import numpy as np import matplotlib.pyplot as plt from utils import read_mca figsize = (12, 8) data20, live_time = read_mca("../data/calib_detector_gain20_5peaks.mca") data50, live_time = read_mca("../data/calib_detector_gain50_5peaks.mca") data100, live_time = read_mca("../data/calib_detector_gain100_4peaks.mca") channels = np.arange(len(data20)) #Pulse heights from Christians notes pulseHeights20 = np.array([10.3, 20.3, 39.6, 70., 110.]) #mV pulseHeights50 = np.array([5., 10.0, 20., 40., 50.]) #mV pulseHeights100 = np.array([2., 5.0, 10.0, 19.6]) #mV #By eye estimates where the peaks roughly are peaksrough20 = np.array([25, 72, 152, 276, 436]) peaksrough50 = np.array([36, 90, 193, 388, 494]) peaksrough100 = np.array([15, 76, 175, 371]) plt.figure(1) plt.step(channels, data20, lw=2, where='mid', label="Calibration Data") plt.figure(2) plt.step(channels, data50, lw=2, where='mid', label="Calibration Data") plt.figure(3) plt.step(channels, data100, lw=2, where='mid', label="Calibration Data") #Gaussian function def gauss_function(x, a, x0, sigma): return a * np.exp(-(x - x0)**2 / (2 * sigma**2)) # program
import numpy as np import matplotlib.pyplot as plt from utils import read_mca if (__name__ == '__main__'): import sys file_names = sys.argv[1:] plt.figure() for f in file_names: data, live_time = read_mca(f) plt.step(range(len(data)), data / live_time, where='mid', label="%s live time %f s" % (f, live_time)) #channel np.array(range(len(data))) #mask = (channel>300) & (channel<400) #chargsum = sum(np.array(data)[mask]) #print(chargsum) plt.legend() plt.xlabel("Channel", size=20) plt.ylabel("Rate [Hz]", size=20) plt.yscale('log') plt.show()
import numpy as np import matplotlib.pyplot as plt from utils import read_mca if(__name__ == '__main__'): import sys file_names = sys.argv[1:] plt.figure() for f in file_names: data,live_time = read_mca(f) plt.step(range(len(data)),data/live_time,where='mid',label= "%s live time %f s"%(f,live_time)) #channel np.array(range(len(data))) #mask = (channel>300) & (channel<400) #chargsum = sum(np.array(data)[mask]) #print(chargsum) plt.legend() plt.xlabel("Channel",size=20) plt.ylabel("Rate [Hz]",size=20) plt.yscale('log') plt.show()
import numpy as np import matplotlib.pyplot as plt from utils import read_mca figsize = (12,8) data20,live_time = read_mca("../data/calib_detector_gain20_5peaks.mca") data50,live_time = read_mca("../data/calib_detector_gain50_5peaks.mca") data100,live_time = read_mca("../data/calib_detector_gain100_4peaks.mca") channels = np.arange(len(data20)) #Pulse heights from Christians notes pulseHeights20 = np.array([10.3,20.3,39.6,70.,110.])#mV pulseHeights50 = np.array([5.,10.0,20.,40.,50.])#mV pulseHeights100 = np.array([2.,5.0,10.0,19.6])#mV #By eye estimates where the peaks roughly are peaksrough20 = np.array([25,72,152,276,436]) peaksrough50 = np.array([36,90,193,388,494]) peaksrough100 = np.array([15,76,175,371]) plt.figure(1) plt.step(channels,data20, lw = 2, where='mid',label= "Calibration Data") plt.figure(2) plt.step(channels,data50, lw = 2, where='mid',label= "Calibration Data") plt.figure(3) plt.step(channels,data100, lw = 2, where='mid',label= "Calibration Data") #Gaussian function def gauss_function(x, a, x0, sigma): return a*np.exp(-(x-x0)**2/(2*sigma**2)) # program