I would like to be able to change values in iris based on the coordinate, instead of the index.
For example, consider the following cube and say that I wish to set values from -45N to 45N and 160E to 240E to 1:
import iris
import numpy as np
from iris.coords import DimCoord
from iris.cube import Cube
latitude_vals = np.linspace(-90, 90, 4)
longitude_vals = np.linspace(45, 360, 8)
latitude = DimCoord(latitude_vals, standard_name="latitude", units="degrees")
longitude = DimCoord(longitude_vals, standard_name="longitude", units="degrees")
cube = Cube(
np.zeros((4, 8), np.float32), dim_coords_and_dims=[(latitude, 0), (longitude, 1)]
)
In this example, what I want can be done by invoking xarray:
import xarray as xr
da = xr.DataArray.from_iris(cube)
da.loc[dict(latitude=slice(-45, 45), longitude=slice(160, 240))] = 1
But can this be done entirely within iris, without having to resort to specifying the indices manually?
Example of specifying the indices manually:
cube.data[1:3, 3:5] = cube.data[1:3, 3:5] + 1
Update (22 Jan 2021): This is a known issue, see this cross-post and links for related discussion.
Related
I am having trouble adding a basemap to my map. My geodataframe is created using X and Y coords of a bunch of points.
gdf = geo.GeoDataFrame(
df, geometry=gpd.points_from_xy(df['X'], df['Y']))
gdf.set_crs(epsg=3857)
Which look like this:
After using contexily to get a basemap, I cannot get the basemap to properly show up. The coords should be showing the bottom of the Mississippi River Basin.
ax = gdf.plot(color="red", figsize=(9, 9))
cx.add_basemap(ax, zoom=0, crs= gdf.crs)
Let me know if there is anything wrong with my code as to why it is not showing up.
Thanks!
It looks like your data is in WGS84/EPSG:4326 (i.e. lat/lon) coordinates. So I think you're confusing geopandas.GeoDataFrame.set_crs, which tells geopandas what the CRS of the data is, with geopandas.GeoDataFrame.to_crs, which transforms the data from the current CRS to the new one you specify. Also note that neither of these operations are in-place by default. So I think you want:
gdf = geo.GeoDataFrame(
df, geometry=gpd.points_from_xy(df['X'], df['Y'])
)
gdf = gdf.set_crs("epsg:4326")
gdf_mercator = gdf.to_crs("epsg:3857")
This really is same as #Michael Delgado answer. It's simpler to state the CRS at GeoDataFrame construction time. Also make sure you are using correct CRS
MWE
import geopandas as gpd
import geopandas as geo
import pandas as pd
import contextily as cx
# construct a dataframe with X and Y of some points in US
places = gpd.read_file(
gpd.datasets.get_path("naturalearth_cities"),
mask=gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")).loc[
lambda d: d["iso_a3"].eq("USA")
],
)
df = pd.DataFrame({"X": places.geometry.x, "Y": places.geometry.y})
# user code, state CRS at construction time
gdf = geo.GeoDataFrame(
df, geometry=gpd.points_from_xy(df["X"], df["Y"]), crs="epsg:4326"
)
ax = gdf.plot(color="red", figsize=(9, 9))
cx.add_basemap(ax, zoom=0, crs=gdf.crs)
I got a dataframe with the following columns Name (string), size (num), latitude (num), longitude (num), geometry (shapely.geometry.point.Point).
When i'm plotting my points on a map and are trying to annotate each point the annotation is not shown at all. My guess is that this is due to the projection im using.
Here are the lines of codes im running:
import geopandas as gpd
import geoplot as gplt
proj = gplt.crs.AlbersEqualArea()
fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': proj})
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.longitude, df.latitude))
gplt.pointplot(gdf, hue='size', s=15, ax=ax, cmap=palette, legend=True, zorder=10)
for idx, row in gdf.iterrows():
plt.annotate(s=row['Name'], xy=[row['latitude'],row['longitude']])
plt.show()
You need coordinate transformation in
plt.annotate(s=row['Name'], xy=[row['latitude'],row['longitude']])
The transformation should be
xtran = gplt.crs.ccrs.AlbersEqualArea()
Replace that line with
x, y = xtran.transform_point(row['longitude'], row['latitude'], ccrs.PlateCarree())
plt.annotate( s=row['Name'], xy=[x, y] )
This is my code:
import pandas as pd
import geoplot as gplt
import geopandas as gpd
import geoplot.crs as gcrs
import contextily
df = pd.read_csv('dataframe_master.csv', index_col='id')
crs = {'init': 'epsg:4326'}
geometry = [geometry.Point(xy) for xy in zip(df['latitude'], df['longitude'])]
df_geo = gpd.GeoDataFrame(df_geo, crs=crs, geometry=geometry)
test = df_geo[:200000]
test = test.to_crs(epsg=3857)
ax = test.plot(marker='o', markersize=1)
contextily.add_basemap(ax)
plt.show()
And it generates this image:
image, which doesn't show a background map and seems a little distorted.
My coordinate data was originally made with the RD-coordinaten standard (EPSG:28992), which I converted to EPSG:4326 with this code:
lon_l = []
lat_l = []
p1 = Proj(init='epsg:28992')
p2 = Proj(proj='latlong',datum='WGS84')
for row in range(len(df)):
lon, lat, z = transform(p1, p2, df.iloc[row, 7], df.iloc[row, 8], 0.0)
lon_l.append(lon)
lat_l.append(lat)
I did a sanity check on the longitude latitude output by comparing to some online converters, and the output points to the correct locations.
I tried following this solution: https://gis.stackexchange.com/questions/348339/using-crs-epsg3857-but-misalignment-between-stamen-background-and-coordinates-o in case my conversion was missing the "towgs84"part, but the image still looked the same with a slightly different colour.
I figured it out! I should've listed longitude before latitude when building the geometry.
geometry = [geometry.Point(xy) for xy in zip(df['longitude'], df['latitude'])]
I’m experiencing projection errors following a groupby on geodataframe. Below you will find the libraries that I am using:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import holoviews as hv
from holoviews import opts
import panel as pn
from bokeh.resources import INLINE
import geopandas as gpd
import geoviews as gv
from cartopy import crs
hv.extension('bokeh', 'matplotlib')
gv.extension('bokeh')
pd.options.plotting.backend = 'holoviews'
Whilst these are the versions of some key libraries:
bokeh 2.1.1
geopandas 0.6.1
geoviews 1.8.1
holoviews 1.13.3
I have concatenated 3 shapefiles to build a polygon picture of UK healthcare boundaries (links to files provided if needed). Unfortunately, from what i have found the UK doesn’t produce one file that combines all of those, so have had to merge the shape files from the 3 individual countries i’m interested in. The 3 shape files have a size of:
shape file 1 = (https://www.opendatani.gov.uk/dataset/department-of-health-trust-boundaries)
shape file 2 = (https://geoportal.statistics.gov.uk/datasets/5252644ec26e4bffadf9d3661eef4826_4)
shape file 3 = (https://data.gov.uk/dataset/31ab16a2-22da-40d5-b5f0-625bafd76389/local-health-boards-december-2016-ultra-generalised-clipped-boundaries-in-wales)
My code to concat them together is below:
England_CCG.drop(['objectid', 'bng_e', 'bng_n', 'long', 'lat', 'st_areasha', 'st_lengths'], inplace = True, axis = 1 )
Wales_HB.drop(['objectid', 'bng_e', 'bng_n', 'long', 'lat', 'st_areasha', 'st_lengths', 'lhb16nmw'], inplace = True, axis = 1 )
Scotland_HB.drop(['Shape_Leng', 'Shape_Area'], inplace = True, axis = 1)
#NI_HB.drop(['Shape_Leng', 'Shape_Area'], inplace = True, axis = 1 )
England_CCG.rename(columns={'ccg20cd': 'CCG_Code', 'ccg20nm': 'CCG_Name'}, inplace = True )
Wales_HB.rename(columns={'lhb16cd': 'CCG_Code', 'lhb16nm': 'CCG_Name'}, inplace = True )
Scotland_HB.rename(columns={'HBCode': 'CCG_Code', 'HBName': 'CCG_Name'}, inplace = True )
#NI_HB.rename(columns={'TrustCode': 'CCG_Code', 'TrustName': 'CCG_Name'}, inplace = True )
UK_shape = [England_CCG, Wales_HB, Scotland_HB]
Merged_Shapes = gpd.GeoDataFrame(pd.concat(UK_shape))
Each of the files has the same esri projection once joined, and the shape plots perfectly as one when I run:
Test= gv.Polygons(Merged_Shapes, vdims=[('CCG_Name')], crs=crs.OSGB())
This gives me a polygon plot of the UK, with all the area boundaries for each ccg.
To my geodataframe, I then add a new column, called ‘Country’ which attributes each CCG to whatever the country they belong to. So, all the Welsh CCGs are attributed to Wales, all the English ones to England and all the Scottish ones to Scotland. Just a simple additional grouping of the data really.
What I want to achieve is to have a dropdown next to the polygon map I am making, that will show all the CCGs in a particular country when it is selected from the drop down widget. I understand that the way to to do this is by a groupby. However, when I use the following code to achieve this:
c1 = gv.Polygons(Merged_Shapes, vdims=[('CCG_Name','Country')], crs=crs.OSGB()).groupby(['Country'])
I get a long list of projection errors stating:
“WARNING:param.project_path: While projecting a Polygons element from a PlateCarree coordinate reference system (crs) to a Mercator projection none of the projected paths were contained within the bounds specified by the projection. Ensure you have specified the correct coordinate system for your data.”
To which I am left without a map but I retain the widget. Does anyone know what is going wrong here and what a possible solution would be? its been driving me crazy!
Kind regards,
For some reason geoviews doesn't like the OSGB projection then followed by a groupby, as it tries to default back to platecaree projection.
The way I fixed it was to just make the entire dataset project in epsg:4326. For anyone who also runs into this problem, code below (it is a well documented solution:
Merged_Shapes.to_crs({'init': 'epsg:4326'},inplace=True)
gv.Polygons(Merged_Shapes, vdims=[('CCG_Name'),('Country')]).groupby('Country')
The groupby works fine after this.
I need to add a shape to a preexisting image generated using a pyplot (plt). The best way I know of to generate basic shapes quickly is using Imagedraw's predefined shapes. The original data has points with corresponding colors in line_holder and colorholder. I need to add a bounding box (or in this case ellipse) to the plot to make it obvious to the user whether the data is in an acceptable range.
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from PIL import Image
...
lines = LineCollection(mpl.line_holder, colors=mpl.colorholder , linestyle='solid')
plt.axes().add_collection(lines)
plt.axes().set_aspect('equal', 'datalim')
plt.axes().autoscale_view(True,True,True)
plt.draw()
plt.show()
I tried inserting this before the show():
image = Image.new('1',(int(ceil(disc/conv))+2,int(ceil(disc/conv))+1), 1)
draw = ImageDraw.Draw(image)
box=(1, 1, int(ceil(disc/conv)), int(ceil(disc/conv))) #create bounding box
draw.ellipse(box, 1, 0) #draw circle in black
but I cannot find a way to then add this ellipse to the pyplot. Does anyone know how one would go about getting the images together? If it is not possible to add an imagedraw object to a pyplot, are there good alternatives for performing this type of operation?
Matplotlib has several patches (shapes) that appear to meet your needs (and remove PIL as a dependency). They are documented here. A helpful example using shapes is here.
To add an ellipse to a plot, you first create a Ellipse patch and then add that patch to the axes you're currently working on. Beware that Circle's (or Ellipse's with equal minor radii) will appear elliptical if your aspect ratio is not equal.
In your snippet you call plt.axes() several times. This is unnecessary, as it is just returning the current axes object. I think it is clearer to keep the axes object and directly operate on it rather than repeatedly getting the same object via plt.axes(). As far as axes() is used in your snippet, gca() does the same thing. The end of my script demonstrates this.
I've also replaced your add_collection() line by a plotting a single line. These essentially do the same thing and allows my snippet to be executed as a standalone script.
import matplotlib.pyplot as plt
import matplotlib as mpl
# set up your axes object
ax = plt.axes()
ax.set_aspect('equal', 'datalim')
ax.autoscale_view(True, True, True)
# adding a LineCollection is equivalent to plotting a line
# this will run as a stand alone script
x = range(10)
plt.plot( x, x, 'x-')
# add and ellipse to the axes
c = mpl.patches.Ellipse( (5, 5), 1, 6, angle=45)
ax.add_patch(c)
# you can get the current axes a few ways
ax2 = plt.axes()
c2 = mpl.patches.Ellipse( (7, 7), 1, 6, angle=-45, color='green')
ax2.add_patch(c2)
ax3 = plt.gca()
c3 = mpl.patches.Ellipse( (0, 2), 3, 3, color='black')
ax3.add_patch(c3)
print id(ax), id(ax2), id(ax3)
plt.show()