legend ( loc = "lower left" ) # Use an utility function to add tick labels and land and ocean features to the # map. quiverkey ( tmp, 0.17, 0.23, 5, label = "5 knots", coordinates = "figure" ) ax. PlateCarree (), label = "Original", ) ax. values, width = 0.003, scale = 100, color = "tab:red", transform = ccrs. PlateCarree (), label = "Interpolated", ) ax. values, width = 0.0015, scale = 100, color = "tab:blue", transform = ccrs. set_title ( "Uncoupled spline gridding of wind speed" ) tmp = ax. convexhull_mask ( coordinates, grid = grid_full, projection = projection ) # Make maps of the original and gridded wind speed plt. grid ( region = region, spacing = spacing, projection = projection, dims =, ) grid = vd. format ( score )) # Interpolate the wind speed onto a regular geographic grid and mask the data # that are outside of the convex hull of the data points. score ( * test ) print ( "Cross-validation R^2 score: ". The best possible score is 1, meaning a # perfect prediction of the test data. fit ( * train ) # And score on the testing data. Vector (), ), ] ) print ( chain ) # Fit on the training data chain.
![vector 2d processing vector 2d processing](https://i0.wp.com/algorithms.tutorialhorizon.com/files/2020/05/Sort-two-dimensional-array-in-place-1.png)
# Notice that BlockReduce can work on multicomponent data without the use of # Vector. wind_speed_north_knots ), random_state = 2, ) # We'll make a 20 arc-minute grid spacing = 20 / 60 # Chain together a blocked mean to avoid aliasing, a polynomial trend (Spline # usually requires de-trended data), and finally a Spline for each component. train_test_split ( projection ( * coordinates ), ( data. We'll fit the gridder on the # training set and use the testing set to evaluate how well the gridder is # performing. mean ()) # Split the data into a training and testing set. get_region ( coordinates ) # Use a Mercator projection because Spline is a Cartesian gridder projection = pyproj. head ()) # Separate out some of the data into utility variables coordinates = ( data. Import cartopy.crs as ccrs import matplotlib.pyplot as plt import numpy as np import pyproj import verde as vd # Fetch the wind speed data from Texas. Please use the 'id_coordinates' function to define grid coordinates and pass them as the 'coordinates' argument. usr/share/miniconda3/envs/test/lib/python3.9/site-packages/verde/base/base_classes.py:463: FutureWarning: The 'spacing', 'shape' and 'region' arguments will be removed in Verde v2.0.0.
![vector 2d processing vector 2d processing](https://media.nagwa.com/253195202574/en/thumbnail_s.jpeg)
This may change model selection results slightly.
![vector 2d processing vector 2d processing](https://i.stack.imgur.com/f2zy6.png)
usr/share/miniconda3/envs/test/lib/python3.9/site-packages/verde/base/base_classes.py:359: FutureWarning: The default scoring will change from R² to negative root mean squared error (RMSE) in Verde 2.0.0.