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