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Commit 61c07e93 authored by Roger Wolf's avatar Roger Wolf
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cleanup from obsolete or moved files

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Pendel/tools/csv2yml.py deleted 100755 → 0
+ 0
− 95
View file @ 43e31b64
#!/usr/bin/env python3
"""csv2yml.py
read floating-point column-data in very general .txt formats
and write an output block in yaml format
keys taken from 1st header line
Usage:
./cvs2yml [options] <input file name>
Input:
- file name
Output:
- yaml data block
.. moduleauthor:: Guenter Quast <g.quast@kit.edu>
"""
if __name__ == "__main__": # -------------------------------------------
import sys, os, yaml, argparse
from PhyPraKit import csv2yaml
if os.name == 'nt': # interactive mode on windows if error occurs
os.environ['PYTHONINSPECT']='x'
# - - - Parse command-line arguments
# parser = argparse.ArgumentParser(usage=__doc__)
parser = argparse.ArgumentParser(
description="convert csv to yaml format")
parser.add_argument('filename', type=str,
help="name of input file in csv format")
parser.add_argument('-s', '--save',
action='store_const', const=True, default=False,
help="save result in file")
parser.add_argument('-H', '--Header',
action='store_const', const=True, default=False,
help="print csv Header lines")
parser.add_argument('-q', '--quiet',
action='store_const', const=True, default=False,
help="quiet - no output to screen")
parser.add_argument('-d','--delimiter',
type=str, default=',',
help="delimiter, default=','")
parser.add_argument('-n','--header_lines',
type=int, default=1,
help="numer of header lines, default=1")
if len(sys.argv)==1: # print help message if no input given
parser.print_help()
raise ValueError('!!! not input given -exit !')
# collect input from ArgumentParser
args = parser.parse_args()
fnam = args.filename
nlhead = args.header_lines
delim = args.delimiter
showHeader = args.Header
sav_flg = args.save
quiet = args.quiet
if quiet: sav_flg = True
# ---- end argument parsing -------------------
f = open(fnam, 'r')
hlines, ylines = csv2yaml(f, nlhead, delim)
if not quiet:
print('-->', fnam, 'to yaml', end='')
if showHeader:
print(' csv header:')
for l in hlines:
print(l)
# print results to screen
if not quiet:
print(' yaml block:')
for l in ylines:
print(l)
# write to file
if sav_flg:
ymlfn = fnam.split('.')[0] + '.yaml'
with open(ymlfn, 'w') as f:
for l in ylines:
print(l, file=f)
print(' -> yaml saved in file',ymlfn)
Pendel/tools/plotCSV.py deleted 100755 → 0
+ 0
− 100
View file @ 43e31b64
#!/usr/bin/python3
"""plotCSV.py
uses PhyPraKkt.readtxt() to read floating-point column-data in csv format
usage:
./plotCSV.py [options] <input file name>
Input:
- input file in csv format
Options:
- s : character used as field separator, default ','
- H : number of header lines, default 1
Output:
- figure
"""
from PhyPraKit import readtxt
# --- helper function
def wexit(code):
# keep Python window open on MS Windows
import os, sys
if os.name == 'nt':
_ = input('\n ==> type <ret> to end > ')
sys.exit(code)
if __name__ == "__main__":
import sys, argparse, numpy as np, matplotlib.pyplot as plt
# - - - Parse command-line arguments
parser = argparse.ArgumentParser(description = \
"plot contents of CSV file")
parser.add_argument('filename', type=str, nargs='+',
help="name of csv file")
parser.add_argument('-v', '--verbose',
action='store_const', const=True, default=False,
help="full printout to screen")
parser.add_argument('-s', '--separator',
type=str, default=',',
help="character used as field separator ")
parser.add_argument('-H', '--Headerlines',
type=int, default = 1,
help="number of header lines")
if len(sys.argv)==1: # print help message if no input given
parser.print_help()
print(" \n !!! no input file given - exiting \n")
wexit(1)
# collect input from ArgumentParser
args = parser.parse_args()
fname = args.filename[0]
quiet_flg = not args.verbose
separator = args.separator # field separator: , or \t, or \_ or ; etc.
nHlines = args.Headerlines # number of header lines
# print(args)
# end parsing input ------------------------------------------
# read data from file in .txt format
hlines, data = readtxt(fname, delim=separator, nlhead=nHlines)
nColumns = data.shape[0]
nRows = data.shape[1]
keys = hlines[0].split(separator)[:]
# output to terminal
print(hlines)
print(" --> number of columns", nColumns)
print(" --> number of data points", nRows)
# make plots - columns 1, 2, 3, ... vs. column 0
fig=plt.figure(1, figsize=(10, 2.25*nColumns))
fig.tight_layout()
fig.suptitle('contents of file '+fname, size='x-large', color='b')
fig.subplots_adjust(left=0.14, bottom=0.1, right=0.97, top=0.93,
wspace=None, hspace=.33)#
x = data[0]
axes = []
ncol = nColumns - 1
for i in range(1, ncol+1):
axes.append(fig.add_subplot(ncol, 1, i))
ax = axes[i-1]
ax.plot(x, data[i], alpha=0.3, label='Column' + str(i))
ax.set_ylabel(keys[i], size='large')
ax.set_xlabel(keys[0], size='large')
ax.legend(loc='best', numpoints=1, prop={'size':10})
ax.grid()
plt.show()
Pendel/tools/plotData.py deleted 100755 → 0
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#!/usr/bin/env python3
"""**plotData.py** [options] <input file name>
Plot (several) data set(s) with error bars in x- and y- directions
or histograms from file in yaml format
usage:
./plotData.py [options] <input file name>
Input:
- input file in yaml format
Output:
- figure
yaml-format for (x-y) data:
.. code-block:: yaml
title: <title of plot>
x_label: <label for x-axis>
y_label: <label for y-axis>
label: <name of data set>
x_data: [ x values ]
y_data: [ y values ]
x_errors: x-uncertainty or [x-uncertainties]
y_errors: y-uncertainty or [y-uncertainties]
*Remark*: more than one input data sets are also possible.
Data sets and models can be overlayed in one plot if option
`showplots = False` ist specified. Either provide more than
one input file, or use yaml syntax, as shown here:
.. code-block:: yaml
# several input sets to be separated by
...
---
yaml-format for histogram:
.. code-block:: yaml
title: <title of plot>
x_label: <label for x-axis>
y_label: <label for y-axis>
label: <name of data set>
raw_data: [x1, ... , xn]
# define binning
n_bins: n
bin_range: [x_min, x_max]
# alternatively:
# bin edges: [e0, ..., en]
several input sets to be separated by
...
---
In case a model function is supplied, it is overlayed in the
output graph. The corresponding *yaml* block looks as follows:
.. code-block:: yaml
# optional model specification
model_label: <model name>
model_function: |
<Python code of model function>
If no `y_data` or `raw_data` keys are provided, only the model function
is shown. Note that minimalistic `x_data` and `bin_range` or `bin_edges`
information must be given to define the x-range of the graph.
"""
from PhyPraKit import plot_xy_from_yaml,plot_hist_from_yaml
# --- helper function
def wexit(code):
# keep Python window open on MS Windows
import os, sys
if os.name == 'nt':
_ = input('\n ==> type <ret> to end > ')
sys.exit(code)
if __name__ == "__main__": # --------------------------------------
import os, sys, yaml, argparse, matplotlib.pyplot as plt
if os.name == 'nt': # interactive mode on windows if error occurs
os.environ['PYTHONINSPECT']='x'
# - - - Parse command-line arguments
parser = argparse.ArgumentParser(description = \
"Plot data with error bars or a histrogram from file in yaml format")
# parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument('filename', type=str, nargs='+',
help="name(s) of input file(s) in yaml format")
parser.add_argument('-s', '--saveplot',
action='store_const', const=True, default=False,
help="save plot(s) in file(s)")
parser.add_argument('-f','--format',
type=str, default='pdf',
help="graphics output format, default=pdf")
parser.add_argument('-n', '--noplot',
action='store_const', const=True, default=False,
help="suppress ouput of plots on screen")
if len(sys.argv)==1: # print help message if no input given
parser.print_help()
print(" \n !!! no input file given - exiting \n")
wexit(1)
# collect input from ArgumentParser
args = parser.parse_args()
fnames = args.filename
sav_flg = args.saveplot
pltfmt = args.format
plt_flg = not args.noplot
ddata = []
for fnam in fnames:
f = open(fnam,'r')
try:
ymldata = yaml.load_all(f, Loader=yaml.Loader)
except (OSError, yaml.YAMLError) as exception:
print('!!! failed to read configuration file ' + fnam)
print(str(exception))
wexit(1)
data_type = 'xy'
for d in ymldata:
if 'type' in d:
data_type = d['type']
ddata.append(d)
# create a figure
if data_type == 'xy':
fignam = 'plotxyData'
elif data_type == 'histogram':
fignam = 'plothistData'
else:
print('!!! invalid data type', data_type)
wexit(1)
# create figure
figsize = (7.5, 6.5)
figure = plt.figure(num=fignam, figsize=figsize)
# decode yaml input and plot data for each yaml file
for d in ddata:
if data_type == 'xy':
plot_xy_from_yaml(d)
elif data_type == 'histogram':
plot_hist_from_yaml(d)
f.close()
# output to file or screen
if (sav_flg):
oname = fnames[0].split('.')[0] + '.'+pltfmt
plt.savefig( oname )
print(' -> graph saved to file ', oname)
# show plot on screen
if plt_flg:
plt.show()
wexit(0)
Pendel/tools/run_phyFit.py deleted 100755 → 0
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#!/usr/bin/env python3
"""**run_phyFit.py** [options] <input file name>
Perform fit with data and model from yaml file
Uses functions xyFit and hFit from PhyPraKit.phyFit
This code performs fits
- to x-y data with independent and correlated, absolute
and relative uncertainties in the x and y directions
- and to histogram data with a binned likelihood fit.
usage:
./run_phyFit.py [options] <input file name>
./run_phyFit.py --help for help
Input:
- input file in yaml format
output:
- text and/or file, graph depending on options
**yaml format for x-y fit:**
.. code-block:: yaml
label: <str data-set name>
x_label: <str name x-data>
x_data: [ list of float ]
y_label: <str name y-data>
y_data: [ list of float ]
x_errors: <float>, [list of floats], or {dictionary/ies}
y_errors: <float>, [list of floats], or {dictionary/ies}
# optionally, add Gaussian constraints on parameters
parameter_constraints:
<parameter name>:
value: <value>
uncertaintiy: <value>
model_label: <str model name>
model_function: |
<Python code>
format of uncertainty dictionary:
- error_value: <float> or [list of floats]
- correlation_coefficient: 0. or 1.
- relative: true or false
relative errors may be spcified as <float>%
Simple example of *yaml* input:
.. code-block:: yaml
label: 'Test Data'
x_data: [.05,0.36,0.68,0.80,1.09,1.46,1.71,1.83,2.44,2.09,3.72,4.36,4.60]
x_errors: 3%
x_label: 'x values'
y_data: [0.35,0.26,0.52,0.44,0.48,0.55,0.66,0.48,0.75,0.70,0.75,0.80,0.90]
y_errors: [.06,.07,.05,.05,.07,.07,.09,.1,.11,.1,.11,.12,.1]
y_label: 'y values'
model_label: 'Parabolic Fit'
model_function: |
def quadratic_model(x, a=0., b=1., c=0. ):
return a * x*x + b*x + c
**Example of yaml input for histogram fit:**
.. code-block:: yaml
# Example of a fit to histogram data
type: histogram
label: example data
x_label: 'h'
y_label: 'pdf(h)'
# data:
raw_data: [ 79.83,79.63,79.68,79.82,80.81,79.97,79.68,80.32,79.69,79.18,
80.04,79.80,79.98,80.15,79.77,80.30,80.18,80.25,79.88,80.02 ]
n_bins: 15
bin_range: [79., 81.]
# alternatively an array for the bin edges can be specified
#bin_edges: [79., 79.5, 80, 80.5, 81.]
model_density_function: |
def normal_distribution(x, mu=80., sigma=1.):
return np.exp(-0.5*((x - mu)/sigma)** 2)/np.sqrt(2.*np.pi*sigma** 2)
*Remark*: more than one input data sets are also possible.
Data sets and models can be overlayed in one plot if option
`showplots = False` ist specified. Either provide more than
one input file, or use yaml syntax, as shown here:
.. code-block:: yaml
# several input sets to be separated by
...
---
"""
from PhyPraKit.phyFit import xyFit_from_yaml, hFit_from_yaml
from pprint import pprint
# --- helper function
def wexit(code):
# keep Python window open on MS Windows
import os, sys
if os.name == 'nt':
_ = input('\n ==> type <ret> to end > ')
sys.exit(code)
if __name__ == "__main__": # --------------------------------------
#
# xyFit.py: Example of an application of PhyPraKit.phyFit.xyFit_from_yaml()
#
# package imports
import os, sys, argparse, yaml, numpy as np, matplotlib.pyplot as plt
if os.name == 'nt': # interactive mode on windows if error occurs
os.environ['PYTHONINSPECT']='x'
# - - - Parse command-line arguments
parser = argparse.ArgumentParser(description = \
"Perform a fit with PhyPraKit.phyFit package driven by input file")
# parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument('filename', type=str, nargs='+',
help="name(s) of fit input file(s) in yaml format")
parser.add_argument('-v', '--verbose',
action='store_const', const=True, default=False,
help="full printout to screen")
parser.add_argument('-r', '--result_to_file',
action='store_const', const=True, default=False,
help="store results to file")
parser.add_argument('-n', '--noplot',
action='store_const', const=True, default=False,
help="suppress ouput of plots on screen")
parser.add_argument('-s', '--saveplot',
action='store_const', const=True, default=False,
help="save plot(s) in file(s)")
parser.add_argument('-c', '--contour',
action='store_const', const=True, default=False,
help="plot contours and profiles")
parser.add_argument('--noband',
action='store_const', const=True, default=False,
help="suppress 1-sigma band around function")
parser.add_argument('-f','--format',
type=str, default='pdf',
help="graphics output format, default=pdf")
if len(sys.argv)==1: # print help message if no input given
parser.print_help()
print(" \n !!! no input file given - exiting \n")
wexit(1)
# collect input from ArgumentParser
args = parser.parse_args()
fnames=args.filename
quiet_flg = not args.verbose
store_result = args.result_to_file
plt_flg= not args.noplot
sav_flg=args.saveplot
cont_flg=args.contour
band_flg=not args.noband
pltfmt=args.format
# - - - End: Parse command-line arguments
ddata = []
# open and read input yaml file
for fnam in fnames:
f = open(fnam, 'r')
try:
ymldata = yaml.load_all(f, Loader=yaml.Loader)
except (OSError, yaml.YAMLError) as exception:
print('!!! failed to read configuration file ' + fnam)
print(str(exception))
wexit(1)
fitType = 'xy'
for d in ymldata:
if 'type' in d:
fitType = d['type']
ddata.append(d)
f.close()
# select appropriate wrapper
if fitType == 'xy':
fit = xyFit_from_yaml
elif fitType == 'histogram':
fit = hFit_from_yaml
else:
print('!!! unsupported type of fit:', fitType)
wexit(1)
for fd in ddata:
if 'type' in fd.keys():
fitType = fd['type']
print("*==*", sys.argv[0], "received valid yaml data for fit:")
if 'parametric_model' in fd: # for complex kafe2go format
pprint(fd, compact=True)
else: # "nice" printout for simple xyFit format
print(' ** Type of Fit:', fitType)
for key in fd:
if type(fd[key]) is not type([]): # got a scalar or string
print(key + ': ', fd[key])
elif type(fd[key][0]) is not type({}): # got list of scalars
print(key + ': ', fd[key])
else: # got list of uncertainty dictionaries
print(key+':')
for d in fd[key]:
for k in d:
print(' '+ k +': ', d[k], end=' ')
print()
# run fit
rdict = fit(fd, # the input dictionary defining the fit
plot=plt_flg, # show plot of data and model
plot_band=band_flg, # plot model confidence-band
plot_cor=cont_flg, # plot profiles likelihood and contours
showplots= False, # show plots on screen
quiet=quiet_flg, # suppress informative printout
return_fitObject=False
)
# print results to illustrate how to use output
print('\n*==* Fit Result:')
pvals, perrs, cor, chi2, pnams= rdict.values()
print(" chi2: {:.3g}".format(chi2))
print(" parameter names: ", pnams)
print(" parameter values: ", pvals)
np.set_printoptions(precision=3)
print(" neg. parameter errors: ", perrs[:,0])
print(" pos. parameter errors: ", perrs[:,1])
print(" correlation matrix : \n", cor)
if store_result:
outfile = (fnames[0].split('.')[0]+ '.result')
with open(outfile, 'a') as outf:
for key in rdict:
print("{}\n".format(key), rdict[key], file=outf)
print(' -> result saved to file ', outfile)
if (sav_flg):
# save all figures to file(s)
n_fig = 0
tag = ''
for n in plt.get_fignums():
plt.figure(n)
oname = (fnames[0].split('.')[0] + '%s.' + pltfmt) %(tag)
plt.savefig( oname)
print(' -> figure saved to file ', oname)
n_fig += 1
tag = '_'+str(n_fig)
else:
# show on screen
plt.show()
wexit(0)
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