Skip to content

Commit

Permalink
Merge pull request #94 from nunobrum/dev_space
Browse files Browse the repository at this point in the history 
Dev space
  • Loading branch information
@nunobrum
nunobrum committed May 12, 2023
2 parents 5c99747 + ce3b9a3 commit 6f58797
Show file tree
Hide file tree
Showing 9 changed files with 379 additions and 351 deletions.
319 changes: 160 additions & 159 deletions PyLTSpice/Histogram.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -61,172 +61,173 @@
__author__ = "Nuno Canto Brum <me@nunobrum.com>"
__copyright__ = "Copyright 2017, Fribourg Switzerland"

#!/usr/bin/env python
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt

from optparse import OptionParser

usage = "usage: %prog [options] LOG_FILE TRACE"
opts = OptionParser(usage=usage, version="%prog 0.1")
# opts.add_option('v', "var", action="store", type="string", dest="trace", help="The trace to be used in the histogram")
opts.add_option('-s', "--sigma", action="store", type="int", dest="sigma", default=3,
help="Sigma to be used in the distribution fit. Default=3")
opts.add_option('-n', "--nbins", action="store", type="int", dest="nbins", default=20,
help="Number of bins to be used in the histogram. Default=20")
opts.add_option('-c', "--condition", action="append", type="string", dest="filters",
help="Filter condition writen in python. More than one expression can be added but each expression "
"should be preceded by -c.\n" +
"EXAMPLE: -c V(N001)>4 -c parameter==1 -c I(V1)<0.5" )
opts.add_option('-f', "--format", action="store", type="string", dest="format",
help="Format string for the X axis. Example: -f %3.4f")
# opts.add_option('-p', "--scaling",action="store", type="string", dest="prescaling",
# help="Prescaling function to be applied to the input value.")
opts.add_option('-t', "--title", action="store", type="string", dest="title",
help="Title to appear on the top of the histogram.")
opts.add_option('-r', "--range", action="store", type="string", dest="range",
help="Range of the X axis to use for the histogram in the form min:max. Example: -r -1:1")
opts.add_option('-C', "--clipboard", action="store_true", dest="clipboard",
help="If the data from the clipboard is to be used.")
# opts.add_option('-x', "--xname", action="store", dest="xname", help="Name for the variable displayed")
opts.add_option('-o', "--output", action="store", type="string", dest="imagefile",
help="Output the image to a file. Argument is the name of the image File with png extension.\n"
"Example: -o image.png")
opts.add_option('-1', "--nonorm", action="store_false", dest="normalized", default=True,
help="Doesn't normalize the histogram so that area of the bell curve is 1.")
(options, args) = opts.parse_args()

values = []


if options.clipboard:
try:
import clipboard
except ImportError:
print("Failed to load clipboard package. Use PiP to install it.")
exit(1)
if len(args) > 0:
TRACE = args[-1]
else:
TRACE = "var"
text = clipboard.paste()
for line in text.split('\n'):
def main():
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt

from optparse import OptionParser

usage = "usage: %prog [options] LOG_FILE TRACE"
opts = OptionParser(usage=usage, version="%prog 0.1")
# opts.add_option('v', "var", action="store", type="string", dest="trace", help="The trace to be used in the histogram")
opts.add_option('-s', "--sigma", action="store", type="int", dest="sigma", default=3,
help="Sigma to be used in the distribution fit. Default=3")
opts.add_option('-n', "--nbins", action="store", type="int", dest="nbins", default=20,
help="Number of bins to be used in the histogram. Default=20")
opts.add_option('-c', "--condition", action="append", type="string", dest="filters",
help="Filter condition writen in python. More than one expression can be added but each expression "
"should be preceded by -c.\n" +
"EXAMPLE: -c V(N001)>4 -c parameter==1 -c I(V1)<0.5" )
opts.add_option('-f', "--format", action="store", type="string", dest="format",
help="Format string for the X axis. Example: -f %3.4f")
# opts.add_option('-p', "--scaling",action="store", type="string", dest="prescaling",
# help="Prescaling function to be applied to the input value.")
opts.add_option('-t', "--title", action="store", type="string", dest="title",
help="Title to appear on the top of the histogram.")
opts.add_option('-r', "--range", action="store", type="string", dest="range",
help="Range of the X axis to use for the histogram in the form min:max. Example: -r -1:1")
opts.add_option('-C', "--clipboard", action="store_true", dest="clipboard",
help="If the data from the clipboard is to be used.")
# opts.add_option('-x', "--xname", action="store", dest="xname", help="Name for the variable displayed")
opts.add_option('-o', "--output", action="store", type="string", dest="imagefile",
help="Output the image to a file. Argument is the name of the image File with png extension.\n"
"Example: -o image.png")
opts.add_option('-1', "--nonorm", action="store_false", dest="normalized", default=True,
help="Doesn't normalize the histogram so that area of the bell curve is 1.")
(options, args) = opts.parse_args()

values = []


if options.clipboard:
try:
values.append(float(line))
except ValueError:
print("Failed to convert line: '", line, "'")
elif len(args) == 0:
opts.print_help()
exit(-1)
else:
if len(args) < 2:
opts.error("Wrong number of parameters. Need to give the filename and the trace.")
import clipboard
except ImportError:
print("Failed to load clipboard package. Use PiP to install it.")
exit(1)
if len(args) > 0:
TRACE = args[-1]
else:
TRACE = "var"
text = clipboard.paste()
for line in text.split('\n'):
try:
values.append(float(line))
except ValueError:
print("Failed to convert line: '", line, "'")
elif len(args) == 0:
opts.print_help()
exit(-1)
TRACE = args[1]
logfile = args[0]

if not options.filters is None:
print("Filters Applied:", options.filters)
else:
print("No filters defined")

log = open(logfile,'r')
header = log.readline().rstrip('\r\n')
vars = header.split('\t')
try:
sav_col = vars.index(TRACE)
except ValueError:
log.close()
print("File '%s' doesn't have trace '%s'" % (logfile, TRACE))
print("LOG FILE contains %s" % vars)
exit(-1)
if len(args) < 2:
opts.error("Wrong number of parameters. Need to give the filename and the trace.")
opts.print_help()
exit(-1)
TRACE = args[1]
logfile = args[0]

if (options.filters is None) or (len(options.filters) == 0):
for line in log:
#print(line)
vs = line.split('\t')
values.append(float(vs[sav_col]))
else:
for line in log:
vs = map(float,line.split('\t'))
env = dict(zip(vars,vs))

for expression in options.filters:
test = eval(expression, None, env)
if test == False:
break
else:
values.append(float(env[TRACE]))
log.close()

if len(values) == 0:
print("No elements found")
elif len(values) < options.nbins:
print("Not enough elements for an histogram")
else:
x = np.array(values, dtype=float)
mu = x.mean()
mn = x.min()
mx = x.max()
sd = np.std(x)
sigmin = mu - options.sigma*sd
sigmax = mu + options.sigma*sd

if options.range is None:
# Automatic calculation of the range
axisXmin = mu - (options.sigma+1)*sd
axisXmax = mu + (options.sigma + 1) * sd

if mn < axisXmin:
axisXmin = mn

if mx > axisXmax:
axisXmax = mx
else:
if not options.filters is None:
print("Filters Applied:", options.filters)
else:
print("No filters defined")

log = open(logfile,'r')
header = log.readline().rstrip('\r\n')
vars = header.split('\t')
try:
smin, smax = options.range.split(":")
axisXmin = float(smin)
axisXmax = float(smax)
except:
opts.error("Invalid range setting")
sav_col = vars.index(TRACE)
except ValueError:
log.close()
print("File '%s' doesn't have trace '%s'" % (logfile, TRACE))
print("LOG FILE contains %s" % vars)
exit(-1)
if options.format:
fmt = options.format
else:
fmt = "%f"

print("Collected %d elements" % len(values))
print("Distributing in %d bins" % options.nbins)
print("Minimum is " + fmt % mn)
print("Maximum is " + fmt % mx)
print("Mean is " + fmt % mu)
print("Standard Deviation is " + fmt % sd)
print(("Sigma %d boundaries are " + fmt + " and " + fmt) % (options.sigma, sigmin, sigmax))
n, bins, patches = plt.hist(x, options.nbins, density=options.normalized, facecolor='green', alpha=0.75,
range=(axisXmin, axisXmax))
axisYmax = n.max() * 1.1

if options.normalized:
# add a 'best fit' line
y = norm.pdf(bins, mu, sd)
l = plt.plot(bins, y, 'r--', linewidth=1)
plt.axvspan(mu - options.sigma*sd, mu + options.sigma*sd, alpha=0.2, color="cyan")
plt.ylabel('Distribution [Normalised]')
else:
plt.ylabel('Distribution')
plt.xlabel(TRACE)

if options.title is None:
title = (r'$\mathrm{Histogram\ of\ %s:}\ \mu='+fmt+r',\ stdev='+fmt+r',\ \sigma=%d$') % (TRACE, mu, sd, options.sigma)
else:
title = options.title
plt.title(title)
if (options.filters is None) or (len(options.filters) == 0):
for line in log:
#print(line)
vs = line.split('\t')
values.append(float(vs[sav_col]))
else:
for line in log:
vs = map(float,line.split('\t'))
env = dict(zip(vars,vs))

for expression in options.filters:
test = eval(expression, None, env)
if test == False:
break
else:
values.append(float(env[TRACE]))
log.close()

plt.axis([axisXmin, axisXmax, 0, axisYmax ])
plt.grid(True)
if options.imagefile is not None:
plt.savefig(options.imagefile)
if len(values) == 0:
print("No elements found")
elif len(values) < options.nbins:
print("Not enough elements for an histogram")
else:
plt.show()
x = np.array(values, dtype=float)
mu = x.mean()
mn = x.min()
mx = x.max()
sd = np.std(x)
sigmin = mu - options.sigma*sd
sigmax = mu + options.sigma*sd

if options.range is None:
# Automatic calculation of the range
axisXmin = mu - (options.sigma+1)*sd
axisXmax = mu + (options.sigma + 1) * sd

if mn < axisXmin:
axisXmin = mn

if mx > axisXmax:
axisXmax = mx
else:
try:
smin, smax = options.range.split(":")
axisXmin = float(smin)
axisXmax = float(smax)
except:
opts.error("Invalid range setting")
exit(-1)
if options.format:
fmt = options.format
else:
fmt = "%f"

print("Collected %d elements" % len(values))
print("Distributing in %d bins" % options.nbins)
print("Minimum is " + fmt % mn)
print("Maximum is " + fmt % mx)
print("Mean is " + fmt % mu)
print("Standard Deviation is " + fmt % sd)
print(("Sigma %d boundaries are " + fmt + " and " + fmt) % (options.sigma, sigmin, sigmax))
n, bins, patches = plt.hist(x, options.nbins, density=options.normalized, facecolor='green', alpha=0.75,
range=(axisXmin, axisXmax))
axisYmax = n.max() * 1.1

if options.normalized:
# add a 'best fit' line
y = norm.pdf(bins, mu, sd)
l = plt.plot(bins, y, 'r--', linewidth=1)
plt.axvspan(mu - options.sigma*sd, mu + options.sigma*sd, alpha=0.2, color="cyan")
plt.ylabel('Distribution [Normalised]')
else:
plt.ylabel('Distribution')
plt.xlabel(TRACE)

if options.title is None:
title = (r'$\mathrm{Histogram\ of\ %s:}\ \mu='+fmt+r',\ stdev='+fmt+r',\ \sigma=%d$') % (TRACE, mu, sd, options.sigma)
else:
title = options.title
plt.title(title)

plt.axis([axisXmin, axisXmax, 0, axisYmax ])
plt.grid(True)
if options.imagefile is not None:
plt.savefig(options.imagefile)
else:
plt.show()
main()
Loading

0 comments on commit 6f58797

Please sign in to comment.