, remaining) = parser.parse_known_args(args=args)
return (1, namespace)
namespace = argparse.Namespace()
end = 1
while (end <= len(args)):
try:
(namespace, remaining) = parser.parse_known_args(args=args[:end])
if remaining:
return ((end - 1), namespace)
except SnakeParseException:
pass
end += 1
return (end, namespace) | Parses the args with the given parsers until an unknown argument is
encountered. | src/snakeparse/api.py | _parse_known_args | nh13/snakeparse | 47 | python | @staticmethod
def _parse_known_args(parser: argparse.ArgumentParser, args: Sequence[str]) -> Tuple[(int, argparse.Namespace)]:
'Parses the args with the given parsers until an unknown argument is\n encountered.'
if (not args):
(namespace, remaining) = parser.parse_known_args(args=args)
return (1, namespace)
namespace = argparse.Namespace()
end = 1
while (end <= len(args)):
try:
(namespace, remaining) = parser.parse_known_args(args=args[:end])
if remaining:
return ((end - 1), namespace)
except SnakeParseException:
pass
end += 1
return (end, namespace) | @staticmethod
def _parse_known_args(parser: argparse.ArgumentParser, args: Sequence[str]) -> Tuple[(int, argparse.Namespace)]:
'Parses the args with the given parsers until an unknown argument is\n encountered.'
if (not args):
(namespace, remaining) = parser.parse_known_args(args=args)
return (1, namespace)
namespace = argparse.Namespace()
end = 1
while (end <= len(args)):
try:
(namespace, remaining) = parser.parse_known_args(args=args[:end])
if remaining:
return ((end - 1), namespace)
except SnakeParseException:
pass
end += 1
return (end, namespace)<|docstring|>Parses the args with the given parsers until an unknown argument is
encountered.<|endoftext|> |
6177a944de600c869f96e966eacd8403f85b7c0fea94c56b31000d9b709cbe71 | @staticmethod
def usage_short(prog: str='snakeparse', workflow_name: str=None) -> str:
'A one line usage to display at the top of any usage or help message.'
if (workflow_name is None):
workflow_name = '[workflow name]'
return f'{prog} [snakeparse options] [snakemake options] {workflow_name} [workflow options]'.lstrip(' ') | A one line usage to display at the top of any usage or help message. | src/snakeparse/api.py | usage_short | nh13/snakeparse | 47 | python | @staticmethod
def usage_short(prog: str='snakeparse', workflow_name: str=None) -> str:
if (workflow_name is None):
workflow_name = '[workflow name]'
return f'{prog} [snakeparse options] [snakemake options] {workflow_name} [workflow options]'.lstrip(' ') | @staticmethod
def usage_short(prog: str='snakeparse', workflow_name: str=None) -> str:
if (workflow_name is None):
workflow_name = '[workflow name]'
return f'{prog} [snakeparse options] [snakemake options] {workflow_name} [workflow options]'.lstrip(' ')<|docstring|>A one line usage to display at the top of any usage or help message.<|endoftext|> |
da6939b99b3a1d2aaa3f28bc2f5b88c94d827afa5fd8c44e6215e14679352a84 | def _usage(self, message: Optional[str]=None, exit: bool=True) -> None:
'The long usage that lists all the available workflows.'
terminal_size = shutil.get_terminal_size(fallback=(80, 24))
group_name_columns = 38
group_description_columns = (terminal_size.columns - group_name_columns)
workflow_name_columns = (group_name_columns - 3)
workflow_description_columns = (group_description_columns - 1)
self.file.write((('Usage: ' + self._usage_short()) + '\n'))
self.file.write(f'''Version: {__version__}
''')
if self._config_usage:
self.file.write('\n')
SnakeParseConfig.config_parser().print_help(file=self.file, suppress=False)
if self.config.workflows:
self.file.write('\nAvailable Workflows:\n')
self._print_line()
groups = OrderedDict(self.config.groups)
for wf in self.config.workflows.values():
if (wf.group not in groups):
groups[wf.group] = None
for (group, desc) in groups.items():
name = (('Worfklows' if (group is None) else group) + ':')
desc = ('' if (desc is None) else desc)
self.file.write(f'''{name:<{group_name_columns}}{desc:<{group_description_columns}}
''')
for wf in self.config.workflows.values():
if (wf.group != group):
continue
desc = (str(wf.snakefile) if (wf.description is None) else wf.description)
self.file.write(f''' {wf.name:<{workflow_name_columns}}{desc:<{workflow_description_columns}}
''')
if self.debug:
self.file.write(f''' snakefile: {wf.snakefile}
''')
self._print_line()
else:
self.file.write('\nNo workflows configured.\n')
self._print_line()
if (message is not None):
self.file.write(f'''
{message}
''')
if exit:
sys.exit(2) | The long usage that lists all the available workflows. | src/snakeparse/api.py | _usage | nh13/snakeparse | 47 | python | def _usage(self, message: Optional[str]=None, exit: bool=True) -> None:
terminal_size = shutil.get_terminal_size(fallback=(80, 24))
group_name_columns = 38
group_description_columns = (terminal_size.columns - group_name_columns)
workflow_name_columns = (group_name_columns - 3)
workflow_description_columns = (group_description_columns - 1)
self.file.write((('Usage: ' + self._usage_short()) + '\n'))
self.file.write(f'Version: {__version__}
')
if self._config_usage:
self.file.write('\n')
SnakeParseConfig.config_parser().print_help(file=self.file, suppress=False)
if self.config.workflows:
self.file.write('\nAvailable Workflows:\n')
self._print_line()
groups = OrderedDict(self.config.groups)
for wf in self.config.workflows.values():
if (wf.group not in groups):
groups[wf.group] = None
for (group, desc) in groups.items():
name = (('Worfklows' if (group is None) else group) + ':')
desc = ( if (desc is None) else desc)
self.file.write(f'{name:<{group_name_columns}}{desc:<{group_description_columns}}
')
for wf in self.config.workflows.values():
if (wf.group != group):
continue
desc = (str(wf.snakefile) if (wf.description is None) else wf.description)
self.file.write(f' {wf.name:<{workflow_name_columns}}{desc:<{workflow_description_columns}}
')
if self.debug:
self.file.write(f' snakefile: {wf.snakefile}
')
self._print_line()
else:
self.file.write('\nNo workflows configured.\n')
self._print_line()
if (message is not None):
self.file.write(f'
{message}
')
if exit:
sys.exit(2) | def _usage(self, message: Optional[str]=None, exit: bool=True) -> None:
terminal_size = shutil.get_terminal_size(fallback=(80, 24))
group_name_columns = 38
group_description_columns = (terminal_size.columns - group_name_columns)
workflow_name_columns = (group_name_columns - 3)
workflow_description_columns = (group_description_columns - 1)
self.file.write((('Usage: ' + self._usage_short()) + '\n'))
self.file.write(f'Version: {__version__}
')
if self._config_usage:
self.file.write('\n')
SnakeParseConfig.config_parser().print_help(file=self.file, suppress=False)
if self.config.workflows:
self.file.write('\nAvailable Workflows:\n')
self._print_line()
groups = OrderedDict(self.config.groups)
for wf in self.config.workflows.values():
if (wf.group not in groups):
groups[wf.group] = None
for (group, desc) in groups.items():
name = (('Worfklows' if (group is None) else group) + ':')
desc = ( if (desc is None) else desc)
self.file.write(f'{name:<{group_name_columns}}{desc:<{group_description_columns}}
')
for wf in self.config.workflows.values():
if (wf.group != group):
continue
desc = (str(wf.snakefile) if (wf.description is None) else wf.description)
self.file.write(f' {wf.name:<{workflow_name_columns}}{desc:<{workflow_description_columns}}
')
if self.debug:
self.file.write(f' snakefile: {wf.snakefile}
')
self._print_line()
else:
self.file.write('\nNo workflows configured.\n')
self._print_line()
if (message is not None):
self.file.write(f'
{message}
')
if exit:
sys.exit(2)<|docstring|>The long usage that lists all the available workflows.<|endoftext|> |
c19045eb9832add32f16ed9f7321b2cfa6e60f6131e1bbbe5fc940c180c0c280 | def makesocket(path):
'Create a socket file, return True successfully, fail to return False.'
if (not os.path.exists(path)):
try:
sock = socket.socket(socket.AF_UNIX)
sock.bind(path)
except Exception as e:
return False
return True
return False | Create a socket file, return True successfully, fail to return False. | py/makesocket.py | makesocket | rgb-24bit/scripts | 0 | python | def makesocket(path):
if (not os.path.exists(path)):
try:
sock = socket.socket(socket.AF_UNIX)
sock.bind(path)
except Exception as e:
return False
return True
return False | def makesocket(path):
if (not os.path.exists(path)):
try:
sock = socket.socket(socket.AF_UNIX)
sock.bind(path)
except Exception as e:
return False
return True
return False<|docstring|>Create a socket file, return True successfully, fail to return False.<|endoftext|> |
02ce0855768f2e857952c5def51151dd0e0a0468e836c5e2bb2a48c3e6685075 | def parse_args():
'Command line arguments parsing.'
parser = argparse.ArgumentParser(prog='makesocket', description=DESCRIPTION)
parser.add_argument('-v', '--version', action='version', version=('%(prog)s ' + VERSION))
parser.add_argument('-p', '--path', action='store', dest='path', type=str, default=None, help='Specify the path to the socket file to be created')
return parser.parse_args() | Command line arguments parsing. | py/makesocket.py | parse_args | rgb-24bit/scripts | 0 | python | def parse_args():
parser = argparse.ArgumentParser(prog='makesocket', description=DESCRIPTION)
parser.add_argument('-v', '--version', action='version', version=('%(prog)s ' + VERSION))
parser.add_argument('-p', '--path', action='store', dest='path', type=str, default=None, help='Specify the path to the socket file to be created')
return parser.parse_args() | def parse_args():
parser = argparse.ArgumentParser(prog='makesocket', description=DESCRIPTION)
parser.add_argument('-v', '--version', action='version', version=('%(prog)s ' + VERSION))
parser.add_argument('-p', '--path', action='store', dest='path', type=str, default=None, help='Specify the path to the socket file to be created')
return parser.parse_args()<|docstring|>Command line arguments parsing.<|endoftext|> |
9a83a98d88e3ad73b669582ed998a4a9ced0e5a044acdf35fb059b34c4156204 | @staticmethod
def _to_state_space(tau, dt=0.05):
'\n Args:\n tau (float): time constant\n dt (float): discrte time\n Returns:\n A (numpy.ndarray): discrete A matrix \n B (numpy.ndarray): discrete B matrix \n '
Ac = np.array([[((- 1.0) / tau), 0.0, 0.0, 0.0], [0.0, ((- 1.0) / tau), 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]])
Bc = np.array([[(1.0 / tau), 0.0], [0.0, (1.0 / tau)], [0.0, 0.0], [0.0, 0.0]])
A = scipy.linalg.expm((dt * Ac))
B = np.zeros_like(Bc)
for m in range(Bc.shape[0]):
for n in range(Bc.shape[1]):
integrate_fn = (lambda tau: np.matmul(scipy.linalg.expm((Ac * tau)), Bc)[(m, n)])
sol = integrate.quad(integrate_fn, 0, dt)
B[(m, n)] = sol[0]
return (A, B) | Args:
tau (float): time constant
dt (float): discrte time
Returns:
A (numpy.ndarray): discrete A matrix
B (numpy.ndarray): discrete B matrix | PythonLinearNonlinearControl/envs/first_order_lag.py | _to_state_space | Geonhee-LEE/PythonLinearNonlinearControl | 425 | python | @staticmethod
def _to_state_space(tau, dt=0.05):
'\n Args:\n tau (float): time constant\n dt (float): discrte time\n Returns:\n A (numpy.ndarray): discrete A matrix \n B (numpy.ndarray): discrete B matrix \n '
Ac = np.array([[((- 1.0) / tau), 0.0, 0.0, 0.0], [0.0, ((- 1.0) / tau), 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]])
Bc = np.array([[(1.0 / tau), 0.0], [0.0, (1.0 / tau)], [0.0, 0.0], [0.0, 0.0]])
A = scipy.linalg.expm((dt * Ac))
B = np.zeros_like(Bc)
for m in range(Bc.shape[0]):
for n in range(Bc.shape[1]):
integrate_fn = (lambda tau: np.matmul(scipy.linalg.expm((Ac * tau)), Bc)[(m, n)])
sol = integrate.quad(integrate_fn, 0, dt)
B[(m, n)] = sol[0]
return (A, B) | @staticmethod
def _to_state_space(tau, dt=0.05):
'\n Args:\n tau (float): time constant\n dt (float): discrte time\n Returns:\n A (numpy.ndarray): discrete A matrix \n B (numpy.ndarray): discrete B matrix \n '
Ac = np.array([[((- 1.0) / tau), 0.0, 0.0, 0.0], [0.0, ((- 1.0) / tau), 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]])
Bc = np.array([[(1.0 / tau), 0.0], [0.0, (1.0 / tau)], [0.0, 0.0], [0.0, 0.0]])
A = scipy.linalg.expm((dt * Ac))
B = np.zeros_like(Bc)
for m in range(Bc.shape[0]):
for n in range(Bc.shape[1]):
integrate_fn = (lambda tau: np.matmul(scipy.linalg.expm((Ac * tau)), Bc)[(m, n)])
sol = integrate.quad(integrate_fn, 0, dt)
B[(m, n)] = sol[0]
return (A, B)<|docstring|>Args:
tau (float): time constant
dt (float): discrte time
Returns:
A (numpy.ndarray): discrete A matrix
B (numpy.ndarray): discrete B matrix<|endoftext|> |
c9aacfda65f300cb3cd90067c2b0693390f3e237755ba46b0488898dce9ac5a4 | def reset(self, init_x=None):
' reset state\n Returns:\n init_x (numpy.ndarray): initial state, shape(state_size, ) \n info (dict): information\n '
self.step_count = 0
self.curr_x = np.zeros(self.config['state_size'])
if (init_x is not None):
self.curr_x = init_x
self.g_x = np.array([0.0, 0, (- 2.0), 3.0])
self.history_x = []
self.history_g_x = []
return (self.curr_x, {'goal_state': self.g_x}) | reset state
Returns:
init_x (numpy.ndarray): initial state, shape(state_size, )
info (dict): information | PythonLinearNonlinearControl/envs/first_order_lag.py | reset | Geonhee-LEE/PythonLinearNonlinearControl | 425 | python | def reset(self, init_x=None):
' reset state\n Returns:\n init_x (numpy.ndarray): initial state, shape(state_size, ) \n info (dict): information\n '
self.step_count = 0
self.curr_x = np.zeros(self.config['state_size'])
if (init_x is not None):
self.curr_x = init_x
self.g_x = np.array([0.0, 0, (- 2.0), 3.0])
self.history_x = []
self.history_g_x = []
return (self.curr_x, {'goal_state': self.g_x}) | def reset(self, init_x=None):
' reset state\n Returns:\n init_x (numpy.ndarray): initial state, shape(state_size, ) \n info (dict): information\n '
self.step_count = 0
self.curr_x = np.zeros(self.config['state_size'])
if (init_x is not None):
self.curr_x = init_x
self.g_x = np.array([0.0, 0, (- 2.0), 3.0])
self.history_x = []
self.history_g_x = []
return (self.curr_x, {'goal_state': self.g_x})<|docstring|>reset state
Returns:
init_x (numpy.ndarray): initial state, shape(state_size, )
info (dict): information<|endoftext|> |
dd0ca5b44f6e12f8d78e98e1329d457ff60cc66ab40e327c8eb1631fe5b1243a | def step(self, u):
'\n Args:\n u (numpy.ndarray) : input, shape(input_size, )\n Returns:\n next_x (numpy.ndarray): next state, shape(state_size, ) \n cost (float): costs\n done (bool): end the simulation or not\n info (dict): information \n '
u = np.clip(u, self.config['input_lower_bound'], self.config['input_upper_bound'])
next_x = (np.matmul(self.A, self.curr_x[(:, np.newaxis)]) + np.matmul(self.B, u[(:, np.newaxis)]))
cost = 0
cost = np.sum((u ** 2))
cost += np.sum(((self.curr_x - self.g_x) ** 2))
self.history_x.append(next_x.flatten())
self.history_g_x.append(self.g_x.flatten())
self.curr_x = next_x.flatten()
self.step_count += 1
return (next_x.flatten(), cost, (self.step_count > self.config['max_step']), {'goal_state': self.g_x}) | Args:
u (numpy.ndarray) : input, shape(input_size, )
Returns:
next_x (numpy.ndarray): next state, shape(state_size, )
cost (float): costs
done (bool): end the simulation or not
info (dict): information | PythonLinearNonlinearControl/envs/first_order_lag.py | step | Geonhee-LEE/PythonLinearNonlinearControl | 425 | python | def step(self, u):
'\n Args:\n u (numpy.ndarray) : input, shape(input_size, )\n Returns:\n next_x (numpy.ndarray): next state, shape(state_size, ) \n cost (float): costs\n done (bool): end the simulation or not\n info (dict): information \n '
u = np.clip(u, self.config['input_lower_bound'], self.config['input_upper_bound'])
next_x = (np.matmul(self.A, self.curr_x[(:, np.newaxis)]) + np.matmul(self.B, u[(:, np.newaxis)]))
cost = 0
cost = np.sum((u ** 2))
cost += np.sum(((self.curr_x - self.g_x) ** 2))
self.history_x.append(next_x.flatten())
self.history_g_x.append(self.g_x.flatten())
self.curr_x = next_x.flatten()
self.step_count += 1
return (next_x.flatten(), cost, (self.step_count > self.config['max_step']), {'goal_state': self.g_x}) | def step(self, u):
'\n Args:\n u (numpy.ndarray) : input, shape(input_size, )\n Returns:\n next_x (numpy.ndarray): next state, shape(state_size, ) \n cost (float): costs\n done (bool): end the simulation or not\n info (dict): information \n '
u = np.clip(u, self.config['input_lower_bound'], self.config['input_upper_bound'])
next_x = (np.matmul(self.A, self.curr_x[(:, np.newaxis)]) + np.matmul(self.B, u[(:, np.newaxis)]))
cost = 0
cost = np.sum((u ** 2))
cost += np.sum(((self.curr_x - self.g_x) ** 2))
self.history_x.append(next_x.flatten())
self.history_g_x.append(self.g_x.flatten())
self.curr_x = next_x.flatten()
self.step_count += 1
return (next_x.flatten(), cost, (self.step_count > self.config['max_step']), {'goal_state': self.g_x})<|docstring|>Args:
u (numpy.ndarray) : input, shape(input_size, )
Returns:
next_x (numpy.ndarray): next state, shape(state_size, )
cost (float): costs
done (bool): end the simulation or not
info (dict): information<|endoftext|> |
4f8a2f3ddab0ef5970ec6577cffb93b72f11aeb0c64264d55cf46db7efc281f7 | def dsa_view(redirect_name=None):
'Decorate djangos-social-auth views. Will check and retrieve backend\n or return HttpResponseServerError if backend is not found.\n\n redirect_name parameter is used to build redirect URL used by backend.\n '
def dec(func):
@wraps(func)
def wrapper(request, backend, *args, **kwargs):
if redirect_name:
redirect = reverse(redirect_name, args=(backend,))
else:
redirect = request.path
request.social_auth_backend = get_backend(backend, request, redirect)
if (request.social_auth_backend is None):
raise WrongBackend(backend)
return func(request, request.social_auth_backend, *args, **kwargs)
return wrapper
return dec | Decorate djangos-social-auth views. Will check and retrieve backend
or return HttpResponseServerError if backend is not found.
redirect_name parameter is used to build redirect URL used by backend. | src/social_auth/decorators.py | dsa_view | legalosLOTR/sentry | 4 | python | def dsa_view(redirect_name=None):
'Decorate djangos-social-auth views. Will check and retrieve backend\n or return HttpResponseServerError if backend is not found.\n\n redirect_name parameter is used to build redirect URL used by backend.\n '
def dec(func):
@wraps(func)
def wrapper(request, backend, *args, **kwargs):
if redirect_name:
redirect = reverse(redirect_name, args=(backend,))
else:
redirect = request.path
request.social_auth_backend = get_backend(backend, request, redirect)
if (request.social_auth_backend is None):
raise WrongBackend(backend)
return func(request, request.social_auth_backend, *args, **kwargs)
return wrapper
return dec | def dsa_view(redirect_name=None):
'Decorate djangos-social-auth views. Will check and retrieve backend\n or return HttpResponseServerError if backend is not found.\n\n redirect_name parameter is used to build redirect URL used by backend.\n '
def dec(func):
@wraps(func)
def wrapper(request, backend, *args, **kwargs):
if redirect_name:
redirect = reverse(redirect_name, args=(backend,))
else:
redirect = request.path
request.social_auth_backend = get_backend(backend, request, redirect)
if (request.social_auth_backend is None):
raise WrongBackend(backend)
return func(request, request.social_auth_backend, *args, **kwargs)
return wrapper
return dec<|docstring|>Decorate djangos-social-auth views. Will check and retrieve backend
or return HttpResponseServerError if backend is not found.
redirect_name parameter is used to build redirect URL used by backend.<|endoftext|> |
3565921a6660ed7d652393a4f67ac283ef1ef5e4db75d01564f8ec17759e5ec3 | @persist_csv(target=(BASE_RESULTS_DIR + '/vte_whitened_scores.csv'), enabled=True, out_transform=(lambda x: x[2]))
@persist_json(target=(BASE_RESULTS_DIR + '/vte_whitened_params.json'), enabled=True, out_transform=(lambda x: x[1]))
@persist_pickle(target=(BASE_RESULTS_DIR + '/vte_whitened.pickle'), enabled=True, out_transform=(lambda x: x[0]))
def fit_pca_whitened_classifiers(cv=5, n_jobs=(- 1), verbose=False, report=False, random_seed=None):
'Fit classifiers to [non-]undersampled PCA-whitened input data.\n \n .. note:: Spits a lot of ``liblinear`` convergence warnings.\n\n We start with the top 7 columns by univariate ROC AUC for the VTE data.\n We perform a whitening PCA transform of the data and then fit classifiers\n with balanced class weights. Formerly oversampling of the minority class\n was done with the use of a :class:`sklearn.model_selection.PredefinedSplit`\n to prevent the oversampled data from leaking into the validation sets\n during the grid search (all oversampled data appended to end of training\n set and now allowed to be part of validation sets), but the improvement was\n not as much as one would have hoped (actually worse). So we ended up going\n back to just using balanced class weights.\n\n Use 5-fold (by default) cross-validation to choose the best parameters,\n refit on best, evaluate accuracy, precision, recall, ROC AUC.\n\n Note that we need a scaler before doing PCA. Use F1 score to pick model.\n\n :param cv: Number of CV splits to make when doing grid search.\n :type cv: int, optional\n :param n_jobs: Number of jobs to run in parallel when grid searching.\n Defaults to ``-1`` to distribute load to all threads.\n :type n_jobs: int, optional\n :param verbose: Verbosity of the\n :class:`~sklearn.model_selection.GridSearchCV` during searching/fitting.\n :type verbose: bool, optional\n :param report: If ``True``, print to stdout a report on model scores.\n :type report: bool, optional\n :param random_seed: A int seed to pass for multiple calls to this function\n to be reproducible. Leave ``None`` for stochastic behavior.\n :type random_state: int, optional\n :rtype: tuple\n '
if (cv < 3):
raise ValueError('cv folds must be 3 or more')
best_cols = list(pd.read_csv((BASE_RESULTS_DIR + '/vte_selected_cols.csv'), index_col=0).index)
(X_train, X_test, y_train, y_test) = vte_slp_factory(data_transform=replace_hdl_tot_chol_with_ratio, inputs=best_cols, targets=VTE_OUTPUT_COLS, dropna=True, random_state=random_seed)
scaler = StandardScaler()
scaler.fit(X_train)
(X_train, X_test) = (scaler.transform(X_train), scaler.transform(X_test))
pca = PCA(whiten=True, random_state=random_seed)
pca.fit(X_train)
(X_train, X_test) = (pca.transform(X_train), pca.transform(X_test))
base_names = ('l2_logistic', 'l2_linsvc', 'bagged_l2_logistic', 'bagged_l2_linsvc', 'rbf_svc', 'xgboost', 'random_forest')
lrc_l2_grid = dict(penalty=['l2'], C=[1], fit_intercept=[True], max_iter=[100], class_weight=['balanced'])
lsvc_l2_grid = dict(penalty=['l2'], loss=['hinge', 'squared_hinge'], dual=[True], random_state=[random_seed], C=[1, 5, 10], fit_intercept=[True], class_weight=['balanced'])
bag_lrc_l2_grid = dict(base_estimator=[LogisticRegression(fit_intercept=True, class_weight='balanced')], n_estimators=[100, 200, 400], random_state=[random_seed])
bag_lsvc_l2_grid = dict(base_estimator=[LinearSVC(loss='hinge', fit_intercept=True, class_weight='balanced', random_state=random_seed)], n_estimators=[100, 200, 400], random_state=[random_seed])
rbf_svc_grid = dict(C=[0.1, 1, 5], kernel=['rbf'], gamma=['scale', 'auto'], class_weight=['balanced'])
neg_pos_ratio = ((y_train == 0).sum() / (y_train == 1).sum())
xgb_grid = dict(max_depth=[3], n_estimators=[400, 600, 800], learning_rate=[0.1], booster=['gbtree'], subsample=[0.5], reg_lambda=[0.1, 1], random_state=[random_seed], scale_pos_weight=[neg_pos_ratio])
rf_grid = dict(max_depth=[6, 12, 24], n_estimators=[100, 200, 400], criterion=['entropy'], random_state=[random_seed], class_weight=['balanced'])
base_models = (LogisticRegression(), LinearSVC(), BaggingClassifier(), BaggingClassifier(), SVC(), XGBClassifier(), RandomForestClassifier())
base_names = ('l2_logistic', 'l2_linsvc', 'bagged_l2_logistic', 'bagged_l2_linsvc', 'rbf_svc', 'xgboost', 'random_forest')
param_grids = (lrc_l2_grid, lsvc_l2_grid, bag_lrc_l2_grid, bag_lsvc_l2_grid, rbf_svc_grid, xgb_grid, rf_grid)
mdata = {}
mparams = {}
mscores = pd.DataFrame(index=base_names, columns=['accuracy', 'precision', 'recall', 'roc_auc'])
for (base_name, base_model, param_grid) in zip(base_names, base_models, param_grids):
model = GridSearchCV(base_model, param_grid, scoring='f1', cv=cv, n_jobs=n_jobs, verbose=int(verbose))
model.fit(X_train, y_train)
mdata[base_name] = model
params = model.best_estimator_.get_params()
for name in params.keys():
if hasattr(params[name], 'get_params'):
params[name] = params[name].get_params()
mparams[base_name] = params
y_pred = model.predict(X_test)
if hasattr(model, 'decision_function'):
y_pred_scores = model.decision_function(X_test)
elif hasattr(model, 'predict_proba'):
y_pred_scores = model.predict_proba(X_test)[(:, 1)]
else:
print(f"warning: {model.__class__.__name__} can't compute ROC AUC score; does not have decision_function or predict_proba", file=sys.stderr)
y_pred_scores = None
mscores.loc[(base_name, :)] = (accuracy_score(y_test, y_pred), precision_score(y_test, y_pred), recall_score(y_test, y_pred), (np.nan if (y_pred_scores is None) else roc_auc_score(y_test, y_pred_scores)))
if report:
print('---- classifier quality metrics ', end='')
print(('-' * 48), end='\n\n')
print(mscores)
return (mdata, mparams, mscores) | Fit classifiers to [non-]undersampled PCA-whitened input data.
.. note:: Spits a lot of ``liblinear`` convergence warnings.
We start with the top 7 columns by univariate ROC AUC for the VTE data.
We perform a whitening PCA transform of the data and then fit classifiers
with balanced class weights. Formerly oversampling of the minority class
was done with the use of a :class:`sklearn.model_selection.PredefinedSplit`
to prevent the oversampled data from leaking into the validation sets
during the grid search (all oversampled data appended to end of training
set and now allowed to be part of validation sets), but the improvement was
not as much as one would have hoped (actually worse). So we ended up going
back to just using balanced class weights.
Use 5-fold (by default) cross-validation to choose the best parameters,
refit on best, evaluate accuracy, precision, recall, ROC AUC.
Note that we need a scaler before doing PCA. Use F1 score to pick model.
:param cv: Number of CV splits to make when doing grid search.
:type cv: int, optional
:param n_jobs: Number of jobs to run in parallel when grid searching.
Defaults to ``-1`` to distribute load to all threads.
:type n_jobs: int, optional
:param verbose: Verbosity of the
:class:`~sklearn.model_selection.GridSearchCV` during searching/fitting.
:type verbose: bool, optional
:param report: If ``True``, print to stdout a report on model scores.
:type report: bool, optional
:param random_seed: A int seed to pass for multiple calls to this function
to be reproducible. Leave ``None`` for stochastic behavior.
:type random_state: int, optional
:rtype: tuple | mtml/modeling/vte/mixed_models.py | fit_pca_whitened_classifiers | crb479/mcdevitt-trauma-ml | 5 | python | @persist_csv(target=(BASE_RESULTS_DIR + '/vte_whitened_scores.csv'), enabled=True, out_transform=(lambda x: x[2]))
@persist_json(target=(BASE_RESULTS_DIR + '/vte_whitened_params.json'), enabled=True, out_transform=(lambda x: x[1]))
@persist_pickle(target=(BASE_RESULTS_DIR + '/vte_whitened.pickle'), enabled=True, out_transform=(lambda x: x[0]))
def fit_pca_whitened_classifiers(cv=5, n_jobs=(- 1), verbose=False, report=False, random_seed=None):
'Fit classifiers to [non-]undersampled PCA-whitened input data.\n \n .. note:: Spits a lot of ``liblinear`` convergence warnings.\n\n We start with the top 7 columns by univariate ROC AUC for the VTE data.\n We perform a whitening PCA transform of the data and then fit classifiers\n with balanced class weights. Formerly oversampling of the minority class\n was done with the use of a :class:`sklearn.model_selection.PredefinedSplit`\n to prevent the oversampled data from leaking into the validation sets\n during the grid search (all oversampled data appended to end of training\n set and now allowed to be part of validation sets), but the improvement was\n not as much as one would have hoped (actually worse). So we ended up going\n back to just using balanced class weights.\n\n Use 5-fold (by default) cross-validation to choose the best parameters,\n refit on best, evaluate accuracy, precision, recall, ROC AUC.\n\n Note that we need a scaler before doing PCA. Use F1 score to pick model.\n\n :param cv: Number of CV splits to make when doing grid search.\n :type cv: int, optional\n :param n_jobs: Number of jobs to run in parallel when grid searching.\n Defaults to ``-1`` to distribute load to all threads.\n :type n_jobs: int, optional\n :param verbose: Verbosity of the\n :class:`~sklearn.model_selection.GridSearchCV` during searching/fitting.\n :type verbose: bool, optional\n :param report: If ``True``, print to stdout a report on model scores.\n :type report: bool, optional\n :param random_seed: A int seed to pass for multiple calls to this function\n to be reproducible. Leave ``None`` for stochastic behavior.\n :type random_state: int, optional\n :rtype: tuple\n '
if (cv < 3):
raise ValueError('cv folds must be 3 or more')
best_cols = list(pd.read_csv((BASE_RESULTS_DIR + '/vte_selected_cols.csv'), index_col=0).index)
(X_train, X_test, y_train, y_test) = vte_slp_factory(data_transform=replace_hdl_tot_chol_with_ratio, inputs=best_cols, targets=VTE_OUTPUT_COLS, dropna=True, random_state=random_seed)
scaler = StandardScaler()
scaler.fit(X_train)
(X_train, X_test) = (scaler.transform(X_train), scaler.transform(X_test))
pca = PCA(whiten=True, random_state=random_seed)
pca.fit(X_train)
(X_train, X_test) = (pca.transform(X_train), pca.transform(X_test))
base_names = ('l2_logistic', 'l2_linsvc', 'bagged_l2_logistic', 'bagged_l2_linsvc', 'rbf_svc', 'xgboost', 'random_forest')
lrc_l2_grid = dict(penalty=['l2'], C=[1], fit_intercept=[True], max_iter=[100], class_weight=['balanced'])
lsvc_l2_grid = dict(penalty=['l2'], loss=['hinge', 'squared_hinge'], dual=[True], random_state=[random_seed], C=[1, 5, 10], fit_intercept=[True], class_weight=['balanced'])
bag_lrc_l2_grid = dict(base_estimator=[LogisticRegression(fit_intercept=True, class_weight='balanced')], n_estimators=[100, 200, 400], random_state=[random_seed])
bag_lsvc_l2_grid = dict(base_estimator=[LinearSVC(loss='hinge', fit_intercept=True, class_weight='balanced', random_state=random_seed)], n_estimators=[100, 200, 400], random_state=[random_seed])
rbf_svc_grid = dict(C=[0.1, 1, 5], kernel=['rbf'], gamma=['scale', 'auto'], class_weight=['balanced'])
neg_pos_ratio = ((y_train == 0).sum() / (y_train == 1).sum())
xgb_grid = dict(max_depth=[3], n_estimators=[400, 600, 800], learning_rate=[0.1], booster=['gbtree'], subsample=[0.5], reg_lambda=[0.1, 1], random_state=[random_seed], scale_pos_weight=[neg_pos_ratio])
rf_grid = dict(max_depth=[6, 12, 24], n_estimators=[100, 200, 400], criterion=['entropy'], random_state=[random_seed], class_weight=['balanced'])
base_models = (LogisticRegression(), LinearSVC(), BaggingClassifier(), BaggingClassifier(), SVC(), XGBClassifier(), RandomForestClassifier())
base_names = ('l2_logistic', 'l2_linsvc', 'bagged_l2_logistic', 'bagged_l2_linsvc', 'rbf_svc', 'xgboost', 'random_forest')
param_grids = (lrc_l2_grid, lsvc_l2_grid, bag_lrc_l2_grid, bag_lsvc_l2_grid, rbf_svc_grid, xgb_grid, rf_grid)
mdata = {}
mparams = {}
mscores = pd.DataFrame(index=base_names, columns=['accuracy', 'precision', 'recall', 'roc_auc'])
for (base_name, base_model, param_grid) in zip(base_names, base_models, param_grids):
model = GridSearchCV(base_model, param_grid, scoring='f1', cv=cv, n_jobs=n_jobs, verbose=int(verbose))
model.fit(X_train, y_train)
mdata[base_name] = model
params = model.best_estimator_.get_params()
for name in params.keys():
if hasattr(params[name], 'get_params'):
params[name] = params[name].get_params()
mparams[base_name] = params
y_pred = model.predict(X_test)
if hasattr(model, 'decision_function'):
y_pred_scores = model.decision_function(X_test)
elif hasattr(model, 'predict_proba'):
y_pred_scores = model.predict_proba(X_test)[(:, 1)]
else:
print(f"warning: {model.__class__.__name__} can't compute ROC AUC score; does not have decision_function or predict_proba", file=sys.stderr)
y_pred_scores = None
mscores.loc[(base_name, :)] = (accuracy_score(y_test, y_pred), precision_score(y_test, y_pred), recall_score(y_test, y_pred), (np.nan if (y_pred_scores is None) else roc_auc_score(y_test, y_pred_scores)))
if report:
print('---- classifier quality metrics ', end=)
print(('-' * 48), end='\n\n')
print(mscores)
return (mdata, mparams, mscores) | @persist_csv(target=(BASE_RESULTS_DIR + '/vte_whitened_scores.csv'), enabled=True, out_transform=(lambda x: x[2]))
@persist_json(target=(BASE_RESULTS_DIR + '/vte_whitened_params.json'), enabled=True, out_transform=(lambda x: x[1]))
@persist_pickle(target=(BASE_RESULTS_DIR + '/vte_whitened.pickle'), enabled=True, out_transform=(lambda x: x[0]))
def fit_pca_whitened_classifiers(cv=5, n_jobs=(- 1), verbose=False, report=False, random_seed=None):
'Fit classifiers to [non-]undersampled PCA-whitened input data.\n \n .. note:: Spits a lot of ``liblinear`` convergence warnings.\n\n We start with the top 7 columns by univariate ROC AUC for the VTE data.\n We perform a whitening PCA transform of the data and then fit classifiers\n with balanced class weights. Formerly oversampling of the minority class\n was done with the use of a :class:`sklearn.model_selection.PredefinedSplit`\n to prevent the oversampled data from leaking into the validation sets\n during the grid search (all oversampled data appended to end of training\n set and now allowed to be part of validation sets), but the improvement was\n not as much as one would have hoped (actually worse). So we ended up going\n back to just using balanced class weights.\n\n Use 5-fold (by default) cross-validation to choose the best parameters,\n refit on best, evaluate accuracy, precision, recall, ROC AUC.\n\n Note that we need a scaler before doing PCA. Use F1 score to pick model.\n\n :param cv: Number of CV splits to make when doing grid search.\n :type cv: int, optional\n :param n_jobs: Number of jobs to run in parallel when grid searching.\n Defaults to ``-1`` to distribute load to all threads.\n :type n_jobs: int, optional\n :param verbose: Verbosity of the\n :class:`~sklearn.model_selection.GridSearchCV` during searching/fitting.\n :type verbose: bool, optional\n :param report: If ``True``, print to stdout a report on model scores.\n :type report: bool, optional\n :param random_seed: A int seed to pass for multiple calls to this function\n to be reproducible. Leave ``None`` for stochastic behavior.\n :type random_state: int, optional\n :rtype: tuple\n '
if (cv < 3):
raise ValueError('cv folds must be 3 or more')
best_cols = list(pd.read_csv((BASE_RESULTS_DIR + '/vte_selected_cols.csv'), index_col=0).index)
(X_train, X_test, y_train, y_test) = vte_slp_factory(data_transform=replace_hdl_tot_chol_with_ratio, inputs=best_cols, targets=VTE_OUTPUT_COLS, dropna=True, random_state=random_seed)
scaler = StandardScaler()
scaler.fit(X_train)
(X_train, X_test) = (scaler.transform(X_train), scaler.transform(X_test))
pca = PCA(whiten=True, random_state=random_seed)
pca.fit(X_train)
(X_train, X_test) = (pca.transform(X_train), pca.transform(X_test))
base_names = ('l2_logistic', 'l2_linsvc', 'bagged_l2_logistic', 'bagged_l2_linsvc', 'rbf_svc', 'xgboost', 'random_forest')
lrc_l2_grid = dict(penalty=['l2'], C=[1], fit_intercept=[True], max_iter=[100], class_weight=['balanced'])
lsvc_l2_grid = dict(penalty=['l2'], loss=['hinge', 'squared_hinge'], dual=[True], random_state=[random_seed], C=[1, 5, 10], fit_intercept=[True], class_weight=['balanced'])
bag_lrc_l2_grid = dict(base_estimator=[LogisticRegression(fit_intercept=True, class_weight='balanced')], n_estimators=[100, 200, 400], random_state=[random_seed])
bag_lsvc_l2_grid = dict(base_estimator=[LinearSVC(loss='hinge', fit_intercept=True, class_weight='balanced', random_state=random_seed)], n_estimators=[100, 200, 400], random_state=[random_seed])
rbf_svc_grid = dict(C=[0.1, 1, 5], kernel=['rbf'], gamma=['scale', 'auto'], class_weight=['balanced'])
neg_pos_ratio = ((y_train == 0).sum() / (y_train == 1).sum())
xgb_grid = dict(max_depth=[3], n_estimators=[400, 600, 800], learning_rate=[0.1], booster=['gbtree'], subsample=[0.5], reg_lambda=[0.1, 1], random_state=[random_seed], scale_pos_weight=[neg_pos_ratio])
rf_grid = dict(max_depth=[6, 12, 24], n_estimators=[100, 200, 400], criterion=['entropy'], random_state=[random_seed], class_weight=['balanced'])
base_models = (LogisticRegression(), LinearSVC(), BaggingClassifier(), BaggingClassifier(), SVC(), XGBClassifier(), RandomForestClassifier())
base_names = ('l2_logistic', 'l2_linsvc', 'bagged_l2_logistic', 'bagged_l2_linsvc', 'rbf_svc', 'xgboost', 'random_forest')
param_grids = (lrc_l2_grid, lsvc_l2_grid, bag_lrc_l2_grid, bag_lsvc_l2_grid, rbf_svc_grid, xgb_grid, rf_grid)
mdata = {}
mparams = {}
mscores = pd.DataFrame(index=base_names, columns=['accuracy', 'precision', 'recall', 'roc_auc'])
for (base_name, base_model, param_grid) in zip(base_names, base_models, param_grids):
model = GridSearchCV(base_model, param_grid, scoring='f1', cv=cv, n_jobs=n_jobs, verbose=int(verbose))
model.fit(X_train, y_train)
mdata[base_name] = model
params = model.best_estimator_.get_params()
for name in params.keys():
if hasattr(params[name], 'get_params'):
params[name] = params[name].get_params()
mparams[base_name] = params
y_pred = model.predict(X_test)
if hasattr(model, 'decision_function'):
y_pred_scores = model.decision_function(X_test)
elif hasattr(model, 'predict_proba'):
y_pred_scores = model.predict_proba(X_test)[(:, 1)]
else:
print(f"warning: {model.__class__.__name__} can't compute ROC AUC score; does not have decision_function or predict_proba", file=sys.stderr)
y_pred_scores = None
mscores.loc[(base_name, :)] = (accuracy_score(y_test, y_pred), precision_score(y_test, y_pred), recall_score(y_test, y_pred), (np.nan if (y_pred_scores is None) else roc_auc_score(y_test, y_pred_scores)))
if report:
print('---- classifier quality metrics ', end=)
print(('-' * 48), end='\n\n')
print(mscores)
return (mdata, mparams, mscores)<|docstring|>Fit classifiers to [non-]undersampled PCA-whitened input data.
.. note:: Spits a lot of ``liblinear`` convergence warnings.
We start with the top 7 columns by univariate ROC AUC for the VTE data.
We perform a whitening PCA transform of the data and then fit classifiers
with balanced class weights. Formerly oversampling of the minority class
was done with the use of a :class:`sklearn.model_selection.PredefinedSplit`
to prevent the oversampled data from leaking into the validation sets
during the grid search (all oversampled data appended to end of training
set and now allowed to be part of validation sets), but the improvement was
not as much as one would have hoped (actually worse). So we ended up going
back to just using balanced class weights.
Use 5-fold (by default) cross-validation to choose the best parameters,
refit on best, evaluate accuracy, precision, recall, ROC AUC.
Note that we need a scaler before doing PCA. Use F1 score to pick model.
:param cv: Number of CV splits to make when doing grid search.
:type cv: int, optional
:param n_jobs: Number of jobs to run in parallel when grid searching.
Defaults to ``-1`` to distribute load to all threads.
:type n_jobs: int, optional
:param verbose: Verbosity of the
:class:`~sklearn.model_selection.GridSearchCV` during searching/fitting.
:type verbose: bool, optional
:param report: If ``True``, print to stdout a report on model scores.
:type report: bool, optional
:param random_seed: A int seed to pass for multiple calls to this function
to be reproducible. Leave ``None`` for stochastic behavior.
:type random_state: int, optional
:rtype: tuple<|endoftext|> |
38617f9a6eee4b86b18d236ee43e8b8e1edc4668034232b8458464a22c729d71 | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
def test_ip_association_and_allocation_of_other_project(self, net_get, fixed_get):
'Makes sure that we cannot deallocaate or disassociate\n a public ip of other project.\n '
net_get.return_value = dict(test_network.fake_network, **networks[1])
context1 = context.RequestContext('user', 'project1')
context2 = context.RequestContext('user', 'project2')
float_ip = db.floating_ip_create(context1.elevated(), {'address': '1.2.3.4', 'project_id': context1.project_id})
float_addr = float_ip['address']
instance = db.instance_create(context1, {'project_id': 'project1'})
fix_addr = db.fixed_ip_associate_pool(context1.elevated(), 1, instance['uuid']).address
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr, instance_uuid=instance.uuid, network=dict(test_network.fake_network, **networks[1]))
self.assertRaises(exception.NotAuthorized, self.network.associate_floating_ip, context2, float_addr, fix_addr)
self.assertRaises(exception.NotAuthorized, self.network.deallocate_floating_ip, context2, float_addr)
self.network.associate_floating_ip(context1, float_addr, fix_addr)
self.assertRaises(exception.NotAuthorized, self.network.disassociate_floating_ip, context2, float_addr)
self.network.disassociate_floating_ip(context1, float_addr)
self.network.deallocate_floating_ip(context1, float_addr)
self.network.deallocate_fixed_ip(context1, fix_addr, 'fake')
db.floating_ip_destroy(context1.elevated(), float_addr)
db.fixed_ip_disassociate(context1.elevated(), fix_addr) | Makes sure that we cannot deallocaate or disassociate
a public ip of other project. | nova/tests/network/test_manager.py | test_ip_association_and_allocation_of_other_project | bopopescu/nova-week | 7 | python | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
def test_ip_association_and_allocation_of_other_project(self, net_get, fixed_get):
'Makes sure that we cannot deallocaate or disassociate\n a public ip of other project.\n '
net_get.return_value = dict(test_network.fake_network, **networks[1])
context1 = context.RequestContext('user', 'project1')
context2 = context.RequestContext('user', 'project2')
float_ip = db.floating_ip_create(context1.elevated(), {'address': '1.2.3.4', 'project_id': context1.project_id})
float_addr = float_ip['address']
instance = db.instance_create(context1, {'project_id': 'project1'})
fix_addr = db.fixed_ip_associate_pool(context1.elevated(), 1, instance['uuid']).address
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr, instance_uuid=instance.uuid, network=dict(test_network.fake_network, **networks[1]))
self.assertRaises(exception.NotAuthorized, self.network.associate_floating_ip, context2, float_addr, fix_addr)
self.assertRaises(exception.NotAuthorized, self.network.deallocate_floating_ip, context2, float_addr)
self.network.associate_floating_ip(context1, float_addr, fix_addr)
self.assertRaises(exception.NotAuthorized, self.network.disassociate_floating_ip, context2, float_addr)
self.network.disassociate_floating_ip(context1, float_addr)
self.network.deallocate_floating_ip(context1, float_addr)
self.network.deallocate_fixed_ip(context1, fix_addr, 'fake')
db.floating_ip_destroy(context1.elevated(), float_addr)
db.fixed_ip_disassociate(context1.elevated(), fix_addr) | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
def test_ip_association_and_allocation_of_other_project(self, net_get, fixed_get):
'Makes sure that we cannot deallocaate or disassociate\n a public ip of other project.\n '
net_get.return_value = dict(test_network.fake_network, **networks[1])
context1 = context.RequestContext('user', 'project1')
context2 = context.RequestContext('user', 'project2')
float_ip = db.floating_ip_create(context1.elevated(), {'address': '1.2.3.4', 'project_id': context1.project_id})
float_addr = float_ip['address']
instance = db.instance_create(context1, {'project_id': 'project1'})
fix_addr = db.fixed_ip_associate_pool(context1.elevated(), 1, instance['uuid']).address
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr, instance_uuid=instance.uuid, network=dict(test_network.fake_network, **networks[1]))
self.assertRaises(exception.NotAuthorized, self.network.associate_floating_ip, context2, float_addr, fix_addr)
self.assertRaises(exception.NotAuthorized, self.network.deallocate_floating_ip, context2, float_addr)
self.network.associate_floating_ip(context1, float_addr, fix_addr)
self.assertRaises(exception.NotAuthorized, self.network.disassociate_floating_ip, context2, float_addr)
self.network.disassociate_floating_ip(context1, float_addr)
self.network.deallocate_floating_ip(context1, float_addr)
self.network.deallocate_fixed_ip(context1, fix_addr, 'fake')
db.floating_ip_destroy(context1.elevated(), float_addr)
db.fixed_ip_disassociate(context1.elevated(), fix_addr)<|docstring|>Makes sure that we cannot deallocaate or disassociate
a public ip of other project.<|endoftext|> |
747115fb527bb14ad0fe18b1f368a55b975378fa86e01a1d89ad6645294abf3c | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
@mock.patch('nova.db.fixed_ip_update')
def test_deallocate_fixed(self, fixed_update, net_get, fixed_get):
"Verify that release is called properly.\n\n Ensures https://bugs.launchpad.net/nova/+bug/973442 doesn't return\n "
net_get.return_value = dict(test_network.fake_network, **networks[1])
def vif_get(_context, _vif_id):
return vifs[0]
self.stubs.Set(db, 'virtual_interface_get', vif_get)
context1 = context.RequestContext('user', 'project1')
instance = db.instance_create(context1, {'project_id': 'project1'})
elevated = context1.elevated()
fix_addr = db.fixed_ip_associate_pool(elevated, 1, instance['uuid'])
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr.address, instance_uuid=instance.uuid, allocated=True, virtual_interface_id=3, network=dict(test_network.fake_network, **networks[1]))
self.flags(force_dhcp_release=True)
self.mox.StubOutWithMock(linux_net, 'release_dhcp')
linux_net.release_dhcp(networks[1]['bridge'], fix_addr.address, 'DE:AD:BE:EF:00:00')
self.mox.ReplayAll()
self.network.deallocate_fixed_ip(context1, fix_addr.address, 'fake')
fixed_update.assert_called_once_with(context1, fix_addr.address, {'allocated': False, 'virtual_interface_id': None}) | Verify that release is called properly.
Ensures https://bugs.launchpad.net/nova/+bug/973442 doesn't return | nova/tests/network/test_manager.py | test_deallocate_fixed | bopopescu/nova-week | 7 | python | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
@mock.patch('nova.db.fixed_ip_update')
def test_deallocate_fixed(self, fixed_update, net_get, fixed_get):
"Verify that release is called properly.\n\n Ensures https://bugs.launchpad.net/nova/+bug/973442 doesn't return\n "
net_get.return_value = dict(test_network.fake_network, **networks[1])
def vif_get(_context, _vif_id):
return vifs[0]
self.stubs.Set(db, 'virtual_interface_get', vif_get)
context1 = context.RequestContext('user', 'project1')
instance = db.instance_create(context1, {'project_id': 'project1'})
elevated = context1.elevated()
fix_addr = db.fixed_ip_associate_pool(elevated, 1, instance['uuid'])
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr.address, instance_uuid=instance.uuid, allocated=True, virtual_interface_id=3, network=dict(test_network.fake_network, **networks[1]))
self.flags(force_dhcp_release=True)
self.mox.StubOutWithMock(linux_net, 'release_dhcp')
linux_net.release_dhcp(networks[1]['bridge'], fix_addr.address, 'DE:AD:BE:EF:00:00')
self.mox.ReplayAll()
self.network.deallocate_fixed_ip(context1, fix_addr.address, 'fake')
fixed_update.assert_called_once_with(context1, fix_addr.address, {'allocated': False, 'virtual_interface_id': None}) | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
@mock.patch('nova.db.fixed_ip_update')
def test_deallocate_fixed(self, fixed_update, net_get, fixed_get):
"Verify that release is called properly.\n\n Ensures https://bugs.launchpad.net/nova/+bug/973442 doesn't return\n "
net_get.return_value = dict(test_network.fake_network, **networks[1])
def vif_get(_context, _vif_id):
return vifs[0]
self.stubs.Set(db, 'virtual_interface_get', vif_get)
context1 = context.RequestContext('user', 'project1')
instance = db.instance_create(context1, {'project_id': 'project1'})
elevated = context1.elevated()
fix_addr = db.fixed_ip_associate_pool(elevated, 1, instance['uuid'])
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr.address, instance_uuid=instance.uuid, allocated=True, virtual_interface_id=3, network=dict(test_network.fake_network, **networks[1]))
self.flags(force_dhcp_release=True)
self.mox.StubOutWithMock(linux_net, 'release_dhcp')
linux_net.release_dhcp(networks[1]['bridge'], fix_addr.address, 'DE:AD:BE:EF:00:00')
self.mox.ReplayAll()
self.network.deallocate_fixed_ip(context1, fix_addr.address, 'fake')
fixed_update.assert_called_once_with(context1, fix_addr.address, {'allocated': False, 'virtual_interface_id': None})<|docstring|>Verify that release is called properly.
Ensures https://bugs.launchpad.net/nova/+bug/973442 doesn't return<|endoftext|> |
1924425acdb0030490527a738a9dcdc717e63b014764a8a89b4bc9107566aad4 | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
@mock.patch('nova.db.fixed_ip_update')
def test_deallocate_fixed_no_vif(self, fixed_update, net_get, fixed_get):
"Verify that deallocate doesn't raise when no vif is returned.\n\n Ensures https://bugs.launchpad.net/nova/+bug/968457 doesn't return\n "
net_get.return_value = dict(test_network.fake_network, **networks[1])
def vif_get(_context, _vif_id):
return None
self.stubs.Set(db, 'virtual_interface_get', vif_get)
context1 = context.RequestContext('user', 'project1')
instance = db.instance_create(context1, {'project_id': 'project1'})
elevated = context1.elevated()
fix_addr = db.fixed_ip_associate_pool(elevated, 1, instance['uuid'])
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr.address, allocated=True, virtual_interface_id=3, instance_uuid=instance.uuid, network=dict(test_network.fake_network, **networks[1]))
self.flags(force_dhcp_release=True)
fixed_update.return_value = fixed_get.return_value
self.network.deallocate_fixed_ip(context1, fix_addr.address, 'fake')
fixed_update.assert_called_once_with(context1, fix_addr.address, {'allocated': False, 'virtual_interface_id': None}) | Verify that deallocate doesn't raise when no vif is returned.
Ensures https://bugs.launchpad.net/nova/+bug/968457 doesn't return | nova/tests/network/test_manager.py | test_deallocate_fixed_no_vif | bopopescu/nova-week | 7 | python | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
@mock.patch('nova.db.fixed_ip_update')
def test_deallocate_fixed_no_vif(self, fixed_update, net_get, fixed_get):
"Verify that deallocate doesn't raise when no vif is returned.\n\n Ensures https://bugs.launchpad.net/nova/+bug/968457 doesn't return\n "
net_get.return_value = dict(test_network.fake_network, **networks[1])
def vif_get(_context, _vif_id):
return None
self.stubs.Set(db, 'virtual_interface_get', vif_get)
context1 = context.RequestContext('user', 'project1')
instance = db.instance_create(context1, {'project_id': 'project1'})
elevated = context1.elevated()
fix_addr = db.fixed_ip_associate_pool(elevated, 1, instance['uuid'])
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr.address, allocated=True, virtual_interface_id=3, instance_uuid=instance.uuid, network=dict(test_network.fake_network, **networks[1]))
self.flags(force_dhcp_release=True)
fixed_update.return_value = fixed_get.return_value
self.network.deallocate_fixed_ip(context1, fix_addr.address, 'fake')
fixed_update.assert_called_once_with(context1, fix_addr.address, {'allocated': False, 'virtual_interface_id': None}) | @mock.patch('nova.db.fixed_ip_get_by_address')
@mock.patch('nova.db.network_get')
@mock.patch('nova.db.fixed_ip_update')
def test_deallocate_fixed_no_vif(self, fixed_update, net_get, fixed_get):
"Verify that deallocate doesn't raise when no vif is returned.\n\n Ensures https://bugs.launchpad.net/nova/+bug/968457 doesn't return\n "
net_get.return_value = dict(test_network.fake_network, **networks[1])
def vif_get(_context, _vif_id):
return None
self.stubs.Set(db, 'virtual_interface_get', vif_get)
context1 = context.RequestContext('user', 'project1')
instance = db.instance_create(context1, {'project_id': 'project1'})
elevated = context1.elevated()
fix_addr = db.fixed_ip_associate_pool(elevated, 1, instance['uuid'])
fixed_get.return_value = dict(test_fixed_ip.fake_fixed_ip, address=fix_addr.address, allocated=True, virtual_interface_id=3, instance_uuid=instance.uuid, network=dict(test_network.fake_network, **networks[1]))
self.flags(force_dhcp_release=True)
fixed_update.return_value = fixed_get.return_value
self.network.deallocate_fixed_ip(context1, fix_addr.address, 'fake')
fixed_update.assert_called_once_with(context1, fix_addr.address, {'allocated': False, 'virtual_interface_id': None})<|docstring|>Verify that deallocate doesn't raise when no vif is returned.
Ensures https://bugs.launchpad.net/nova/+bug/968457 doesn't return<|endoftext|> |
e99f7105142e20e19114e1160a89472e6d0d01bb55ebace4521c7dc9c47ffb35 | def test_flatdhcpmanager_dynamic_fixed_range(self):
'Test FlatDHCPManager NAT rules for fixed_range.'
self.network = network_manager.FlatDHCPManager(host=HOST)
self.network.db = db
self._test_init_host_dynamic_fixed_range(self.network) | Test FlatDHCPManager NAT rules for fixed_range. | nova/tests/network/test_manager.py | test_flatdhcpmanager_dynamic_fixed_range | bopopescu/nova-week | 7 | python | def test_flatdhcpmanager_dynamic_fixed_range(self):
self.network = network_manager.FlatDHCPManager(host=HOST)
self.network.db = db
self._test_init_host_dynamic_fixed_range(self.network) | def test_flatdhcpmanager_dynamic_fixed_range(self):
self.network = network_manager.FlatDHCPManager(host=HOST)
self.network.db = db
self._test_init_host_dynamic_fixed_range(self.network)<|docstring|>Test FlatDHCPManager NAT rules for fixed_range.<|endoftext|> |
12bd51bb56b82b3004c52980e6fb3ba79ba1e973deebcf6d88dfd78386a2dc2f | def test_vlanmanager_dynamic_fixed_range(self):
'Test VlanManager NAT rules for fixed_range.'
self.network = network_manager.VlanManager(host=HOST)
self.network.db = db
self._test_init_host_dynamic_fixed_range(self.network) | Test VlanManager NAT rules for fixed_range. | nova/tests/network/test_manager.py | test_vlanmanager_dynamic_fixed_range | bopopescu/nova-week | 7 | python | def test_vlanmanager_dynamic_fixed_range(self):
self.network = network_manager.VlanManager(host=HOST)
self.network.db = db
self._test_init_host_dynamic_fixed_range(self.network) | def test_vlanmanager_dynamic_fixed_range(self):
self.network = network_manager.VlanManager(host=HOST)
self.network.db = db
self._test_init_host_dynamic_fixed_range(self.network)<|docstring|>Test VlanManager NAT rules for fixed_range.<|endoftext|> |
cbc8057a4d136b19f7e9e5fb7585aac986feae634aa960a69737e2ee9523c2fd | def test_rpc_allocate(self):
"Test to verify bug 855030 doesn't resurface.\n\n Mekes sure _rpc_allocate_fixed_ip returns a value so the call\n returns properly and the greenpool completes.\n "
address = '10.10.10.10'
def fake_allocate(*args, **kwargs):
return address
def fake_network_get(*args, **kwargs):
return test_network.fake_network
self.stubs.Set(self.rpc_fixed, 'allocate_fixed_ip', fake_allocate)
self.stubs.Set(self.rpc_fixed.db, 'network_get', fake_network_get)
rval = self.rpc_fixed._rpc_allocate_fixed_ip(self.context, 'fake_instance', 'fake_network')
self.assertEqual(rval, address) | Test to verify bug 855030 doesn't resurface.
Mekes sure _rpc_allocate_fixed_ip returns a value so the call
returns properly and the greenpool completes. | nova/tests/network/test_manager.py | test_rpc_allocate | bopopescu/nova-week | 7 | python | def test_rpc_allocate(self):
"Test to verify bug 855030 doesn't resurface.\n\n Mekes sure _rpc_allocate_fixed_ip returns a value so the call\n returns properly and the greenpool completes.\n "
address = '10.10.10.10'
def fake_allocate(*args, **kwargs):
return address
def fake_network_get(*args, **kwargs):
return test_network.fake_network
self.stubs.Set(self.rpc_fixed, 'allocate_fixed_ip', fake_allocate)
self.stubs.Set(self.rpc_fixed.db, 'network_get', fake_network_get)
rval = self.rpc_fixed._rpc_allocate_fixed_ip(self.context, 'fake_instance', 'fake_network')
self.assertEqual(rval, address) | def test_rpc_allocate(self):
"Test to verify bug 855030 doesn't resurface.\n\n Mekes sure _rpc_allocate_fixed_ip returns a value so the call\n returns properly and the greenpool completes.\n "
address = '10.10.10.10'
def fake_allocate(*args, **kwargs):
return address
def fake_network_get(*args, **kwargs):
return test_network.fake_network
self.stubs.Set(self.rpc_fixed, 'allocate_fixed_ip', fake_allocate)
self.stubs.Set(self.rpc_fixed.db, 'network_get', fake_network_get)
rval = self.rpc_fixed._rpc_allocate_fixed_ip(self.context, 'fake_instance', 'fake_network')
self.assertEqual(rval, address)<|docstring|>Test to verify bug 855030 doesn't resurface.
Mekes sure _rpc_allocate_fixed_ip returns a value so the call
returns properly and the greenpool completes.<|endoftext|> |
a2ed9b791a6561dcddb274dae2b02daf68511af016a03d1aae3fa458bdd8d26b | def authenticate():
'Sends a 401 response that enables basic auth'
return Response('Unauthorized', 401, {'WWW-Authenticate': 'Basic realm="Login Required"'}) | Sends a 401 response that enables basic auth | gosecure_app.py | authenticate | nkrios/goSecure | 790 | python | def authenticate():
return Response('Unauthorized', 401, {'WWW-Authenticate': 'Basic realm="Login Required"'}) | def authenticate():
return Response('Unauthorized', 401, {'WWW-Authenticate': 'Basic realm="Login Required"'})<|docstring|>Sends a 401 response that enables basic auth<|endoftext|> |
f7a3eb9b2cd4676e04444e5ea98f2d6de40566cfb2b1d91caafffbc64e359d2f | def _initialize(self, resource=None, id_query=False, reset=False, **keywargs):
'Opens an I/O session to the instrument.'
super(agilentBase8340, self)._initialize(resource, id_query, reset, **keywargs)
if (not self._driver_operation_simulate):
self._clear()
if (id_query and (not self._driver_operation_simulate)):
id = self.identity.instrument_model
id_check = self._instrument_id
id_short = id[:len(id_check)]
if (id_short != id_check):
raise Exception('Instrument ID mismatch, expecting %s, got %s', id_check, id_short)
if reset:
self.utility_reset() | Opens an I/O session to the instrument. | ivi/agilent/agilentBase8340.py | _initialize | sacherjj/python-ivi | 161 | python | def _initialize(self, resource=None, id_query=False, reset=False, **keywargs):
super(agilentBase8340, self)._initialize(resource, id_query, reset, **keywargs)
if (not self._driver_operation_simulate):
self._clear()
if (id_query and (not self._driver_operation_simulate)):
id = self.identity.instrument_model
id_check = self._instrument_id
id_short = id[:len(id_check)]
if (id_short != id_check):
raise Exception('Instrument ID mismatch, expecting %s, got %s', id_check, id_short)
if reset:
self.utility_reset() | def _initialize(self, resource=None, id_query=False, reset=False, **keywargs):
super(agilentBase8340, self)._initialize(resource, id_query, reset, **keywargs)
if (not self._driver_operation_simulate):
self._clear()
if (id_query and (not self._driver_operation_simulate)):
id = self.identity.instrument_model
id_check = self._instrument_id
id_short = id[:len(id_check)]
if (id_short != id_check):
raise Exception('Instrument ID mismatch, expecting %s, got %s', id_check, id_short)
if reset:
self.utility_reset()<|docstring|>Opens an I/O session to the instrument.<|endoftext|> |
a49021676acda828a4da445733070097209d65e1619c14bb544ab947eed09d37 | def __init__(self, command, option=None, pattern='EOS', timeout_second=10):
'* Get communication with unix process using pexpect module.'
self.command = command
self.timeout_second = timeout_second
self.pattern = pattern
self.option = option
self.launch_process(command) | * Get communication with unix process using pexpect module. | JapaneseTokenizer/common/sever_handler.py | __init__ | fumankaitori/JapaneseTokenizers | 134 | python | def __init__(self, command, option=None, pattern='EOS', timeout_second=10):
self.command = command
self.timeout_second = timeout_second
self.pattern = pattern
self.option = option
self.launch_process(command) | def __init__(self, command, option=None, pattern='EOS', timeout_second=10):
self.command = command
self.timeout_second = timeout_second
self.pattern = pattern
self.option = option
self.launch_process(command)<|docstring|>* Get communication with unix process using pexpect module.<|endoftext|> |
cc7ca6c6af49b98950e4ad26b4af9548d21be8ec8daa044928b6da0b823ed7d8 | def launch_process(self, command):
'* What you can do\n - It starts process and keep it.\n '
if (not (self.option is None)):
command_plus_option = ((self.command + ' ') + self.option)
else:
command_plus_option = self.command
if six.PY3:
if (shutil.which(command) is None):
raise Exception('No command at {}'.format(command))
else:
self.process_analyzer = pexpect.spawnu(command_plus_option)
self.process_id = self.process_analyzer.pid
else:
doc_command_string = "echo '' | {}".format(command)
command_check = os.system(doc_command_string)
if (not (command_check == 0)):
raise Exception('No command at {}'.format(command))
else:
self.process_analyzer = pexpect.spawnu(command_plus_option)
self.process_id = self.process_analyzer.pid | * What you can do
- It starts process and keep it. | JapaneseTokenizer/common/sever_handler.py | launch_process | fumankaitori/JapaneseTokenizers | 134 | python | def launch_process(self, command):
'* What you can do\n - It starts process and keep it.\n '
if (not (self.option is None)):
command_plus_option = ((self.command + ' ') + self.option)
else:
command_plus_option = self.command
if six.PY3:
if (shutil.which(command) is None):
raise Exception('No command at {}'.format(command))
else:
self.process_analyzer = pexpect.spawnu(command_plus_option)
self.process_id = self.process_analyzer.pid
else:
doc_command_string = "echo | {}".format(command)
command_check = os.system(doc_command_string)
if (not (command_check == 0)):
raise Exception('No command at {}'.format(command))
else:
self.process_analyzer = pexpect.spawnu(command_plus_option)
self.process_id = self.process_analyzer.pid | def launch_process(self, command):
'* What you can do\n - It starts process and keep it.\n '
if (not (self.option is None)):
command_plus_option = ((self.command + ' ') + self.option)
else:
command_plus_option = self.command
if six.PY3:
if (shutil.which(command) is None):
raise Exception('No command at {}'.format(command))
else:
self.process_analyzer = pexpect.spawnu(command_plus_option)
self.process_id = self.process_analyzer.pid
else:
doc_command_string = "echo | {}".format(command)
command_check = os.system(doc_command_string)
if (not (command_check == 0)):
raise Exception('No command at {}'.format(command))
else:
self.process_analyzer = pexpect.spawnu(command_plus_option)
self.process_id = self.process_analyzer.pid<|docstring|>* What you can do
- It starts process and keep it.<|endoftext|> |
b8beef1ec02da132e2e6aac8c496ac1a3407e7ed279d2ef08fc35d57760fefce | def stop_process(self):
"* What you can do\n - You're able to stop the process which this instance has now.\n "
if hasattr(self, 'process_analyzer'):
self.process_analyzer.kill(sig=9)
else:
pass
return True | * What you can do
- You're able to stop the process which this instance has now. | JapaneseTokenizer/common/sever_handler.py | stop_process | fumankaitori/JapaneseTokenizers | 134 | python | def stop_process(self):
"* What you can do\n - You're able to stop the process which this instance has now.\n "
if hasattr(self, 'process_analyzer'):
self.process_analyzer.kill(sig=9)
else:
pass
return True | def stop_process(self):
"* What you can do\n - You're able to stop the process which this instance has now.\n "
if hasattr(self, 'process_analyzer'):
self.process_analyzer.kill(sig=9)
else:
pass
return True<|docstring|>* What you can do
- You're able to stop the process which this instance has now.<|endoftext|> |
20d0968cad3dc9989bc9563899eed7a638f809594feb14103080f0559a0550a9 | def __query(self, input_string):
'* What you can do\n - It takes the result of Juman++\n - This function monitors time which takes for getting the result.\n '
signal.signal(signal.SIGALRM, self.__notify_handler)
signal.alarm(self.timeout_second)
self.process_analyzer.sendline(input_string)
buffer = ''
while True:
line_string = self.process_analyzer.readline()
if (line_string.strip() == input_string):
'Skip if process returns the same input string'
continue
elif (line_string.strip() == self.pattern):
buffer += line_string
signal.alarm(0)
return buffer
else:
buffer += line_string | * What you can do
- It takes the result of Juman++
- This function monitors time which takes for getting the result. | JapaneseTokenizer/common/sever_handler.py | __query | fumankaitori/JapaneseTokenizers | 134 | python | def __query(self, input_string):
'* What you can do\n - It takes the result of Juman++\n - This function monitors time which takes for getting the result.\n '
signal.signal(signal.SIGALRM, self.__notify_handler)
signal.alarm(self.timeout_second)
self.process_analyzer.sendline(input_string)
buffer =
while True:
line_string = self.process_analyzer.readline()
if (line_string.strip() == input_string):
'Skip if process returns the same input string'
continue
elif (line_string.strip() == self.pattern):
buffer += line_string
signal.alarm(0)
return buffer
else:
buffer += line_string | def __query(self, input_string):
'* What you can do\n - It takes the result of Juman++\n - This function monitors time which takes for getting the result.\n '
signal.signal(signal.SIGALRM, self.__notify_handler)
signal.alarm(self.timeout_second)
self.process_analyzer.sendline(input_string)
buffer =
while True:
line_string = self.process_analyzer.readline()
if (line_string.strip() == input_string):
'Skip if process returns the same input string'
continue
elif (line_string.strip() == self.pattern):
buffer += line_string
signal.alarm(0)
return buffer
else:
buffer += line_string<|docstring|>* What you can do
- It takes the result of Juman++
- This function monitors time which takes for getting the result.<|endoftext|> |
8bea8e77fc144aa140d2e1f5e79ded0ec2cacd6aaa738e8eb0d7bbc4dd056220 | def __init__(self, ax, s='Text', x=0, y=0, strings=None, text_id=None, **text_kwargs):
'\n\t\t:param ax:\n\t\t:param s:\n\t\t:param x:\n\t\t:param y:\n\t\t:param strings: list of strings, used for animating text\n\t\t:param text_kwargs:\n\t\t'
super().__init__(x=x, y=y, **text_kwargs)
self.id = text_id
self.strings = strings
self.c = 0
ax.add_artist(self) | :param ax:
:param s:
:param x:
:param y:
:param strings: list of strings, used for animating text
:param text_kwargs: | viseng/annotations.py | __init__ | OllieBoyne/vis-eng | 0 | python | def __init__(self, ax, s='Text', x=0, y=0, strings=None, text_id=None, **text_kwargs):
'\n\t\t:param ax:\n\t\t:param s:\n\t\t:param x:\n\t\t:param y:\n\t\t:param strings: list of strings, used for animating text\n\t\t:param text_kwargs:\n\t\t'
super().__init__(x=x, y=y, **text_kwargs)
self.id = text_id
self.strings = strings
self.c = 0
ax.add_artist(self) | def __init__(self, ax, s='Text', x=0, y=0, strings=None, text_id=None, **text_kwargs):
'\n\t\t:param ax:\n\t\t:param s:\n\t\t:param x:\n\t\t:param y:\n\t\t:param strings: list of strings, used for animating text\n\t\t:param text_kwargs:\n\t\t'
super().__init__(x=x, y=y, **text_kwargs)
self.id = text_id
self.strings = strings
self.c = 0
ax.add_artist(self)<|docstring|>:param ax:
:param s:
:param x:
:param y:
:param strings: list of strings, used for animating text
:param text_kwargs:<|endoftext|> |
a23d4a7a0df80d500faca29c0395903c843e81c81e0fb7b86f79b0de58fccd7a | def __len__(self):
'Length if animated, in number of provided strings'
return len(self.strings) | Length if animated, in number of provided strings | viseng/annotations.py | __len__ | OllieBoyne/vis-eng | 0 | python | def __len__(self):
return len(self.strings) | def __len__(self):
return len(self.strings)<|docstring|>Length if animated, in number of provided strings<|endoftext|> |
6100d50c0962d6981b5304b602a9fc5fc16877aeb41c58d2eef4514d42e94fd6 | def __init__(self):
'construction '
mediaBase.MediaBase.__init__(self)
self.pre = (self.prefix + 'querypipe')
self.pipeline_name = self.pre
self.client = media_client.MediaClient(media_config.config) | construction | test/media/qa_test/test_query_pipeline.py | __init__ | yunfan/bce-sdk-python | 22 | python | def __init__(self):
' '
mediaBase.MediaBase.__init__(self)
self.pre = (self.prefix + 'querypipe')
self.pipeline_name = self.pre
self.client = media_client.MediaClient(media_config.config) | def __init__(self):
' '
mediaBase.MediaBase.__init__(self)
self.pre = (self.prefix + 'querypipe')
self.pipeline_name = self.pre
self.client = media_client.MediaClient(media_config.config)<|docstring|>construction<|endoftext|> |
4afca31964e9129fe380fc601c0a13a27736f3e8a7847cf9c280fba4d7b688b4 | def setUp(self):
'create env'
ret = self.client.create_pipeline(self.pipeline_name, self.sourceBucket, self.targetBucket)
nose.tools.assert_is_not_none(ret) | create env | test/media/qa_test/test_query_pipeline.py | setUp | yunfan/bce-sdk-python | 22 | python | def setUp(self):
ret = self.client.create_pipeline(self.pipeline_name, self.sourceBucket, self.targetBucket)
nose.tools.assert_is_not_none(ret) | def setUp(self):
ret = self.client.create_pipeline(self.pipeline_name, self.sourceBucket, self.targetBucket)
nose.tools.assert_is_not_none(ret)<|docstring|>create env<|endoftext|> |
cb26f65e96eb90f829b468e04f34f8081e4a0ae11a4cc89bbd037d8c5adab65f | def tearDown(self):
'clear env'
result = self.client.list_pipelines()
for each_val in result.pipelines:
pipeline_name = each_val.pipeline_name
if pipeline_name.startswith(self.pre):
resp = self.client.delete_pipeline(pipeline_name)
nose.tools.assert_is_not_none(resp) | clear env | test/media/qa_test/test_query_pipeline.py | tearDown | yunfan/bce-sdk-python | 22 | python | def tearDown(self):
result = self.client.list_pipelines()
for each_val in result.pipelines:
pipeline_name = each_val.pipeline_name
if pipeline_name.startswith(self.pre):
resp = self.client.delete_pipeline(pipeline_name)
nose.tools.assert_is_not_none(resp) | def tearDown(self):
result = self.client.list_pipelines()
for each_val in result.pipelines:
pipeline_name = each_val.pipeline_name
if pipeline_name.startswith(self.pre):
resp = self.client.delete_pipeline(pipeline_name)
nose.tools.assert_is_not_none(resp)<|docstring|>clear env<|endoftext|> |
50839e72d3eab8a82b03ab5d0074dad4f1ffdc3fb9471b6094a4972dbcab823d | def test_query_pipeline_exsit(self):
'query exsit pipeline'
resp = self.client.get_pipeline(self.pipeline_name)
nose.tools.assert_is_not_none(resp)
nose.tools.assert_equal(resp.state, 'ACTIVE')
nose.tools.assert_equal(resp.pipeline_name, self.pipeline_name) | query exsit pipeline | test/media/qa_test/test_query_pipeline.py | test_query_pipeline_exsit | yunfan/bce-sdk-python | 22 | python | def test_query_pipeline_exsit(self):
resp = self.client.get_pipeline(self.pipeline_name)
nose.tools.assert_is_not_none(resp)
nose.tools.assert_equal(resp.state, 'ACTIVE')
nose.tools.assert_equal(resp.pipeline_name, self.pipeline_name) | def test_query_pipeline_exsit(self):
resp = self.client.get_pipeline(self.pipeline_name)
nose.tools.assert_is_not_none(resp)
nose.tools.assert_equal(resp.state, 'ACTIVE')
nose.tools.assert_equal(resp.pipeline_name, self.pipeline_name)<|docstring|>query exsit pipeline<|endoftext|> |
84aaa5cf5124e7438bbcf6d09bd09bdbd7998dee7057be3658d8cb1ef8e3e35b | def test_query_pipeline_is_deleted(self):
'query deleted pipeline'
resp = self.client.delete_pipeline(self.pipeline_name)
nose.tools.assert_is_not_none(resp)
resp = self.client.get_pipeline(self.pipeline_name)
nose.tools.assert_equal(resp.state, 'INACTIVE') | query deleted pipeline | test/media/qa_test/test_query_pipeline.py | test_query_pipeline_is_deleted | yunfan/bce-sdk-python | 22 | python | def test_query_pipeline_is_deleted(self):
resp = self.client.delete_pipeline(self.pipeline_name)
nose.tools.assert_is_not_none(resp)
resp = self.client.get_pipeline(self.pipeline_name)
nose.tools.assert_equal(resp.state, 'INACTIVE') | def test_query_pipeline_is_deleted(self):
resp = self.client.delete_pipeline(self.pipeline_name)
nose.tools.assert_is_not_none(resp)
resp = self.client.get_pipeline(self.pipeline_name)
nose.tools.assert_equal(resp.state, 'INACTIVE')<|docstring|>query deleted pipeline<|endoftext|> |
c88ff1a1d4d89d399cc1a877d0df2e9b94477cfe3f6e1f553f46d27a50d16773 | def test_query_pipeline_is_name_empty(self):
'pipeline name is empty'
with nose.tools.assert_raises_regexp(BceClientError, "pipeline_name can't be empty string"):
resp = self.client.get_pipeline('') | pipeline name is empty | test/media/qa_test/test_query_pipeline.py | test_query_pipeline_is_name_empty | yunfan/bce-sdk-python | 22 | python | def test_query_pipeline_is_name_empty(self):
with nose.tools.assert_raises_regexp(BceClientError, "pipeline_name can't be empty string"):
resp = self.client.get_pipeline() | def test_query_pipeline_is_name_empty(self):
with nose.tools.assert_raises_regexp(BceClientError, "pipeline_name can't be empty string"):
resp = self.client.get_pipeline()<|docstring|>pipeline name is empty<|endoftext|> |
5242f74bff3d20d8a6bca609353a3d3bedf56bb6f2d1d0f588414250a547d6ae | def test_query_pipeline_is_name_none(self):
'pipeline name is none'
with nose.tools.assert_raises_regexp(ValueError, 'arg "pipeline_name" should not be None'):
self.client.get_pipeline(None) | pipeline name is none | test/media/qa_test/test_query_pipeline.py | test_query_pipeline_is_name_none | yunfan/bce-sdk-python | 22 | python | def test_query_pipeline_is_name_none(self):
with nose.tools.assert_raises_regexp(ValueError, 'arg "pipeline_name" should not be None'):
self.client.get_pipeline(None) | def test_query_pipeline_is_name_none(self):
with nose.tools.assert_raises_regexp(ValueError, 'arg "pipeline_name" should not be None'):
self.client.get_pipeline(None)<|docstring|>pipeline name is none<|endoftext|> |
4935a36ae65882d45fc4218eec87f2b71818e609b5de9809be8e271479c4e46d | def test_query_pipeline_not_exist(self):
'pipeline name is not exist'
pipeline_name = 'not_exist_pipeline'
try:
self.client.get_pipeline(pipeline_name)
except BceHttpClientError as e:
if isinstance(e.last_error, BceServerError):
assert e.last_error.message.startswith('The requested pipeline does not exist')
else:
assert (True == False) | pipeline name is not exist | test/media/qa_test/test_query_pipeline.py | test_query_pipeline_not_exist | yunfan/bce-sdk-python | 22 | python | def test_query_pipeline_not_exist(self):
pipeline_name = 'not_exist_pipeline'
try:
self.client.get_pipeline(pipeline_name)
except BceHttpClientError as e:
if isinstance(e.last_error, BceServerError):
assert e.last_error.message.startswith('The requested pipeline does not exist')
else:
assert (True == False) | def test_query_pipeline_not_exist(self):
pipeline_name = 'not_exist_pipeline'
try:
self.client.get_pipeline(pipeline_name)
except BceHttpClientError as e:
if isinstance(e.last_error, BceServerError):
assert e.last_error.message.startswith('The requested pipeline does not exist')
else:
assert (True == False)<|docstring|>pipeline name is not exist<|endoftext|> |
5a03ac2979fb029c6c30b869cbd9a3a9e0789ae126d1c6c71e39aad4a749f497 | def drink_search(searchterm):
'\n Returns the results from a search on the LCBO website\n :param searchterm: (string) Search term/key words\n :return: A results object containing a list of the search results.\n '
searchterm = re.sub(' ', '%20', searchterm)
url = 'https://www.lcbo.com/webapp/wcs/stores/servlet/SearchDisplay?storeId=10203&searchTerm={}'.format(searchterm)
try:
res = {'result': []}
req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req).read()
soup = BeautifulSoup(webpage, 'lxml')
product_ref = soup.find('div', class_='product_listing_container')
product_names = product_ref.findAll('div', class_='product_name')
product_prices = product_ref.findAll('div', class_='product_price')
for product in product_names:
try:
product_name = product.find('a').contents[0]
product_link = product.find('a').attrs.get('href', None)
except:
print('drink data not available')
continue
data = {'name': product_name, 'link': product_link}
res['result'].append(data)
for i in range(len(product_prices)):
try:
price = product_prices[i].find('span').contents[0]
except Exception as e:
print('price data not available')
print(e)
continue
res['result'][i]['price'] = price.strip('\t\n')
except Exception as e:
print('failed to get page')
print(e)
res = None
return res | Returns the results from a search on the LCBO website
:param searchterm: (string) Search term/key words
:return: A results object containing a list of the search results. | drinkSearch.py | drink_search | raunakb007/LCBgo | 0 | python | def drink_search(searchterm):
'\n Returns the results from a search on the LCBO website\n :param searchterm: (string) Search term/key words\n :return: A results object containing a list of the search results.\n '
searchterm = re.sub(' ', '%20', searchterm)
url = 'https://www.lcbo.com/webapp/wcs/stores/servlet/SearchDisplay?storeId=10203&searchTerm={}'.format(searchterm)
try:
res = {'result': []}
req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req).read()
soup = BeautifulSoup(webpage, 'lxml')
product_ref = soup.find('div', class_='product_listing_container')
product_names = product_ref.findAll('div', class_='product_name')
product_prices = product_ref.findAll('div', class_='product_price')
for product in product_names:
try:
product_name = product.find('a').contents[0]
product_link = product.find('a').attrs.get('href', None)
except:
print('drink data not available')
continue
data = {'name': product_name, 'link': product_link}
res['result'].append(data)
for i in range(len(product_prices)):
try:
price = product_prices[i].find('span').contents[0]
except Exception as e:
print('price data not available')
print(e)
continue
res['result'][i]['price'] = price.strip('\t\n')
except Exception as e:
print('failed to get page')
print(e)
res = None
return res | def drink_search(searchterm):
'\n Returns the results from a search on the LCBO website\n :param searchterm: (string) Search term/key words\n :return: A results object containing a list of the search results.\n '
searchterm = re.sub(' ', '%20', searchterm)
url = 'https://www.lcbo.com/webapp/wcs/stores/servlet/SearchDisplay?storeId=10203&searchTerm={}'.format(searchterm)
try:
res = {'result': []}
req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req).read()
soup = BeautifulSoup(webpage, 'lxml')
product_ref = soup.find('div', class_='product_listing_container')
product_names = product_ref.findAll('div', class_='product_name')
product_prices = product_ref.findAll('div', class_='product_price')
for product in product_names:
try:
product_name = product.find('a').contents[0]
product_link = product.find('a').attrs.get('href', None)
except:
print('drink data not available')
continue
data = {'name': product_name, 'link': product_link}
res['result'].append(data)
for i in range(len(product_prices)):
try:
price = product_prices[i].find('span').contents[0]
except Exception as e:
print('price data not available')
print(e)
continue
res['result'][i]['price'] = price.strip('\t\n')
except Exception as e:
print('failed to get page')
print(e)
res = None
return res<|docstring|>Returns the results from a search on the LCBO website
:param searchterm: (string) Search term/key words
:return: A results object containing a list of the search results.<|endoftext|> |
e6b2c582e0b2d5498c15766fcf1f7bb3e801230862959da988d65ae00d66fc1e | def quick_inspect_amplitudes(N: np.array, E: np.array, C: np.array, s: int=1, logx: bool=False, logy: bool=False, loglog: bool=False, cmap: str='viridis', **kwargs) -> None:
"A utility function to quickly visualise the distribution\n of the WA p-p amplitudes on the N and E components. \n\n Args:\n N (np.array): The p-p WA amplitudes in mm measured on the North component.\n E (np.array): The p-p WA amplitudes in mm measured on the North component.\n C (np.array): [description]\n s (int, optional): [description]. Defaults to 1.\n logx (bool, optional): [description]. Defaults to False.\n logy (bool, optional): [description]. Defaults to False.\n loglog (bool, optional): [description]. Defaults to False.\n cmap (str, optional): [description]. Defaults to 'viridis'.\n "
s_scale = s
diff = (N - E)
s = np.std(diff)
weights = (np.ones_like(diff) / float(len(diff)))
(fig, axes) = plt.subplots(1, 2, figsize=((7 * 2.5), 6))
h = axes[0].scatter(N, E, c=C, cmap=cmap, **kwargs)
oneone = [(np.min([N.min(), E.min()]) * 1.1), (np.max([N.max(), E.max()]) * 1.1)]
axes[0].plot(oneone, oneone, 'k--', label='1:1')
axes[0].plot(oneone, (oneone + (s * s_scale)), 'r--', label=f'+/- {s_scale}s: {(s_scale * s):.2f}')
axes[0].plot(oneone, (oneone - (s * s_scale)), 'r--')
xlabel = 'log10 p-p E-W [mm]'
ylabel = 'log10 p-p N-S [mm]'
axes[0].set_xlabel(xlabel)
axes[0].set_ylabel(ylabel)
axes[0].legend()
fig.colorbar(h, ax=axes[0])
out = axes[1].hist(diff, bins=30, weights=weights, edgecolor='black', color='blue')
axes[1].vlines((s * s_scale), 0, out[0].max(), color='r', linestyles='dashed', label=f'+/- {s_scale}s: {(s_scale * s):.2f}')
axes[1].vlines(((- s) * s_scale), 0, out[0].max(), color='r', linestyles='dashed')
axes[1].legend()
axes[1].set_xlabel('log10 p-p differences') | A utility function to quickly visualise the distribution
of the WA p-p amplitudes on the N and E components.
Args:
N (np.array): The p-p WA amplitudes in mm measured on the North component.
E (np.array): The p-p WA amplitudes in mm measured on the North component.
C (np.array): [description]
s (int, optional): [description]. Defaults to 1.
logx (bool, optional): [description]. Defaults to False.
logy (bool, optional): [description]. Defaults to False.
loglog (bool, optional): [description]. Defaults to False.
cmap (str, optional): [description]. Defaults to 'viridis'. | catops/catops/plotting.py | quick_inspect_amplitudes | uofuseismo/YPMLRecalibration | 0 | python | def quick_inspect_amplitudes(N: np.array, E: np.array, C: np.array, s: int=1, logx: bool=False, logy: bool=False, loglog: bool=False, cmap: str='viridis', **kwargs) -> None:
"A utility function to quickly visualise the distribution\n of the WA p-p amplitudes on the N and E components. \n\n Args:\n N (np.array): The p-p WA amplitudes in mm measured on the North component.\n E (np.array): The p-p WA amplitudes in mm measured on the North component.\n C (np.array): [description]\n s (int, optional): [description]. Defaults to 1.\n logx (bool, optional): [description]. Defaults to False.\n logy (bool, optional): [description]. Defaults to False.\n loglog (bool, optional): [description]. Defaults to False.\n cmap (str, optional): [description]. Defaults to 'viridis'.\n "
s_scale = s
diff = (N - E)
s = np.std(diff)
weights = (np.ones_like(diff) / float(len(diff)))
(fig, axes) = plt.subplots(1, 2, figsize=((7 * 2.5), 6))
h = axes[0].scatter(N, E, c=C, cmap=cmap, **kwargs)
oneone = [(np.min([N.min(), E.min()]) * 1.1), (np.max([N.max(), E.max()]) * 1.1)]
axes[0].plot(oneone, oneone, 'k--', label='1:1')
axes[0].plot(oneone, (oneone + (s * s_scale)), 'r--', label=f'+/- {s_scale}s: {(s_scale * s):.2f}')
axes[0].plot(oneone, (oneone - (s * s_scale)), 'r--')
xlabel = 'log10 p-p E-W [mm]'
ylabel = 'log10 p-p N-S [mm]'
axes[0].set_xlabel(xlabel)
axes[0].set_ylabel(ylabel)
axes[0].legend()
fig.colorbar(h, ax=axes[0])
out = axes[1].hist(diff, bins=30, weights=weights, edgecolor='black', color='blue')
axes[1].vlines((s * s_scale), 0, out[0].max(), color='r', linestyles='dashed', label=f'+/- {s_scale}s: {(s_scale * s):.2f}')
axes[1].vlines(((- s) * s_scale), 0, out[0].max(), color='r', linestyles='dashed')
axes[1].legend()
axes[1].set_xlabel('log10 p-p differences') | def quick_inspect_amplitudes(N: np.array, E: np.array, C: np.array, s: int=1, logx: bool=False, logy: bool=False, loglog: bool=False, cmap: str='viridis', **kwargs) -> None:
"A utility function to quickly visualise the distribution\n of the WA p-p amplitudes on the N and E components. \n\n Args:\n N (np.array): The p-p WA amplitudes in mm measured on the North component.\n E (np.array): The p-p WA amplitudes in mm measured on the North component.\n C (np.array): [description]\n s (int, optional): [description]. Defaults to 1.\n logx (bool, optional): [description]. Defaults to False.\n logy (bool, optional): [description]. Defaults to False.\n loglog (bool, optional): [description]. Defaults to False.\n cmap (str, optional): [description]. Defaults to 'viridis'.\n "
s_scale = s
diff = (N - E)
s = np.std(diff)
weights = (np.ones_like(diff) / float(len(diff)))
(fig, axes) = plt.subplots(1, 2, figsize=((7 * 2.5), 6))
h = axes[0].scatter(N, E, c=C, cmap=cmap, **kwargs)
oneone = [(np.min([N.min(), E.min()]) * 1.1), (np.max([N.max(), E.max()]) * 1.1)]
axes[0].plot(oneone, oneone, 'k--', label='1:1')
axes[0].plot(oneone, (oneone + (s * s_scale)), 'r--', label=f'+/- {s_scale}s: {(s_scale * s):.2f}')
axes[0].plot(oneone, (oneone - (s * s_scale)), 'r--')
xlabel = 'log10 p-p E-W [mm]'
ylabel = 'log10 p-p N-S [mm]'
axes[0].set_xlabel(xlabel)
axes[0].set_ylabel(ylabel)
axes[0].legend()
fig.colorbar(h, ax=axes[0])
out = axes[1].hist(diff, bins=30, weights=weights, edgecolor='black', color='blue')
axes[1].vlines((s * s_scale), 0, out[0].max(), color='r', linestyles='dashed', label=f'+/- {s_scale}s: {(s_scale * s):.2f}')
axes[1].vlines(((- s) * s_scale), 0, out[0].max(), color='r', linestyles='dashed')
axes[1].legend()
axes[1].set_xlabel('log10 p-p differences')<|docstring|>A utility function to quickly visualise the distribution
of the WA p-p amplitudes on the N and E components.
Args:
N (np.array): The p-p WA amplitudes in mm measured on the North component.
E (np.array): The p-p WA amplitudes in mm measured on the North component.
C (np.array): [description]
s (int, optional): [description]. Defaults to 1.
logx (bool, optional): [description]. Defaults to False.
logy (bool, optional): [description]. Defaults to False.
loglog (bool, optional): [description]. Defaults to False.
cmap (str, optional): [description]. Defaults to 'viridis'.<|endoftext|> |
c7a9a7dc8bddbbe8775fa2c143821e77eebd3b6ee30d4327155fcd54e2ccdf78 | def magnitude_distance_plot(M: np.array, Dist: np.array, Dep: np.array, A: np.array) -> None:
'Plot the event magnitude (M) versus distance relationship,\n with the relevent side distributions. Bonus, also plots\n the depth distribution of causitive events, which is also of \n interest.\n\n Args:\n M (np.array): The event magnitudes.\n Dist (np.array): The source reciever distance (Rhyp or Repi, km).\n Dep (np.array): The focal depths of the events (km).\n A (np.array): The half p-p WA horizontal amplitude (mm).\n '
hkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Dist) + (1.0 / len(Dist))))
hdepkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Dep) + (1.0 / len(Dep))))
fac = 2.6
fig = plt.figure(constrained_layout=False, figsize=((7 * fac), (3 * fac)))
gs1 = fig.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48, wspace=0.2, hspace=0.05)
ax1 = fig.add_subplot(gs1[(:1, 0:(- 1))])
ax2 = fig.add_subplot(gs1[(1:, :(- 1))])
ax3 = fig.add_subplot(gs1[(1:, (- 1):)])
ax4 = fig.add_subplot(gs1[(0, (- 1))])
ax1.xaxis.set_visible(False)
ax1.set_ylabel('Frac. in bin')
ax1.set_xlim(np.log10([1, 200]))
ax2.set_xscale('log')
ax2.set_ylabel('Cat. Mag.')
ax2.set_xlabel('$R_{hyp}$ [km]')
ax2.xaxis.set_major_formatter(ticker.FuncFormatter((lambda y, pos: '{{:.{:1d}f}}'.format(int(np.maximum((- np.log10(y)), 0))).format(y))))
ax2.set_xlim([1, 200])
ax3.yaxis.set_visible(False)
ax3.set_ylabel('Cat. Mag.')
ax3.set_xlabel('Frac. in bin')
ax3.yaxis.set_label_position('right')
ax3.yaxis.tick_right()
ax4.set_xlabel('Depth [km]')
ax4.xaxis.set_label_position('top')
ax4.yaxis.tick_right()
ax4.xaxis.tick_top()
ax1.hist(np.log10(Dist), **hkwargs)
ax3.hist(M, orientation='horizontal', **hkwargs)
ax4.hist(Dep, **hdepkwargs)
sout = ax2.scatter(Dist, M, c=A, lw=1, cmap='viridis', s=10)
cbaxes = inset_axes(ax2, width='35%', height='3%', loc=2)
cbar = fig.colorbar(sout, cax=cbaxes, orientation='horizontal')
cbar.set_label('$\\mathrm{log(A [mm]}$)', rotation=0, fontsize=14, horizontalalignment='center') | Plot the event magnitude (M) versus distance relationship,
with the relevent side distributions. Bonus, also plots
the depth distribution of causitive events, which is also of
interest.
Args:
M (np.array): The event magnitudes.
Dist (np.array): The source reciever distance (Rhyp or Repi, km).
Dep (np.array): The focal depths of the events (km).
A (np.array): The half p-p WA horizontal amplitude (mm). | catops/catops/plotting.py | magnitude_distance_plot | uofuseismo/YPMLRecalibration | 0 | python | def magnitude_distance_plot(M: np.array, Dist: np.array, Dep: np.array, A: np.array) -> None:
'Plot the event magnitude (M) versus distance relationship,\n with the relevent side distributions. Bonus, also plots\n the depth distribution of causitive events, which is also of \n interest.\n\n Args:\n M (np.array): The event magnitudes.\n Dist (np.array): The source reciever distance (Rhyp or Repi, km).\n Dep (np.array): The focal depths of the events (km).\n A (np.array): The half p-p WA horizontal amplitude (mm).\n '
hkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Dist) + (1.0 / len(Dist))))
hdepkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Dep) + (1.0 / len(Dep))))
fac = 2.6
fig = plt.figure(constrained_layout=False, figsize=((7 * fac), (3 * fac)))
gs1 = fig.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48, wspace=0.2, hspace=0.05)
ax1 = fig.add_subplot(gs1[(:1, 0:(- 1))])
ax2 = fig.add_subplot(gs1[(1:, :(- 1))])
ax3 = fig.add_subplot(gs1[(1:, (- 1):)])
ax4 = fig.add_subplot(gs1[(0, (- 1))])
ax1.xaxis.set_visible(False)
ax1.set_ylabel('Frac. in bin')
ax1.set_xlim(np.log10([1, 200]))
ax2.set_xscale('log')
ax2.set_ylabel('Cat. Mag.')
ax2.set_xlabel('$R_{hyp}$ [km]')
ax2.xaxis.set_major_formatter(ticker.FuncFormatter((lambda y, pos: '{{:.{:1d}f}}'.format(int(np.maximum((- np.log10(y)), 0))).format(y))))
ax2.set_xlim([1, 200])
ax3.yaxis.set_visible(False)
ax3.set_ylabel('Cat. Mag.')
ax3.set_xlabel('Frac. in bin')
ax3.yaxis.set_label_position('right')
ax3.yaxis.tick_right()
ax4.set_xlabel('Depth [km]')
ax4.xaxis.set_label_position('top')
ax4.yaxis.tick_right()
ax4.xaxis.tick_top()
ax1.hist(np.log10(Dist), **hkwargs)
ax3.hist(M, orientation='horizontal', **hkwargs)
ax4.hist(Dep, **hdepkwargs)
sout = ax2.scatter(Dist, M, c=A, lw=1, cmap='viridis', s=10)
cbaxes = inset_axes(ax2, width='35%', height='3%', loc=2)
cbar = fig.colorbar(sout, cax=cbaxes, orientation='horizontal')
cbar.set_label('$\\mathrm{log(A [mm]}$)', rotation=0, fontsize=14, horizontalalignment='center') | def magnitude_distance_plot(M: np.array, Dist: np.array, Dep: np.array, A: np.array) -> None:
'Plot the event magnitude (M) versus distance relationship,\n with the relevent side distributions. Bonus, also plots\n the depth distribution of causitive events, which is also of \n interest.\n\n Args:\n M (np.array): The event magnitudes.\n Dist (np.array): The source reciever distance (Rhyp or Repi, km).\n Dep (np.array): The focal depths of the events (km).\n A (np.array): The half p-p WA horizontal amplitude (mm).\n '
hkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Dist) + (1.0 / len(Dist))))
hdepkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Dep) + (1.0 / len(Dep))))
fac = 2.6
fig = plt.figure(constrained_layout=False, figsize=((7 * fac), (3 * fac)))
gs1 = fig.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48, wspace=0.2, hspace=0.05)
ax1 = fig.add_subplot(gs1[(:1, 0:(- 1))])
ax2 = fig.add_subplot(gs1[(1:, :(- 1))])
ax3 = fig.add_subplot(gs1[(1:, (- 1):)])
ax4 = fig.add_subplot(gs1[(0, (- 1))])
ax1.xaxis.set_visible(False)
ax1.set_ylabel('Frac. in bin')
ax1.set_xlim(np.log10([1, 200]))
ax2.set_xscale('log')
ax2.set_ylabel('Cat. Mag.')
ax2.set_xlabel('$R_{hyp}$ [km]')
ax2.xaxis.set_major_formatter(ticker.FuncFormatter((lambda y, pos: '{{:.{:1d}f}}'.format(int(np.maximum((- np.log10(y)), 0))).format(y))))
ax2.set_xlim([1, 200])
ax3.yaxis.set_visible(False)
ax3.set_ylabel('Cat. Mag.')
ax3.set_xlabel('Frac. in bin')
ax3.yaxis.set_label_position('right')
ax3.yaxis.tick_right()
ax4.set_xlabel('Depth [km]')
ax4.xaxis.set_label_position('top')
ax4.yaxis.tick_right()
ax4.xaxis.tick_top()
ax1.hist(np.log10(Dist), **hkwargs)
ax3.hist(M, orientation='horizontal', **hkwargs)
ax4.hist(Dep, **hdepkwargs)
sout = ax2.scatter(Dist, M, c=A, lw=1, cmap='viridis', s=10)
cbaxes = inset_axes(ax2, width='35%', height='3%', loc=2)
cbar = fig.colorbar(sout, cax=cbaxes, orientation='horizontal')
cbar.set_label('$\\mathrm{log(A [mm]}$)', rotation=0, fontsize=14, horizontalalignment='center')<|docstring|>Plot the event magnitude (M) versus distance relationship,
with the relevent side distributions. Bonus, also plots
the depth distribution of causitive events, which is also of
interest.
Args:
M (np.array): The event magnitudes.
Dist (np.array): The source reciever distance (Rhyp or Repi, km).
Dep (np.array): The focal depths of the events (km).
A (np.array): The half p-p WA horizontal amplitude (mm).<|endoftext|> |
f27c142b4b5bba698a6dd0579594d3ee1527df044629d89fcdf02d144f76260d | def spatial_distribution_plot(Lon: np.array, Lat: np.array, Dep: np.array) -> None:
'[summary]\n\n Args:\n Lon (np.array): [description]\n Lat (np.array): [description]\n Dep (np.array): [description]\n '
hkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Lon) + (1.0 / len(Lon))))
fac = 2.75
fig = plt.figure(constrained_layout=False, figsize=((7 * fac), (3 * fac)))
gs1 = fig.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48, wspace=0.15, hspace=0.15)
ax1 = fig.add_subplot(gs1[(:1, 0:(- 1))])
ax2 = fig.add_subplot(gs1[(1:, :(- 1))])
ax3 = fig.add_subplot(gs1[(1:, (- 1):)])
ax4 = fig.add_subplot(gs1[(0, (- 1))])
ax1.xaxis.set_visible(False)
ax1.set_ylabel('Frac. in bin')
ax2.set_ylabel('Latitude [deg]')
ax2.set_xlabel('Longitude [deg]')
ax3.yaxis.set_visible(False)
ax3.set_ylabel('Cat. Mag.')
ax3.set_xlabel('Frac. in bin')
ax3.yaxis.set_label_position('right')
ax3.yaxis.tick_right()
ax4.set_xlabel('Depth [km]')
ax4.xaxis.set_label_position('top')
ax4.yaxis.tick_right()
ax4.xaxis.tick_top()
ax1.hist(Lon, **hkwargs)
ax3.hist(Lat, orientation='horizontal', **hkwargs)
ax4.hist(Dep, **hkwargs)
sout = ax2.scatter(Lon, Lat, c=Dep, lw=1, cmap='Greys_r', s=10)
ax2.xaxis.set_major_locator(plt.MultipleLocator(0.5))
ax2.yaxis.set_major_locator(plt.MultipleLocator(0.25))
cbaxes = inset_axes(ax2, width='2.5%', height='55%', loc='lower left')
cbar = fig.colorbar(sout, cax=cbaxes)
cbar.set_label('Depth [km]', rotation=90) | [summary]
Args:
Lon (np.array): [description]
Lat (np.array): [description]
Dep (np.array): [description] | catops/catops/plotting.py | spatial_distribution_plot | uofuseismo/YPMLRecalibration | 0 | python | def spatial_distribution_plot(Lon: np.array, Lat: np.array, Dep: np.array) -> None:
'[summary]\n\n Args:\n Lon (np.array): [description]\n Lat (np.array): [description]\n Dep (np.array): [description]\n '
hkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Lon) + (1.0 / len(Lon))))
fac = 2.75
fig = plt.figure(constrained_layout=False, figsize=((7 * fac), (3 * fac)))
gs1 = fig.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48, wspace=0.15, hspace=0.15)
ax1 = fig.add_subplot(gs1[(:1, 0:(- 1))])
ax2 = fig.add_subplot(gs1[(1:, :(- 1))])
ax3 = fig.add_subplot(gs1[(1:, (- 1):)])
ax4 = fig.add_subplot(gs1[(0, (- 1))])
ax1.xaxis.set_visible(False)
ax1.set_ylabel('Frac. in bin')
ax2.set_ylabel('Latitude [deg]')
ax2.set_xlabel('Longitude [deg]')
ax3.yaxis.set_visible(False)
ax3.set_ylabel('Cat. Mag.')
ax3.set_xlabel('Frac. in bin')
ax3.yaxis.set_label_position('right')
ax3.yaxis.tick_right()
ax4.set_xlabel('Depth [km]')
ax4.xaxis.set_label_position('top')
ax4.yaxis.tick_right()
ax4.xaxis.tick_top()
ax1.hist(Lon, **hkwargs)
ax3.hist(Lat, orientation='horizontal', **hkwargs)
ax4.hist(Dep, **hkwargs)
sout = ax2.scatter(Lon, Lat, c=Dep, lw=1, cmap='Greys_r', s=10)
ax2.xaxis.set_major_locator(plt.MultipleLocator(0.5))
ax2.yaxis.set_major_locator(plt.MultipleLocator(0.25))
cbaxes = inset_axes(ax2, width='2.5%', height='55%', loc='lower left')
cbar = fig.colorbar(sout, cax=cbaxes)
cbar.set_label('Depth [km]', rotation=90) | def spatial_distribution_plot(Lon: np.array, Lat: np.array, Dep: np.array) -> None:
'[summary]\n\n Args:\n Lon (np.array): [description]\n Lat (np.array): [description]\n Dep (np.array): [description]\n '
hkwargs = dict(bottom=0.0, color='.8', edgecolor='k', rwidth=0.8, weights=(np.zeros_like(Lon) + (1.0 / len(Lon))))
fac = 2.75
fig = plt.figure(constrained_layout=False, figsize=((7 * fac), (3 * fac)))
gs1 = fig.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48, wspace=0.15, hspace=0.15)
ax1 = fig.add_subplot(gs1[(:1, 0:(- 1))])
ax2 = fig.add_subplot(gs1[(1:, :(- 1))])
ax3 = fig.add_subplot(gs1[(1:, (- 1):)])
ax4 = fig.add_subplot(gs1[(0, (- 1))])
ax1.xaxis.set_visible(False)
ax1.set_ylabel('Frac. in bin')
ax2.set_ylabel('Latitude [deg]')
ax2.set_xlabel('Longitude [deg]')
ax3.yaxis.set_visible(False)
ax3.set_ylabel('Cat. Mag.')
ax3.set_xlabel('Frac. in bin')
ax3.yaxis.set_label_position('right')
ax3.yaxis.tick_right()
ax4.set_xlabel('Depth [km]')
ax4.xaxis.set_label_position('top')
ax4.yaxis.tick_right()
ax4.xaxis.tick_top()
ax1.hist(Lon, **hkwargs)
ax3.hist(Lat, orientation='horizontal', **hkwargs)
ax4.hist(Dep, **hkwargs)
sout = ax2.scatter(Lon, Lat, c=Dep, lw=1, cmap='Greys_r', s=10)
ax2.xaxis.set_major_locator(plt.MultipleLocator(0.5))
ax2.yaxis.set_major_locator(plt.MultipleLocator(0.25))
cbaxes = inset_axes(ax2, width='2.5%', height='55%', loc='lower left')
cbar = fig.colorbar(sout, cax=cbaxes)
cbar.set_label('Depth [km]', rotation=90)<|docstring|>[summary]
Args:
Lon (np.array): [description]
Lat (np.array): [description]
Dep (np.array): [description]<|endoftext|> |
577ef67f9095c7790966721827725d53ad789315b8c3ce9bae1d116130c72ed7 | def pytest_generate_tests(metafunc):
'\n Parametrize tests over targets\n '
if ('target' in metafunc.fixturenames):
targets = [('verilator', None)]
if shutil.which('irun'):
targets.append(('system-verilog', 'ncsim'))
if shutil.which('vcs'):
targets.append(('system-verilog', 'vcs'))
if shutil.which('iverilog'):
targets.append(('system-verilog', 'iverilog'))
metafunc.parametrize('target,simulator', targets) | Parametrize tests over targets | tests/test_expressions.py | pytest_generate_tests | standanley/fault | 0 | python | def pytest_generate_tests(metafunc):
'\n \n '
if ('target' in metafunc.fixturenames):
targets = [('verilator', None)]
if shutil.which('irun'):
targets.append(('system-verilog', 'ncsim'))
if shutil.which('vcs'):
targets.append(('system-verilog', 'vcs'))
if shutil.which('iverilog'):
targets.append(('system-verilog', 'iverilog'))
metafunc.parametrize('target,simulator', targets) | def pytest_generate_tests(metafunc):
'\n \n '
if ('target' in metafunc.fixturenames):
targets = [('verilator', None)]
if shutil.which('irun'):
targets.append(('system-verilog', 'ncsim'))
if shutil.which('vcs'):
targets.append(('system-verilog', 'vcs'))
if shutil.which('iverilog'):
targets.append(('system-verilog', 'iverilog'))
metafunc.parametrize('target,simulator', targets)<|docstring|>Parametrize tests over targets<|endoftext|> |
bec389bb93c0b9e87853efea167c7f8a75fb1cc5f193396025a10f89ac72ac79 | @pytest.mark.parametrize('op', ['add', 'truediv', 'and_', 'xor', 'or_', 'lshift', 'rshift', 'mod', 'mul', 'rshift', 'sub', 'lt', 'le', 'eq', 'ne', 'gt', 'ge'])
def test_binop_two_signals_setattr(target, simulator, op):
'\n Test that we can and two output signals for an expect\n '
if (op == 'mod'):
pytest.skip('urem missing from coreir verilog backend')
BinaryOpCircuit = gen_binary_op_circuit(op)
tester = fault.Tester(BinaryOpCircuit)
for _ in range(5):
(I0, I1) = gen_random_inputs(op)
print(I0, I1)
tester.eval()
tester.circuit.O.expect(getattr(operator, op)(tester.circuit.I0_out, tester.circuit.I1_out))
tester.circuit.O.expect(getattr(operator, op)(tester.circuit.I0, tester.circuit.I1))
run_test(tester, target, simulator) | Test that we can and two output signals for an expect | tests/test_expressions.py | test_binop_two_signals_setattr | standanley/fault | 0 | python | @pytest.mark.parametrize('op', ['add', 'truediv', 'and_', 'xor', 'or_', 'lshift', 'rshift', 'mod', 'mul', 'rshift', 'sub', 'lt', 'le', 'eq', 'ne', 'gt', 'ge'])
def test_binop_two_signals_setattr(target, simulator, op):
'\n \n '
if (op == 'mod'):
pytest.skip('urem missing from coreir verilog backend')
BinaryOpCircuit = gen_binary_op_circuit(op)
tester = fault.Tester(BinaryOpCircuit)
for _ in range(5):
(I0, I1) = gen_random_inputs(op)
print(I0, I1)
tester.eval()
tester.circuit.O.expect(getattr(operator, op)(tester.circuit.I0_out, tester.circuit.I1_out))
tester.circuit.O.expect(getattr(operator, op)(tester.circuit.I0, tester.circuit.I1))
run_test(tester, target, simulator) | @pytest.mark.parametrize('op', ['add', 'truediv', 'and_', 'xor', 'or_', 'lshift', 'rshift', 'mod', 'mul', 'rshift', 'sub', 'lt', 'le', 'eq', 'ne', 'gt', 'ge'])
def test_binop_two_signals_setattr(target, simulator, op):
'\n \n '
if (op == 'mod'):
pytest.skip('urem missing from coreir verilog backend')
BinaryOpCircuit = gen_binary_op_circuit(op)
tester = fault.Tester(BinaryOpCircuit)
for _ in range(5):
(I0, I1) = gen_random_inputs(op)
print(I0, I1)
tester.eval()
tester.circuit.O.expect(getattr(operator, op)(tester.circuit.I0_out, tester.circuit.I1_out))
tester.circuit.O.expect(getattr(operator, op)(tester.circuit.I0, tester.circuit.I1))
run_test(tester, target, simulator)<|docstring|>Test that we can and two output signals for an expect<|endoftext|> |
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