Added uncertainities - single graph

singleenerg_uncertshow.py

+#!/usr/bin/env python3
+from rawprasslib import load_raw
+from rawautoparams import load_params
+from matplotlib import pyplot as plt
+import errno
+import numpy as np
+import os
+import time
+import scipy.optimize as spopt
+
+# SCRIPT DOES NOT CHECK FOR SANITY OF THE OUTCOME !!!
+# THAT'S YOUR JOB !!!
+
+# AE == appearance energy from LCQ obtained by sigmoid fitting
+# E_tcid == threshold collision induced dissociation energy from Armentrout
+# red dots .. residual intensity (TIC -fragment - parent)
+# blue dots .. relative intensity of the fragment
+# orange vertial bars .. standard deviation (std)
+# black vertilcal bars .. std of the mean (usually too small to see)
+# black lines - sigmoid fit, tangent in the inflex point.
+
+# Ab-initio conversion ratios derivation
+ev = 1.602176634e-19
+mol = 6.02214076e23
+farad = ev*mol
+ev2kj = farad/1000
+kj2ev = 1/ev2kj
+
+factor = 10.502
+
+filename = "BH_MeMe"
+outfilename = "777_uncertshow"
+
+parent = (272.8, 274.6)
+daughter = (194.4, 195.8)
+
+
+def boltzmann(x, bottom, top, xhalf, slope):
+    """boltzmann sigmoid curve function"""
+    y = bottom + (top - bottom) / (1 + np.exp((x-xhalf)/slope))
+    return y
+
+
+def fitline(x, a):
+    return x*a
+
+
+def get_tangentparams(parameters):
+    """generate a, b for y = ax + b from sigomid fit parameters"""
+    bottom, top, xhalf, slope = parameters[0]
+    a = -(top - bottom) / (4*slope)
+    yhalf = top + (bottom - top) / 2
+    b = yhalf - a * xhalf
+    return a, b
+
+
+def get_r_sqrd(function, xdata, ydata, popt):
+    """routine for getting fit probability"""
+    residuals = ydata - function(xdata, *popt)
+    ss_res = np.sum(residuals**2)
+    ss_tot = np.sum((ydata-np.mean(ydata))**2)
+    r_sqrd = 1 - (ss_res / ss_tot)
+    return r_sqrd
+
+
+figure = plt.figure(figsize=(5, 1.5), dpi=300, constrained_layout=True)
+
+print("processing %s..." % filename)
+
+# data readout and processing
+filename = filename+".RAW"
+if not os.path.isfile(filename):
+    raise FileNotFoundError(
+        errno.ENOENT, os.strerror(errno.ENOENT), filename)
+matrix = load_raw(filename)[0]
+legends = load_params(filename)
+normenergs = np.asarray(legends[1]).T[4]
+times = matrix[0][0]
+assert len(times) == len(normenergs)
+masses = matrix[1]
+parentargs = np.where((masses > parent[0]) & (masses < parent[1]))
+daughterargs = np.where((masses > daughter[0]) & (masses < daughter[1]))
+# tic == zero -> bad scan
+tic = np.sum(matrix[2].T, axis=0)
+assert len(times) == len(tic)
+good = np.where(tic > 0)
+parentints = np.divide(np.sum(matrix[2].T[parentargs], axis=0), tic)[good]
+daughterints = np.divide(np.sum(matrix[2].T[daughterargs], axis=0), tic)[good]
+rest = 1 - parentints - daughterints
+
+# fitting
+x = normenergs[good]
+y = daughterints
+xaver = np.unique(x)
+xarrs = [np.where(x == xval) for xval in xaver]
+yaver = [np.average(y[arr]) for arr in xarrs]
+stds = [np.std(y[arr], ddof=1) for arr in xarrs]
+stdmeans = [stds[m]/np.sqrt(len(xarrs)) for m, arr in enumerate(xarrs)]
+try:
+    guess = [min(y), max(y), x[np.argmax(y > (max(y)/2))], 0.1]
+    parameters = spopt.curve_fit(boltzmann, x, y, guess)
+    print(parameters[0])
+except RuntimeError:
+    print("optimization failed, giving up")
+    raise RuntimeError
+
+a, b = get_tangentparams(parameters)
+zerocross = -b/a
+yline = [0, max(y)]
+xline = [zerocross, (yline[1]-b)/a]
+rsqrd = get_r_sqrd(boltzmann, x, y, parameters[0])
+xfit = np.linspace(x[0], x[-1], 500)
+yfit = boltzmann(xfit, *parameters[0])
+plot = figure.add_subplot(ylabel="$I_{frag}\ /\ \Sigma I$",
+                          xlim=(min(x), max(x)), ylim=(-0.1, 1.1))
+plot.spines['top'].set_visible(False)
+plot.spines['right'].set_visible(False)
+plot.errorbar(xaver, yaver, yerr=stdmeans, zorder=10,
+              color=[0, 0, 0, 0.5], linestyle='None')
+plot.errorbar(xaver, yaver, yerr=stds, zorder=9,
+              color=[1, 0.5, 0, 1], linestyle='None')
+plot.plot(x, rest, '.', color=[1, 0.8, 0.8, 0.1])
+plot.plot(x, daughterints, '.', color=[0.4, 0.7, 1, 0.1])
+plot.plot(xfit, yfit, color=[0, 0, 0, 0.4], zorder=11)
+plot.plot(xline, yline, color=[0, 0, 0, 0.7], zorder=12)
+plot.plot(xaver, yaver, '.', color=[0, 0, 0, 1], zorder=13)
+plot.text(0.01, 1,
+          "{}\n$BDE_{{AE}}={:.2f}$\n$BDE={:.2f}\ eV$\n$BDE={:.2f}\ kJ/mol$\n$r^2={:.4f}$"
+          .format(filename, zerocross, (zerocross*factor/farad)*1000, 
+                  zerocross*factor, rsqrd),
+          transform=plot.axes.transAxes, va='top')
+print(zerocross)
+print("DONE! (%s)" % filename)
+
+plt.savefig(outfilename+".jpg")