I am trying to grid plots on a map, but the grid is not showing up and I am getting a ValueError.
It says ValueError: not enough values to unpack (expected 2, got 1)
import numpy as np
import matplotlib.pyplot as plt
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.crs as ccrs
avglonlist=[-63.414436532479854, -63.41382404937334, -63.41320293629234, -63.4126322428388, -63.412060546875, -63.41134304470493]
avglatlist=[44.5523500343606, 44.55130764100617, 44.550250391568596, 44.54927937825529, 44.54830612909229, 44.5470865885415]
klist=['0.1243', '0.1304', '0.1321', '0.1281', '0.1358', '0.1105']
ax = plt.axes(projection=ccrs.PlateCarree())
#ax.set_extent((-65.0, -58, 40, 47.7), crs=crs_latlon)
ax.set_extent((-64.0, -61, 42.5, 45.0), crs=ccrs.PlateCarree())
#Add coastlines and meridians/parallels (Cartopy-specific).
plt.gca().coastlines('10m')
gl=ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=1, color='gray', alpha=0.5, linestyle='-')
gl.xlabels_top = False
gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
# Add a title, legend, and display.
ax.set_title("Mission #13: Attenuation Coeffiecient\n2012-06-12 17:48:00 til 2012-07-08 12:10:00")
plt.pcolormesh(avglonlist, avglatlist, klist, transform=ccrs.PlateCarree())
plt.clim(0.0,0.5)
plt.show()
Related
I am drawing multiple horizontal and vertical lines using ax.hlines() and ax.vlines() respectively. I want to assign values to these lines using the array P and the order of assignment is presented in the expected output.
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import numpy as np
fig,ax = plt.subplots(1)
n=3
for i in range(0,n):
for j in range(0,n):
rect = mpl.patches.Rectangle((200+200*i,200+200*j),10*n, 10*n, linewidth=1, edgecolor='black', facecolor='black')
ax.add_patch(rect)
ax.hlines(200+200*i+5*n, 200, 200*n, zorder=0)
ax.vlines(200+200*j+5*n, 200, 200*n, zorder=0)
ax.set_xlim(left = 0, right = 220*n)
ax.set_ylim(bottom = 0, top = 220*n)
plt.show()
#########################################
P=np.array([[1.9],
[4.9],
[6.1],
[8.2],
[1.8],
[5.8],
[9.7],
[7.3],
[8.9],
[2.5],
[9.9],
[0.7]])
#########################################
The current output is
The expected output is
Values bar is added following #Davide_sd.
I'm not sure if this sovles your problem.
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import numpy as np
from matplotlib.colors import Normalize
from matplotlib import cm
fig,ax = plt.subplots(1)
n=3
P=np.array([[1.9],
[4.9],
[6.1],
[8.2],
[1.8],
[5.8],
[9.7],
[7.3],
[8.9],
[2.5],
[9.9],
[0.7]])
color = cm.get_cmap('Blues')
norm = Normalize(vmin=0, vmax=10)
color_list = []
for i in range(len(P)):
color_list.append(color(P[i]/10))
print(color_list)
id = 0
for j in range(0, n):
for k in range(n-1):
ax.hlines(200+200*(n-j-1)+5*n, 200*(k+1)+5*n, 200*(k+2)+5*n, zorder=0, colors=color_list[id])
id += 1
for i in range(0, n):
rect = mpl.patches.Rectangle((200+200*i, 200+200*j), 10*n, 10*n, linewidth=1, edgecolor='black', facecolor='black')
ax.add_patch(rect)
if j < n-1:
ax.vlines(200+200*i+5*n, 200*(n-1-j)+5*n, 200*(n-j)+5*n, zorder=0, colors=color_list[id])
id += 1
cb = fig.colorbar(cm.ScalarMappable(cmap=color, norm=norm))
cb.set_label("Values")
ax.set_xlim(left = 0, right = 220*n)
ax.set_ylim(bottom = 0, top = 220*n)
plt.show()
And the ouput is like:
You need to use a colormap, Normalize and ScalarMappable in order to create a colorbar.
Here is the procedure:
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import matplotlib.cm as cm
from matplotlib.colors import Normalize
import numpy as np
#########################################
P=np.array([[1.9],
[4.9],
[6.1],
[8.2],
[1.8],
[5.8],
[9.7],
[7.3],
[8.9],
[2.5],
[9.9],
[0.7]])
#########################################
# normalize the values. Values between 0 and 10 will be
# normalized to values from 0 and 1.
norm = Normalize(vmin=0, vmax=10)
Pnorm = norm(P)
# choose an appropriate colormap
cmap = cm.Blues
fig,ax = plt.subplots(1)
n=3
k = 0
for i in range(0,n):
for j in range(0,n):
rect = mpl.patches.Rectangle((200+200*i,200+200*j),10*n, 10*n, linewidth=1, edgecolor='black', facecolor='black')
ax.add_patch(rect)
# extract the color from the colormap
ax.hlines(200+200*i+5*n, 200, 200*n, zorder=0, color=cmap(Pnorm[k]))
ax.vlines(200+200*j+5*n, 200, 200*n, zorder=0, color=cmap(Pnorm[k]))
k += 1
cb = fig.colorbar(ScalarMappable(cmap=cmap, norm=norm))
cb.set_label("Values")
ax.set_xlim(left = 0, right = 220*n)
ax.set_ylim(bottom = 0, top = 220*n)
plt.show()
There is a problem with the way you are currently plotting lines, as they are overlapping. You need to fix it!
I'm still very new to programming and trying to create a contour plot of alkalinity across Hawaii using Cartopy. I will need to interpolate the point values called MODIFIED_TA against an x-y mesh grid but have not been able to figure out how to do this. The code I'm using is:
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import cartopy.crs as ccrs
import cartopy.mpl.ticker as cticker
import statistics
from scipy.interpolate import UnivariateSpline
import numpy as np
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import warnings
warnings.filterwarnings("ignore") # ignoring the warning prompts.
%matplotlib inline
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree(central_longitude=-170))
landgreen = cfeature.NaturalEarthFeature('physical', 'land', '110m',
edgecolor='face', facecolor='green')
oceanaqua = cfeature.NaturalEarthFeature('physical', 'ocean', '110m',
edgecolor='face', facecolor='aqua')
ax.set_extent([-151.5, -162, 18, 24], ccrs.PlateCarree())
ax.set_title('TOTAL ALKALINITY')
ax.add_feature(landgreen)
ax.add_feature(cfeature.OCEAN, color = 'k')
ax.gridlines(draw_labels=True)
lon_formatter = cticker.LongitudeFormatter()
lat_formatter = cticker.LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.grid(linewidth=2, color='black', alpha=0.5, linestyle='--')
lons = all_data.LONGITUDE[:]
lats = all_data.LATITUDE[:]
alk = all_data.MODIFIED_TA[:]
x1,y1 = np.meshgrid(lons,lats)
z1,z2 = np.meshgrid(all_data.MODIFIED_TA,all_data.MODIFIED_TA)
plt.tricontourf(lons,lats,alk, transform=ccrs.PlateCarree(), cmap=cm.gist_rainbow)
plt.colorbar(shrink=0.5)
plt.title('$A_{T}$ VALUES', color = 'k', fontname='Times New Roman',size = 23)
plt.plot()
The result is nothing like what I was hoping for and again, I'm not sure how to interpolate this value so that it comes out as a smooth gradient across the x/y coordinate grid. Any help would be greatly appreciated!
See output here
It's hard to tell for sure without being able to see your data. I tried to create a MRE and it worked. I would start by seeing if this works.
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.mpl.ticker as cticker
import numpy as np
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree(central_longitude=-170))
ax.set_extent([-151.5, -162, 18, 24], ccrs.PlateCarree())
ax.add_feature(cfeature.OCEAN)
ax.gridlines(draw_labels=True)
lon_formatter = cticker.LongitudeFormatter()
lat_formatter = cticker.LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.grid(linewidth=2, color='black', alpha=0.5, linestyle='--')
lons = np.random.random(80) * 7 - 160
lats = np.random.random(80) * 4 + 19
alk = np.cos(lons * 10 * np.pi / 180) * np.sin(lats * 20 / 180)
plt.plot(lons, lats, 'k.', transform = ccrs.PlateCarree())
plt.tricontourf(lons,lats,alk, transform=ccrs.PlateCarree(), alpha = 0.5)
plt.colorbar(shrink=0.5)
plt.title('$A_{T}$ VALUES', color = 'k', fontname='Times New Roman',size = 23)
If it does work, then what I'd look at would include:
What are the dimensions of all_data.LONGITUDE, all_data.LATITUDE, all_data.MOTIFIED_TA?
Are there duplicate values?
Does it work when you plot it outside of a projection?
If my example does not work, then it suggests there is something about your install, in which case update it if you can. If the problem still persists, perhaps there is a bug cartopy that needs reporting or a conflict with other packages.
Sorry, I cannot help further.
Is there a way to fill a country with an image similar to R solution using custom library here:
I have a solution where the face colour is filled for instance the below where Italy is blue. However, I would like to add the Italian flag. Is there a way in Python (I have not found much after searching) or is something like QGIS needed:
#create a map where I can load images in to fill the countries
import cartopy
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cartopy.io.shapereader as shpreader
flag = "italy.png" #this is a locally saved png.
plt.figure(figsize=(15, 15)) #size of plot
ax = plt.axes(projection=cartopy.crs.TransverseMercator(25))
ax.add_feature(cartopy.feature.BORDERS, linestyle='-', alpha=1)
ax.coastlines(resolution='110m') #simplifies the border lines
ax.add_feature(cartopy.feature.OCEAN, facecolor="#40e0d0") #colour of ocean
# ax.gridlines() #adds global grid lines
ax.set_extent ((-7.5, 50, 34, 69), cartopy.crs.PlateCarree()) #makes it european
shpfilename = shpreader.natural_earth(resolution='110m',
category='cultural',
name='admin_0_countries')
for country in shpreader.Reader(shpfilename).records():
if country.attributes['NAME_LONG'] == "Italy":
ax.add_geometries(country.geometry, ccrs.PlateCarree(),
facecolor="blue",
#no attribute like this img= "fd",
label=country.attributes['NAME_LONG'])
plt.show()
Any help, much appreciated!
Here is a demo code that does what you need. As a matter of fact, cartopy logo uses this technique to create.
import cartopy
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cartopy.io.shapereader as shpreader
import matplotlib.patches as mpatches
import numpy as np
imdat1 = plt.imread('flag-of-italy.jpg', format='jpg') # use your flag
plt.figure(figsize=(8, 8))
ax = plt.axes(projection=cartopy.crs.TransverseMercator(25))
ax.add_feature(cartopy.feature.BORDERS, linestyle='-', alpha=1)
ax.coastlines(resolution='110m')
ax.add_feature(cartopy.feature.OCEAN, facecolor="#40e0d0")
# ax.gridlines() #adds global grid lines
ax.set_extent ((-7.5, 50, 24, 69), cartopy.crs.PlateCarree())
shpfilename = shpreader.natural_earth(resolution='110m',
category='cultural',
name='admin_0_countries')
italy_ctry = None #use this to grab italy's
for country in shpreader.Reader(shpfilename).records():
if country.attributes['NAME_LONG'] == "Italy":
italy_ctry = country
ax.add_geometries(country.geometry, ccrs.PlateCarree(),
facecolor="none",
alpha=0.7,
zorder=2,
label=country.attributes['NAME_LONG'])
# create mpatch from `italy` geometry
cg = italy_ctry.geometry
cg2 = cg.simplify(0.02)
if cg2.geometryType()=='MultiPolygon':
# if == `Polygon`, dont need to loop
for ea in cg2.geoms:
cg2xy = ea.exterior.xy # tuple of (x,y)
xys = []
for ea in zip(cg2xy[0], cg2xy[1]):
#print(ea[0],ea[1])
xys.append([ea[0],ea[1]])
# add a patch
poly = mpatches.Polygon(xys, closed=True, ec='r', \
lw=2, fc='yellow', \
transform=ccrs.PlateCarree(), \
alpha=0.5, zorder=30)
plate_carree_transform = ccrs.PlateCarree()._as_mpl_transform(ax)
xtent1 = (6.519950, 17.122259, 35.783370, 47.962952)
imdat2 = ax.imshow(imdat1, origin='upper', extent=xtent1, \
transform=ccrs.PlateCarree(), \
zorder=15, alpha=.9)
##imdat2 = ax.stock_img() #for testing
imdat2.set_clip_path(mpatches.Path(xys), transform=plate_carree_transform)
pass
plt.show()
The sample plot (varies with the flag in use):
I am trying to smooth my color map using pcolormesh shading='gouraud' argument, but it failed, returned the follow error, which I do not understand.
"/usr/local/anaconda/lib/python2.7/site- packages/matplotlib/collections.py", line 1822, in draw
gc, triangles, colors, transform.frozen())
File "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_ps.py", line 876, in draw_gouraud_triangles
('colors', 'u1', (3,))])
TypeError: data type not understood File
I have my code as below:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib.mlab import griddata
from matplotlib.ticker import AutoMinorLocator
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.basemap import Basemap
x = np.loadtxt('data.txt',usecols=[0])
y = np.loadtxt('data.txt',usecols=[1])
s = np.loadtxt('data.txt',usecols=[2])
N = 36j
M = 18j
extent = (min(x), max(x), min(y), max(y))
xx,yy = np.mgrid[extent[0]:extent[1]:N, extent[2]:extent[3]:M]
ss = griddata(x, y, s, xx, yy, interp='linear')
fig, ax = plt.subplots()
#m = Basemap(projection='hammer',lon_0=0,resolution=None)
m = Basemap(projection='kav7',lon_0=0,resolution=None)
m.drawmapboundary(fill_color='0.75')
im = m.pcolormesh(xx,yy,ss,shading='gouraud',cmap=plt.cm.jet,latlon=True)
m.drawparallels(np.arange(-90.,99.,30.))
m.drawmeridians(np.arange(-180.,180.,60.))
cb = m.colorbar(im,"bottom", size="5%", pad="2%", ticks=[-2,-1,0,1,2,3,4,5])
plt.show()
When the shading argument is flat shading = 'flat', then this works very well, but the color is not smooth. Any one can offer me some idea how to approach this problem?
I am trying to plot some bathymetry data using cartopy. I slice out a section of the data and plot it over a Mercator projection. It produces a map that looks ok but I get the following error,
IllegalArgumentException: Invalid number of points in LinearRing found 3 - must be 0 or >= 4
Shell is not a LinearRing
Should I be worried about this? My code is below,
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.feature as cfeature
from netCDF4 import Dataset
# load data and slice out region of interest
_file = 'GEBCO_2014_2D.nc'
gebco = Dataset(_file, mode='r')
g_lons = gebco.variables['lon'][:]
g_lon_inds = np.where((g_lons>=-30) & (g_lons<=10))[0]
g_lons = g_lons[g_lon_inds]
g_lats = gebco.variables['lat'][:]
g_lat_inds = np.where((g_lats>=40) & (g_lats<=65))[0]
g_lats = g_lats[g_lat_inds]
d = gebco.variables['elevation'][g_lat_inds, g_lon_inds]
gebco.close()
# plot data
projection=ccrs.Mercator()
extent = [-30, 10, 40, 65]
fig = plt.figure(figsize=(13.3, 10))
ax = fig.add_subplot(111, projection=projection)
lon_labels = np.arange(-30, 20, 10)
lat_labels = np.arange(40, 75, 10)
gl = ax.gridlines(draw_labels=True, xlocs=lon_labels, ylocs=lat_labels)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
ax.set_extent(extent, crs=ccrs.PlateCarree())
coastline_10m = cfeature.NaturalEarthFeature('physical', 'coastline', '10m',
edgecolor='k', alpha=0.6,
facecolor=cfeature.COLORS['land'])
ax.add_feature(coastline_10m)
CS = plt.contour(g_lons, g_lats, d, [-1000,-150],
colors='k', alpha=0.4, linewidth=0.5, zorder=1,
transform=ccrs.PlateCarree())
plt.clabel(CS, inline=True, fontsize=10, fmt='%i')