added lots of .py

dioderata/knife_edge.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+
+from time import sleep
+from collections import deque
+import numpy as np
+import scipy.signal
+from scipy.special import erf
+from scipy.optimize import curve_fit
+
+from zoetrope import aniplot as plt
+from bstrd import BS, bsstream
+plt.blit = False
+plt.style.use('ggplot')
+
+# config
+#chname_diode  = "SLAAR11-LSCP1-FNS:CH0:VAL_GET"
+chname_diode = "SARES11-GES1:CH1_VAL_GET"
+chname_i0 = "SAROP11-PBPS110:INTENSITY"
+chname_xhuber = "SARES11-XSAM125:ENC_X1_BS"
+chname_yhuber = "SARES11-XSAM125:ENC_Y1_BS"
+chname_events = "SAR-CVME-TIFALL4:EvtSet"
+length = 5000
+
+# create channel
+ch_diode  = BS(chname_diode)
+ch_i0 = BS(chname_i0)
+ch_xhuber = BS(chname_xhuber)
+ch_yhuber = BS(chname_yhuber)
+ch_evts = BS(chname_events)
+
+n = 100
+sigs = np.empty(n)
+i0s = np.empty(n)
+x_pos = np.empty(n)
+y_pos = np.empty(n)
+evts = np.empty((n, 256))
+
+# create a buffer for the plotting
+#ke_sigs = deque(maxlen=length)
+#x_poses = deque(maxlen=length)
+#y_poses = deque(maxlen=length)
+ke_sigs = [] 
+x_poses = [] 
+y_poses = [] 
+i_zeroes = []
+
+# fit stuff for knife edge scans
+def errfunc_fwhm(x, x0, amplitude, width, offset):
+    return offset + amplitude*erf((x0-x)*2*np.sqrt(np.log(2))/(np.abs(width)))         #d is fwhm
+
+# create the empty plot
+pd = plt.plot([0])
+
+# some plot settings
+plt.suptitle(chname_diode)
+plt.fig.set_figheight(5)
+plt.fig.set_figwidth(5)
+plt.tight_layout()
+
+for counter, data in zip(plt.show(), bsstream):
+    print(counter)
+
+    for i in range(n):
+#        sigs[i] = ch_diode.get()
+#        i0s[i] = ch_i0.get()
+#        x_pos[i] = ch_xhuber.get()
+#        y_pos[i] = ch_yhuber.get()
+        ke_sigs.append(ch_diode.get())
+        x_poses.append(ch_xhuber.get())
+        y_poses.append(ch_yhuber.get())
+        i_zeroes.append(ch_i0.get())
+        next(bsstream) # this gets the next set of data
+
+#    sig_norm = sigs#/np.asarray(i0s)
+#    ke_sigs.append(sig_norm)
+
+    pd.set(x_poses, np.asarray(ke_sigs)/(i_zeroes))
+#    popt, pcov = curve_fit(errfunc_fwhm, np.asarray(x_poses), np.asarray(ke_sigs)/(i_zeroes), p0=[np.mean(x_poses), (np.max(ke_sigs)-np.min(ke_sigs)), 10, 0])
+#    pd.set(x_poses, errfunc_fwhm(x_poses, *popt))
+
+    print(np.shape(x_poses))
+
+    # this, I need to move into the library
+    pd.ax.relim()
+    pd.ax.autoscale_view()
+#    pd.ax.suptitle("{}, {}".format(popt[0], popt[2]))
+
+bsstream.close()