389 lines
11 KiB
Python
389 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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#
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# Copyright (C) 2000-2005 by Yasushi Saito (yasushi.saito@gmail.com)
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#
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# Jockey is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 2, or (at your option) any
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# later version.
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#
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# Jockey is distributed in the hope that it will be useful, but WITHOUT
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# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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# for more details.
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#
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import pychart_util
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import copy
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import math
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def _convert_item(v, typ, line):
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if typ == "a":
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try:
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i = float(v)
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except ValueError: # non-number
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i = v
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return i
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elif typ == "d":
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try:
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return int(v)
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except ValueError:
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raise ValueError, "Can't convert %s to int; line=%s" % (v, line)
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elif typ == "f":
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try:
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return float(v)
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except ValueError:
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raise ValueError, "Can't convert %s to float; line=%s" % (v, line)
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elif typ == "s":
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return v
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else:
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raise ValueError, "Unknown conversion type, type=%s; line=%s" % (typ,line)
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def parse_line(line, delim):
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if delim.find("%") < 0:
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return [ _convert_item(item, "a", None) for item in line.split(delim) ]
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data = []
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idx = 0 # indexes delim
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ch = 'f'
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sep = ','
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while idx < len(delim):
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if delim[idx] != '%':
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raise ValueError, "bad delimitor: '" + delim + "'"
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ch = delim[idx+1]
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idx += 2
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sep = ""
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while idx < len(delim) and delim[idx] != '%':
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sep += delim[idx]
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idx += 1
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xx = line.split(sep, 1)
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data.append(_convert_item(xx[0], ch, line))
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if len(xx) >= 2:
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line = xx[1]
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else:
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line = ""
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break
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if line != "":
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for item in line.split(sep):
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data.append(_convert_item(item, ch, line))
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return data
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def escape_string(str):
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return str.replace("/", "//")
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def extract_rows(data, *rows):
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"""Extract rows specified in the argument list.
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>>> chart_data.extract_rows([[10,20], [30,40], [50,60]], 1, 2)
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[[30,40],[50,60]]
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"""
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try:
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# for python 2.2
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# return [data[r] for r in rows]
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out = []
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for r in rows:
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out.append(data[r])
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return out
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except IndexError:
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raise IndexError, "data=%s rows=%s" % (data, rows)
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return out
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def extract_columns(data, *cols):
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"""Extract columns specified in the argument list.
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>>> chart_data.extract_columns([[10,20], [30,40], [50,60]], 0)
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[[10],[30],[50]]
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"""
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out = []
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try:
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# for python 2.2:
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# return [ [r[c] for c in cols] for r in data]
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for r in data:
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col = []
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for c in cols:
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col.append(r[c])
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out.append(col)
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except IndexError:
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raise IndexError, "data=%s col=%s" % (data, col)
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return out
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def moving_average(data, xcol, ycol, width):
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"""Compute the moving average of YCOL'th column of each sample point
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in DATA. In particular, for each element I in DATA,
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this function extracts up to WIDTH*2+1 elements, consisting of
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I itself, WIDTH elements before I, and WIDTH
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elements after I. It then computes the mean of the YCOL'th
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column of these elements, and it composes a two-element sample
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consisting of XCOL'th element and the mean.
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>>> data = [[10,20], [20,30], [30,50], [40,70], [50,5]]
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... chart_data.moving_average(data, 0, 1, 1)
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[(10, 25.0), (20, 33.333333333333336), (30, 50.0), (40, 41.666666666666664), (50, 37.5)]
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The above value actually represents:
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[(10, (20+30)/2), (20, (20+30+50)/3), (30, (30+50+70)/3),
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(40, (50+70+5)/3), (50, (70+5)/2)]
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"""
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out = []
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try:
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for i in range(len(data)):
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n = 0
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total = 0
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for j in range(i-width, i+width+1):
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if j >= 0 and j < len(data):
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total += data[j][ycol]
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n += 1
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out.append((data[i][xcol], float(total) / n))
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except IndexError:
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raise IndexError, "bad data: %s,xcol=%d,ycol=%d,width=%d" % (data,xcol,ycol,width)
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return out
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def filter(func, data):
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"""Parameter <func> must be a single-argument
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function that takes a sequence (i.e.,
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a sample point) and returns a boolean. This procedure calls <func> on
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each element in <data> and returns a list comprising elements for
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which <func> returns True.
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>>> data = [[1,5], [2,10], [3,13], [4,16]]
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... chart_data.filter(lambda x: x[1] % 2 == 0, data)
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[[2,10], [4,16]].
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"""
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out = []
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for r in data:
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if func(r):
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out.append(r)
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return out
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def transform(func, data):
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"""Apply <func> on each element in <data> and return the list
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consisting of the return values from <func>.
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>>> data = [[10,20], [30,40], [50,60]]
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... chart_data.transform(lambda x: [x[0], x[1]+1], data)
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[[10, 21], [30, 41], [50, 61]]
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"""
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out = []
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for r in data:
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out.append(func(r))
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return out
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def aggregate_rows(data, col):
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out = copy.deepcopy(data)
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total = 0
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for r in out:
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total += r[col]
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r[col] = total
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return out
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def empty_line_p(s):
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return s.strip() == ""
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def fread_csv(fd, delim = ','):
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"""This function is similar to read_csv, except that it reads from
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an open file handle <fd>, or any object that provides method "readline".
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fd = open("foo", "r")
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data = chart_data.fread_csv(fd, ",") """
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data = []
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line = fd.readline()
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while line != "":
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if line[0] != '#' and not empty_line_p(line):
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data.append(parse_line(line, delim))
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line = fd.readline()
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return data
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def read_csv(path, delim = ','):
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"""This function reads
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comma-separated values from file <path>. Empty lines and lines
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beginning with "#" are ignored. Parameter <delim> specifies how
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a line is separated into values. If it does not contain the
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letter "%", then <delim> marks the end of a value.
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Otherwise, this function acts like scanf in C:
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chart_data.read_csv("file", "%d,%s:%d")
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Paramter <delim> currently supports
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only three conversion format specifiers:
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"d"(int), "f"(double), and "s"(string)."""
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f = open(path)
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data = fread_csv(f, delim)
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f.close()
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return data
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def fwrite_csv(fd, data):
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"""This function writes comma-separated <data> to <fd>. Parameter <fd> must be a file-like object
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that supports the |write()| method."""
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for v in data:
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fd.write(",".join([str(x) for x in v]))
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fd.write("\n")
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def write_csv(path, data):
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"""This function writes comma-separated values to <path>."""
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fd = file(path, "w")
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fwrite_csv(fd, data)
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fd.close()
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def read_str(delim = ',', *lines):
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"""This function is similar to read_csv, but it reads data from the
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list of <lines>.
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fd = open("foo", "r")
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data = chart_data.read_str(",", fd.readlines())"""
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data = []
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for line in lines:
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com = parse_line(line, delim)
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data.append(com)
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return data
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def func(f, xmin, xmax, step = None):
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"""Create sample points from function <f>, which must be a
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single-parameter function that returns a number (e.g., math.sin).
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Parameters <xmin> and <xmax> specify the first and last X values, and
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<step> specifies the sampling interval.
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>>> chart_data.func(math.sin, 0, math.pi * 4, math.pi / 2)
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[(0, 0.0), (1.5707963267948966, 1.0), (3.1415926535897931, 1.2246063538223773e-16), (4.7123889803846897, -1.0), (6.2831853071795862, -2.4492127076447545e-16), (7.8539816339744828, 1.0), (9.4247779607693793, 3.6738190614671318e-16), (10.995574287564276, -1.0)]
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"""
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data = []
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x = xmin
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if not step:
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step = (xmax - xmin) / 100.0
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while x < xmax:
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data.append((x, f(x)))
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x += step
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return data
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def _nr_data(data, col):
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nr_data = 0
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for d in data:
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nr_data += d[col]
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return nr_data
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def median(data, freq_col=1):
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"""Compute the median of the <freq_col>'th column of the values is <data>.
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>>> chart_data.median([(10,20), (20,4), (30,5)], 0)
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20
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>>> chart_data.median([(10,20), (20,4), (30,5)], 1)
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5.
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"""
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nr_data = _nr_data(data, freq_col)
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median_idx = nr_data / 2
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i = 0
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for d in data:
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i += d[freq_col]
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if i >= median_idx:
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return d
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raise Exception, "??? median ???"
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def cut_extremes(data, cutoff_percentage, freq_col=1):
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nr_data = _nr_data(data, freq_col)
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min_idx = nr_data * cutoff_percentage / 100.0
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max_idx = nr_data * (100 - cutoff_percentage) / 100.0
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r = []
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i = 0
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for d in data:
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if i < min_idx:
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if i + d[freq_col] >= min_idx:
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x = copy.deepcopy(d)
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x[freq_col] = x[freq_col] - (min_idx - i)
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r.append(x)
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i += d[freq_col]
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continue
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elif i + d[freq_col] >= max_idx:
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if i < max_idx and i + d[freq_col] >= max_idx:
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x = copy.deepcopy(d)
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x[freq_col] = x[freq_col] - (max_idx - i)
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r.append(x)
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break
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i += d[freq_col]
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r.append(d)
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return r
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def mean(data, val_col, freq_col):
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nr_data = 0
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sum = 0
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for d in data:
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sum += d[val_col] * d[freq_col]
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nr_data += d[freq_col]
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if nr_data == 0:
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raise IndexError, "data is empty"
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return sum / float(nr_data)
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def mean_samples(data, xcol, ycollist):
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"""Create a sample list that contains
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the mean of the original list.
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>>> chart_data.mean_samples([ [1, 10, 15], [2, 5, 10], [3, 8, 33] ], 0, (1, 2))
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[(1, 12.5), (2, 7.5), (3, 20.5)]
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"""
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out = []
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numcol = len(ycollist)
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try:
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for elem in data:
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v = 0
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for col in ycollist:
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v += elem[col]
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out.append( (elem[xcol], float(v) / numcol) )
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except IndexError:
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raise IndexError, "bad data: %s,xcol=%d,ycollist=%s" % (data,xcol,ycollist)
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return out
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def stddev_samples(data, xcol, ycollist, delta = 1.0):
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"""Create a sample list that contains the mean and standard deviation of the original list. Each element in the returned list contains following values: [MEAN, STDDEV, MEAN - STDDEV*delta, MEAN + STDDEV*delta].
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>>> chart_data.stddev_samples([ [1, 10, 15, 12, 15], [2, 5, 10, 5, 10], [3, 32, 33, 35, 36], [4,16,66, 67, 68] ], 0, range(1,5))
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[(1, 13.0, 2.1213203435596424, 10.878679656440358, 15.121320343559642), (2, 7.5, 2.5, 5.0, 10.0), (3, 34.0, 1.5811388300841898, 32.418861169915807, 35.581138830084193), (4, 54.25, 22.094965489902897, 32.155034510097103, 76.344965489902904)]
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"""
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out = []
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numcol = len(ycollist)
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try:
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for elem in data:
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total = 0
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for col in ycollist:
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total += elem[col]
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mean = float(total) / numcol
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variance = 0
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for col in ycollist:
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variance += (mean - elem[col]) ** 2
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stddev = math.sqrt(variance / numcol) * delta
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out.append( (elem[xcol], mean, stddev, mean-stddev, mean+stddev) )
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except IndexError:
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raise IndexError, "bad data: %s,xcol=%d,ycollist=%s" % (data,xcol,ycollist)
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return out
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def nearest_match(data, col, val):
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min_delta = None
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match = None
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for d in data:
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if min_delta == None or abs(d[col] - val) < min_delta:
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min_delta = abs(d[col] - val)
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match = d
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pychart_util.warn("XXX ", match)
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return match
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