def meta_data_text(clicked,sites_open,environment, region, year_min, year_max):
species = 'Ozone'
if year_min and year_max:
sites_df = LoadData.Get_Species_Sites(species,environment,region,sites_open,
year_min,year_max)
table = dash_table.DataTable(
id = 'o3_site_table',
columns = [{"name": i, "id": i} for i in sites_df.columns],
data= sites_df.to_dict('records'),
sort_action = 'native',
style_table={'maxHeight':288,
'overflowX': 'scroll'},
style_header={
'textAlign': 'center',
'backgroundColor': 'white',
'fontWeight': 'bold'},
style_cell={'textAlign': 'left'},
style_as_list_view=True,
# row_selectable="multi",
)
count_estimate = str(LoadData.Estimate_data_count(species, sites_df,year_min, year_max))
if len(count_estimate) > 6:
message = 'There are approximately {} million data points. This will take a while to load and process.'.format(count_estimate[:-6])
elif len(count_estimate) <= 6 and len(count_estimate) > 4:
message = 'Approximately {} data points found'.format(int(count_estimate) - int(count_estimate)%1000)
else:
message = 'Approximately {} data points found'.format(count_estimate)
output = html.Div(children = [html.P(message, style = {'color':'red'}),
html.Br(),table])
else:
output = html.P('Select years to analyse')
return output
def GetData(sites):
"""
This function loops gathers all the analysis modules needed and imports
them.
Function IN:
parameters (OPTIONAL, LIST):
Fucntion OUT:
argout:
Description of what the fuction returns if any
"""
# Needs fucntion to specify the filenames and how the data should
# be opened. For now lets just keep it simple with out Heathfield data
# If there are no paramters given (and there should be)
# then use some sample data
# Get dataframe from LoadData.FromCSV. Leaving the input blank will get
# the Heathfield data.
if 'Heathfield' in sites:
df = LoadData.FromCSV()
df = TidyData.DateClean_Heathfeild(df)
elif 'Edinburgh' in sites:
df = LoadData.Edinburgh_Data()
df.set_index('Date and Time', inplace=True)
# Use the DateClean function to make the date into a datetime format
# Drop last line as this is usually bogus data
return df[:-1]
def Site_Summary(site_name, species):
site_object = LoadData.get_site_info_object(site_name)
site_variables_list = LoadData.Get_Site_Variables(site_name)
for v in site_variables_list:
if 'modelled' in v.lower().split():
site_variables_list.remove(v)
if 'temperature' in v.lower().split():
site_variables_list.remove(v)
if 'pressure' in v.lower().split():
site_variables_list.remove(v)
if site_object.environment_type.lower()[0] in ['a', 'e', 'i', 'o', 'u']:
prefix = 'an'
else:
prefix = 'a'
summary = html.Div(
id='site_summary',
children=[
html.Br(),
html.P('%s is %s %s site in the %s region, opened in %s.' %
(site_name, prefix, site_object.environment_type.lower(),
site_object.region, site_object.date_open.year)),
html.Br(),
html.P('This is a %s site measuring the following species:' %
site_object.site_type),
html.Ul([html.Li(x) for x in site_variables_list])
])
return summary
def Site_Week_Summary(site_name, species):
df = LoadData.get_recent_site_data(site_name, species, days_ago=7)
plot = [
go.Scatter(x=df.index,
y=df.Concentration.values,
mode='lines',
name=species)
]
plot_title = '%s at %s between %s and %s' % (
species, site_name, df.index[0].date(), df.index[-1].date())
# Find the unit for the species
unit = LoadData.Get_Unit('AURN', species)
ytitle = '%s (%s)' % (species, unit)
layout = go.Layout(
title=plot_title,
xaxis=dict(title='Date'),
yaxis=dict(title=ytitle),
images=[
dict(source="assets/UoE_Geosciences_2_colour.jpg",
xref="paper",
yref="paper",
x=.6,
y=0.95,
sizex=0.25,
sizey=0.25,
xanchor="right",
yanchor="bottom"),
dict(source="assets/ukri-nerc-logo-600x160.png",
xref="paper",
yref="paper",
x=0.83,
y=0.95,
sizex=0.2,
sizey=0.2,
xanchor="right",
yanchor="bottom"),
dict(source="assets/DEFRA-logo.png",
xref="paper",
yref="paper",
x=1,
y=0.95,
sizex=0.13,
sizey=0.13,
xanchor="right",
yanchor="bottom"),
],
)
plot = dcc.Graph(id='map_site_timeseries',
figure={
'data': plot,
'layout': layout
})
return plot
def comparison_plot_renderer(data, variable_options, site_choice, DataResample,
start_date, end_date, medianswitch, title, xtitle,
ytitle, label_format):
if not data:
return ''
data = data.split(',')
# Find min year for site
start_year, end_year = LoadData.get_site_year_range_db(data[1])
df = load_station_data(data[0], data[1],
[start_year, int(data[3])], data[4:])
if not isinstance(df, pd.DataFrame):
return ''
variable_options = data[4:]
from dataplot.DataTools.AnalysisTools import ComparisonPlots
# if comparison_tabs == 'week_comp':
return ComparisonPlots.CompareWeeks(df,
variable_options=variable_options,
site_choice=site_choice,
DataResample=DataResample,
start_date=start_date,
end_date=end_date,
show_median=medianswitch,
title=title,
xtitle=xtitle,
ytitle=ytitle,
label_format=label_format)
def varaible_list(site):
site_vars = LoadData.get_site_variables_db(site)
var_options = [{
'label': i.replace('<sub>', '').replace('</sub>', ''),
'value': i
} for i in site_vars]
return var_options, False
def get_site_minimum_year(site):
start_year, end_year = LoadData.get_site_year_range_db(site)
options = [{
'label': i,
'value': i
} for i in range(start_year, end_year + 1)]
return options
def load_station_data(site_type, sites, years, variables):
if sites:
if site_type == 'DEFRA AURN':
# df = LoadData.Get_AURN_data( sites, years, variables)
df = LoadData.Get_One_Site_Data(sites, years, variables)
else:
print("Don't have any other data yet")
else:
df = 0
return df
def get_colourbychoices(data, value):
if not data:
return ''
data = data.split(',')
df = load_station_data(data[0], data[1],
[int(data[2]), int(data[3])], data[4:])
if not isinstance(df, pd.DataFrame):
return ''
variable_list = LoadData.get_site_variables_db(data[1])
var_options = [{'label': i, 'value': i} for i in variable_list]
return var_options
def site_info_message(site_info_string):
## Currently only works with one site chosen
info = site_info_string.split(',')
site_type, sites, min_year, max_year = info[0], info[1], str(info[2]), str(
info[3])
site_info = LoadData.get_site_info_object(sites)
env_type = site_info.environment_type
gov_region = site_info.region
message = "Plotting data for the %s site %s between %s and %s, which is a %s site in %s.\n" % (
site_type, sites, min_year, max_year, env_type, gov_region)
return message
def Fill_Year_DEFRA_Data(year):
## This will be a module to fill up the db with the past values
## Likely/hopefully only need this the once.
all_sites_query = site_info.objects.all()
## Need to prioritise input as this takes an absolute age.
for site in all_sites_query:
site_name = site.site_name
site_code = site.site_code
site_open = site.site_open
date_open = site.date_open
date_closed = site.date_closed
# This skips sites that have already been added to the database
### THIS IS NOT A SMART WAY OF DOING THIS BUT IS A TEMP BODGE
if measurement_data.objects.filter(date_and_time__year=year).filter(
site_id=site_info.objects.filter(
site_name=site_name)).exists():
continue
# Don't include sites that are just a quick PM10 site
# Only inlcudes Brighton Roadside PM10 & Northampton PM10
if 'PM10' in site_name:
continue
if date_open.year > year:
continue
# Load in dataframe - could be a memory issue here with the
# site open the longest
if site_open:
date_closed = dt.now()
# For the time being only get 2018 data
if site_open:
print('Getting data for %s: %d - %d (%s)' %
(site_name, date_open.year, date_closed.year, site_code))
df = LoadData.Get_AURN_data(site_name, [year, year],
drop_status_and_units=False)
DEFRA_AURN_data_to_db(df, site_code)
print('Submitted to database')
def Get_Latest_AURN_Data(site_name, year):
# Just add the latest data to the database. This relies on all variables
# in a site being updated at the same time. Which I think is correct.
df = LoadData.Get_AURN_data(site_name, [year, year],
drop_status_and_units=False)
# Get the site code - I need a cleaner way of doing this...
filename = 'dataplot/InfoFiles/DEFRA_AURN_sites_info.csv'
sites = pd.read_csv(filename)
site_code = sites['Site Code'].loc[sites['Site Name'] == site_name]
site_code = site_code.values[0]
# Query the site info based on the site code
site_id = site_info.objects.get(site_code=site_code)
# Get the latest date and time in the database for a given site
site_measurements = measurement_data.objects.filter(site_id=site_id)
most_recent_date = site_measurements.latest('date_and_time').date_and_time
trimmed_df = df.loc[df.index > most_recent_date]
DEFRA_AURN_data_to_db(trimmed_df, site_code)
def fill_maximum_year(site):
start_year, end_year = LoadData.get_site_year_range_db(site)
return end_year
def Update_DEFRA_Data(site_name):
## Find where the database still has unverified data in and see
## if the DEFRA site has been updated.
site_id = site_info.objects.filter(site_code=site_code)
site_data = measurement_info.objects.filter(site_id=site_id)
# Only get data that hasn't been verified but isn't unknown
queried_data = site_data.exclude(verified='V').exclude(verified='U')
# Into a dataframe for ease of use
current_data = pd.DataFrame.from_records(queried_data.values(
'date_and_time', 'value', 'verified'),
index='date_and_time')
# Get the years that still have unverified data
years = current_data.index.year.unique().values
# If the range of years is more than two then its unlikely it'll
# ever be verifed so ignore it
if len(years) > 2:
years[years[0], years[1]]
if max(years) < dt.now().year - 1:
# If the maximum year is more than a year ago then don't bother doing anything
pass
if min(years) < dt.now().year - 1:
# If the minimum year is more than a year ago then only use recent year
years = [years[0]]
# Load in the data again
new_df = LoadData.Get_AURN_data(site_name, [years[0], years[-1]],
drop_status_and_units=False)
pollutant_cols = []
status_cols = []
unit_cols = []
for c in df.columns:
if c.split('.')[0].lower() == 'status':
status_cols.append(c)
elif c.split('.')[0].lower() == 'unit':
unit_cols.append(c)
else:
pollutant_cols.append(c)
# Need to now update the database
for i, col in enumerate(pollutant_cols):
# Get the relevant status for the measurement
status_col = df[status_cols[i]]
status_col.replace('R', 'V', inplace=True)
# Fill the nan values with 'U' for unknown - although this will rarely be
# a problem as all nan status have a matching nan measurement
status_col.dropna(inplace=True)
status_col.fillna('U')
chemical_formula = Get_Chemical_Formula(col)
measurement_name = 'DEFRA_AURN_%s' % chemical_formula
temp_col = new_df[col]
temp_col.dropna(inplace=True)
for x in range(len(temp_col)):
# Filter the data by measurement_id, site and time
# There should only be one data entry for each of these
data_entry = measurement_data.objects.filter(
measurement_id=measurement_name).filter(
site_id=site_id).filter(date_and_time=temp_col.index[x])[0]
if data_entry.verified == 'V':
continue
elif status_col[x] in ['U', 'N']:
continue
elif status_col[x] == data_entry.verified:
continue
else:
data_entry.verified = status_col[x]
data_entry.value = temp_col[x]
data_entry.save()
def main_site_map(environment, region, species):
mapbox_access_token = 'pk.eyJ1IjoiZG91Z2ZpbmNoIiwiYSI6ImNqZHhjYnpqeDBteDAyd3FsZXM4ZGdqdTAifQ.xLS22vmqzVYR0SAEDWdLpQ'
# site_df = LoadData.get_all_site_info(environment, region)
# random_sizes = np.random.randint(20, size = len(site_df))
# For the time being lets just set variable and time
date = datetime(2017, 12, 14, 12)
variable = species
vals_df = LoadData.all_sites_one_var_data(date, variable, region,
environment)
unit = LoadData.Get_Unit('AURN', species)
size_scale = 1
variable_vals = vals_df.value * size_scale
hover_text = [
'%s: %.3f %s' % (vals_df.index.tolist()[x], variable_vals[x], unit)
for x in range(len(variable_vals))
]
data = [
go.Scattermapbox(
lat=vals_df.latitude.tolist(),
lon=vals_df.longitude.tolist(),
mode='markers',
# customdata = final_df.index.tolist(),
marker=go.scattermapbox.Marker(
color=variable_vals.tolist(),
colorscale='Viridis',
showscale=True,
size=14,
colorbar=dict(title=species + ' ' + unit, titleside='right'),
# opacity = 0.85,
# color = chosen_hour,
# cmax = last_day.max(axis = 1).max(),
# colorbar = {'title':var_choice}
),
text=hover_text,
)
]
layout = go.Layout(
showlegend=False,
autosize=True,
# showlegend = True,
height=750,
hovermode='closest',
margin={
'l': 0.2,
'r': 0.2,
't': 0.2,
'b': 0.2
},
mapbox=dict(accesstoken=mapbox_access_token,
bearing=0,
center=dict(lat=55, lon=-3.2),
pitch=0,
zoom=4.5),
)
fig = dict(data=data, layout=layout)
return len(vals_df), fig
continue
site_year_open = site.date_open.year
site_year_closed = site.date_closed
if site_year_closed:
site_year_closed = site_year_closed.year
else:
site_year_closed = dt.now().year
site_year_open = 2020
for year in range(site_year_open, site_year_closed + 1):
print('Processing {} data for site {}'.format(year, site.site_name))
try:
df = LoadData.Get_AURN_data(site.site_name, [year, year],
drop_status_and_units=False)
except (HTTPError, URLError) as e:
print('No web data for {} {}'.format(site.site_name, year))
continue
pollutant_cols = []
status_cols = []
unit_cols = []
for c in df.columns:
if c.split('.')[0].lower() == 'status':
status_cols.append(c)
elif c.split('.')[0].lower() == 'unit':
unit_cols.append(c)
else:
pollutant_cols.append(c)
def site_count_data(species,split_by):
site_count_df = LoadData.Yearly_Site_Count(species,split_by = split_by)
return site_count_df
def uk_ozone():
## Get the sites availble from the DEFRA AURN network
site_regions = LoadData.AURN_regions()
region_choices = ['All'] + site_regions
region_options = [{'label': i.strip(), 'value': i.strip()} for i in region_choices]
site_envs = LoadData.AURN_environment_types()
env_choices = ['All'] + site_envs
env_options = [{'label': i.strip(), 'value': i.strip()} for i in env_choices]
#### Start the page layout
page_layout = html.Div(id = 'full_page_container', children =
### The first items are for the common attributes (ie site)
[
html.Div(className = 'page-header', children = [
html.Div(id = 'home-logo-holder', children = [html.A(id = 'home-logo', href="/")]),
html.Div(id = 'page-header-holder', children = [html.A('UK Atmosphere',id = "page-header-text", href = "/")]),
]),
html.Div(className = 'page-body',children = [
html.H3('Analysis for UK ozone from DEFRA AURN sites.'),
html.Br(),
html.Label('Select a region:'),
dcc.Dropdown(id = 'o3_region_choice',
multi = True,
options = region_options,
value = 'All'),
html.Br(),
html.Label('Select an environment type:'),
dcc.Dropdown(id = 'o3_env_choice',
multi = True,
options = env_options,
value = 'All'),
html.Br(),
html.Label('Select a range of years:'),
dcc.Dropdown(
id = 'o3_minimum_year',
placeholder = 'Select start year...',
value = 2000
),
html.P('To'),
dcc.Dropdown(
id = 'o3_maximum_year',
placeholder = 'Select end year...',
),
html.Br(),
daq.BooleanSwitch(id = 'o3_site_open', on = False,
label = 'Only use sites currently open',
labelPosition = 'top'),
html.Br(),
html.Button('Find Ozone Data', id = 'o3_go_button'),
html.Br(),
html.Br(),
dcc.Loading(id="o3_meta_data_load", children=[
html.Div(id = 'o3_meta_data_text')],type="dot"),
html.Hr(),
html.Button('Load Ozone Data', id = 'o3_load_button'),# disabled = True),
html.Hr(),
html.Div(id = 'o3_data_values_holder',children = [
daq.GraduatedBar(id = 'o3_load_bar',
size = 500,
# max = 100,
value = 0,
showCurrentValue=True),
html.Div(id = 'o3_dataframe-holder'),
dcc.Interval(id = 'Interval',interval = 500),
dcc.Store(id = 'load_id_store'),
html.Div(id = 'loaded_sites2'),
html.Div(id = 'tester_output')]),
### Create a div to place the dataframe while its being used but not
### viewable by the user. Make data Json - very slow when being read
html.Div(id = 'o3_metadata-holder', style = {'display': 'none'}),
### ************************** Site Count ***************************
html.Div(id = 'O3_SiteCountHolder', className = 'plot_holder', children = [
dcc.Loading(id="loading-sitecount", children=[
html.Div(id = 'O3_SiteCountPlot')],type="dot", className = 'main_plot'),
html.Div(id = 'O3_SiteCountTools', className = 'plot_tools', children = [
html.H3('Site Count Plot Tools:'),
html.Br(),
html.Label('Plot Title'),
dcc.Input( id = 'O3_SiteCountTitle',
placeholder = 'Enter Title',
value = ''),
html.Br(),
html.Br(),
html.Label('Spliy by:'),
dcc.RadioItems(id = 'Site_Count_Split',
options = [{'label': i, 'value': i} for i in ['Total', 'Environment Type','Region',]],
value = 'Total'),
html.Br(),
]),
]),
html.Hr(),
html.Br(),
html.Label(),
dcc.RadioItems(id = 'O3_Env_or_Regions',
options = [{'label': i, 'value': i} for i in ['Environment Type', 'Region']],
value = 'Environment Type',
labelStyle={'display': 'inline-block'}),
### Each placeholder for plots and their individual controls go below
### ************************** TimeSeries ***************************
html.Div(id = 'O3_TimeSeriesHolder', className = 'plot_holder', children = [
html.Div(id = 'O3_TimeSeries', className = 'main_plot'),
html.Div(id = 'TimeSeriesTools', className = 'plot_tools', children = [
html.H3('Time Series Tools:'),
html.Br(),
html.Label('Plot Title'),
dcc.Input( id = 'O3_TimeSeriesTitle',
placeholder = 'Enter Title',
value = ''),
html.Br(),
html.Label('X Axis Label'),
dcc.Input( id = 'O3_TimeSeriesXTitle',
placeholder = 'Enter X axis label',
value = 'Year'),
html.Br(),
html.Label('Y Axis Label'),
dcc.Input( id = 'O3_TimeSeriesYTitle',
placeholder = 'Enter Y axis label',
value = ''),
html.Br(),
dcc.RadioItems(id = 'O3_TimeSeriesLabelFormat',
options = [{'label': i, 'value': i} for i in ['Variable Name', 'Chemical Formula',]],
value = 'Variable Name'),
html.Br(),
html.Label('Value Type'),
dcc.RadioItems(id = 'O3_ValueType',
options = [{'label': i, 'value': i} for i in ['Annual Mean', 'Annual Maximum','Annual Minimum']],
value = 'Annual Mean'),
html.Br(),
html.Label('Line Type'),
dcc.RadioItems(id = 'O3_TimeSeriesLineOrScatter',
options = [{'label': i, 'value': i} for i in ['Scatter', 'Line', 'Line & Scatter']],
value = 'Line & Scatter',
),
])
]),
html.Hr(),
### ********************* Trend Table *********************************
html.Div(id = 'o3_trend_table'),
html.Hr(),
### ********************* Gamma plot *********************************
html.Div(id = 'O3_Gamma_Plot_Holder', className = 'plot_holder', children = [
html.Div(id = 'O3_Gamma_Plot', className = 'main_plot'),
html.Div(id = 'O3_GammaTools', className = 'plot_tools', children = [
html.H3('Gamma Plot Tools:'),
html.Br(),
html.Label('Plot Title'),
dcc.Input( id = 'O3_GammaTitle',
placeholder = 'Enter Title',
value = ''),
html.Br(),
]),
]),
html.Hr(),
### ********************* YearlyExceed *********************************
html.Div(id = 'O3_YearlyExceedHolder', className = 'plot_holder', children = [
html.Div(id = 'O3_YearlyExceed',className = 'main_plot'),
# html.Div(id = 'Correlation', className = 'main_plot'),
html.Div(id = 'O3_YearlyExceedTools', className = 'plot_tools', children = [
html.H3('Yearly Exceedance Tools:'),
html.Br(),
html.Label('Plot Title'),
dcc.Input( id = 'O3_YearlyExceedTitle',
placeholder = 'Enter Title',
value = ''),
html.Br(),
]),
]),
html.Hr(),
### ********************* Yearly siteExceed *********************************
html.Div(id = 'O3_YearlySiteExceedHolder', className = 'plot_holder', children = [
html.Div(id = 'O3_YearlySiteExceed',className = 'main_plot'),
# html.Div(id = 'Correlation', className = 'main_plot'),
html.Div(id = 'O3_YearlySiteExceedTools', className = 'plot_tools', children = [
html.H3('Yearly Site Exceedance Tools:'),
html.Br(),
html.Label('Plot Title'),
dcc.Input( id = 'O3_YearlySiteExceedTitle',
placeholder = 'Enter Title',
value = ''),
html.Br(),
]),
]),
html.Hr(),
### ********************* MonthlyExceed *********************************
html.Div(id = 'O3_MonthlyExceedHolder', className = 'plot_holder', children = [
html.Div(id = 'O3_MonthlyExceed', className = 'main_plot'),
# html.Div(id = 'DiurnalCycle', className = 'main_plot'),
html.Div(id = 'O3_MonthlyExceedTools', className = 'plot_tools', children = [
html.H3('Monthly Exceedance Tools:'),
html.Br(),
html.Label('Plot Title'),
dcc.Input( id = 'O3_MonthlyExceedTitle',
placeholder = 'Enter Title',
value = ''),
html.Br(),
]),
]),
html.Hr(),
### ******************* WeeklyExceed ***************************
html.Div(id = 'O3_WeeklyExceedHolder', className = 'plot_holder', children = [
html.Div(id = 'O3_WeeklyExceed',className = 'main_plot'),
# html.Div(id = 'HourlyBoxplots', className = 'main_plot'),
html.Div(id = 'O3_WeeklyExceedTools', className = 'plot_tools', children = [
html.H3('Weekly Exceedance Tools:'),
html.Br(),
html.Label('Plot Title'),
dcc.Input( id = 'O3_WeeklyExceedTitle',
placeholder = 'Enter Title',
value = ''),
html.Br(),
]),
]),
html.Br(),
html.Hr(),
### ********************* ExceedMap *********************************
html.Div(id = 'ExceedMapHolder', className = 'plot_holder', children = [
html.Div(id = 'O3_ExceedMap', className = 'main_plot'),
# html.Div(id = 'WeeklyCycle', className = 'main_plot'),
html.Div(id = 'O3_ExceedMapTools', className = 'plot_tools', children = [
html.H3('Exceedance Map Tools:'),
html.Br(),
html.Label('Plot Title'),
dcc.Input( id = 'O3_ExceedMapTitle',
placeholder = 'Enter Title',
value = ''),
html.Br(),
]),
]),
html.Hr(),
])])
return page_layout
def get_o3_meta_data(species, environment, region, year_start, year_end):
if year_start and year_end:
df = LoadData.get_all_species_obvs(species, environment, region,year_start, year_end)
else:
df = 0
return df
def list_available_sites(site_region, env_choice, open_sites_only=True):
sites = LoadData.AURN_site_list_db(site_region, env_choice)
sites = list(sites)
sites.sort()
options = [{'label': i, 'value': i} for i in sites]
return options
site_year_closed = site.date_closed
if site_year_open < 2010:
site_year_open = 2010 # This is when modelled met started
site_year_open = 2020
if site_year_closed:
site_year_closed = site_year_closed.year
else:
site_year_closed = dt.now().year
for year in range(site_year_open, site_year_closed + 1):
# print(year)
site_code = site.site_code
# try:
df = LoadData.Get_AURN_Met_Data(site_code, year)
# except (HTTPError, URLError, KeyError) as e:
# continue
if type(df) != pd.core.frame.DataFrame:
continue
all_entries = []
for var in df.columns:
filters = {
'site_id': site,
'date_and_time__year': year,
'measurement_id': var_ids[var]
}
avail_data = measurement_data.objects.filter(**filters)
if len(avail_data):
def DEFRA_map_page():
site_regions = LoadData.AURN_regions()
region_choices = ['All'] + site_regions
region_options = [{'label': i.strip(), 'value': i.strip()} for i in region_choices]
site_envs = LoadData.AURN_environment_types()
env_choices = ['All'] + site_envs
env_options = [{'label': i.strip(), 'value': i.strip()} for i in env_choices]
all_species = LoadData.get_all_aurn_species()
species_options = [{'label': i.replace('<sub>','').replace('</sub>',''), 'value': i} for i in all_species]
page_layout = html.Div(id ='full_page_container', children =
[
html.Div(className = 'page-header', children = [
html.Div(id = 'home-logo-holder', children = [html.A(id = 'home-logo', href="/")]),
html.Div(id = 'page-header-holder', children = [html.A('UK Atmosphere',id = "page-header-text", href = "/")]),
]),
html.Div(className = 'page-body',children = [
html.H3('Map of DEFRA AURN sites'),
html.Div(className = 'tool_explainer', children = [
html.P('Select a pollutant and location on the map to see recent measurements'),
]),
html.Div(className = 'map_data_selection', children = [
html.Label('Select an environment:'),
dcc.Dropdown(id = 'map_env_choice',
multi = False,
options = env_options,
value = 'All'),
html.Br(),
html.Label('Select a region:'),
dcc.Dropdown(id = 'map_region_choice',
multi = False,
options = region_options,
value = 'All'),
html.Br(),
html.Label('Select a species:'),
dcc.Dropdown(id = 'map_species_choice',
multi = False,
options = species_options,
value = 'Ozone'),
html.Br(),
]),
# Map layout will go here
html.Div(id = 'map_output_holder', children = [
html.Div(id = 'main_map_holder', children = [
html.Div(id = 'site_counter_output'),
dcc.Loading(id="loading-main-map", children=[
dcc.Graph(id = 'main_map', config = {'scrollZoom': True},
)],type="dot"),
]),
html.Div(id = 'site_plot_from_map', children = [
html.H4(id = 'site_name_from_map'),
dcc.Tabs(id="map_tabs", value='site_sum', children=[
dcc.Tab(label='Site Summary', value='site_sum'),
dcc.Tab(label='Last 7 Days', value='site_week'),
dcc.Tab(label='Yearly Stats', value='yearly_stats'),
]),
html.Div(id = 'map_site_info'),
]),
])
])
])
return page_layout
