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SF.net SVN: matplotlib: [5098] trunk/matplotlib
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From: <jd...@us...> - 2008-04-29 15:15:02
|
Revision: 5098
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5098&view=rev
Author: jdh2358
Date: 2008-04-29 08:14:36 -0700 (Tue, 29 Apr 2008)
Log Message:
-----------
changed numpy abbrev from npy to np
Modified Paths:
--------------
trunk/matplotlib/lib/matplotlib/art3d.py
trunk/matplotlib/lib/matplotlib/axes.py
trunk/matplotlib/lib/matplotlib/axes3d.py
trunk/matplotlib/lib/matplotlib/axis3d.py
trunk/matplotlib/lib/matplotlib/backend_bases.py
trunk/matplotlib/lib/matplotlib/cbook.py
trunk/matplotlib/lib/matplotlib/cm.py
trunk/matplotlib/lib/matplotlib/collections.py
trunk/matplotlib/lib/matplotlib/colorbar.py
trunk/matplotlib/lib/matplotlib/colors.py
trunk/matplotlib/lib/matplotlib/contour.py
trunk/matplotlib/lib/matplotlib/dates.py
trunk/matplotlib/lib/matplotlib/dviread.py
trunk/matplotlib/lib/matplotlib/finance.py
trunk/matplotlib/lib/matplotlib/image.py
trunk/matplotlib/lib/matplotlib/legend.py
trunk/matplotlib/lib/matplotlib/lines.py
trunk/matplotlib/lib/matplotlib/mlab.py
trunk/matplotlib/lib/matplotlib/patches.py
trunk/matplotlib/lib/matplotlib/path.py
trunk/matplotlib/lib/matplotlib/proj3d.py
trunk/matplotlib/lib/matplotlib/pylab.py
trunk/matplotlib/lib/matplotlib/quiver.py
trunk/matplotlib/lib/matplotlib/scale.py
trunk/matplotlib/lib/matplotlib/texmanager.py
trunk/matplotlib/lib/matplotlib/ticker.py
trunk/matplotlib/lib/matplotlib/transforms.py
trunk/matplotlib/lib/matplotlib/widgets.py
trunk/matplotlib/setupext.py
trunk/matplotlib/src/_backend_agg.cpp
Modified: trunk/matplotlib/lib/matplotlib/art3d.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/art3d.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/art3d.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -11,7 +11,7 @@
from colors import Normalize
-import numpy as npy
+import numpy as np
import proj3d
class Wrap2D:
@@ -253,8 +253,8 @@
segis.append((si,ei))
si = ei
xs,ys,zs = zip(*points)
- ones = npy.ones(len(xs))
- self.vec = npy.array([xs,ys,zs,ones])
+ ones = np.ones(len(xs))
+ self.vec = np.array([xs,ys,zs,ones])
self.segis = segis
def draw3d(self, renderer):
@@ -326,7 +326,7 @@
source = image._A
w,h,p = source.shape
X,Y = meshgrid(arange(w),arange(h))
- Z = npy.zeros((w,h))
+ Z = np.zeros((w,h))
tX,tY,tZ = proj3d.transform(X.flat,Y.flat,Z.flat,M)
tX = reshape(tX,(w,h))
tY = reshape(tY,(w,h))
Modified: trunk/matplotlib/lib/matplotlib/axes.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/axes.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -1,7 +1,7 @@
from __future__ import division, generators
import math, warnings, new
-import numpy as npy
+import numpy as np
from numpy import ma
import matplotlib
@@ -212,16 +212,16 @@
def _xy_from_y(self, y):
if self.axes.yaxis is not None:
b = self.axes.yaxis.update_units(y)
- if b: return npy.arange(len(y)), y, False
+ if b: return np.arange(len(y)), y, False
if not ma.isMaskedArray(y):
- y = npy.asarray(y)
+ y = np.asarray(y)
if len(y.shape) == 1:
- y = y[:,npy.newaxis]
+ y = y[:,np.newaxis]
nr, nc = y.shape
- x = npy.arange(nr)
+ x = np.arange(nr)
if len(x.shape) == 1:
- x = x[:,npy.newaxis]
+ x = x[:,np.newaxis]
return x,y, True
def _xy_from_xy(self, x, y):
@@ -235,18 +235,18 @@
x = ma.asarray(x)
y = ma.asarray(y)
if len(x.shape) == 1:
- x = x[:,npy.newaxis]
+ x = x[:,np.newaxis]
if len(y.shape) == 1:
- y = y[:,npy.newaxis]
+ y = y[:,np.newaxis]
nrx, ncx = x.shape
nry, ncy = y.shape
assert nrx == nry, 'Dimensions of x and y are incompatible'
if ncx == ncy:
return x, y, True
if ncx == 1:
- x = npy.repeat(x, ncy, axis=1)
+ x = np.repeat(x, ncy, axis=1)
if ncy == 1:
- y = npy.repeat(y, ncx, axis=1)
+ y = np.repeat(y, ncx, axis=1)
assert x.shape == y.shape, 'Dimensions of x and y are incompatible'
return x, y, True
@@ -1231,7 +1231,7 @@
# Otherwise, it will compute the bounds of it's current data
# and the data in xydata
if not ma.isMaskedArray(xys):
- xys = npy.asarray(xys)
+ xys = np.asarray(xys)
self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits)
self.ignore_existing_data_limits = False
@@ -2071,7 +2071,7 @@
dx = 0.5 * (dx + dy)
dy = dx
- alpha = npy.power(10.0, (dx, dy))
+ alpha = np.power(10.0, (dx, dy))
start = p.trans_inverse.transform_point((p.x, p.y))
lim_points = p.lim.get_points()
result = start + alpha * (lim_points - start)
@@ -2200,7 +2200,7 @@
def dist_x_y(p1, x, y):
'x and y are arrays; return the distance to the closest point'
x1, y1 = p1
- return min(npy.sqrt((x-x1)**2+(y-y1)**2))
+ return min(np.sqrt((x-x1)**2+(y-y1)**2))
def dist(a):
if isinstance(a, Text):
@@ -2217,7 +2217,7 @@
ydata = a.get_ydata(orig=False)
xt, yt = a.get_transform().numerix_x_y(xdata, ydata)
- return dist_x_y(xywin, npy.asarray(xt), npy.asarray(yt))
+ return dist_x_y(xywin, np.asarray(xt), np.asarray(yt))
artists = self.lines + self.patches + self.texts
if callable(among):
@@ -2601,14 +2601,14 @@
if not iterable(y): y = [y]
if not iterable(xmin): xmin = [xmin]
if not iterable(xmax): xmax = [xmax]
- y = npy.asarray(y)
- xmin = npy.asarray(xmin)
- xmax = npy.asarray(xmax)
+ y = np.asarray(y)
+ xmin = np.asarray(xmin)
+ xmax = np.asarray(xmax)
if len(xmin)==1:
- xmin = npy.resize( xmin, y.shape )
+ xmin = np.resize( xmin, y.shape )
if len(xmax)==1:
- xmax = npy.resize( xmax, y.shape )
+ xmax = np.resize( xmax, y.shape )
if len(xmin)!=len(y):
raise ValueError, 'xmin and y are unequal sized sequences'
@@ -2669,26 +2669,26 @@
if not iterable(x): x = [x]
if not iterable(ymin): ymin = [ymin]
if not iterable(ymax): ymax = [ymax]
- x = npy.asarray(x)
- ymin = npy.asarray(ymin)
- ymax = npy.asarray(ymax)
+ x = np.asarray(x)
+ ymin = np.asarray(ymin)
+ ymax = np.asarray(ymax)
if len(ymin)==1:
- ymin = npy.resize( ymin, x.shape )
+ ymin = np.resize( ymin, x.shape )
if len(ymax)==1:
- ymax = npy.resize( ymax, x.shape )
+ ymax = np.resize( ymax, x.shape )
if len(ymin)!=len(x):
raise ValueError, 'ymin and x are unequal sized sequences'
if len(ymax)!=len(x):
raise ValueError, 'ymax and x are unequal sized sequences'
- Y = npy.array([ymin, ymax]).T
+ Y = np.array([ymin, ymax]).T
verts = [ ((thisx, thisymin), (thisx, thisymax))
for thisx, (thisymin, thisymax) in zip(x,Y)]
#print 'creating line collection'
coll = mcoll.LineCollection(verts, colors=colors,
- linestyles=linestyles, label=label)
+ linestyles=linestyles, label=label)
self.add_collection(coll)
coll.update(kwargs)
@@ -3063,19 +3063,19 @@
if Nx!=len(y):
raise ValueError('x and y must be equal length')
- x = detrend(npy.asarray(x))
- y = detrend(npy.asarray(y))
+ x = detrend(np.asarray(x))
+ y = detrend(np.asarray(y))
- c = npy.correlate(x, y, mode=2)
+ c = np.correlate(x, y, mode=2)
- if normed: c/= npy.sqrt(npy.dot(x,x) * npy.dot(y,y))
+ if normed: c/= np.sqrt(np.dot(x,x) * np.dot(y,y))
if maxlags is None: maxlags = Nx - 1
if maxlags >= Nx or maxlags < 1:
raise ValueError('maglags must be None or strictly positive < %d'%Nx)
- lags = npy.arange(-maxlags,maxlags+1)
+ lags = np.arange(-maxlags,maxlags+1)
c = c[Nx-1-maxlags:Nx+maxlags]
@@ -3358,10 +3358,10 @@
# do not convert to array here as unit info is lost
- #left = npy.asarray(left)
- #height = npy.asarray(height)
- #width = npy.asarray(width)
- #bottom = npy.asarray(bottom)
+ #left = np.asarray(left)
+ #height = np.asarray(height)
+ #width = np.asarray(width)
+ #bottom = np.asarray(bottom)
if len(linewidth) == 1: linewidth = linewidth * nbars
@@ -3469,16 +3469,16 @@
if adjust_xlim:
xmin, xmax = self.dataLim.intervalx
- xmin = npy.amin(width)
+ xmin = np.amin(width)
if xerr is not None:
- xmin = xmin - npy.amax(xerr)
+ xmin = xmin - np.amax(xerr)
xmin = max(xmin*0.9, 1e-100)
self.dataLim.intervalx = (xmin, xmax)
if adjust_ylim:
ymin, ymax = self.dataLim.intervaly
- ymin = npy.amin(height)
+ ymin = np.amin(height)
if yerr is not None:
- ymin = ymin - npy.amax(yerr)
+ ymin = ymin - np.amax(yerr)
ymin = max(ymin*0.9, 1e-100)
self.dataLim.intervaly = (ymin, ymax)
self.autoscale_view()
@@ -3596,7 +3596,7 @@
l, = self.plot([thisx,thisx], [0, thisy], linefmt)
stemlines.append(l)
- baseline, = self.plot([npy.amin(x), npy.amax(x)], [0,0], basefmt)
+ baseline, = self.plot([np.amin(x), np.amax(x)], [0,0], basefmt)
self.hold(remember_hold)
@@ -3658,10 +3658,10 @@
"""
self.set_frame_on(False)
- x = npy.asarray(x).astype(npy.float32)
+ x = np.asarray(x).astype(np.float32)
sx = float(x.sum())
- if sx>1: x = npy.divide(x,sx)
+ if sx>1: x = np.divide(x,sx)
if labels is None: labels = ['']*len(x)
if explode is None: explode = [0]*len(x)
@@ -3841,17 +3841,17 @@
# arrays fine here, they are booleans and hence not units
if not iterable(lolims):
- lolims = npy.asarray([lolims]*len(x), bool)
- else: lolims = npy.asarray(lolims, bool)
+ lolims = np.asarray([lolims]*len(x), bool)
+ else: lolims = np.asarray(lolims, bool)
- if not iterable(uplims): uplims = npy.array([uplims]*len(x), bool)
- else: uplims = npy.asarray(uplims, bool)
+ if not iterable(uplims): uplims = np.array([uplims]*len(x), bool)
+ else: uplims = np.asarray(uplims, bool)
- if not iterable(xlolims): xlolims = npy.array([xlolims]*len(x), bool)
- else: xlolims = npy.asarray(xlolims, bool)
+ if not iterable(xlolims): xlolims = np.array([xlolims]*len(x), bool)
+ else: xlolims = np.asarray(xlolims, bool)
- if not iterable(xuplims): xuplims = npy.array([xuplims]*len(x), bool)
- else: xuplims = npy.asarray(xuplims, bool)
+ if not iterable(xuplims): xuplims = np.array([xuplims]*len(x), bool)
+ else: xuplims = np.asarray(xuplims, bool)
def xywhere(xs, ys, mask):
"""
@@ -4032,26 +4032,26 @@
distance = max(positions) - min(positions)
widths = min(0.15*max(distance,1.0), 0.5)
if isinstance(widths, float) or isinstance(widths, int):
- widths = npy.ones((col,), float) * widths
+ widths = np.ones((col,), float) * widths
# loop through columns, adding each to plot
self.hold(True)
for i,pos in enumerate(positions):
- d = npy.ravel(x[i])
+ d = np.ravel(x[i])
row = len(d)
# get median and quartiles
q1, med, q3 = mlab.prctile(d,[25,50,75])
# get high extreme
iq = q3 - q1
hi_val = q3 + whis*iq
- wisk_hi = npy.compress( d <= hi_val , d )
+ wisk_hi = np.compress( d <= hi_val , d )
if len(wisk_hi) == 0:
wisk_hi = q3
else:
wisk_hi = max(wisk_hi)
# get low extreme
lo_val = q1 - whis*iq
- wisk_lo = npy.compress( d >= lo_val, d )
+ wisk_lo = np.compress( d >= lo_val, d )
if len(wisk_lo) == 0:
wisk_lo = q1
else:
@@ -4062,16 +4062,16 @@
flier_hi_x = []
flier_lo_x = []
if len(sym) != 0:
- flier_hi = npy.compress( d > wisk_hi, d )
- flier_lo = npy.compress( d < wisk_lo, d )
- flier_hi_x = npy.ones(flier_hi.shape[0]) * pos
- flier_lo_x = npy.ones(flier_lo.shape[0]) * pos
+ flier_hi = np.compress( d > wisk_hi, d )
+ flier_lo = np.compress( d < wisk_lo, d )
+ flier_hi_x = np.ones(flier_hi.shape[0]) * pos
+ flier_lo_x = np.ones(flier_lo.shape[0]) * pos
# get x locations for fliers, whisker, whisker cap and box sides
box_x_min = pos - widths[i] * 0.5
box_x_max = pos + widths[i] * 0.5
- wisk_x = npy.ones(2) * pos
+ wisk_x = np.ones(2) * pos
cap_x_min = pos - widths[i] * 0.25
cap_x_max = pos + widths[i] * 0.25
@@ -4089,8 +4089,8 @@
med_x = [box_x_min, box_x_max]
# calculate 'notch' plot
else:
- notch_max = med + 1.57*iq/npy.sqrt(row)
- notch_min = med - 1.57*iq/npy.sqrt(row)
+ notch_max = med + 1.57*iq/np.sqrt(row)
+ notch_min = med - 1.57*iq/np.sqrt(row)
if notch_max > q3:
notch_max = q3
if notch_min < q1:
@@ -4267,7 +4267,7 @@
# mapping, not interpretation as rgb or rgba.
if not is_string_like(c):
- sh = npy.shape(c)
+ sh = np.shape(c)
if len(sh) == 1 and sh[0] == len(x):
colors = None # use cmap, norm after collection is created
else:
@@ -4324,7 +4324,7 @@
symstyle = marker[1]
else:
- verts = npy.asarray(marker[0])
+ verts = np.asarray(marker[0])
if sym is not None:
if symstyle==0:
@@ -4357,11 +4357,11 @@
else:
# MGDTODO: This has dpi problems
# rescale verts
- rescale = npy.sqrt(max(verts[:,0]**2+verts[:,1]**2))
+ rescale = np.sqrt(max(verts[:,0]**2+verts[:,1]**2))
verts /= rescale
- scales = npy.asarray(scales)
- scales = npy.sqrt(scales * self.figure.dpi / 72.)
+ scales = np.asarray(scales)
+ scales = np.sqrt(scales * self.figure.dpi / 72.)
if len(scales)==1:
verts = [scales[0]*verts]
else:
@@ -4382,7 +4382,7 @@
if colors is None:
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
- collection.set_array(npy.asarray(c))
+ collection.set_array(np.asarray(c))
collection.set_cmap(cmap)
collection.set_norm(norm)
@@ -4394,10 +4394,10 @@
temp_x = x
temp_y = y
- minx = npy.amin(temp_x)
- maxx = npy.amax(temp_x)
- miny = npy.amin(temp_y)
- maxy = npy.amax(temp_y)
+ minx = np.amin(temp_x)
+ maxx = np.amax(temp_x)
+ miny = np.amin(temp_y)
+ maxy = np.amax(temp_y)
w = maxx-minx
h = maxy-miny
@@ -4513,16 +4513,16 @@
nx = gridsize
ny = int(nx/math.sqrt(3))
# Count the number of data in each hexagon
- x = npy.array(x, float)
- y = npy.array(y, float)
+ x = np.array(x, float)
+ y = np.array(y, float)
if xscale=='log':
- x = npy.log10(x)
+ x = np.log10(x)
if yscale=='log':
- y = npy.log10(y)
- xmin = npy.amin(x)
- xmax = npy.amax(x)
- ymin = npy.amin(y)
- ymax = npy.amax(y)
+ y = np.log10(y)
+ xmin = np.amin(x)
+ xmax = np.amax(x)
+ ymin = np.amin(y)
+ ymax = np.amax(y)
# In the x-direction, the hexagons exactly cover the region from
# xmin to xmax. Need some padding to avoid roundoff errors.
padding = 1.e-9 * (xmax - xmin)
@@ -4532,17 +4532,17 @@
sy = (ymax-ymin) / ny
x = (x-xmin)/sx
y = (y-ymin)/sy
- ix1 = npy.round(x).astype(int)
- iy1 = npy.round(y).astype(int)
- ix2 = npy.floor(x).astype(int)
- iy2 = npy.floor(y).astype(int)
+ ix1 = np.round(x).astype(int)
+ iy1 = np.round(y).astype(int)
+ ix2 = np.floor(x).astype(int)
+ iy2 = np.floor(y).astype(int)
nx1 = nx + 1
ny1 = ny + 1
nx2 = nx
ny2 = ny
n = nx1*ny1+nx2*ny2
- counts = npy.zeros(n)
+ counts = np.zeros(n)
lattice1 = counts[:nx1*ny1]
lattice2 = counts[nx1*ny1:]
lattice1.shape = (nx1,ny1)
@@ -4558,16 +4558,16 @@
else:
lattice2[ix2[i], iy2[i]]+=1
- px = xmin + sx * npy.array([ 0.5, 0.5, 0.0, -0.5, -0.5, 0.0])
- py = ymin + sy * npy.array([-0.5, 0.5, 1.0, 0.5, -0.5, -1.0]) / 3.0
+ px = xmin + sx * np.array([ 0.5, 0.5, 0.0, -0.5, -0.5, 0.0])
+ py = ymin + sy * np.array([-0.5, 0.5, 1.0, 0.5, -0.5, -1.0]) / 3.0
- polygons = npy.zeros((6, n, 2), float)
- polygons[:,:nx1*ny1,0] = npy.repeat(npy.arange(nx1), ny1)
- polygons[:,:nx1*ny1,1] = npy.tile(npy.arange(ny1), nx1)
- polygons[:,nx1*ny1:,0] = npy.repeat(npy.arange(nx2) + 0.5, ny2)
- polygons[:,nx1*ny1:,1] = npy.tile(npy.arange(ny2), nx2) + 0.5
+ polygons = np.zeros((6, n, 2), float)
+ polygons[:,:nx1*ny1,0] = np.repeat(np.arange(nx1), ny1)
+ polygons[:,:nx1*ny1,1] = np.tile(np.arange(ny1), nx1)
+ polygons[:,nx1*ny1:,0] = np.repeat(np.arange(nx2) + 0.5, ny2)
+ polygons[:,nx1*ny1:,1] = np.tile(np.arange(ny2), nx2) + 0.5
- polygons = npy.transpose(polygons, axes=[1,0,2])
+ polygons = np.transpose(polygons, axes=[1,0,2])
polygons[:,:,0] *= sx
polygons[:,:,1] *= sy
polygons[:,:,0] += px
@@ -4607,13 +4607,13 @@
# Transform the counts if needed
if bins=='log':
- counts = npy.log10(counts+1)
+ counts = np.log10(counts+1)
elif bins!=None:
if not iterable(bins):
minimum, maximum = min(counts), max(counts)
bins-=1 # one less edge than bins
- bins = minimum + (maximum-minimum)*npy.arange(bins)/bins
- bins = npy.sort(bins)
+ bins = minimum + (maximum-minimum)*np.arange(bins)/bins
+ bins = np.sort(bins)
counts = bins.searchsorted(counts)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
@@ -4847,7 +4847,7 @@
if len(args)==1:
C = args[0]
numRows, numCols = C.shape
- X, Y = npy.meshgrid(npy.arange(numCols+1), npy.arange(numRows+1) )
+ X, Y = np.meshgrid(np.arange(numCols+1), np.arange(numRows+1) )
elif len(args)==3:
X, Y, C = args
else:
@@ -4936,8 +4936,8 @@
Similarly for meshgrid:
- x = npy.arange(5)
- y = npy.arange(3)
+ x = np.arange(5)
+ y = np.arange(3)
X, Y = meshgrid(x,y)
is equivalent to
@@ -4990,8 +4990,8 @@
# don't plot if C or any of the surrounding vertices are masked.
mask = ma.getmaskarray(C)[0:Ny-1,0:Nx-1]+xymask
- newaxis = npy.newaxis
- compress = npy.compress
+ newaxis = np.newaxis
+ compress = np.compress
ravelmask = (mask==0).ravel()
X1 = compress(ravelmask, ma.filled(X[0:-1,0:-1]).ravel())
@@ -5004,7 +5004,7 @@
Y4 = compress(ravelmask, ma.filled(Y[0:-1,1:]).ravel())
npoly = len(X1)
- xy = npy.concatenate((X1[:,newaxis], Y1[:,newaxis],
+ xy = np.concatenate((X1[:,newaxis], Y1[:,newaxis],
X2[:,newaxis], Y2[:,newaxis],
X3[:,newaxis], Y3[:,newaxis],
X4[:,newaxis], Y4[:,newaxis],
@@ -5043,10 +5043,10 @@
x = X.compressed()
y = Y.compressed()
- minx = npy.amin(x)
- maxx = npy.amax(x)
- miny = npy.amin(y)
- maxy = npy.amax(y)
+ minx = np.amin(x)
+ maxx = np.amax(x)
+ miny = np.amin(y)
+ maxy = np.amax(y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim( corners)
@@ -5127,7 +5127,7 @@
X = X.ravel()
Y = Y.ravel()
- coords = npy.zeros(((Nx * Ny), 2), dtype=float)
+ coords = np.zeros(((Nx * Ny), 2), dtype=float)
coords[:, 0] = X
coords[:, 1] = Y
@@ -5151,10 +5151,10 @@
self.grid(False)
- minx = npy.amin(X)
- maxx = npy.amax(X)
- miny = npy.amin(Y)
- maxy = npy.amax(Y)
+ minx = np.amin(X)
+ maxx = np.amax(X)
+ miny = np.amin(Y)
+ maxy = np.amax(Y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim( corners)
@@ -5250,16 +5250,16 @@
y = [0, nr]
elif len(args) == 3:
x, y = args[:2]
- x = npy.asarray(x)
- y = npy.asarray(y)
+ x = np.asarray(x)
+ y = np.asarray(y)
if x.ndim == 1 and y.ndim == 1:
if x.size == 2 and y.size == 2:
style = "image"
else:
- dx = npy.diff(x)
- dy = npy.diff(y)
- if (npy.ptp(dx) < 0.01*npy.abs(dx.mean()) and
- npy.ptp(dy) < 0.01*npy.abs(dy.mean())):
+ dx = np.diff(x)
+ dy = np.diff(y)
+ if (np.ptp(dx) < 0.01*np.abs(dx.mean()) and
+ np.ptp(dy) < 0.01*np.abs(dy.mean())):
style = "image"
else:
style = "pcolorimage"
@@ -5283,7 +5283,7 @@
# The following needs to be cleaned up; the renderer
# requires separate contiguous arrays for X and Y,
# but the QuadMesh class requires the 2D array.
- coords = npy.empty(((Nx * Ny), 2), npy.float64)
+ coords = np.empty(((Nx * Ny), 2), np.float64)
coords[:, 0] = X
coords[:, 1] = Y
@@ -5328,7 +5328,7 @@
ret.set_clim(vmin, vmax)
else:
ret.autoscale_None()
- self.update_datalim(npy.array([[xl, yb], [xr, yt]]))
+ self.update_datalim(np.array([[xl, yb], [xr, yt]]))
self.autoscale_view(tight=True)
return ret
@@ -5427,7 +5427,7 @@
# trapezoidal integration of the probability density function
pdf, bins, patches = ax.hist(...)
- print npy.trapz(pdf, bins)
+ print np.trapz(pdf, bins)
align = 'edge' | 'center'. Interprets bins either as edge
or center values
@@ -5445,7 +5445,7 @@
%(Rectangle)s
"""
if not self._hold: self.cla()
- n, bins = npy.histogram(x, bins, range=None, normed=normed)
+ n, bins = np.histogram(x, bins, range=None, normed=normed)
if width is None: width = 0.9*(bins[1]-bins[0])
if orientation == 'horizontal':
patches = self.barh(bins, n, height=width, left=bottom,
@@ -5498,7 +5498,7 @@
Returns the tuple Pxx, freqs
- For plotting, the power is plotted as 10*npy.log10(pxx) for decibels,
+ For plotting, the power is plotted as 10*np.log10(pxx) for decibels,
though pxx itself is returned
Refs:
@@ -5514,17 +5514,17 @@
pxx.shape = len(freqs),
freqs += Fc
- self.plot(freqs, 10*npy.log10(pxx), **kwargs)
+ self.plot(freqs, 10*np.log10(pxx), **kwargs)
self.set_xlabel('Frequency')
self.set_ylabel('Power Spectrum (dB)')
self.grid(True)
vmin, vmax = self.viewLim.intervaly
intv = vmax-vmin
- logi = int(npy.log10(intv))
+ logi = int(np.log10(intv))
if logi==0: logi=.1
step = 10*logi
#print vmin, vmax, step, intv, math.floor(vmin), math.ceil(vmax)+1
- ticks = npy.arange(math.floor(vmin), math.ceil(vmax)+1, step)
+ ticks = np.arange(math.floor(vmin), math.ceil(vmax)+1, step)
self.set_yticks(ticks)
return pxx, freqs
@@ -5546,7 +5546,7 @@
See the PSD help for a description of the optional parameters.
Returns the tuple Pxy, freqs. Pxy is the cross spectrum (complex
- valued), and 10*npy.log10(|Pxy|) is plotted
+ valued), and 10*np.log10(|Pxy|) is plotted
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
@@ -5561,16 +5561,16 @@
# pxy is complex
freqs += Fc
- self.plot(freqs, 10*npy.log10(npy.absolute(pxy)), **kwargs)
+ self.plot(freqs, 10*np.log10(np.absolute(pxy)), **kwargs)
self.set_xlabel('Frequency')
self.set_ylabel('Cross Spectrum Magnitude (dB)')
self.grid(True)
vmin, vmax = self.viewLim.intervaly
intv = vmax-vmin
- step = 10*int(npy.log10(intv))
+ step = 10*int(np.log10(intv))
- ticks = npy.arange(math.floor(vmin), math.ceil(vmax)+1, step)
+ ticks = np.arange(math.floor(vmin), math.ceil(vmax)+1, step)
self.set_yticks(ticks)
return pxy, freqs
@@ -5655,10 +5655,10 @@
window, noverlap)
- Z = 10*npy.log10(Pxx)
- Z = npy.flipud(Z)
+ Z = 10*np.log10(Pxx)
+ Z = np.flipud(Z)
- if xextent is None: xextent = 0, npy.amax(bins)
+ if xextent is None: xextent = 0, np.amax(bins)
xmin, xmax = xextent
freqs += Fc
extent = xmin, xmax, freqs[0], freqs[-1]
@@ -5718,9 +5718,9 @@
if marker is None and markersize is None:
if hasattr(Z, 'tocoo'):
raise TypeError, "Image mode does not support scipy.sparse arrays"
- Z = npy.asarray(Z)
+ Z = np.asarray(Z)
if precision is None: mask = Z!=0.
- else: mask = npy.absolute(Z)>precision
+ else: mask = np.absolute(Z)>precision
if 'cmap' not in kwargs:
kwargs['cmap'] = mcolors.ListedColormap(['w', 'k'], name='binary')
@@ -5735,9 +5735,9 @@
x = c.col
z = c.data
else:
- Z = npy.asarray(Z)
+ Z = np.asarray(Z)
if precision is None: mask = Z!=0.
- else: mask = npy.absolute(Z)>precision
+ else: mask = np.absolute(Z)>precision
y,x,z = mlab.get_xyz_where(mask, mask)
if marker is None: marker = 's'
if markersize is None: markersize = 10
@@ -5780,7 +5780,7 @@
Returns: an image.AxesImage instance
'''
- Z = npy.asarray(Z)
+ Z = np.asarray(Z)
nr, nc = Z.shape
extent = [-0.5, nc-0.5, nr-0.5, -0.5]
kw = {'extent': extent,
Modified: trunk/matplotlib/lib/matplotlib/axes3d.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes3d.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/axes3d.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -15,7 +15,7 @@
import cbook
from transforms import unit_bbox
-import numpy as npy
+import numpy as np
from colors import Normalize
import art3d
@@ -184,7 +184,7 @@
pass
def auto_scale_xyz(self, X,Y,Z=None,had_data=None):
- x,y,z = map(npy.asarray, (X,Y,Z))
+ x,y,z = map(np.asarray, (X,Y,Z))
try:
x,y = X.flat,Y.flat
if Z is not None:
@@ -274,7 +274,7 @@
point.
"""
- relev,razim = npy.pi * self.elev/180, npy.pi * self.azim/180
+ relev,razim = np.pi * self.elev/180, np.pi * self.azim/180
xmin,xmax = self.get_w_xlim()
ymin,ymax = self.get_w_ylim()
@@ -286,29 +286,29 @@
zmin,zmax)
# look into the middle of the new coordinates
- R = npy.array([0.5,0.5,0.5])
+ R = np.array([0.5,0.5,0.5])
#
- xp = R[0] + npy.cos(razim)*npy.cos(relev)*self.dist
- yp = R[1] + npy.sin(razim)*npy.cos(relev)*self.dist
- zp = R[2] + npy.sin(relev)*self.dist
+ xp = R[0] + np.cos(razim)*np.cos(relev)*self.dist
+ yp = R[1] + np.sin(razim)*np.cos(relev)*self.dist
+ zp = R[2] + np.sin(relev)*self.dist
- E = npy.array((xp, yp, zp))
+ E = np.array((xp, yp, zp))
#
self.eye = E
self.vvec = R - E
self.vvec = self.vvec / proj3d.mod(self.vvec)
- if abs(relev) > npy.pi/2:
+ if abs(relev) > np.pi/2:
# upside down
- V = npy.array((0,0,-1))
+ V = np.array((0,0,-1))
else:
- V = npy.array((0,0,1))
+ V = np.array((0,0,1))
zfront,zback = -self.dist,self.dist
viewM = proj3d.view_transformation(E,R,V)
perspM = proj3d.persp_transformation(zfront,zback)
- M0 = npy.dot(viewM,worldM)
- M = npy.dot(perspM,M0)
+ M0 = np.dot(viewM,worldM)
+ M = np.dot(perspM,M0)
return M
def mouse_init(self):
@@ -382,8 +382,8 @@
# scale the z value to match
x0,y0,z0 = p0
x1,y1,z1 = p1
- d0 = npy.hypot(x0-xd,y0-yd)
- d1 = npy.hypot(x1-xd,y1-yd)
+ d0 = np.hypot(x0-xd,y0-yd)
+ d1 = np.hypot(x1-xd,y1-yd)
dt = d0+d1
z = d1/dt * z0 + d0/dt * z1
#print 'mid', edgei, d0, d1, z0, z1, z
@@ -503,14 +503,14 @@
had_data = self.has_data()
rows, cols = Z.shape
- tX,tY,tZ = npy.transpose(X), npy.transpose(Y), npy.transpose(Z)
+ tX,tY,tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)
rstride = cbook.popd(kwargs, 'rstride', 10)
cstride = cbook.popd(kwargs, 'cstride', 10)
#
polys = []
boxes = []
- for rs in npy.arange(0,rows-1,rstride):
- for cs in npy.arange(0,cols-1,cstride):
+ for rs in np.arange(0,rows-1,rstride):
+ for cs in np.arange(0,cols-1,cstride):
ps = []
corners = []
for a,ta in [(X,tX),(Y,tY),(Z,tZ)]:
@@ -521,9 +521,9 @@
zright = ta[cs][rs:min(rows,rs+rstride+1):]
zright = zright[::-1]
corners.append([ztop[0],ztop[-1],zbase[0],zbase[-1]])
- z = npy.concatenate((ztop,zleft,zbase,zright))
+ z = np.concatenate((ztop,zleft,zbase,zright))
ps.append(z)
- boxes.append(map(npy.array,zip(*corners)))
+ boxes.append(map(np.array,zip(*corners)))
polys.append(zip(*ps))
#
lines = []
@@ -532,10 +532,10 @@
n = proj3d.cross(box[0]-box[1],
box[0]-box[2])
n = n/proj3d.mod(n)*5
- shade.append(npy.dot(n,[-1,-1,0.5]))
+ shade.append(np.dot(n,[-1,-1,0.5]))
lines.append((box[0],n+box[0]))
#
- color = npy.array([0,0,1,1])
+ color = np.array([0,0,1,1])
norm = Normalize(min(shade),max(shade))
colors = [color * (0.5+norm(v)*0.5) for v in shade]
for c in colors: c[3] = 1
@@ -553,7 +553,7 @@
had_data = self.has_data()
rows,cols = Z.shape
- tX,tY,tZ = npy.transpose(X), npy.transpose(Y), npy.transpose(Z)
+ tX,tY,tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)
rii = [i for i in range(0,rows,rstride)]+[rows-1]
cii = [i for i in range(0,cols,cstride)]+[cols-1]
@@ -718,8 +718,8 @@
def get_test_data(delta=0.05):
from mlab import bivariate_normal
- x = y = npy.arange(-3.0, 3.0, delta)
- X, Y = npy.meshgrid(x,y)
+ x = y = np.arange(-3.0, 3.0, delta)
+ X, Y = np.meshgrid(x,y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
@@ -764,8 +764,8 @@
def test_plot():
ax = Axes3D()
- xs = npy.arange(0,4*npy.pi+0.1,0.1)
- ys = npy.sin(xs)
+ xs = np.arange(0,4*np.pi+0.1,0.1)
+ ys = np.sin(xs)
ax.plot(xs,ys, label='zl')
ax.plot(xs,ys+max(xs),label='zh')
ax.plot(xs,ys,dir='x', label='xl')
@@ -785,7 +785,7 @@
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
ax = Axes3D()
- xs = npy.arange(0,10,0.4)
+ xs = np.arange(0,10,0.4)
verts = []
zs = [0.0,1.0,2.0,3.0]
for z in zs:
@@ -817,7 +817,7 @@
ax = Axes3D()
for c,z in zip(['r','g','b','y'],[30,20,10,0]):
- xs = npy.arange(20)
+ xs = np.arange(20)
ys = [random.random() for x in xs]
ax.bar(xs,ys,z=z,dir='y',color=c)
#ax.plot(xs,ys)
Modified: trunk/matplotlib/lib/matplotlib/axis3d.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axis3d.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/axis3d.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -13,7 +13,7 @@
import art3d
import proj3d
-import numpy as npy
+import numpy as np
def norm_angle(a):
"""Return angle between -180 and +180"""
@@ -51,8 +51,8 @@
# Compute the dash end points
# The 'c' prefix is for canvas coordinates
- cxy = npy.array(transform.xy_tup((x, y)))
- cd = npy.array([cos_theta, sin_theta])
+ cxy = np.array(transform.xy_tup((x, y)))
+ cd = np.array([cos_theta, sin_theta])
c1 = cxy+dashpush*cd
c2 = cxy+(dashpush+dashlength)*cd
(x1, y1) = transform.inverse_xy_tup(tuple(c1))
@@ -75,9 +75,9 @@
# well enough yet.
we = self._mytext.get_window_extent(renderer=renderer)
w, h = we.width(), we.height()
- off = npy.array([cos_theta*(w/2+2)-1,sin_theta*(h+1)-1])
- off = npy.array([cos_theta*(w/2),sin_theta*(h/2)])
- dir = npy.array([cos_theta,sin_theta])*dashpad
+ off = np.array([cos_theta*(w/2+2)-1,sin_theta*(h+1)-1])
+ off = np.array([cos_theta*(w/2),sin_theta*(h/2)])
+ dir = np.array([cos_theta,sin_theta])*dashpad
cw = c2 + off +dir
self._mytext.set_position(transform.inverse_xy_tup(tuple(cw)))
Modified: trunk/matplotlib/lib/matplotlib/backend_bases.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/backend_bases.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/backend_bases.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -6,7 +6,7 @@
from __future__ import division
import os
-import numpy as npy
+import numpy as np
import matplotlib.cbook as cbook
import matplotlib.colors as colors
import matplotlib._image as _image
@@ -122,10 +122,10 @@
meshWidth, meshHeight, coordinates)
if showedges:
- edgecolors = npy.array([[0.0, 0.0, 0.0, 1.0]], npy.float_)
+ edgecolors = np.array([[0.0, 0.0, 0.0, 1.0]], np.float_)
else:
edgecolors = facecolors
- linewidths = npy.array([1.0], npy.float_)
+ linewidths = np.array([1.0], np.float_)
return self.draw_path_collection(
master_transform, cliprect, clippath, clippath_trans,
@@ -1569,7 +1569,7 @@
a.set_ylim((y0, y1))
elif self._button_pressed == 3:
if a.get_xscale()=='log':
- alpha=npy.log(Xmax/Xmin)/npy.log(x1/x0)
+ alpha=np.log(Xmax/Xmin)/np.log(x1/x0)
rx1=pow(Xmin/x0,alpha)*Xmin
rx2=pow(Xmax/x0,alpha)*Xmin
else:
@@ -1577,7 +1577,7 @@
rx1=alpha*(Xmin-x0)+Xmin
rx2=alpha*(Xmax-x0)+Xmin
if a.get_yscale()=='log':
- alpha=npy.log(Ymax/Ymin)/npy.log(y1/y0)
+ alpha=np.log(Ymax/Ymin)/np.log(y1/y0)
ry1=pow(Ymin/y0,alpha)*Ymin
ry2=pow(Ymax/y0,alpha)*Ymin
else:
Modified: trunk/matplotlib/lib/matplotlib/cbook.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/cbook.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/cbook.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -5,7 +5,7 @@
from __future__ import generators
import re, os, errno, sys, StringIO, traceback, locale
import time, datetime
-import numpy as npy
+import numpy as np
try:
set = set
@@ -856,7 +856,7 @@
class MemoryMonitor:
def __init__(self, nmax=20000):
self._nmax = nmax
- self._mem = npy.zeros((self._nmax,), npy.int32)
+ self._mem = np.zeros((self._nmax,), np.int32)
self.clear()
def clear(self):
@@ -892,7 +892,7 @@
print "Warning: array size was too small for the number of calls."
def xy(self, i0=0, isub=1):
- x = npy.arange(i0, self._n, isub)
+ x = np.arange(i0, self._n, isub)
return x, self._mem[i0:self._n:isub]
def plot(self, i0=0, isub=1, fig=None):
@@ -1051,11 +1051,11 @@
def simple_linear_interpolation(a, steps):
- steps = npy.floor(steps)
+ steps = np.floor(steps)
new_length = ((len(a) - 1) * steps) + 1
new_shape = list(a.shape)
new_shape[0] = new_length
- result = npy.zeros(new_shape, a.dtype)
+ result = np.zeros(new_shape, a.dtype)
result[0] = a[0]
a0 = a[0:-1]
Modified: trunk/matplotlib/lib/matplotlib/cm.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/cm.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/cm.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -2,7 +2,7 @@
This module contains the instantiations of color mapping classes
"""
-import numpy as npy
+import numpy as np
from numpy import ma
import matplotlib as mpl
import matplotlib.colors as colors
@@ -56,18 +56,18 @@
try:
if x.ndim == 3:
if x.shape[2] == 3:
- if x.dtype == npy.uint8:
- alpha = npy.array(alpha*255, npy.uint8)
+ if x.dtype == np.uint8:
+ alpha = np.array(alpha*255, np.uint8)
m, n = x.shape[:2]
- xx = npy.empty(shape=(m,n,4), dtype = x.dtype)
+ xx = np.empty(shape=(m,n,4), dtype = x.dtype)
xx[:,:,:3] = x
xx[:,:,3] = alpha
elif x.shape[2] == 4:
xx = x
else:
raise ValueError("third dimension must be 3 or 4")
- if bytes and xx.dtype != npy.uint8:
- xx = (xx * 255).astype(npy.uint8)
+ if bytes and xx.dtype != np.uint8:
+ xx = (xx * 255).astype(np.uint8)
return xx
except AttributeError:
pass
Modified: trunk/matplotlib/lib/matplotlib/collections.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/collections.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/collections.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -8,7 +8,7 @@
line segemnts)
"""
import math, warnings
-import numpy as npy
+import numpy as np
import matplotlib as mpl
import matplotlib.cbook as cbook
import matplotlib.colors as _colors # avoid conflict with kwarg
@@ -51,7 +51,7 @@
draw time a call to scalar mappable will be made to set the face
colors.
"""
- _offsets = npy.array([], npy.float_)
+ _offsets = np.array([], np.float_)
_transOffset = transforms.IdentityTransform()
_transforms = []
@@ -84,11 +84,11 @@
self.set_antialiased(antialiaseds)
self._uniform_offsets = None
- self._offsets = npy.array([], npy.float_)
+ self._offsets = np.array([], np.float_)
if offsets is not None:
- offsets = npy.asarray(offsets, npy.float_)
+ offsets = np.asarray(offsets, np.float_)
if len(offsets.shape) == 1:
- offsets = offsets[npy.newaxis,:] # Make it Nx2.
+ offsets = offsets[np.newaxis,:] # Make it Nx2.
if transOffset is not None:
Affine2D = transforms.Affine2D
self._offsets = offsets
@@ -137,7 +137,7 @@
if not transOffset.is_affine:
offsets = transOffset.transform_non_affine(offsets)
transOffset = transOffset.get_affine()
- offsets = npy.asarray(offsets, npy.float_)
+ offsets = np.asarray(offsets, np.float_)
result = mpath.get_path_collection_extents(
transform.frozen(), paths, self.get_transforms(),
@@ -166,7 +166,7 @@
ys = self.convert_yunits(self._offsets[:1])
offsets = zip(xs, ys)
- offsets = npy.asarray(offsets, npy.float_)
+ offsets = np.asarray(offsets, np.float_)
self.update_scalarmappable()
@@ -206,7 +206,7 @@
ind = mpath.point_in_path_collection(
mouseevent.x, mouseevent.y, self._pickradius,
transform.frozen(), paths, self.get_transforms(),
- npy.asarray(self._offsets, npy.float_),
+ np.asarray(self._offsets, np.float_),
self._transOffset.frozen(), len(self._facecolors))
return len(ind)>0,dict(ind=ind)
@@ -424,7 +424,7 @@
# By converting to floats now, we can avoid that on every draw.
self._coordinates = self._coordinates.reshape((meshHeight + 1, meshWidth + 1, 2))
- self._coordinates = npy.array(self._coordinates, npy.float_)
+ self._coordinates = np.array(self._coordinates, np.float_)
def get_paths(self, dataTrans=None):
if self._paths is None:
@@ -439,11 +439,11 @@
c = coordinates
# We could let the Path constructor generate the codes for us,
# but this is faster, since we know they'll always be the same
- codes = npy.array(
+ codes = np.array(
[Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY],
Path.code_type)
- points = npy.concatenate((
+ points = np.concatenate((
c[0:-1, 0:-1],
c[0:-1, 1: ],
c[1: , 1: ],
@@ -470,7 +470,7 @@
ys = self.convert_yunits(self._offsets[:1])
offsets = zip(xs, ys)
- offsets = npy.asarray(offsets, npy.float_)
+ offsets = np.asarray(offsets, np.float_)
if self.check_update('array'):
self.update_scalarmappable()
@@ -556,8 +556,8 @@
Example: see examples/dynamic_collection.py for complete example
- offsets = npy.random.rand(20,2)
- facecolors = [cm.jet(x) for x in npy.random.rand(20)]
+ offsets = np.random.rand(20,2)
+ facecolors = [cm.jet(x) for x in np.random.rand(20)]
black = (0,0,0,1)
collection = RegularPolyCollection(
@@ -584,7 +584,7 @@
# in points^2
self._transforms = [
transforms.Affine2D().rotate(-self._rotation).scale(
- (npy.sqrt(x) * renderer.dpi / 72.0) / npy.sqrt(npy.pi))
+ (np.sqrt(x) * renderer.dpi / 72.0) / np.sqrt(np.pi))
for x in self._sizes]
return Collection.draw(self, renderer)
@@ -679,7 +679,7 @@
pickradius=pickradius,
**kwargs)
- self._facecolors = npy.array([])
+ self._facecolors = np.array([])
self.set_segments(segments)
def get_paths(self):
@@ -687,7 +687,7 @@
def set_segments(self, segments):
if segments is None: return
- segments = [npy.asarray(seg, npy.float_) for seg in segments]
+ segments = [np.asarray(seg, np.float_) for seg in segments]
if self._uniform_offsets is not None:
segments = self._add_offsets(segments)
self._paths = [mpath.Path(seg) for seg in segments]
Modified: trunk/matplotlib/lib/matplotlib/colorbar.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/colorbar.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/colorbar.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -15,7 +15,7 @@
'''
-import numpy as npy
+import numpy as np
import matplotlib as mpl
import matplotlib.colors as colors
import matplotlib.cm as cm
@@ -185,7 +185,7 @@
self._process_values()
self._find_range()
X, Y = self._mesh()
- C = self._values[:,npy.newaxis]
+ C = self._values[:,np.newaxis]
self._config_axes(X, Y)
if self.filled:
self._add_solids(X, Y, C)
@@ -248,13 +248,13 @@
'''
N = X.shape[0]
ii = [0, 1, N-2, N-1, 2*N-1, 2*N-2, N+1, N, 0]
- x = npy.take(npy.ravel(npy.transpose(X)), ii)
- y = npy.take(npy.ravel(npy.transpose(Y)), ii)
+ x = np.take(np.ravel(np.transpose(X)), ii)
+ y = np.take(np.ravel(np.transpose(Y)), ii)
x = x.reshape((len(x), 1))
y = y.reshape((len(y), 1))
if self.orientation == 'horizontal':
- return npy.hstack((y, x))
- return npy.hstack((x, y))
+ return np.hstack((y, x))
+ return np.hstack((x, y))
def _edges(self, X, Y):
'''
@@ -276,7 +276,7 @@
if self.orientation == 'vertical':
args = (X, Y, C)
else:
- args = (npy.transpose(Y), npy.transpose(X), npy.transpose(C))
+ args = (np.transpose(Y), np.transpose(X), np.transpose(C))
kw = {'cmap':self.cmap, 'norm':self.norm,
'shading':'flat', 'alpha':self.alpha}
# Save, set, and restore hold state to keep pcolor from
@@ -303,8 +303,8 @@
dummy, y = self._locate(levels)
if len(y) <> N:
raise ValueError("levels are outside colorbar range")
- x = npy.array([0.0, 1.0])
- X, Y = npy.meshgrid(x,y)
+ x = np.array([0.0, 1.0])
+ X, Y = np.meshgrid(x,y)
if self.orientation == 'vertical':
xy = [zip(X[i], Y[i]) for i in range(N)]
else:
@@ -348,7 +348,7 @@
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
- b = npy.array(locator())
+ b = np.array(locator())
b, ticks = self._locate(b)
formatter.set_locs(b)
ticklabels = [formatter(t, i) for i, t in enumerate(b)]
@@ -364,32 +364,32 @@
if b is None:
b = self.boundaries
if b is not None:
- self._boundaries = npy.asarray(b, dtype=float)
+ self._boundaries = np.asarray(b, dtype=float)
if self.values is None:
self._values = 0.5*(self._boundaries[:-1]
+ self._boundaries[1:])
if isinstance(self.norm, colors.NoNorm):
- self._values = (self._values + 0.00001).astype(npy.int16)
+ self._values = (self._values + 0.00001).astype(np.int16)
return
- self._values = npy.array(self.values)
+ self._values = np.array(self.values)
return
if self.values is not None:
- self._values = npy.array(self.values)
+ self._values = np.array(self.values)
if self.boundaries is None:
- b = npy.zeros(len(self.values)+1, 'd')
+ b = np.zeros(len(self.values)+1, 'd')
b[1:-1] = 0.5*(self._values[:-1] - self._values[1:])
b[0] = 2.0*b[1] - b[2]
b[-1] = 2.0*b[-2] - b[-3]
self._boundaries = b
return
- self._boundaries = npy.array(self.boundaries)
+ self._boundaries = np.array(self.boundaries)
return
# Neither boundaries nor values are specified;
# make reasonable ones based on cmap and norm.
if isinstance(self.norm, colors.NoNorm):
b = self._uniform_y(self.cmap.N+1) * self.cmap.N - 0.5
- v = npy.zeros((len(b)-1,), dtype=npy.int16)
- v[self._inside] = npy.arange(self.cmap.N, dtype=npy.int16)
+ v = np.zeros((len(b)-1,), dtype=np.int16)
+ v[self._inside] = np.arange(self.cmap.N, dtype=np.int16)
if self.extend in ('both', 'min'):
v[0] = -1
if self.extend in ('both', 'max'):
@@ -403,8 +403,8 @@
b = [b[0]-1] + b
if self.extend in ('both', 'max'):
b = b + [b[-1] + 1]
- b = npy.array(b)
- v = npy.zeros((len(b)-1,), dtype=float)
+ b = np.array(b)
+ v = np.zeros((len(b)-1,), dtype=float)
bi = self.norm.boundaries
v[self._inside] = 0.5*(bi[:-1] + bi[1:])
if self.extend in ('both', 'min'):
@@ -461,19 +461,19 @@
spaced boundaries, plus ends if required.
'''
if self.extend == 'neither':
- y = npy.linspace(0, 1, N)
+ y = np.linspace(0, 1, N)
else:
if self.extend == 'both':
- y = npy.zeros(N + 2, 'd')
+ y = np.zeros(N + 2, 'd')
y[0] = -0.05
y[-1] = 1.05
elif self.extend == 'min':
- y = npy.zeros(N + 1, 'd')
+ y = np.zeros(N + 1, 'd')
y[0] = -0.05
else:
- y = npy.zeros(N + 1, 'd')
+ y = np.zeros(N + 1, 'd')
y[-1] = 1.05
- y[self._inside] = npy.linspace(0, 1, N)
+ y[self._inside] = np.linspace(0, 1, N)
return y
def _proportional_y(self):
@@ -503,13 +503,13 @@
transposition for a horizontal colorbar are done outside
this function.
'''
- x = npy.array([0.0, 1.0])
+ x = np.array([0.0, 1.0])
if self.spacing == 'uniform':
y = self._uniform_y(self._central_N())
else:
y = self._proportional_y()
self._y = y
- X, Y = npy.meshgrid(x,y)
+ X, Y = np.meshgrid(x,y)
if self.extend in ('min', 'both'):
X[0,:] = 0.5
if self.extend in ('max', 'both'):
@@ -535,19 +535,19 @@
# floating point errors.
xn = self.norm(x, clip=False).filled()
in_cond = (xn > -0.001) & (xn < 1.001)
- xn = npy.compress(in_cond, xn)
- xout = npy.compress(in_cond, x)
+ xn = np.compress(in_cond, xn)
+ xout = np.compress(in_cond, x)
# The rest is linear interpolation with clipping.
y = self._y
N = len(b)
- ii = npy.minimum(npy.searchsorted(b, xn), N-1)
- i0 = npy.maximum(ii - 1, 0)
+ ii = np.minimum(np.searchsorted(b, xn), N-1)
+ i0 = np.maximum(ii - 1, 0)
#db = b[ii] - b[i0]
- db = npy.take(b, ii) - npy.take(b, i0)
- db = npy.where(i0==ii, 1.0, db)
+ db = np.take(b, ii) - np.take(b, i0)
+ db = np.where(i0==ii, 1.0, db)
#dy = y[ii] - y[i0]
- dy = npy.take(y, ii) - npy.take(y, i0)
- z = npy.take(y, i0) + (xn-npy.take(b,i0))*dy/db
+ dy = np.take(y, ii) - np.take(y, i0)
+ z = np.take(y, i0) + (xn-np.take(b,i0))*dy/db
return xout, z
def set_alpha(self, alpha):
Modified: trunk/matplotlib/lib/matplotlib/colors.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/colors.py 2008-04-29 14:22:48 UTC (rev 5097)
+++ trunk/matplotlib/lib/matplotlib/colors.py 2008-04-29 15:14:36 UTC (rev 5098)
@@ -34,7 +34,7 @@
'chartreuse' are supported.
"""
import re
-import numpy as npy
+import numpy as np
from numpy import ma
import matplotlib.cbook as cbook
@@ -320,23 +320,23 @@
"""
try:
if c.lower() == 'none':
- return npy.zeros((0,4), dtype=npy.float_)
+ return np.zeros((0,4), dtype=np.float_)
except AttributeError:
pass
if len(c) == 0:
- return npy.zeros((0,4), dtype=npy.float_)
+ return np.zeros((0,4), dtype=np.float_)
try:
result = [self.to_rgba(c, alpha)]
except ValueError:
# If c is a list it must be maintained as the same list
# with modified items so that items can be appended to
# it. This is needed for examples/dynamic_collections.py.
- if not isinstance(c, (list, npy.ndarray)): # specific; don't need duck-typing
+ if not isinstance(c, (list, np.ndarray)): # specific; don't need duck-typing
c = list(c)
for i, cc in enumerate(c):
c[i] = self.to_rgba(cc, alpha) # change in place
result = c
- return npy.asarray(result, npy.float_)
+ return np.asarray(result, np.float_)
colorConverter = ColorConverter()
@@ -358,7 +358,7 @@
gives the closest value for values of x between 0 and 1.
"""
try:
- adata = npy.array(data)
+ adata = np.array(data)
except:
raise TypeError("data must be convertable to an array")
shape = adata.shape
@@ -372,21 +372,21 @@
if x[0] != 0. or x[-1] != 1.0:
raise ValueError(
"data mapping points must start with x=0. and end with x=1")
- if npy.sometrue(npy.sort(x)-x):
+ if np.sometrue(np.sort(x)-x):
raise ValueError(
"data mapping points must have x in increasing order")
# begin generation of lookup table
x = x * (N-1)
- lut = npy.zeros((N,), npy.float)
- xind = npy.arange(float(N))
- ind = npy.searchsorted(x, xind)[1:-1]
+ lut = np.zeros((N,), np.float)
+ xind = np.arange(float(N))
+ ind = np.searchsorted(x, xind)[1:-1]
lut[1:-1] = ( ((xind[1:-1] - x[ind-1]) / (x[ind] - x[ind-1]))
* (y0[ind] - y1[ind-1]) + y1[ind-1])
lut[0] = y1[0]
lut[-1] = y0[-1]
# ensure that the lut is confined to values between 0 and 1 by clipping it
- npy.clip(lut, 0.0, 1.0)
+ np.clip(lut, 0.0, 1.0)
#lut = where(lut > 1., 1., lut)
#lut = where(lut < 0., 0., lut)
return lut
@@ -437,26 +437,26 @@
mask_bad = None
if not cbook.iterable(X):
vtype = 'scalar'
- xa = npy.array([X])
+ xa = np.array([X])
else:
vtype = 'array'
xma = ma.asarray(X)
xa = xma.filled(0)
mask_bad = ma.getmask(xma)
- if xa.dtype.char in npy.typecodes['Float']:
- npy.putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less than 1.
+ if xa.dtype.char in np.typecodes['Float']:
+ np.putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less than 1.
xa = (xa * self.N).astype(int)
# Set the over-range indices before the under-range;
# otherwise the under-range values get converted to over-range.
- npy.putmask(xa, xa>self.N-1, self._i_over)
- npy.putmask(xa, xa<0, self._i_under)
+ np.putmask(xa, xa>self.N-1, self._i_over)
+ np.putmask(xa, xa<0, self._i_under)
if mask_bad is not None and mask_bad.shape == xa.shape:
- npy.putmask(xa, mask_bad, self._i_bad)
+ np.putmask(xa, mask_bad, self._i_bad)
if bytes:
- lut = (self._lut * 255).astype(npy.uint8)
+ lut = (self._lut * 255).astype(np.uint8)
else:
lut = self._lut
- rgba = npy.empty(shape=xa.shape+(4,), dtype=lut.dtype)
+ rgba = np.empty(shape=xa.shape+(4,), dtype=lut.dtype)
lut.take(xa, axis=0, mode='clip', out=rgba)
# twice as fast as lut[xa];
# using the clip or wrap mode and providing an
@@ -501,8 +501,8 @@
raise NotImplementedError("Abstract class only")
def is_gray(self):
- return (npy.alltrue(self._lut[:,0] == self._lut[:,1])
- and npy.alltrue(self._lut[:,0] == self._lut[:,2]))
+ return (np.alltrue(self._lut[:,0] == self._lut[:,1])
+ and np.alltrue(self._lut[:,0] == self._lut[:,2]))
class LinearSegmentedColormap(Colormap):
@@ -527,7 +527,7 @@
self._segmentdata = segmentdata
def _init(self):
- self._lut = npy.ones((self.N + 3, 4), npy.float)
+ self._lut = np.ones((self.N + 3, 4), np.float)
self._lut[:-3, 0] = makeMappingArray(self.N, self._segmentdata['red'])
self._lut[:-3, 1] = makeMappingArray(self.N, self._segmentdata['green'])
self._lut[:-3, 2] = makeMappingArray(self.N, self._segmentdata['blue'])
@@ -579,9 +579,9 @@
def _init(self):
- rgb = npy.array([colorConverter.to_rgb(c)
- for c in self.colors], npy.float)
- self._lut = npy.zeros((self.N + 3, 4), npy.float)
+ rgb = np.array([colorConverter.to_rgb(c)
+ for c in self.colors], np.float)
+ self._lut = np.zeros((self.N + 3, 4), np.float)
self._lut[:-3, :-1] = rgb
self._lut[:-3, -1] = 1
self._isinit = True
@@ -615,10 +615,10 @@
if cbook.iterable(value):
vtype = 'array'
- val = ma.asarray(value).astype(npy.float)
+ val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
- val = ma.array([value]).astype(npy.float)
+ val = ma.array([value]).astype(np.float)
self.autoscale_None(val)
vmin, vmax = self.vmin, self.vmax
@@ -629,7 +629,7 @@
else:
if clip:
mask = ma.getmask(val)
- val = ma.array(npy.clip(val.filled(vmax), vmin, vmax),
+ val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val-vmin) * (1.0/(vmax-vmin))
if vtype == 'scalar':
@@ -674,10 +674,10 @@
if cbook.iterable(value):
vtype = 'array'
- val = ma.asarray(value).astype(npy.float)
+ val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
- val = ma.array([value]).astype(npy.float)
+ val = ma.array([value]).astype(np.float)
self.autoscale_None(val)
vmin, vmax = self.vmin, self.vmax
@@ -690,9 +690,9 @@
else:
if clip:
mask = ma.getmask(val)
- val = ma.array(npy.clip(val.filled(vmax), vmin, vmax),
+ val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
- result = (ma.log(val)-npy.log(vmin))/(npy.log(vmax)-npy.log(vmin))
+ result = (ma.log(val)-np.log(vmin))/(np.log(vmax)-np.log(vmin))
if vtype == 'scalar':
result = result[0]
return result
@@ -737,7 +737,7 @@
self.clip = clip
self.vmin = boundari...
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