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 = {
'imshow_options': {
'cmap': cmap,
'norm': norm,
},
'title': title,
}
# make a plot template
p = plotter_solo.Plotter(array=arr,
tstamps=tstamps,
extent=extent,
plotter_options=plotter_options)
# function to save one time frame
def saveone(i):
ts = tstamps[i]
# oname = wdir / ts.strftime('img-%m%d%H%M.png')
oname = wdir / f'{i:04}.png'
footnote = str(ts)
p(oname, tidx=i, footnote=footnote)
# save all frames in parallel
# 68 for stampede, 24 for ls5
with Pool(24) as pool:
# save better resolution image
mpl.rcParams['savefig.dpi'] = 300
fname = '../data/tseries_ch4_1min_conc_co_fl.dat'
with open(fname) as f:
dat = reader.Reader(f, slice(60 * 12, 60 * 12 + 1))
g = dat['grid']
ext = (g['x0'], g['x0'] + g['dx'] * g['nx'], g['y0'],
g['y0'] + g['dy'] * g['ny'])
ext = [_ * 1000 for _ in ext]
print(ext)
arr = dat['v'][:, ::-1, :]
# https://ocefpaf.github.io/python4oceanographers/blog/2015/03/02/geotiff/
bga = pu.background_adder('../resources/gamma3_res2.tif')
p = psolo.Plotter(
arr,
dat['ts'],
extent=ext,
plotter_options={
'customize_once': bga.set_background,
#'customize_after': bga.refresh_background,
})
p('r.png')
# i may get image from NAIP or something and use it as background?
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,
x=x,
y=y,
projection=LambertConformalTCEQ(),
plotter_options=plotter_options)
# make single image file (for QA)
ntsteps = len(tstamps)
p.savefig((odir / oname).with_suffix('.png'), tidx=min(16 * 60, ntsteps - 1))
# make mpeg file
p.savemp4(odir / oname, wdir=wdir)
# save better resolution image
mpl.rcParams['savefig.dpi'] = 300
fname = '../data/tseries_ch4_1min_conc_co_fl.dat'
with open(fname) as f:
dat = reader.Reader(f, slice(60 * 12, 60 * 12 + 1))
g = dat['grid']
ext = (g['x0'], g['x0'] + g['dx'] * g['nx'], g['y0'],
g['y0'] + g['dy'] * g['ny'])
ext = [_ * 1000 for _ in ext]
print(ext)
arr = dat['v'][:, ::-1, :]
p = psolo.Plotter(arr, dat['ts'], extent=ext)
# the number is "zoom level"
# i found max i can go is 22, but it still ugly
# p.ax.add_image(GoogleTiles(), 22)#, alpha=.1)
# p.ax.add_image(GoogleTiles(style='satellite'), 22)#, alpha=.1)
# https://ocefpaf.github.io/python4oceanographers/blog/2015/03/02/geotiff/
ds = gdal.Open('../resources/gamma3_res2.tif')
data = ds.ReadAsArray()
gt = ds.GetGeoTransform()
projection = p.ax.projection # i know they match
extent = (gt[0], gt[0] + ds.RasterXSize * gt[1],
gt[3] + ds.RasterYSize * gt[5], gt[3])
print(extent)
extent = p.ax.get_xlim() + p.ax.get_ylim()
print(extent)
