[IMP] openerp python module.

- Some logging code moved from netsvc.py to loglevels.py
- Changed imports to use the new openerp module
- config and netsvc initialization calls move to openerp-server.py
- Moved openerp-server.py outside the old bin directory
- Some imports in tools moved inside the methods to break mutual-dependencies

bzr revid: vmt@openerp.com-20110207125723-ooee7d7ng5elmkso

1 files changed, 0 insertions, 388 deletions

diff --git a/bin/pychart/chart_data.py b/bin/pychart/chart_data.py
deleted file mode 100644
index dc805ee1762..00000000000
--- a/bin/pychart/chart_data.py
+++ /dev/null
@@ -1,388 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# 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: '" + delim + "'"
-        ch = delim[idx+1]
-        idx += 2
-        sep = ""
-        while idx < len(delim) and delim[idx] != '%':
-            sep += delim[idx]
-            idx += 1
-        xx = line.split(sep, 1)
-        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 fread_csv(fd, delim = ','):
-    """This function is similar to read_csv, except that it reads from
-    an open file handle <fd>, or any object that provides method "readline".
-
-fd = open("foo", "r")
-data = chart_data.fread_csv(fd, ",") """
-    
-    data = []
-    line = fd.readline()
-    while line != "":
-        if line[0] != '#' and not empty_line_p(line):
-            data.append(parse_line(line, delim))
-        line = fd.readline()
-    return data
-
-def read_csv(path, delim = ','):
-    """This function reads
-    comma-separated values from file <path>. 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)."""
-        
-    f = open(path)
-    data = fread_csv(f, delim)
-    f.close()
-    return data
-
-def fwrite_csv(fd, data):
-    """This function writes comma-separated <data> to <fd>. Parameter <fd> must be a file-like object
-    that supports the |write()| method."""
-    for v in data:
-        fd.write(",".join([str(x) for x in v]))
-        fd.write("\n")
-        
-def write_csv(path, data):
-    """This function writes comma-separated values to <path>."""
-    fd = file(path, "w")
-    fwrite_csv(fd, data)
-    fd.close()
-    
-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