Download the fonts from https://github.com/stipub/stixfonts
dirstix = "/net2/rnd/dev/stixfonts/fonts/static_ttf"
import matplotlib.font_manager as font_manager
for font in font_manager.findSystemFonts(dirstix):
font_manager.fontManager.addfont(font)
# Set font family globally
plt.rcParams['font.family'] = 'STIX Two Text'
| name: meatpotatoes | |
| channels: | |
| - conda-forge | |
| dependencies: | |
| - python | |
| - pip | |
| - black | |
| - cartopy | |
| - cftime | |
| - cmocean |
| import xarray as xr | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| from matplotlib import animation | |
| import matplotlib.image as image | |
| import cartopy.crs as ccrs | |
| from owslib.wms import WebMapService | |
| from matplotlib import cm | |
| import os | |
| from xgcm import Grid |
| import cmocean | |
| import cartopy.crs as ccrs | |
| import matplotlib.pyplot as plt | |
| # extract temperature and depth for a given time | |
| snapshot = CCS2.isel(time=50) | |
| temp_snapshot = snapshot['temp'] | |
| depth_snapshot = snapshot['z_rho'] | |
| # compute temperature at 100 meters depth using xgcm.transform |
| import matplotlib.pyplot as plt | |
| import cmocean | |
| plt.figure(figsize=[10,8]) | |
| CCS2['v'].isel(time=12, yq=500).plot(x='lon_v', y='z_v', | |
| vmin=-0.25, vmax=0.25, | |
| cmap=cmocean.cm.balance, | |
| subplot_kws={'facecolor': 'grey'}) |
| def compute_depth_layers(ds, grid, hmin=0.1): | |
| """ compute depths of ROMS vertical levels (Vtransform = 2) """ | |
| # compute vertical transformation functional | |
| S_rho = (ds.hc * ds.s_rho + ds.Cs_r * ds.h) / (ds.hc + ds.h) | |
| S_w = (ds.hc * ds.s_w + ds.Cs_w * ds.h) / (ds.hc + ds.h) | |
| # compute depth of rho (layers) and w (interfaces) points | |
| z_rho = ds.zeta + (ds.zeta + ds.h) * S_rho | |
| z_w = ds.zeta + (ds.zeta + ds.h) * S_w |
| import xarray as xr | |
| from xgcm import Grid | |
| CCS2 = xr.open_zarr(CCS2_store, consolidated=True) | |
| # Create xgcm grid object | |
| grid_ccs2 = Grid(CCS2, coords={'X': {'center': 'xh', 'outer': 'xq'}, | |
| 'Y': {'center': 'yh', 'outer': 'yq'}, | |
| 'Z': {'center': 's_rho', 'outer': 's_w'}}, | |
| periodic=False) |
| import xarray as xr | |
| import glob | |
| # use glob to create a list of files to convert | |
| filelist = glob.glob('data/CCS2_avg_*.nc') | |
| # path to grid file | |
| gridfile = 'grid/CCS2_grid.nc' | |
| # output store name | |
| store_name = 'CCS2_store' |
| def add_coords(ds): | |
| """ set coordinate variables as xarray coordinates | |
| Parameters: | |
| ds (xarray.Dataset): ROMS dataset | |
| """ | |
| ds = ds.set_coords(['Cs_r', 'Cs_w', 'hc', 'h', 'Vtransform', 'time', | |
| 'lon_rho', 'lon_v', 'lon_u', 'lon_psi', | |
| 'lat_rho', 'lat_v', 'lat_u', 'lat_psi']) | |
| return ds |
| def rename_dims(ds): | |
| """ rename dimensions | |
| Parameters: | |
| ds (xarray.Dataset): ROMS dataset | |
| """ | |
| ds = ds.rename({'xi_rho': 'xh', 'xi_v': 'xh', 'xi_u': 'xq', 'xi_psi': 'xq', | |
| 'eta_rho': 'yh', 'eta_v': 'yq', 'eta_u': 'yh', 'eta_psi': 'yq', | |
| 'ocean_time': 'time' |