def tester_bgm_w2c():
"""wms, specify projection/extent lcc"""
from plotter import calpost_reader as reader
import cartopy.crs as ccrs
with open('../data/tseries_ch4_1min_conc_co_fl.dat') as f:
dat = reader.Reader(f, slice(60 * 12, 60 * 12 + 10))
# background
plotter_options = {
'contour_options': {
'alpha': .2
},
'background_manager':
BackgroundManager(wms_options={
'wms':
'https://services.nationalmap.gov/arcgis/services/USGSNAIPImagery/ImageServer/WMSServer',
'layers': '0'
},
projection=LambertConformalTCEQ(),
extent=[-465000, -460000, -908000, -901000]),
'customize_once':
lambda p: p.ax.gridlines(draw_labels=True),
}
x = dat['x'] * 1000
y = dat['y'] * 1000
p = Plotter(dat['v'], dat['ts'], x=x, y=y, plotter_options=plotter_options)
p(outdir / 'test_bgm_w2c.png')
def tester_bgm_b2a():
"""specify extent/projection and bg image"""
from plotter import calpost_reader as reader
import cartopy.crs as ccrs
with open('../data/tseries_ch4_1min_conc_co_fl.dat') as f:
dat = reader.Reader(f, slice(60 * 12, 60 * 12 + 10))
# background
plotter_options = {
'contour_options': {
'alpha': .2
},
'background_manager':
BackgroundManager(bgfile=bgfile_lcc,
projection=LambertConformalTCEQ(),
extent=[-465000.0, -460000.0, -902000.0, -907000.0]),
'customize_once':
lambda p: p.ax.gridlines(draw_labels=True),
}
x = dat['x'] * 1000
y = dat['y'] * 1000
p = Plotter(dat['v'], dat['ts'], x=x, y=y, plotter_options=plotter_options)
p(outdir / 'test_bgm_b2a.png')
lambda p:
# adjust_text() repels labels from each other
adjust_text(
# make list of annotation
list(
# this part creates annotation for each point
p.ax.annotate(_.Site_Label, (_.geometry.x, _.geometry.y,),
zorder=11, fontsize='xx-small')
# goes across all points but filter by Site_Label
for _ in df.itertuples() # if _.Site_Label in (f'{site}',)
),
),
# modeled box
lambda p: p.ax.add_geometries(
[Polygon([(extent[x], extent[y]) for x, y in ((0, 2), (0, 3), (1, 3), (1, 2), (0, 2))])],
crs=LambertConformalTCEQ(), facecolor='none', edgecolor='white', lw=.6,
),
]}
# clone the options and let each has own title
plotter_options = [{**plotter_options, 'title': title} for title in titles]
# colorbar goes to entire figure
figure_options = {
'colorbar_options': {
'label': r'$CH_4$ (ppbV)',
}
}
# make a plot template
# mpl.rcParams.update({'font.size': 8})
(
_.geometry.x,
_.geometry.y,
),
zorder=11,
fontsize='xx-small',
)
# goes across all points but filter by Site_Label
for _ in df_shp.itertuples()), ),
# modeled box
lambda p: p.ax.add_geometries(
[
Polygon([(extent[x], extent[y])
for x, y in ((0, 2), (0, 3), (1, 3), (1, 2), (0, 2))])
],
crs=LambertConformalTCEQ(),
facecolor='none',
edgecolor='black',
lw=.6,
),
# grid lines
lambda p: add_gridlines(p.ax, {
'bottom_labels': False,
'right_labels': False
})
]
}
# make a plot template
p = plotter_solo.Plotter(array=arr,
tstamps=tstamps,
cmap=colors.ListedColormap(['#e41a1c', '#377eb8', '#ffff33'])
),
# Shannon's "original" box
lambda p: p.ax.add_geometries(
[Polygon([(-101.8834373, 31.71350603),
(-101.8664281, 31.71727773),
(-101.8748762, 31.75052556),
(-101.8942724, 31.74599821),
(-101.8834373, 31.71350603),
])],
crs=ccrs.PlateCarree(), facecolor='none', edgecolor='white', lw=.6,
),
# modeled box
lambda p: p.ax.add_geometries(
[Polygon([(extent[x], extent[y]) for x, y in ((0, 2), (0, 3), (1, 3), (1, 2), (0, 2))])],
crs=LambertConformalTCEQ(), facecolor='none', edgecolor='white', lw=.6, ls='--',
),
]}
# clone the options and let each has own title
plotter_options = [{**plotter_options, 'title': title} for title in titles]
# colorbar goes to entire figure
figure_options = {
'colorbar_options': {
'label': r'$CH_4$ (ppbV)',
}
}
# make a plot template
def tester_s5(quiet=True):
"""show contour from calpost with all the bells and whistles"""
from plotter import calpost_reader as reader
from plotter.plotter_util import LambertConformalTCEQ
import rasterio
import cartopy.crs as ccrs
import geopandas as gpd
import matplotlib.colors as colors
from shapely.geometry import Polygon
from adjustText import adjust_text
import tempfile
from pathlib import Path
import shlex
import subprocess
# background (extent is used as plot's extent)
b = rasterio.open(bgfile)
bext = [b.transform[2], b.transform[2] + b.transform[0] * b.width,
b.transform[5] + b.transform[4] * b.height, b.transform[5]]
# source locations
df = gpd.read_file(shpfile)
df = df.to_crs('EPSG:3857')
# read the data
title = 'Flare'
with open('../data/tseries_ch4_1min_conc_co_fl.dat') as f:
dat = reader.Reader(f, slice(60 * 12, 60 * 12 + 10))
# grab necessary info
arr = dat['v']
tstamps = dat['ts']
grid = dat['grid']
extent = [
grid['x0'], grid['x0'] + grid['nx'] * grid['dx'],
grid['y0'], grid['y0'] + grid['ny'] * grid['dy'],
]
# distance in calpost is in km
extent = [_ * 1000 for _ in extent]
x = dat['x'] * 1000
y = dat['y'] * 1000
# convert unit of array from g/m3 tp ppb
# mwt g/mol
# molar volume m3/mol
arr = arr / 16.043 * 0.024465403697038 * 1e9
# Mrinali/Gary's surfer color scale
cmap = colors.ListedColormap([
'#D6FAFE', '#02FEFF', '#C4FFC4', '#01FE02',
'#FFEE02', '#FAB979', '#EF6601', '#FC0100', ])
cmap.set_under('#FFFFFF')
# Define a normalization from values -> colors
bndry = [1, 10, 50, 100, 200, 500, 1000, 2000]
norm = colors.BoundaryNorm(bndry, len(bndry))
plotter_options = {
'extent': bext, 'projection': ccrs.epsg(3857),
'title': title,
'contour_options': {
'levels': bndry,
'cmap': cmap,
'norm': norm,
'alpha': .5,
},
'colorbar_options': {
'label': r'$CH_4$ (ppbV)',
},
'customize_once': [
# background
lambda p: p.ax.imshow(b.read()[:3, :, :].transpose((1, 2, 0)),
extent=bext, origin='upper'),
# emission points
lambda p: df.plot(ax=p.ax, column='kls', categorical=True, legend=False, zorder=10,
markersize=2,
# got red/blue/yellow from colorbrewer's Set1
cmap=colors.ListedColormap(['#e41a1c', '#377eb8', '#ffff33'])),
# emission point annotations
lambda p: adjust_text(
# make list of annotation
list(
# this part creates annotation for each point
p.ax.text(_.geometry.x, _.geometry.y, _.Site_Label, zorder=11, size=6)
# goes across all points but filter by Site_Label
for _ in df.itertuples() if _.Site_Label in ('F1', 'op3_w1', 'S4')
),
# draw arrow from point to annotation
arrowprops={'arrowstyle': '-'}
),
# modeled box
lambda p: p.ax.add_geometries(
[Polygon([(extent[x],extent[y]) for x,y in ((0,2), (0,3), (1,3), (1,2), (0,2))])],
crs=LambertConformalTCEQ(), facecolor='none', edgecolor='white', lw=.6,
),
]}
# make a plot template
p = Plotter(arr, dat['ts'], x=x, y=y, plotter_options=plotter_options)
# make a plot
p(outdir / 'test_s5.png')
# make an animation
with tempfile.TemporaryDirectory() as wdir:
workdir = Path(wdir)
# function to save one time frame
def saveone(i):
ts = tstamps[i]
pname = workdir / f'{i:04}.png'
footnote = str(ts)
p(pname, tidx=i, footnote=footnote)
n = len(tstamps)
for i, ts in enumerate(tstamps):
saveone(i)
# make mpeg file
if quiet:
loglevel = '-loglevel error'
else:
loglevel = ''
cmd = f'ffmpeg {loglevel} -i "{workdir / "%04d.png"}" -vframes 2880 -crf 3 -vcodec libx264 -pix_fmt yuv420p -f mp4 -y "{outdir / "test_pr2b.mp4"}"'
subprocess.run(shlex.split(cmd))
fname_toy = 'toy_model_allstations.txt'
grid_toy = {
'x0': -464.4,
'y0': -906.7,
'nx': 34,
'ny': 47,
'dx': 0.1,
'dy': 0.1
}
grid_pilot = {
'x0': -440.8,
'y0': -730.0,
'nx': 48,
'ny': 44,
'dx': 0.2,
'dy': 0.2
}
hrc_toy = HysplitReceptorCoords(fname_toy, grid_toy,
LambertConformalTCEQ())
hrc_pilot = HysplitReceptorCoords(fname_pilot, grid_pilot,
LambertConformalHRRR())
print(
hrc_pilot.get_index(
pd.DataFrame(
{'rdx': [1, 2, 3, 11]},
index=[1, 2, 3, 11],
)))