misc_histo.py
5.71 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
title = "Histogram"
tip = "density distribution on a map"
onein = 1
import numpy as np
import scipy.stats as ss
from scipy import optimize, signal
try:
from nodfitting import curve_fit
except:
from scipy.optimize import curve_fit
from guiqwt import pyplot
from guidata.qt.QtGui import QMessageBox
from guidata.dataset.datatypes import DataSet
from guidata.dataset.dataitems import (IntItem, StringItem, ChoiceItem, FloatItem, BoolItem)
from guiqwt.config import _
class NOD3_App:
def __init__(self, parent):
self.parent = parent
self.parent.activateWindow()
def Error(self, msg):
QMessageBox.critical(self.parent.parent(), title,
_(u"Error:")+"\n%s" % str(msg))
def compute_app(self, **args):
class FuncParam(DataSet):
amin = FloatItem('min')
amax = FloatItem('max')
same = BoolItem("Set scale", default=False)
fit = BoolItem("Gauss fit", default=True)
name = title.replace(" ", "")
if args == {}:
param = FuncParam(_(title), "Histogram noise profile")
else:
param = self.parent.ScriptParameter(name, args)
# if no parameter needed set param to None. activate next line
#param = None
self.parent.compute_11(name, lambda m, p: self.function(m, p), param, onein)
def hist(self, data, lh, x0, txt):
try:
self.parent.N += 1
except:
self.parent.N = 1
self.parent.fig = pyplot.figure("Intensity distribution")
self.Nanz = len(self.parent._get_selected_rows())
self.ix = max(1, int(np.sqrt(self.Nanz+0.25)-0.5 + 0.99))
self.iy = int(float(self.Nanz)/float(self.ix) + 0.99)
pyplot.subplot(self.ix, self.iy, self.parent.N)
if txt != "": pyplot.legend()
pyplot.hist(data, title=txt, color='blue')
pyplot.xlabel("Intensity")
pyplot.ylabel("#bins")
pyplot.zlabel("distribution")
if self.parent.N == self.Nanz: pyplot.show(mainloop=False)
def plot(self, xy, lh, x0, txt, std, filename, p):
def gauss(X, a, x0, dx, off, slope):
x = X - dx
return off + slope*x + a*np.exp(-(x*x)/(2*x0*x0))
def fit(f, x, y, p0):
xtol = 1.49012e-08
Error = True
while Error:
#try:
if 1:
results = curve_fit(f, x, y, p0=p0, xtol=xtol)
popt = results[0]
Error = False
#except:
else:
Error = True
xtol *= 2.0
if xtol > 1.0:
return []
return popt
x = xy[1][:lh]
y = xy[0][:lh]
#for i in range(len(x)):
# print x[i], y[i]
#print
try:
self.parent.N += 1
except:
self.parent.N = 1
self.parent.fig = pyplot.figure("Noise distribution of " + filename)
self.Nanz = len(self.parent._get_selected_rows())
self.ix = max(1, int(np.sqrt(self.Nanz+0.25)-0.5 + 0.99))
self.iy = int(float(self.Nanz)/float(self.ix) + 0.99)
rms = None
if p.fit:
p0 = (max(y), len(x)/4, 0.0, 0.0, 0.0)
popt = fit(gauss, x, y, p0)
if popt != []:
rms = abs(popt[1])
off = popt[2]
pyplot.subplot(self.ix, self.iy, self.parent.N)
if txt != "": pyplot.legend()
if rms != None:
pyplot.plot(x, y, "b*", label=txt)
pyplot.plot(x, gauss(x, *popt), "r-", label=str("RMS=%.2f" % rms))
pyplot.plot(x, gauss(x, *popt), "", label=str("Off=%.2f" % off))
self.parent.hist_f = gauss(x, *popt)
self.parent.hist_f_rms = rms
self.parent.hist_f_off = off
else:
pyplot.plot(x, y, "b-", label=txt)
self.parent.hist_f = None
self.parent.hist_x = x
self.parent.hist_y = y
pyplot.xlabel("Intensity")
pyplot.ylabel("#bins")
pyplot.zlabel("distribution")
if self.parent.N == self.Nanz: pyplot.show(mainloop=False)
def function(self, m, p):
hdata = m.data
ny, nx = hdata.shape
mask = ~np.isnan(hdata)
hdata = hdata[mask]
med = max(5*np.median(hdata), 5*abs(hdata.min()))
if p.same:
bins = 32
hdata = np.where(hdata == hdata.min(), p.amin, hdata)
hdata = np.where(hdata == hdata.max(), p.amax, hdata)
hdata = np.where(hdata < p.amin, np.nan, hdata)
hdata = np.where(hdata > p.amax, np.nan, hdata)
else:
bins = min(int(np.sqrt(nx*ny)), 32)
hdata = np.where(abs(hdata) < med, hdata, med)
mask = ~np.isnan(hdata)
hist, bin_edges = np.histogram(hdata[mask], bins=bins)
dbin = bin_edges[1] - bin_edges[0]
#hist /= dbin
bin_edges += dbin/2.0
hist = hist[1:]
bin_edges = bin_edges[1:-1]
#bin_edges = bin_edges[:-1]
lh = len(hist)
for i in range(len(hist)):
if abs(bin_edges[i]) < med: lh = i
hmax = 0.0
imax = 0
for i in range(lh):
if hist[i] > imax:
hmax = bin_edges[i]
imax = hist[i]
if "EXTNAME" in m.header: txt = m.header["EXTNAME"].replace("MAP-", "")
elif "MAPTYPE" in m.header: txt = m.header["MAPTYPE"]
else: txt = "?"
if hasattr(self.parent, 'fname'):
filename = self.parent.fname
else:
filename = ""
self.plot([hist, bin_edges], lh, hmax, txt, np.std(hdata), filename, p)
#self.hist(hdata, lh, hmax, txt)
return [], p