def test_dist(self):
# Test that we can specify distributions both by name and as objects.
svals1, ppcc1 = stats.ppcc_plot(self.x, -10, 10, dist='tukeylambda')
svals2, ppcc2 = stats.ppcc_plot(self.x, -10, 10, dist=stats.tukeylambda)
assert_allclose(svals1, svals2, rtol=1e-20)
assert_allclose(ppcc1, ppcc2, rtol=1e-20)
# Test that 'tukeylambda' is the default dist
svals3, ppcc3 = stats.ppcc_plot(self.x, -10, 10)
assert_allclose(svals1, svals3, rtol=1e-20)
assert_allclose(ppcc1, ppcc3, rtol=1e-20)
def test_plot_kwarg(self):
# Check with the matplotlib.pyplot module
fig = plt.figure()
fig.add_subplot(111)
stats.ppcc_plot(self.x, -20, 20, plot=plt)
plt.close()
# Check that a Matplotlib Axes object is accepted
fig.add_subplot(111)
ax = fig.add_subplot(111)
stats.ppcc_plot(self.x, -20, 20, plot=ax)
plt.close()
def test_plot_kwarg(self):
# Check with the matplotlib.pyplot module
fig = plt.figure()
fig.add_subplot(111)
stats.ppcc_plot(self.x, -20, 20, plot=plt)
plt.close()
# Check that a Matplotlib Axes object is accepted
fig.add_subplot(111)
ax = fig.add_subplot(111)
stats.ppcc_plot(self.x, -20, 20, plot=ax)
plt.close()
def test_empty(self):
# For consistency with probplot return for one empty array,
# ppcc contains all zeros and svals is the same as for normal array
# input.
svals, ppcc = stats.ppcc_plot([], 0, 1)
assert_allclose(svals, np.linspace(0, 1, num=80))
assert_allclose(ppcc, np.zeros(80, dtype=float))
def test_empty(self):
# For consistency with probplot return for one empty array,
# ppcc contains all zeros and svals is the same as for normal array
# input.
svals, ppcc = stats.ppcc_plot([], 0, 1)
assert_allclose(svals, np.linspace(0, 1, num=80))
assert_allclose(ppcc, np.zeros(80, dtype=float))
def test_basic(self):
N = 5
svals, ppcc = stats.ppcc_plot(self.x, -10, 10, N=N)
ppcc_expected = [
0.21139644, 0.21384059, 0.98766719, 0.97980182, 0.93519298
]
assert_allclose(svals, np.linspace(-10, 10, num=N))
assert_allclose(ppcc, ppcc_expected)
def ppcc_plot(series, lambda_min=-5, lambda_max=5, dist="tukeylambda",
N=100, ax=None, save=False, show=True):
if ax is None:
fig, ax = plt.subplots()
svals, ppcc = stats.ppcc_plot(series, lambda_min, lambda_max,
dist=dist, plot=ax, N=N)
show_and_save_plot(show=show, save=save, filename="ppcc.png")
max_corr_value = ppcc.max()
max_corr_lambda = svals[ppcc.argmax()]
return svals, ppcc, max_corr_value, max_corr_lambda
def test_basic(self):
N = 5
svals, ppcc = stats.ppcc_plot(self.x, -10, 10, N=N)
ppcc_expected = [0.21139644, 0.21384059, 0.98766719, 0.97980182, 0.93519298]
assert_allclose(svals, np.linspace(-10, 10, num=N))
assert_allclose(ppcc, ppcc_expected)
# First we generate some random data from a Tukey-Lambda distribution, # with shape parameter -0.7: from scipy import stats import matplotlib.pyplot as plt np.random.seed(1234567) x = stats.tukeylambda.rvs(-0.7, loc=2, scale=0.5, size=10000) + 1e4 # Now we explore this data with a PPCC plot as well as the related # probability plot and Box-Cox normplot. A red line is drawn where we # expect the PPCC value to be maximal (at the shape parameter -0.7 used # above): fig = plt.figure(figsize=(12, 4)) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) res = stats.probplot(x, plot=ax1) res = stats.boxcox_normplot(x, -5, 5, plot=ax2) res = stats.ppcc_plot(x, -5, 5, plot=ax3) ax3.vlines(-0.7, 0, 1, colors='r', label='Expected shape value') plt.show()
def redraw(self):
def bootstrap(data, num_samples, statistic, alpha):
n = len(data)
idx = random.randint(0, n, (num_samples, n))
samples = data[idx]
stat = np.sort(statistic(samples, 1))
return (stat[int((alpha / 2.0) * num_samples)], stat[int(
(1 - alpha / 2.0) * num_samples)], samples)
data = DS.Raw.iloc[DS.Ir, DS.Ic]
data = data.assign(Lr=DS.Lr[DS.Ir])
data = data.assign(Cr=DS.Cr[DS.Ir])
data = data[[self.YcomboBox.currentText(), 'Lr', 'Cr']]
if data.shape[1] != 3:
QtWidgets.QMessageBox.critical(self,'Error',"More columns have the same name",\
QtWidgets.QMessageBox.Ok)
return ()
Nnan = data[self.YcomboBox.currentText()].isnull().all()
data = data.dropna()
Lr = data['Lr'].values
Cr = data['Cr'].values
data = data.drop('Lr', axis=1)
data = data.drop('Cr', axis=1)
Y = data.values.ravel()
if Y.dtype == 'float' and Y.dtype == 'int':
QtWidgets.QMessageBox.critical(self,'Error',"Some values are not numbers!",\
QtWidgets.QMessageBox.Ok)
return ()
color = 'blue'
fig = Figure()
ax = fig.add_subplot(111)
if self.CcheckBox.isChecked():
color = DS.Cc[self.YcomboBox.currentIndex() - 1]
if self.BoxradioButton.isChecked():
medianprops = dict(marker='D',
markeredgecolor='black',
markerfacecolor=color)
ax.boxplot(Y, vert=1, medianprops=medianprops)
if self.YcheckBox.isChecked():
if self.YlineEdit.text():
ax.set_ylabel(self.YlineEdit.text())
if self.XcheckBox.isChecked():
if self.XlineEdit.text():
ax.set_xlabel(self.YlineEdit.text())
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
elif self.NormalityradioButton.isChecked():
stats.probplot(Y, plot=ax)
if self.XcheckBox.isChecked():
if self.XlineEdit.text():
ax.set_xlabel('Normal N(0,1) Statistic Medians')
if self.YcheckBox.isChecked():
if self.YlineEdit.text():
ax.set_ylabel('Ordered Responce')
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
elif self.PPCCradioButton.isChecked():
stats.ppcc_plot(Y, -6, 6, plot=ax)
if self.XcheckBox.isChecked():
if self.XlineEdit.text():
ax.set_xlabel('Shape Values')
if self.YcheckBox.isChecked():
if self.YlineEdit.text():
ax.set_ylabel('Prob.Plot.Corr.Coef.')
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
elif self.BoxCoxradioButton.isChecked():
fig = Figure()
if (Y <= 0).all():
QtWidgets.QMessageBox.critical(
self, 'Error', "Data must be strictly positive!",
QtWidgets.QMessageBox.Ok)
return ()
bins = np.linspace(np.amin(Y), np.amax(Y), 21)
ax1 = fig.add_subplot(2,
2,
1,
title="Original " +
self.YcomboBox.currentText())
ax1.hist(Y,
bins=bins,
histtype='bar',
color=color,
alpha=0.5,
orientation="vertical",
label="x")
if Nnan:
ax1.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
trans_y, lambda_ = stats.boxcox(Y)
ax2 = fig.add_subplot(2,
2,
2,
title='Transformed Data (lambda=' +
str(round(lambda_, 2)) + ')')
bins = np.linspace(np.amin(trans_y), np.amax(trans_y), 21)
ax2.hist(trans_y,
bins=bins,
histtype='bar',
color=color,
alpha=0.5,
orientation="vertical",
label="x Transformed")
ax3 = fig.add_subplot(2,
2,
3,
title="Original " +
self.YcomboBox.currentText())
stats.probplot(Y, dist='norm', plot=ax3)
ax4 = fig.add_subplot(2, 2, 4)
stats.probplot(trans_y, dist='norm', plot=ax4)
ax4.set_title('Transformed Data (lambda=' +
str(round(lambda_, 2)) + ')')
elif self.logisticradioButton.isChecked():
stats.probplot(Y, dist=stats.logistic, plot=ax)
ax.set_xlabel('Quantiles')
ax.set_ylabel("Ordered " + self.YcomboBox.currentText())
ax.set_title('Logistic', fontsize=12)
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
elif self.laplaceradioButton.isChecked():
stats.probplot(Y, dist=stats.laplace, plot=ax)
ax.set_xlabel('Quantiles')
ax.set_ylabel('Ordered ' + self.YcomboBox.currentText())
ax.set_title('Laplace', fontsize=12)
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
elif self.logammaradioButton.isChecked():
shape = float(self.logammadoubleSpinBox.value())
stats.probplot(Y, dist=stats.loggamma, sparams=shape, plot=ax)
ax.set_xlabel('Quantiles')
ax.set_ylabel("Ordered " + self.YcomboBox.currentText())
ax.set_title('Log Gamma with shape ' + str(shape), fontsize=12)
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
elif self.lognormalradioButton.isChecked():
shape = float(self.lognormdoubleSpinBox.value()) / 10.
stats.probplot(Y, dist=stats.lognorm, sparams=(shape), plot=ax)
ax.set_xlabel('Quantiles')
ax.set_ylabel("Ordered " + self.YcomboBox.currentText())
ax.set_title('Log norm with shape ' + str(shape), fontsize=12)
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
elif self.BootmeanradioButton.isChecked():
fig = Figure()
ax1 = fig.add_subplot(1,
2,
1,
title="Historgram of " +
self.YcomboBox.currentText())
low, high, samples = bootstrap(Y, self.spinBox.value(), np.mean,
0.05)
points = np.mean(samples, 1)
ax1.hist(points, 50, histtype='step')
if Nnan:
ax1.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
ax2 = fig.add_subplot(1, 2, 2, title='Bootstrap 95% CI for mean')
ax2.scatter(0.1 * (random.random(len(points)) - 0.5), points)
ax2.plot([0.19, 0.21], [low, low], 'r', linewidth=2)
ax2.plot([0.19, 0.21], [high, high], 'r', linewidth=2)
ax2.plot([0.2, 0.2], [low, high], 'r', linewidth=2)
ax2.set_xlim([-0.2, 0.3])
elif self.BootsdradioButton.isChecked():
fig = Figure()
ax1 = fig.add_subplot(1,
2,
1,
title="Historgram of " +
self.YcomboBox.currentText())
low, high, samples = bootstrap(Y, self.spinBox.value(), np.std,
0.05)
points = np.std(samples, 1)
ax1.hist(points, 50, histtype='step')
if Nnan:
ax1.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
ax2 = fig.add_subplot(
1, 2, 2, title='Bootstrap 95% CI for standard deviation')
ax2.scatter(0.1 * (random.random(len(points)) - 0.5), points)
ax2.plot([0.19, 0.21], [low, low], 'r', linewidth=2)
ax2.plot([0.19, 0.21], [high, high], 'r', linewidth=2)
ax2.plot([0.2, 0.2], [low, high], 'r', linewidth=2)
ax2.set_xlim([-0.2, 0.3])
elif self.histogramradioButton.isChecked():
iqr = np.percentile(Y, [75, 25])
iqr = iqr[0] - iqr[1]
n = Y.shape[0]
dy = abs(float(Y.max()) - float(Y.min()))
nbins = np.floor(dy / (2 * iqr) * n**(1 / 3)) + 1
nbins = 2 * nbins
bins = np.linspace(float(Y.min()), float(Y.max()), nbins)
ax.hist(Y,
bins=bins,
histtype='bar',
color=color,
alpha=0.5,
orientation='vertical',
label="X")
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
elif self.trendradioButton.isChecked():
nr = len(Y)
ind = np.array(range(1, nr + 1))
color_point = 'red'
color_line = 'blue'
if self.CcheckBox.isChecked():
color_line = DS.Cc[self.YcomboBox.currentIndex() - 1]
color_point = Cr
ax.scatter(ind, Y, marker='o', color=color_point)
if self.LcheckBox.isChecked():
ax.plot(ind, Y, color=color_line)
if (nr > 30):
itick = np.linspace(0, nr - 1, 20).astype(int)
ltick = Lr[itick]
else:
itick = ind
ltick = Lr
ax.set_xticks(itick)
ax.set_xticklabels(ltick, rotation='vertical')
ax.set_xlabel('Object')
ax.set_ylabel(self.YcomboBox.currentText())
if Nnan:
ax.annotate('NaN present',
xy=(0.05, 0.95),
xycoords='axes fraction')
if self.TcheckBox.isChecked():
if self.TlineEdit.text():
ax.set_title(self.TlineEdit.text())
else:
ax.set_title('')
if self.XcheckBox.isChecked():
if self.XlineEdit.text():
ax.set_xlabel(self.XlineEdit.text())
else:
ax.set_xlabel('')
if self.YcheckBox.isChecked():
if self.YlineEdit.text():
ax.set_ylabel(self.YlineEdit.text())
else:
ax.set_ylabel('')
if self.XGcheckBox.isChecked():
ax.xaxis.grid(True)
else:
ax.xaxis.grid(False)
if self.YGcheckBox.isChecked():
ax.yaxis.grid(True)
else:
ax.yaxis.grid(False)
if not self.XMcheckBox.isChecked():
ax.tick_params(axis='x',
which='both',
bottom='off',
top='off',
labelbottom='off')
if not self.YMcheckBox.isChecked():
ax.tick_params(axis='y',
which='both',
left='off',
right='off',
labelleft='off')
self.rmmpl()
self.addmpl(fig)
