diff options
Diffstat (limited to 'utils/raspberrypi/ctt/ctt_noise.py')
| -rw-r--r-- | utils/raspberrypi/ctt/ctt_noise.py | 26 |
1 files changed, 13 insertions, 13 deletions
diff --git a/utils/raspberrypi/ctt/ctt_noise.py b/utils/raspberrypi/ctt/ctt_noise.py index b84cf0ca..f258bc6d 100644 --- a/utils/raspberrypi/ctt/ctt_noise.py +++ b/utils/raspberrypi/ctt/ctt_noise.py @@ -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: |