| Index: trunk/tools/wsor/editor_lifecycle/normplot.py |
| — | — | @@ -0,0 +1,76 @@ |
| | 2 | +#!/usr/bin/python |
| | 3 | + |
| | 4 | +import os |
| | 5 | +import sys |
| | 6 | + |
| | 7 | +import numpy as np |
| | 8 | +import matplotlib.pyplot as pp |
| | 9 | + |
| | 10 | +from argparse import ArgumentParser |
| | 11 | +from collections import defaultdict |
| | 12 | +from datetime import datetime |
| | 13 | +from matplotlib.font_manager import FontProperties |
| | 14 | + |
| | 15 | +__prog__ = os.path.basename(__file__) |
| | 16 | + |
| | 17 | +parser = ArgumentParser(description=__doc__) |
| | 18 | +parser.add_argument('input_paths', metavar='data', nargs='+') |
| | 19 | +parser.add_argument('-t', '--title', required=1) |
| | 20 | +parser.add_argument('-xlim', nargs=2, metavar='YEAR', type=int) |
| | 21 | +parser.add_argument('-ylim', nargs=2, metavar='VALUE', type=float) |
| | 22 | + |
| | 23 | +conv = defaultdict(lambda k : float) |
| | 24 | +conv[0] = lambda k : datetime.strptime(k, '%Y-%m') |
| | 25 | +markers = 'ov^<>sp*D' |
| | 26 | +colors = 'brcmgyk' |
| | 27 | +M = len(markers) |
| | 28 | +C = len(colors) |
| | 29 | +labeltempl = r'$10^{%d} \leq a < 10^{%d}$' |
| | 30 | + |
| | 31 | +if __name__ == '__main__': |
| | 32 | + ns = parser.parse_args() |
| | 33 | + |
| | 34 | + lines = [] |
| | 35 | + |
| | 36 | + fig = pp.figure(figsize=(8,4)) |
| | 37 | + ax = fig.add_axes(pp.axes([.1,.1,.65,.8], axisbg='whitesmoke')) |
| | 38 | + |
| | 39 | + for i, path in enumerate(ns.input_paths): |
| | 40 | + try: |
| | 41 | + data = np.loadtxt(path, converters=conv, dtype=object, skiprows=1) |
| | 42 | + except IOError, e: |
| | 43 | + print >> sys.stderr, '%s: skipping %s because: %s' % (__prog__,\ |
| | 44 | + path, e.message or e.args[1]) |
| | 45 | + continue |
| | 46 | + act, peak_date, peak_date_err, peak, peak_err = np.asfarray(data[:,1:]).T |
| | 47 | + a = act[0] |
| | 48 | + cohort = data[:, 0] |
| | 49 | + idx = np.argsort(cohort) |
| | 50 | + cohort = cohort[idx] |
| | 51 | + peak = peak[idx] / peak.mean() |
| | 52 | + l, = ax.plot(cohort, peak, marker=markers[i % M], color=colors[i % C], |
| | 53 | + ls='none', mec=colors[i % C], label=labeltempl % (a-1, a), ms=8, |
| | 54 | + alpha=.65) |
| | 55 | + lines.append(l) |
| | 56 | + |
| | 57 | + pp.figlegend(lines, [ l.get_label() for l in lines ], |
| | 58 | + loc='center right', prop=FontProperties(size='medium')) |
| | 59 | + |
| | 60 | + if ns.xlim: |
| | 61 | + pp.xlim(datetime(ns.xlim[0],1,1), datetime(ns.xlim[1],1,1)) |
| | 62 | + if ns.ylim: |
| | 63 | + pp.ylim(ns.ylim) |
| | 64 | + |
| | 65 | + pp.xlabel('cohort') |
| | 66 | + pp.ylabel(r'normalized activity $\frac{a_p}{< a_p >}$') |
| | 67 | + |
| | 68 | + pp.title(ns.title) |
| | 69 | + ax.minorticks_on() |
| | 70 | + ax.grid("on") |
| | 71 | + pp.draw() |
| | 72 | + |
| | 73 | + output_path = ns.title.replace(' ', '_').lower() + '.pdf' |
| | 74 | + pp.savefig(output_path, fmt='pdf') |
| | 75 | + print 'figure saved to %s' % output_path |
| | 76 | + |
| | 77 | + pp.show() |