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