Add Exercise_01

Author: BHFock

notebooks/.notebook_shadow_copies/Exercise_01.md

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+---
+jupyter:
+  jupytext:
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.14.4
+  kernelspec:
+    display_name: Python 3 (ipykernel)
+    language: python
+    name: python3
+---
+
+<!-- #region tags=[] -->
+# LFRic Iris data manipulation and visualisation practical
+
+Let's apply what we've learned about data processing and visualisation of LFRic data in Iris and PyVista with the two exercises. \
+
+The aim is to use the prompts to write the code yourself, but we have also provided a separate notebook containing the answers if you are stuck \
+
+All the information needed to write the code for this practical can be found in the notebooks in the first part of this practical \
+
+Note: this is delivered in Jupyter labs, but sometime the PyVista and GeoVista plotting is laggy in labs. If you prefer you can run in ipython.
+
+
+<!-- #endregion -->
+
+<!-- #region tags=[] -->
+## Exercise 1 - Regrid LFRic to UM and plot data 
+
+In this exercise you will take LFRic data, regrid it to UM data and then plot the differences
+<!-- #endregion -->
+
+**Step 1** To begin, we need to import the neccesary packages that we will need for this exercise.
+
+```python
+%matplotlib inline
+import pyvista as pv
+import geovista as gv
+import geovista.theme
+import iris.quickplot as qplt
+import iris
+from geovista import GeoPlotter
+from esmf_regrid.experimental.unstructured_scheme import MeshToGridESMFRegridder, GridToMeshESMFRegridder
+from iris.experimental.ugrid.load import PARSE_UGRID_ON_LOAD
+pv.rcParams["use_ipyvtk"] = True
+iris.FUTURE.datum_support = True  # avoids some warnings
+```
+
+The pv_conversions script contains two functions which convert LFRic cubes to pyvista objects. Load these two functions:
+
+```python
+from pv_conversions import pv_from_lfric_cube
+```
+
+**Step 2** Use iris.load_cube to load in the lfric data above, and select the diagnostic 'surface_air_pressure'. Print the cube - is it a mesh or grid? \
+    (hint: you will need to use PARSE_UGRID_ON_LOAD.context() )
+
+```python tags=[]
+# Define the location of the data and file names
+data_path = 
+lfric_path = data_path + '20210324T0000Z_lf_ugrid.nc'
+um_path = data_path + '20210324T0000Z_um_latlon.nc'
+
+with PARSE_UGRID_ON_LOAD.context():
+    lfric_rho = iris.load_cube(lfric_path, 'surface_air_pressure')
+print(lfric_rho)
+```
+
+<!-- #region tags=[] -->
+**Step 4** Select the first timestep from the data so we have a 2D cube
+<!-- #endregion -->
+
+```python tags=[]
+
+```
+
+**Step 3** Transform the LFRic cube into a pyvista object using one of the functions from pv_conversions
+
+```python
+
+```
+
+**Step 5** Use GeoPlotter to plot the data using PyVista. You will need to use plotter.show() to display the plot. \
+
+You can now observe Surface Air Pressue on a 3D globe. 
+
+```python tags=[]
+
+```
+
+**Step 6** Now, lets regrid some LFRic data onto a lat/lon grid \
+
+Use iris.load_cube to load the reference grid and print the cube. You can use the equivelent UM data loaded above for this. 
+
+```python
+
+```
+
+**Step 7** Create a regridder by using MeshToGridESMRegridder. Make sure the mesh and grid are the correct way round, consult the regridding section of the notebooks for help.
+
+```python
+
+```
+
+**Step 8** Now, regrid the LFRic data using the regridder you created. Print the result - is your LFRic data regridded?
+
+```python
+
+```
+
+**Step 9** Using qplt.pcolormesh plot the regridded LFRic data
+
+```python tags=[]
+
+```
+
+**Step 10** We can use the UM data loaded as reference for the regridding to compare to the reggrided LFRic data.\
+Now select the first timestep of the UM data loaded in step 6
+
+```python
+
+```
+
+**Step 11** Now calulate the difference between the LFRic regridded data and native UM data
+
+```python
+
+```
+
+**Step 12** Now plot the original UM data and the regridded LFRic data and the difference side by side. \
+(hint: use plt.subplot(1,3,1) and try adding a title and coastlines to the plot)
+
+```python
+
+```
+
+```python
+
+```

notebooks/.notebook_shadow_copies/Exercise_01_answers.md

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+---
+jupyter:
+  jupytext:
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.14.4
+  kernelspec:
+    display_name: Python 3 (ipykernel)
+    language: python
+    name: python3
+---
+
+<!-- #region tags=[] -->
+# LFRic Iris data manipulation and visualisation practical
+
+Let's apply what we've learned about data processing and visualisation of LFRic data in Iris and PyVista with the two exercises. \
+
+The aim is to use the prompts to write the code yourself, but we have also provided a separate notebook containing the answers if you are stuck \
+
+All the information needed to write the code for this practical can be found in the notebooks in the first part of this practical \
+
+Note: this is delivered in Jupyter labs, but sometime the PyVista and GeoVista plotting is laggy in labs. If you prefer you can run in ipython.
+
+
+<!-- #endregion -->
+
+<!-- #region tags=[] -->
+## Exercise 1 - Regrid LFRic to UM and plot data 
+
+In this exercise you will take LFRic data, regrid it to UM data and then plot the differences
+<!-- #endregion -->
+
+**Step 1** To begin, we need to import the neccesary packages that we will need for this exercise.
+
+```python tags=[]
+%matplotlib inline
+import pyvista as pv
+import geovista as gv
+import geovista.theme
+import iris.quickplot as qplt
+import iris
+from geovista import GeoPlotter
+from esmf_regrid.experimental.unstructured_scheme import MeshToGridESMFRegridder, GridToMeshESMFRegridder
+from iris.experimental.ugrid.load import PARSE_UGRID_ON_LOAD
+pv.rcParams["use_ipyvtk"] = True
+iris.FUTURE.datum_support = True  # avoids some warnings
+
+```
+
+The pv_conversions script contains two functions which convert LFRic cubes to pyvista objects. Load these two functions:
+
+```python tags=[]
+from pv_conversions import pv_from_lfric_cube
+```
+
+**Step 2** Use iris.load_cube to load in the lfric data above, and select the diagnostic 'surface_air_pressure'. Print the cube - is it a mesh or grid? \
+    (hint: you will need to use PARSE_UGRID_ON_LOAD.context() )
+
+```python tags=[]
+# Define the location of the data and file names
+data_path = '/scratch/bfock/example_data_iris-mesh-tutorial'
+data_path = '/scratch/bfock/example_data_u-ct674/'
+
+lfric_path = data_path + '20210324T0000Z_lf_ugrid.nc'
+um_path = data_path + '20210324T0000Z_um_latlon.nc'
+
+with PARSE_UGRID_ON_LOAD.context():
+    lfric_rho = iris.load_cube(lfric_path, 'surface_air_pressure')
+print(lfric_rho)
+```
+
+<!-- #region tags=[] -->
+**Step 4** Select the first timestep from the data so we have a 2D cube
+<!-- #endregion -->
+
+```python tags=[]
+lfric_rho_t0 = lfric_rho[0]
+```
+
+**Step 3** Transform the LFRic cube into a pyvista object using one of the functions from pv_conversions
+
+```python tags=[]
+lfric_pv = pv_from_lfric_cube(lfric_rho_t0)
+```
+
+**Step 5** Use GeoPlotter to plot the data using PyVista. You will need to use plotter.show() to display the plot. \
+
+You can now observe Surface Air Pressue on a 3D globe. 
+
+```python tags=[]
+# Plot the data using pyvista
+plotter = GeoPlotter()
+_ = plotter.add_mesh(lfric_pv)
+plotter.show()
+```
+
+**Step 6** Now, lets regrid some LFRic data onto a lat/lon grid \
+
+Use iris.load_cube to load the reference grid and print the cube. You can use the equivelent UM data loaded above for this. 
+
+```python tags=[]
+um_rho = iris.load_cube(um_path, 'surface_air_pressure')
+print(um_rho)
+```
+
+**Step 7** Create a regridder by using MeshToGridESMRegridder. Make sure the mesh and grid are the correct way round, consult the regridding section of the notebooks for help.
+
+```python tags=[]
+# Create the regrider
+regridder = MeshToGridESMFRegridder(lfric_rho_t0, um_rho)
+```
+
+**Step 8** Now, regrid the LFRic data using the regridder you created. Print the result - is your LFRic data regridded?
+
+```python tags=[]
+result = regridder(lfric_rho_t0)
+print(result)
+```
+
+**Step 9** Using qplt.pcolormesh plot the regridded LFRic data
+
+```python tags=[]
+qplt.pcolormesh(result)
+```
+
+**Step 10** We can use the UM data loaded as reference for the regridding to compare to the reggrided LFRic data.\
+Now select the first timestep of the UM data loaded in step 6
+
+```python tags=[]
+um_rho_t0 = um_rho[0]
+```
+
+**Step 11** Now calulate the difference between the LFRic regridded data and native UM data
+
+```python tags=[]
+difference = result - um_rho_t0
+```
+
+**Step 12** Now plot the original UM data and the regridded LFRic data and the difference side by side. \
+(hint: use plt.subplot(1,3,1) and try adding a title and coastlines to the plot)
+
+```python
+qplt.plt.figure(figsize=(20,12))
+
+qplt.plt.subplot(1, 3, 1)
+qplt.pcolormesh(result)
+qplt.plt.title("LFRic data")
+qplt.plt.gca().coastlines()
+
+qplt.plt.subplot(1, 3, 2)
+qplt.pcolormesh(um_rho_t0)
+qplt.plt.title("UM data")
+qplt.plt.gca().coastlines()
+
+qplt.plt.subplot(1, 3, 3)
+qplt.pcolormesh(difference, cmap="bwr")
+qplt.plt.title('Difference')
+qplt.plt.gca().coastlines()
+
+qplt.plt.show()
+```
+
+```python
+
+```