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app.py
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app.py
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from dash import Dash, dcc, html, Input, Output, State, exceptions, ctx
import dash_design_kit as ddk
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import dash_bootstrap_components as dbc
import dash_mantine_components as dmc
from dash_iconify import DashIconify
import flask
import urllib
import pandas as pd
import numpy as np
import zc
import constants
import db
import colorcet as cc
from urllib.request import urlopen
import dash_ag_grid as dag
import json
# import ssl
# ssl._create_default_https_context = ssl._create_unverified_context
cones_url = 'https://data.pmel.noaa.gov/pmel/erddap/tabledap/osmc_cones.csv?latitude,longitude,name,index,time&orderBy("name,index")'
data_url = 'https://data.pmel.noaa.gov/pmel/erddap/tabledap/osmc_rt_60'
# data_url = 'http://dunkel.pmel.noaa.gov:8336/erddap/tabledap/osmc_rt60'
app = Dash(
__name__,
external_stylesheets=[dbc.themes.BOOTSTRAP, dbc.icons.BOOTSTRAP],
title='OSMC'
)
server = app.server # expose server variable for Procfile
version = '1.0'
center = {'lon': 0.0, 'lat': 0.0}
zoom = 1.4
height_of_row=345
header_footer_fudge = 150
map_height = 520
def cc_color_set(index, palette):
rgb = px.colors.convert_to_RGB_255(palette[index])
hexi = '#%02x%02x%02x' % rgb
return hexi
def cc_color_set_transparent(index, palette, alpha):
h = palette[index].lstrip('#')
rgb = tuple(int(h[i:i+2], 16) for i in (0, 2, 4))
color = 'rgba(' + str(rgb[0]) + ',' + str(rgb[1]) + ',' + str(rgb[2]) + ',' + str(alpha) + ')'
return color
marker_size = 9
trace_size = 12
platform_color = {
'ARGO' : cc_color_set(0, cc.glasbey_bw_minc_20),
'TAGGED ANIMAL': cc_color_set(1, cc.glasbey_bw_minc_20),
'C-MAN WEATHER STATIONS': cc_color_set(2, cc.glasbey_bw_minc_20),
'CLIMATE REFERENCE MOORED BUOYS': cc_color_set(3, cc.glasbey_bw_minc_20),
'DRIFTING BUOYS': cc_color_set(4, cc.glasbey_bw_minc_20),
'GLIDERS': cc_color_set(5, cc.glasbey_bw_minc_20),
'ICE BUOYS': cc_color_set(6, cc.glasbey_bw_minc_20),
'MOORED BUOYS': cc_color_set(7, cc.glasbey_bw_minc_20),
'RESEARCH': cc_color_set(8, cc.glasbey_bw_minc_20),
'SHIPS': cc_color_set(9, cc.glasbey_bw_minc_20),
'SHORE AND BOTTOM STATIONS': cc_color_set(10, cc.glasbey_bw_minc_20),
'TIDE GAUGE STATIONS': cc_color_set(11, cc.glasbey_bw_minc_20),
'TROPICAL MOORED BUOYS': cc_color_set(12, cc.glasbey_bw_minc_20),
'TSUNAMI WARNING STATIONS': cc_color_set(21, cc.glasbey_bw_minc_20),
'UNKNOWN': cc_color_set(14, cc.glasbey_bw_minc_20),
'UNCREWED SURFACE VEHICLE': cc_color_set(15, cc.glasbey_bw_minc_20),
'VOLUNTEER OBSERVING SHIPS': cc_color_set(16, cc.glasbey_bw_minc_20),
'VOSCLIM': cc_color_set(17, cc.glasbey_bw_minc_20),
'WEATHER AND OCEAN OBS': cc_color_set(18, cc.glasbey_bw_minc_20),
'WEATHER BUOYS': cc_color_set(19, cc.glasbey_bw_minc_20),
'WEATHER OBS': cc_color_set(20, cc.glasbey_bw_minc_20),
}
# platform_color = {
# 'ARGO' : '#0040FF',
# 'AUTONOMOUS PINNIPEDS': '#FF0000',
# 'C-MAN WEATHER STATIONS': '#FF7F00',
# 'CLIMATE REFERENCE MOORED BUOYS': '#FFD400',
# 'DRIFTING BUOYS': '#FFFF00',
# 'GLIDERS': '#BFFF00',
# 'ICE BUOYS': '#6AFF00',
# 'MOORED BUOYS': '#00EAFF',
# 'RESEARCH': '#AA00FF',
# 'SHIPS': '#FF00AA',
# 'SHORE AND BOTTOM STATIONS': '#EDB9B9',
# 'TIDE GAUGE STATIONS': '#E7E9B9',
# 'TROPICAL MOORED BUOYS': '#B9EDE0',
# 'TSUNAMI WARNING STATIONS': '#B9D7ED',
# 'UNKNOWN': '#DCB9ED',
# 'UNMANNED SURFACE VEHICLE': '#8F2323',
# 'VOLUNTEER OBSERVING SHIPS': '#8F6A23',
# 'VOSCLIM': '#23628F',
# 'WEATHER AND OCEAN OBS': '#6B238F',
# 'WEATHER BUOYS': '#000000',
# 'WEATHER OBS': '#737373',
# }
varByPlatform_defs = [
{"field": "platform_type", "pinned": "left", 'headerName': "Platform Type"},
{"field": "count", "pinned": "left", "headerName":"Number of Platforms of this Type", 'type': 'rightAligned', "valueFormatter": {"function": "d3.format(',d')(params.value)"}},
{"field":'total', "headerName": 'Total', 'type': 'rightAligned', "valueFormatter": {"function": "d3.format(',d')(params.value)"}}
]
for var in constants.surface_variables:
varByPlatform_defs.append({"field":var, "headerName": constants.long_names[var], 'type': 'rightAligned', "valueFormatter": {"function": "d3.format(',d')(params.value)"},})
for var in constants.depth_variables:
varByPlatform_defs.append({"field":var, "headerName": constants.long_names[var], 'type': 'rightAligned', "valueFormatter": {"function": "d3.format(',d')(params.value)"}})
country_color = {
'AUSTRALIA' : cc_color_set(21, cc.glasbey_bw_minc_20),
'BAHAMAS': cc_color_set(22, cc.glasbey_bw_minc_20),
'BENIN' : cc_color_set(23, cc.glasbey_bw_minc_20),
'BRAZIL' : cc_color_set(24, cc.glasbey_bw_minc_20),
'BULGARIA' : cc_color_set(25, cc.glasbey_bw_minc_20),
'CANADA' : cc_color_set(26, cc.glasbey_bw_minc_20),
'CHINA' : cc_color_set(27, cc.glasbey_bw_minc_20),
'CROATIA' : cc_color_set(28, cc.glasbey_bw_minc_20),
'EL SALVADOR' : cc_color_set(29, cc.glasbey_bw_minc_20),
'EUROPEAN UNION' : cc_color_set(30, cc.glasbey_bw_minc_20),
'FRANCE' : cc_color_set(31, cc.glasbey_bw_minc_20),
'GERMANY' : cc_color_set(32, cc.glasbey_bw_minc_20),
'GREECE' : cc_color_set(33, cc.glasbey_bw_minc_20),
'HONG KONG' : cc_color_set(34, cc.glasbey_bw_minc_20),
'INDIA' : cc_color_set(35, cc.glasbey_bw_minc_20),
'IRAN, ISLAMIC REPUBLIC OF' : cc_color_set(36, cc.glasbey_bw_minc_20),
'IRELAND' : cc_color_set(37, cc.glasbey_bw_minc_20),
'ISRAEL' : cc_color_set(38, cc.glasbey_bw_minc_20),
'ITALY' : cc_color_set(39, cc.glasbey_bw_minc_20),
'JAPAN' : cc_color_set(40, cc.glasbey_bw_minc_20),
'KOREA, REPUBLIC OF' : cc_color_set(41, cc.glasbey_bw_minc_20),
'NETHERLANDS' : cc_color_set(42, cc.glasbey_bw_minc_20),
'NEW ZEALAND' : cc_color_set(43, cc.glasbey_bw_minc_20),
'NORWAY' : cc_color_set(44, cc.glasbey_bw_minc_20),
'PHILIPPINES' : cc_color_set(45, cc.glasbey_bw_minc_20),
'POLAND' : cc_color_set(46, cc.glasbey_bw_minc_20),
'PORTUGAL' : cc_color_set(47, cc.glasbey_bw_minc_20),
'ROMANIA' : cc_color_set(48, cc.glasbey_bw_minc_20),
'RUSSIAN FEDERATION' : cc_color_set(49, cc.glasbey_bw_minc_20),
'SOUTH AFRICA' : cc_color_set(50, cc.glasbey_bw_minc_20),
'SPAIN' : cc_color_set(51, cc.glasbey_bw_minc_20),
'SYRIAN ARAB REPUBLIC' : cc_color_set(52, cc.glasbey_bw_minc_20),
'UNITED KINGDOM' : cc_color_set(53, cc.glasbey_bw_minc_20),
'UNITED STATES' : cc_color_set(54, cc.glasbey_bw_minc_20),
'UNKNOWN' : cc_color_set(55, cc.glasbey_bw_minc_20),
}
test_file = 'data/nc_osmc_data_rt_test.csv'
options_from_OSMC_LAS=[
{'value':'ID' , 'label':'All Parameters'},
{ 'value':'SST','label': 'Sea Surface Temperature'},
{ 'value':'SSS','label': 'Sea Surface Salinity'},
{ 'value':'ZTMP','label': 'Temperature Profile'},
{ 'value':'SLP','label': 'Sea Level Pressure'},
{ 'value':'ATMP','label': 'Air Temperature'},
{ 'value':'ZSAL','label': 'Salinity'},
{ 'value':'WINDSPD','label': 'Wind Speed'},
{ 'value':'WINDDIR','label': 'Wind Direction'},
{ 'value':'CLOUDS','label': 'Clouds'},
{ 'value':'PRECIP','label': 'Precipitation'},
{ 'value':'WVHT','label': 'Wave Height'},
{ 'value':'FCO2W','label':'Fugacity of CO2 in Seawater'},
{ 'value':'WATER_COL_HT','label': 'Water Column Height'},
{ 'value':'WATERLEVEL_WRT_LCD','label': 'Tidal Elevation WRT local chart datum'},
{ 'value':'WATERLEVEL_MET_RES','label': 'Meteorological Residual Tidal Elevation'}
]
no_data_graph = go.Figure()
no_data_graph = no_data_graph.update_layout(
xaxis = { "visible": False },
yaxis = { "visible": False },
annotations = [
{
"text": "No data found for selected platform...",
"xref": "paper",
"yref": "paper",
"showarrow": False,
"font": {
"size": 28
}
}
]
)
select_graph = go.Figure()
select_graph = select_graph.update_layout(
xaxis = { "visible": False },
yaxis = { "visible": False },
annotations = [
{
"text": "Select a platform from the map or menu...",
"xref": "paper",
"yref": "paper",
"showarrow": False,
"font": {
"size": 28
}
}
]
)
app.layout = ddk.App([
dcc.Location(id='url', refresh=False),
dcc.Interval(id='trigger', n_intervals=0,
max_intervals=0, #<-- only run once
interval=1),
dcc.Store(id='map-info'),
dcc.Store(id='ui-state'),
dcc.Store(id='download-url'),
ddk.Header([
ddk.Logo(src=app.get_asset_url('noaa-logo-rgb-2022.png'), style={'height':'90px'}),
ddk.Title('Observing System Monitoring Center'),
dcc.Loading(html.Div(id='map-loader', style={'float': 'right', 'display': 'none'}))
]),
# ddk.Block(width=.95, children=[
# html.Div('Parts of the US government are closed. This site will not be updated; however, NOAA websites and social media channels necessary to protect lives and property will be maintained. See ', style={'display':'inline'}),
# html.A('www.weather.gov', href='https://www.weather.gov', style={'display':'inline'}),
# html.Div(' for critical weather information. To learn more, see ', style={'display': 'inline'}),
# html.A('www.commerce.gov', href='https://www.commerce.gov', style={'display':'inline'}),
# html.Div('.', style={'display':'inline'}),
# ], style={'margin-left': '40px'}),
ddk.Block (id='control-block', width=20, children=[
ddk.Card(
children=[
ddk.Row(ddk.Title('Country:', style={'font-size':'.8em', 'padding-left':'5px'})),
dcc.Dropdown(
id='country',
clearable=True,
multi=True,
style={'padding-left': '10px'},
options=[
{'value': "AUSTRALIA", 'label': 'Australia'},
{'value': "BAHAMAS", 'label': 'Bahamas'},
{'value': 'BENIN', 'label': 'Benin'},
{'value': "BRAZIL", 'label': 'Brazil'},
{'value': 'BULGARIA', 'label': 'Bulgaria'},
{'value': "CANADA", 'label': 'Canada'},
{'value': "CHINA", 'label': 'China'},
{'value': 'CROATIA', 'label': 'Croatia'},
{'value': 'EL SALVADOR', 'label': 'El Salvador'},
{'value': "EUROPEAN UNION", 'label': 'European Union'},
{'value': "FRANCE", 'label': 'France'},
{'value': "GERMANY", 'label': 'Germany'},
{'value': 'GREECE', 'label' : 'Greece'},
{'value': 'HONG KONG', 'label': 'Hong Kong'},
{'value': "INDIA", 'label': 'India'},
{'value': 'IRAN, ISLAMIC REPUBLIC OF', 'label' : 'Iran, Islamic Republic of'},
{'value': "IRELAND", 'label': 'Ireland'},
{'value': 'ISRAEL', 'label': 'Israel'},
{'value': 'ITALY', 'label': 'Italy'},
{'value': "JAPAN", 'label': 'Japan'},
{'value': "KOREA, REPUBLIC OF", 'label': 'South Korea'},
{'value': 'NETHERLANDS', 'label' : 'Netherlands'},
{'value': 'NEW ZEALAND', 'label' : 'New Zealand'},
{'value': 'NORWAY', 'label' : 'Norway'},
{'value': 'PHILIPPINES', 'label' : 'Philippines'},
{'value': 'POLAND', 'label' : 'Poland'},
{'value': 'PORTUGAL', 'label' : 'Portugal'},
{'value': 'ROMANIA', 'label' : 'Romania'},
{'value': 'RUSSIAN FEDERATION', 'label' : 'Russian Federation'},
{'value': "SPAIN", 'label': 'Spain'},
{'value': "SOUTH AFRICA", 'label': 'South Africa'},
{'value': 'SYRIAN ARAB REPUBLIC', 'label' : 'Syrian Arab Republic'},
{'value': "UKRAINE", 'label': 'Ukraine'},
{'value': "UNITED KINGDOM", 'label': 'United Kingdom'},
{'value': "UNITED STATES", 'label': 'United States'},
{'value': 'UNKNOWN', 'label': 'Unknown'},
],
),
ddk.Row(ddk.Title('Parameter:', style={'font-size':'.8em', 'padding-left': '5px'})),
dcc.Dropdown(
id='variable',
clearable=True,
multi=True,
style={'padding-left': '10px'},
options=[
{'value':'sst','label': 'Sea Surface Temperature'},
{'value':'ztmp','label': 'Temperature Profile'},
{'value':'slp','label': 'Sea Level Pressure'},
{'value':'atmp','label': 'Air Temperature'},
{'value':'zsal','label': 'Salinity'},
{'value':'windspd','label': 'Wind Speed'},
{'value':'winddir','label': 'Wind Direction'},
{'value':'clouds','label': 'Clouds'},
{'value': 'dewpoint', 'label': 'Dew Point Temperature'},
{'value': 'hur', 'label': 'Relative Humidity'},
{'value': 'wvht', 'label': 'Sea Surface Wave Significant Height'},
{'value': 'waterlevel_met_res', 'label': 'Meteorological Residual Tidal Elevation'},
{'value': 'waterlevel_wrt_lcd', 'label': 'Tidal Elevation WRT Local Chart Datum'},
{'value': 'water_col_ht', 'label': 'Water Column Height'}
]
),
ddk.Row(ddk.Title('Platform Type:', style={'font-size':'.8em', 'padding-left': '5px'})),
dcc.Dropdown(
id='platform-type',
clearable=True,
multi=True,
style={'padding-left': '10px'},
options=[
{'label': 'ARGO', 'value': 'ARGO'},
{'label': 'C-MAN WEATHER STATIONS', 'value': 'C-MAN WEATHER STATIONS'},
{'label': 'CLIMATE REFERENCE MOORED BUOYS', 'value': 'CLIMATE REFERENCE MOORED BUOYS'},
{'label': 'DRIFTING BUOYS', 'value': 'DRIFTING BUOYS'},
{'label': 'GLIDERS', 'value': 'GLIDERS'},
{'label': 'ICE BUOYS', 'value': 'ICE BUOYS'},
{'label': 'MOORED BUOYS', 'value': 'MOORED BUOYS'},
{'label': 'RESEARCH', 'value': 'RESEARCH'},
{'label': 'SHIPS', 'value': 'SHIPS'},
{'label': 'SHORE AND BOTTOM STATIONS', 'value': 'SHORE AND BOTTOM STATIONS'},
{'label': 'TAGGED ANIMAL', 'value': 'TAGGED ANIMAL'},
{'label': 'TIDE GAUGE STATIONS', 'value': 'TIDE GAUGE STATIONS'},
{'label': 'TROPICAL MOORED BUOYS', 'value': 'TROPICAL MOORED BUOYS'},
{'label': 'TSUNAMI WARNING STATIONS', 'value': 'TSUNAMI WARNING STATIONS'},
{'label': 'UNKNOWN', 'value': 'UNKNOWN'},
{'label': 'UNCREWED SURFACE VEHICLE', 'value': 'UNCREWED SURFACE VEHICLE'},
{'label': 'VOLUNTEER OBSERVING SHIPS', 'value': 'VOLUNTEER OBSERVING SHIPS'},
{'label': 'VOSCLIM', 'value': 'VOSCLIM'},
{'label': 'WEATHER AND OCEAN OBS', 'value': 'WEATHER AND OCEAN OBS'},
{'label': 'WEATHER BUOYS', 'value': 'WEATHER BUOYS'},
{'label': 'WEATHER OBS', 'value': 'WEATHER OBS'}
]
),
ddk.Row(ddk.Title('Color Markers by:', style={'font-size':'.8em', 'padding-left': '5px'})),
dcc.RadioItems(
id='color-by',
options=[
{'label': 'Platform Type', 'value': 'platform_type'},
{'label': 'Country', 'value': 'country'},
],
),
ddk.Row(ddk.Title('Find Platforms:', style={'font-size':'.8em', 'padding-left': '5px'})),
dcc.Dropdown(
id='platform-code',
clearable=True,
multi=False,
style={'padding-left': '10px'},
),
html.Hr(style={'border': '1px solid black'}),
ddk.Row(ddk.Title('Plot Options:', style={'font-size':'.8em', 'padding-left': '5px'})),
dcc.Dropdown(
id='markers',
options=[
{'label': 'Both lines and markers', 'value': 'both'},
{'label': 'Markers only', 'value': 'markers'},
{'label': 'Lines only', 'value': 'lines'}
],
value='lines',
clearable=False,
multi=False
),
html.Hr(style={'border': '1px solid black'}),
ddk.Row(dcc.Loading(html.A(html.Button(id='download-button', children=['Download'], style={'font-size':'.8em', 'padding-left': '5px'},), target='_blank', id='download-link'))),
dcc.Dropdown(
style={'margin-right': '5px'},
id='download-format',
options=[
{'label': 'CSV', 'value': '.csv'},
{'label': 'netCDF', 'value': '.ncCF'},
{'label': 'HTML', 'value': '.htmlTable'}
],
placeholder='Select Download Format',
multi=False
)
]
)
]),
ddk.Block(width=80, children=[
ddk.Card(id='map-card', children=[
ddk.CardHeader(id='map-card-header',
modal=True,
modal_config={'height': 90, 'width': 95},
fullscreen=True,
children=[
html.Button(id='counts-button', children=['Data Counts Table'], style={'font-size':'.8em', 'width':'300px'},)
]),
ddk.Graph(id='location-map', style={'padding-left': '20px'}),
]),
]),
ddk.Block(width=100, children=[
ddk.Card(width=100, children=[
dcc.Loading(
ddk.CardHeader(id='plots-header',
title='Plots...',
modal=True,
modal_config={'height': 90, 'width': 95},
fullscreen=True,
children=[
html.Button(id='info-action', children=[html.Span([
html.I(id='info-icon', className="bi bi-info-circle", ),
' Platform Information'
])]),
]
)
),
dcc.Loading(
dmc.Alert(children=
[
html.Div(id='info-body', children=[html.H1('.'),html.H2('.')]),
],
title='Fecthing additional information',
id='info-popover',
withCloseButton=True,
hide=True,
color='gray'
),
),
dcc.Loading(dcc.Graph(id='plots', style={'padding-left': '20px'})),
])
]),
ddk.Card(style={'margin-bottom': '10px'}, children=[
dbc.Col(width=12, children=[
ddk.Block(children=[
dbc.Row(children=[
dbc.Col(width=1, children=[
html.Img(src='https://www.pmel.noaa.gov/sites/default/files/PMEL-meatball-logo-sm.png',
height=100,
width=100),
]),
dbc.Col(width=10, children=[
html.Div(children=[
dcc.Link('National Oceanic and Atmospheric Administration',
href='https://www.noaa.gov/'),
]),
html.Div(children=[
dcc.Link('Pacific Marine Environmental Laboratory', href='https://www.pmel.noaa.gov/'),
]),
html.Div(children=[
dcc.Link('oar.pmel.webmaster@noaa.gov', href='mailto:oar.pmel.webmaster@noaa.gov')
]),
html.Div(children=[
dcc.Link('DOC |', href='https://www.commerce.gov/'),
dcc.Link(' NOAA |', href='https://www.noaa.gov/'),
dcc.Link(' OAR |', href='https://www.research.noaa.gov/'),
dcc.Link(' PMEL |', href='https://www.pmel.noaa.gov/'),
dcc.Link(' Privacy Policy |', href='https://www.noaa.gov/disclaimer'),
dcc.Link(' Disclaimer |', href='https://www.noaa.gov/disclaimer'),
dcc.Link(' Accessibility', href='https://www.pmel.noaa.gov/accessibility')
])
]),
dbc.Col(width=1, children=[
html.Div(style={'font-size': '1.1rem', 'position': 'absolute', 'bottom': '0'},
children=[version])
])
])
])
])
]),
dmc.Modal(
size="90%",
title="Platform Counts",
id="counts-modal",
overflow="outside",
zIndex=10000,
children=[
ddk.Card(style={'height': '80vh'}, children=[
ddk.CardHeader(id='table-header', children='Number of Observations per Variable by Platform Type'),
dag.AgGrid(id='nobsByVarAndPlatform', columnDefs=varByPlatform_defs, style={'height':'75vh'})
]),
],
),
dcc.Clipboard()
])
@app.callback(
[
Output("counts-modal", "opened"),
],
[
Input("counts-button", "n_clicks"),
],
[
State("counts-modal", "opened"),
],prevent_initial_call=True,
)
def model_state(nc1, opened):
return not opened,
@app.callback(
[
Output("nobsByVarAndPlatform", "rowData"),
Output("table-header", "children")
],
[
Input("counts-modal", "opened"),
]
)
def get_table(is_open):
if is_open:
nobs_df = db.get_nobs('platform_type')
counts_df = db.get_platform_counts('platform_type')
all_df = counts_df.merge(nobs_df, how='inner', on='platform_type')
all_df.loc['total']= all_df.sum(numeric_only=True)
all_df.at['total', 'platform_type'] = "TOTAL"
r = db.get_range('time')
title = 'Number of Observations per Variable by Platform Type from ' + r.loc[0]['min_time'] + ' to ' + r.loc[0]['max_time']
return [all_df.to_dict("records"), title]
else:
raise exceptions.PreventUpdate
@app.callback(
Output("info-popover", "hide"),
Input("info-action", "n_clicks"),
State("info-popover", "hide"),
prevent_initial_call=True,
)
def alert(n_clicks, hide):
return not hide
@app.callback(
Output('info-body', 'children'),
Output('info-popover', 'title'),
Input('info-action', 'n_clicks'),
Input('ui-state', 'data'),
State('info-popover', 'hide'),
prevent_initial_call=True
)
def fetch_info(click, in_info_ui_state, in_hide):
tid = ctx.triggered_id
if in_info_ui_state is not None and (tid == 'info-action' or in_hide == False):
info_ui_state = json.loads(in_info_ui_state)
if info_ui_state['platform_code'] is None:
return html.Div(html.H5('No platform selected.')), 'You must select a platform'
else:
if info_ui_state['platform_code'] is not None:
url = 'https://data.pmel.noaa.gov/generic/erddap/tabledap/wmo_list.csv?&WMO="' + info_ui_state['platform_code'] + '"&orderByMax("time")'
try:
extra_info = pd.read_csv(url, skiprows=[1])
# Make a table. Include only columns which have data (!='nan')
datatable_columns = []
for index, row in extra_info.iterrows():
for column in extra_info.columns:
if column != 'WMO':
value = str(row[column])
if value != 'nan':
datatable_columns.append({'name': column, 'id': column})
return html.Div(ddk.DataTable(columns=datatable_columns, data=extra_info.to_dict('records'), editable=False)), 'Platform Metadata for ' + str(info_ui_state['platform_code'])
except Exception as e:
print(e)
return html.Div(children=[html.H5('No metadata found...'),]),'No extra information found for: ' + info_ui_state['platform_code']
else:
return html.Div(html.H5('No platform selected.')), 'You must select a platform'
else:
raise exceptions.PreventUpdate
##
# This runs on-load and builds the control and sets the options and values of the controls based on the state of the URL.
@app.callback([
Output('country', 'value'),
Output('variable', 'value'),
Output('platform-type', 'value'),
Output('color-by', 'value'),
Output('platform-code', 'options'),
Output('platform-code', 'value'),
Output('markers', 'value'),
],[
Input('trigger', 'n_intervals'),
]
)
def read_url(trigger):
out_platform_code = None
out_line_marker_setting = 'lines'
out_platform_type = None
out_color_by = 'platform_type'
out_variable = None
out_country = None
url = flask.request.referrer
parts = urllib.parse.urlparse(url)
params = urllib.parse.parse_qs(parts.query)
if 'markers' in params:
line_plot_markers = params['markers'][0]
if 'platform_code' in params:
out_platform_code = params['platform_code'][0]
if 'platform_type' in params:
out_platform_type = params['platform_type']
if 'color_by' in params:
out_color_by = params['color_by'][0]
if 'variable' in params:
out_variable = params['variable']
if 'country' in params:
out_country = params['country']
map_df = db.get_locations()
counts_df = db.get_counts()
if out_variable is not None:
query = ""
if isinstance(out_variable, list):
if len(out_variable) > 0:
for qidx, qvar in enumerate(out_variable):
if qidx == 0:
query = query + qvar + '> 0'
else:
query = query + ' or ' + qvar + '> 0'
elif isinstance(out_variable, str):
query = query + out_variable + '> 0'
if len(query) > 1:
has_data = counts_df.query(query)
map_df = map_df[map_df['platform_code'].isin(has_data['platform_code'])]
if out_platform_type is not None:
if isinstance(out_platform_type, str):
map_df = map_df.loc[map_df['platform_type'] == out_platform_type]
elif isinstance(out_platform_type, list):
query = ''
for pi, plat in enumerate(out_platform_type):
if pi == 0:
query = query + 'platform_type==\''+plat+'\''
else:
query = query + 'or platform_type==\''+plat+'\''
if len(query) > 1:
map_df = map_df.query(query)
if out_country is not None:
if isinstance(out_country, str):
map_df = map_df.loc[map_df['country'] == out_country]
elif isinstance(out_country, list):
query = ''
for ci, cntry in enumerate(out_country):
if ci == 0:
query = query + 'country==\''+cntry+'\''
else:
query = query + 'or country==\''+cntry+'\''
if len(query) > 1:
map_df = map_df.query(query)
codes_to_show = pd.unique(map_df['platform_code'])
platform_code_options = []
codes_to_show.sort()
for codey in codes_to_show:
platform_code_options.append({'label': codey, 'value': codey})
return [out_country, out_variable, out_platform_type, out_color_by, platform_code_options, out_platform_code, out_line_marker_setting]
@app.callback([
Output('platform-code', 'options', allow_duplicate=True),
],[
Input('variable', 'value'),
Input('platform-type', 'value'),
Input('country', 'value'),
], prevent_initial_call=True)
def set_platform_list(list_variable_in, list_platform_type_in, list_country_in):
map_df = db.get_locations()
counts_df = db.get_counts()
if list_variable_in is not None:
query = ""
if isinstance(list_variable_in, list):
if len(list_variable_in) > 0:
for qidx, qvar in enumerate(list_variable_in):
if qidx == 0:
query = query + qvar + '> 0'
else:
query = query + ' or ' + qvar + '> 0'
elif isinstance(list_variable_in, str):
query = query + list_variable_in + '> 0'
if len(query) > 1:
has_data = counts_df.query(query)
map_df = map_df[map_df['platform_code'].isin(has_data['platform_code'])]
if list_platform_type_in is not None:
if isinstance(list_platform_type_in, str):
map_df = map_df.loc[map_df['platform_type'] == list_platform_type_in]
elif isinstance(list_platform_type_in, list):
query = ''
for pi, plat in enumerate(list_platform_type_in):
if pi == 0:
query = query + 'platform_type==\''+plat+'\''
else:
query = query + 'or platform_type==\''+plat+'\''
if len(query) > 1:
map_df = map_df.query(query)
if list_country_in is not None:
if isinstance(list_country_in, str):
map_df = map_df.loc[map_df['country'] == list_country_in]
elif isinstance(list_country_in, list):
query = ''
for ci, cntry in enumerate(list_country_in):
if ci == 0:
query = query + 'country==\''+cntry+'\''
else:
query = query + 'or country==\''+cntry+'\''
if len(query) > 1:
map_df = map_df.query(query)
codes_to_show = pd.unique(map_df['platform_code'])
platform_code_options = []
codes_to_show.sort()
for codey in codes_to_show:
platform_code_options.append({'label': codey, 'value': codey})
return [platform_code_options]
@app.callback(
[
Output('platform-code', 'value', allow_duplicate=True)
],
[
Input('location-map', 'clickData')
], prevent_initial_call=True
)
def set_platform_code_from_map(state_in_click):
out_platform_code = None
if state_in_click is not None:
fst_point = state_in_click['points'][0]
out_platform_code = fst_point['customdata']
return [out_platform_code]
@app.callback(
[
Output('ui-state', 'data'),
Output('url', 'search')
],
[
Input('variable', 'value'),
Input('platform-type', 'value'),
Input('country', 'value'),
Input('color-by', 'value'),
Input('platform-code', 'value'),
Input('markers', 'value'),
# Input('map-info', 'data')
], prevent_initial_call=True
) # , state_in_map_info
def set_ui_state(state_in_variable, state_in_platform_type, state_in_country, state_in_color_by, state_in_pcode, state_in_markers):
trigger_id = ctx.triggered_id
ui_state_out = {}
query = '?'
out_variable = None
if state_in_variable is not None:
if isinstance(state_in_variable, list):
for vidx, v in enumerate(state_in_variable):
if vidx == 0:
query = query + 'variable='+v
else:
query = query + '&variable='+v
else:
query = query + 'variable='+out_variable
out_variable = state_in_variable
ui_state_out['variable'] = out_variable
out_platform_type = None
if state_in_platform_type is not None:
out_platform_type = state_in_platform_type
if isinstance(state_in_platform_type, list):
for pt in state_in_platform_type:
if len(query) > 1:
query = query + '&'
query = query + 'platform_type=' + pt
else:
if len(query) > 1:
query = query + '&'
query = query + 'platform_type=' + out_platform_type
ui_state_out['platform_type'] = out_platform_type
out_country = None
if state_in_country is not None:
out_country = state_in_country
if isinstance(state_in_country, list):
for c in state_in_country:
if len(query) > 1:
query = query + '&'
query = query + 'country=' + c
else:
query = query + 'country=' + out_country
ui_state_out['country'] = out_country
out_color_by = 'platform_type'
if state_in_color_by is not None:
out_color_by = state_in_color_by
if len(query) > 1:
query = query + '&'
query = query + 'color_by=' + out_color_by
ui_state_out['color_by'] = out_color_by
out_platform_code = state_in_pcode
if out_platform_code is not None:
if len(query) > 1:
query = query + '&'
query = query + 'platform_code=' + out_platform_code
ui_state_out['platform_code'] = out_platform_code
out_markers = 'lines'
if state_in_markers is not None:
out_markers = state_in_markers
ui_state_out['markers'] = out_markers
# if state_in_map_info is not None:
# query_map_info = json.loads(state_in_map_info)
# location_center = query_map_info['center']
# location_zoom = query_map_info['zoom']
# query = query + '&lat=' + str(location_center['lat']) + '&lon=' + str(location_center['lon']) + '&zoom='+str(location_zoom)
return [json.dumps(ui_state_out), query]
@app.callback(
[
Output('location-map', 'figure'),
Output('map-card-header', 'title'),
Output('map-loader', 'children'),
],
[
Input('ui-state', 'data'),
],
prevent_initial_call=True
)
def show_platforms(in_ui_state):
cones_df = None
try:
cones_df = pd.read_csv(cones_url, skiprows=[1])
except:
print('load_platforms: No cones found.')
if in_ui_state is not None and len(in_ui_state) > 0:
map_ui_state = json.loads(in_ui_state)
else:
raise exceptions.PreventUpdate
location_center = center
location_zoom = zoom
map_df = db.get_locations()
map_df.dropna(subset=['platform_type'], inplace=True)
counts_df = db.get_counts()
color_by = 'platform_type'
if 'color_by' in map_ui_state:
color_by = map_ui_state['color_by']
if color_by == 'platform_type':
color_map = platform_color
if color_by == 'country':
color_map = country_color
platform_trace = None
selection_code = None
selection_code = map_ui_state['platform_code']
if selection_code is not None:
data_df = db.get_data(selection_code)
trace_df = data_df.loc[data_df['platform_code']==selection_code]
platform_trace = go.Scattermapbox(lat=trace_df["latitude"], lon=trace_df["longitude"],
hovertext=trace_df['trace_text'],
hoverlabel = {'namelength': 0,},
mode='markers',
marker=dict(color=trace_df["millis"], colorscale='Greys', size=trace_size), name=str(selection_code),
uid=9000)
map_in_variable = map_ui_state['variable']
map_in_platform_type = map_ui_state['platform_type']
map_in_country = map_ui_state['country']
ui_revision = '*base*'
if map_in_variable is not None:
ui_revision = '*variable*'
query = ""
if isinstance(map_in_variable, list):
if len(map_in_variable) > 0:
for qidx, qvar in enumerate(map_in_variable):
if qidx == 0:
query = query + qvar + ' > 0'
else:
query = query + ' or ' + qvar + ' > 0'
elif isinstance(map_in_variable, str):
query = query + map_in_variable + ' > 0'
if len(query) > 1:
has_data = counts_df.query(query)
map_df = map_df[map_df['platform_code'].isin(has_data['platform_code'])]
if map_in_platform_type is not None:
ui_revision = '*platform_type*'
if isinstance(map_in_platform_type, str):
map_df = map_df.loc[map_df['platform_type'] == map_in_platform_type]
elif isinstance(map_in_platform_type, list):
query = ''
for pi, plat in enumerate(map_in_platform_type):
if pi == 0:
query = query + 'platform_type==\''+plat+'\''
else:
query = query + 'or platform_type==\''+plat+'\''
if len(query) > 1:
map_df = map_df.query(query)
if map_in_country is not None:
ui_revision = '*country*'
if isinstance(map_in_country, str):
map_df = map_df.loc[map_df['country'] == map_in_country]
elif isinstance(map_in_country, list):
query = ''
for ci, cntry in enumerate(map_in_country):
if ci == 0:
query = query + 'country==\''+cntry+'\''
else:
query = query + 'or country==\''+cntry+'\''
if len(query) > 1:
map_df = map_df.query(query)
platform_count = map_df.shape[0]
title = 'Platform Locations - ' + str(platform_count) + ' platforms reported.'
location_map = go.Figure()
categories = map_df[color_by].unique().tolist()
categories.sort()
for icat, category in enumerate(categories):
map_trace_df = map_df.loc[map_df[color_by] == category]
if category in color_map:
marker_color = color_map[category]
else:
marker_color = '#FF5F1F'
platform_dots = go.Scattermapbox(lat=map_trace_df["latitude"], lon=map_trace_df["longitude"], mode='markers',
marker=dict(color=marker_color, size=marker_size), name=str(category),
hovertext=map_trace_df['trace_text'],
hoverlabel = {'namelength': 0,},
customdata=map_trace_df['platform_code'],
uid=icat)
location_map.add_trace(platform_dots)
# location_map.update_layout(uirevision=ui_revision)
if platform_trace is not None:
location_map.add_trace(platform_trace)
location_map.update_layout(
height=map_height,
mapbox_style="white-bg",
mapbox_layers=[
{
"below": 'traces',
"sourcetype": "raster",
"sourceattribution": "Powered by Esri",
"source": [
"https://ibasemaps-api.arcgis.com/arcgis/rest/services/Ocean/World_Ocean_Base/MapServer/tile/{z}/{y}/{x}?token=" + constants.ESRI_API_KEY
]
}
],
mapbox_zoom=location_zoom,
mapbox_center=location_center,
margin={"r": 0, "t": 0, "l": 0, "b": 0},
legend=dict(
orientation="v",
x=-.01,
),
modebar_orientation='v',
)
# location_map.update_traces(cluster={'enabled': True, 'maxzoom': 2, 'color': '#444455'})
cone_colors = px.colors.qualitative.Light24
if cones_df is not None and cones_df.shape[0] > 1:
cones = cones_df['name'].unique()
for inx, cone in enumerate(cones):
fill_color = cc_color_set_transparent(inx, px.colors.qualitative.Light24, 0.5)
line_color = cone_colors[inx]
x = cones_df.loc[cones_df['name'] == cone, 'longitude']
y = cones_df.loc[cones_df['name'] == cone, 'latitude']
cone_map = go.Scattermapbox(lon=x,
lat=y,
mode='lines',
name=cone,
fill='toself',
hoverinfo='name',
hoverlabel={'namelength': -1},
fillcolor=fill_color,
line=dict(color=line_color))
location_map.add_trace(cone_map)
location_map.update_traces(showlegend=True)
return [location_map, title, 'done']
@app.callback([
Output('plots', 'figure'),
Output('plots-header', 'title'),
Output('download-url', 'data'),
Output('download-format', 'value')
],[
Input('ui-state', 'data')
], prevent_initial_call=True)
def make_plots(plot_in_ui_state):
if plot_in_ui_state is not None:
ui_state = json.loads(plot_in_ui_state)
else: