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test.py
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import xarray as xr
import pickle
import os
from copy import deepcopy
import re
import numpy as np
import scipy.stats as st
from tqdm import tqdm
from src.hls_funcs.masks import shp2mask
from src.hls_funcs.predict import predict_biomass
from src.hls_funcs.predict import pred_bm, pred_cov, pred_bm_se, pred_bm_thresh
from src.objects.charts import gauge_obj
import param
import panel as pn
pn.extension('echarts')
import datetime as dt
import geoviews as gv
from cartopy import crs
import affine
import holoviews as hv
from holoviews import streams
from bokeh.models.formatters import PrintfTickFormatter
import numpy as np
#import rioxarray
import hvplot.pandas
import hvplot.xarray
hv.extension('bokeh', logo=False)
import datetime
from pyproj import Proj
from distributed import Client, LocalCluster
cluster = LocalCluster(n_workers=5, threads_per_worker=2, dashboard_address=":9898")
client = Client(cluster)
inDIR = "D:/LMF_STARFM_netcdf/"
all_files = [f for f in os.listdir(inDIR) if f.endswith('.nc')]
#dat = xr.open_dataset(os.path.join(inDIR, all_files[-1]))
yrs = np.arange(2013, 2021)
sub_files = [f for f in all_files if any(str(n) in f for n in yrs)]
da_list = []
for yr in yrs:
f_path = os.path.join(inDIR, [f for f in all_files if str(yr) in f][0])
dat_tmp = xr.open_dataset(f_path).chunk(dict(DOY=1, x=-1, y=-1))
date_list = [datetime.datetime(yr, 1, 1) +
datetime.timedelta(days=d) for d in (dat_tmp['DOY'].values.astype('float') - 1)]
dat_tmp = dat_tmp.rename(dict(DOY='date'))
dat_tmp.coords['date'] = [d.date() for d in date_list]
dat_tmp.coords['date'] = dat_tmp.coords['date'].astype('datetime64[ns]')
da_list.append(dat_tmp)
dat = xr.concat(da_list, dim='date').sortby('date')
#dat = dat.chunk(dict(DOY=1, x=-1, y=-1))
bm_mod = pickle.load(open('C:/SPK_local/models/CPER_HLS_to_VOR_biomass_model_lr_simp.pk', 'rb'))
cov_mod = pickle.load(open('C:/SPK_local/models/CPER_HLS_to_LPI_cover_pls_binned_model.pk', 'rb'))
vars_list_xr = []
for idx, v in enumerate(band_list):
vars_list_xr.append(func_dict[v](dat))
vars_list_xr[idx].name = v
dat_xr = xr.merge(vars_list_xr)
dat_xr
da_cov = pred_cov(dat_xr, cov_mod)
from rasterio.plot import show
show(da_cov['SD'].sel(date='2020-10-01'))
ends_dict = {
'SD': {
'ndvi': 0.30,
'dfi': 16,
'bai_126': 155},
'GREEN': {
'ndvi': 0.55,
'dfi': 10,
'bai_126': 160},
'BARE': {
'ndvi': 0.10,
'dfi': 8,
'bai_126': 140}}
class App(param.Parameterized):
date_init = datetime.datetime(int(dat['YEAR'].max()), 6, 15)
thresh_init = 500
bm_mean1 = 0
da_bm = pred_bm(dat, bm_mod)
da_bm.name = 'Biomass'
da_bm = da_bm.where(da_bm > 0)
da_bm_sel = da_bm.sel(date=date_init).persist()
da_cov = pred_cov(dat, ends_dict)
da_cov = da_cov.to_array(dim='type')
da_cov = da_cov.where((da_cov < 1.0) | (da_cov.isnull()), 1.0)
da_cov_sel = da_cov.sel(date=date_init).persist()
da_se = pred_bm_se(dat, bm_mod)
da_se = da_se.where(da_bm.notnull())
da_thresh = pred_bm_thresh(da_bm, da_se, thresh_init)
da_thresh.name = 'Threshold'
da_thresh_sel = da_thresh.sel(date=date_init).persist()
datCRS = crs.UTM(13)
mapCRS = crs.GOOGLE_MERCATOR
datProj = Proj(datCRS.proj4_init)
mapProj = Proj(mapCRS.proj4_init)
map_args = dict(crs=datCRS, rasterize=True, project=False, dynamic=True)
map_opts = dict(projection=mapCRS, responsive=False, xticks=None, yticks=None, width=900, height=700,
padding=0, tools=['pan', 'wheel_zoom', 'box_zoom'],
active_tools=['pan', 'wheel_zoom'], toolbar='left')
poly_opts = dict(fill_color=['', ''], fill_alpha=[0.0, 0.0], line_color=['#1b9e77', '#d95f02'],
line_width=[3, 3])
gauge_opts = dict(height=200, width=300)
bg_col='#ffffff'
css = '''
.bk.box1 {
background: #ffffff;
border-radius: 5px;
border: 1px black solid;
}
'''
pn.extension(raw_css=[css])
basemap = param.ObjectSelector(default="Satellite", objects=["Satellite", "Map"])
alpha = param.Number(default=1.0)
date = param.CalendarDate(default=date_init)
thresh = param.Integer(default=thresh_init)
#action = param.Action(lambda self: self.param.trigger('action'), 'Compute')
action = pn.widgets.Button(name='Save regions and \ncompute stats',
width=200)
#action.on_click(lambda self: self.param.trigger('action'))
action_val = action
cov_map = da_cov_sel.hvplot.rgb(x='x', y='y', bands='type',
**map_args).opts(**map_opts)
bm_map = da_bm_sel.hvplot(x='x', y='y',
cmap='Viridis', clim=(100, 1000), colorbar=False,
**map_args).opts(**map_opts)
thresh_map = da_thresh_sel.hvplot(x='x', y='y',
cmap='YlOrRd', clim=(0.05, 0.95), colorbar=False,
**map_args).opts(**map_opts)
tiles = gv.tile_sources.EsriImagery.opts(projection=mapCRS, backend='bokeh')
polys = hv.Polygons([])
def __init__(self, **params):
super(App, self).__init__(**params)
self.gauge_obj = deepcopy(gauge_obj)
self.poly_stream = streams.PolyDraw(source=self.polys, drag=True, num_objects=2,
show_vertices=True, styles=self.poly_opts)
self.edit_stream = streams.PolyEdit(source=self.polys, shared=True)
self.select_stream = streams.Selection1D(source=self.polys)
self.startX, self.endX = (float(self.da_bm['x'].min().values), float(self.da_bm['x'].max().values))
self.startY, self.endY = (float(self.da_bm['y'].min().values), float(self.da_bm['y'].max().values))
self.cov_stats = ''
self.bm_stats = ''
self.thresh_stats = ''
#self.stats_dict = {0: self.cov_stats,
# 1: self.bm_stats,
# 2: self.thresh_stats}
#self.stats = self.stats_dict[self.active_tab]
#self.text2.jscallback(args={'gauge1': self.gauge_pane1}, code="""
#gauge1.data.series[0].data[0].value = cb_obj.value
#gauge1.properties.data.change.emit()
#""")
self.all_maps = pn.Tabs(('Cover', pn.Row(self.tiles * self.cov_map * self.polys, self.cov_stats)),
('Biomass', pn.Row(self.tiles * self.bm_map * self.polys, self.bm_stats)),
('Threshold', pn.Row(self.tiles * self.thresh_map * self.polys, self.thresh_stats)))
self.active_tab = self.all_maps.active
def keep_zoom(self, x_range, y_range):
map_x_range, map_y_range = self.mapProj(x_range, y_range, inverse=True)
(self.startX, self.endX), (self.startY, self.endY) = self.datProj(map_x_range, map_y_range, inverse=False)
@param.depends('basemap')
def map_base(self):
if self.basemap == "Satellite":
self.tiles = gv.tile_sources.EsriImagery(projection=self.mapCRS, backend='bokeh')
elif self.basemap == "Map":
self.tiles = gv.tile_sources.Wikipedia(projection=self.mapCRS, backend='bokeh')
return self.tiles
@param.depends('date', watch=True)
def bm_date(self):
self.date_init = np.datetime64(self.date)
self.da_bm_sel = self.da_bm.sel(date=self.date_init).persist()
self.bm_map = self.da_bm_sel.hvplot(x='x', y='y',
xlim=(self.startX, self.endX),
ylim=(self.startY, self.endY),
cmap='Viridis', clim=(100, 1000),
colorbar=False,
**self.map_args).opts(alpha=self.alpha,
**self.map_opts)
self.bm_map.streams[-1].add_subscriber(self.keep_zoom)
return self.bm_map
@param.depends('date', watch=True)
def cov_date(self):
self.date_init = np.datetime64(self.date)
self.da_cov_sel = self.da_cov.sel(date=self.date_init).persist()
self.cov_map = self.da_cov_sel.hvplot.rgb(x='x', y='y',
xlim=(self.startX, self.endX),
ylim=(self.startY, self.endY),
bands='type',
**self.map_args).opts(alpha=self.alpha,
**self.map_opts)
return self.cov_map
@param.depends('date', 'thresh', watch=True)
def thresh_date(self):
self.date_init = np.datetime64(self.date)
self.thresh_init = self.thresh
self.da_thresh = pred_bm_thresh(self.da_bm, self.da_se, self.thresh_init)
self.da_thresh_sel = self.da_thresh.sel(date=self.date_init).persist()
self.thresh_map = self.da_thresh_sel.hvplot(x='x', y='y',
xlim=(self.startX, self.endX),
ylim=(self.startY, self.endY),
cmap='YlOrRd', clim=(0.05, 0.95), colorbar=False,
**self.map_args).opts(alpha=self.alpha,
**self.map_opts)
self.bm_map.streams[-1].add_subscriber(self.keep_zoom)
return self.thresh_map
@param.depends('alpha', watch=True)
def cov_alpha(self):
return self.cov_map.opts(alpha=self.alpha,
xlim=self.bm_map.streams[-1].x_range,
ylim=self.bm_map.streams[-1].y_range,
**self.map_opts)
@param.depends('alpha', watch=True)
def bm_alpha(self):
self.bm_map = self.bm_map.opts(alpha=self.alpha,
xlim=self.bm_map.streams[-1].x_range,
ylim=self.bm_map.streams[-1].y_range,
**self.map_opts)
self.bm_map.streams[-1].add_subscriber(self.keep_zoom)
return self.bm_map
@param.depends('alpha', watch=True)
def thresh_alpha(self):
return self.thresh_map.opts(alpha=self.alpha,
xlim=self.bm_map.streams[-1].x_range,
ylim=self.bm_map.streams[-1].y_range,
**self.map_opts)
@param.depends('action.param.value', watch=True)
def show_hist(self):
if self.poly_stream.data is None:
self.cov_stats = ''
self.bm_stats = ''
self.thresh_stats = ''
else:
self.bm_stats = 'Yes'
thresh_list = []
bm_list = []
cov_list = []
ts_yr_list = []
ts_avg_list = []
for idx, ps_c in enumerate(self.poly_stream.data['line_color']):
xs_map, ys_map = self.mapProj(self.poly_stream.data['xs'][idx],
self.poly_stream.data['ys'][idx], inverse=True)
xs_dat, ys_dat = self.datProj(xs_map, ys_map, inverse=False)
geometries = {
"type": "Polygon",
"coordinates": [
list(map(list, zip(xs_dat, ys_dat)))
]
}
ta = affine.Affine(30.0, 0.0, float(self.da_bm_sel['x'].min()),
0.0, -30.0, float(self.da_bm_sel['y'].max()))
poly_mask = shp2mask([geometries], self.da_bm_sel,
transform=ta, outshape=self.da_bm_sel.shape, default_value=1)
da_bm_tmp = self.da_bm_sel.where(poly_mask == 1)
bm_hist_tmp = da_bm_tmp.hvplot.hist('Biomass', xlim=(0, 2000),
bins=np.arange(0, 10000, 20))\
.opts(height=200, width=300, fill_color=ps_c, fill_alpha=0.6,
line_color='black', line_width=0.5, line_alpha=0.6,
bgcolor=self.bg_col).options(toolbar=None)
markdown = pn.pane.Markdown('## Region stats', height=50,
style={'font-family': "serif",
'color': ps_c})
thresh_pct = round(float(da_bm_tmp.where(da_bm_tmp < self.thresh_init).count())/
float(da_bm_tmp.count()) * 100, 0)
thresh_text = pn.pane.Markdown(f'**{thresh_pct}%** of the region is estimated to have biomass ' +
f'less than {self.thresh_init} kg/ha.',
style={'font-family': "Helvetica"})
thresh_list.append(pn.Column(pn.Row(pn.layout.HSpacer(), markdown, pn.layout.HSpacer()),
bm_hist_tmp * hv.VLine(x=self.thresh_init).opts(line_color='black'),
thresh_text,
css_classes=['box1'], margin=5))
bm_gauge_obj = deepcopy(self.gauge_obj)
bm_gauge_obj['series'][0]['data'][0]['value'] = int(da_bm_tmp.mean().values)
bm_gauge_pane = pn.pane.ECharts(bm_gauge_obj, **self.gauge_opts)
bm_list.append(pn.Column(pn.Row(pn.layout.HSpacer(),markdown, pn.layout.HSpacer()),
bm_gauge_pane,
css_classes=['box1'], margin=5))
#yr = int(self.da_bm_sel.YEAR.values)
#ts_bm_yr_tmp = self.da_bm.where(poly_mask == 1).sel(date=slice(datetime.datetime(yr, 5, 1),
# datetime.datetime(yr, 10, 31)))
#ts_yr_list.append(ts_bm_yr_tmp.mean(dim=['x', 'y']).hvplot.line(x='date',
# y='Biomass').opts(line_color=ps_c))
#ts_bm_avg_tmp = self.da_bm.where(poly_mask == 1).groupby(da_bm.date.dt.dayofyear).mean(dim=['x', 'y'])
#ts_avg_list.append(ts_bm_avg_tmp.hvplot.line(x='date', y='Biomass').opts(line_color=ps_c))
da_cov_tmp = self.da_cov_sel.where(poly_mask == 1)
cov_factors = list(da_cov_tmp.type.values)
cov_vals = [round(float(da_cov_tmp.sel(type=f).mean().values), 2) for f in cov_factors]
from bokeh.models import NumeralTickFormatter
pct_fmt = NumeralTickFormatter(format="0%")
cov_colors = hv.Cycle(['red', 'green', 'blue'])
cov_scatter_tmp = hv.Overlay([hv.Scatter(f) for f in list(zip(cov_factors, cov_vals))]) \
.options({'Scatter': dict(xformatter=pct_fmt,
size=15,
fill_color=cov_colors,
line_color=cov_colors,
ylim=(0, 1))})
cov_spike_tmp = hv.Overlay([hv.Spikes(f) for f in cov_scatter_tmp])\
.options({'Spikes': dict(color=cov_colors, line_width=4,
labelled=[], invert_axes=True, color_index=None,
ylim=(0, 1))})
cov_list.append(pn.Column(pn.Row(pn.layout.HSpacer(), markdown, pn.layout.HSpacer()),
(cov_spike_tmp * cov_scatter_tmp).options(height=200,
width=300,
bgcolor=self.bg_col,
toolbar=None),
css_classes=['box1'], margin=5))
self.polys=self.poly_stream.element.opts(xlim=(self.startX, self.endX),
ylim=(self.startY, self.endY))
self.poly_stream = streams.PolyDraw(source=self.polys, drag=True, num_objects=2,
show_vertices=True, styles=self.poly_opts)
self.edit_stream = streams.PolyEdit(source=self.polys, shared=True)
#self.gauge_pane1.object['series'][0]['data'][0]['value'] = int(da_bm_tmp.mean().values)
#self.gauge_pane2.object['series'][0]['data'][0]['value'] = int(da_bm_tmp.mean().values) + 100
self.thresh_stats = pn.Column(*thresh_list)
self.bm_stats = pn.Column(*bm_list)
self.cov_stats = pn.Column(*cov_list)
def view_all(self):
#self.da_bm_sel.name = 'Biomass'
self.active_tab = self.all_maps.active
self.bm_map.streams[-1].add_subscriber(self.keep_zoom)
#self.stats = self.stats_dict[self.active_tab]
base = hv.DynamicMap(self.map_base)
cov = self.cov_map
bm = self.bm_map
thresh = self.thresh_map
self.all_maps = pn.Tabs(('Cover', pn.Row(base * cov * self.polys, self.cov_stats)),
('Biomass', pn.Row(base * bm * self.polys, self.bm_stats)),
('Threshold', pn.Row(base * thresh * self.polys, self.thresh_stats)),
active=self.active_tab)
return pn.Column(self.all_maps)
viewer = App()
pn.serve(pn.Row(pn.Column(pn.Param(viewer.param,
widgets={'date': pn.widgets.DatePicker(name='Calendar',
value=datetime.datetime(int(dat['YEAR'].max()), 6, 15).date(),
width=200),
'alpha': pn.widgets.FloatSlider(name='Map transparency',
value=1.0,
start=0.0, end=1.0,
step=0.1,
width=200),
'thresh': pn.widgets.IntSlider(name='Threshold',
start=200, end=2000,
step=25, value=500,
format=PrintfTickFormatter(
format='%d kg/ha'),
width=200),
'basemap': pn.widgets.Select(name="Change basemap",
options=["Satellite", "Map"],
width=200),
#'action': pn.widgets.Button(name='Save regions and \ncompute stats',
# width=200)
}), viewer.action_button),
viewer.view_all))
layout = pn.template.ReactTemplate(
site="Shortgrass Explorer",
title="HoloViews",
theme=pn.template.react.DarkTheme,
row_height=100,
)
layout.sidebar[:] = [pn.Column(pn.Param(viewer.param,
widgets={'date': pn.widgets.DatePicker(name='Calendar',
value=datetime.datetime(int(dat['YEAR'].max()), 6, 15).date()),
'alpha': pn.widgets.FloatSlider(name='Map transparency',
value=1.0,
start=0.0, end=1.0,
step=0.1),
'thresh': pn.widgets.IntSlider(name='Threshold',
start=200, end=2000,
step=25, value=500,
format=PrintfTickFormatter(
format='%d kg/ha')),
'basemap': pn.widgets.Select(name="Change basemap",
options=["Satellite", "Map"]),
'action': pn.widgets.Button(name='Save regions and \ncompute stats')}))]
layout.main[:, :] = viewer.view_all
pn.serve(layout)
import param
import panel as pn
class A(param.Parameterized):
action = param.Event(label="param mapped")
class B(param.Parameterized):
def __init__(self, a, **params):
super().__init__(**params)
a.param.watch(self.compute, 'action')
def compute(self, event):
print(event)
a = A()
b = B(a)
pn.serve(pn.Column(a.param), show=True)
factors = ["a", "b", "c", "d", "e", "f", "g", "h"]
x = [50, 40, 65, 10, 25, 37, 80, 60]
scatter = hv.Scatter((factors, x)).opts(size=15, fill_color="orange", line_color="green")
spikes = hv.Spikes(scatter).opts(color='green', line_width=4, labelled=[], invert_axes=True, color_index=None)
plot = (spikes * scatter).options(toolbar=None, bgcolor='#f0f0f0')
css = '''
.bk.box1 {
background: #f0f0f0;
border-radius: 5px;
border: 1px black solid;
margin-top: 10% 5% 10%;
margin-bottom:10%
}
'''
markdown = pn.pane.Markdown('## Area info',
style={'font-family': "serif",
'text-align': 'center',
'color': '#1b9e77'})
pn.extension(raw_css=[css])
layout = pn.template.ReactTemplate(
site="Awesome Panel",
title="HoloViews",
theme=pn.template.react.DarkTheme,
row_height=200,
)
layout.main[0:2, 0:4] = pn.Column(pn.Row(pn.layout.HSpacer(), markdown, pn.layout.HSpacer()),
plot, css_classes=['box1'], margin=5, sizing_mode='stretch_both')
layout.main[3:4, 0:3] = pn.Column(plot, css_classes=['box1'], margin=5,sizing_mode='stretch_both')
pn.serve(layout)
da_bm = pred_bm(dat, bm_mod)
da_bm = da_bm.where(da_bm > 0)
da_bm.name = 'Biomass'
da_cov = pred_cov(dat, ends_dict)
da_cov = da_cov.to_array(dim='type')
da_cov = da_cov.where((da_cov < 1.0) | (da_cov.isnull()), 1.0)
da_se = pred_bm_se(dat, bm_mod)
da_se = da_se.where(da_bm.notnull())
hv.config.image_rtol = 0.01
datCRS = crs.UTM(13)
mapCRS = crs.GOOGLE_MERCATOR
datProj = Proj(datCRS.proj4_init)
mapProj = Proj(mapCRS.proj4_init)
#map_args = dict(crs=datCRS, rasterize=True, project=True, dynamic=True)
map_args = dict(rasterize=True, dynamic=True)
map_opts = dict(projection=mapCRS, responsive=False, width=900, height=500,
padding=0, tools=['pan', 'wheel_zoom', 'box_zoom', 'poly_edit'],
active_tools=['pan', 'wheel_zoom'])
x, y = datProj(da_bm.x.values, da_bm.y.values, inverse=True)
bm_sub = da_bm.isel(date=150)
bm_sub['x'] = x
bm_sub['y'] = y
bm_map = bm_sub.hvplot(x='x', y='y', geo=True,
**map_args).opts(**map_opts)
img_stream = streams.RangeXY(source=bm_map)
tiles = gv.tile_sources.EsriImagery.opts(projection=mapCRS, backend='bokeh')
pn.serve(pn.Pane(tiles * bm_map))
class Test(param.Parameterized):
bm_sub = da_bm.isel(date=150)
bm_map = bm_sub.hvplot(x='x', y='y',
**map_args).opts(**map_opts)
poly = hv.Polygons([])
poly_stream = streams.PolyDraw(source=poly, drag=True, num_objects=4,
show_vertices=True, styles={
'fill_color': ['red', 'green', 'blue'],
'fill_alpha': [0.6, 0.6, 0.6]
})
poly_edit = streams.PolyEdit(source=poly, shared=True)
select_stream = streams.Selection1D(source=poly)
point_holder = hv.Points([10])
point_holder_hv = pn.pane.HoloViews(point_holder)
#slider = pn.widgets.IntSlider(start=0, end=1000)
gauge1 = gauge_obj
gauge_pane = pn.pane.ECharts(gauge1, width=400, height=400)
#slider.jscallback(args={'gauge': gauge_pane}, value="""
# gauge.data.series[0].data[0].value = cb_obj.value
# gauge.properties.data.change.emit()
# """)
point_holder_hv.jscallback(args={'gauge': gauge_pane}, value="""
gauge.data.series[0].data[0].value = y_range.start
gauge.properties.data.change.emit()
""")
def __init__(self, **params):
super(Test, self).__init__(**params)
def watch_poly(self, index):
if len(index) > 0:
data = self.poly_stream.data
xs_map, ys_map = mapProj([data['xs'][i] for i in index][0],
[data['ys'][i] for i in index][0], inverse=True)
xs_dat, ys_dat = datProj(xs_map, ys_map, inverse=False)
geometries = {
"type": "Polygon",
"coordinates": [
list(map(list, zip(xs_dat, ys_dat)))
]
}
ta = affine.Affine(30.0, 0.0, float(self.bm_sub['x'].min()),
0.0, -30.0, float(self.bm_sub['y'].max()))
poly_mask = shp2mask([geometries], self.bm_sub,
transform=ta, outshape=self.bm_sub.shape, default_value=1)
da_bm_tmp = self.bm_sub.where(poly_mask == 1)
#self.slider.value = int(da_bm_tmp.mean().values)
self.point_holder = hv.Points([int(da_bm_tmp.mean().values)])
#@param.depends('slider')
def show(self):
self.select_stream.add_subscriber(self.watch_poly)
#gauge_pane = pn.pane.ECharts(self.gauge1, width=400, height=400)
return pn.Row(pn.Pane(self.bm_map*self.poly), pn.Column(self.gauge_pane))
Tester = Test()
pn.serve(Tester.show)
pn.serve(pn.Row(pn.Pane(bm_map*poly_out), pn.Column(gauge_pane, slider)))
gauge1 = gauge_obj
gauge_pane = pn.pane.ECharts(gauge1, width=200, height=250)
pn.serve(gauge_pane)
x=1
test = pn.widgets.Button(name='Click')
def button_f(event):
print(event)
x+=1
test.on_click(button_f)
class Tester2(param.Parameterized):
def __init__(self):
super(Tester2, self)
#self.action = param.Action(lambda x: x.param.trigger('action'), label='Compute')
self.button = pn.widgets.Button(name='Click')
self.text = pn.widgets.TextInput(value='Ready')
self.a=5
self.button.on_click(self.b)
def b(self, event):
super(Tester2, self)
self.text.value = 'Clicked {0} times'.format(self.button.clicks)
self.a = self.a + self.button.clicks
print(self.a)
test = Tester2()
pn.serve(pn.Row(test.button, test.text))
pn.serve(test)
gauge = {
'tooltip': {
'formatter': '{a} <br/>{b} : {c}%'
},
'series': [
{
'name': 'Gauge',
'type': 'gauge',
'min': 0,
'max': 1000,
'detail': {'formatter': '{value}%'},
'data': [{'value': 50, 'name': 'Value'}]
}
]
}
slider = pn.widgets.IntSlider(start=0, end=1000)
gauge_pane = pn.pane.ECharts(gauge, width=400, height=400)
slider.jscallback(args={'gauge': gauge_pane}, value="""
gauge.data.series[0].data[0].value = cb_obj.value
gauge.properties.data.change.emit()
""")
pn.serve(pn.Column(gauge_pane, slider))
def load_bm(date):
bm_map = da_bm.sel(DOY=date).hvplot(x='x', y='y', tiles='EsriImagery', crs=crs.UTM(13),
cmap='Viridis', clim=(200, 1800), colorbar=False,
data_aspect=0.6).opts(responsive=True,
xticks=None,
yticks=None)
print(bm_map.range(dimension='x'))
return bm_map
def load_cov(date):
cv_map = da_cov.sel(DOY=date).hvplot.rgb(x='x', y='y',
bands='type', tiles='EsriImagery',
crs=crs.UTM(13),
data_aspect=0.6).opts(responsive=True,
xticks=None,
yticks=None)
return cv_map
def load_thresh(date, thresh):
da_thresh = pred_bm_thresh(da_bm, da_se, thresh)
thresh_map = da_thresh.sel(DOY=date).hvplot(x='x', y='y', tiles='EsriImagery', crs=crs.UTM(13),
cmap='YlOrRd', clim=(0.05, 0.95), colorbar=False,
data_aspect=0.6).opts(responsive=True,
xticks=None,
yticks=None)
return thresh_map
date_picker = pn.widgets.DatePicker(name='Calendar',
value=datetime.datetime(2016, 6, 15).date())
thresh_picker = pn.widgets.IntSlider(name='Threshold', start=200, end=2000, step=25, value=500,
format=PrintfTickFormatter(format='%d kg/ha'))
@pn.depends(date=date_picker.param.value)
def load_map_bm(date):
p = load_bm(date.timetuple().tm_yday)
return p
@pn.depends(date=date_picker.param.value)
def load_map_cov(date):
return load_cov(date.timetuple().tm_yday)
@pn.depends(date=date_picker.param.value, thresh=thresh_picker.param.value)
def load_map_thresh(date, thresh):
return load_thresh(date.timetuple().tm_yday, thresh)
pn.serve(pn.Row(pn.WidgetBox('# Interactive tools:',
date_picker, thresh_picker, width=250),
pn.Tabs(('Cover', load_map_cov), ('Biomass', load_map_bm), ('Threshold', load_map_thresh)),
sizing_mode='stretch_both'))
geometries = \
{'type': 'Polygon', 'coordinates': [
[[515775.58844153915, 4523045.2408434935],
[518012.1479929752, 4522208.065559676],
[515722.8805455199, 4519434.631583949],
[513485.08678470645, 4520633.901147823]]
]}
da_bm_sel = da_bm.isel(date=150)
ta = affine.Affine(30.0, 0.0, float(da_bm_sel['x'].min()),
0.0, -30.0, float(da_bm_sel['y'].max()))
poly_mask = shp2mask([geometries], da_bm_sel,
transform=ta, outshape=da_bm_sel.shape, default_value=1)
show(poly_mask)
da_bm_sel.where(poly_mask == 1)
class App(param.Parameterized):
date_init = datetime.datetime(dat['YEAR'].values, 6, 15).date()
doy_init = date_init.timetuple().tm_yday
thresh_init = 500
da_bm = pred_bm(dat, bm_mod)
da_bm = da_bm.where(da_bm > 0)
da_cov = pred_cov(dat, ends_dict)
da_cov = da_cov.to_array(dim='type')
da_cov = da_cov.where((da_cov < 1.0) | (da_cov.isnull()), 1.0)
da_se = pred_bm_se(dat, bm_mod)
da_se = da_se.where(da_bm.notnull())
da_thresh = pred_bm_thresh(da_bm, da_se, thresh_init)
bm_map = da_bm.sel(DOY=doy_init).hvplot(x='x', y='y', rasterize=True,
cmap='Viridis', clim=(100, 1000), colorbar=False).opts(responsive=False,
xticks=None,
yticks=None)
cv_map = da_cov.sel(DOY=doy_init).hvplot.rgb(x='x', y='y',
bands='type', rasterize=True).opts(responsive=False,
xticks=None,
yticks=None)
thresh_map = da_thresh.sel(DOY=doy_init).hvplot(x='x', y='y', rasterize=True,
cmap='YlOrRd', clim=(0.05, 0.95), colorbar=False).opts(responsive=False,
xticks=None,
yticks=None)
def keep_zoom(self, x_range, y_range):
self.startX, self.endX = x_range
self.startY, self.endY = y_range
#rangexy = streams.RangeXY(source = bm_map, x_range=(startX,endX), y_range=(startY,endY))
#date_picker = pn.widgets.DatePicker(name='Calendar',
# value=datetime.datetime(da_bm['YEAR'].values, 6, 15).date())
#thresh_picker = pn.widgets.IntSlider(name='Threshold', start=200, end=2000, step=25, value=500,
# format=PrintfTickFormatter(format='%d kg/ha'))
date = param.CalendarDate(default=date_init)
thresh = param.Integer(default=thresh_init)
# create a button that when pushed triggers 'button'
button = param.Action(lambda x: x.param.trigger('button'), label='Get XY')
def __init__(self, **params):
super(App, self).__init__(**params)
self.startX, self.endX = (508305.0, 538305.0)
self.startY, self.endY = (4504575.0, 4534575.0)
self.xr = (508305.0, 538305.0)
self.yr = (4504575.0, 4534575.0)
self.t = datetime.datetime.now()
@param.depends('button')
def view_xy(self):
x = self.bm_map.streams[-1].x_range
y = self.bm_map.streams[-1].y_range
return str(x) + ',' + str(y)
@param.depends('button')
def view_xy2(self):
self.t = datetime.datetime.now()
self.bm_map.streams[-1].add_subscriber(self.keep_zoom)
x = (self.startX, self.endX)
y = (self.startY, self.endY)
return str(x) + ',' + str(y)
@param.depends('button')
def view_t(self):
return self.t
@param.depends('date')
def view_bm(self):
self.doy_init = self.date.timetuple().tm_yday
self.bm_map = self.da_bm.sel(DOY=self.doy_init).hvplot(x='x', y='y',
#xlim=(self.startX, self.endX),
#ylim=(self.startY, self.endY),
cmap='Viridis', clim=(100, 1000),
colorbar=False,
rasterize=True).opts(responsive=False,
xticks=None,
yticks=None)
self.bm_map.streams[-1].add_subscriber(self.keep_zoom)
self.bm_map = self.bm_map.redim.range(x=(self.startX, self.endX), y=(self.startY, self.endY))
return self.bm_map
@param.depends('date')
def view_cov(self):
self.doy_init = self.date.timetuple().tm_yday
self.cv_map = self.da_cov.sel(DOY=self.doy_init).hvplot.rgb(x='x', y='y',
#xlim=(self.startX, self.endX),
#ylim=(self.startY, self.endY),
bands='type', rasterize=True).opts(responsive=False,
xticks=None,
yticks=None)
self.cv_map.streams[-1].add_subscriber(self.keep_zoom)
self.cv_map = self.cv_map.redim.range(x=(self.startX, self.endX), y=(self.startY, self.endY))
return self.cv_map
@param.depends('date', 'thresh')
def view_thresh(self):
self.doy_init = self.date.timetuple().tm_yday
self.thresh_init = self.thresh
self.da_thresh = pred_bm_thresh(self.da_bm, self.da_se, self.thresh_init)
self.thresh_map = self.da_thresh \
.sel(DOY=self.doy_init).hvplot(x='x', y='y',
#xlim=(self.startX, self.endX),
#ylim=(self.startY, self.endY),
cmap='YlOrRd', clim=(0.05, 0.95),
colorbar=False, rasterize=True).opts(responsive=False,
xticks=None,
yticks=None)
self.thresh_map.streams[-1].add_subscriber(self.keep_zoom)
self.thresh_map = self.thresh_map.redim.range(x=(self.startX, self.endX), y=(self.startY, self.endY))
return self.thresh_map
viewer = App()
pn.serve(pn.Row(pn.Column(pn.Param(viewer.param,
widgets={'date': pn.widgets.DatePicker(name='Calendar',
value=datetime.datetime(dat['YEAR'].values, 6, 15).date()),
'thresh': pn.widgets.IntSlider(name='Threshold',
start=200, end=2000,
step=25, value=500,
format=PrintfTickFormatter(
format='%d kg/ha'))}),
viewer.view_xy,
viewer.view_xy2,
viewer.view_t),
pn.Tabs(('Biomass', viewer.view_bm),
('Cover', viewer.view_cov),
('Threshold', viewer.view_thresh))))
test = da_bm.sel(DOY=100).hvplot(x='x', y='y', rasterize=True,
#xlim=xr,
#ylim=yr,
cmap='Viridis', clim=(200, 1800), colorbar=False).opts(responsive=False,
xticks=None,
yticks=None)
pn.Row(test).servable()
test = da_bm.sel(DOY=100).hvplot(x='x', y='y', tiles='EsriImagery', crs=crs.UTM(13), rasterize=True,
cmap='Viridis', clim=(200, 1800), colorbar=False,
data_aspect=0.6).opts(responsive=True,
xticks=None,
yticks=None)
test
import numpy as np
import pandas as pd
import panel as pn
import hvplot.pandas
from holoviews import streams
import param
class AppTest(param.Parameterized):
data = 10*np.random.rand(100,4)
df = pd.DataFrame(data=data,columns=['x','y','z1' ,'z2'])
color_val = param.Selector(default='z1',objects=['z1','z2'])
plot = df.hvplot.scatter(x='x',y='y',c='z1')
startX,endX = plot.range('x')
startY,endY = plot.range('y')
def keep_zoom(self,x_range,y_range):
self.startX,self.endX = x_range
self.startY,self.endY = y_range
@param.depends('color_val')
def view(self):
self.plot = self.df.hvplot.scatter(x='x',y='y',c=self.color_val)
self.plot = self.plot.redim.range(x=(self.startX,self.endX), y=(self.startY,self.endY))
rangexy = streams.RangeXY(source = self.plot, x_range=(self.startX,self.endX), y_range=(self.startY,self.endY))
rangexy.add_subscriber(self.keep_zoom)
return self.plot
viewer = AppTest()
pn.serve(pn.Column(pn.Param(viewer.param,widgets={'color_val':pn.widgets.RadioButtonGroup}),viewer.view))
import holoviews as hv
import numpy as np
import pandas as pd
import panel as pn
hv.extension('bokeh')
renderer = hv.renderer('bokeh')
x_range_fmt = "({:.3f}, {:.3f})"
# Create dummy dataset
num_pts = 1000
# Create scatterplots
hv_plots = [da_bm.sel(DOY=100).hvplot().opts(axiswise=True) for i in range(2)]
txt_xrange_doc = [pn.widgets.StaticText(name="x_range using get_root", value='x_range: (,)') for i in range(2)]
txt_xrange_renderer = [pn.widgets.StaticText(name="x_range using get_plot", value='x_range: (,)') for i in range(2)]
txt_xrange_stream = [pn.widgets.StaticText(name="x_range using streams", value='x_range: (,)') for i in range(2)]
# Create streams linked to x_range
rng = [hv.streams.RangeX(source=hv_plot) for hv_plot in hv_plots]
def query_x_range(event):
for i in range(2):
# Query x_range by using get_root on panel to obtain bokeh document
xrng_doc = panel.get_root().children[i].children[0].x_range
xrng_str = x_range_fmt.format(xrng_doc.start, xrng_doc.end)
txt_xrange_doc[i].value = xrng_str
# Query x_range by using holoviews renderer to get plot handles
xrng_renderer = renderer.get_plot(panel[i][0].object).handles['x_range']
xrng_str = x_range_fmt.format(xrng_renderer.start, xrng_renderer.end)
txt_xrange_renderer[i].value = xrng_str
# Query x_range stream object
xrng_stream = rng[i].x_range
xrng_str = x_range_fmt.format(xrng_stream[0], xrng_stream[1])
txt_xrange_stream[i].value = xrng_str
update_button = pn.widgets.Button(name='Query figure x_range', button_type='primary')
update_button.on_click(query_x_range)
# Create panel
panel = pn.Row(pn.Column(hv_plots[0], txt_xrange_doc[0], txt_xrange_renderer[0], txt_xrange_stream[0]),
pn.Column(hv_plots[1], txt_xrange_doc[1], txt_xrange_renderer[1], txt_xrange_stream[1], update_button))
pn.serve(panel)
def load_bm(date):
bm_map = da_bm.sel(DOY=date).hvplot(x='x', y='y', tiles='EsriImagery', crs=ccrs.UTM(13),
cmap='Viridis', clim=(100, 1000), colorbar=False,
data_aspect=0.6).opts(responsive=True,
xticks=None,
yticks=None)
print(bm_map.range(dimension='x'))
return bm_map
def load_cov(date):
cv_map = da_cov.sel(DOY=date).hvplot.rgb(x='x', y='y',
bands='type', tiles='EsriImagery',
crs=ccrs.UTM(13), data_aspect=0.6).opts(responsive=True,
xticks=None,
yticks=None)
return cv_map
def load_thresh(date, thresh):
da_thresh = pred_bm_thresh(dat, bm_mod, thresh)
thresh_map = da_thresh.sel(DOY=date).hvplot(x='x', y='y', tiles='EsriImagery', crs=ccrs.UTM(13),
cmap='YlOrRd', clim=(0.05, 0.95), colorbar=False,
data_aspect=0.6).opts(responsive=True,
xticks=None,
yticks=None)
return thresh_map
def keep_zoom(self,x_range,y_range):
self.startX,self.endX = x_range
self.startY,self.endY = y_range
# date_slider = pn.widgets.IntSlider(name='Date Slider',
# start=0, end=len(da_bm.time), value=0)
date_picker = pn.widgets.DatePicker(name='Calendar',
value=datetime.datetime(da_bm['YEAR'].values, 6, 15).date())
thresh_picker = pn.widgets.IntSlider(name='Threshold', start=200, end=2000, step=25, value=500,
format=PrintfTickFormatter(format='%d kg/ha'))
@pn.depends(date=date_picker.param.value)
def load_map_bm(self):
p = load_bm(self.date.timetuple().tm_yday)
startX, endX = p.range('x')
startY, endY = p.range('y')
self.plot = self.plot.redim.range(x=(self.startX, self.endX), y=(self.startY, self.endY))
rangexy = streams.RangeXY(source=self.plot, x_range=(self.startX, self.endX), y_range=(self.startY, self.endY))
rangexy.add_subscriber(self.keep_zoom)
return p
@pn.depends(date=date_picker.param.value)
def load_map_cov(date):
return load_cov(date.timetuple().tm_yday)
@pn.depends(date=date_picker.param.value, thresh=thresh_picker.param.value)
def load_map_thresh(date, thresh):
return load_thresh(date.timetuple().tm_yday, thresh)
def query_x_range(event):
for i in range(2):
# Query x_range by using get_root on panel to obtain bokeh document
xrng_doc = panel.get_root().children[i].children[0].x_range
xrng_str = x_range_fmt.format(xrng_doc.start, xrng_doc.end)
txt_xrange_doc[i].value = xrng_str
# Query x_range by using holoviews renderer to get plot handles
xrng_renderer = renderer.get_plot(panel[i][0].object).handles['x_range']
xrng_str = x_range_fmt.format(xrng_renderer.start, xrng_renderer.end)
txt_xrange_renderer[i].value = xrng_str
# Query x_range stream object
xrng_stream = rng[i].x_range
xrng_str = x_range_fmt.format(xrng_stream[0], xrng_stream[1])