From 032ce7efa3c5f3bed38c2769c7bf12d98fa4c31d Mon Sep 17 00:00:00 2001
From: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com>
Date: Mon, 14 Oct 2024 11:48:53 -0600
Subject: [PATCH] remove NCL_vector_5.py until we have a curly vector
 implementation

---
 Gallery/Vectors/NCL_vector_5.py | 236 --------------------------------
 1 file changed, 236 deletions(-)
 delete mode 100644 Gallery/Vectors/NCL_vector_5.py

diff --git a/Gallery/Vectors/NCL_vector_5.py b/Gallery/Vectors/NCL_vector_5.py
deleted file mode 100644
index 67e1c2a7c..000000000
--- a/Gallery/Vectors/NCL_vector_5.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""
-NCL_vector_5.py
-===============
- A vector pressure/height plot
-
-This script illustrates the following concepts:
-   - Using streamplot to resemble curly vectors
-   - Drawing pressure/height vectors over filled contours
-   - Using inset_axes() to create additional axes for color bars
-   - Interpolate to user specified pressure levels
-   - Using the geocat-comp method `interp_hybrid_to_pressure <https://geocat-comp.readthedocs.io/en/latest/user_api/generated/geocat.comp.interp_hybrid_to_pressure.html#geocat.comp.interp_hybrid_to_pressure>`_
-   - Using a different color scheme to follow `best practices <https://geocat-examples.readthedocs.io/en/latest/gallery/Colors/CB_Temperature.html#sphx-glr-gallery-colors-cb-temperature-py` for visualizations
-
-See following URLs to see the reproduced NCL plot & script:
-    - Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/vector_5.ncl
-    - Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/vector_5_lg.png
-"""
-
-###############################################################################
-# Import packages:
-
-import numpy as np
-import xarray as xr
-from matplotlib import pyplot as plt
-from scipy.interpolate import interp2d
-from mpl_toolkits.axes_grid1.inset_locator import inset_axes
-import metpy.calc as mpcalc
-from metpy.units import units
-
-import geocat.datafiles as gdf
-import geocat.viz as gv
-from geocat.comp import interp_hybrid_to_pressure
-
-import warnings
-
-###############################################################################
-# Read in data:
-ds = xr.open_dataset(gdf.get("netcdf_files/atmos.nc"), decode_times=False)
-
-# Define an array of surface pressures
-pnew = np.arange(200, 901, 50)
-
-# Read in variables
-P0mb = 1000.
-hyam = ds.hyam  # get a coefficiants
-hybm = ds.hybm  # get b coefficiants
-PS = ds.PS  # get pressure
-PS = PS / 100  # Convert from Pascal to mb
-
-# Suppress userwarnings from metpy package
-warnings.filterwarnings("ignore")
-
-# Read in variables from data interpolated to pressure levels
-# interp_hybrid_to_pressure is the Python version of vinth2p in NCL script
-T = interp_hybrid_to_pressure(data=ds.T,
-                              ps=PS,
-                              hyam=hyam,
-                              hybm=hybm,
-                              p0=P0mb,
-                              new_levels=pnew,
-                              method='log')
-W = interp_hybrid_to_pressure(data=ds.OMEGA,
-                              ps=PS,
-                              hyam=hyam,
-                              hybm=hybm,
-                              p0=P0mb,
-                              new_levels=pnew,
-                              method='log')
-V = interp_hybrid_to_pressure(data=ds.V,
-                              ps=PS,
-                              hyam=hyam,
-                              hybm=hybm,
-                              p0=P0mb,
-                              new_levels=pnew,
-                              method='log')
-
-# Extract data
-T = T.isel(time=0).sel(lon=170, method="nearest")
-W = W.isel(time=0).sel(lon=170, method="nearest")
-V = V.isel(time=0).sel(lon=170, method="nearest")
-
-# Scale W
-wscaler = np.mean(W)
-vscaler = np.mean(V)
-scale = abs(vscaler / wscaler)
-
-# We need to flip the sign of wscale to make sure that the vertical component
-# of the streamplot is correct. We are currently unsure why this is needed yet since this
-# is not in the original NCL script. We will continue to research into implementing
-# curly vectors in Matplotlib
-wscale = W * scale * -1
-
-###############################################################################
-# Plot:
-
-# Generate figure (set its size (width, height) in inches)
-fig = plt.figure(figsize=(10, 12))
-
-# Generate axes using Cartopy and draw coastlines
-ax = plt.axes()
-
-# Specify which contours should be drawn
-levels = np.linspace(200, 300, 11)
-
-# # Plot contour lines
-T.plot.contour(ax=ax,
-               levels=levels,
-               colors='black',
-               linewidths=0.5,
-               linestyles='solid',
-               add_labels=False)
-
-# # Plot filled contours
-colors = T.plot.contourf(ax=ax,
-                         levels=levels,
-                         cmap='viridis',
-                         add_labels=False,
-                         add_colorbar=False)
-
-# We attempt to recreate curly vectors with Matplotlib's streamplot function.
-# streamplot requires the input parameter x, y to be evenly spaced strictly increasing arrays.
-# Therefore we interpolate the original dataset onto an evenly spaced grid that we generate below.
-
-# regularly spaced grid spanning the domain of x and y
-xi = np.linspace(T['lat'].min(), T['lat'].max(), T['lat'].size)
-yi = np.linspace(T['plev'].min(), T['plev'].max(), T['plev'].size)
-
-# use xarray's interp to interpolate onto the new grid
-uCi = V.interp(lat=xi, plev=yi)
-vCi = wscale.interp(lat=xi, plev=yi)
-
-# Use streamplot to match curly vector
-ax.streamplot(xi,
-              yi,
-              uCi,
-              vCi,
-              linewidth=0.5,
-              density=2.0,
-              arrowsize=0.7,
-              arrowstyle='->',
-              color='black',
-              integration_direction='backward')
-
-# Draw legend for vector plot
-ax.add_patch(
-    plt.Rectangle((52, 960),
-                  37,
-                  56,
-                  facecolor='white',
-                  edgecolor='black',
-                  clip_on=False))
-
-# Add quiverkey
-# Draw translucent vector plot to be set as input for quiverkey
-Q = ax.quiver(T['lat'], T['plev'], V, wscale, alpha=0, scale=400)
-ax.quiverkey(Q,
-             0.828,
-             0.120,
-             30,
-             '3',
-             labelpos='N',
-             coordinates='figure',
-             color='black',
-             alpha=1,
-             fontproperties={'size': 13})
-ax.quiverkey(Q,
-             0.828,
-             0.120,
-             30,
-             'Reference Vector',
-             labelpos='S',
-             coordinates='figure',
-             color='black',
-             alpha=1,
-             fontproperties={'size': 13})
-
-# Use geocat.viz.util convenience function to add minor and major tick lines
-gv.add_major_minor_ticks(ax, x_minor_per_major=3, labelsize=16)
-
-# Use geocat.viz.util convenience function to set axes tick values
-gv.set_axes_limits_and_ticks(ax,
-                             xlim=(-88, 88),
-                             ylim=(900, 200),
-                             xticks=np.arange(-60, 61, 30),
-                             yticks=np.array(
-                                 [200, 250, 300, 400, 500, 700, 850]),
-                             xticklabels=['60S', '30S', '0', '30N', '60N'])
-
-# Use geocat.viz.util convenience function to add titles and the pressure label
-gv.set_titles_and_labels(ax,
-                         maintitle="Pressure/Height Vector",
-                         maintitlefontsize=28,
-                         ylabel='Pressure (mb)',
-                         labelfontsize=28)
-
-# Create second y-axis to show geo-potential height.
-axRHS = gv.add_height_from_pressure_axis(ax, heights=[4, 8, 12])
-
-# Force the plot to be square by setting the aspect ratio to 1
-ax.set_box_aspect(1)
-axRHS.set_box_aspect(1)
-
-# Set tick lengths
-ax.tick_params('both', which='major', length=12, pad=9)
-ax.tick_params('both', which='minor', length=8, pad=9)
-
-# Turn off minor ticks on Y axis on the left hand side
-ax.tick_params(axis='y', which='minor', left=False, right=False)
-
-# Call tight_layout function before adding the color bar to prevent user warnings
-plt.tight_layout()
-
-# Create inset axes for color bars
-cax = inset_axes(ax,
-                 width='97%',
-                 height='8%',
-                 loc='lower left',
-                 bbox_to_anchor=(0.03, -0.27, 1, 1),
-                 bbox_transform=ax.transAxes,
-                 borderpad=0)
-
-# Add a colorbar
-cab = plt.colorbar(colors,
-                   cax=cax,
-                   orientation='horizontal',
-                   ticks=levels[:-2:2],
-                   extendfrac='auto',
-                   extendrect=True,
-                   drawedges=True,
-                   spacing='uniform')
-
-# Set colorbar ticklabel font size
-cab.ax.xaxis.set_tick_params(length=0, labelsize=16)
-
-# Show plot
-plt.show()