def post_frame(self,t,data2d,data3d):
if self.plot_mode is None:
return
import matplotlib.pyplot as plt
import stompy.plot.cmap as scmap
if self.fig is None:
self.fig=plt.figure()
ax=self.fig.add_subplot(1,1,1)
# min/max over entire period
#vmin=self.data['value'].min()
#vmax=self.data['value'].max()
# min/max for this moment
vmin=data2d.min()
vmax=data2d.max()
cmap=scmap.load_gradient('turbo.cpt')
self.coll=self.grid.plot_cells(values=data2d,clim=[vmin,vmax],
lw=0.5,
cmap=cmap,ax=ax)
self.coll.set_edgecolor('face')
ax.axis('equal')
plt.colorbar(self.coll,ax=ax)
self.txt=ax.text(0.05,0.05,"text",transform=ax.transAxes)
else:
self.coll.set_array(data2d)
self.txt.set_text( str(t) )
if self.plot_mode=='each':
self.fig.canvas.draw()
plt.pause(0.01)
elif self.plot_mode=='save':
time_str=utils.to_datetime(t).strftime('%Y%m%dT%H%M')
self.fig.savefig('extrap-%s.png'%time_str)
# Load in Lidar and pre-compiled DEMs, particularly in N Marsh
#
import os, shutil
import pandas as pd
from stompy.spatial import field, wkb2shp
import six
import matplotlib.pyplot as plt
import numpy as np
import stompy.plot.cmap as scmap
from stompy.plot import plot_wkb
from stompy import utils
from stompy.spatial import interp_orthogonal
six.moves.reload_module(interp_orthogonal)
turbo = scmap.load_gradient('turbo.cpt')
turbo_gray = scmap.load_gradient('turbo.cpt')
turbo_gray.set_over("0.8")
sst = scmap.load_gradient('oc-sst.cpt')
##
fig_dir = "figs-20201211"
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
##
version = "existing" # "asbuilt"
date_str = "20201230"
scat = ax.scatter(xyz_samples[:, 0],
xyz_samples[:, 1],
15,
xyz_samples[:, 2],
cmap='jet')
plot_utils.cbar(scat, label='m NAVD88', ax=ax, orientation='horizontal')
img = dem.plot(ax=ax, interpolation='nearest', cmap='jet')
crange = [-6, 2.25]
scat.set_clim(crange)
img.set_clim(crange)
div_cmap = scmap.load_gradient('ViBlGrWhYeOrRe.cpt')
# And show the difference:
scat = ax2.scatter(xyz_samples[:, 0],
xyz_samples[:, 1],
15,
delta,
cmap='seismic')
plot_utils.cbar(scat, label='ADCP - DEM (m)', ax=ax2, orientation='horizontal')
img = dem.plot(ax=ax2, interpolation='nearest', cmap='gray')
scat.set_clim([-1, 1])
img.set_clim([-10, 5])
for a in [ax, ax2]:
- north_channel_and_ditch-1m.tif
- lagoon-1m.tif
"""
from stompy.grid import unstructured_grid
import stompy.grid.quad_laplacian as quads
from stompy.spatial import wkb2shp
import matplotlib.pyplot as plt
import stompy.plot.cmap as scmap
from stompy.spatial import field
from stompy import utils
import numpy as np
import six
import os
turbo = scmap.load_gradient('turbo.cpt')
##
fig_dir = "figs-20201230"
os.path.exists(fig_dir) or os.makedirs(fig_dir)
##
all_points = wkb2shp.shp2geom('../data/cbec/cbec-survey/All_Points.shp',
target_srs='EPSG:26910')
xyz = np.array([np.array(p) for p in all_points['geom']])
xyz[:, 2] = all_points['Z_m']
##
gen = unstructured_grid.UnstructuredGrid.read_pickle(
def test_load_gradient():
assert cmap.load_gradient('hot_desaturated.cpt') is not None
centr=g_complete.cells_centroid()
g_complete.cells['depth'][sel_cells]=dem(centr[sel_cells])
sel_edges=np.nonzero(np.isnan(g_complete.edges['edge_depth']))[0]
e2c=g_complete.edge_to_cells()
nc1=e2c[sel_edges,0].copy()
nc2=e2c[sel_edges,1].copy()
nc1[nc1<0]=nc2[nc1<0]
nc2[nc2<0]=nc1[nc2<0]
cell_depth=g_complete.cells['depth']
g_complete.edges['edge_depth'][sel_edges] = np.maximum( cell_depth[nc1],
cell_depth[nc2] )
##
import stompy.plot.cmap as scmap
cmap=scmap.load_gradient('hot_desaturated.cpt')
plt.figure(1).clf()
g_complete.plot_cells(values=g_complete.cells['depth'],clim=[-4000,10],cmap=cmap)
g_complete.plot_edges(values=g_complete.edges['edge_depth'],clim=[-4000,10],cmap=cmap)
plt.setp(plt.gca().collections,clim=[-100,10])
##
g_complete.write_ugrid('spliced_grids_01_bathy.nc',overwrite=True)
import numpy as np
import pandas as pd
import stompy.model.delft.waq_scenario as dwaq
from stompy.memoize import memoize
import logging as log
from stompy import utils
from stompy import filters
from stompy.plot import plot_wkb
import netCDF4
import warnings
from stompy.model.data_comparison import calc_metrics
import os
import six
from matplotlib import gridspec
cmap = scmap.load_gradient('turbo.cpt') # a less offensive 'jet'
def hydro_name(hydro):
"""
Convenience method -- get a representative name for a hydro
object to use in naming runs. Attempts hydro.name, falls
back on base dir if a HydroFiles object.
"""
try:
return hydro.name
except AttributeError:
pass
if isinstance(hydro, dwaq.HydroFiles):
return os.path.basename(os.path.dirname(hydro.hyd_path))
raise Exception("Can't figure out good name for %s" % hydro)
"""
Calculate several distance along axis metrics for the CSC
grid and compare.
"""
import six
import numpy as np
import matplotlib.pyplot as plt
from stompy.grid import unstructured_grid
from stompy import utils
from sklearn.linear_model import LinearRegression
import stompy.plot.cmap as scmap
from stompy.plot import plot_utils
cmap=scmap.load_gradient('ncview_banded.cpt')
##
grid_file="~/src/csc/dflowfm/CacheSloughComplex_v111-edit19fix-bathy.nc"
six.moves.reload_module(unstructured_grid)
g=unstructured_grid.UnstructuredGrid.read_ugrid(grid_file)
ds=g.write_to_xarray()
##
# Define a downstream d=0
x_down=np.array([610016, 4215971.])
##
from stompy.model import unstructured_diffuser
import matplotlib.pyplot as plt
import prepare_pings
from stompy.spatial import field
import stompy.plot.cmap as scmap
from stompy.plot import plot_utils
import six
##
dem = field.GdalGrid("../../bathy/junction-composite-20200604-w_smooth.tif")
##
six.moves.reload_module(prepare_pings)
years = [2018, 2019, 2020]
plt.figure(1).clf()
fig, axs = plt.subplots(1, len(years), figsize=(6.5, 3.75), num=1)
terrain = scmap.load_gradient('BlueYellowRed.cpt')
for ax, year in zip(axs, years):
rx_locs = prepare_pings.rx_locations(year=year)
ax.plot(rx_locs.x, rx_locs.y, 'go')
img = dem.plot(ax=ax, cmap=terrain, vmin=-9, vmax=10)
plt.setp(ax.get_xticklabels(), visible=0)
plt.setp(ax.get_yticklabels(), visible=0)
#plot_utils.scalebar(ax=ax,
from stompy.grid import unstructured_grid
import matplotlib.pyplot as plt
from matplotlib import colors
from matplotlib.transforms import Bbox
import stompy.plot.cmap as scmap
from stompy.plot import plot_utils
##
sfb_ocean = "."
grid_ocean = unstructured_grid.UnstructuredGrid.from_ugrid(
os.path.join(sfb_ocean, "grid-merge-suisun", "spliced-bathy.nc"))
##
cmap = scmap.load_gradient('oc-sst.cpt', reverse=True)
fig = plt.figure(1)
fig.clf()
fig.set_size_inches((7, 9), forward=True)
ax = fig.add_axes([0, 0, 1, 1])
ax.xaxis.set_visible(0)
ax.yaxis.set_visible(0)
grid_ocean.plot_edges(ax=ax, lw=0.5, color='k', alpha=0.2)
ccoll = grid_ocean.plot_cells(ax=ax,
values=(-grid_ocean.cells['z_bed']).clip(
1, np.inf),
norm=colors.LogNorm(),
cmap=cmap)
cax = fig.add_axes([0.07, 0.15, 0.03, 0.35])
salt = np.zeros(G.Ncells(), 'f8')
for proc in procs:
# steps 3 and 4 should be clear of the 3h ramp up.
one_salt = all_ds[proc].sa1.isel(time=time, laydim=9).values
salt[global_to_G[all_ds[proc].FlowElemGlobalNr.values]] = one_salt
##
plt.figure(1).clf()
fig, ax = plt.subplots(num=1)
coll = G.plot_cells(ax=ax, lw=0.5, values=salt)
coll.set_clim([10, 36])
coll.set_cmap(scmap.load_gradient('hot_desaturated.cpt'))
plt.setp(coll, edgecolor='face')
plt.colorbar(coll)
ax.xaxis.set_visible(0)
ax.yaxis.set_visible(0)
fig.tight_layout()
##
def update_salt(tidx):
salt = np.zeros(G.Ncells(), 'f8')
for proc in procs:
# steps 3 and 4 should be clear of the 3h ramp up.
from stompy.spatial import field
import six
import numpy as np
import glob
import numpy as np
import skimage.graph
from scipy import ndimage
from stompy import memoize, utils
import matplotlib.pyplot as plt
import stompy.plot.cmap as scmap
turbo = scmap.load_gradient('turbo.cpt')
import subprocess
##
# 9k x 10k
dem = field.GdalGrid('../bathy/DEM_VegCorr_DWR_2m_NAVD88.tif')
##
from stompy.spatial import wkb2shp
six.moves.reload_module(wkb2shp)
gdb_fn = "../bathy/levees/ESRI ArcGIS/SFBayShoreInventory__SFEI_2016.gdb"
levees = wkb2shp.shp2geom(gdb_fn,
layer_patt='.*with_elevation',
target_srs='EPSG:26910')
# 'San_Francisco_Bay_Shore_Inventory__100ft_segments_with_elevation'
##
ax=ax,
zorder=-.5)
cax = fig.add_axes([0.08, 0.2, 0.3, 0.03])
return fig, ax, cax
##
fig, ax, cax = setup_figure(1)
Gvar = np.zeros(G.Ncells(), 'f8')
coll = G.plot_cells(values=Gvar, clip=zoom, ax=ax)
cmap = scmap.load_gradient('StepSeq25.cpt')
plt.setp(coll, lw=1, edgecolor='face', clim=[0, 33], cmap=cmap)
date_txt = ax.text(0.08, 0.27, "1111-22-33 44:55", transform=ax.transAxes)
cell_mask = G.cell_clip_mask(zoom)
def set_time(t_idx):
Gvar[:] = merge(
lambda ds: ds['sa1'].isel(time=t_idx).isel(laydim=0).values)
depth = merge(lambda ds: ds['waterdepth'].isel(time=t_idx).values)
coll.set_array(np.ma.array(Gvar[cell_mask], mask=depth[cell_mask] < 0.05))
t = dss[0].time.values[t_idx]
date_txt.set_text(utils.to_datetime(t).strftime('%Y-%m-%d %H:%M'))
