* Introducing "wpylib" which is the collection of my small python utilities.

* graph_digitizer: Utility to help me digitize numbers from a graph image
  files (JPG/PNG plots).
master
wirawan 13 years ago
commit 6e1b0c505c
  1. 13
      __init__.py
  2. 85
      graph_digitizer.py

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# $Id: __init__.py,v 1.1 2009-12-04 19:30:26 wirawan Exp $
#
# wpylib main module
# Created: 20091204
# Wirawan Purwanto
#
# Main wpylib package. It is just a stub.
# The "wpylib" namespace contains all simple tools that I am making
# in the course of my research and hobby.
# In the future, useful modules will continue their lives as separate
# modules outside the "wpylib" jail. :-)
#
pass

@ -0,0 +1,85 @@
#!/usr/bin/ipython -pylab
#
# $Id: graph_digitizer.py,v 1.1 2009-12-04 19:30:26 wirawan Exp $
#
# Created: 20091204
# Wirawan Purwanto
#
# Simple and dirty utility module to digitize a graph (e.g. those image files
# obtained from a journal article PDF).
#
import numpy
def make_matrix(Str, debug=None):
"""Simple tool to convert a string like
'''1 2 3
4 5 6
7 8 9'''
into a numpy matrix (or, actually, an array object)."""
if isinstance(Str, numpy.matrix):
return numpy.array(Str)
elif isinstance(Str, numpy.ndarray):
if len(Str.shape) == 2:
return Str.copy()
else:
raise ValueError, "Cannot make matrix out of non-2D array"
Str2 = ";".join([ row.rstrip().rstrip(";") for row in Str.split("\n") if row.strip() != "" ])
rslt = numpy.matrix(Str2)
if debug: print rslt
return numpy.array(rslt)
def get_axis_scaler(data, axis):
"""Simple routine to obtain the scaling factor from pixel coordinate to
x or y value. The `data' string argument is a literal table like:
xpixel ypixel xvalue
...
or
xpixel ypixel yvalue
...
Only linear scale is supported."""
from scipy import stats
datamtx = make_matrix(data)
if axis == "x":
xx = datamtx[:,0]
yy = datamtx[:,2]
else:
xx = datamtx[:,1]
yy = datamtx[:,2]
# example from
# http://www2.warwick.ac.uk/fac/sci/moac/currentstudents/peter_cock/python/lin_reg
(gradient, intercept, r_value, p_value, std_err) = stats.linregress(xx,yy)
print gradient, intercept, r_value, p_value, std_err
#return (float(gradient[0]), float(intercept[0]))
return (gradient, intercept)
class axes_scaler:
"""The main engine to "unscale" the graph's data points from pixel (x,y) to
true axis (x,y) value. Only linear axis is supported here."""
def __init__(self, data_x, data_y):
"""Initialize the axis scalers (x and y) from a given `pixel -> axis value'
mapping."""
self.init(data_x, data_y)
def init(self, data_x, data_y):
self.xscaler = get_axis_scaler(data_x, "x")
self.yscaler = get_axis_scaler(data_y, "y")
def __call__(self, x, y):
return ((self.xscaler[0]*x + self.xscaler[1]), \
(self.yscaler[0]*y + self.yscaler[1]))
def scale_many(self, data):
mtx = make_matrix(data)
rslt = []
for row in mtx:
(x, y) = row[0], row[1]
rslt.append(list( self(x, y) ))
#print x, y
return numpy.array(rslt)
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