NekoCWD/libcamera
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utils: raspberrypi: ctt: Fix pycodestyle E231

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E231 missing whitespace after ','
E231 missing whitespace after ':'

Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
This commit is contained in:
Laurent Pinchart 2020-05-02 03:32:00 +03:00
parent 7a653369cb
commit 93a133fb17
11 changed files with 493 additions and 493 deletions
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26
utils/raspberrypi/ctt/ctt_noise.py
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@ -12,7 +12,7 @@ Find noise standard deviation and fit to model:
noise std = a + b*sqrt(pixel mean)
"""
def noise(Cam,Img,plot):
def noise(Cam, Img, plot):
Cam.log += '\nProcessing image: {}'.format(Img.name)
stds = []
means = []
@ -36,14 +36,14 @@ def noise(Cam,Img,plot):
"""
stds = np.array(stds)
means = np.array(means)
means = np.clip(np.array(means),0,None)
means = np.clip(np.array(means), 0, None)
sq_means = np.sqrt(means)
"""
least squares fit model
"""
fit = np.polyfit(sq_means,stds,1)
fit = np.polyfit(sq_means, stds, 1)
Cam.log += '\nBlack level = {}'.format(Img.blacklevel_16)
Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1]))
Cam.log += ' slope = {:.3f}'.format(fit[0])
@ -59,8 +59,8 @@ def noise(Cam,Img,plot):
fit_score_norm = fit_score - fit_std
anom_ind = np.where(fit_score_norm > 1)
fit_score_norm.sort()
sq_means_clean = np.delete(sq_means,anom_ind)
stds_clean = np.delete(stds,anom_ind)
sq_means_clean = np.delete(sq_means, anom_ind)
stds_clean = np.delete(stds, anom_ind)
removed = len(stds) - len(stds_clean)
if removed != 0:
Cam.log += '\nIdentified and removed {} anomalies.'.format(removed)
@ -68,7 +68,7 @@ def noise(Cam,Img,plot):
"""
recalculate fit with outliers removed
"""
fit = np.polyfit(sq_means_clean,stds_clean,1)
fit = np.polyfit(sq_means_clean, stds_clean, 1)
Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1]))
Cam.log += ' slope = {:.3f}'.format(fit[0])
@ -81,7 +81,7 @@ def noise(Cam,Img,plot):
corrected = 1
ones = np.ones(len(means))
y_data = stds/sq_means
fit2 = np.polyfit(ones,y_data,0)
fit2 = np.polyfit(ones, y_data, 0)
Cam.log += '\nOffset below zero. Fit recalculated with zero offset'
Cam.log += '\nNoise profile: offset = 0'
Cam.log += ' slope = {:.3f}'.format(fit2[0])
@ -94,13 +94,13 @@ def noise(Cam,Img,plot):
if plot:
x = np.arange(sq_means.max()//0.88)
fit_plot = x*fit[0] + fit[1]
plt.scatter(sq_means,stds,label='data',color='blue')
plt.scatter(sq_means[anom_ind],stds[anom_ind],color='orange',label='anomalies')
plt.plot(x,fit_plot,label='fit',color='red',ls=':')
plt.scatter(sq_means, stds, label='data', color='blue')
plt.scatter(sq_means[anom_ind], stds[anom_ind], color='orange', label='anomalies')
plt.plot(x, fit_plot, label='fit', color='red', ls=':')
if fit[1] < 0:
fit_plot_2 = x*fit2[0]
plt.plot(x,fit_plot_2,label='fit 0 intercept',color='green',ls='--')
plt.plot(0,0)
plt.plot(x, fit_plot_2, label='fit 0 intercept', color='green', ls='--')
plt.plot(0, 0)
plt.title('Noise Plot\nImg: {}'.format(Img.str))
plt.legend(loc = 'upper left')
plt.xlabel('Sqrt Pixel Value')
@ -116,7 +116,7 @@ def noise(Cam,Img,plot):
"""
Cam.log += '\n'
if corrected:
fit = [fit2[0],0]
fit = [fit2[0], 0]
return fit
else: