added most up-to-date versions of the SwissMX tools/utils
This commit is contained in:
Appleby Martin Vears
166
edge_scan/edge_scan.py
Normal file
166
edge_scan/edge_scan.py
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@@ -0,0 +1,166 @@
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#!/usr/bin/env python3
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# authors M.Appleby
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"""
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# aim
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Calculate FWHM of x-ray beam from an edge scan
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# protocol
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complete edge scan
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## IMPORTANT ##
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- save data as .txt file
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# usage
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python convert-scan-for-pyfai.py -j <jugfrau-name> -s <path to scan file> -n <name of output file>
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# output
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creates a .png of fit including FWHM title
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"""
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from scipy import stats
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from scipy.optimize import curve_fit
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from scipy.signal import peak_widths, find_peaks
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from scipy import asarray as ar,exp
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from math import sqrt, pi
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import argparse
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def gaus(x, *p):
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A, mu, sigma = p
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return A * np.exp(-(x-mu)**2/(2.*sigma**2))
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def sigmoid(x, *p):
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L, x0, k, b = p
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return L / (1 + np.exp(-k * (x -x0))) + b
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def getFWHM(filename, output_name, motor):
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motor = motor.capitalize()
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df = pd.read_csv(filename, sep='\t')
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#df = df[ [0,1] ]
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df = df.set_index( f"SAR-EXPMX:MOT_F{motor}.VAL", drop=True )
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print(df)
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print(df.columns)
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x_label=df.index.name
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y_label=df.columns[0]
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x_vals = df.index.tolist()
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y_vals = df[y_label].tolist()
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plt.plot(x_vals, y_vals, label = 'edge_scan')
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plt.ylabel('Intensity (counts)')
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plt.xlabel('Motor position (mm)')
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L_guess = np.max(y_vals)
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b_guess = np.min(y_vals)
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x0_guess = x_vals[np.argmin(np.abs(y_vals - (L_guess + b_guess) / 2))]
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k_guess = 1 / (x_vals[-1] - x_vals[0])
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s0 = [L_guess, x0_guess, k_guess, b_guess]
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params, params_covariance = curve_fit(sigmoid, x_vals, y_vals, p0=s0)
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#L_fit, x0_fit, k_fit, b_fit = params
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plt.plot(x_vals, y_vals)
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plt.plot(x_vals, sigmoid(x_vals, *params))
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plt.show()
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y_fit = sigmoid(x_vals, *params)
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dydx = np.gradient(y_fit, x_vals)
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if motor == 'Y':
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dydx=-dydx
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mean = x_vals[np.argmax(dydx)]
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sigma = (np.max(x_vals) - np.min(x_vals))/4
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print(mean, sigma)
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A= np.max(dydx)
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print(x_vals, '\\', y_vals)
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print(dydx)
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dydx[np.isnan(dydx)] = 0
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print(dydx)
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p0 = [A, mean, sigma]
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parameters, covariance = curve_fit( gaus, x_vals, dydx, p0=p0 )
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x0_op = parameters[1]
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sigma_op = parameters[2]
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print(parameters)
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gauss_y = gaus(x_vals,*parameters)
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FWHM_x = np.abs(2*np.sqrt(2*np.log(2))*sigma_op)
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plt.plot(x_vals, dydx, label = 'derivative')
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plt.plot(x_vals, gauss_y,label='Gaussian fit',color ='orange')
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plt.fill_between(x_vals,gauss_y,color='orange',alpha=0.5)
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plt.axvspan(x0_op+FWHM_x/2,x0_op-FWHM_x/2, color='green', alpha=0.75, lw=0, label='FWHM = {0}'.format(FWHM_x))
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#print(FWHM_x)
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#plt.plot(x_label, y_label, data=df)
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plt.legend()
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plt.show()
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# find peak centre
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peaks = find_peaks( gauss_y )
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fwhm_peak = peak_widths( gauss_y, peaks[0], rel_height=0.5 )
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fwhm_str = x_vals[int( round( fwhm_peak[2][0], 0 ))]
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fwhm_end = x_vals[int( round( fwhm_peak[3][0], 0 ))]
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fwhm = fwhm_str - fwhm_end
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fwhm_height = gauss_y[int( round( fwhm_peak[2][0], 0 ))]
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# find 1/e2 peak width
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full_peak = peak_widths( gauss_y, peaks[0], rel_height=0.865 )
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full_str = x_vals[int( round( full_peak[2][0], 0 ))]
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full_end = x_vals[int( round( full_peak[3][0], 0 ))]
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full = full_str - full_end
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full_height = gauss_y[int( round( full_peak[2][0], 0 ))]
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print( "FWHM = {0}".format( fwhm ) )
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print(FWHM_x)
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print( "1/e2 = {0}".format( full ) )
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#plt.savefig(output_name+filename.split('/')[-1]+'FWHM_{0}.png'.format(FWHM_x))
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return
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-p",
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"--path",
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help="path of input and output file, not currently in use",
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type=str,
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default="/sf/cristallina/data/p21224/res/pshell/edge_scans/"
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)
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parser.add_argument(
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"-i",
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"--input",
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help="location of input file",
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type=str,
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default="/sf/cristallina/data/p21224/res/pshell/edge_scans/0.5_x/0.5_x"
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)
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parser.add_argument(
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"-o",
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"--output",
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help="output path to save figure",
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type=str,
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default="/sf/cristallina/data/p21224/res/pshell/edge_scans/"
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)
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parser.add_argument(
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"-m",
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"--motor",
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help="X or Y",
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type=str,
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default="X"
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)
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args = parser.parse_args()
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getFWHM(args.input, args.output, args.motor)
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166
edge_scan/edge_scan_laser.py
Normal file
166
edge_scan/edge_scan_laser.py
Normal file
@@ -0,0 +1,166 @@
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#!/usr/bin/env python3
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# authors M.Appleby
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"""
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# aim
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Calculate FWHM of x-ray beam from an edge scan
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# protocol
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complete edge scan
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## IMPORTANT ##
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- save data as .txt file
|
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|
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# usage
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python convert-scan-for-pyfai.py -j <jugfrau-name> -s <path to scan file> -n <name of output file>
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# output
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creates a .png of fit including FWHM title
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"""
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from scipy import stats
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from scipy.optimize import curve_fit
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from scipy.signal import peak_widths, find_peaks
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from scipy import asarray as ar,exp
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from math import sqrt, pi
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import argparse
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def gaus(x, *p):
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A, mu, sigma = p
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return A * np.exp(-(x-mu)**2/(2.*sigma**2))
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def sigmoid(x, *p):
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L, x0, k, b = p
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return L / (1 + np.exp(-k * (x -x0))) + b
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def getFWHM(filename, output_name, motor):
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motor = motor.capitalize()
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df = pd.read_csv(filename, sep=',')
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#df = df[ [0,1] ]
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df = df.set_index( f"x-position", drop=True )
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print(df)
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print(df.columns)
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x_label=df.index.name
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y_label=df.columns[0]
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x_vals = df.index.tolist()
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y_vals = df[y_label].tolist()
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plt.plot(x_vals, y_vals, label = 'edge_scan')
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plt.ylabel('Intensity (counts)')
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plt.xlabel('Motor position (mm)')
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L_guess = np.max(y_vals)
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b_guess = np.min(y_vals)
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x0_guess = x_vals[np.argmin(np.abs(y_vals - (L_guess + b_guess) / 2))]
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k_guess = 1 / (x_vals[-1] - x_vals[0])
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s0 = [L_guess, x0_guess, k_guess, b_guess]
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params, params_covariance = curve_fit(sigmoid, x_vals, y_vals, p0=s0)
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#L_fit, x0_fit, k_fit, b_fit = params
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plt.plot(x_vals, y_vals)
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plt.plot(x_vals, sigmoid(x_vals, *params))
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plt.show()
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y_fit = sigmoid(x_vals, *params)
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dydx = np.gradient(y_fit, x_vals)
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if motor == 'Y':
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dydx=-dydx
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mean = x_vals[np.argmax(dydx)]
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sigma = (np.max(x_vals) - np.min(x_vals))/4
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print(mean, sigma)
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A= np.max(dydx)
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print(x_vals, '\\', y_vals)
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print(dydx)
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dydx[np.isnan(dydx)] = 0
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print(dydx)
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p0 = [A, mean, sigma]
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parameters, covariance = curve_fit( gaus, x_vals, dydx, p0=p0 )
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x0_op = parameters[1]
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sigma_op = parameters[2]
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print(parameters)
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gauss_y = gaus(x_vals,*parameters)
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FWHM_x = np.abs(2*np.sqrt(2*np.log(2))*sigma_op)
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plt.plot(x_vals, dydx, label = 'derivative')
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plt.plot(x_vals, gauss_y,label='Gaussian fit',color ='orange')
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plt.fill_between(x_vals,gauss_y,color='orange',alpha=0.5)
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plt.axvspan(x0_op+FWHM_x/2,x0_op-FWHM_x/2, color='green', alpha=0.75, lw=0, label='FWHM = {0}'.format(FWHM_x))
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#print(FWHM_x)
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#plt.plot(x_label, y_label, data=df)
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plt.legend()
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plt.show()
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# find peak centre
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peaks = find_peaks( gauss_y )
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fwhm_peak = peak_widths( gauss_y, peaks[0], rel_height=0.5 )
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fwhm_str = x_vals[int( round( fwhm_peak[2][0], 0 ))]
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fwhm_end = x_vals[int( round( fwhm_peak[3][0], 0 ))]
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fwhm = fwhm_str - fwhm_end
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fwhm_height = gauss_y[int( round( fwhm_peak[2][0], 0 ))]
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# find 1/e2 peak width
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full_peak = peak_widths( gauss_y, peaks[0], rel_height=0.865 )
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full_str = x_vals[int( round( full_peak[2][0], 0 ))]
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full_end = x_vals[int( round( full_peak[3][0], 0 ))]
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full = full_str - full_end
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full_height = gauss_y[int( round( full_peak[2][0], 0 ))]
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print( "FWHM = {0}".format( fwhm ) )
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print(FWHM_x)
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print( "1/e2 = {0}".format( full ) )
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#plt.savefig(output_name+filename.split('/')[-1]+'FWHM_{0}.png'.format(FWHM_x))
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return
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-p",
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"--path",
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help="path of input and output file, not currently in use",
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type=str,
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default="/sf/cristallina/data/p21224/res/pshell/edge_scans/"
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)
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parser.add_argument(
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"-i",
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"--input",
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help="location of input file",
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type=str,
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default="/sf/cristallina/data/p21224/res/pshell/edge_scans/0.5_x/0.5_x"
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)
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parser.add_argument(
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"-o",
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"--output",
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help="output path to save figure",
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type=str,
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default="/sf/cristallina/data/p21224/res/pshell/edge_scans/"
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)
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parser.add_argument(
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"-m",
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"--motor",
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help="X or Y",
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type=str,
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default="X"
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)
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args = parser.parse_args()
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getFWHM(args.input, args.output, args.motor)
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155
edge_scan/edge_scan_slic.py
Normal file
155
edge_scan/edge_scan_slic.py
Normal file
@@ -0,0 +1,155 @@
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#!/usr/bin/env python3
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# authors M.Appleby + some code donated by J.Beale
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"""
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Martin needs to tidy this
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# aim
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Calculate FWHM of x-ray beam from an edge scan
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# protocol
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give run directory
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To run and edge scan in Slic - may be a limit on how far and fast motor can move
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# to execute directly in slic:
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from devices.knife_edge import KnifeEdge
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kn = KnifeEdge
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scan = Scanner(default_acquisitions=[daq], condition=None)
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scan.scan1D(kn.x, 4, 5, 0.1, 10, 'test_knife_edge_evr_only', detectors=[], channels=["SARES30-LSCP1-FNS:CH5:VAL_GET"], pvs=[])
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"""
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import os, glob
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from scipy import stats
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from scipy.optimize import curve_fit
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from scipy.signal import peak_widths, find_peaks
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from scipy import asarray as ar,exp
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from math import sqrt, pi
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from scipy.signal import savgol_filter
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import argparse
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from sfdata import SFDataFiles, SFScanInfo
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def loadData(directory):
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num_of_acqs=len([name for name in os.listdir(os.path.join(directory,'data'))])
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print(num_of_acqs)
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diode_data = []
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for acquisition in range(1,num_of_acqs+1):
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with SFDataFiles(os.path.join(directory,'data','acq{:04}.BSDATA.h5'.format(acquisition))) as data:
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diode = data["SARES30-LSCP1-FNS:CH5:VAL_GET"].data
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diode = diode.mean()
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diode_data.append(diode)
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scan = SFScanInfo(os.path.join(directory,'meta','scan.json'))
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motor_positions = scan.readbacks
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print(motor_positions)
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print(len(diode_data), len(motor_positions))
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return motor_positions, diode_data
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def gauss(x, *p):
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A, mu, sigma = p
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return A * np.exp(-(x-mu)**2/(2.*sigma**2))
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#def gauss(x, C, A, x0, sigma):
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# return C + A*np.exp(-(x-x0)**2/(2.*sigma**2))
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def getFWHM(motor_positions, diode_data):
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#print(df.columns)
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x_label='x motor'
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y_label='diode'
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x_vals = motor_positions*1000
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y_vals = [i for i in diode_data]
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y_vals = savgol_filter(y_vals, 15, 3)
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fig, ax1 = plt.subplots()
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ax1.scatter(x_vals, y_vals, label='edge_scan', color='blue')
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plt.ylabel('Intensity (counts)')
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plt.xlabel('Motor position (um)')
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#plt.show()
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dydx = np.gradient(y_vals, x_vals)
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#dydx = dydx/max(dydx)
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mean = sum((x_vals)*dydx)/sum(dydx)
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sigma = sum(dydx*(mean)**2)/sum(dydx)
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A= 1/(sigma*2*sqrt(pi))
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dydx[np.isnan(dydx)] = 0
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p0 = [100, mean, 5 ]
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parameters, covariance = curve_fit( gauss, x_vals, dydx, p0=p0 )
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x0_op = parameters[1]
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# gaussian fit
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gauss_y = gauss(x_vals, *parameters)
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FWHM_x = np.abs(2*np.sqrt(2*np.log(2))*parameters[2])
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# find peak centre
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peaks = find_peaks( gauss_y )
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# find fwhm peak width
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fwhm_peak = peak_widths( gauss_y, peaks[0], rel_height=0.5 )
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#fwhm_str = x_vals[int( round( fwhm_peak[2][0], 0 ))]
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#fwhm_end = x_vals[int( round( fwhm_peak[3][0], 0 ))]
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#fwhm = fwhm_str - fwhm_end
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#fwhm_height = gauss_y[int( round( fwhm_peak[2][0], 0 ))]
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# find 1/e2 peak width
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#full_peak = peak_widths( gauss_y, peaks[0], rel_height=0.865 )
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#full_str = x_vals[int( round( full_peak[2][0], 0 ))]
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#full_end = x_vals[int( round( full_peak[3][0], 0 ))]
|
||||
|
||||
#full = full_str - full_end
|
||||
#full_height = gauss_y[int( round( full_peak[2][0], 0 ))]
|
||||
|
||||
#print( "FWHM = {0}".format( fwhm ) )
|
||||
print(FWHM_x)
|
||||
#print( "1/e2 = {0}".format( full ) )
|
||||
|
||||
|
||||
# plot
|
||||
ax2 = ax1.twinx()
|
||||
ax2.plot(x_vals, dydx,label ='derivative',color='red')
|
||||
ax2.plot(x_vals,gauss_y,label='Gaussian fit',color ='orange')
|
||||
|
||||
plt.fill_between(x_vals,gauss_y,color='orange',alpha=0.5)
|
||||
plt.axvspan(x0_op+FWHM_x/2,x0_op-FWHM_x/2, color='blue', alpha=0.75, lw=0, label='FWHM = {0}'.format(FWHM_x))
|
||||
#plt.hlines(fwhm_height, fwhm_str, fwhm_end, color='green', label='FWHM (find_peaks) = {0}'.format(fwhm))
|
||||
#plt.hlines(full_height, full_str, full_end, color='yellow', label='1/e2 = {0}'.format(full))
|
||||
fig.legend(loc="upper left")
|
||||
fig.tight_layout()
|
||||
plt.show()
|
||||
#plt.savefig(output_name+filename.split('/')[-1]+'FWHM_{0}.png'.format(sciFWHM_x))
|
||||
return
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--input",
|
||||
help="location of input file",
|
||||
type=str,
|
||||
default="/sf/cristallina/data/p21736/raw/run0002/"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--type",
|
||||
help="laser or xray",
|
||||
type=str,
|
||||
#default="/sf/cristallina/data/p19150/raw/run0371/"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
x_data, y_data = loadData(args.input)
|
||||
getFWHM(x_data, y_data)
|
||||
|
||||
|
||||
185
edge_scan/edge_scan_v2.py
Normal file
185
edge_scan/edge_scan_v2.py
Normal file
@@ -0,0 +1,185 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# authors M.Appleby
|
||||
|
||||
"""
|
||||
# aim
|
||||
Calculate FWHM of x-ray beam from an edge scan
|
||||
|
||||
# protocol
|
||||
complete edge scan
|
||||
## IMPORTANT ##
|
||||
- save data as .txt file
|
||||
|
||||
# usage
|
||||
python convert-scan-for-pyfai.py -j <jugfrau-name> -s <path to scan file> -n <name of output file>
|
||||
|
||||
# output
|
||||
creates a .png of fit including FWHM title
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy import stats
|
||||
from scipy.optimize import curve_fit
|
||||
from scipy.signal import peak_widths, find_peaks
|
||||
from scipy import asarray as ar,exp
|
||||
from math import sqrt, pi
|
||||
import argparse
|
||||
|
||||
def loadData(directory): # for data taken by slic
|
||||
num_of_acqs=len([name for name in os.listdir(os.path.join(directory,'data'))])
|
||||
print(num_of_acqs)
|
||||
diode_data = []
|
||||
for acquisition in range(1,num_of_acqs+1):
|
||||
with SFDataFiles(os.path.join(directory,'data','acq{:04}.BSDATA.h5'.format(acquisition))) as data:
|
||||
diode = data["SARES30-LSCP1-FNS:CH5:VAL_GET"].data
|
||||
diode = diode.mean()
|
||||
diode_data.append(diode)
|
||||
scan = SFScanInfo(os.path.join(directory,'meta','scan.json'))
|
||||
motor_positions = scan.readbacks
|
||||
print(motor_positions)
|
||||
print(len(diode_data), len(motor_positions))
|
||||
return motor_positions, diode_data
|
||||
|
||||
def gaus(x, *p):
|
||||
A, mu, sigma = p
|
||||
return A * np.exp(-(x-mu)**2/(2.*sigma**2))
|
||||
|
||||
def sigmoid(x, *p):
|
||||
L, x0, k, b = p
|
||||
return L / (1 + np.exp(-k * (x -x0))) + b
|
||||
|
||||
|
||||
def getFWHM(filename, output_name, motor, laser):
|
||||
motor = motor.capitalize()
|
||||
df = pd.read_csv(filename, sep='\t')
|
||||
|
||||
if laser:
|
||||
df = df.set_index( f"x-position", drop=True )
|
||||
else:
|
||||
df = df.set_index( f"SAR-EXPMX:MOT_F{motor}.VAL", drop=True )
|
||||
|
||||
#df = df.set_index( f"SAR-EXPMX:MOT_F{motor}.VAL", drop=True )
|
||||
|
||||
x_label=df.index.name
|
||||
y_label=df.columns[0]
|
||||
x_vals = df.index.tolist()
|
||||
y_vals = df[y_label].tolist()
|
||||
|
||||
plt.plot(x_vals, y_vals, label = 'edge_scan')
|
||||
plt.ylabel('Intensity (counts)')
|
||||
plt.xlabel('Motor position (mm)')
|
||||
|
||||
L_guess = np.max(y_vals)
|
||||
b_guess = np.min(y_vals)
|
||||
x0_guess = x_vals[np.argmin(np.abs(y_vals - (L_guess + b_guess) / 2))]
|
||||
k_guess = 1 / (x_vals[-1] - x_vals[0])
|
||||
|
||||
s0 = [L_guess, x0_guess, k_guess, b_guess]
|
||||
params, params_covariance = curve_fit(sigmoid, x_vals, y_vals, p0=s0)
|
||||
#L_fit, x0_fit, k_fit, b_fit = params
|
||||
plt.plot(x_vals, y_vals)
|
||||
plt.plot(x_vals, sigmoid(x_vals, *params))
|
||||
plt.show()
|
||||
y_fit = sigmoid(x_vals, *params)
|
||||
|
||||
dydx = np.gradient(y_fit, x_vals)
|
||||
if motor == 'Y':
|
||||
dydx=-dydx
|
||||
mean = x_vals[np.argmax(dydx)]
|
||||
sigma = (np.max(x_vals) - np.min(x_vals))/4
|
||||
print(mean, sigma)
|
||||
|
||||
A= np.max(dydx)
|
||||
print(x_vals, '\\', y_vals)
|
||||
print(dydx)
|
||||
dydx[np.isnan(dydx)] = 0
|
||||
print(dydx)
|
||||
|
||||
p0 = [A, mean, sigma]
|
||||
parameters, covariance = curve_fit( gaus, x_vals, dydx, p0=p0 )
|
||||
x0_op = parameters[1]
|
||||
sigma_op = parameters[2]
|
||||
print(parameters)
|
||||
|
||||
gauss_y = gaus(x_vals,*parameters)
|
||||
FWHM_x = np.abs(2*np.sqrt(2*np.log(2))*sigma_op)
|
||||
|
||||
plt.plot(x_vals, dydx, label = 'derivative')
|
||||
plt.plot(x_vals, gauss_y,label='Gaussian fit',color ='orange')
|
||||
plt.fill_between(x_vals,gauss_y,color='orange',alpha=0.5)
|
||||
plt.axvspan(x0_op+FWHM_x/2,x0_op-FWHM_x/2, color='green', alpha=0.75, lw=0, label='FWHM = {0}'.format(FWHM_x))
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
# find peak centre
|
||||
peaks = find_peaks( gauss_y )
|
||||
|
||||
fwhm_peak = peak_widths( gauss_y, peaks[0], rel_height=0.5 )
|
||||
|
||||
fwhm_str = x_vals[int( round( fwhm_peak[2][0], 0 ))]
|
||||
fwhm_end = x_vals[int( round( fwhm_peak[3][0], 0 ))]
|
||||
|
||||
fwhm = fwhm_str - fwhm_end
|
||||
fwhm_height = gauss_y[int( round( fwhm_peak[2][0], 0 ))]
|
||||
|
||||
# find 1/e2 peak width
|
||||
full_peak = peak_widths( gauss_y, peaks[0], rel_height=0.865 )
|
||||
|
||||
full_str = x_vals[int( round( full_peak[2][0], 0 ))]
|
||||
full_end = x_vals[int( round( full_peak[3][0], 0 ))]
|
||||
|
||||
full = full_str - full_end
|
||||
full_height = gauss_y[int( round( full_peak[2][0], 0 ))]
|
||||
|
||||
print( "FWHM = {0}".format( fwhm ) )
|
||||
print(FWHM_x)
|
||||
print( "1/e2 = {0}".format( full ) )
|
||||
|
||||
plt.savefig(output_name+filename.split('/')[-1]+'FWHM_{0}.png'.format(FWHM_x))
|
||||
return
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"-p",
|
||||
"--path",
|
||||
help="path of input and output file, not currently in use",
|
||||
type=str,
|
||||
default="/sf/cristallina/data/p21224/res/pshell/edge_scans/"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--input",
|
||||
help="location of input file",
|
||||
type=str,
|
||||
default="/sf/cristallina/data/p21224/res/pshell/edge_scans/0.5_x/0.5_x"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-o",
|
||||
"--output",
|
||||
help="output path to save figure",
|
||||
type=str,
|
||||
default="/sf/cristallina/data/p21224/res/pshell/edge_scans/"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-m",
|
||||
"--motor",
|
||||
help="X or Y",
|
||||
type=str,
|
||||
default="X"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-l",
|
||||
"--laser",
|
||||
help="True or False",
|
||||
action='store_true'
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
getFWHM(args.input, args.output, args.motor, args.laser)
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user