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Geomedian notebook #1148
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Geomedian notebook #1148
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2e4221d
Geomedian notebook
margaretharrison 005ec26
update GeoMAD with latest version
JM-GA 36a24fe
update tests to use new GeoMAD products
JM-GA 2a6975f
update export netcdf notebook with new GeoMAD
JM-GA edd03c5
Merge branch 'develop' into geomedian
JM-GA cb424ef
Update setup_test_datacube.sh
JM-GA 38c1177
Update setup_test_datacube.sh
JM-GA e2d0fa4
Update setup_test_datacube.sh
JM-GA bf1ce5d
Update to new c3 grid path rows
JM-GA 555e3e9
Update loading_data notebook to use new GeoMAD
JM-GA c3651fc
update plotting notebook with new GeoMAD
JM-GA 02ed2dc
Update parallel processing notebook with new GeoMAD
JM-GA b4b992a
update Animated_timeseries with new GeoMAD
JM-GA 09d81c7
Update DEA_Intertidal with new GeoMAD
JM-GA 6025419
Update DEA_GeoMAD.ipynb
JM-GA 1e69dc2
Expand GeoMAD test tiles
JM-GA ddf6b7c
Update 1_Extract_training_data.ipynb
JM-GA cf4f524
Update 04_Loading_data.ipynb
JM-GA 0cf8ac8
Update 09_Parallel_processing_with_Dask.ipynb
JM-GA b956e10
Update DEA_Intertidal.ipynb
JM-GA 65ba42e
Update Exporting_NetCDFs.ipynb
JM-GA 2b0fdf7
Update Animated_timeseries.ipynb
JM-GA 3a8233a
Update 05_Plotting.ipynb
JM-GA 14d2302
Update 09_Parallel_processing_with_Dask.ipynb
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1,801 changes: 1,013 additions & 788 deletions
1,801
Beginners_guide/09_Parallel_processing_with_Dask.ipynb
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368 changes: 334 additions & 34 deletions
368
Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| """ | ||
| Geomedian widget: generates an interactive visualisation of | ||
| the geomedian summary statistic. | ||
| """ | ||
|
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| # Load modules | ||
| import ipywidgets as widgets | ||
| import matplotlib.pyplot as plt | ||
| from mpl_toolkits.mplot3d import Axes3D | ||
| import numpy as np | ||
| import xarray as xr | ||
| from odc.algo import xr_geomedian | ||
|
|
||
| def run_app(): | ||
|
|
||
| """ | ||
| An interactive app that allows users to visualise the difference between the median and geomedian time-series summary statistics. By modifying the red-green-blue values of three timesteps for a given pixel, the user changes the output summary statistics. | ||
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| This allows a visual representation of the difference through the output values, RGB colour, as well as showing values plotted as a vector on a 3-dimensional space. | ||
|
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| Last modified: December 2021 | ||
| """ | ||
|
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| # Define the red-green-blue sliders for timestep 1 | ||
| p1r = widgets.IntSlider(description='Red', max=255, value=58) | ||
| p1g = widgets.IntSlider(description='Green', max=255, value=153) | ||
| p1b = widgets.IntSlider(description='Blue', max=255, value=68) | ||
|
|
||
| # Define the red-green-blue sliders for timestep 2 | ||
| p2r = widgets.IntSlider(description='Red', max=255, value=208) | ||
| p2g = widgets.IntSlider(description='Green', max=255, value=221) | ||
| p2b = widgets.IntSlider(description='Blue', max=255, value=203) | ||
|
|
||
| # Define the red-green-blue sliders for timestep 3 | ||
| p3r = widgets.IntSlider(description='Red', max=255, value=202) | ||
| p3g = widgets.IntSlider(description='Green', max=255, value=82) | ||
| p3b = widgets.IntSlider(description='Blue', max=255, value=33) | ||
|
|
||
| # Define the median calculation for the timesteps | ||
| def f(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): | ||
| print('Red Median = {}'.format(np.median([p1r, p2r, p3r]))) | ||
| print('Green Median = {}'.format(np.median([p1g, p2g, p3g]))) | ||
| print('Blue Median = {}'.format(np.median([p1b, p2b, p3b]))) | ||
|
|
||
| # Define the geomedian calculation for the timesteps | ||
| def g(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): | ||
| print('Red Geomedian = {:.2f}'.format(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).red.values.ravel()[0])) | ||
| print('Green Geomedian = {:.2f}'.format(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).green.values.ravel()[0])) | ||
| print('Blue Geomedian = {:.2f}'.format(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).blue.values.ravel()[0])) | ||
|
|
||
| # Define the Timestep 1 box colour | ||
| def h(p1r, p1g, p1b): | ||
| fig1, axes1 = plt.subplots(figsize=(2,2)) | ||
| fig1 = plt.imshow([[(p1r, p1g, p1b)]]) | ||
| axes1.set_title('Timestep 1') | ||
| axes1.axis('off') | ||
| plt.show(fig1) | ||
|
|
||
| # Define the Timestep 2 box colour | ||
| def hh(p2r, p2g, p2b): | ||
| fig2, axes2 = plt.subplots(figsize=(2,2)) | ||
| fig2 = plt.imshow([[(p2r, p2g, p2b)]]) | ||
| axes2.set_title('Timestep 2') | ||
| axes2.axis('off') | ||
| plt.show(fig2) | ||
|
|
||
| # Define the Timestep 3 box colour | ||
| def hhh(p3r, p3g, p3b): | ||
| fig3, axes3 = plt.subplots(figsize=(2,2)) | ||
| fig3 = plt.imshow([[(p3r, p3g, p3b)]]) | ||
| axes3.set_title('Timestep 3') | ||
| axes3.axis('off') | ||
| plt.show(fig3) | ||
|
|
||
| # Define the Median RGB colour box | ||
| def i(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): | ||
| fig4, axes4 = plt.subplots(figsize=(3,3)) | ||
| fig4 = plt.imshow([[(int(np.median([p1r, p2r, p3r])), int(np.median([p1g, p2g, p3g])), int(np.median([p1b, p2b, p3b])))]]) | ||
| axes4.set_title('Median RGB - All timesteps') | ||
| axes4.axis('off') | ||
| plt.show(fig4) | ||
|
|
||
| # Define the Geomedian RGB colour box | ||
| def ii(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): | ||
| fig5, axes5 = plt.subplots(figsize=(3,3)) | ||
| fig5 = plt.imshow([[(int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).red.values.ravel()[0]), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).green.values.ravel()[0]), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).blue.values.ravel()[0]))]]) | ||
| axes5.set_title('Geomedian RGB - All timesteps') | ||
| axes5.axis('off') | ||
| plt.show(fig5) | ||
|
|
||
| # Define 3-D axis to display vectors on | ||
| def j(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): | ||
| fig6 = plt.figure() | ||
| axes6 = fig6.add_subplot(111, projection='3d') | ||
| x = [p1r, p2r, p3r, int(np.median([p1r, p2r, p3r])), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).red.values.ravel()[0])] | ||
| y = [p1g, p2g, p3g, int(np.median([p1g, p2g, p3g])), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).green.values.ravel()[0])] | ||
| z = [p1b, p2b, p3b, int(np.median([p1b, p2b, p3b])), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).blue.values.ravel()[0])] | ||
| labels = [' 1', ' 2', ' 3', ' median', ' geomedian'] | ||
| axes6.scatter(x, y, z, c=['black','black','black','r', 'blue'], marker='o') | ||
| axes6.set_xlabel('Red') | ||
| axes6.set_ylabel('Green') | ||
| axes6.set_zlabel('Blue') | ||
| axes6.set_xlim3d(0, 255) | ||
| axes6.set_ylim3d(0, 255) | ||
| axes6.set_zlim3d(0, 255) | ||
| for ax, ay, az, label in zip(x, y, z, labels): | ||
| axes6.text(ax, ay, az, label) | ||
| plt.title('Each band represents a dimension.') | ||
| plt.show() | ||
|
|
||
| # Define outputs | ||
| outf = widgets.interactive_output(f, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) | ||
| outg = widgets.interactive_output(g, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) | ||
|
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| outh = widgets.interactive_output(h, {'p1r': p1r, 'p1g': p1g, 'p1b': p1b}) | ||
| outhh = widgets.interactive_output(hh, {'p2r': p2r, 'p2g': p2g, 'p2b': p2b}) | ||
| outhhh = widgets.interactive_output(hhh, {'p3r': p3r, 'p3g': p3g, 'p3b': p3b}) | ||
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| outi = widgets.interactive_output(i, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) | ||
| outii = widgets.interactive_output(ii, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) | ||
|
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| outj = widgets.interactive_output(j, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) | ||
|
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| app_output = widgets.HBox([widgets.VBox([widgets.HBox([outh, widgets.VBox([ p1r, p1g, p1b])]), widgets.HBox([outhh, widgets.VBox([p2r, p2g, p2b])]), widgets.HBox([outhhh, widgets.VBox([ p3r, p3g, p3b])])]), widgets.VBox([widgets.HBox([widgets.VBox([outf, outi]), widgets.VBox([outg, outii])]), outj])]) | ||
|
|
||
| return app_output |
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Hey @JM-GA, everything looks awesome here, great work. One tiny minor comment is that the new data now includes a chunk of nodata here - might be good to add in a call to
datacube.utils.masking import mask_invalid_datato this example so that users can correctly treat nodata values in their analysis. But not a major issue!There was a problem hiding this comment.
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Thanks @robbibt - good idea! I have added in handing of no data pixels into the notebook