We Keep Coding

Resampling and re-projecting weather satellite image

The current question is somewhat similar to a resampling question on:
Resample question on Stackoverflow
However, my specific problem is that I only have a partial satellite image and not the full image. As a result, I'm not sure how to proceed. Here's what I've tried so far:
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
def transform_extent_pts(extent_pts, map_proj, pt_crs):
xul, yul = map_proj.transform_point(
x = extent_pts[0],
y = extent_pts[3],
src_crs = pt_crs)
xlr, ylr = map_proj.transform_point(
x = extent_pts[1],
y = extent_pts[2],
src_crs = pt_crs)
return [xul, xlr, ylr, yul]
sat_image1 = ROOT
df = plt.imread(sat_image1)
# re-project to Mercator
map_proj = ccrs.Mercator()
# Image extent in Geostationary coordinates:
data_crs = ccrs.Geostationary(central_longitude=0.0)
ax2 = plt.axes(projection=data_crs)
img_extent_sat = ax2.get_extent(crs=data_crs)
img_extent_sat = [1.03*x for x in img_extent_sat]
#img_extent_sat=[-32.150361957, 30.150361957, 7.150361956, 42.150361956]
# Convert to Mercator
img_extent_merc = transform_extent_pts(img_extent_sat, map_proj, ccrs.Geodetic())
plt.close()
fig = plt.figure(figsize=(10,10))
ax = plt.axes(projection=map_proj)
ax.coastlines(color='blue')
ax.gridlines(color='black', alpha=0.5, linestyle='--', linewidth=0.75, draw_labels=True)
# Map extent in degrees (PlateCarree) coordinates:
map_extent_deg = (50., -20., -40., 40.) # African continent
map_extent_deg = (-31, 38.009232, 2.880476, 42)
# Convert to Mercator
map_extent_merc = transform_extent_pts(map_extent_deg, map_proj, ccrs.Geodetic())
ax.set_extent(map_extent_merc, map_proj)
plt.imshow(df, origin='upper', transform=data_crs, extent=img_extent_sat)
The main issue I'm facing is that the projection of my data seems to be incorrect, and there might also be a small misalignment. I'm now wondering how I can change the projection of my data. Could you provide some guidance on how to accomplish this?
I've tried a few things, but unfortunately the image is only available in PNG format. The "transform" function, such as using "geostationary," doesn't seem to work with this format. Are there any other options available? Can the projection be changed afterwards, or is that not possible? I assume that the exact coordinates can be obtained by shifting the image.
Perhaps the two red dots can be helpful. I know their coordinates, and they should align with the two white squares:
plt.plot(sta_lon, sta_lat, marker='o', color='red', markersize=8,
alpha=0.7, transform=ccrs.Geodetic())
plt.text(sta_lon, sta_lat+0.25, sta_name, ha='center', fontsize=18,
color='red', transform=ccrs.Geodetic())

How do I get the transformed data of a cartopy geodetic plot?

how do I get all the data of the transformed line of the "handle" - Line2D object in the following code:
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
ax = plt.axes(projection=ccrs.PlateCarree())
ax.stock_img()
ny_lon, ny_lat = -75, 43
delhi_lon, delhi_lat = 77.23, 28.61
handle = plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
color='blue', linewidth=2, marker='o',
transform=ccrs.Geodetic(),
)
plt.show()
To be more clear:
I'm not looking for the output of "handle[0].get_data()", since this just prints my original longitude and latitude, but im looking for the the data of the geodetic line drawn on the map.

I found the answer!
According to this question, you can access the data of the transformation via the following code snippet:
[handle] = plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat], color='blue', linewidth=2, marker='o', transform=ccrs.Geodetic())
t_path = handle._get_transformed_path()
path_in_data_coords, _ = t_path.get_transformed_path_and_affine()
print(path_in_data_coords.vertices)
In the answer to this question there is also a second approach.

Let me do some computation and plot checks on the code provided by the OP.
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
ax = plt.axes(projection=ccrs.PlateCarree())
ax.stock_img()
ny_lon, ny_lat = -75, 43
delhi_lon, delhi_lat = 77.23, 28.61
# Plot geodetic path in thick 'blue' line
handle = plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
color='blue', linewidth=10, marker='o',
transform=ccrs.Geodetic(),
)
# Get the geodetic path's coordinates to plot on top in 'red'
t_path = handle[0]._get_transformed_path()
path_in_data_coords, _ = t_path.get_transformed_path_and_affine()
ax.plot(path_in_data_coords.vertices[:,0],
path_in_data_coords.vertices[:,1],
color='red', lw=2)
plt.show()
And, the output plot is:
Congratulations to the OP.
(Extension part 1)
Now, let us compute the length of the geodesic path using the coordinates obtained above. My proposed code is:
# (*** Continued from the code above ***)
import cartopy.geodesic as geodesic
import numpy as np
# defining the earth shape on which to make calculations
myGeod = geodesic.Geodesic(6378137.0, 1/298.257223563)
# get (lat,long) lists from (long,lat) of the geodesic path
latlonlists = []
[latlonlists.append([lat,lon]) for lon,lat in zip(path_in_data_coords.vertices[:,0], path_in_data_coords.vertices[:,1])]
#print(latlonlists)
# compute length of the geodesic
geodesic_in_meters = myGeod.geometry_length(np.array(latlonlists))
print(geodesic_in_meters) # output: 17554975.077432975

Plot polylines on top of OSMnx map

Using the OSMnx library, I'm trying to draw lines as polygons on top of a base map (with per-defined coordinates not adhering to the underlying network), but with no luck. I'm certain that the coordinates I have are inside of the boundary, and I get no error when adding them.
Here's my current code, which generates the base map and also adds a multi polygon layer below the network. So it's possible to add polygons, which makes me think there might a projection issue with my coordinates, but I haven't had any luck setting different projections.
Any help would be much appreciated!
import matplotlib.pyplot as plt
from descartes import PolygonPatch
from shapely.geometry import Polygon, MultiPolygon
import osmnx as ox
ox.config(log_console=True, use_cache=True)
ox.__version__
def plot(geometries):
# get the place shape
gdf = ox.gdf_from_place('Copenhagen Municipality,Denmark')
gdf = ox.project_gdf(gdf)
# get the street network, with retain_all=True to retain all the disconnected islands' networks
G = ox.graph_from_place('Copenhagen Municipality,Denmark', network_type='drive', retain_all=True)
G = ox.project_graph(G)
fig, ax = ox.plot_graph(G, fig_height=10, show=False, close=False, edge_color='#777777')
# Add shape from gdf
for geometry in gdf['geometry'].tolist():
if isinstance(geometry, (Polygon, MultiPolygon)):
if isinstance(geometry, Polygon):
geometry = MultiPolygon([geometry])
for polygon in geometry:
patch = PolygonPatch(polygon, fc='#cccccc', ec='k', linewidth=3, alpha=0.1, zorder=-1)
ax.add_patch(patch)
# Add lines:
for geometry in geometries:
if isinstance(geometry, (Polygon, MultiPolygon)):
if isinstance(geometry, Polygon):
geometry = MultiPolygon([geometry])
for polygon in geometry:
patch = PolygonPatch(polygon, fc='#148024', ec='#777777', linewidth=10, alpha=1, zorder=2)
ax.add_patch(patch)
plt.savefig('images/cph.png', alpha=True, dpi=300)
plot(geometries)
geometries is a list which contains polygons like these:
POLYGON ((55.6938796 12.5584122, 55.6929711 12.5585957, 55.6921317 12.5579927, 55.6916918 12.5564539, 55.6909246 12.5553629, 55.6901215 12.554119, 55.6891181 12.5531433, 55.6881469 12.5526575, 55.687502 12.5538862, 55.6866445 12.5530816, 55.6856769 12.5524416, 55.6848185 12.5515929, 55.6838506 12.551074, 55.6829915 12.5504047, 55.6821492 12.5498124, 55.6812104 12.5492503, 55.680311 12.5486803, 55.6792187 12.547724, 55.6783172 12.5472156, 55.6774282 12.5466767, 55.6765291 12.5461124, 55.6755652 12.5453961, 55.6747743 12.5445313, 55.6738159 12.5439029, 55.673417 12.5454132, 55.6733398 12.5470051, 55.6731045 12.5486561, 55.6726013 12.5501493, 55.6727833 12.5520672, 55.6716717 12.5525378, 55.6706619 12.5528382, 55.6698239 12.5521737))
POLYGON ((55.6693768 12.5509383, 55.6684025 12.5511539, 55.6677405 12.5500371, 55.6668188 12.5501435, 55.6658323 12.550075, 55.665264 12.5487917, 55.6649187 12.5473085, 55.6645313 12.5457653))

In geopandas dataframes, the geo-coordinates are (longitude, latitude). Here is a simple demonstration code that plots some sample data.
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import osmnx as ox
from shapely import wkt #need wkt.loads
# simple plot of OSM data
gdf = ox.gdf_from_place('Copenhagen Municipality,Denmark')
gdf = gpd.GeoDataFrame(gdf, crs={'init': 'epsg:4326'}) #set CRS
ax1 = gdf.plot(color='lightgray') # grab axis as `ax1` for reuse
# prep the polygons to plot on the axis `ax1`
# use (longitude latitude), and the last point must equal the 1st
pgon1 = "POLYGON((12.5584122 55.6938796, 12.5585957 55.6929711, 12.5579927 55.6921317, 12.5564539 55.6916918, 12.5553629 55.6909246, 12.554119 55.6901215, 12.5531433 55.6891181, 12.5526575 55.6881469, 12.5538862 55.687502, 12.5530816 55.6866445, 12.5524416 55.6856769, 12.5515929 55.6848185, 12.551074 55.6838506, 12.5504047 55.6829915, 12.5498124 55.6821492, 12.5492503 55.6812104, 12.5486803 55.680311, 12.547724 55.6792187, 12.5472156 55.6783172, 12.5466767 55.6774282, 12.5461124 55.6765291, 12.5453961 55.6755652, 12.5445313 55.6747743, 12.5439029 55.6738159, 12.5454132 55.673417, 12.5470051 55.6733398, 12.5486561 55.6731045, 12.5501493 55.6726013, 12.5520672 55.6727833, 12.5525378 55.6716717, 12.5528382 55.6706619, 12.5521737 55.6698239, 12.5584122 55.6938796))"
pgon2 = "POLYGON((12.5509383 55.6693768, 12.5511539 55.6684025, 12.5500371 55.6677405, 12.5501435 55.6668188, 12.550075 55.6658323, 12.5487917 55.665264, 12.5473085 55.6649187, 12.5457653 55.6645313, 12.5509383 55.6693768))"
# create dataframe of the 2 polygons
d = {'col1': [1, 2], 'wkt': [pgon1, pgon2]}
df = pd.DataFrame( data=d )
# make geo-dataframe from it
geometry = [wkt.loads(pgon) for pgon in df.wkt]
gdf2 = gpd.GeoDataFrame(df, \
crs={'init': 'epsg:4326'}, \
geometry=geometry)
# plot it as red polygons
gdf2.plot(ax=ax1, color='red', zorder=5)
The output plot:

Plotting a map using geopandas and matplotlib

I have small csv that has 6 coordinates from Birmingham England. I read the csv with pandas then transformed it into GeoPandas DataFrame changing my latitude and longitude columns with Shapely Points. I am now trying to plot my GeoDataframe and all I can see are the points. How do I get the Birmingham map represented as well? A good documentation source on GeoPandas would be strongly appreciated too.
from shapely.geometry import Point
import geopandas as gpd
import pandas as pd
df = pd.read_csv('SiteLocation.csv')
df['Coordinates'] = list(zip(df.LONG, df.LAT))
df['Coordinates'] = df['Coordinates'].apply(Point)
# Building the GeoDataframe
geo_df = gpd.GeoDataFrame(df, geometry='Coordinates')
geo_df.plot()

The GeoPandas documentation contains an example on how to add a background to a map (https://geopandas.readthedocs.io/en/latest/gallery/plotting_basemap_background.html), which is explained in more detail below.
You will have to deal with tiles, that are (png) images served through a web server, with a URL like
http://.../Z/X/Y.png, where Z is the zoom level, and X and Y identify the tile
And geopandas's doc shows how to set tiles as backgrounds for your plots, fetching the correct ones and doing all the otherwise difficult job of spatial syncing, etc...
Installation
Assuming GeoPandas is already installed, you need the contextily package in addition. If you are under windows, you may want to pick a look at How to install Contextily?
Use case
Create a python script and define the contextily helper function
import contextily as ctx
def add_basemap(ax, zoom, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png'):
xmin, xmax, ymin, ymax = ax.axis()
basemap, extent = ctx.bounds2img(xmin, ymin, xmax, ymax, zoom=zoom, url=url)
ax.imshow(basemap, extent=extent, interpolation='bilinear')
# restore original x/y limits
ax.axis((xmin, xmax, ymin, ymax))
and play
import matplotlib.pyplot as plt
from shapely.geometry import Point
import geopandas as gpd
import pandas as pd
# Let's define our raw data, whose epsg is 4326
df = pd.DataFrame({
'LAT' :[-22.266415, -20.684157],
'LONG' :[166.452764, 164.956089],
})
df['coords'] = list(zip(df.LONG, df.LAT))
# ... turn them into geodataframe, and convert our
# epsg into 3857, since web map tiles are typically
# provided as such.
geo_df = gpd.GeoDataFrame(
df, crs ={'init': 'epsg:4326'},
geometry = df['coords'].apply(Point)
).to_crs(epsg=3857)
# ... and make the plot
ax = geo_df.plot(
figsize= (5, 5),
alpha = 1
)
add_basemap(ax, zoom=10)
ax.set_axis_off()
plt.title('Kaledonia : From Hienghène to Nouméa')
plt.show()
Note: you can play with the zoom to find the good resolution for the map. E.g./I.e. :
... and such resolutions implicitly call for changing the x/y limits.

Just want to add the use case concerning zooming whereby the basemap is updated according to the new xlim and ylim coordinates. A solution I have come up with is:
First set callbacks on the ax that can detect xlim_changed and ylim_changed
Once both have been detected as changed get the new plot_area calling ax.get_xlim() and ax.get_ylim()
Then clear the ax and re-plot the basemap and any other data
Example for a world map showing the capitals. You notice when you zoom in the resolution of the map is being updated.
import geopandas as gpd
import matplotlib.pyplot as plt
import contextily as ctx
figsize = (12, 10)
osm_url = 'http://tile.stamen.com/terrain/{z}/{x}/{y}.png'
EPSG_OSM = 3857
EPSG_WGS84 = 4326
class MapTools:
def __init__(self):
self.cities = gpd.read_file(
gpd.datasets.get_path('naturalearth_cities'))
self.cities.crs = EPSG_WGS84
self.cities = self.convert_to_osm(self.cities)
self.fig, self.ax = plt.subplots(nrows=1, ncols=1, figsize=figsize)
self.callbacks_connect()
# get extent of the map for all cities
self.cities.plot(ax=self.ax)
self.plot_area = self.ax.axis()
def convert_to_osm(self, df):
return df.to_crs(epsg=EPSG_OSM)
def callbacks_connect(self):
self.zoomcallx = self.ax.callbacks.connect(
'xlim_changed', self.on_limx_change)
self.zoomcally = self.ax.callbacks.connect(
'ylim_changed', self.on_limy_change)
self.x_called = False
self.y_called = False
def callbacks_disconnect(self):
self.ax.callbacks.disconnect(self.zoomcallx)
self.ax.callbacks.disconnect(self.zoomcally)
def on_limx_change(self, _):
self.x_called = True
if self.y_called:
self.on_lim_change()
def on_limy_change(self, _):
self.y_called = True
if self.x_called:
self.on_lim_change()
def on_lim_change(self):
xlim = self.ax.get_xlim()
ylim = self.ax.get_ylim()
self.plot_area = (*xlim, *ylim)
self.blit_map()
def add_base_map_osm(self):
if abs(self.plot_area[1] - self.plot_area[0]) < 100:
zoom = 13
else:
zoom = 'auto'
try:
basemap, extent = ctx.bounds2img(
self.plot_area[0], self.plot_area[2],
self.plot_area[1], self.plot_area[3],
zoom=zoom,
url=osm_url,)
self.ax.imshow(basemap, extent=extent, interpolation='bilinear')
except Exception as e:
print(f'unable to load map: {e}')
def blit_map(self):
self.ax.cla()
self.callbacks_disconnect()
cities = self.cities.cx[
self.plot_area[0]:self.plot_area[1],
self.plot_area[2]:self.plot_area[3]]
cities.plot(ax=self.ax, color='red', markersize=3)
print('*'*80)
print(self.plot_area)
print(f'{len(cities)} cities in plot area')
self.add_base_map_osm()
self.callbacks_connect()
#staticmethod
def show():
plt.show()
def main():
map_tools = MapTools()
map_tools.show()
if __name__ == '__main__':
main()
Runs on Linux Python3.8 with following pip installs
affine==2.3.0
attrs==19.3.0
autopep8==1.4.4
Cartopy==0.17.0
certifi==2019.11.28
chardet==3.0.4
Click==7.0
click-plugins==1.1.1
cligj==0.5.0
contextily==1.0rc2
cycler==0.10.0
descartes==1.1.0
Fiona==1.8.11
geographiclib==1.50
geopandas==0.6.2
geopy==1.20.0
idna==2.8
joblib==0.14.0
kiwisolver==1.1.0
matplotlib==3.1.2
mercantile==1.1.2
more-itertools==8.0.0
munch==2.5.0
numpy==1.17.4
packaging==19.2
pandas==0.25.3
Pillow==6.2.1
pluggy==0.13.1
py==1.8.0
pycodestyle==2.5.0
pyparsing==2.4.5
pyproj==2.4.1
pyshp==2.1.0
pytest==5.3.1
python-dateutil==2.8.1
pytz==2019.3
rasterio==1.1.1
requests==2.22.0
Rtree==0.9.1
Shapely==1.6.4.post2
six==1.13.0
snuggs==1.4.7
urllib3==1.25.7
wcwidth==0.1.7
Note especially requirement for contextily==1.0rc2
On windows I use Conda (P3.7.3) and don't forget to set the User variables:
GDAL c:\Users\<username>\Anaconda3\envs\<your environment>\Library\share\gdal
PROJLIB c:\Users\<username>\Anaconda3\envs\<your environment>\Library\share

Try df.unary_union. The function will aggregate points into a single geometry.
Jupyter Notebook can plot it

how to plot geotiff data in specific area (lat/lon) with python

I have a geotiff raster data sets with elevation data init and i want to plot it in specific area, such as 60°E - 70° E ，70°S - 80°E.
I have a bit of code from here,but the pcolormesh seem couldn't plot my geotif.it's all red. picture. The picture is shown by imshow as really picture
When I try to make a plot with this code below:
path = "F:\\Mosaic_h1112v28_ps.tif"
dataset = gdal.Open(path)
data = dataset.ReadAsArray()
x0, dx, dxdy, y0, dydx, dy = dataset.GetGeoTransform()
nrows, ncols = data.shape
londata = np.linspace(x0, x0+dx*ncols)
latdata = np.linspace(y0, y0+dy*nrows)
lons, lats = np.meshgrid(lonarray, latarray)
fig = plt.figure(figsize=(8, 8))
m = Basemap(projection='lcc', lon_0=67.5, lat_0=-68.5, height=950000,
width=580000, resolution='h')
m.drawcoastlines()
x, y = m(lons, lats)
Then i dont know how to continue it . I just want to use imshow, but the imshow dont specify area(lat/lon).
I will really appreciate your help.

It's a good question, here is my solution.
Required packages: georaster with its dependencies (gdal, etc).
Data for demo purposes downloadable from http://dwtkns.com/srtm/
import georaster
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
fig = plt.figure(figsize=(8,8))
# full path to the geotiff file
fpath = r"C:\\path_to_your\geotiff_file\srtm_57_10.tif" # Thailand east
# read extent of image without loading
# good for values in degrees lat/long
# geotiff may use other coordinates and projection
my_image = georaster.SingleBandRaster(fpath, load_data=False)
# grab limits of image's extent
minx, maxx, miny, maxy = my_image.extent
# set Basemap with slightly larger extents
# set resolution at intermediate level "i"
m = Basemap( projection='cyl', \
llcrnrlon=minx-2, \
llcrnrlat=miny-2, \
urcrnrlon=maxx+2, \
urcrnrlat=maxy+2, \
resolution='i')
m.drawcoastlines(color="gray")
m.fillcontinents(color='beige')
# load the geotiff image, assign it a variable
image = georaster.SingleBandRaster( fpath, \
load_data=(minx, maxx, miny, maxy), \
latlon=True)
# plot the image on matplotlib active axes
# set zorder to put the image on top of coastlines and continent areas
# set alpha to let the hidden graphics show through
plt.imshow(image.r, extent=(minx, maxx, miny, maxy), zorder=10, alpha=0.6)
plt.show()
The resulting plot:
Edit1
My original answer places focus on how to plot simple geotiff image on the most basic projection with Basemap. A better answer was not possible without access to all required resources (i.e. geotiff file).
Here I try to improve my answer.
I have clipped a small portion from whole world geotiff file. Then reproject (warp) it to LCC projection specifications defined by Basemap() to be used. All the process were done with GDAL softwares. The resulting file is named "lcc_2.tiff". With this geotiff file, the plotting of the image is done with the code below.
The most important part is that geotiff file must have the same coordinate system (same projection) as the projection used by Basemap.
import georaster
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
fig = plt.figure(figsize=(8,8))
m = Basemap(projection='lcc', lon_0=67.5, lat_0=-68.5, \
height=950000, width=580000, resolution='h')
m.drawcoastlines()
m.fillcontinents(color='beige')
image = georaster.SingleBandRaster( "lcc_2.tiff", latlon=False)
plt.imshow(image.r, extent=image.extent, zorder=10, alpha=0.6)
plt.show()
The output map:

Here is my solution.
1. Import GEOTIF file and transform it into 2-D array data
from osgeo import gdal
pathToRaster = r'./xxxx.tif'
raster = gdal.Open(pathToRaster, gdal.GA_ReadOnly)
data = raster.GetRasterBand(1).ReadAsArray()
data = data[::-1]
2. Plot it using Pcolormesh
kk = plt.pcolormesh(data,cmap = plt.cm.Reds,alpha = 0.45, zorder =2)

You can use rioxarray
import rioxarray as rio
ds = rio.open_rasterio(path)
# Example lat lon range for subset
geometries = [
{
'type': 'Polygon',
'coordinates': [[
[33.97301017665958, -118.45830810580743],
[33.96660083660732, -118.37455741054782],
[33.92304171545437, -118.37151348516299],
[33.915042933806724, -118.42909440702563]
]]
}
]
clipped = ds.rio.clip(geometries)
clipped.plot()