103 lines
2.2 KiB
Python
103 lines
2.2 KiB
Python
#! /usr/bin/python4
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#import scipy.optimize as opt
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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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import VisTools.plotting as vt
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import VisTools.tex as vtt
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import sys
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#import uncertainties as unc
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#import uncertainties.unumpy as unp
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try:
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append = str(sys.argv[1])
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except:
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append= ''
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plt.ioff()
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plt.style.use('bmh')
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plt.rcParams['axes.facecolor'] = 'white'
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fig, axarr = plt.subplots(
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2,
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1,
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figsize=(10, 8),
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gridspec_kw={'height_ratios':[4, 1]}
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)
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plt.subplots_adjust(hspace=.5)
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data = pd.read_csv("./" + __file__[:-3].split("_")[1] + append + ".csv", comment="#")
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fits = np.array([])
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errs = np.array([])
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colors = np.array([])
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data['dv_pit48'] /= 2
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data['Dv'] = data['v_pit48'] - data['v_set']
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data['Dvk'] = data['v_keith'] - data['v_set']
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print(data)
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# fit to abs dist
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linfnc = lambda x, m, c: x*m+c
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def plot_and_linfit(col: str, fil, l: str):
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plot = axarr[0].errorbar(
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data[fil]['v_set'],
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data[fil][col],
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alpha=0.3,
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label=None,
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fmt='.',
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antialiased=True
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)
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vals = vt.lm_plot(
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data[fil],
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'v_set',
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col,
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None,
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'dv_pit48',
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l,
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fig=axarr[0],
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color=plot[0].get_color()
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)
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return vals, plot
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for i in range(8):
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val, p = plot_and_linfit(
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'Dv',
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data.cycles == i,
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'$f_{}={{m:.3f}}\\cdot x{{c:+.3f}}V$'.format(i))
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fits = np.append(fits, val[0])
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#errs = np.append(errs, np.sqrt(pcov[0][0]))
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colors = np.append(colors, p[0].get_color() )
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plot_and_linfit('Dvk', data.cycles == 0, 'reference measurement')
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print(fits[0].n)
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for i in range(8):
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axarr[1].errorbar(
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i,
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fits[i].n,
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yerr=fits[i].s,
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fmt='.',
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#c = colors[i],
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)
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axarr[0].set_xlabel('V$_{set}$')
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#axarr[0].set_title("PowerIt ADC Calibration: dependency on measurement cycles")
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axarr[0].set_ylabel(r'$\Delta$V$_{IN}$ / V')
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axarr[1].set_xlabel('scaler value')
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axarr[1].set_ylabel(r'$m = \Delta$Gain')
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axarr[0].legend(
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bbox_to_anchor=(0, -.29, 1, 1),
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loc="lower left",
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mode="expand",
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borderaxespad=0,
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ncol=3)
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plt.savefig(
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"./" + __file__[:-3].split("_")[1] + append + ".pdf",
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dpi=1000,
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bbox_inches='tight')
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vtt.unc_tolatex(fits[0], "0")
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