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def clusterQuality(self, cluster, fet=1):
'\n returns the L-ratio and Isolation Distance measures\n calculated on the principal components of the energy in a spike matrix\n '
if (self.waveforms is None):
return None
(nSpikes, nElectrodes, _) = self.waveforms.shape
wvs = self.waveforms.copy()
E = np.sqrt(np.nansum((self.waveforms ** 2), axis=2))
zeroIdx = (np.sum(E, 0) == [0, 0, 0, 0])
E = E[:, (~ zeroIdx)]
wvs = wvs[:, (~ zeroIdx), :]
normdWaves = (wvs.T / E.T).T
PCA_m = self.getParam(normdWaves, 'PCA', fet=fet)
badIdx = (np.sum(PCA_m, axis=0) == 0)
PCA_m = PCA_m[:, (~ badIdx)]
idx = (self.spk_clusters == cluster)
nClustSpikes = np.count_nonzero(idx)
try:
d = self._mahal(PCA_m, PCA_m[idx, :])
M_noise = d[(~ idx)]
df = np.prod((fet, nElectrodes))
from scipy import stats
L = np.sum((1 - stats.chi2.cdf(M_noise, df)))
L_ratio = (L / nClustSpikes)
if (nClustSpikes < (nSpikes / 2)):
M_noise.sort()
isolation_dist = M_noise[nClustSpikes]
else:
isolation_dist = np.nan
except Exception:
isolation_dist = L_ratio = np.nan
return (L_ratio, isolation_dist) | -791,362,355,700,851,100 | returns the L-ratio and Isolation Distance measures
calculated on the principal components of the energy in a spike matrix | ephysiopy/common/spikecalcs.py | clusterQuality | rhayman/ephysiopy | python | def clusterQuality(self, cluster, fet=1):
'\n returns the L-ratio and Isolation Distance measures\n calculated on the principal components of the energy in a spike matrix\n '
if (self.waveforms is None):
return None
(nSpikes, nElectrodes, _) = self.waveforms.shape
wvs = self.waveforms.copy()
E = np.sqrt(np.nansum((self.waveforms ** 2), axis=2))
zeroIdx = (np.sum(E, 0) == [0, 0, 0, 0])
E = E[:, (~ zeroIdx)]
wvs = wvs[:, (~ zeroIdx), :]
normdWaves = (wvs.T / E.T).T
PCA_m = self.getParam(normdWaves, 'PCA', fet=fet)
badIdx = (np.sum(PCA_m, axis=0) == 0)
PCA_m = PCA_m[:, (~ badIdx)]
idx = (self.spk_clusters == cluster)
nClustSpikes = np.count_nonzero(idx)
try:
d = self._mahal(PCA_m, PCA_m[idx, :])
M_noise = d[(~ idx)]
df = np.prod((fet, nElectrodes))
from scipy import stats
L = np.sum((1 - stats.chi2.cdf(M_noise, df)))
L_ratio = (L / nClustSpikes)
if (nClustSpikes < (nSpikes / 2)):
M_noise.sort()
isolation_dist = M_noise[nClustSpikes]
else:
isolation_dist = np.nan
except Exception:
isolation_dist = L_ratio = np.nan
return (L_ratio, isolation_dist) |
def _mahal(self, u, v):
"\n gets the mahalanobis distance between two vectors u and v\n a blatant copy of the Mathworks fcn as it doesn't require the\n covariance matrix to be calculated which is a pain if there\n are NaNs in the matrix\n "
u_sz = u.shape
v_sz = v.shape
if (u_sz[1] != v_sz[1]):
warnings.warn('Input size mismatch: matrices must have same num of columns')
if (v_sz[0] < v_sz[1]):
warnings.warn('Too few rows: v must have more rows than columns')
if (np.any(np.imag(u)) or np.any(np.imag(v))):
warnings.warn('No complex inputs are allowed')
m = np.nanmean(v, axis=0)
M = np.tile(m, reps=(u_sz[0], 1))
C = (v - np.tile(m, reps=(v_sz[0], 1)))
(_, R) = np.linalg.qr(C)
ri = np.linalg.solve(R.T, (u - M).T)
d = (np.sum((ri * ri), 0).T * (v_sz[0] - 1))
return d | -7,699,833,769,099,782,000 | gets the mahalanobis distance between two vectors u and v
a blatant copy of the Mathworks fcn as it doesn't require the
covariance matrix to be calculated which is a pain if there
are NaNs in the matrix | ephysiopy/common/spikecalcs.py | _mahal | rhayman/ephysiopy | python | def _mahal(self, u, v):
"\n gets the mahalanobis distance between two vectors u and v\n a blatant copy of the Mathworks fcn as it doesn't require the\n covariance matrix to be calculated which is a pain if there\n are NaNs in the matrix\n "
u_sz = u.shape
v_sz = v.shape
if (u_sz[1] != v_sz[1]):
warnings.warn('Input size mismatch: matrices must have same num of columns')
if (v_sz[0] < v_sz[1]):
warnings.warn('Too few rows: v must have more rows than columns')
if (np.any(np.imag(u)) or np.any(np.imag(v))):
warnings.warn('No complex inputs are allowed')
m = np.nanmean(v, axis=0)
M = np.tile(m, reps=(u_sz[0], 1))
C = (v - np.tile(m, reps=(v_sz[0], 1)))
(_, R) = np.linalg.qr(C)
ri = np.linalg.solve(R.T, (u - M).T)
d = (np.sum((ri * ri), 0).T * (v_sz[0] - 1))
return d |
def thetaModIdx(self, x1):
'\n Calculates a theta modulation index of a spike train based on the cells\n autocorrelogram\n\n Parameters\n ----------\n x1: np.array\n The spike time-series\n Returns\n -------\n thetaMod: float\n The difference of the values at the first peak and trough of the\n autocorrelogram\n '
y = self.xcorr(x1)
(corr, _) = np.histogram(y[(y != 0)], bins=201, range=np.array([(- 500), 500]))
from scipy.signal import periodogram
(freqs, power) = periodogram(corr, fs=200, return_onesided=True)
b = signal.boxcar(3)
h = signal.filtfilt(b, 3, power)
sqd_amp = (h ** 2)
theta_band_max_idx = np.nonzero((sqd_amp == np.max(sqd_amp[np.logical_and((freqs > 6), (freqs < 11))])))[0][0]
mtbp = np.mean(sqd_amp[(theta_band_max_idx - 1):(theta_band_max_idx + 1)])
other_band_idx = np.logical_and((freqs > 2), (freqs < 50))
mobp = np.mean(sqd_amp[other_band_idx])
return ((mtbp - mobp) / (mtbp + mobp)) | 8,879,195,594,928,940,000 | Calculates a theta modulation index of a spike train based on the cells
autocorrelogram
Parameters
----------
x1: np.array
The spike time-series
Returns
-------
thetaMod: float
The difference of the values at the first peak and trough of the
autocorrelogram | ephysiopy/common/spikecalcs.py | thetaModIdx | rhayman/ephysiopy | python | def thetaModIdx(self, x1):
'\n Calculates a theta modulation index of a spike train based on the cells\n autocorrelogram\n\n Parameters\n ----------\n x1: np.array\n The spike time-series\n Returns\n -------\n thetaMod: float\n The difference of the values at the first peak and trough of the\n autocorrelogram\n '
y = self.xcorr(x1)
(corr, _) = np.histogram(y[(y != 0)], bins=201, range=np.array([(- 500), 500]))
from scipy.signal import periodogram
(freqs, power) = periodogram(corr, fs=200, return_onesided=True)
b = signal.boxcar(3)
h = signal.filtfilt(b, 3, power)
sqd_amp = (h ** 2)
theta_band_max_idx = np.nonzero((sqd_amp == np.max(sqd_amp[np.logical_and((freqs > 6), (freqs < 11))])))[0][0]
mtbp = np.mean(sqd_amp[(theta_band_max_idx - 1):(theta_band_max_idx + 1)])
other_band_idx = np.logical_and((freqs > 2), (freqs < 50))
mobp = np.mean(sqd_amp[other_band_idx])
return ((mtbp - mobp) / (mtbp + mobp)) |
def thetaModIdxV2(self, x1):
'\n This is a simpler alternative to the thetaModIdx method in that it\n calculates the difference between the normalized temporal\n autocorrelogram at the trough between 50-70ms and the\n peak between 100-140ms over their sum (data is binned into 5ms bins)\n\n Measure used in Cacucci et al., 2004 and Kropff et al 2015\n '
y = self.xcorr(x1)
(corr, bins) = np.histogram(y[(y != 0)], bins=201, range=np.array([(- 500), 500]))
bins = bins[0:(- 1)]
corr = (corr / float(np.max(corr)))
thetaAntiPhase = np.min(corr[np.logical_and((bins > 50), (bins < 70))])
thetaPhase = np.max(corr[np.logical_and((bins > 100), (bins < 140))])
return ((thetaPhase - thetaAntiPhase) / (thetaPhase + thetaAntiPhase)) | -8,577,724,921,252,220,000 | This is a simpler alternative to the thetaModIdx method in that it
calculates the difference between the normalized temporal
autocorrelogram at the trough between 50-70ms and the
peak between 100-140ms over their sum (data is binned into 5ms bins)
Measure used in Cacucci et al., 2004 and Kropff et al 2015 | ephysiopy/common/spikecalcs.py | thetaModIdxV2 | rhayman/ephysiopy | python | def thetaModIdxV2(self, x1):
'\n This is a simpler alternative to the thetaModIdx method in that it\n calculates the difference between the normalized temporal\n autocorrelogram at the trough between 50-70ms and the\n peak between 100-140ms over their sum (data is binned into 5ms bins)\n\n Measure used in Cacucci et al., 2004 and Kropff et al 2015\n '
y = self.xcorr(x1)
(corr, bins) = np.histogram(y[(y != 0)], bins=201, range=np.array([(- 500), 500]))
bins = bins[0:(- 1)]
corr = (corr / float(np.max(corr)))
thetaAntiPhase = np.min(corr[np.logical_and((bins > 50), (bins < 70))])
thetaPhase = np.max(corr[np.logical_and((bins > 100), (bins < 140))])
return ((thetaPhase - thetaAntiPhase) / (thetaPhase + thetaAntiPhase)) |
def thetaBandMaxFreq(self, x1):
'\n Calculates the frequency with the max power in the theta band (6-12Hz)\n of a spike trains autocorrelogram. Partly to look for differences\n in theta frequency in different running directions a la Blair\n See Welday paper - https://doi.org/10.1523/jneurosci.0712-11.2011\n '
y = self.xcorr(x1)
(corr, _) = np.histogram(y[(y != 0)], bins=201, range=np.array([(- 500), 500]))
from scipy.signal import periodogram
(freqs, power) = periodogram(corr, fs=200, return_onesided=True)
power_masked = np.ma.MaskedArray(power, np.logical_or((freqs < 6), (freqs > 12)))
return freqs[np.argmax(power_masked)] | 5,875,807,191,050,528,000 | Calculates the frequency with the max power in the theta band (6-12Hz)
of a spike trains autocorrelogram. Partly to look for differences
in theta frequency in different running directions a la Blair
See Welday paper - https://doi.org/10.1523/jneurosci.0712-11.2011 | ephysiopy/common/spikecalcs.py | thetaBandMaxFreq | rhayman/ephysiopy | python | def thetaBandMaxFreq(self, x1):
'\n Calculates the frequency with the max power in the theta band (6-12Hz)\n of a spike trains autocorrelogram. Partly to look for differences\n in theta frequency in different running directions a la Blair\n See Welday paper - https://doi.org/10.1523/jneurosci.0712-11.2011\n '
y = self.xcorr(x1)
(corr, _) = np.histogram(y[(y != 0)], bins=201, range=np.array([(- 500), 500]))
from scipy.signal import periodogram
(freqs, power) = periodogram(corr, fs=200, return_onesided=True)
power_masked = np.ma.MaskedArray(power, np.logical_or((freqs < 6), (freqs > 12)))
return freqs[np.argmax(power_masked)] |
def smoothSpikePosCount(self, x1, npos, sigma=3.0, shuffle=None):
'\n Returns a spike train the same length as num pos samples that has been\n smoothed in time with a gaussian kernel M in width and standard\n deviation equal to sigma\n\n Parameters\n --------------\n x1 : np.array\n The pos indices the spikes occured at\n npos : int\n The number of position samples captured\n sigma : float\n the standard deviation of the gaussian used to smooth the spike\n train\n shuffle: int\n The number of seconds to shift the spike train by. Default None\n\n Returns\n -----------\n smoothed_spikes : np.array\n The smoothed spike train\n '
spk_hist = np.bincount(x1, minlength=npos)
if (shuffle is not None):
spk_hist = np.roll(spk_hist, int((shuffle * 50)))
h = signal.gaussian(13, sigma)
h = (h / float(np.sum(h)))
return signal.filtfilt(h.ravel(), 1, spk_hist) | -6,908,954,358,747,898,000 | Returns a spike train the same length as num pos samples that has been
smoothed in time with a gaussian kernel M in width and standard
deviation equal to sigma
Parameters
--------------
x1 : np.array
The pos indices the spikes occured at
npos : int
The number of position samples captured
sigma : float
the standard deviation of the gaussian used to smooth the spike
train
shuffle: int
The number of seconds to shift the spike train by. Default None
Returns
-----------
smoothed_spikes : np.array
The smoothed spike train | ephysiopy/common/spikecalcs.py | smoothSpikePosCount | rhayman/ephysiopy | python | def smoothSpikePosCount(self, x1, npos, sigma=3.0, shuffle=None):
'\n Returns a spike train the same length as num pos samples that has been\n smoothed in time with a gaussian kernel M in width and standard\n deviation equal to sigma\n\n Parameters\n --------------\n x1 : np.array\n The pos indices the spikes occured at\n npos : int\n The number of position samples captured\n sigma : float\n the standard deviation of the gaussian used to smooth the spike\n train\n shuffle: int\n The number of seconds to shift the spike train by. Default None\n\n Returns\n -----------\n smoothed_spikes : np.array\n The smoothed spike train\n '
spk_hist = np.bincount(x1, minlength=npos)
if (shuffle is not None):
spk_hist = np.roll(spk_hist, int((shuffle * 50)))
h = signal.gaussian(13, sigma)
h = (h / float(np.sum(h)))
return signal.filtfilt(h.ravel(), 1, spk_hist) |
def ifr_sp_corr(self, x1, speed, minSpeed=2.0, maxSpeed=40.0, sigma=3, shuffle=False, nShuffles=100, minTime=30, plot=False):
'\n x1 : np.array\n The indices of pos at which the cluster fired\n speed: np.array (1 x nSamples)\n instantaneous speed\n minSpeed: int\n speeds below this value are ignored - defaults to 2cm/s as with\n Kropff et al., 2015\n '
speed = speed.ravel()
posSampRate = 50
nSamples = len(speed)
spk_hist = np.bincount(x1, minlength=nSamples)
h = signal.gaussian(13, sigma)
h = (h / float(np.sum(h)))
lowSpeedIdx = (speed < minSpeed)
highSpeedIdx = (speed > maxSpeed)
speed_filt = speed[(~ np.logical_or(lowSpeedIdx, highSpeedIdx))]
spk_hist_filt = spk_hist[(~ np.logical_or(lowSpeedIdx, highSpeedIdx))]
spk_sm = signal.filtfilt(h.ravel(), 1, spk_hist_filt)
sm_spk_rate = (spk_sm * posSampRate)
res = stats.pearsonr(sm_spk_rate, speed_filt)
if plot:
(_, sp_bin_edges) = np.histogram(speed_filt, bins=50)
sp_dig = np.digitize(speed_filt, sp_bin_edges, right=True)
spks_per_sp_bin = [spk_hist_filt[(sp_dig == i)] for i in range(len(sp_bin_edges))]
rate_per_sp_bin = []
for x in spks_per_sp_bin:
rate_per_sp_bin.append((np.mean(x) * posSampRate))
rate_filter = signal.gaussian(5, 1.0)
rate_filter = (rate_filter / np.sum(rate_filter))
binned_spk_rate = signal.filtfilt(rate_filter, 1, rate_per_sp_bin)
spk_binning_edges = np.linspace(np.min(sm_spk_rate), np.max(sm_spk_rate), len(sp_bin_edges))
(speed_mesh, spk_mesh) = np.meshgrid(sp_bin_edges, spk_binning_edges)
(binned_rate, _, _) = np.histogram2d(speed_filt, sm_spk_rate, bins=[sp_bin_edges, spk_binning_edges])
from ephysiopy.common.utils import blurImage
sm_binned_rate = blurImage(binned_rate, 5)
fig = plt.figure()
ax = fig.add_subplot(111)
from matplotlib.colors import LogNorm
speed_mesh = speed_mesh[:(- 1), :(- 1)]
spk_mesh = spk_mesh[:(- 1), :(- 1)]
ax.pcolormesh(speed_mesh, spk_mesh, sm_binned_rate, norm=LogNorm(), alpha=0.5, shading='nearest', edgecolors='None')
ax.plot(sp_bin_edges, binned_spk_rate, 'r')
lr = stats.linregress(speed_filt, sm_spk_rate)
end_point = (lr.intercept + ((sp_bin_edges[(- 1)] - sp_bin_edges[0]) * lr.slope))
ax.plot([np.min(sp_bin_edges), np.max(sp_bin_edges)], [lr.intercept, end_point], 'r--')
ax.set_xlim(np.min(sp_bin_edges), np.max(sp_bin_edges[(- 2)]))
ax.set_ylim(0, (np.nanmax(binned_spk_rate) * 1.1))
ax.set_ylabel('Firing rate(Hz)')
ax.set_xlabel('Running speed(cm/s)')
ax.set_title('Intercept: {0:.3f} Slope: {1:.5f}\nPearson: {2:.5f}'.format(lr.intercept, lr.slope, lr.rvalue))
if shuffle:
timeSteps = np.random.randint((30 * posSampRate), (nSamples - (30 * posSampRate)), nShuffles)
shuffled_results = []
for t in timeSteps:
spk_count = np.roll(spk_hist, t)
spk_count_filt = spk_count[(~ lowSpeedIdx)]
spk_count_sm = signal.filtfilt(h.ravel(), 1, spk_count_filt)
shuffled_results.append(stats.pearsonr(spk_count_sm, speed_filt)[0])
if plot:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.hist(np.abs(shuffled_results), 20)
ylims = ax.get_ylim()
ax.vlines(res, ylims[0], ylims[1], 'r')
if isinstance(fig, plt.Figure):
return fig | -7,873,371,224,588,896,000 | x1 : np.array
The indices of pos at which the cluster fired
speed: np.array (1 x nSamples)
instantaneous speed
minSpeed: int
speeds below this value are ignored - defaults to 2cm/s as with
Kropff et al., 2015 | ephysiopy/common/spikecalcs.py | ifr_sp_corr | rhayman/ephysiopy | python | def ifr_sp_corr(self, x1, speed, minSpeed=2.0, maxSpeed=40.0, sigma=3, shuffle=False, nShuffles=100, minTime=30, plot=False):
'\n x1 : np.array\n The indices of pos at which the cluster fired\n speed: np.array (1 x nSamples)\n instantaneous speed\n minSpeed: int\n speeds below this value are ignored - defaults to 2cm/s as with\n Kropff et al., 2015\n '
speed = speed.ravel()
posSampRate = 50
nSamples = len(speed)
spk_hist = np.bincount(x1, minlength=nSamples)
h = signal.gaussian(13, sigma)
h = (h / float(np.sum(h)))
lowSpeedIdx = (speed < minSpeed)
highSpeedIdx = (speed > maxSpeed)
speed_filt = speed[(~ np.logical_or(lowSpeedIdx, highSpeedIdx))]
spk_hist_filt = spk_hist[(~ np.logical_or(lowSpeedIdx, highSpeedIdx))]
spk_sm = signal.filtfilt(h.ravel(), 1, spk_hist_filt)
sm_spk_rate = (spk_sm * posSampRate)
res = stats.pearsonr(sm_spk_rate, speed_filt)
if plot:
(_, sp_bin_edges) = np.histogram(speed_filt, bins=50)
sp_dig = np.digitize(speed_filt, sp_bin_edges, right=True)
spks_per_sp_bin = [spk_hist_filt[(sp_dig == i)] for i in range(len(sp_bin_edges))]
rate_per_sp_bin = []
for x in spks_per_sp_bin:
rate_per_sp_bin.append((np.mean(x) * posSampRate))
rate_filter = signal.gaussian(5, 1.0)
rate_filter = (rate_filter / np.sum(rate_filter))
binned_spk_rate = signal.filtfilt(rate_filter, 1, rate_per_sp_bin)
spk_binning_edges = np.linspace(np.min(sm_spk_rate), np.max(sm_spk_rate), len(sp_bin_edges))
(speed_mesh, spk_mesh) = np.meshgrid(sp_bin_edges, spk_binning_edges)
(binned_rate, _, _) = np.histogram2d(speed_filt, sm_spk_rate, bins=[sp_bin_edges, spk_binning_edges])
from ephysiopy.common.utils import blurImage
sm_binned_rate = blurImage(binned_rate, 5)
fig = plt.figure()
ax = fig.add_subplot(111)
from matplotlib.colors import LogNorm
speed_mesh = speed_mesh[:(- 1), :(- 1)]
spk_mesh = spk_mesh[:(- 1), :(- 1)]
ax.pcolormesh(speed_mesh, spk_mesh, sm_binned_rate, norm=LogNorm(), alpha=0.5, shading='nearest', edgecolors='None')
ax.plot(sp_bin_edges, binned_spk_rate, 'r')
lr = stats.linregress(speed_filt, sm_spk_rate)
end_point = (lr.intercept + ((sp_bin_edges[(- 1)] - sp_bin_edges[0]) * lr.slope))
ax.plot([np.min(sp_bin_edges), np.max(sp_bin_edges)], [lr.intercept, end_point], 'r--')
ax.set_xlim(np.min(sp_bin_edges), np.max(sp_bin_edges[(- 2)]))
ax.set_ylim(0, (np.nanmax(binned_spk_rate) * 1.1))
ax.set_ylabel('Firing rate(Hz)')
ax.set_xlabel('Running speed(cm/s)')
ax.set_title('Intercept: {0:.3f} Slope: {1:.5f}\nPearson: {2:.5f}'.format(lr.intercept, lr.slope, lr.rvalue))
if shuffle:
timeSteps = np.random.randint((30 * posSampRate), (nSamples - (30 * posSampRate)), nShuffles)
shuffled_results = []
for t in timeSteps:
spk_count = np.roll(spk_hist, t)
spk_count_filt = spk_count[(~ lowSpeedIdx)]
spk_count_sm = signal.filtfilt(h.ravel(), 1, spk_count_filt)
shuffled_results.append(stats.pearsonr(spk_count_sm, speed_filt)[0])
if plot:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.hist(np.abs(shuffled_results), 20)
ylims = ax.get_ylim()
ax.vlines(res, ylims[0], ylims[1], 'r')
if isinstance(fig, plt.Figure):
return fig |
def half_amp_dur(self, waveforms):
'\n Half amplitude duration of a spike\n\n Parameters\n ----------\n A: ndarray\n An nSpikes x nElectrodes x nSamples array\n\n Returns\n -------\n had: float\n The half-amplitude duration for the channel (electrode) that has\n the strongest (highest amplitude) signal. Units are ms\n '
from scipy import optimize
best_chan = np.argmax(np.max(np.mean(waveforms, 0), 1))
mn_wvs = np.mean(waveforms, 0)
wvs = mn_wvs[best_chan, :]
half_amp = (np.max(wvs) / 2)
half_amp = (np.zeros_like(wvs) + half_amp)
t = np.linspace(0, (1 / 1000.0), 50)
from scipy.interpolate import BPoly
p1 = BPoly.from_derivatives(t, wvs[:, np.newaxis])
p2 = BPoly.from_derivatives(t, half_amp[:, np.newaxis])
xs = np.r_[(t, t)]
xs.sort()
x_min = xs.min()
x_max = xs.max()
x_mid = (xs[:(- 1)] + (np.diff(xs) / 2))
roots = set()
for val in x_mid:
(root, infodict, ier, mesg) = optimize.fsolve((lambda x: (p1(x) - p2(x))), val, full_output=True)
if ((ier == 1) and (x_min < root < x_max)):
roots.add(root[0])
roots = list(roots)
if (len(roots) > 1):
r = np.abs(np.diff(roots[0:2]))[0]
else:
r = np.nan
return r | 3,749,305,657,729,343,500 | Half amplitude duration of a spike
Parameters
----------
A: ndarray
An nSpikes x nElectrodes x nSamples array
Returns
-------
had: float
The half-amplitude duration for the channel (electrode) that has
the strongest (highest amplitude) signal. Units are ms | ephysiopy/common/spikecalcs.py | half_amp_dur | rhayman/ephysiopy | python | def half_amp_dur(self, waveforms):
'\n Half amplitude duration of a spike\n\n Parameters\n ----------\n A: ndarray\n An nSpikes x nElectrodes x nSamples array\n\n Returns\n -------\n had: float\n The half-amplitude duration for the channel (electrode) that has\n the strongest (highest amplitude) signal. Units are ms\n '
from scipy import optimize
best_chan = np.argmax(np.max(np.mean(waveforms, 0), 1))
mn_wvs = np.mean(waveforms, 0)
wvs = mn_wvs[best_chan, :]
half_amp = (np.max(wvs) / 2)
half_amp = (np.zeros_like(wvs) + half_amp)
t = np.linspace(0, (1 / 1000.0), 50)
from scipy.interpolate import BPoly
p1 = BPoly.from_derivatives(t, wvs[:, np.newaxis])
p2 = BPoly.from_derivatives(t, half_amp[:, np.newaxis])
xs = np.r_[(t, t)]
xs.sort()
x_min = xs.min()
x_max = xs.max()
x_mid = (xs[:(- 1)] + (np.diff(xs) / 2))
roots = set()
for val in x_mid:
(root, infodict, ier, mesg) = optimize.fsolve((lambda x: (p1(x) - p2(x))), val, full_output=True)
if ((ier == 1) and (x_min < root < x_max)):
roots.add(root[0])
roots = list(roots)
if (len(roots) > 1):
r = np.abs(np.diff(roots[0:2]))[0]
else:
r = np.nan
return r |
def p2t_time(self, waveforms):
'\n The peak to trough time of a spike in ms\n\n Parameters\n ----------\n cluster: int\n the cluster whose waveforms are to be analysed\n\n Returns\n -------\n p2t: float\n The mean peak-to-trough time for the channel (electrode) that has\n the strongest (highest amplitude) signal. Units are ms\n '
best_chan = np.argmax(np.max(np.mean(waveforms, 0), 1))
tP = self.getParam(waveforms, param='tP')
tT = self.getParam(waveforms, param='tT')
mn_tP = np.mean(tP, 0)
mn_tT = np.mean(tT, 0)
p2t = np.abs((mn_tP[best_chan] - mn_tT[best_chan]))
return (p2t * 1000) | 3,933,080,969,911,125,500 | The peak to trough time of a spike in ms
Parameters
----------
cluster: int
the cluster whose waveforms are to be analysed
Returns
-------
p2t: float
The mean peak-to-trough time for the channel (electrode) that has
the strongest (highest amplitude) signal. Units are ms | ephysiopy/common/spikecalcs.py | p2t_time | rhayman/ephysiopy | python | def p2t_time(self, waveforms):
'\n The peak to trough time of a spike in ms\n\n Parameters\n ----------\n cluster: int\n the cluster whose waveforms are to be analysed\n\n Returns\n -------\n p2t: float\n The mean peak-to-trough time for the channel (electrode) that has\n the strongest (highest amplitude) signal. Units are ms\n '
best_chan = np.argmax(np.max(np.mean(waveforms, 0), 1))
tP = self.getParam(waveforms, param='tP')
tT = self.getParam(waveforms, param='tT')
mn_tP = np.mean(tP, 0)
mn_tT = np.mean(tT, 0)
p2t = np.abs((mn_tP[best_chan] - mn_tT[best_chan]))
return (p2t * 1000) |
def plotClusterSpace(self, waveforms, param='Amp', clusts=None, bins=256, **kwargs):
'\n TODO: aspect of plot boxes in ImageGrid not right as scaled by range of\n values now\n '
from ephysiopy.dacq2py.tintcolours import colours as tcols
import matplotlib.colors as colors
from itertools import combinations
from mpl_toolkits.axes_grid1 import ImageGrid
self.scaling = np.full(4, 15)
amps = self.getParam(waveforms, param=param)
bad_electrodes = np.setdiff1d(np.array(range(4)), np.array(np.sum(amps, 0).nonzero())[0])
cmap = np.tile(tcols[0], (bins, 1))
cmap[0] = (1, 1, 1)
cmap = colors.ListedColormap(cmap)
cmap._init()
alpha_vals = np.ones((cmap.N + 3))
alpha_vals[0] = 0
cmap._lut[:, (- 1)] = alpha_vals
cmb = combinations(range(4), 2)
if ('fig' in kwargs):
fig = kwargs['fig']
else:
fig = plt.figure(figsize=(8, 6))
grid = ImageGrid(fig, 111, nrows_ncols=(2, 3), axes_pad=0.1, aspect=False)
if ('Amp' in param):
myRange = np.vstack(((self.scaling * 0), (self.scaling * 2)))
else:
myRange = None
clustCMap0 = np.tile(tcols[0], (bins, 1))
clustCMap0[0] = (1, 1, 1)
clustCMap0 = colors.ListedColormap(clustCMap0)
clustCMap0._init()
clustCMap0._lut[:, (- 1)] = alpha_vals
for (i, c) in enumerate(cmb):
if (c not in bad_electrodes):
(h, ye, xe) = np.histogram2d(amps[:, c[0]], amps[:, c[1]], range=myRange[:, c].T, bins=bins)
(x, y) = np.meshgrid(xe[0:(- 1)], ye[0:(- 1)])
grid[i].pcolormesh(x, y, h, cmap=clustCMap0, shading='nearest', edgecolors='face')
(h, ye, xe) = np.histogram2d(amps[:, c[0]], amps[:, c[1]], range=myRange[:, c].T, bins=bins)
clustCMap = np.tile(tcols[1], (bins, 1))
clustCMap[0] = (1, 1, 1)
clustCMap = colors.ListedColormap(clustCMap)
clustCMap._init()
clustCMap._lut[:, (- 1)] = alpha_vals
grid[i].pcolormesh(x, y, h, cmap=clustCMap, shading='nearest', edgecolors='face')
s = ((str((c[0] + 1)) + ' v ') + str((c[1] + 1)))
grid[i].text(0.05, 0.95, s, va='top', ha='left', size='small', color='k', transform=grid[i].transAxes)
grid[i].set_xlim(xe.min(), xe.max())
grid[i].set_ylim(ye.min(), ye.max())
plt.setp([a.get_xticklabels() for a in grid], visible=False)
plt.setp([a.get_yticklabels() for a in grid], visible=False)
return fig | 2,114,415,613,261,320,700 | TODO: aspect of plot boxes in ImageGrid not right as scaled by range of
values now | ephysiopy/common/spikecalcs.py | plotClusterSpace | rhayman/ephysiopy | python | def plotClusterSpace(self, waveforms, param='Amp', clusts=None, bins=256, **kwargs):
'\n TODO: aspect of plot boxes in ImageGrid not right as scaled by range of\n values now\n '
from ephysiopy.dacq2py.tintcolours import colours as tcols
import matplotlib.colors as colors
from itertools import combinations
from mpl_toolkits.axes_grid1 import ImageGrid
self.scaling = np.full(4, 15)
amps = self.getParam(waveforms, param=param)
bad_electrodes = np.setdiff1d(np.array(range(4)), np.array(np.sum(amps, 0).nonzero())[0])
cmap = np.tile(tcols[0], (bins, 1))
cmap[0] = (1, 1, 1)
cmap = colors.ListedColormap(cmap)
cmap._init()
alpha_vals = np.ones((cmap.N + 3))
alpha_vals[0] = 0
cmap._lut[:, (- 1)] = alpha_vals
cmb = combinations(range(4), 2)
if ('fig' in kwargs):
fig = kwargs['fig']
else:
fig = plt.figure(figsize=(8, 6))
grid = ImageGrid(fig, 111, nrows_ncols=(2, 3), axes_pad=0.1, aspect=False)
if ('Amp' in param):
myRange = np.vstack(((self.scaling * 0), (self.scaling * 2)))
else:
myRange = None
clustCMap0 = np.tile(tcols[0], (bins, 1))
clustCMap0[0] = (1, 1, 1)
clustCMap0 = colors.ListedColormap(clustCMap0)
clustCMap0._init()
clustCMap0._lut[:, (- 1)] = alpha_vals
for (i, c) in enumerate(cmb):
if (c not in bad_electrodes):
(h, ye, xe) = np.histogram2d(amps[:, c[0]], amps[:, c[1]], range=myRange[:, c].T, bins=bins)
(x, y) = np.meshgrid(xe[0:(- 1)], ye[0:(- 1)])
grid[i].pcolormesh(x, y, h, cmap=clustCMap0, shading='nearest', edgecolors='face')
(h, ye, xe) = np.histogram2d(amps[:, c[0]], amps[:, c[1]], range=myRange[:, c].T, bins=bins)
clustCMap = np.tile(tcols[1], (bins, 1))
clustCMap[0] = (1, 1, 1)
clustCMap = colors.ListedColormap(clustCMap)
clustCMap._init()
clustCMap._lut[:, (- 1)] = alpha_vals
grid[i].pcolormesh(x, y, h, cmap=clustCMap, shading='nearest', edgecolors='face')
s = ((str((c[0] + 1)) + ' v ') + str((c[1] + 1)))
grid[i].text(0.05, 0.95, s, va='top', ha='left', size='small', color='k', transform=grid[i].transAxes)
grid[i].set_xlim(xe.min(), xe.max())
grid[i].set_ylim(ye.min(), ye.max())
plt.setp([a.get_xticklabels() for a in grid], visible=False)
plt.setp([a.get_yticklabels() for a in grid], visible=False)
return fig |
def _fix_int_dtypes(self, df: pd.DataFrame) -> None:
'\n Mutate DataFrame to set dtypes for int columns containing NaN values."\n '
for col in df:
if (('float' in df[col].dtype.name) and df[col].hasnans):
notna_series = df[col].dropna().values
if np.isclose(notna_series, notna_series.astype(int)).all():
df[col] = np.where(df[col].isnull(), None, df[col])
df[col] = df[col].astype(pd.Int64Dtype()) | 1,217,929,678,337,796,400 | Mutate DataFrame to set dtypes for int columns containing NaN values." | airflow/providers/amazon/aws/transfers/mysql_to_s3.py | _fix_int_dtypes | alphasights/airflow | python | def _fix_int_dtypes(self, df: pd.DataFrame) -> None:
'\n \n '
for col in df:
if (('float' in df[col].dtype.name) and df[col].hasnans):
notna_series = df[col].dropna().values
if np.isclose(notna_series, notna_series.astype(int)).all():
df[col] = np.where(df[col].isnull(), None, df[col])
df[col] = df[col].astype(pd.Int64Dtype()) |
def test_transforms():
'Test basic transforms'
xfm = np.random.randn(4, 4).astype(np.float32)
new_xfm = xfm.dot(rotate(180, (1, 0, 0)).dot(rotate((- 90), (0, 1, 0))))
new_xfm = new_xfm.dot(rotate(90, (0, 0, 1)).dot(rotate(90, (0, 1, 0))))
new_xfm = new_xfm.dot(rotate(90, (1, 0, 0)))
assert_allclose(xfm, new_xfm)
new_xfm = translate((1, (- 1), 1)).dot(translate(((- 1), 1, (- 1)))).dot(xfm)
assert_allclose(xfm, new_xfm)
new_xfm = scale((1, 2, 3)).dot(scale((1, (1.0 / 2.0), (1.0 / 3.0)))).dot(xfm)
assert_allclose(xfm, new_xfm)
xfm = ortho((- 1), 1, (- 1), 1, (- 1), 1)
assert_equal(xfm.shape, (4, 4))
xfm = frustum((- 1), 1, (- 1), 1, (- 1), 1)
assert_equal(xfm.shape, (4, 4))
xfm = perspective(1, 1, (- 1), 1)
assert_equal(xfm.shape, (4, 4)) | -5,003,356,567,224,349,000 | Test basic transforms | vispy/util/tests/test_transforms.py | test_transforms | izaid/vispy | python | def test_transforms():
xfm = np.random.randn(4, 4).astype(np.float32)
new_xfm = xfm.dot(rotate(180, (1, 0, 0)).dot(rotate((- 90), (0, 1, 0))))
new_xfm = new_xfm.dot(rotate(90, (0, 0, 1)).dot(rotate(90, (0, 1, 0))))
new_xfm = new_xfm.dot(rotate(90, (1, 0, 0)))
assert_allclose(xfm, new_xfm)
new_xfm = translate((1, (- 1), 1)).dot(translate(((- 1), 1, (- 1)))).dot(xfm)
assert_allclose(xfm, new_xfm)
new_xfm = scale((1, 2, 3)).dot(scale((1, (1.0 / 2.0), (1.0 / 3.0)))).dot(xfm)
assert_allclose(xfm, new_xfm)
xfm = ortho((- 1), 1, (- 1), 1, (- 1), 1)
assert_equal(xfm.shape, (4, 4))
xfm = frustum((- 1), 1, (- 1), 1, (- 1), 1)
assert_equal(xfm.shape, (4, 4))
xfm = perspective(1, 1, (- 1), 1)
assert_equal(xfm.shape, (4, 4)) |
@property
def search_path(self):
'\n Search first the vendor package then as a natural package.\n '
(yield (self.vendor_pkg + '.'))
(yield '') | -4,364,949,470,265,435,600 | Search first the vendor package then as a natural package. | virtual/lib/python3.8/site-packages/setuptools/extern/__init__.py | search_path | MARTIN-OMOLLO/PITCH | python | @property
def search_path(self):
'\n \n '
(yield (self.vendor_pkg + '.'))
(yield ) |
def _module_matches_namespace(self, fullname):
'Figure out if the target module is vendored.'
(root, base, target) = fullname.partition((self.root_name + '.'))
return ((not root) and any(map(target.startswith, self.vendored_names))) | 1,613,554,549,028,283,000 | Figure out if the target module is vendored. | virtual/lib/python3.8/site-packages/setuptools/extern/__init__.py | _module_matches_namespace | MARTIN-OMOLLO/PITCH | python | def _module_matches_namespace(self, fullname):
(root, base, target) = fullname.partition((self.root_name + '.'))
return ((not root) and any(map(target.startswith, self.vendored_names))) |
def load_module(self, fullname):
'\n Iterate over the search path to locate and load fullname.\n '
(root, base, target) = fullname.partition((self.root_name + '.'))
for prefix in self.search_path:
try:
extant = (prefix + target)
__import__(extant)
mod = sys.modules[extant]
sys.modules[fullname] = mod
return mod
except ImportError:
pass
else:
raise ImportError("The '{target}' package is required; normally this is bundled with this package so if you get this warning, consult the packager of your distribution.".format(**locals())) | -5,310,695,123,598,882,000 | Iterate over the search path to locate and load fullname. | virtual/lib/python3.8/site-packages/setuptools/extern/__init__.py | load_module | MARTIN-OMOLLO/PITCH | python | def load_module(self, fullname):
'\n \n '
(root, base, target) = fullname.partition((self.root_name + '.'))
for prefix in self.search_path:
try:
extant = (prefix + target)
__import__(extant)
mod = sys.modules[extant]
sys.modules[fullname] = mod
return mod
except ImportError:
pass
else:
raise ImportError("The '{target}' package is required; normally this is bundled with this package so if you get this warning, consult the packager of your distribution.".format(**locals())) |
def find_spec(self, fullname, path=None, target=None):
'Return a module spec for vendored names.'
return (importlib.util.spec_from_loader(fullname, self) if self._module_matches_namespace(fullname) else None) | 7,781,275,529,721,239,000 | Return a module spec for vendored names. | virtual/lib/python3.8/site-packages/setuptools/extern/__init__.py | find_spec | MARTIN-OMOLLO/PITCH | python | def find_spec(self, fullname, path=None, target=None):
return (importlib.util.spec_from_loader(fullname, self) if self._module_matches_namespace(fullname) else None) |
def install(self):
'\n Install this importer into sys.meta_path if not already present.\n '
if (self not in sys.meta_path):
sys.meta_path.append(self) | 7,688,392,835,112,288,000 | Install this importer into sys.meta_path if not already present. | virtual/lib/python3.8/site-packages/setuptools/extern/__init__.py | install | MARTIN-OMOLLO/PITCH | python | def install(self):
'\n \n '
if (self not in sys.meta_path):
sys.meta_path.append(self) |
def passthrough(x):
'Return x.'
return x | -3,003,717,923,206,876,700 | Return x. | tests/tools_tests.py | passthrough | nasqueron/pywikibot | python | def passthrough(x):
return x |
def test_wrapper(self):
'Create a test instance and verify the wrapper redirects.'
obj = self.DummyClass()
wrapped = tools.ContextManagerWrapper(obj)
self.assertIs(wrapped.class_var, obj.class_var)
self.assertIs(wrapped.instance_var, obj.instance_var)
self.assertIs(wrapped._wrapped, obj)
self.assertFalse(obj.closed)
with wrapped as unwrapped:
self.assertFalse(obj.closed)
self.assertIs(unwrapped, obj)
unwrapped.class_var = 47
self.assertTrue(obj.closed)
self.assertEqual(wrapped.class_var, 47) | -4,267,953,912,771,476,000 | Create a test instance and verify the wrapper redirects. | tests/tools_tests.py | test_wrapper | nasqueron/pywikibot | python | def test_wrapper(self):
obj = self.DummyClass()
wrapped = tools.ContextManagerWrapper(obj)
self.assertIs(wrapped.class_var, obj.class_var)
self.assertIs(wrapped.instance_var, obj.instance_var)
self.assertIs(wrapped._wrapped, obj)
self.assertFalse(obj.closed)
with wrapped as unwrapped:
self.assertFalse(obj.closed)
self.assertIs(unwrapped, obj)
unwrapped.class_var = 47
self.assertTrue(obj.closed)
self.assertEqual(wrapped.class_var, 47) |
def test_exec_wrapper(self):
'Check that the wrapper permits exceptions.'
wrapper = tools.ContextManagerWrapper(self.DummyClass())
self.assertFalse(wrapper.closed)
with self.assertRaisesRegex(ZeroDivisionError, '(integer division or modulo by zero|division by zero)'):
with wrapper:
(1 / 0)
self.assertTrue(wrapper.closed) | 6,113,006,524,318,023,000 | Check that the wrapper permits exceptions. | tests/tools_tests.py | test_exec_wrapper | nasqueron/pywikibot | python | def test_exec_wrapper(self):
wrapper = tools.ContextManagerWrapper(self.DummyClass())
self.assertFalse(wrapper.closed)
with self.assertRaisesRegex(ZeroDivisionError, '(integer division or modulo by zero|division by zero)'):
with wrapper:
(1 / 0)
self.assertTrue(wrapper.closed) |
@classmethod
def setUpClass(cls):
'Define base_file and original_content.'
super(OpenArchiveTestCase, cls).setUpClass()
cls.base_file = join_xml_data_path('article-pyrus.xml')
with open(cls.base_file, 'rb') as f:
cls.original_content = f.read().replace(b'\r\n', b'\n') | -3,331,918,296,333,598,000 | Define base_file and original_content. | tests/tools_tests.py | setUpClass | nasqueron/pywikibot | python | @classmethod
def setUpClass(cls):
super(OpenArchiveTestCase, cls).setUpClass()
cls.base_file = join_xml_data_path('article-pyrus.xml')
with open(cls.base_file, 'rb') as f:
cls.original_content = f.read().replace(b'\r\n', b'\n') |
def _get_content(self, *args, **kwargs):
'Use open_archive and return content using a with-statement.'
with tools.open_archive(*args, **kwargs) as f:
return f.read().replace(b'\r\n', b'\n') | 9,127,821,848,596,652,000 | Use open_archive and return content using a with-statement. | tests/tools_tests.py | _get_content | nasqueron/pywikibot | python | def _get_content(self, *args, **kwargs):
with tools.open_archive(*args, **kwargs) as f:
return f.read().replace(b'\r\n', b'\n') |
def test_open_archive_normal(self):
'Test open_archive with no compression in the standard library.'
self.assertEqual(self._get_content(self.base_file), self.original_content) | 3,668,411,129,950,133,000 | Test open_archive with no compression in the standard library. | tests/tools_tests.py | test_open_archive_normal | nasqueron/pywikibot | python | def test_open_archive_normal(self):
self.assertEqual(self._get_content(self.base_file), self.original_content) |
def test_open_archive_bz2(self):
'Test open_archive with bz2 compressor in the standard library.'
self.assertEqual(self._get_content((self.base_file + '.bz2')), self.original_content)
self.assertEqual(self._get_content((self.base_file + '.bz2'), use_extension=False), self.original_content) | 1,617,084,888,435,620,000 | Test open_archive with bz2 compressor in the standard library. | tests/tools_tests.py | test_open_archive_bz2 | nasqueron/pywikibot | python | def test_open_archive_bz2(self):
self.assertEqual(self._get_content((self.base_file + '.bz2')), self.original_content)
self.assertEqual(self._get_content((self.base_file + '.bz2'), use_extension=False), self.original_content) |
@require_modules('bz2file')
def test_open_archive_with_bz2file(self):
'Test open_archive when bz2file library.'
old_bz2 = tools.bz2
try:
tools.bz2 = __import__('bz2file')
self.assertEqual(self._get_content((self.base_file + '.bz2')), self.original_content)
self.assertEqual(self._get_content((self.base_file + '.bz2'), use_extension=False), self.original_content)
finally:
tools.bz2 = old_bz2 | -4,334,069,004,180,986,000 | Test open_archive when bz2file library. | tests/tools_tests.py | test_open_archive_with_bz2file | nasqueron/pywikibot | python | @require_modules('bz2file')
def test_open_archive_with_bz2file(self):
old_bz2 = tools.bz2
try:
tools.bz2 = __import__('bz2file')
self.assertEqual(self._get_content((self.base_file + '.bz2')), self.original_content)
self.assertEqual(self._get_content((self.base_file + '.bz2'), use_extension=False), self.original_content)
finally:
tools.bz2 = old_bz2 |
def test_open_archive_without_bz2(self):
'Test open_archive when bz2 and bz2file are not available.'
old_bz2 = tools.bz2
BZ2_IMPORT_ERROR = 'This is a fake exception message that is used when bz2 and bz2file is not importable'
try:
tools.bz2 = ImportError(BZ2_IMPORT_ERROR)
self.assertRaisesRegex(ImportError, BZ2_IMPORT_ERROR, self._get_content, (self.base_file + '.bz2'))
finally:
tools.bz2 = old_bz2 | -2,222,823,590,668,212,000 | Test open_archive when bz2 and bz2file are not available. | tests/tools_tests.py | test_open_archive_without_bz2 | nasqueron/pywikibot | python | def test_open_archive_without_bz2(self):
old_bz2 = tools.bz2
BZ2_IMPORT_ERROR = 'This is a fake exception message that is used when bz2 and bz2file is not importable'
try:
tools.bz2 = ImportError(BZ2_IMPORT_ERROR)
self.assertRaisesRegex(ImportError, BZ2_IMPORT_ERROR, self._get_content, (self.base_file + '.bz2'))
finally:
tools.bz2 = old_bz2 |
def test_open_archive_gz(self):
'Test open_archive with gz compressor in the standard library.'
self.assertEqual(self._get_content((self.base_file + '.gz')), self.original_content) | -7,963,129,132,435,812,000 | Test open_archive with gz compressor in the standard library. | tests/tools_tests.py | test_open_archive_gz | nasqueron/pywikibot | python | def test_open_archive_gz(self):
self.assertEqual(self._get_content((self.base_file + '.gz')), self.original_content) |
def test_open_archive_7z(self):
'Test open_archive with 7za if installed.'
FAILED_TO_OPEN_7ZA = 'Unexpected STDERR output from 7za '
try:
subprocess.Popen(['7za'], stdout=subprocess.PIPE).stdout.close()
except OSError:
raise unittest.SkipTest('7za not installed')
self.assertEqual(self._get_content((self.base_file + '.7z')), self.original_content)
self.assertRaisesRegex(OSError, FAILED_TO_OPEN_7ZA, self._get_content, (self.base_file + '_invalid.7z'), use_extension=True) | 7,100,321,156,077,318,000 | Test open_archive with 7za if installed. | tests/tools_tests.py | test_open_archive_7z | nasqueron/pywikibot | python | def test_open_archive_7z(self):
FAILED_TO_OPEN_7ZA = 'Unexpected STDERR output from 7za '
try:
subprocess.Popen(['7za'], stdout=subprocess.PIPE).stdout.close()
except OSError:
raise unittest.SkipTest('7za not installed')
self.assertEqual(self._get_content((self.base_file + '.7z')), self.original_content)
self.assertRaisesRegex(OSError, FAILED_TO_OPEN_7ZA, self._get_content, (self.base_file + '_invalid.7z'), use_extension=True) |
def _get_content(self, *args, **kwargs):
'Use open_compressed and return content using a with-statement.'
if (kwargs.get('use_extension') is False):
kwargs['use_extension'] = True
with tools.open_compressed(*args, **kwargs) as f:
content = f.read().replace(b'\r\n', b'\n')
self.assertOneDeprecation(self.INSTEAD)
return content | -4,683,509,650,639,901,000 | Use open_compressed and return content using a with-statement. | tests/tools_tests.py | _get_content | nasqueron/pywikibot | python | def _get_content(self, *args, **kwargs):
if (kwargs.get('use_extension') is False):
kwargs['use_extension'] = True
with tools.open_compressed(*args, **kwargs) as f:
content = f.read().replace(b'\r\n', b'\n')
self.assertOneDeprecation(self.INSTEAD)
return content |
@classmethod
def setUpClass(cls):
'Define base_file and original_content.'
super(OpenArchiveWriteTestCase, cls).setUpClass()
cls.base_file = join_xml_data_path('article-pyrus.xml')
with open(cls.base_file, 'rb') as f:
cls.original_content = f.read().replace(b'\r\n', b'\n') | -8,544,897,831,322,012,000 | Define base_file and original_content. | tests/tools_tests.py | setUpClass | nasqueron/pywikibot | python | @classmethod
def setUpClass(cls):
super(OpenArchiveWriteTestCase, cls).setUpClass()
cls.base_file = join_xml_data_path('article-pyrus.xml')
with open(cls.base_file, 'rb') as f:
cls.original_content = f.read().replace(b'\r\n', b'\n') |
def test_invalid_modes(self):
'Test various invalid mode configurations.'
INVALID_MODE_RA = 'Invalid mode: "ra"'
INVALID_MODE_RT = 'Invalid mode: "rt"'
INVALID_MODE_BR = 'Invalid mode: "br"'
MN_DETECTION_ONLY = 'Magic number detection only when reading'
self.assertRaisesRegex(ValueError, INVALID_MODE_RA, tools.open_archive, '/dev/null', 'ra')
self.assertRaisesRegex(ValueError, INVALID_MODE_RT, tools.open_archive, '/dev/null', 'rt')
self.assertRaisesRegex(ValueError, INVALID_MODE_BR, tools.open_archive, '/dev/null', 'br')
self.assertRaisesRegex(ValueError, MN_DETECTION_ONLY, tools.open_archive, '/dev/null', 'wb', False) | 1,863,774,189,160,259,600 | Test various invalid mode configurations. | tests/tools_tests.py | test_invalid_modes | nasqueron/pywikibot | python | def test_invalid_modes(self):
INVALID_MODE_RA = 'Invalid mode: "ra"'
INVALID_MODE_RT = 'Invalid mode: "rt"'
INVALID_MODE_BR = 'Invalid mode: "br"'
MN_DETECTION_ONLY = 'Magic number detection only when reading'
self.assertRaisesRegex(ValueError, INVALID_MODE_RA, tools.open_archive, '/dev/null', 'ra')
self.assertRaisesRegex(ValueError, INVALID_MODE_RT, tools.open_archive, '/dev/null', 'rt')
self.assertRaisesRegex(ValueError, INVALID_MODE_BR, tools.open_archive, '/dev/null', 'br')
self.assertRaisesRegex(ValueError, MN_DETECTION_ONLY, tools.open_archive, '/dev/null', 'wb', False) |
def test_binary_mode(self):
'Test that it uses binary mode.'
with tools.open_archive(self.base_file, 'r') as f:
self.assertEqual(f.mode, 'rb')
self.assertIsInstance(f.read(), bytes) | 1,719,401,108,466,029,300 | Test that it uses binary mode. | tests/tools_tests.py | test_binary_mode | nasqueron/pywikibot | python | def test_binary_mode(self):
with tools.open_archive(self.base_file, 'r') as f:
self.assertEqual(f.mode, 'rb')
self.assertIsInstance(f.read(), bytes) |
def test_write_archive_bz2(self):
'Test writing a bz2 archive.'
content = self._write_content('.bz2')
with open((self.base_file + '.bz2'), 'rb') as f:
self.assertEqual(content, f.read()) | 5,309,385,862,999,258,000 | Test writing a bz2 archive. | tests/tools_tests.py | test_write_archive_bz2 | nasqueron/pywikibot | python | def test_write_archive_bz2(self):
content = self._write_content('.bz2')
with open((self.base_file + '.bz2'), 'rb') as f:
self.assertEqual(content, f.read()) |
def test_write_archive_gz(self):
'Test writing a gz archive.'
content = self._write_content('.gz')
self.assertEqual(content[:3], b'\x1f\x8b\x08') | -2,919,605,937,170,006,500 | Test writing a gz archive. | tests/tools_tests.py | test_write_archive_gz | nasqueron/pywikibot | python | def test_write_archive_gz(self):
content = self._write_content('.gz')
self.assertEqual(content[:3], b'\x1f\x8b\x08') |
def test_write_archive_7z(self):
'Test writing an archive as a 7z archive.'
FAILED_TO_WRITE_7Z = 'It is not possible to write a 7z file.'
self.assertRaisesRegex(NotImplementedError, FAILED_TO_WRITE_7Z, tools.open_archive, '/dev/null.7z', mode='wb') | 559,217,299,130,519,900 | Test writing an archive as a 7z archive. | tests/tools_tests.py | test_write_archive_7z | nasqueron/pywikibot | python | def test_write_archive_7z(self):
FAILED_TO_WRITE_7Z = 'It is not possible to write a 7z file.'
self.assertRaisesRegex(NotImplementedError, FAILED_TO_WRITE_7Z, tools.open_archive, '/dev/null.7z', mode='wb') |
def test_single(self):
'Test that it returns the dict itself when there is only one.'
self.assertEqual(tools.merge_unique_dicts(self.dct1), self.dct1)
self.assertEqual(tools.merge_unique_dicts(**self.dct1), self.dct1) | -1,434,746,132,220,353,800 | Test that it returns the dict itself when there is only one. | tests/tools_tests.py | test_single | nasqueron/pywikibot | python | def test_single(self):
self.assertEqual(tools.merge_unique_dicts(self.dct1), self.dct1)
self.assertEqual(tools.merge_unique_dicts(**self.dct1), self.dct1) |
def test_multiple(self):
'Test that it actually merges dicts.'
self.assertEqual(tools.merge_unique_dicts(self.dct1, self.dct2), self.dct_both)
self.assertEqual(tools.merge_unique_dicts(self.dct2, **self.dct1), self.dct_both) | -3,050,643,579,955,607,000 | Test that it actually merges dicts. | tests/tools_tests.py | test_multiple | nasqueron/pywikibot | python | def test_multiple(self):
self.assertEqual(tools.merge_unique_dicts(self.dct1, self.dct2), self.dct_both)
self.assertEqual(tools.merge_unique_dicts(self.dct2, **self.dct1), self.dct_both) |
def test_different_type(self):
'Test that the keys can be different types.'
self.assertEqual(tools.merge_unique_dicts({'1': 'str'}, {1: 'int'}), {'1': 'str', 1: 'int'}) | 5,023,755,342,220,285,000 | Test that the keys can be different types. | tests/tools_tests.py | test_different_type | nasqueron/pywikibot | python | def test_different_type(self):
self.assertEqual(tools.merge_unique_dicts({'1': 'str'}, {1: 'int'}), {'1': 'str', 1: 'int'}) |
def test_conflict(self):
'Test that it detects conflicts.'
self.assertRaisesRegex(ValueError, '42', tools.merge_unique_dicts, self.dct1, **{'42': 'bad'})
self.assertRaisesRegex(ValueError, '42', tools.merge_unique_dicts, self.dct1, self.dct1)
self.assertRaisesRegex(ValueError, '42', tools.merge_unique_dicts, self.dct1, **self.dct1) | -6,342,496,012,056,625,000 | Test that it detects conflicts. | tests/tools_tests.py | test_conflict | nasqueron/pywikibot | python | def test_conflict(self):
self.assertRaisesRegex(ValueError, '42', tools.merge_unique_dicts, self.dct1, **{'42': 'bad'})
self.assertRaisesRegex(ValueError, '42', tools.merge_unique_dicts, self.dct1, self.dct1)
self.assertRaisesRegex(ValueError, '42', tools.merge_unique_dicts, self.dct1, **self.dct1) |
def test_show_default_marker(self):
'Test marker is shown without kwargs.'
stop = 2
it = list(tools.islice_with_ellipsis(self.it, stop))
self.assertEqual(len(it), (stop + 1))
self.assertEqual(it[:(- 1)], self.it[:stop])
self.assertEqual(it[(- 1)], '…') | -5,647,136,174,958,706,000 | Test marker is shown without kwargs. | tests/tools_tests.py | test_show_default_marker | nasqueron/pywikibot | python | def test_show_default_marker(self):
stop = 2
it = list(tools.islice_with_ellipsis(self.it, stop))
self.assertEqual(len(it), (stop + 1))
self.assertEqual(it[:(- 1)], self.it[:stop])
self.assertEqual(it[(- 1)], '…') |
def test_show_custom_marker(self):
'Test correct marker is shown with kwargs..'
stop = 2
it = list(tools.islice_with_ellipsis(self.it, stop, marker='new'))
self.assertEqual(len(it), (stop + 1))
self.assertEqual(it[:(- 1)], self.it[:stop])
self.assertNotEqual(it[(- 1)], '…')
self.assertEqual(it[(- 1)], 'new') | -1,190,494,465,889,196,800 | Test correct marker is shown with kwargs.. | tests/tools_tests.py | test_show_custom_marker | nasqueron/pywikibot | python | def test_show_custom_marker(self):
stop = 2
it = list(tools.islice_with_ellipsis(self.it, stop, marker='new'))
self.assertEqual(len(it), (stop + 1))
self.assertEqual(it[:(- 1)], self.it[:stop])
self.assertNotEqual(it[(- 1)], '…')
self.assertEqual(it[(- 1)], 'new') |
def test_show_marker_with_start_stop(self):
'Test marker is shown with start and stop without kwargs.'
start = 1
stop = 3
it = list(tools.islice_with_ellipsis(self.it, start, stop))
self.assertEqual(len(it), ((stop - start) + 1))
self.assertEqual(it[:(- 1)], self.it[start:stop])
self.assertEqual(it[(- 1)], '…') | 3,370,575,341,154,751,000 | Test marker is shown with start and stop without kwargs. | tests/tools_tests.py | test_show_marker_with_start_stop | nasqueron/pywikibot | python | def test_show_marker_with_start_stop(self):
start = 1
stop = 3
it = list(tools.islice_with_ellipsis(self.it, start, stop))
self.assertEqual(len(it), ((stop - start) + 1))
self.assertEqual(it[:(- 1)], self.it[start:stop])
self.assertEqual(it[(- 1)], '…') |
def test_show_custom_marker_with_start_stop(self):
'Test marker is shown with start and stop with kwargs.'
start = 1
stop = 3
it = list(tools.islice_with_ellipsis(self.it, start, stop, marker='new'))
self.assertEqual(len(it), ((stop - start) + 1))
self.assertEqual(it[:(- 1)], self.it[start:stop])
self.assertNotEqual(it[(- 1)], '…')
self.assertEqual(it[(- 1)], 'new') | 2,889,475,869,026,697,000 | Test marker is shown with start and stop with kwargs. | tests/tools_tests.py | test_show_custom_marker_with_start_stop | nasqueron/pywikibot | python | def test_show_custom_marker_with_start_stop(self):
start = 1
stop = 3
it = list(tools.islice_with_ellipsis(self.it, start, stop, marker='new'))
self.assertEqual(len(it), ((stop - start) + 1))
self.assertEqual(it[:(- 1)], self.it[start:stop])
self.assertNotEqual(it[(- 1)], '…')
self.assertEqual(it[(- 1)], 'new') |
def test_show_marker_with_stop_zero(self):
'Test marker is shown with stop for non empty iterable.'
stop = 0
it = list(tools.islice_with_ellipsis(self.it, stop))
self.assertEqual(len(it), (stop + 1))
self.assertEqual(it[(- 1)], '…') | 8,517,314,535,977,047,000 | Test marker is shown with stop for non empty iterable. | tests/tools_tests.py | test_show_marker_with_stop_zero | nasqueron/pywikibot | python | def test_show_marker_with_stop_zero(self):
stop = 0
it = list(tools.islice_with_ellipsis(self.it, stop))
self.assertEqual(len(it), (stop + 1))
self.assertEqual(it[(- 1)], '…') |
def test_do_not_show_marker_with_stop_zero(self):
'Test marker is shown with stop for empty iterable.'
stop = 0
it = list(tools.islice_with_ellipsis(self.it_null, stop))
self.assertEqual(len(it), stop) | -2,326,337,750,535,510,500 | Test marker is shown with stop for empty iterable. | tests/tools_tests.py | test_do_not_show_marker_with_stop_zero | nasqueron/pywikibot | python | def test_do_not_show_marker_with_stop_zero(self):
stop = 0
it = list(tools.islice_with_ellipsis(self.it_null, stop))
self.assertEqual(len(it), stop) |
def test_do_not_show_marker(self):
'Test marker is not shown when no marker is specified.'
import itertools
stop = 2
it_1 = list(tools.islice_with_ellipsis(self.it, stop, marker=None))
it_2 = list(itertools.islice(self.it, stop))
self.assertEqual(it_1, it_2) | -4,157,401,792,266,222,000 | Test marker is not shown when no marker is specified. | tests/tools_tests.py | test_do_not_show_marker | nasqueron/pywikibot | python | def test_do_not_show_marker(self):
import itertools
stop = 2
it_1 = list(tools.islice_with_ellipsis(self.it, stop, marker=None))
it_2 = list(itertools.islice(self.it, stop))
self.assertEqual(it_1, it_2) |
def test_do_not_show_marker_when_get_all(self):
'Test marker is not shown when all elements are retrieved.'
stop = None
it = list(tools.islice_with_ellipsis(self.it, stop))
self.assertEqual(len(it), len(self.it))
self.assertEqual(it, self.it)
self.assertNotEqual(it[(- 1)], '…') | -6,378,311,575,050,977,000 | Test marker is not shown when all elements are retrieved. | tests/tools_tests.py | test_do_not_show_marker_when_get_all | nasqueron/pywikibot | python | def test_do_not_show_marker_when_get_all(self):
stop = None
it = list(tools.islice_with_ellipsis(self.it, stop))
self.assertEqual(len(it), len(self.it))
self.assertEqual(it, self.it)
self.assertNotEqual(it[(- 1)], '…') |
def test_accept_only_keyword_marker(self):
"Test that the only kwargs accepted is 'marker'."
GENERATOR_NOT_CALLABLE = "'generator' object is not callable"
self.assertRaisesRegex(TypeError, GENERATOR_NOT_CALLABLE, tools.islice_with_ellipsis(self.it, 1, t='')) | 7,600,603,567,777,064,000 | Test that the only kwargs accepted is 'marker'. | tests/tools_tests.py | test_accept_only_keyword_marker | nasqueron/pywikibot | python | def test_accept_only_keyword_marker(self):
GENERATOR_NOT_CALLABLE = "'generator' object is not callable"
self.assertRaisesRegex(TypeError, GENERATOR_NOT_CALLABLE, tools.islice_with_ellipsis(self.it, 1, t=)) |
def __contains__(self, item):
'Override to not process some items.'
if (item in self.skip_list):
return True
else:
return super(SkipList, self).__contains__(item) | 5,224,048,271,776,758,000 | Override to not process some items. | tests/tools_tests.py | __contains__ | nasqueron/pywikibot | python | def __contains__(self, item):
if (item in self.skip_list):
return True
else:
return super(SkipList, self).__contains__(item) |
def add(self, item):
'Override to not add some items.'
if (item in self.process_again_list):
return
else:
return super(ProcessAgainList, self).add(item) | -8,827,108,811,324,081,000 | Override to not add some items. | tests/tools_tests.py | add | nasqueron/pywikibot | python | def add(self, item):
if (item in self.process_again_list):
return
else:
return super(ProcessAgainList, self).add(item) |
def __contains__(self, item):
'Override to stop on encountering items.'
if (item in self.stop_list):
raise StopIteration
else:
return super(ContainsStopList, self).__contains__(item) | -8,770,174,865,988,611,000 | Override to stop on encountering items. | tests/tools_tests.py | __contains__ | nasqueron/pywikibot | python | def __contains__(self, item):
if (item in self.stop_list):
raise StopIteration
else:
return super(ContainsStopList, self).__contains__(item) |
def add(self, item):
'Override to not continue on encountering items.'
if (item in self.stop_list):
raise StopIteration
else:
super(AddStopList, self).add(item) | -3,560,789,555,579,234,300 | Override to not continue on encountering items. | tests/tools_tests.py | add | nasqueron/pywikibot | python | def add(self, item):
if (item in self.stop_list):
raise StopIteration
else:
super(AddStopList, self).add(item) |
def _test_dedup_int(self, deduped, deduper, key=None):
'Test filter_unique results for int.'
if (not key):
key = passthrough
self.assertEqual(len(deduped), 0)
self.assertEqual(next(deduper), 1)
self.assertEqual(next(deduper), 3)
if (key in (hash, passthrough)):
if isinstance(deduped, tools.OrderedDict):
self.assertEqual(list(deduped.keys()), [1, 3])
elif isinstance(deduped, collections.Mapping):
self.assertCountEqual(list(deduped.keys()), [1, 3])
else:
self.assertEqual(deduped, set([1, 3]))
self.assertEqual(next(deduper), 2)
self.assertEqual(next(deduper), 4)
if (key in (hash, passthrough)):
if isinstance(deduped, tools.OrderedDict):
self.assertEqual(list(deduped.keys()), [1, 3, 2, 4])
elif isinstance(deduped, collections.Mapping):
self.assertCountEqual(list(deduped.keys()), [1, 2, 3, 4])
else:
self.assertEqual(deduped, set([1, 2, 3, 4]))
self.assertRaises(StopIteration, next, deduper) | 6,018,367,884,307,071,000 | Test filter_unique results for int. | tests/tools_tests.py | _test_dedup_int | nasqueron/pywikibot | python | def _test_dedup_int(self, deduped, deduper, key=None):
if (not key):
key = passthrough
self.assertEqual(len(deduped), 0)
self.assertEqual(next(deduper), 1)
self.assertEqual(next(deduper), 3)
if (key in (hash, passthrough)):
if isinstance(deduped, tools.OrderedDict):
self.assertEqual(list(deduped.keys()), [1, 3])
elif isinstance(deduped, collections.Mapping):
self.assertCountEqual(list(deduped.keys()), [1, 3])
else:
self.assertEqual(deduped, set([1, 3]))
self.assertEqual(next(deduper), 2)
self.assertEqual(next(deduper), 4)
if (key in (hash, passthrough)):
if isinstance(deduped, tools.OrderedDict):
self.assertEqual(list(deduped.keys()), [1, 3, 2, 4])
elif isinstance(deduped, collections.Mapping):
self.assertCountEqual(list(deduped.keys()), [1, 2, 3, 4])
else:
self.assertEqual(deduped, set([1, 2, 3, 4]))
self.assertRaises(StopIteration, next, deduper) |
def _test_dedup_str(self, deduped, deduper, key=None):
'Test filter_unique results for str.'
if (not key):
key = passthrough
self.assertEqual(len(deduped), 0)
self.assertEqual(next(deduper), '1')
self.assertEqual(next(deduper), '3')
if (key in (hash, passthrough)):
if isinstance(deduped, collections.Mapping):
self.assertEqual(deduped.keys(), [key('1'), key('3')])
else:
self.assertEqual(deduped, set([key('1'), key('3')]))
self.assertEqual(next(deduper), '2')
self.assertEqual(next(deduper), '4')
if (key in (hash, passthrough)):
if isinstance(deduped, collections.Mapping):
self.assertEqual(deduped.keys(), [key(i) for i in self.strs])
else:
self.assertEqual(deduped, set((key(i) for i in self.strs)))
self.assertRaises(StopIteration, next, deduper) | 2,363,046,913,805,858,300 | Test filter_unique results for str. | tests/tools_tests.py | _test_dedup_str | nasqueron/pywikibot | python | def _test_dedup_str(self, deduped, deduper, key=None):
if (not key):
key = passthrough
self.assertEqual(len(deduped), 0)
self.assertEqual(next(deduper), '1')
self.assertEqual(next(deduper), '3')
if (key in (hash, passthrough)):
if isinstance(deduped, collections.Mapping):
self.assertEqual(deduped.keys(), [key('1'), key('3')])
else:
self.assertEqual(deduped, set([key('1'), key('3')]))
self.assertEqual(next(deduper), '2')
self.assertEqual(next(deduper), '4')
if (key in (hash, passthrough)):
if isinstance(deduped, collections.Mapping):
self.assertEqual(deduped.keys(), [key(i) for i in self.strs])
else:
self.assertEqual(deduped, set((key(i) for i in self.strs)))
self.assertRaises(StopIteration, next, deduper) |
def test_set(self):
'Test filter_unique with a set.'
deduped = set()
deduper = tools.filter_unique(self.ints, container=deduped)
self._test_dedup_int(deduped, deduper) | -4,737,709,360,573,862,000 | Test filter_unique with a set. | tests/tools_tests.py | test_set | nasqueron/pywikibot | python | def test_set(self):
deduped = set()
deduper = tools.filter_unique(self.ints, container=deduped)
self._test_dedup_int(deduped, deduper) |
def test_dict(self):
'Test filter_unique with a dict.'
deduped = {}
deduper = tools.filter_unique(self.ints, container=deduped)
self._test_dedup_int(deduped, deduper) | 63,968,957,205,018,136 | Test filter_unique with a dict. | tests/tools_tests.py | test_dict | nasqueron/pywikibot | python | def test_dict(self):
deduped = {}
deduper = tools.filter_unique(self.ints, container=deduped)
self._test_dedup_int(deduped, deduper) |
def test_OrderedDict(self):
'Test filter_unique with a OrderedDict.'
deduped = tools.OrderedDict()
deduper = tools.filter_unique(self.ints, container=deduped)
self._test_dedup_int(deduped, deduper) | -548,688,444,290,108,200 | Test filter_unique with a OrderedDict. | tests/tools_tests.py | test_OrderedDict | nasqueron/pywikibot | python | def test_OrderedDict(self):
deduped = tools.OrderedDict()
deduper = tools.filter_unique(self.ints, container=deduped)
self._test_dedup_int(deduped, deduper) |
def test_int_hash(self):
'Test filter_unique with ints using hash as key.'
deduped = set()
deduper = tools.filter_unique(self.ints, container=deduped, key=hash)
self._test_dedup_int(deduped, deduper, hash) | 2,554,941,613,787,697,700 | Test filter_unique with ints using hash as key. | tests/tools_tests.py | test_int_hash | nasqueron/pywikibot | python | def test_int_hash(self):
deduped = set()
deduper = tools.filter_unique(self.ints, container=deduped, key=hash)
self._test_dedup_int(deduped, deduper, hash) |
def test_int_id(self):
'Test filter_unique with ints using id as key.'
deduped = set()
deduper = tools.filter_unique(self.ints, container=deduped, key=id)
self._test_dedup_int(deduped, deduper, id) | 7,581,880,135,276,090,000 | Test filter_unique with ints using id as key. | tests/tools_tests.py | test_int_id | nasqueron/pywikibot | python | def test_int_id(self):
deduped = set()
deduper = tools.filter_unique(self.ints, container=deduped, key=id)
self._test_dedup_int(deduped, deduper, id) |
def test_obj(self):
'Test filter_unique with objects.'
deduped = set()
deduper = tools.filter_unique(self.decs, container=deduped)
self._test_dedup_int(deduped, deduper) | 581,004,810,495,251,000 | Test filter_unique with objects. | tests/tools_tests.py | test_obj | nasqueron/pywikibot | python | def test_obj(self):
deduped = set()
deduper = tools.filter_unique(self.decs, container=deduped)
self._test_dedup_int(deduped, deduper) |
def test_obj_hash(self):
'Test filter_unique with objects using hash as key.'
deduped = set()
deduper = tools.filter_unique(self.decs, container=deduped, key=hash)
self._test_dedup_int(deduped, deduper, hash) | 6,557,149,698,377,117,000 | Test filter_unique with objects using hash as key. | tests/tools_tests.py | test_obj_hash | nasqueron/pywikibot | python | def test_obj_hash(self):
deduped = set()
deduper = tools.filter_unique(self.decs, container=deduped, key=hash)
self._test_dedup_int(deduped, deduper, hash) |
def test_obj_id(self):
'Test filter_unique with objects using id as key, which fails.'
deduped = set()
deduper = tools.filter_unique(self.decs, container=deduped, key=id)
self.assertEqual(len(deduped), 0)
for _ in self.decs:
self.assertEqual(id(next(deduper)), deduped.pop())
self.assertRaises(StopIteration, next, deduper)
deduper_ids = list(tools.filter_unique(self.decs, key=id))
self.assertNotEqual(len(deduper_ids), len(set(deduper_ids))) | 7,523,221,529,014,139,000 | Test filter_unique with objects using id as key, which fails. | tests/tools_tests.py | test_obj_id | nasqueron/pywikibot | python | def test_obj_id(self):
deduped = set()
deduper = tools.filter_unique(self.decs, container=deduped, key=id)
self.assertEqual(len(deduped), 0)
for _ in self.decs:
self.assertEqual(id(next(deduper)), deduped.pop())
self.assertRaises(StopIteration, next, deduper)
deduper_ids = list(tools.filter_unique(self.decs, key=id))
self.assertNotEqual(len(deduper_ids), len(set(deduper_ids))) |
def test_str(self):
'Test filter_unique with str.'
deduped = set()
deduper = tools.filter_unique(self.strs, container=deduped)
self._test_dedup_str(deduped, deduper) | -7,648,199,100,553,032,000 | Test filter_unique with str. | tests/tools_tests.py | test_str | nasqueron/pywikibot | python | def test_str(self):
deduped = set()
deduper = tools.filter_unique(self.strs, container=deduped)
self._test_dedup_str(deduped, deduper) |
def test_str_hash(self):
'Test filter_unique with str using hash as key.'
deduped = set()
deduper = tools.filter_unique(self.strs, container=deduped, key=hash)
self._test_dedup_str(deduped, deduper, hash) | 2,405,488,532,924,476,000 | Test filter_unique with str using hash as key. | tests/tools_tests.py | test_str_hash | nasqueron/pywikibot | python | def test_str_hash(self):
deduped = set()
deduper = tools.filter_unique(self.strs, container=deduped, key=hash)
self._test_dedup_str(deduped, deduper, hash) |
@unittest.skipIf((not tools.PY2), 'str in Py3 behave like objects and id as key fails')
def test_str_id(self):
'Test str using id as key.'
deduped = set()
deduper = tools.filter_unique(self.strs, container=deduped, key=id)
self._test_dedup_str(deduped, deduper, id) | -1,790,066,798,828,475,000 | Test str using id as key. | tests/tools_tests.py | test_str_id | nasqueron/pywikibot | python | @unittest.skipIf((not tools.PY2), 'str in Py3 behave like objects and id as key fails')
def test_str_id(self):
deduped = set()
deduper = tools.filter_unique(self.strs, container=deduped, key=id)
self._test_dedup_str(deduped, deduper, id) |
def test_for_resumable(self):
'Test filter_unique is resumable after a for loop.'
gen2 = tools.filter_unique(self.ints)
deduped = []
for item in gen2:
deduped.append(item)
if (len(deduped) == 3):
break
self.assertEqual(deduped, [1, 3, 2])
last = next(gen2)
self.assertEqual(last, 4)
self.assertRaises(StopIteration, next, gen2) | -5,043,707,769,899,936,000 | Test filter_unique is resumable after a for loop. | tests/tools_tests.py | test_for_resumable | nasqueron/pywikibot | python | def test_for_resumable(self):
gen2 = tools.filter_unique(self.ints)
deduped = []
for item in gen2:
deduped.append(item)
if (len(deduped) == 3):
break
self.assertEqual(deduped, [1, 3, 2])
last = next(gen2)
self.assertEqual(last, 4)
self.assertRaises(StopIteration, next, gen2) |
def test_skip(self):
'Test filter_unique with a container that skips items.'
deduped = SkipList()
deduper = tools.filter_unique(self.ints, container=deduped)
deduped_out = list(deduper)
self.assertCountEqual(deduped, deduped_out)
self.assertEqual(deduped, set([2, 4])) | 1,663,633,413,850,212,600 | Test filter_unique with a container that skips items. | tests/tools_tests.py | test_skip | nasqueron/pywikibot | python | def test_skip(self):
deduped = SkipList()
deduper = tools.filter_unique(self.ints, container=deduped)
deduped_out = list(deduper)
self.assertCountEqual(deduped, deduped_out)
self.assertEqual(deduped, set([2, 4])) |
def test_process_again(self):
'Test filter_unique with an ignoring container.'
deduped = ProcessAgainList()
deduper = tools.filter_unique(self.ints, container=deduped)
deduped_out = list(deduper)
self.assertEqual(deduped_out, [1, 3, 2, 1, 1, 4])
self.assertEqual(deduped, set([2, 4])) | -1,911,708,214,690,534,700 | Test filter_unique with an ignoring container. | tests/tools_tests.py | test_process_again | nasqueron/pywikibot | python | def test_process_again(self):
deduped = ProcessAgainList()
deduper = tools.filter_unique(self.ints, container=deduped)
deduped_out = list(deduper)
self.assertEqual(deduped_out, [1, 3, 2, 1, 1, 4])
self.assertEqual(deduped, set([2, 4])) |
def test_stop(self):
'Test filter_unique with an ignoring container.'
deduped = ContainsStopList()
deduped.stop_list = [2]
deduper = tools.filter_unique(self.ints, container=deduped)
deduped_out = list(deduper)
self.assertCountEqual(deduped, deduped_out)
self.assertEqual(deduped, set([1, 3]))
self.assertRaises(StopIteration, next, deduper)
deduped = AddStopList()
deduped.stop_list = [4]
deduper = tools.filter_unique(self.ints, container=deduped)
deduped_out = list(deduper)
self.assertCountEqual(deduped, deduped_out)
self.assertEqual(deduped, set([1, 2, 3]))
self.assertRaises(StopIteration, next, deduper) | -6,087,706,758,696,055,000 | Test filter_unique with an ignoring container. | tests/tools_tests.py | test_stop | nasqueron/pywikibot | python | def test_stop(self):
deduped = ContainsStopList()
deduped.stop_list = [2]
deduper = tools.filter_unique(self.ints, container=deduped)
deduped_out = list(deduper)
self.assertCountEqual(deduped, deduped_out)
self.assertEqual(deduped, set([1, 3]))
self.assertRaises(StopIteration, next, deduper)
deduped = AddStopList()
deduped.stop_list = [4]
deduper = tools.filter_unique(self.ints, container=deduped)
deduped_out = list(deduper)
self.assertCountEqual(deduped, deduped_out)
self.assertEqual(deduped, set([1, 2, 3]))
self.assertRaises(StopIteration, next, deduper) |
def __new__(cls, name, bases, dct):
'Create a new test case class.'
def create_test(method):
def test_method(self):
'Test getargspec.'
expected = method(1, 2)
returned = self.getargspec(method)
self.assertEqual(returned, expected)
self.assertIsInstance(returned, self.expected_class)
self.assertNoDeprecation()
return test_method
for (attr, tested_method) in list(dct.items()):
if attr.startswith('_method_test_'):
suffix = attr[len('_method_test_'):]
cls.add_method(dct, ('test_method_' + suffix), create_test(tested_method), doc_suffix='on {0}'.format(suffix))
dct['net'] = False
return super(MetaTestArgSpec, cls).__new__(cls, name, bases, dct) | -8,840,490,780,989,338,000 | Create a new test case class. | tests/tools_tests.py | __new__ | nasqueron/pywikibot | python | def __new__(cls, name, bases, dct):
def create_test(method):
def test_method(self):
'Test getargspec.'
expected = method(1, 2)
returned = self.getargspec(method)
self.assertEqual(returned, expected)
self.assertIsInstance(returned, self.expected_class)
self.assertNoDeprecation()
return test_method
for (attr, tested_method) in list(dct.items()):
if attr.startswith('_method_test_'):
suffix = attr[len('_method_test_'):]
cls.add_method(dct, ('test_method_' + suffix), create_test(tested_method), doc_suffix='on {0}'.format(suffix))
dct['net'] = False
return super(MetaTestArgSpec, cls).__new__(cls, name, bases, dct) |
def _method_test_args(self, param):
'Test method with two positional arguments.'
return (['self', 'param'], None, None, None) | 6,077,924,824,687,960,000 | Test method with two positional arguments. | tests/tools_tests.py | _method_test_args | nasqueron/pywikibot | python | def _method_test_args(self, param):
return (['self', 'param'], None, None, None) |
def _method_test_kwargs(self, param=42):
'Test method with one positional and one keyword argument.'
return (['self', 'param'], None, None, (42,)) | 2,637,302,384,798,835,700 | Test method with one positional and one keyword argument. | tests/tools_tests.py | _method_test_kwargs | nasqueron/pywikibot | python | def _method_test_kwargs(self, param=42):
return (['self', 'param'], None, None, (42,)) |
def _method_test_varargs(self, param, *var):
'Test method with two positional arguments and var args.'
return (['self', 'param'], 'var', None, None) | -8,122,617,885,005,337,000 | Test method with two positional arguments and var args. | tests/tools_tests.py | _method_test_varargs | nasqueron/pywikibot | python | def _method_test_varargs(self, param, *var):
return (['self', 'param'], 'var', None, None) |
def _method_test_varkwargs(self, param, **var):
'Test method with two positional arguments and var kwargs.'
return (['self', 'param'], None, 'var', None) | 6,348,691,979,645,983,000 | Test method with two positional arguments and var kwargs. | tests/tools_tests.py | _method_test_varkwargs | nasqueron/pywikibot | python | def _method_test_varkwargs(self, param, **var):
return (['self', 'param'], None, 'var', None) |
def _method_test_vars(self, param, *args, **kwargs):
'Test method with two positional arguments and both var args.'
return (['self', 'param'], 'args', 'kwargs', None) | -7,342,400,291,848,552,000 | Test method with two positional arguments and both var args. | tests/tools_tests.py | _method_test_vars | nasqueron/pywikibot | python | def _method_test_vars(self, param, *args, **kwargs):
return (['self', 'param'], 'args', 'kwargs', None) |
def getargspec(self, method):
'Call tested getargspec function.'
return tools.getargspec(method) | -3,962,492,708,510,394,000 | Call tested getargspec function. | tests/tools_tests.py | getargspec | nasqueron/pywikibot | python | def getargspec(self, method):
return tools.getargspec(method) |
def getargspec(self, method):
"Call inspect's getargspec function."
with warnings.catch_warnings():
if (tools.PYTHON_VERSION >= (3, 5)):
warnings.simplefilter('ignore', DeprecationWarning)
return inspect.getargspec(method) | -6,408,349,756,444,476,000 | Call inspect's getargspec function. | tests/tools_tests.py | getargspec | nasqueron/pywikibot | python | def getargspec(self, method):
with warnings.catch_warnings():
if (tools.PYTHON_VERSION >= (3, 5)):
warnings.simplefilter('ignore', DeprecationWarning)
return inspect.getargspec(method) |
def patch(self, name):
'Patch up <name> in self.setUp.'
patcher = mock.patch(name)
self.addCleanup(patcher.stop)
return patcher.start() | -8,015,875,073,429,200,000 | Patch up <name> in self.setUp. | tests/tools_tests.py | patch | nasqueron/pywikibot | python | def patch(self, name):
patcher = mock.patch(name)
self.addCleanup(patcher.stop)
return patcher.start() |
def setUp(self):
'Patch a variety of dependencies.'
super(TestFileModeChecker, self).setUp()
self.stat = self.patch('os.stat')
self.chmod = self.patch('os.chmod')
self.file = '~FakeFile' | 1,632,057,437,270,286,800 | Patch a variety of dependencies. | tests/tools_tests.py | setUp | nasqueron/pywikibot | python | def setUp(self):
super(TestFileModeChecker, self).setUp()
self.stat = self.patch('os.stat')
self.chmod = self.patch('os.chmod')
self.file = '~FakeFile' |
def test_auto_chmod_for_dir(self):
'Do not chmod files that have mode private_files_permission.'
self.stat.return_value.st_mode = 16768
tools.file_mode_checker(self.file, mode=384)
self.stat.assert_called_with(self.file)
self.assertFalse(self.chmod.called) | -778,208,593,761,305,900 | Do not chmod files that have mode private_files_permission. | tests/tools_tests.py | test_auto_chmod_for_dir | nasqueron/pywikibot | python | def test_auto_chmod_for_dir(self):
self.stat.return_value.st_mode = 16768
tools.file_mode_checker(self.file, mode=384)
self.stat.assert_called_with(self.file)
self.assertFalse(self.chmod.called) |
def test_auto_chmod_OK(self):
'Do not chmod files that have mode private_files_permission.'
self.stat.return_value.st_mode = 33152
tools.file_mode_checker(self.file, mode=384)
self.stat.assert_called_with(self.file)
self.assertFalse(self.chmod.called) | 150,273,854,964,530,340 | Do not chmod files that have mode private_files_permission. | tests/tools_tests.py | test_auto_chmod_OK | nasqueron/pywikibot | python | def test_auto_chmod_OK(self):
self.stat.return_value.st_mode = 33152
tools.file_mode_checker(self.file, mode=384)
self.stat.assert_called_with(self.file)
self.assertFalse(self.chmod.called) |
def test_auto_chmod_not_OK(self):
'Chmod files that do not have mode private_files_permission.'
self.stat.return_value.st_mode = 33188
tools.file_mode_checker(self.file, mode=384)
self.stat.assert_called_with(self.file)
self.chmod.assert_called_once_with(self.file, 384) | -8,264,303,141,372,550,000 | Chmod files that do not have mode private_files_permission. | tests/tools_tests.py | test_auto_chmod_not_OK | nasqueron/pywikibot | python | def test_auto_chmod_not_OK(self):
self.stat.return_value.st_mode = 33188
tools.file_mode_checker(self.file, mode=384)
self.stat.assert_called_with(self.file)
self.chmod.assert_called_once_with(self.file, 384) |
def setUp(self):
'Setup tests.'
super(TestFileShaCalculator, self).setUp() | 2,380,888,882,065,735,700 | Setup tests. | tests/tools_tests.py | setUp | nasqueron/pywikibot | python | def setUp(self):
super(TestFileShaCalculator, self).setUp() |
def test_md5_complete_calculation(self):
'Test md5 of complete file.'
res = tools.compute_file_hash(self.filename, sha='md5')
self.assertIn(res, ('5d7265e290e6733e1e2020630262a6f3', '2c941f2fa7e6e629d165708eb02b67f7')) | -8,072,161,407,093,740,000 | Test md5 of complete file. | tests/tools_tests.py | test_md5_complete_calculation | nasqueron/pywikibot | python | def test_md5_complete_calculation(self):
res = tools.compute_file_hash(self.filename, sha='md5')
self.assertIn(res, ('5d7265e290e6733e1e2020630262a6f3', '2c941f2fa7e6e629d165708eb02b67f7')) |
def test_md5_partial_calculation(self):
'Test md5 of partial file (1024 bytes).'
res = tools.compute_file_hash(self.filename, sha='md5', bytes_to_read=1024)
self.assertIn(res, ('edf6e1accead082b6b831a0a600704bc', 'be0227b6d490baa49e6d7e131c7f596b')) | 6,977,463,010,757,062,000 | Test md5 of partial file (1024 bytes). | tests/tools_tests.py | test_md5_partial_calculation | nasqueron/pywikibot | python | def test_md5_partial_calculation(self):
res = tools.compute_file_hash(self.filename, sha='md5', bytes_to_read=1024)
self.assertIn(res, ('edf6e1accead082b6b831a0a600704bc', 'be0227b6d490baa49e6d7e131c7f596b')) |
def test_sha1_complete_calculation(self):
'Test sha1 of complete file.'
res = tools.compute_file_hash(self.filename, sha='sha1')
self.assertIn(res, ('1c12696e1119493a625aa818a35c41916ce32d0c', '146121e6d0461916c9a0fab00dc718acdb6a6b14')) | 1,132,385,081,542,025,700 | Test sha1 of complete file. | tests/tools_tests.py | test_sha1_complete_calculation | nasqueron/pywikibot | python | def test_sha1_complete_calculation(self):
res = tools.compute_file_hash(self.filename, sha='sha1')
self.assertIn(res, ('1c12696e1119493a625aa818a35c41916ce32d0c', '146121e6d0461916c9a0fab00dc718acdb6a6b14')) |
def test_sha1_partial_calculation(self):
'Test sha1 of partial file (1024 bytes).'
res = tools.compute_file_hash(self.filename, sha='sha1', bytes_to_read=1024)
self.assertIn(res, ('e56fa7bd5cfdf6bb7e2d8649dd9216c03e7271e6', '617ce7d539848885b52355ed597a042dae1e726f')) | 2,189,167,603,415,891,000 | Test sha1 of partial file (1024 bytes). | tests/tools_tests.py | test_sha1_partial_calculation | nasqueron/pywikibot | python | def test_sha1_partial_calculation(self):
res = tools.compute_file_hash(self.filename, sha='sha1', bytes_to_read=1024)
self.assertIn(res, ('e56fa7bd5cfdf6bb7e2d8649dd9216c03e7271e6', '617ce7d539848885b52355ed597a042dae1e726f')) |
def test_sha224_complete_calculation(self):
'Test sha224 of complete file.'
res = tools.compute_file_hash(self.filename, sha='sha224')
self.assertIn(res, ('3d350d9d9eca074bd299cb5ffe1b325a9f589b2bcd7ba1c033ab4d33', '4a2cf33b7da01f7b0530b2cc624e1180c8651b20198e9387aee0c767')) | -1,636,782,885,229,818,600 | Test sha224 of complete file. | tests/tools_tests.py | test_sha224_complete_calculation | nasqueron/pywikibot | python | def test_sha224_complete_calculation(self):
res = tools.compute_file_hash(self.filename, sha='sha224')
self.assertIn(res, ('3d350d9d9eca074bd299cb5ffe1b325a9f589b2bcd7ba1c033ab4d33', '4a2cf33b7da01f7b0530b2cc624e1180c8651b20198e9387aee0c767')) |
def test_sha224_partial_calculation(self):
'Test sha224 of partial file (1024 bytes).'
res = tools.compute_file_hash(self.filename, sha='sha224', bytes_to_read=1024)
self.assertIn(res, ('affa8cb79656a9b6244a079f8af91c9271e382aa9d5aa412b599e169', '486467144e683aefd420d576250c4cc984e6d7bf10c85d36e3d249d2')) | 9,067,358,240,009,453,000 | Test sha224 of partial file (1024 bytes). | tests/tools_tests.py | test_sha224_partial_calculation | nasqueron/pywikibot | python | def test_sha224_partial_calculation(self):
res = tools.compute_file_hash(self.filename, sha='sha224', bytes_to_read=1024)
self.assertIn(res, ('affa8cb79656a9b6244a079f8af91c9271e382aa9d5aa412b599e169', '486467144e683aefd420d576250c4cc984e6d7bf10c85d36e3d249d2')) |
@classproperty
def bar(cls):
'Class property method.'
return cls._bar | 1,559,728,526,125,838,600 | Class property method. | tests/tools_tests.py | bar | nasqueron/pywikibot | python | @classproperty
def bar(cls):
return cls._bar |
def test_classproperty(self):
'Test for classproperty decorator.'
self.assertEqual(Foo.bar, 'baz')
self.assertEqual(Foo.bar, Foo._bar) | 6,343,514,514,572,085,000 | Test for classproperty decorator. | tests/tools_tests.py | test_classproperty | nasqueron/pywikibot | python | def test_classproperty(self):
self.assertEqual(Foo.bar, 'baz')
self.assertEqual(Foo.bar, Foo._bar) |
def __init__(self):
'Create instance with dummy values.'
self.instance_var = 1337
self.closed = False | 5,299,018,231,384,815,000 | Create instance with dummy values. | tests/tools_tests.py | __init__ | nasqueron/pywikibot | python | def __init__(self):
self.instance_var = 1337
self.closed = False |
def close(self):
'Just store that it has been closed.'
self.closed = True | -4,535,228,252,227,769,000 | Just store that it has been closed. | tests/tools_tests.py | close | nasqueron/pywikibot | python | def close(self):
self.closed = True |
def test_method(self):
'Test getargspec.'
expected = method(1, 2)
returned = self.getargspec(method)
self.assertEqual(returned, expected)
self.assertIsInstance(returned, self.expected_class)
self.assertNoDeprecation() | -8,777,568,974,570,606,000 | Test getargspec. | tests/tools_tests.py | test_method | nasqueron/pywikibot | python | def test_method(self):
expected = method(1, 2)
returned = self.getargspec(method)
self.assertEqual(returned, expected)
self.assertIsInstance(returned, self.expected_class)
self.assertNoDeprecation() |
def path_to_url(path):
'\n Convert a path to a file: URL. The path will be made absolute and have\n quoted path parts.\n '
path = os.path.normpath(os.path.abspath(path))
url = urlparse.urljoin('file:', urllib2.pathname2url(path))
return url | 1,249,888,226,016,398,300 | Convert a path to a file: URL. The path will be made absolute and have
quoted path parts. | poetry/packages/utils/utils.py | path_to_url | jancespivo/poetry | python | def path_to_url(path):
'\n Convert a path to a file: URL. The path will be made absolute and have\n quoted path parts.\n '
path = os.path.normpath(os.path.abspath(path))
url = urlparse.urljoin('file:', urllib2.pathname2url(path))
return url |
def is_installable_dir(path):
'Return True if `path` is a directory containing a setup.py file.'
if (not os.path.isdir(path)):
return False
setup_py = os.path.join(path, 'setup.py')
if os.path.isfile(setup_py):
return True
return False | -7,312,794,735,601,680,000 | Return True if `path` is a directory containing a setup.py file. | poetry/packages/utils/utils.py | is_installable_dir | jancespivo/poetry | python | def is_installable_dir(path):
if (not os.path.isdir(path)):
return False
setup_py = os.path.join(path, 'setup.py')
if os.path.isfile(setup_py):
return True
return False |
def is_archive_file(name):
'Return True if `name` is a considered as an archive file.'
ext = splitext(name)[1].lower()
if (ext in ARCHIVE_EXTENSIONS):
return True
return False | -1,030,535,540,759,138,600 | Return True if `name` is a considered as an archive file. | poetry/packages/utils/utils.py | is_archive_file | jancespivo/poetry | python | def is_archive_file(name):
ext = splitext(name)[1].lower()
if (ext in ARCHIVE_EXTENSIONS):
return True
return False |
def splitext(path):
'Like os.path.splitext, but take off .tar too'
(base, ext) = posixpath.splitext(path)
if base.lower().endswith('.tar'):
ext = (base[(- 4):] + ext)
base = base[:(- 4)]
return (base, ext) | 7,530,089,719,296,582,000 | Like os.path.splitext, but take off .tar too | poetry/packages/utils/utils.py | splitext | jancespivo/poetry | python | def splitext(path):
(base, ext) = posixpath.splitext(path)
if base.lower().endswith('.tar'):
ext = (base[(- 4):] + ext)
base = base[:(- 4)]
return (base, ext) |
def __init__(self, A, a=1.0, dtype=None, copy=False):
'Initializes `expm_multiply_parallel`. \n\n Parameters\n -----------\n A : {array_like, scipy.sparse matrix}\n The operator (matrix) whose exponential is to be calculated.\n a : scalar, optional\n scalar value multiplying generator matrix :math:`A` in matrix exponential: :math:`\\mathrm{e}^{aA}`.\n dtype : numpy.dtype, optional\n data type specified for the total operator :math:`\\mathrm{e}^{aA}`. Default is: `numpy.result_type(A.dtype,min_scalar_type(a),float64)`.\n copy : bool, optional\n if `True` the matrix is copied otherwise the matrix is stored by reference. \n\n '
if (_np.array(a).ndim == 0):
self._a = a
else:
raise ValueError('a must be scalar value.')
self._A = _sp.csr_matrix(A, copy=copy)
if (A.shape[0] != A.shape[1]):
raise ValueError('A must be a square matrix.')
a_dtype_min = _np.min_scalar_type(self._a)
if (dtype is None):
self._dtype = _np.result_type(A.dtype, a_dtype_min, _np.float64)
else:
min_dtype = _np.result_type(A.dtype, a_dtype_min, _np.float32)
if (not _np.can_cast(min_dtype, dtype)):
raise ValueError('dtype not sufficient to represent a*A to at least float32 precision.')
self._dtype = dtype
tol = (_np.finfo(self._dtype).eps / 2)
tol_dtype = _np.finfo(self._dtype).eps.dtype
self._tol = _np.array(tol, dtype=tol_dtype)
mu = (_wrapper_csr_trace(self._A.indptr, self._A.indices, self._A.data) / self._A.shape[0])
self._mu = _np.array(mu, dtype=self._dtype)
self._A_1_norm = _wrapper_csr_1_norm(self._A.indptr, self._A.indices, self._A.data, self._mu)
self._calculate_partition() | 1,455,410,426,730,194,700 | Initializes `expm_multiply_parallel`.
Parameters
-----------
A : {array_like, scipy.sparse matrix}
The operator (matrix) whose exponential is to be calculated.
a : scalar, optional
scalar value multiplying generator matrix :math:`A` in matrix exponential: :math:`\mathrm{e}^{aA}`.
dtype : numpy.dtype, optional
data type specified for the total operator :math:`\mathrm{e}^{aA}`. Default is: `numpy.result_type(A.dtype,min_scalar_type(a),float64)`.
copy : bool, optional
if `True` the matrix is copied otherwise the matrix is stored by reference. | quspin/tools/expm_multiply_parallel_core/expm_multiply_parallel_core.py | __init__ | markusschmitt/QuSpin | python | def __init__(self, A, a=1.0, dtype=None, copy=False):
'Initializes `expm_multiply_parallel`. \n\n Parameters\n -----------\n A : {array_like, scipy.sparse matrix}\n The operator (matrix) whose exponential is to be calculated.\n a : scalar, optional\n scalar value multiplying generator matrix :math:`A` in matrix exponential: :math:`\\mathrm{e}^{aA}`.\n dtype : numpy.dtype, optional\n data type specified for the total operator :math:`\\mathrm{e}^{aA}`. Default is: `numpy.result_type(A.dtype,min_scalar_type(a),float64)`.\n copy : bool, optional\n if `True` the matrix is copied otherwise the matrix is stored by reference. \n\n '
if (_np.array(a).ndim == 0):
self._a = a
else:
raise ValueError('a must be scalar value.')
self._A = _sp.csr_matrix(A, copy=copy)
if (A.shape[0] != A.shape[1]):
raise ValueError('A must be a square matrix.')
a_dtype_min = _np.min_scalar_type(self._a)
if (dtype is None):
self._dtype = _np.result_type(A.dtype, a_dtype_min, _np.float64)
else:
min_dtype = _np.result_type(A.dtype, a_dtype_min, _np.float32)
if (not _np.can_cast(min_dtype, dtype)):
raise ValueError('dtype not sufficient to represent a*A to at least float32 precision.')
self._dtype = dtype
tol = (_np.finfo(self._dtype).eps / 2)
tol_dtype = _np.finfo(self._dtype).eps.dtype
self._tol = _np.array(tol, dtype=tol_dtype)
mu = (_wrapper_csr_trace(self._A.indptr, self._A.indices, self._A.data) / self._A.shape[0])
self._mu = _np.array(mu, dtype=self._dtype)
self._A_1_norm = _wrapper_csr_1_norm(self._A.indptr, self._A.indices, self._A.data, self._mu)
self._calculate_partition() |
@property
def a(self):
'scalar: value multiplying generator matrix :math:`A` in matrix exponential: :math:`\\mathrm{e}^{aA}`'
return self._a | -3,851,025,710,648,072,700 | scalar: value multiplying generator matrix :math:`A` in matrix exponential: :math:`\mathrm{e}^{aA}` | quspin/tools/expm_multiply_parallel_core/expm_multiply_parallel_core.py | a | markusschmitt/QuSpin | python | @property
def a(self):
'scalar: value multiplying generator matrix :math:`A` in matrix exponential: :math:`\\mathrm{e}^{aA}`'
return self._a |
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