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Hi Zhangtao,

When I run that code I do not see the 2nd subplot. If I add
"fig.canvas.draw()" to the bottom, it appears, but it is not the same width
as the upper subplot.


On Tue, Nov 18, 2014 at 10:15 AM, zhangtao <tao...@gm...> wrote:

> from matplotlib.pylab import *
> fig = plt.figure(1)
> fig.clf()
> data = np.random.random((3,3))
> xaxis = np.arange(0,3)
> yaxis = np.arange(0,3)
> ax1 = fig.add_subplot(211)
> im = ax1.imshow(data, interpolation='none')
> c = ax1.contour(xaxis, yaxis, data, colors='k')
> #----------------
> # draw ax1 first, so you can get proper position of ax1
> fig.canvas.draw()
> point_bl, point_tr = ax1.get_position().get_points()  # ax1's bottom-left
> and top-right
> ax2 = fig.add_subplot(212)
> space = 0.45  # space between ax2's bottom left and ax1's bottom left in
> percentage
> ax2.set_position([point_bl[0], point_bl[1]-space,
>                   point_tr[0] - point_bl[0], point_tr[1] - point_bl[1]])
> plt.show()
>
>
> You can adjust that variable "space" as you need.
>
> If someone has some more easy ways to do this, please let me know.
>
>
>
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Thanks Ben! That helped. I ended up using this method instead…

map.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1500, verbose=
True)

Works great!

- Jorma




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What you are seeing is the fact that the adjacent cells share the same
coordinates, so neighboring cells overlap by one pixel. This is only
visible when alpha != 1. This is a tricky issue to solve, but I could have
sworn we made some progress on that front by setting "snap" to False
somewhere. There have been past discussions about it, for sure...

Ben Root

On Wed, Nov 19, 2014 at 12:57 PM, Loïc Estève <loi...@in...> wrote:

> Thanks for the suggestions, I have tried the easiest one for now, namely
> pcolormesh, see attached plot. The alpha colormap look great but I can't
> seem to figure out how to prevent the edges of the cells from being
> visible. I tried using edgecolors='none' to no avail. I guess
> retrospectively that is similar to the lines we see in the colormap on the
> right.
>
> The snippet I am using:
>
> import numpy as np
>
> import matplotlib.pyplot as plt
> from matplotlib.colors import LinearSegmentedColormap
>
> import matplotlib
>
> matplotlib.rcParams['figure.facecolor'] = 'white'
>
> cm_dict = {'red':  ((0.0, 1.0, 1.0),
>                     (1.0, 1.0, 1.0)),
>            'green': ((0.0, 0.0, 0.0),
>                      (1.0, 0.0, 0.0)),
>            'blue':  ((0.0, 0.0, 0.0),
>                      (1.0, 0.0, 0.0)),
>            'alpha': ((0.0, 0.0, 0.0),
>                      (1.0, 1.0, 1.0))
>           }
>
> my_cm = LinearSegmentedColormap('my_cm', cm_dict)
>
> vals = np.tile(np.linspace(-1, 1, 30), (20, 1))
>
> fig = plt.figure()
> ax = plt.pcolormesh(vals, cmap=my_cm)
> plt.colorbar()
> plt.show()
>
> Cheers,
> Loïc
>

Thanks for the suggestions, I have tried the easiest one for now, namely 
pcolormesh, see attached plot. The alpha colormap look great but I can't 
seem to figure out how to prevent the edges of the cells from being 
visible. I tried using edgecolors='none' to no avail. I guess 
retrospectively that is similar to the lines we see in the colormap on 
the right.

The snippet I am using:

import numpy as np

import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

import matplotlib

matplotlib.rcParams['figure.facecolor'] = 'white'

cm_dict = {'red':  ((0.0, 1.0, 1.0),
                     (1.0, 1.0, 1.0)),
            'green': ((0.0, 0.0, 0.0),
                      (1.0, 0.0, 0.0)),
            'blue':  ((0.0, 0.0, 0.0),
                      (1.0, 0.0, 0.0)),
            'alpha': ((0.0, 0.0, 0.0),
                      (1.0, 1.0, 1.0))
           }

my_cm = LinearSegmentedColormap('my_cm', cm_dict)

vals = np.tile(np.linspace(-1, 1, 30), (20, 1))

fig = plt.figure()
ax = plt.pcolormesh(vals, cmap=my_cm)
plt.colorbar()
plt.show()

Cheers,
Loïc

The underlying mechanism is a pcolormesh(). The key thing to remember when
going back and forth between imshow and pcolors is that coordinates for
imshow refer to the center of the pixel, while coordinates for pcolors
refer to the corners, IIRC (I might have that backwards).

Cheers!
Ben Root

On Wed, Nov 19, 2014 at 10:23 AM, Gael Varoquaux <
gae...@no...> wrote:

> On Wed, Nov 19, 2014 at 10:20:23AM -0500, Benjamin Root wrote:
> > Notice that the colormap looks fine for the colorbar because it
> > isn't using imshow() under the hood.
>
> As a short-term workaround (I work with Loic, and I it would help me a
> lot if his problem was solved with a hack), can we leverage the mechanism
> used to plot the colorbar ourselves?
>
> That doesn't preclude fixing the problem in master, of course.
>
> Cheers,
>
> Gaël
>

Did you try pcolormesh?

Cheers,  Jody

> On Nov 19, 2014, at  7:23 AM, Gael Varoquaux <gae...@no...> wrote:
> 
> On Wed, Nov 19, 2014 at 10:20:23AM -0500, Benjamin Root wrote:
>> Notice that the colormap looks fine for the colorbar because it
>> isn't using imshow() under the hood.
> 
> As a short-term workaround (I work with Loic, and I it would help me a
> lot if his problem was solved with a hack), can we leverage the mechanism
> used to plot the colorbar ourselves?
> 
> That doesn't preclude fixing the problem in master, of course.
> 
> Cheers,
> 
> Gaël
> 
> ------------------------------------------------------------------------------
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On Wed, Nov 19, 2014 at 10:20:23AM -0500, Benjamin Root wrote:
> Notice that the colormap looks fine for the colorbar because it
> isn't using imshow() under the hood.

As a short-term workaround (I work with Loic, and I it would help me a
lot if his problem was solved with a hack), can we leverage the mechanism
used to plot the colorbar ourselves?

That doesn't preclude fixing the problem in master, of course.

Cheers,

Gaël

Confirmed. I am going to wager that this might be related to some of the
work that is being done right now in master to improve alpha handling,
particularly with images. Notice that the colormap looks fine for the
colorbar because it isn't using imshow() under the hood.

First, if you could try using the development version of matplotlib, we
have updated the AGG codebase. Maybe that might make an improvement off the
bat. If not, you can follow this PR that is a work in progress that might
be relevant: https://github.com/matplotlib/matplotlib/pull/3783

I hope this helps!
Ben Root


On Wed, Nov 19, 2014 at 9:46 AM, Loic Esteve <loi...@in...> wrote:

> Dear all,
>
> I am trying to create a colormap with a single color (red in the example
> below) where the alpha varies from 0 to 1. It does look like I am getting
> some grayish color near the low alpha values (around alpha = 0.2). Is that
> expected somehow?
>
> The plot I get is here:
>
> https://cloud.githubusercontent.com/assets/1680079/5084457/7d2d3790-6f06-11e4-9021-5b9e77e6a9c4.png
>
> I am using matplotlib 1.4.2.
>
> Here is a snippet which reproduces the issue.
>
> import numpy as np
>
> import matplotlib.pyplot as plt
> from matplotlib.colors import LinearSegmentedColormap
>
> import matplotlib
>
> matplotlib.rcParams['figure.facecolor'] = 'white'
>
> cm_dict = {'red':  ((0.0, 1.0, 1.0),
>                     (1.0, 1.0, 1.0)),
>            'green': ((0.0, 0.0, 0.0),
>                      (1.0, 0.0, 0.0)),
>            'blue':  ((0.0, 0.0, 0.0),
>                      (1.0, 0.0, 0.0)),
>            'alpha': ((0.0, 0.0, 0.0),
>                      (1.0, 1.0, 1.0))
>           }
>
> my_cm = LinearSegmentedColormap('my_cm', cm_dict)
>
> vals = np.tile(np.linspace(-1, 1, 30), (20, 1))
>
> fig = plt.figure()
> ax = plt.imshow(vals, cmap=my_cm)
> plt.colorbar()
> plt.show()
>
> Cheers,
> Loïc
>
>
>
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>

Dear all, 

I am trying to create a colormap with a single color (red in the example below) where the alpha varies from 0 to 1. It does look like I am getting some grayish color near the low alpha values (around alpha = 0.2). Is that expected somehow? 

The plot I get is here: 
https://cloud.githubusercontent.com/assets/1680079/5084457/7d2d3790-6f06-11e4-9021-5b9e77e6a9c4.png 

I am using matplotlib 1.4.2. 

Here is a snippet which reproduces the issue. 

import numpy as np 

import matplotlib.pyplot as plt 
from matplotlib.colors import LinearSegmentedColormap 

import matplotlib 

matplotlib.rcParams['figure.facecolor'] = 'white' 

cm_dict = {'red': ((0.0, 1.0, 1.0), 
(1.0, 1.0, 1.0)), 
'green': ((0.0, 0.0, 0.0), 
(1.0, 0.0, 0.0)), 
'blue': ((0.0, 0.0, 0.0), 
(1.0, 0.0, 0.0)), 
'alpha': ((0.0, 0.0, 0.0), 
(1.0, 1.0, 1.0)) 
} 

my_cm = LinearSegmentedColormap('my_cm', cm_dict) 

vals = np.tile(np.linspace(-1, 1, 30), (20, 1)) 

fig = plt.figure() 
ax = plt.imshow(vals, cmap=my_cm) 
plt.colorbar() 
plt.show() 

Cheers, 
Loïc 

That function is merely using the (relatively) lower res image that comes
packaged with basemap, and comes with features to help downsample it if
needed. I think you can get higher res images using the wmsimage() method.

I hope that points you to the right direction!

Cheers!
Ben Root


On Tue, Nov 18, 2014 at 9:09 PM, jorma <al...@jo...> wrote:

> Hi Folks -
>
> i have a simply Python script below that shows a low res basemap of Maui
> using the Blue Marble imagery. Can anyone provide info on how to create a
> high res map? Thanks!
>
> - Jorma
>
>
> from mpl_toolkits.basemap import Basemap
> import matplotlib.pyplot as plt
> import numpy as np
>
> map = Basemap(projection='merc', lat_0 = 21, lon_0 = -156,
>     resolution = 'f', area_thresh = 0.1,
>     llcrnrlon=-156.753926, llcrnrlat=20.537775,
>     urcrnrlon=-155.958679, urcrnrlat=21.058561)
>
> map.bluemarble()
> map.drawcoastlines()
> map.drawcountries()
> map.drawmapboundary()
>
> lon = -156.325691
> lat = 20.922652
> x,y = map(lon, lat)
> map.plot(x, y, 'bo', markersize=14)
>
> plt.show()
>
>
>
>
>
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Hi Folks -

i have a simply Python script below that shows a low res basemap of Maui
using the Blue Marble imagery. Can anyone provide info on how to create a
high res map? Thanks!

- Jorma


from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
 
map = Basemap(projection='merc', lat_0 = 21, lon_0 = -156,
    resolution = 'f', area_thresh = 0.1,
    llcrnrlon=-156.753926, llcrnrlat=20.537775,
    urcrnrlon=-155.958679, urcrnrlat=21.058561)

map.bluemarble() 
map.drawcoastlines()
map.drawcountries()
map.drawmapboundary()
 
lon = -156.325691
lat = 20.922652
x,y = map(lon, lat)
map.plot(x, y, 'bo', markersize=14)
 
plt.show()





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