I'm trying to open a png-Image to plot this image on a basemap of cartopy.
I already followed these instructions on:
https://scitools.org.uk/cartopy/docs/v0.15/examples/geostationary.html
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy
from PIL import Image
def create_map(image):
res = '10m'
proj = ccrs.NorthPolarStereo(central_longitude=10.0)
img = plt.imread(image)
img_extent = (2.0715, 15.72, 46.9526, 54.5877)
ax = plt.axes(projection = proj)
ax.set_extent = ([3.0889, 17.1128, 46.1827, 55.5482])
land_10m = cfeature.NaturalEarthFeature('physical', 'land', res,
edgecolor = 'face',
facecolor=cfeature.COLORS['land'],
zorder=0)
state_provinces_10m = cfeature.NaturalEarthFeature(category = 'cultural',
name = 'admin_1_states_provinces_lines',
scale = res,
facecolor = none)
ax.add_feature(state_provinces_10m, edgecolor='gray')
ax.add_feature(land_10m)
ax.add_feature(cartopy.feature.BORDERS.with_scale(res), linestyle='-', linewith=1)
ax.add_feature(cartopy.feature.COASTLINE.with_scale(res), linestyle='-')
plt.imshow(img, origin='upper', extent=img_extent, transform = proj)
plt.show()
create_map('image.png')
My results are a basemap of the defined extent but without my image. What i am doing wrong?
regards
Your transform argument for imshow is almost certainly incorrect. An image extent of (2.0715, 15.72, 46.9526, 54.5877) in North polar stereographic projection is a very small region near the North Pole, which is not within your desired map extent. From context it looks like the extent is specified in geographic coordinates, in which case the solution should be to use transform=ccrs.PlateCarree() in your imshow call.
In general I recommend being explicit about what your coordinate system is at all times, so I would suggest
def create_map(image):
res = '10m'
proj = ccrs.NorthPolarStereo(central_longitude=10.0)
img = plt.imread(image)
img_extent = (2.0715, 15.72, 46.9526, 54.5877)
ax = plt.axes(projection = proj)
# EXPLICIT CRS HERE:
ax.set_extent([3.0889, 17.1128, 46.1827, 55.5482], crs=ccrs.PlateCarree())
land_10m = cfeature.NaturalEarthFeature('physical', 'land', res,
edgecolor = 'face',
facecolor=cfeature.COLORS['land'],
zorder=0)
state_provinces_10m = cfeature.NaturalEarthFeature(category = 'cultural',
name = 'admin_1_states_provinces_lines',
scale = res,
facecolor = none)
ax.add_feature(state_provinces_10m, edgecolor='gray')
ax.add_feature(land_10m)
ax.add_feature(cartopy.feature.BORDERS.with_scale(res), linestyle='-', linewith=1)
ax.add_feature(cartopy.feature.COASTLINE.with_scale(res), linestyle='-')
# USE CORRECT CRS HERE
plt.imshow(img, origin='upper', extent=img_extent, transform=ccrs.PlateCarree())
plt.show()
This documentation provides guidance on transforms/projections in Cartopy: https://scitools.org.uk/cartopy/docs/latest/tutorials/understanding_transform.html
Related
I have a dictionary named density, I am trying to create a US state map as the color shows the density of the state. I am trying to replicate this use Basemap (Python) to plot US with 50 states
however I am getting error.
This is my data:
density = {'NY': 648.0,
'FL': 696.0,
'TX': 833.0,
'CA': 927.0,
'PA': 472.0,
'OH': 721.0,
'NJ': 645.0,
'IL': 607.0,
'MI': 570.0,
'AZ': 616.0,
'GA': 799.0,
'MD': 652.0,
'NC': 720.0,
'LA': 546.0,
'TN': 806.0,
'MO': 564.0,
'SC': 574.0,
'VA': 818.0,
'IN': 780.0,
'AL': 619.0,
'MA': 626.0,
'WA': 749.0,
'KY': 680.0,
'WI': 615.0,
'OK': 633.0,
'MN': 743.0,
'IA': 543.0,
'WV': 599.0,
'MS': 695.0,
'AR': 698.0,
'OR': 878.0,
'CO': 782.0,
'NV': 930.0,
'KS': 637.0,
'CT': 1078.0,
'UT': 580.0,
'NM': 667.0,
'NE': 552.0,
'PR': 698.0,
'ME': 702.0,
'ID': 679.0,
'DE': 845.0,
'NH': 668.0,
'RI': 616.0,
'HI': 1131.0,
'DC': 711.0,
'MT': 653.0,
'SD': 495.0,
'ND': 685.0,
'VT': 754.0,
'AK': 1080.0,
'WY': 1028.0,
'VI': 1261.0,
'GU': 889.0}
Here is my code which I get the error.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap as Basemap
from matplotlib.colors import rgb2hex
from matplotlib.patches import Polygon
m = Basemap(llcrnrlon=-119,llcrnrlat=22,urcrnrlon=-64,urcrnrlat=49,
projection='lcc',lat_1=33,lat_2=45,lon_0=-95)
shp_info = m.readshapefile('st99_d00','states',drawbounds=True)
colors={}
statenames=[]
cmap = plt.cm.hot # use 'hot' colormap
vmin = 0; vmax = 450 # set range.
for shapedict in m.states_info:
statename = shapedict['NAME']
if statename not in ['District of Columbia','Puerto Rico']:
pop = popdensity[statename]
colors[statename] = cmap(1.-np.sqrt((pop-vmin)/(vmax-vmin)))[:3]
statenames.append(statename)
ax = plt.gca() # get current axes instance
for nshape,seg in enumerate(m.states):
if statenames[nshape] not in ['Puerto Rico', 'District of Columbia']:
if statenames[nshape] == 'Alaska':
seg = list(map(lambda (x,y): (0.35*x + 1100000, 0.35*y-1300000), seg))
if statenames[nshape] == 'Hawaii':
seg = list(map(lambda (x,y): (x + 5100000, y-900000), seg))
color = rgb2hex(colors[statenames[nshape]])
poly = Polygon(seg,facecolor=color,edgecolor=color)
ax.add_patch(poly)
plt.title('******')
plt.show()
I am confused what I need to do to this code work.
I am new to pyhton, any help and feedback is highly appreciated.
TIA!
I've been having some trouble getting the following animation of DFS to run.
I believe it might be because there is no background canvas, but I'm not sure exactly how to fix this, as all other similar implementations online use plt.plot rather than nx.draw in saving images to be displayed.
Can someone offer any guidance?
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib.animation import ArtistAnimation
fig = plt.figure()
ax = plt.gca()
colors = [0]*len(g)
cmap = plt.get_cmap("autumn")
g = nx.random_tree(20)
pos = nx.fruchterman_reingold_layout(g, k=0.1)
ims = [[nx.draw_networkx(g, pos, node_color = colors, cmap = cmap, ax = ax, vmin=0.0, vmax=1.0)]]
artists = [(nx.draw_networkx(g, pos, node_color = colors, cmap = cmap, ax = ax, vmin=0.0, vmax=1.0),)]
stack = [0]
while stack:
node = stack.pop()
if colors[node]:
continue
colors[node] = 0.8
stack += list(g[node].keys())
img = nx.draw_networkx(g, pos, node_color=colors, cmap=cmap, ax=ax, vmin=0.0, vmax=1.0)
ims += [img]
anim = ArtistAnimation(fig, ims, blit = True)
# plt.show()
The problem with your code is that nx.draw_networkx() doesn't return anything and it's always easier to use FuncAnimation method instead. At first, you need to create a color generator to make the animation function switch to the next color set by each call. Then using the FuncAnimation you can animate your frames (plots):
import networkx as nx
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.animation as animation
matplotlib.use('TkAgg')
plt.ion()
g = nx.random_tree(20)
colors = [0] * len(g)
cmap = plt.get_cmap('autumn')
pos = nx.fruchterman_reingold_layout(g, k=0.1)
# here you make the generator
def change_colors():
stack = [0]
yield colors
while stack:
node = stack.pop()
if colors[node]:
continue
colors[node] = 0.8
stack += list(g[node].keys())
yield colors
# instantiate your generator
color_gen = change_colors()
def update_frame(n):
# clear the plot
plt.cla()
# here switch to the next colors
colors = next(color_gen)
# then draw
nx.draw(g, pos, with_labels=True, node_color=colors, cmap=cmap, vmin=0.0, vmax=1.0)
ani = animation.FuncAnimation(plt.gcf(), update_frame, repeat=False, interval=1000)
plt.ioff()
plt.show()
which will give you:
you can save it as a gif file by replacing plt.show() with ani.save('anim.gif', writer='imagemagick').
I'm trying to get a colorbar for the following minimal example of my code.
g1 = gridspec.GridSpec(1, 1)
f, ((ax0)) = plt.subplots(1, 1)
ax0 = subplot(g1[0])
cmap = matplotlib.cm.get_cmap('viridis')
for i in linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
im = ax0.plot(x,y,color=rgba)
f.colorbar(im)
I also tried f.colorbar(cmap)
Probably pretty obvious, but I get errors such as
'ListedColormap' object has no attribute 'autoscale_None'
In reality, the value defining i is more complex, but I think this should do the trick. My data is plotted with plot and not with imshow (for which I know how to make the colormap).
The answers so far seem overly complicated. fig.colorbar() expects a ScalarMappable as its first argument. Often ScalarMappables are produced by imshow or contourplots and are readily avaible.
In this case you would need to define your custom ScalarMappable to provide to the colorbar.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
cmap = plt.cm.get_cmap('viridis')
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
im = ax.plot(x,y,color=rgba)
sm = plt.cm.ScalarMappable(cmap=cmap)
sm.set_array([])
fig.colorbar(sm)
plt.show()
You should pass an Image or ContourSet when you call colorbar on a Figure.
You can make an image of the data points by calling plt.imshow with the data. You can start with this:
data = []
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
ax0.plot(x,y,color=rgba)
data.append([x, y])
image = plt.imshow(data)
figure.colorbar(image)
plt.show()
Reference:
https://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.colorbar
Oluwafemi Sule's solution almost works, but it plots the matrix into the same figure as the lines. Here a solution that opens a second figure, does the imshow call on that second figure, uses the result to draw the colorbar in the first figure, and then closes the second figure before calling plt.show():
import matplotlib
from matplotlib import pyplot as plt
from matplotlib import gridspec
import numpy as np
cmap = matplotlib.cm.get_cmap('viridis')
g1 = gridspec.GridSpec(1, 1)
f0, ((ax0)) = plt.subplots(1, 1)
f1, ((ax1)) = plt.subplots(1, 1)
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
ax0.plot(x,y,color=rgba)
data = np.linspace(0,1,100).reshape((10,10))
image = ax1.imshow(data)
f0.colorbar(image)
plt.close(f1)
plt.show()
The result looks like this:
I am quite the novice at matplotlib, so bear with me. I have the code below that plots a cylindrical equidistant grid of precipitation. I set the 'extents' limits that finally aligned my basemap with the data. Now, it appears to have "broken" my plt.text capability as I can no longer see the text 'Precipitation Rate (mm/hour)'. Thanks for any help.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from pylab import *
import pickle
from mpl_toolkits.basemap import Basemap
fp = open('uneven_rgb.pkl', 'rb')
uneven_rgb = pickle.load(fp)
fp.close()
num_lon = 1440
num_lat = 400
precipfile = "/Users/bolvin/3B43.20111001.7.HDF_precip.bin"
fileobj = open(precipfile, mode='rb') # Open file as read only binary
data = np.fromfile (fileobj, dtype ='f')
datat = np.reshape(data, (num_lon, num_lat), order = 'FORTRAN')
datam = datat * 24.0
my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',uneven_rgb)
plt.figure(figsize = (20,10))
mapproj = Basemap(projection = 'cyl', llcrnrlat=-50.0, llcrnrlon=0.0, urcrnrlat=50.0,urcrnrlon=360.0)
mapproj.drawcoastlines()
mapproj.drawcountries()
mapproj.drawparallels(np.array([-30.0, 0.0, 30.0]), labels=[0,0,0,0])
mapproj.drawmeridians(np.array([90.0, 180.0, 270.0]), labels=[0,0,0,0])
myplot = plt.imshow(datam.T, interpolation = 'nearest', cmap = my_cmap, vmin = 0.0, vmax = 20.0, extent = (0.0, 360.0, -50.0, 50.0))
plt.title('October 2011 3B43 Precipitation', fontsize = 36, y = 1.03)
plt.text(1.0, 435.0, 'Precipitation Rate (mm/hour)', size = 20)
cbar = plt.colorbar(myplot, orientation='horizontal', shrink = 0.5, pad = 0.03)
cbar.ax.tick_params(labelsize=20)
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.get_yaxis().set_visible(False)
plt.show()
fileobj.close()
plt.text gets as first argument the x and y coordinates on which your text will be put.
As you transformed your imshow plot into the bordes 0-360 for x and -50 to 50 for y, y=435 is not in the plot anymore.
You can check your limits with plt.gca().get_xlim().
You have to move it somewhere in your limits.
Your defining the units you are plotting with this text, right? So the natural place for this would be the label of the colorbar:
cbar = plt.colorbar(myplot, orientation='horizontal', shrink = 0.5,
pad = 0.03, label='Precipitation Rate (mm/hour)')
If you want to insert a small plot inside a bigger one you can use Axes, like here.
The problem is that I don't know how to do the same inside a subplot.
I have several subplots and I would like to plot a small plot inside each subplot.
The example code would be something like this:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
for i in range(4):
ax = fig.add_subplot(2,2,i)
ax.plot(np.arange(11),np.arange(11),'b')
#b = ax.axes([0.7,0.7,0.2,0.2])
#it gives an error, AxesSubplot is not callable
#b = plt.axes([0.7,0.7,0.2,0.2])
#plt.plot(np.arange(3),np.arange(3)+11,'g')
#it plots the small plot in the selected position of the whole figure, not inside the subplot
Any ideas?
I wrote a function very similar to plt.axes. You could use it for plotting yours sub-subplots. There is an example...
import matplotlib.pyplot as plt
import numpy as np
#def add_subplot_axes(ax,rect,facecolor='w'): # matplotlib 2.0+
def add_subplot_axes(ax,rect,axisbg='w'):
fig = plt.gcf()
box = ax.get_position()
width = box.width
height = box.height
inax_position = ax.transAxes.transform(rect[0:2])
transFigure = fig.transFigure.inverted()
infig_position = transFigure.transform(inax_position)
x = infig_position[0]
y = infig_position[1]
width *= rect[2]
height *= rect[3] # <= Typo was here
#subax = fig.add_axes([x,y,width,height],facecolor=facecolor) # matplotlib 2.0+
subax = fig.add_axes([x,y,width,height],axisbg=axisbg)
x_labelsize = subax.get_xticklabels()[0].get_size()
y_labelsize = subax.get_yticklabels()[0].get_size()
x_labelsize *= rect[2]**0.5
y_labelsize *= rect[3]**0.5
subax.xaxis.set_tick_params(labelsize=x_labelsize)
subax.yaxis.set_tick_params(labelsize=y_labelsize)
return subax
def example1():
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111)
rect = [0.2,0.2,0.7,0.7]
ax1 = add_subplot_axes(ax,rect)
ax2 = add_subplot_axes(ax1,rect)
ax3 = add_subplot_axes(ax2,rect)
plt.show()
def example2():
fig = plt.figure(figsize=(10,10))
axes = []
subpos = [0.2,0.6,0.3,0.3]
x = np.linspace(-np.pi,np.pi)
for i in range(4):
axes.append(fig.add_subplot(2,2,i))
for axis in axes:
axis.set_xlim(-np.pi,np.pi)
axis.set_ylim(-1,3)
axis.plot(x,np.sin(x))
subax1 = add_subplot_axes(axis,subpos)
subax2 = add_subplot_axes(subax1,subpos)
subax1.plot(x,np.sin(x))
subax2.plot(x,np.sin(x))
if __name__ == '__main__':
example2()
plt.show()
You can now do this with matplotlibs inset_axes method (see docs):
from mpl_toolkits.axes_grid.inset_locator import inset_axes
inset_axes = inset_axes(parent_axes,
width="30%", # width = 30% of parent_bbox
height=1., # height : 1 inch
loc=3)
Update: As Kuti pointed out, for matplotlib version 2.1 or above, you should change the import statement to:
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
There is now also a full example showing all different options available.
From matplotlib 3.0 on, you can use matplotlib.axes.Axes.inset_axes:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2,2)
for ax in axes.flat:
ax.plot(np.arange(11),np.arange(11))
ins = ax.inset_axes([0.7,0.7,0.2,0.2])
plt.show()
The difference to mpl_toolkits.axes_grid.inset_locator.inset_axes mentionned in #jrieke's answer is that this is a lot easier to use (no extra imports etc.), but has the drawback of being slightly less flexible (no argument for padding or corner locations).
source: https://matplotlib.org/examples/pylab_examples/axes_demo.html
from mpl_toolkits.axes_grid.inset_locator import inset_axes
import matplotlib.pyplot as plt
import numpy as np
# create some data to use for the plot
dt = 0.001
t = np.arange(0.0, 10.0, dt)
r = np.exp(-t[:1000]/0.05) # impulse response
x = np.random.randn(len(t))
s = np.convolve(x, r)[:len(x)]*dt # colored noise
fig = plt.figure(figsize=(9, 4),facecolor='white')
ax = fig.add_subplot(121)
# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Subplot 1: \n Gaussian colored noise')
# this is an inset axes over the main axes
inset_axes = inset_axes(ax,
width="50%", # width = 30% of parent_bbox
height=1.0, # height : 1 inch
loc=1)
n, bins, patches = plt.hist(s, 400, normed=1)
#plt.title('Probability')
plt.xticks([])
plt.yticks([])
ax = fig.add_subplot(122)
# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Subplot 2: \n Gaussian colored noise')
plt.tight_layout()
plt.show()