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fix: instances of plot.show changed to plot.draw in fit module

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This commit is contained in:
Fabian Joswig 2021-12-07 08:27:24 +00:00
parent 757d8ade06
commit f223b12cc2
3 changed files with 6 additions and 5 deletions
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1
.gitignore vendored
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@ -8,3 +8,4 @@ examples/B1k2_pcac_plateau.p
examples/Untitled.* examples/Untitled.*
core.* core.*
*.swp *.swp
htmlcov

6
pyerrors/fits.py
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@ -678,7 +678,7 @@ def qqplot(x, o_y, func, p):
plt.xlabel('Theoretical quantiles') plt.xlabel('Theoretical quantiles')
plt.ylabel('Ordered Values') plt.ylabel('Ordered Values')
plt.legend() plt.legend()
plt.show() plt.draw()
def residual_plot(x, y, func, fit_res): def residual_plot(x, y, func, fit_res):
@ -707,7 +707,7 @@ def residual_plot(x, y, func, fit_res):
ax1.set_xlim([xstart, xstop]) ax1.set_xlim([xstart, xstop])
ax1.set_ylabel('Residuals') ax1.set_ylabel('Residuals')
plt.subplots_adjust(wspace=None, hspace=None) plt.subplots_adjust(wspace=None, hspace=None)
plt.show() plt.draw()
def covariance_matrix(y): def covariance_matrix(y):
@ -782,7 +782,7 @@ def ks_test(obs=None):
loc_max_diff = np.argmax(np.abs(diffs)) loc_max_diff = np.argmax(np.abs(diffs))
loc = Xs[loc_max_diff] loc = Xs[loc_max_diff]
plt.annotate(s='', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0)) plt.annotate(s='', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
plt.show() plt.draw()
print(scipy.stats.kstest(Qs, 'uniform')) print(scipy.stats.kstest(Qs, 'uniform'))

4
tests/fits_test.py
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@ -29,7 +29,7 @@ def test_least_squares():
y = a[0] * np.exp(-a[1] * x) y = a[0] * np.exp(-a[1] * x)
return y return y
out = pe.least_squares(x, oy, func) out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True)
beta = out.fit_parameters beta = out.fit_parameters
for i in range(2): for i in range(2):
@ -133,7 +133,7 @@ def test_total_least_squares():
odr.set_job(fit_type=0, deriv=1) odr.set_job(fit_type=0, deriv=1)
output = odr.run() output = odr.run()
out = pe.total_least_squares(ox, oy, func) out = pe.total_least_squares(ox, oy, func, expected_chisquare=True)
beta = out.fit_parameters beta = out.fit_parameters
for i in range(2): for i in range(2):