def main(data_sequence_folder: str, limit: int, start: int, out: str):
root = Path(data_sequence_folder)
img_folder_path = root.joinpath("imgs/")
npz_filepath = root.joinpath("rendered.npz")
data = np.load(npz_filepath)
datasource = {key: data[key] for key in data.files}
images_filepaths = list(img_folder_path.glob("*.png"))
images_filepaths.sort()
limit = min(start + limit, len(images_filepaths) - 1)
with_slides = {}
for idx, filepath in enumerate(tqdm(images_filepaths[start:limit])):
dataframe = dict([(key, str(value[start + idx]))
for key, value in datasource.items()])
image = hv.RGB.load_image(str(filepath))
image.opts(title=f"Frame #{idx+1}")
table = hv.Div(pformat(dataframe).replace("\n", "</br>"))
fig = image + table
with_slides[idx + 1] = fig
data.close()
hmap = hv.HoloMap(with_slides, "frame")
hmap = hmap.collate()
path_info = hv.Div(f"Sequence from {str(img_folder_path.parent)}")
layout = hv.Layout([path_info] + [hmap])
hv.save(layout, "holomap.html")
def selection_callback(index):
if not index:
return hv.Div("")
divtext = f'<table width={pad} border=1 cellpadding=10 align=center valign=center>'
for i, j in grouped(index, 2):
value_s = '{:f}'.format(df_i[value][i])
value_s2 = '{:f}'.format(df_i[value][j])
divtext += '<tr>'
divtext += f'<td align=center valign=center><br> {i} Value: {value_s}</br></td>' + "\n"
divtext += f'<td align=center valign=center><br> {j} Value: {value_s2}</br></td>' + "\n"
divtext += '</tr><tr>'
divtext += f'<td align=center valign=center><img src={df_i.loc[i, "img_name_raw"]} width={w} height={h}></td>'
divtext += f'<td align=center valign=center><img src={df_i.loc[j, "img_name_raw"]} width={w} height={h}></td>'
divtext += '</tr>'
if len(index) % 2 == 1:
value_s = '{:f}'.format(df_i[value][index[-1]])
divtext += '<tr>'
divtext += f'<td align=center valign=center><br> {index[-1]} Value: {value_s}</br></td>' + "\n"
divtext += f'<td align=center valign=center><br> </br></td>' + "\n"
divtext += '</tr><tr>'
divtext += f'<td align=center valign=center><img src={df_i.loc[index[-1], "img_name_raw"]} width={w} height={h}></td>'
divtext += f'<td align=center valign=center></td>'
divtext += '</tr>'
divtext += '</table>'
return hv.Div(str(divtext))
def portfolio_text(ds):
arr = ds.array()
ret, vol, sharpe = get_ret_vol_sr(arr[0, 2:])
text = """
The selected portfolio has a volatility of %.2f, a return of %.2f
and Sharpe ratio of %.2f.
""" % (vol, ret, sharpe)
return hv.Div(text).opts(height=40)
def display(
self,
defaultpath: Optional[str] = None,
match: Optional[str] = None,
callback: Optional[Callable[['_BasePathSelector'], Any]] = None
) -> pn.Column:
"create the view"
if defaultpath is not None:
self.paths.value = defaultpath
if match is not None:
self.match.value = match
if callback is not None:
self._cback = callback
self._col = pn.Column(pn.Row(self.paths, self.match), self.selector,
pn.Pane(hv.Div("")))
self._on_update()
return self._col
def _on_newtracks(self, *_):
"update the values"
old = self._col.objects[:-1]
obj = hv.Div("No tracks selected"
) if not self.selector.value else self._new_display()
if self._cback:
self._cback(self)
if obj is None:
return
try:
self._col.pop(-1)
self._col.append(pn.Pane(obj))
except KeyError: # panel bug
for _ in range(5):
try:
self._col.clear()
self._col.extend(old + [pn.Pane(obj)])
break
except KeyError: # panel bug
pass
def _new_display(self):
track = self.track
beads = list(track.beads.keys())
if not beads:
return pn.Pane("No beads in this track")
sel = pnw.Select(
name="Plot",
value="cleancycles",
options=["cleanbeads", "cleancycles", "cycles", "peaks"])
lst = pnw.DiscreteSlider(name="Beads", value=beads[0], options=beads)
pane = pn.Pane(hv.Div(""))
def _show(_):
dmap = getattr(track, sel.value).display.display()
pane.object = dmap[lst.value]
lst.param.watch(_show, "value")
sel.param.watch(_show, "value")
col = pn.Column(pn.Row(lst, sel), pane)
_show(None)
return col
"""
import math
import time
import holoviews as hv
import hvplot.pandas # pylint: disable=unused-import
import pandas as pd
import panel as pn
import param
from application.config import site
COLOR = "#E1477E"
EMPTY_DATAFRAME = pd.DataFrame(columns=["x", "y"])
EMPTY_PLOT = hv.Div("Click UPDATE to load!")
SPINNER_URL = (
"https://github.com/MarcSkovMadsen/awesome-panel/blob/master/"
"application/pages/gallery/dataexplorer_loading/spinner.gif?raw=true")
SPINNER_HTML = f"<img application='{SPINNER_URL}' style='width:100%'"
APPLICATION = site.create_application(
url="data-explorer-loading",
name="Data Explorer Loading",
author="Marc Skov Madsen",
introduction=
"Shows how to provide progress information to a user during computation",
description=__doc__,
thumbnail_url="dataexplorer_loading.png",
documentation_url="",
code_url="dataexplorer_loading/dataexplorer_loading.py",
gif_url="",
imgs_pv = ds_sel['p_values'].hvplot.contour(**hvc_opts)
temp = xr.apply_ufunc(combine_pvalues_ufunc, ds_sel['p_values'], input_core_dims=[['ens']], \
output_core_dims = [[]], vectorize = True, dask = 'allowed')
hvc_opts = dict(width=300, dynamic=True, \
x = 'x', y = 'y', colorbar = False, \
logy = True, cmap = ['black', 'gray'], \
levels=[0.01,0.05])
imgs_pv2 = temp.hvplot.contour(**hvc_opts)
# -
# ## Layout
# By combining every panel in a layout, we get clear interactive dashboard
hl = hv.HLine(0).opts(color='gray', line_dash='dotted')
dmap = dmap.opts(xticks=[(i, ens_name) for i, ens_name in enumerate(ens_ls)])
first_panel = im * imgs_pv * polys
second_panel = (dmap * hl * hlines).relabel('ROI drawer')
hv_div = hv.Div(
f"""<h1>{invar} response to {sel_reg} for {month_names[sel_month-1]}</h1>"""
)
second_row = (
(im2 * imgs_pv2).relabel('Model average (p-values combined using Z-score)')
+ hv_div)
layout = ((first_panel + second_panel).opts(
opts.Curve(width=400, framewise=True),
opts.Polygons(fill_alpha=0.2, line_color='green', fill_color='green'),
opts.VLine(color='black')) + second_row).cols(2)
layout
def plot():
#################################################################333333
# Final plot.
# High Significant
from shapely.geometry import Point, Polygon
import pandas as pd
import numpy as np
import geoviews as gv
import bokeh
import panel as pn
import holoviews as hv
from holoviews import opts
from holoviews.operation.datashader import datashade, rasterize
hv.extension("bokeh")
import math
from bokeh.models import HoverTool
from scipy.interpolate import griddata
from scipy.io import loadmat
import datetime
from dateutil.parser import parse
from datetime import timedelta
import time
strtt = time.time()
datamat = loadmat('IOEC_ECM_noDA_20190703_masked.mat')
# datamat = loadmat('IOEC_ECM_DA_20191121_masked.mat')
Xp = datamat['Xp']
Yp = datamat['Yp']
# Xp=np.where(((Xp>= 85) & (Xp<=88) & (Yp>=19) & (Yp<=22)),Xp,88)
# Yp=np.where(((Xp>= 85) & (Xp<=88) & (Yp>=19) & (Yp<=22)),Yp,22)
strt = datetime.datetime(2019, 7, 4, 0, 0)
end = datetime.datetime(2019, 1, 13, 0, 0)
from bokeh.models.callbacks import CustomJS
def perdelta(strt, end, delta):
curr = strt
while curr < end:
yield curr
curr += delta
# Read element file
tri_new = pd.read_csv('fort.ele',
delim_whitespace=True,
names=('A', 'B', 'C', 'D'),
usecols=[1, 2, 3],
skiprows=1,
dtype={'D': np.int})
dateList = []
def plotsecond(datetime, regions='Whole_East_Coast'):
dat = datetime
# datetostr=result.strftime("%Y%b%d%H")
dt = parse(str(dat))
yr = dt.year
mn = dt.month
d = dt.day
hr = dt.hour
mi = dt.minute
if hr < 10:
hr = '0' + str(hr)
else:
d = str(d)
hr = str(hr)
if int(d) < 10:
d = '0' + str(d)
else:
d = str(d)
# varname = 'Hsig_' + str(yr) + '0' + str(mn) + str(d) + '_' + hr + '0000'
Xp = datamat['Xp']
Yp = datamat['Yp']
x = Xp.flatten()
y = Yp.flatten()
Longitude = x
Latitude = y
pkname = 'PkDir_' + str(yr) + '0' + str(mn) + str(
d) + '_' + hr + '0000'
pkvalue = datamat[pkname]
if regions is 'Odisha':
pkvalue = np.where(
((Xp >= 85) & (Xp <= 88) & (Yp >= 19) & (Yp <= 22)), pkvalue,
np.nan).flatten()
elif regions is 'Andra_Pradesh':
pkvalue = np.where(
((Xp >= 79) & (Xp <= 85) & (Yp >= 13) & (Yp <= 19)), pkvalue,
np.nan).flatten()
elif regions is 'Tamil_Nadu':
pkvalue = np.where(
((Xp >= 77) & (Xp <= 83) & (Yp >= 7) & (Yp <= 14)), pkvalue,
np.nan).flatten()
elif regions is 'Whole_East_Coast':
pkvalue = pkvalue.flatten()
else:
# data = get_data4region(data,**odisha)
pkvalue = pkvalue.flatten()
pkvalue = pkvalue.flatten()
d = pkvalue * (math.pi / 180)
# target grid to interpolate to
xt = np.arange(76.937, 92.008, 0.1)
yt = np.arange(1.482, 22.461, 0.1)
xi, yi = np.meshgrid(xt, yt)
di = griddata((Longitude, Latitude), d, (xi, yi))
dfcoast = pd.read_csv('ECpolygonTwoDegreeOffsetBorder.txt',
delim_whitespace=True,
names=('X', 'Y'))
dfcoast['geometry'] = dfcoast.apply(lambda row: Point(row.X, row.Y),
axis=1)
poly = Polygon([(p.x, p.y) for p in dfcoast.geometry])
arr = np.zeros((len(yt), len(xt)))
for i in range(len(xt)):
for j in range(len(yt)):
point = Point(xt[i], yt[j])
arr[j, i] = poly.contains(point)
mask = (xi > 79.7817) & (xi < 81.2718) & (yi > 7.6951) & (yi < 9.7406)
di[mask] = np.nan
di[arr == False] = np.nan
U = np.cos(di)
V = np.sin(di)
mag = np.sqrt(U**2 + V**2)
angle = (np.pi / 2.) - np.arctan2(U / mag, V / mag)
vec = gv.VectorField(
(xi[::5, ::5], yi[::5, ::5], angle[::5, ::5], mag[::5, ::5]))
return vec
def plotthis(datetime, regions='Whole_East_Coast'):
dat = datetime
# datetostr=result.strftime("%Y%b%d%H")
dt = parse(str(dat))
yr = dt.year
mn = dt.month
d = dt.day
hr = dt.hour
mi = dt.minute
if hr < 10:
hr = '0' + str(hr)
else:
d = str(d)
hr = str(hr)
if int(d) < 10:
d = '0' + str(d)
else:
d = str(d)
varname = 'Hsig_' + str(yr) + '0' + str(mn) + str(
d) + '_' + hr + '0000'
x = Xp.flatten()
y = Yp.flatten()
z = datamat[varname]
if regions is 'Odisha':
z = np.where(((Xp >= 85) & (Xp <= 88) & (Yp >= 19) & (Yp <= 22)),
z, np.nan).flatten()
elif regions is 'Andra_Pradesh':
z = np.where(((Xp >= 79) & (Xp <= 85) & (Yp >= 13) & (Yp <= 19)),
z, np.nan).flatten()
elif regions is 'Tamil_Nadu':
z = np.where(((Xp >= 77) & (Xp <= 83) & (Yp >= 7) & (Yp <= 14)), z,
np.nan).flatten()
elif regions is 'Whole_East_Coast':
z = z.flatten()
else:
# data = get_data4region(data,**odisha)
z = z.flatten()
# z = z.flatten()
Longitude = x
Latitude = y
High_Significant = z
pts = np.stack((Longitude, Latitude, High_Significant)).T
verts = pd.DataFrame(
np.stack((Longitude, Latitude, High_Significant)).T,
columns=['Longitude', 'Latitude', ' High_Significant'])
# openStreet Background.
tri_sub = tri_new.apply(lambda x: x - 1)
ggpoints = gv.Points(verts, vdims=[' High_Significant'])
ggsubraster = rasterize(gv.TriMesh((tri_sub, gv.Points(verts))))
tri = gv.TriMesh((tri_sub, gv.Points(verts)))
return tri
allplot = {
(k.strftime("%Y-%m-%d %H:%M:%S"), r): plotthis(k, r)
for k in perdelta(strt, strt + timedelta(days=2), timedelta(hours=18))
for r in ['Odisha', 'Andra_Pradesh', 'Whole_East_Coast', 'Tamil_Nadu']
}
allplot2 = {
(k.strftime("%Y-%m-%d %H:%M:%S"), r): plotsecond(k, r)
for k in perdelta(strt, strt + timedelta(days=2), timedelta(hours=18))
for r in ['Odisha', 'Andra_Pradesh', 'Whole_East_Coast', 'Tamil_Nadu']
}
df_div = hv.Div("""
<figure>
<img src="https://i.ibb.co/S0t5GWb/imglogo.png" height='80' width='90' vspace='-10'>
""")
df_div1 = hv.Div("""
 <center><b><p style="color:#B22222";font-size:80px;font-family:Times new roman><h1 style=font-size:20px;margin-left:2.5em;margin-top:-1em;color:#B22222>Indian National Center for Ocean Information Services<br />
(INCOIS)</h1></p></b></center>
""")
colorbar_opts = {
'major_label_overrides': {
0: '0',
0.5: '0.5',
1: '1',
1.5: '1.5',
2: '2',
2.5: '2.5',
3: '3',
3.5: '>3.5',
3.8: '>4 ',
3.9: '>3.9',
},
'major_label_text_align': 'left',
'major_label_text_font_style': 'bold',
}
levels = [
0,
0.2,
0.4,
0.6,
0.8,
1,
1.2,
1.4,
1.6,
1.8,
2,
2.2,
2.5,
3,
]
def disable_logo(plot, element):
plot.state.toolbar.logo = None
hv.plotting.bokeh.ElementPlot.finalize_hooks.append(disable_logo)
# logo1 = hv.RGB.load_image("imglogo.png")
logo1 = hv.RGB.load_image("https://i.ibb.co/7VXRPCS/logo1.png")
def absolute_position(plot, element):
glyph = plot.handles['glyph']
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
glyph.dh_units = 'screen'
glyph.dw_units = 'screen'
glyph.dh = 60
glyph.dw = 90
glyph.x = x_range.start
glyph.y = y_range.start
xcode = CustomJS(code="glyph.x = cb_obj.start", args={'glyph': glyph})
plot.handles['x_range'].js_on_change('start', xcode)
ycode = CustomJS(code="glyph.y = cb_obj.start", args={'glyph': glyph})
plot.handles['y_range'].js_on_change('start', ycode)
def plot_limits(plot, element):
plot.handles['x_range'].min_interval = 100
plot.handles['x_range'].max_interval = 55000000
# 3000000
plot.handles['y_range'].min_interval = 500
plot.handles['y_range'].max_interval = 900000
opts = dict(
width=700,
height=700,
tools=['hover', 'save', 'wheel_zoom'],
active_tools=['wheel_zoom'],
hooks=[plot_limits, disable_logo],
colorbar=True,
color_levels=15,
colorbar_opts=colorbar_opts,
cmap=[
'#000080',
'#0000cd',
'#0008ff',
'#004cff',
'#0090ff',
'#00d4ff',
'#29ffce',
'#60ff97',
'#97ff60',
'#ceff29',
'#ffe600',
'#ffa700',
# '#ff6800',
'#ff2900',
'#cd0000',
'#800000',
],
clim=(0, 3.76),
title="\tSignificant Wave Height (m) and Direction (°) ",
fontsize={
'title': 18,
'xlabel': 15,
'ylabel': 15,
'ticks': 12
})
tiles = gv.tile_sources.Wikipedia
hmap1 = hv.HoloMap(allplot,
kdims=['Select Date and Time :', 'Select Indian State'])
hmap2 = hv.HoloMap(allplot2,
kdims=['Select Date and Time :', 'Select Indian State'])
dd = df_div.opts(width=70, height=70)
dd1 = df_div1.opts(width=600, height=90)
finalplot = pn.Column(
pn.Row(dd, dd1),
tiles * rasterize(hmap1).options(**opts) * hmap2 *
logo1.opts(hooks=[absolute_position], apply_ranges=False)).servable()
# print("--- %s seconds ---" % (time.time() - strtt))
from bokeh.embed import components
from bokeh.resources import CDN
from bokeh.io import curdoc
doc = curdoc()
script, div = components(finalplot.get_root(doc))
cdn_js0 = CDN.js_files[0]
cdn_js = CDN.js_files[1]
cdn_css = CDN.css_files
print("cdn_js:", cdn_js)
print("cdn_css", cdn_css)
return render_template("plot.html",
script=script,
div=div,
cdn_css=cdn_css,
cdn_js=cdn_js,
cdn_js0=cdn_js0)
verts = pd.DataFrame(np.stack((x, y, z)).T, columns=['X', 'Y', 'Z'])
# #openStreet Background.
# tri_sub = cf.apply(lambda x: x - 1)
# tri_sub=tri_sub[:10]
tri = gv.TriMesh((triangles, gv.Points(verts)))
return tri
allplot = {(k, r): plotthis(k, r) for k, r in zip(z, ['O', 'A', 'T'])}
df_div = hv.Div("""
<figure>
<img src="https://i.ibb.co/S0t5GWb/imglogo.png" height='80' width='90' vspace='-10'>
""")
colorbar_opts = {
'major_label_overrides': {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: '10',
def plot():
#################################################################333333
# Final plot.
# High Significant
from shapely.geometry import Point, Polygon
import pandas as pd
import numpy as np
import geoviews as gv
import bokeh
import panel as pn
import holoviews as hv
from holoviews import opts
from holoviews.operation.datashader import datashade, rasterize
hv.extension("bokeh")
import math
from bokeh.models import HoverTool
from scipy.interpolate import griddata
from scipy.io import loadmat
import datetime
from dateutil.parser import parse
from datetime import timedelta
import time
strtt = time.time()
datamat = loadmat('IOEC_ECM_noDA_20190703_masked.mat')
# datamat = loadmat('IOEC_ECM_DA_20191121_masked.mat')
Xp = datamat['Xp']
Yp = datamat['Yp']
# Xp=np.where(((Xp>= 85) & (Xp<=88) & (Yp>=19) & (Yp<=22)),Xp,88)
# Yp=np.where(((Xp>= 85) & (Xp<=88) & (Yp>=19) & (Yp<=22)),Yp,22)
strt = datetime.datetime(2019, 7, 4, 0, 0)
end = datetime.datetime(2019, 1, 13, 0, 0)
from bokeh.models.callbacks import CustomJS
def perdelta(strt, end, delta):
curr = strt
while curr < end:
yield curr
curr += delta
# Read element file
tri_new = pd.read_csv('fort.ele',
delim_whitespace=True,
names=('A', 'B', 'C', 'D'),
usecols=[1, 2, 3],
skiprows=1,
dtype={'D': np.int})
dateList = []
def plotthis(datetime, regions='Whole_East_Coast'):
dat = datetime
# datetostr=result.strftime("%Y%b%d%H")
dt = parse(str(dat))
yr = dt.year
mn = dt.month
d = dt.day
hr = dt.hour
mi = dt.minute
if hr < 10:
hr = '0' + str(hr)
else:
d = str(d)
hr = str(hr)
if int(d) < 10:
d = '0' + str(d)
else:
d = str(d)
varname = 'Steepn_' + str(yr) + '0' + str(mn) + str(
d) + '_' + hr + '0000'
x = Xp.flatten()
y = Yp.flatten()
z = datamat[varname]
if regions is 'Odisha':
z = np.where(((Xp >= 85) & (Xp <= 88) & (Yp >= 19) & (Yp <= 22)),
z, np.nan).flatten()
elif regions is 'Andra_Pradesh':
z = np.where(((Xp >= 79) & (Xp <= 85) & (Yp >= 13) & (Yp <= 19)),
z, np.nan).flatten()
elif regions is 'Tamil_Nadu':
z = np.where(((Xp >= 77) & (Xp <= 83) & (Yp >= 7) & (Yp <= 14)), z,
np.nan).flatten()
elif regions is 'Whole_East_Coast':
z = z.flatten()
else:
# data = get_data4region(data,**odisha)
z = z.flatten()
# z = z.flatten()
Longitude = x
Latitude = y
HS = z
pts = np.stack((Longitude, Latitude, HS)).T
verts = pd.DataFrame(np.stack((Longitude, Latitude, HS)).T,
columns=['Longitude', 'Latitude', 'HS'])
# openStreet Background.
tri_sub = tri_new.apply(lambda x: x - 1)
ggpoints = gv.Points(verts, vdims=['HS'])
ggsubraster = rasterize(gv.TriMesh((tri_sub, gv.Points(verts))))
tri = gv.TriMesh((tri_sub, gv.Points(verts)))
return tri
allplot = {
(k.strftime("%Y-%m-%d %H:%M:%S"), r): plotthis(k, r)
for k in perdelta(strt, strt + timedelta(days=9), timedelta(hours=3))
for r in ['Odisha', 'Andra_Pradesh', 'Whole_East_Coast', 'Tamil_Nadu']
}
# allplot2={(k.strftime("%Y-%m-%d %H:%M:%S"),r):plotsecond(k,r)for k in perdelta(strt, strt + timedelta(days=1), timedelta(hours=18)) for r in ['Odisha','Andra_Pradesh','Whole_East_Coast','Tamil_Nadu']}
df_div = hv.Div("""
<figure>
<img src="https://i.ibb.co/S0t5GWb/imglogo.png" height='80' width='90' vspace='-10'>
""")
df_div1 = hv.Div("""
 <center><b><p style="color:#B22222";font-size:80px;font-family:Times new roman><h1 style=font-size:20px;margin-left:2.5em;margin-top:-1em;color:#B22222>Indian National Center for Ocean Information Services<br />
(INCOIS)</h1></p></b></center>
""")
df_div2 = hv.Div("""
<html>
<head>
<style>
input[type=button], input[type=submit], input[type=reset] {
background-color: #C6E2FF;
color: DARKVIOLET;
padding: 10px 22px;
text-decoration: none;
margin: 4px 2px;
cursor: pointer;
font-weight: bold;
font-size: 15px;
border: 2px solid light slateblue
}
</style>
</head>
<body>
<input type="button" value=" PRINT " onClick="window.print()">
</body>
</html>
""")
colorbar_opts = {
'major_label_overrides': {
0.00: '0.00',
0.02: '0.02',
0.04: '0.04',
0.06: '0.06',
0.08: '0.08',
0.10: '0.10',
0.12: '0.12',
0.14: '> 0.14',
0.15: '0.15',
0.16: ' ',
0.17: '>0.17',
},
'major_label_text_align': 'left',
'major_label_text_font_style': 'bold',
}
levels = [0.00, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.15, 0.16, 0.17]
def disable_logo(plot, element):
plot.state.toolbar.logo = None
hv.plotting.bokeh.ElementPlot.finalize_hooks.append(disable_logo)
def plot_limits(plot, element):
plot.handles['x_range'].min_interval = 100
plot.handles['x_range'].max_interval = 55000000
# 3000000
plot.handles['y_range'].min_interval = 500
plot.handles['y_range'].max_interval = 900000
opts = dict(
width=700,
height=700,
logz=False,
logx=False,
logy=False,
responsive=True,
active_tools=['wheel_zoom'],
tools=['save', 'wheel_zoom', 'hover'],
hooks=[plot_limits, disable_logo],
colorbar=True,
color_levels=15,
colorbar_opts=colorbar_opts,
cmap=[
'#000080',
'#0000cd',
'#0008ff',
'#004cff',
'#0090ff',
'#00d4ff',
'#29ffce',
'#60ff97',
'#97ff60',
'#ceff29',
'#ffe600',
'#ffa700',
# '#ff6800',
'#ff2900',
'#cd0000',
'#800000',
],
clim=(0.00, 0.15),
title="\t\t\t\t\t\t\t\t Wave Steepness . ",
fontsize={
'title': 18,
'xlabel': 15,
'ylabel': 15,
'ticks': 12
})
tiles = gv.tile_sources.Wikipedia
tiles = gv.tile_sources.Wikipedia
hmap1 = hv.HoloMap(
allplot, kdims=['Select Date and Time :', 'Select Indian States'])
# hmap2 = hv.HoloMap(allplot2, kdims=['Date and Time :','region'])
logo1 = hv.RGB.load_image("https://i.ibb.co/7VXRPCS/logo1.png")
def absolute_position(plot, element):
glyph = plot.handles['glyph']
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
glyph.dh_units = 'screen'
glyph.dw_units = 'screen'
glyph.dh = 60
glyph.dw = 90
# x_range.start=+85
# y_range.start=+20
glyph.x = x_range.start
glyph.y = y_range.start
xcode = CustomJS(code="glyph.x = cb_obj.start", args={'glyph': glyph})
plot.handles['x_range'].js_on_change('start', xcode)
ycode = CustomJS(code="glyph.y = cb_obj.start", args={'glyph': glyph})
plot.handles['y_range'].js_on_change('start', ycode)
# finalplot=tiles*rasterize(hmap1.redim.range(Latitude=(13, 19), Longitude=(79, 85))).options(**opts)*hmap2
dd = df_div.opts(width=90, height=80)
dd1 = df_div1.opts(width=600, height=90)
dd2 = df_div2.opts(width=100, height=10)
finalplot = pn.Column(
pn.Row(dd, dd1),
tiles * rasterize(hmap1).options(**opts) *
logo1.opts(hooks=[absolute_position], apply_ranges=False))
# print("--- %s seconds ---" % (time.time() - strtt))
from bokeh.embed import components
from bokeh.resources import CDN
from bokeh.io import curdoc
doc = curdoc()
script, div = components(finalplot.get_root(doc))
cdn_js0 = CDN.js_files[0]
cdn_js = CDN.js_files[1]
cdn_css = CDN.css_files
print("cdn_js:", cdn_js)
print("cdn_css", cdn_css)
return render_template("plot.html",
script=script,
div=div,
cdn_css=cdn_css,
cdn_js=cdn_js,
cdn_js0=cdn_js0)
else:
tooltips = [
('Exposure', '@exposure_id'),
('camera', '@camera'),
('Date', '@datef'),
(yaxis, '@{}'.format(yaxis)),
]
hover = HoverTool(tooltips=tooltips)
points = hv.Points(df, ['mjd', yaxis],
['exposure_id', 'camera', 'datef', 'dateobs'],
label=yaxis).opts(tools=[hover], size=3)
plot = plot * points
layout = plot.redim.label(x='mjd',
y=yaxis).opts(sizing_mode='scale_width',
height=150,
padding=0.1,
fontsize='1.2vw',
toolbar='above',
active_tools=["box_zoom"],
title='Camera: {}'.format(camera))
doc = renderer.server_doc(layout)
doc.title = 'Time Series'
except Exception as e:
renderer = hv.renderer('bokeh')
print('error', e)
doc = renderer.server_doc(
hv.Div("""
<p style="font-size: 2.2vw"> Couldn't Load Time Series </p>
"""))
doc.title = 'Time Series Error'
def plot():
import numpy as np
import pandas as pd
import geoviews as gv
import holoviews as hv
import panel as pn
from holoviews.operation.datashader import rasterize
hv.extension("bokeh")
from bokeh.models.callbacks import CustomJS
# Some points defining a triangulation over (roughly) Britain.
xy = np.asarray([
[-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888], [-0.054, 0.890],
[-0.045, 0.897], [-0.057, 0.895], [-0.073, 0.900], [-0.087, 0.898],
[-0.090, 0.904], [-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
[-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942], [-0.062, 0.949],
[-0.054, 0.958], [-0.069, 0.954], [-0.087, 0.952], [-0.087, 0.959],
[-0.080, 0.966], [-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
[-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980], [-0.108, 0.980],
[-0.104, 0.987], [-0.102, 0.993], [-0.115, 1.001], [-0.099, 0.996],
[-0.101, 1.007], [-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],
[-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010], [-0.064, 1.005],
[-0.048, 1.005], [-0.031, 1.005], [-0.031, 0.996], [-0.040, 0.987],
[-0.045, 0.980], [-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
[-0.020, 0.954], [-0.006, 0.947], [0.003, 0.935], [0.006, 0.926],
[0.005, 0.921], [0.022, 0.923], [0.033, 0.912], [0.029, 0.905],
[0.017, 0.900], [0.012, 0.895], [0.027, 0.893], [0.019, 0.886],
[0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883], [-0.038, 0.879],
[-0.057, 0.881], [-0.062, 0.876], [-0.078, 0.876], [-0.087, 0.872],
[-0.030, 0.907], [-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
[-0.077, 0.990], [-0.059, 0.993]])
# Make lats + lons
x = abs(xy[:, 0] * 180 / 3.14159)
y = xy[:, 1] * 180 / 3.14159
# A selected triangulation of the points.
triangles = np.asarray([
[67, 66, 1], [65, 2, 66], [1, 66, 2], [64, 2, 65], [63, 3, 64],
[60, 59, 57], [2, 64, 3], [3, 63, 4], [0, 67, 1], [62, 4, 63],
[57, 59, 56], [59, 58, 56], [61, 60, 69], [57, 69, 60], [4, 62, 68],
[6, 5, 9], [61, 68, 62], [69, 68, 61], [9, 5, 70], [6, 8, 7],
[4, 70, 5], [8, 6, 9], [56, 69, 57], [69, 56, 52], [70, 10, 9],
[54, 53, 55], [56, 55, 53], [68, 70, 4], [52, 56, 53], [11, 10, 12],
[69, 71, 68], [68, 13, 70], [10, 70, 13], [51, 50, 52], [13, 68, 71],
[52, 71, 69], [12, 10, 13], [71, 52, 50], [71, 14, 13], [50, 49, 71],
[49, 48, 71], [14, 16, 15], [14, 71, 48], [17, 19, 18], [17, 20, 19],
[48, 16, 14], [48, 47, 16], [47, 46, 16], [16, 46, 45], [23, 22, 24],
[21, 24, 22], [17, 16, 45], [20, 17, 45], [21, 25, 24], [27, 26, 28],
[20, 72, 21], [25, 21, 72], [45, 72, 20], [25, 28, 26], [44, 73, 45],
[72, 45, 73], [28, 25, 29], [29, 25, 31], [43, 73, 44], [73, 43, 40],
[72, 73, 39], [72, 31, 25], [42, 40, 43], [31, 30, 29], [39, 73, 40],
[42, 41, 40], [72, 33, 31], [32, 31, 33], [39, 38, 72], [33, 72, 38],
[33, 38, 34], [37, 35, 38], [34, 38, 35], [35, 37, 36]])
z = np.random.uniform(0, 16, 74)
# print("x",x)
# print("y",y)
# print("z",z)
def plotthis(z, regions='w'):
if regions is 'O':
z = np.where(((x >= 0) & (x <= 4) & (y >= 50) & (y <= 56)), z, np.nan).flatten()
elif regions is 'A':
z = np.where(((x >= 3) & (x <= 4) & (y >= 54) & (y <= 57)), z, np.nan).flatten()
elif regions is 'T':
z = np.where(((x >= -2) & (x <= 3) & (y >= 50) & (y <= 57)), z, np.nan).flatten()
# else:
# # data = get_data4region(data,**odisha)
# z=z.flatten()
print("lx:", len(x), "ly:", len(y), "lz:", len(z))
print("z", z)
verts = pd.DataFrame(np.stack((x, y, z)).T, columns=['X', 'Y', 'Z'])
# #openStreet Background.
# tri_sub = cf.apply(lambda x: x - 1)
# tri_sub=tri_sub[:10]
tri = gv.TriMesh((triangles, gv.Points(verts)))
return tri
allplot = {(k, r): plotthis(k, r) for k, r in zip(z, ['O', 'A', 'T'])}
df_div = hv.Div("""
<figure>
<img src="https://i.ibb.co/5h74S9n/python.png" height='80' width='90' vspace='-10'>
""")
colorbar_opts = {
'major_label_overrides': {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: '10',
11: '11',
12: '12',
13: '13',
14: '>14',
15: '15'
},
'major_label_text_align': 'left', 'major_label_text_font_style': 'bold', }
logo1 = hv.RGB.load_image("https://i.ibb.co/5h74S9n/python.png")
def absolute_position(plot, element):
glyph = plot.handles['glyph']
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
glyph.dh_units = 'screen'
glyph.dw_units = 'screen'
glyph.dh = 60
glyph.dw = 90
glyph.x = x_range.start
glyph.y = y_range.start
xcode = CustomJS(code="glyph.x = cb_obj.start", args={'glyph': glyph})
plot.handles['x_range'].js_on_change('start', xcode)
ycode = CustomJS(code="glyph.y = cb_obj.start", args={'glyph': glyph})
plot.handles['y_range'].js_on_change('start', ycode)
def plot_limits(plot, element):
plot.handles['x_range'].min_interval = 100
plot.handles['x_range'].max_interval = 55000000
# 3000000
plot.handles['y_range'].min_interval = 500
plot.handles['y_range'].max_interval = 900000
opts = dict(width=700, height=700, tools=['hover', 'save', 'wheel_zoom'], active_tools=['wheel_zoom'],
hooks=[plot_limits], colorbar=True, color_levels=15,
colorbar_opts=colorbar_opts, cmap=['#000080',
'#0000cd',
'#0008ff',
'#004cff',
'#0090ff',
'#00d4ff',
'#29ffce',
'#60ff97',
'#97ff60',
'#ceff29',
'#ffe600',
'#ffa700',
'#ff2900',
'#cd0000',
'#800000',
], clim=(0, 15), title="\t\t\t\t\t\t\t\t\t Mean Period (s) ",
fontsize={'title': 18, 'xlabel': 15, 'ylabel': 15, 'ticks': 12})
#tiles = gv.tile_sources.Wikipedia
hmap1 = hv.HoloMap(allplot, kdims=['Select D :', 'Select State'])
dd = df_div.opts(width=70, height=70)
finalplot = pn.Column(pn.Row(dd), rasterize(hmap1).options(**opts) * logo1.opts(hooks=[absolute_position],
apply_ranges=False))
from bokeh.embed import components
from bokeh.resources import CDN
from bokeh.io import curdoc
doc = curdoc()
def modify_doc(doc):
sea = (name='Sea Surface')
doc.add_root(sea.panel().get_root(doc))
# Put all the tabs into one application
#tabs = Tabs(tabs=[tab1, tab2, tab3, tab4, tab5]) # Put the tabs in the current document for display
# doc=curdoc().add_root(finalplot)
script, div = components(finalplot.get_root(doc))
cdn_js0=CDN.js_files[0]
cdn_js = CDN.js_files[1]
cdn_css=CDN.css_files
print("cdn_js:",cdn_js)
print("cdn_css",cdn_css)
return render_template("plot.html",
script=script,
div=div,
cdn_css=cdn_css,
cdn_js=cdn_js,
cdn_js0=cdn_js0)
def set_hv_loading_message(self, message: str):
"""Replaces the plot with a loading message"""
message_plot = hv.Div(
SPINNER_HTML + f"<p align='center'><strong>{message}<strong></p>")
self.plot_pane.object = message_plot
camera,
begin_date=start_date,
end_date=end_date)
df_y = pd.DataFrame(list(outputs_y))
df_x = pd.DataFrame(list(outputs_x))
plot = hv.Curve([])
df_x[yaxis] = df_y['value'].apply(lambda x: x[0])
df_x[xaxis] = df_x['value'].apply(lambda x: x[0])
plot = hv.Points(df_x, [xaxis, yaxis],
['exposure_id', 'camera', 'datef', 'dateobs'])
layout = plot.redim.label(x=xaxis,
y=yaxis).opts(sizing_mode='scale_width',
height=150,
padding=0.1,
fontsize='1.2vw',
toolbar='above',
active_tools=["box_zoom"],
title='Camera: {}'.format(camera))
doc = renderer.server_doc(layout)
doc.title = 'Time Series'
except Exception as e:
renderer = hv.renderer('bokeh')
print('error', e)
doc = renderer.server_doc(
hv.Div("""
<p style="font-size: 2.2vw"> Couldn't Load Regression </p>
"""))
doc.title = 'Regression Error'
def modify_doc(doc):
import pandas as pd
import numpy as np
import geoviews as gv
import bokeh
import panel as pn
import holoviews as hv
from holoviews import opts
from holoviews.operation.datashader import datashade, rasterize
hv.extension("bokeh")
import math
from bokeh.models import HoverTool
from scipy.interpolate import griddata
from scipy.io import loadmat
import datetime
from dateutil.parser import parse
from datetime import timedelta
import time
strtt = time.time()
# datamat = loadmat('IOEC_ECM_noDA_20190703_masked.mat')
datamat = loadmat('newtest_Tm02.mat')
source = ColumnDataSource(data=datamat)
Xp = datamat['Xp']
Yp = datamat['Yp']
# Xp=np.where(((Xp>= 85) & (Xp<=88) & (Yp>=19) & (Yp<=22)),Xp,88)
# Yp=np.where(((Xp>= 85) & (Xp<=88) & (Yp>=19) & (Yp<=22)),Yp,22)
strt = datetime.datetime(2019, 7, 4, 0, 0)
end = datetime.datetime(2019, 1, 13, 0, 0)
from bokeh.models.callbacks import CustomJS
def perdelta(strt, end, delta):
curr = strt
while curr < end:
yield curr
curr += delta
# Read element file
tri_new = pd.read_csv('fort.ele', delim_whitespace=True, names=('A', 'B', 'C', 'D'), usecols=[1, 2, 3], skiprows=1,
dtype={'D': np.int})
dateList = []
def plotthis(datetime, regions='Whole_East_Coast'):
dat = datetime
# datetostr=result.strftime("%Y%b%d%H")
dt = parse(str(dat))
yr = dt.year
mn = dt.month
d = dt.day
hr = dt.hour
mi = dt.minute
if hr < 10:
hr = '0' + str(hr)
else:
d = str(d)
hr = str(hr)
if int(d) < 10:
d = '0' + str(d)
else:
d = str(d)
varname = 'Tm02_' + str(yr) + '0' + str(mn) + str(d) + '_' + hr + '0000'
x = Xp.flatten()
y = Yp.flatten()
z = datamat[varname]
if regions is 'Odisha':
z = np.where(((Xp >= 85) & (Xp <= 88) & (Yp >= 19) & (Yp <= 22)), z, np.nan).flatten()
elif regions is 'Andra_Pradesh':
z = np.where(((Xp >= 79) & (Xp <= 85) & (Yp >= 13) & (Yp <= 19)), z, np.nan).flatten()
elif regions is 'Tamil_Nadu':
z = np.where(((Xp >= 77) & (Xp <= 83) & (Yp >= 7) & (Yp <= 14)), z, np.nan).flatten()
elif regions is 'Whole_East_Coast':
z = z.flatten()
else:
# data = get_data4region(data,**odisha)
z = z.flatten()
# z = z.flatten()
Longitude = x
Latitude = y
MeanWavePeriod = z
pts = np.stack((Longitude, Latitude, MeanWavePeriod)).T
verts = pd.DataFrame(np.stack((Longitude, Latitude, MeanWavePeriod)).T,
columns=['Longitude', 'Latitude', ' MeanWavePeriod'])
# openStreet Background.
tri_sub = tri_new.apply(lambda x: x - 1)
ggpoints = gv.Points(verts, vdims=[' MeanWavePeriod'])
ggsubraster = rasterize(gv.TriMesh((tri_sub, gv.Points(verts))))
tri = gv.TriMesh((tri_sub, gv.Points(verts)))
return tri
allplot = {(k.strftime("%Y-%m-%d %H:%M:%S"), r): plotthis(k, r) for k in
perdelta(strt, strt + timedelta(days=2), timedelta(hours=18)) for r in
['Odisha', 'Andra_Pradesh', 'Whole_East_Coast', 'Tamil_Nadu']}
df_div = hv.Div("""
<figure>
<img src="https://i.ibb.co/S0t5GWb/imglogo.png" height='80' width='90' vspace='-10'>
""")
df_div1 = hv.Div("""
 <center><b><p style="color:#B22222";font-size:80px;font-family:Times new roman><h1 style=font-size:20px;margin-left:2.5em;margin-top:-1em;color:#B22222>Indian National Center for Ocean Information Services<br />
(INCOIS)</h1></p></b></center>
""")
colorbar_opts = {
'major_label_overrides': {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: '10',
11: '11',
12: '12',
13: '13',
14: '>14',
15: '15'
},
'major_label_text_align': 'left', 'major_label_text_font_style': 'bold', }
levels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ]
def disable_logo(plot, element):
plot.state.toolbar.logo = None
hv.plotting.bokeh.ElementPlot.finalize_hooks.append(disable_logo)
# logo1 = hv.RGB.load_image("imglogo.png")
logo1 = hv.RGB.load_image("https://i.ibb.co/7VXRPCS/logo1.png")
def absolute_position(plot, element):
glyph = plot.handles['glyph']
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
glyph.dh_units = 'screen'
glyph.dw_units = 'screen'
glyph.dh = 60
glyph.dw = 90
glyph.x = x_range.start
glyph.y = y_range.start
xcode = CustomJS(code="glyph.x = cb_obj.start", args={'glyph': glyph})
plot.handles['x_range'].js_on_change('start', xcode)
ycode = CustomJS(code="glyph.y = cb_obj.start", args={'glyph': glyph})
plot.handles['y_range'].js_on_change('start', ycode)
def plot_limits(plot, element):
plot.handles['x_range'].min_interval = 100
plot.handles['x_range'].max_interval = 55000000
# 3000000
plot.handles['y_range'].min_interval = 500
plot.handles['y_range'].max_interval = 900000
opts = dict(width=700, height=700, tools=['hover', 'save', 'wheel_zoom'], active_tools=['wheel_zoom'],
hooks=[plot_limits, disable_logo], colorbar=True, color_levels=15,
colorbar_opts=colorbar_opts, cmap=['#000080',
'#0000cd',
'#0008ff',
'#004cff',
'#0090ff',
'#00d4ff',
'#29ffce',
'#60ff97',
'#97ff60',
'#ceff29',
'#ffe600',
'#ffa700',
# '#ff6800',
'#ff2900',
'#cd0000',
'#800000',
], clim=(0, 15), title="\t\t\t\t\t\t\t\t\t Mean Wave Period (s) ",
fontsize={'title': 18, 'xlabel': 15, 'ylabel': 15, 'ticks': 12})
tiles = gv.tile_sources.Wikipedia
print("type(allplot):", type(allplot))
print("allplot :", allplot)
# for k,v in allplot.items():
# print("current key:",k)
# print("current value:",v)
print("allplot.keys()[1]:", list(allplot.keys())[1])
print("allplot.keys()[2]:", list(allplot.keys())[2])
hmap1 = hv.HoloMap(allplot, kdims=['Select Date and Time :', 'Select Indian State'])
print("type(hmap1):", type(hmap1))
print("list(hmap1)[1]", list(hmap1)[1])
dd = df_div.opts(width=70, height=70)
dd1 = df_div1.opts(width=600, height=90)
def callback(attr, old, new):
if new == 0:
finalplot = pn.Column(pn.Row(dd, dd1),
tiles * rasterize(list(hmap1)[1]).options(**opts) * logo1.opts(
hooks=[absolute_position],
apply_ranges=False))
else:
#data = df.rolling('{0}D'.format(new)).mean()
finalplot = pn.Column(pn.Row(dd, dd1),
tiles * rasterize(list(hmap1)[1]).options(**opts) * logo1.opts(
hooks=[absolute_position],
apply_ranges=False))
source.data = ColumnDataSource(data=finalplot).data
slider = Slider(start=0, end=30, value=0, step=1, title="Smoothing by N Days")
slider.on_change('value', callback)
doc.add_root(column(slider, finalplot))
def count(data):
return hv.Div('<p style="font-size:20px">Glaciers selected: {}'.format(len(data)) + "<br>" +
'Area: {:.0f} km² ({:.1f}%)</font>'.format(np.sum(data['area_km2']), np.sum(data['area_km2']) / total_area * 100)).options(height=40)
def count(data):
return hv.Div('<p style="font-size:20px">Glaciers selected: ' +
str(data.dframe().n_glaciers.sum()) +
"</font>").options(height=40)
width=500))
if f == 'year':
yearHisto = np.histogram(to_years(dob), bins=200)
histograms.append(
hv.Histogram(yearHisto,
kdims=['Year of Birth'],
height=500,
width=500))
if f == 'date':
dateHisto = np.histogram(to_date(dob), bins=200)
histograms.append(
hv.Histogram(dateHisto,
kdims=['Dates of Birth'],
height=500,
width=500))
layout = hv.Layout()
#%%
for h in histograms:
layout = layout + h
df_html = df[['DOB']].describe().to_html()
df_div = hv.Div("<div align='right'>" + df_html + "<div>")
layout = layout + df_div
#%%
hv.save(layout, 'dobHistogram.html')
