I am trying to plot a contour and quiver plot over a basemap. When I plot, I get no errors, but only the basemap will show. The netcdf file only has one point in it for lat and long, so I had to create a range of coordinates. Any ideas why this is happening?
import netCDF4
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pylab
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
ncfile = netCDF4.Dataset('30JUNE2012_0400UTC.cdf', 'r')
dbZ = ncfile.variables['MAXDBZF']
u = ncfile.variables['UNEW']
v = ncfile.variables['VNEW']
#print u
#print v
#print dbZ
data = dbZ[0,0]
data.shape
#print data.shape
z_index = 0 # z-level you want to plot (0-19)
U = u[0,z_index, :,:] #[time,z,x,y]
V = v[0,z_index, :,:]
lats = np.linspace(35.0, 41.0, data.shape[0])
lons = np.linspace(-81.0,-73.0, data.shape[1])
# create the map
map = Basemap(llcrnrlat=36,urcrnrlat=40,\
llcrnrlon=-80,urcrnrlon=-74,lat_ts=20,resolution='c')
# load the shapefile, use the name 'states'
map.readshapefile('st99_d00', name='states', drawbounds=True)
# collect the state names from the shapefile attributes so we can
# look up the shape obect for a state by it's name
state_names = []
for shape_dict in map.states_info:
state_names.append(shape_dict['NAME'])
ax = plt.gca() # get current axes instance
x,y = map(*np.meshgrid(lats,lons))
levels = np.arange(5,60,3)
c = map.contourf(x,y,data, levels, cmap='jet')
plt.colorbar()
q=plt.quiver(U,V,width=0.002, scale_units='xy',scale=10)
qk= plt.quiverkey (q,0.95, 1.02, 20, '20m/s', labelpos='N')
plt.show()
Related
I am trying to plot L2 Sea Surface Temperature data and I want to plot it over the globe in a geostationary projection. I tried the following code:
import h5py
import sys
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
# First get data from HDF5 file with h5py:
fn = '/home/swadhin/project/insat/data/3RIMG_30MAR2018_0014_L2B_SST_V01R00.h5'
with h5py.File(fn) as f:
print(list(f.keys()))
image = 'SST'
img_arr = f[image][0,:,:]
# get _FillValue for data masking
img_arr_fill = f[image].attrs['_FillValue'][0]
# retrieve extent of plot from file attributes:
left_lon = f.attrs['left_longitude'][0]
right_lon = f.attrs['right_longitude'][0]
lower_lat = f.attrs['lower_latitude'][0]
upper_lat = f.attrs['upper_latitude'][0]
sat_long = f.attrs['Nominal_Central_Point_Coordinates(degrees)_Latitude_Longitude'][1]
sat_hght = f.attrs['Nominal_Altitude(km)'][0] * 1000.0 # (for meters)
print('Done reading HDF5 file')
## Use np.ma.masked_equal with integer values to
## mask '_FillValue' data in corners:
img_arr_m = np.ma.masked_equal(img_arr, img_arr_fill)
print(img_arr_fill)
print(np.max(img_arr_m))
print(np.min(img_arr_m))
#print(np.shape(img_arr_m))
# # Create Geostationary plot with cartopy and matplotlib
map_proj = ccrs.Geostationary(central_longitude=sat_long,satellite_height=sat_hght)
ax = plt.axes(projection=map_proj)
ax.coastlines(color='black',linewidth = 0.5)
#ax.add_feature(cfeature.BORDERS, edgecolor='white', linewidth=0.25)
#ax.add_feature(cfeature.STATES,edgecolor = 'red',linewidth = 0.5)
ax.gridlines(color='black', alpha=0.5, linestyle='--', linewidth=0.75, draw_labels=True)
#ax.add_geometries(ind_shapes,crs = map_proj, edgecolor = 'black', alpha = 0.5)
map_extend_geos = ax.get_extent(crs=map_proj)
plt.imshow(img_arr_m, interpolation='none',origin='upper',extent=map_extend_geos, cmap = 'jet')
plt.colorbar()
#plt.clim(-10,5)
plt.savefig('/home/swadhin/project/insat/data/l2_sst.png',format = 'png', dpi=1000)
The output I got is not very accurate. There are some SST values over some of the land areas which should not be the case.
I am adding the data here for people who wanna give it a try.
https://drive.google.com/file/d/126oW36JXua-zz3XMUcyZxwPj8UISDgUM/view?usp=sharing
I have checked your HDF5 file, and there are Longitude and Latitude variables in the file. So I think these WGS84 coordinates should be used.
First, the imshow method needs the image boundary information that cannot be obtained.
I also tried the pcolormesh method, but this method can not accept lon/lat array with NaN value.
In conclusion, the contourf seems to be the best choice, but this method still has the disadvantage that it is time-consuming to run.
import h5py
import sys
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
fn ='3RIMG_30MAR2018_0014_L2B_SST_V01R00.h5'
with h5py.File(fn) as f:
print(list(f.keys()))
image = 'SST'
img_arr = f[image][0,:,:]
lon = f['Longitude'][:]*0.01
lat = f['Latitude'][:]*0.01
# # get _FillValue for data masking
img_arr_fill = f[image].attrs['_FillValue'][0]
# # retrieve extent of plot from file attributes:
left_lon = f.attrs['left_longitude'][0]
right_lon = f.attrs['right_longitude'][0]
lower_lat = f.attrs['lower_latitude'][0]
upper_lat = f.attrs['upper_latitude'][0]
sat_long = f.attrs['Nominal_Central_Point_Coordinates(degrees)_Latitude_Longitude'][1]
sat_hght = f.attrs['Nominal_Altitude(km)'][0] * 1000.0 # (for meters)
print('Done reading HDF5 file')
## Use np.ma.masked_equal with integer values to
## mask '_FillValue' data in corners:
img_arr_m = np.ma.masked_equal(img_arr, img_arr_fill)
print(img_arr_fill)
print(np.max(img_arr_m))
print(np.min(img_arr_m))
lon_m = np.ma.masked_equal(lon, 327.67)
lat_m = np.ma.masked_equal(lat, 327.67)
# # Create Geostationary plot with cartopy and matplotlib
map_proj = ccrs.Geostationary(central_longitude=sat_long,satellite_height=sat_hght)
# or map_proj = ccrs.PlateCarree()
ax = plt.axes(projection=map_proj)
ax.set_global()
ax.coastlines(color='black',linewidth = 0.5)
ax.add_feature(cfeature.BORDERS, edgecolor='white', linewidth=0.25)
ax.add_feature(cfeature.STATES,edgecolor = 'red',linewidth = 0.5)
ax.gridlines(color='black', alpha=0.5, linestyle='--', linewidth=0.75, draw_labels=True)
cb = ax.contourf(lon_m,lat_m,img_arr_m, cmap = 'jet',transform = ccrs.PlateCarree())
plt.colorbar(cb)
plt.savefig('l2_sst1.png',format = 'png', dpi=300)
Here is the output figure.
or using a lon-lat projection.
I'm working with netCDF file for plotting the map. However, I'm new to it and there is very little progress and all I did is learning some tutorials. I have some difficulties when overlaying color onto the map. Here is my code (with the downloaded file):
from mpl_toolkits.basemap import Basemap, cm
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
data = Dataset('filename','r')
lat = data.variables['lat'][:]
lon = data.variables['lon'][:]
time = data.variables['time'][:]
lwe_thickness = data.variables['lwe_thickness'][:]
data.close()
mp = Basemap(projection = 'mill',
llcrnrlon=lon.min(),
urcrnrlon=lon.max(),
llcrnrlat=lat.min(),
urcrnrlat=lat.max(),
resolution = 'c')
x, y = np.meshgrid(lon, lat)
x, y = mp(lon, lat)
mp.drawcoastlines()
mp.drawstates()
mp.drawcountries()
mp.drawmapboundary()
parallels = np.arange(0.,90,30.)
mp.drawparallels(parallels,labels=[1,0,0,0])
meridians = np.arange(-180.,180.,30.)
mp.drawmeridians(meridians,labels=[0,0,0,1])
cmesh = mp.pcolormesh(x,y,lwe_thickness,shading='flat',cmap=plt.cm.jet,latlon=True)
cbar = mp.colorbar(cmesh, location='right')
plt.show()
I am reading data from a csv file and plotting contours using python. The code works but when I check shapefile created, I can see only few contour lines are getting plotted whereas when I check image plot there are many more lines. What is going wrong in this code? I think the cs.collections is creating some problem but I am not able to figure it out.
import os
import numpy as np
import pandas as pd
from matplotlib.mlab import griddata
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from shapely.geometry import mapping, Polygon, LineString,MultiLineString
import fiona
# set up plot
plt.clf()
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, axisbg='w', frame_on=False)
# grab data
data = pd.read_csv('Filename.csv', sep=',')
norm = Normalize()
# define map extent
lllon = -180
lllat = -90
urlon = 180
urlat = 90
# Set up Basemap instance
m = Basemap(#projection = 'merc',llcrnrlon = lllon, llcrnrlat = lllat, urcrnrlon = urlon, urcrnrlat = urlat,resolution='h', epsg=4326)
# transform lon / lat coordinates to map projection
data['projected_lon'], data['projected_lat'] = m(*(data.CLON.values, data.CLAT.values))
# grid data
numcols, numrows = 100, 100
xi = np.linspace(data['projected_lon'].min(), data['projected_lon'].max(), numcols)
yi = np.linspace(data['projected_lat'].min(), data['projected_lat'].max(), numrows)
xi, yi = np.meshgrid(xi, yi)
# interpolate
x, y, z = data['projected_lon'].values, data['projected_lat'].values, data.PRES.values
zi = griddata(x, y, z, xi, yi)
# contour plot
cs = plt.contour(xi, yi, zi,zorder=4)
plt.show()
lenvar = (len(cs.collections)) #number of contours
print(lenvar)
#print(cs.collections)
lines = [] # Empty list for contour sections
for i in range(lenvar):
n = i
p = cs.collections[i].get_paths()[0]
v = p.vertices # getting the individual verticies as a numpy.ndarray
x = v[:,0] #takes first column
y = v[:,1] #takes second column
line = LineString([(i[0], i[1]) for i in zip(x,y)]) #Defines Linestring
lines.append(line) #appends to list
schema = {'geometry': 'LineString','properties': {'id': 'int'}} # sets up parameter for shapefile
with fiona.open('ConShp.shp', 'w', 'ESRI Shapefile', schema) as c: # creates new file to be written to
for j in range(len(lines)):
l = (lines[j]) # creates variable
print(l)
print(type(l))
c.write({'geometry': mapping(l),'properties': {'id': j},})
data Sample is like:
Lat, Lon, Value
18.73, 26.34, 5000
20.00, 60.00, 7000
Shapefile_plot_in_GIS
Image of Plot in python
I am trying to create a color mesh plot but the data points and their corresponding colors appear too small.
My script is:
import pandas as pd
import numpy as np
from mpl_toolkits.basemap import Basemap, cm
import matplotlib.pyplot as plt
df = pd.read_csv('data.csv', usecols=[1,2,4])
df = df.apply(pd.to_numeric)
val_pivot_df = df.pivot(index='Latitude', columns='Longitude', values='Bin 1')
lons = val_pivot_df.columns.astype(float)
lats = val_pivot_df.index.astype(float)
fig, ax = plt.subplots(1, figsize=(8,8))
m = Basemap(projection='merc',
llcrnrlat=df.dropna().min().Latitude-5
, urcrnrlat=df.dropna().max().Latitude+5
, llcrnrlon=df.dropna().min().Longitude-5
, urcrnrlon=df.dropna().max().Longitude+5
, resolution='i', area_thresh=10000
)
m.drawcoastlines()
m.drawstates()
m.drawcountries()
m.fillcontinents(color='gray', lake_color='white')
m.drawmapboundary(fill_color='0.3')
x, y = np.meshgrid(lons,lats)
px,py = m(x,y)
data_values = val_pivot_df.values
masked_data = np.ma.masked_invalid(data_values)
cmap = plt.cm.viridis
m.pcolormesh(px, py, masked_data, vmin=0, vmax=8000)
m.colorbar()
plt.show()
I'm looking to get the markersize larger of each data point but I can't seem to find any documentation on how to do this for pcolormesh
There is no marker in a pcolormesh. The size of the colored areas in a pcolor plot is determined by the underlying grid. As an example, if the grid in x direction was [0,1,5,105], the last column would be 100 times larger in size than the first.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
x = [0,1,5,25,27,100]
y = [0,10,20,64,66,100]
X,Y = np.meshgrid(x,y)
Z = np.random.rand(len(y)-1, len(x)-1)
plt.pcolormesh(X,Y,Z)
plt.show()
I'm trying to plot streamlines on a global map with the Robinson projection, but basemap doesn't seem to like the projected co-ordinates. Of course, it works fine for a plain old cylindrical projection, which is regular in the x direction.
Here is an example:
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
u = np.ones((21,21))
v = np.ones((21,21))
lats = np.arange(-90,91,9)
lons = np.arange(-180,181,18)
x,y = np.meshgrid(lons,lats)
# It works for Cylindrical
mp = Basemap(projection='cyl')
xx,yy = mp(x,y)
mp.streamplot(xx,yy,u,v)
mp.drawcoastlines()
plt.show()
# But not Robinson
mp = Basemap(projection='robin',lon_0=0)
xx, yy = mp(x, y)
mp.streamplot(xx,yy,u,v)
mp.drawcoastlines()
plt.show()
It complains about the x co-ordinates, raising:
ValueError: The rows of 'x' must be equal
So is it possible to plot streamlines on Robinson projections?
With the command xx,yy = mp(x,y) a coordinate transformation according to the particular projection is applied to your lon and lats. For most projections this will result in a distorsion of the gird point such that rows of x are no longer equal, hence the error: ValueError: The rows of 'x' must be equal. To fix this you need to re-grid your data, e.g. like this:
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib as plt
u = np.ones((21,21))
v = np.ones((21,21))
lats = np.arange(-90,91,9)
lons = np.arange(-180,181,18)
x,y = np.meshgrid(lons,lats)
mp = Basemap(projection='robin',lon_0=0)
xx, yy = mp(x, y)
# generate a grid that is equally spaced in a plot with the current pojection
lons, lats, xxnew, yynew = mp.makegrid(21,21, returnxy=True)
# project the data onto the new grid
unew = plt.mlab.griddata(xx.flatten(), yy.flatten(),u.flatten(), xxnew, yynew ,interp='linear')
vnew = plt.mlab.griddata(xx.flatten(), yy.flatten(),v.flatten(), xxnew, yynew ,interp='linear')
mp.streamplot(xxnew,yynew,unew,vnew)
mp.drawcoastlines()
plt.show()