def modify_doc(doc):
plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300, bar_color="red")
def cb(attr, old, new):
slider.bar_color = "rgba(255, 255, 0, 1)"
slider.on_change('value', cb)
doc.add_root(column(slider, plot))
def modify_doc(doc):
plot = Plot(height=400, width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300)
def cb(attr, old, new):
slider.title = "baz"
slider.on_change('value', cb)
doc.add_root(column(slider, plot))
def modify_doc(doc):
source = ColumnDataSource(dict(x=[1, 2], y=[1, 1], val=["a", "b"]))
plot = Plot(height=400, width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
plot.add_glyph(source, Circle(x='x', y='y', size=20))
plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data"))))
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300, step=24*3600*1000)
def cb(attr, old, new):
source.data['val'] = [slider.value_as_date.isoformat()]
slider.on_change('value', cb)
doc.add_root(column(slider, plot))
def test_js_on_change_executes(self, bokeh_model_page) -> None:
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300)
slider.js_on_change('value', CustomJS(code=RECORD("value", "cb_obj.value")))
page = bokeh_model_page(slider)
drag_slider(page.driver, ".foo", 150)
results = page.results
assert datetime.fromtimestamp(results['value']/1000) > datetime(*date.fromisoformat("2017-08-04").timetuple()[:3])
assert page.has_no_console_errors()
def test_server_bar_color_updates(
self, bokeh_server_page: BokehServerPage) -> None:
slider = DateSlider(start=start,
end=end,
value=value,
width=300,
bar_color="red")
def modify_doc(doc):
plot = Plot(height=400,
width=400,
x_range=Range1d(0, 1),
y_range=Range1d(0, 1),
min_border=0)
def cb(attr, old, new):
slider.bar_color = "rgba(255, 255, 0, 1)"
slider.on_change('value', cb)
doc.add_root(column(slider, plot))
page = bokeh_server_page(modify_doc)
drag_slider(page.driver, slider, 150)
sleep(1) # noUiSlider does a transition that takes some time
assert get_slider_bar_color(page.driver,
slider) == "rgba(255, 255, 0, 1)"
def test_display(self, bokeh_model_page: BokehModelPage) -> None:
slider = DateSlider(start=start, end=end, value=value, width=300)
page = bokeh_model_page(slider)
children = find_elements_for(page.driver, slider,
"div.bk-input-group > div")
assert len(children) == 2
assert page.has_no_console_errors()
def test_display(self, bokeh_model_page) -> None:
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300)
page = bokeh_model_page(slider)
el = page.driver.find_element_by_css_selector('.foo')
children = el.find_elements_by_css_selector('div.bk-input-group > div')
assert len(children) == 2
assert page.has_no_console_errors()
def test_displays_bar_color(self, bokeh_model_page) -> None:
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300, bar_color="red")
page = bokeh_model_page(slider)
el = page.driver.find_element_by_css_selector('.foo')
assert len(el.find_elements_by_css_selector('div.bk-input-group > div')) == 2
assert get_slider_bar_color(page.driver, ".foo") == "rgba(255, 0, 0, 1)"
assert page.has_no_console_errors()
def test_displays_title(self, bokeh_model_page) -> None:
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300)
page = bokeh_model_page(slider)
el = page.driver.find_element_by_css_selector('.foo')
assert len(el.find_elements_by_css_selector('div.bk-input-group > div')) == 2
assert get_slider_title_text(page.driver, ".foo") == "04 Aug 2017"
assert get_slider_title_value(page.driver, ".foo") == "04 Aug 2017"
assert page.has_no_console_errors()
def test_title_updates(self, bokeh_model_page: BokehModelPage) -> None:
slider = DateSlider(start=start, end=end, value=value, width=300)
page = bokeh_model_page(slider)
assert get_slider_title_value(page.driver, slider) == "04 Aug 2017"
drag_slider(page.driver, slider, 50)
assert get_slider_title_value(page.driver, slider) > "04 Aug 2017"
drag_slider(page.driver, slider, -70)
assert get_slider_title_value(page.driver, slider) == "03 Aug 2017"
assert page.has_no_console_errors()
def modify_doc(doc):
plot = Plot(height=400, width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300)
def cbv(attr, old, new): junk['v'] += 1
def cbvt(attr, old, new): junk['vt'] += 1
slider.on_change('value', cbv)
slider.on_change('value_throttled', cbvt)
doc.add_root(column(slider, plot))
def construct(pgcd, controller=None) :
df_column = "Confirmed"
lastday = pgcd.lastday()
firstday = pgcd.firstday()
mapper = "Log"
# Make carto
title = 'Coronavirus map : Day ' + str(lastday)
tooltips = lutils.tooltips()
tools = [PanTool(), WheelZoomTool(), ResetTool()]
carto = WMap(pgcd, lastday, df_column, title=title, mkind=mapper, tooltips=tooltips, aspect_ratio=2, sizing_mode="scale_both", tools=tools)
# Make a slider object: slider
slider = DateSlider(title="Date", start=firstday, end=lastday, value=lastday, step=1, format="%Y-%d-%m", sizing_mode="stretch_width")
carto.link_on_change("date", slider, postfun=convert_slider_date)
# Make buttons
lambda_callback_bleft = lambda : carto_shift_day(False, carto, slider)
bleft = Button(label="Day -1", button_type="success", width=200)
bleft.on_click(lambda_callback_bleft)
lambda_callback_bright = lambda : carto_shift_day(True, carto, slider)
bright = Button(label="Day +1", button_type="success", width=200)
bright.on_click(lambda_callback_bright)
# Select for carto
options = [df_column for df_column in lutils.columns_description() if df_column not in ["Date"]]
scol = Select(title="", options=options, value=df_column, width=200)
rdesc_column = lambda column : lutils.description(column, reverse=True)
carto.link_on_change("field", scol, postfun=rdesc_column)
smap = Select(title="", options=list(COLOR_MAPPER_NAME.values()), value=COLOR_MAPPER_NAME[mapper], width=200)
rdesc_cmapper = lambda name : lutils.reverse_mapping(COLOR_MAPPER_NAME, name)
carto.link_on_change("mkind", smap, postfun=rdesc_cmapper)
if controller :
fun = lambda : update(slider, carto)
controller.add_receiver("update", fun)
new_signal = lambda country : controller.emit_signal("select_country", country)
carto.add_receiver("doubletap", new_signal)
return column(
carto.figure, slider,
row(bleft, bright, scol, smap, sizing_mode="stretch_width"),
sizing_mode="stretch_both")
def init_widgets(data_start_date, data_end_date, source):
"""Initializes the widgets to be displayed adjacent the plot
Args:
data_start_date: the first date given in the data
data_end_date: the last date given in the data
source: data for the current day that is displayed by the widgets initially
"""
date_slider = DateSlider(title="Date Range: ", start=date.fromisoformat(data_start_date.split("T")[0]), end=date.fromisoformat(data_end_date.split("T")[0]), value=date.fromisoformat(data_end_date.split("T")[0]), step=86400000, sizing_mode="stretch_width")
radio_button_group = RadioButtonGroup(labels=["New Positives", "Cumulative Number of Positives", "Total Number of Tests", "Cumulative Number of Tests"], active=0, sizing_mode="stretch_width")
button = Button(label="Play", button_type="primary")
columns = [
TableColumn(field="name", title="County"),
TableColumn(field="new_positives", title="New Positives"),
]
data_table = DataTable(source=source, columns=columns, sizing_mode="stretch_both", index_position=None)
return date_slider, radio_button_group, button, data_table
def test_server_on_change_round_trip(
self, bokeh_server_page: BokehServerPage) -> None:
slider = DateSlider(start=start,
end=end,
value=value,
width=300,
step=24 * 3600 * 1000)
def modify_doc(doc):
source = ColumnDataSource(dict(x=[1, 2], y=[1, 1], val=["a", "b"]))
plot = Plot(height=400,
width=400,
x_range=Range1d(0, 1),
y_range=Range1d(0, 1),
min_border=0)
plot.add_glyph(source, Circle(x='x', y='y', size=20))
plot.tags.append(
CustomJS(name="custom-action",
args=dict(s=source),
code=RECORD("data", "s.data")))
def cb(attr, old, new):
iso_date = slider.value_as_date.isoformat()
source.data['val'] = [iso_date, iso_date]
slider.on_change('value', cb)
doc.add_root(column(slider, plot))
page = bokeh_server_page(modify_doc)
drag_slider(page.driver, slider, 50)
page.eval_custom_action()
results = page.results
new = results['data']['val']
assert new[0] > '2017-08-04'
drag_slider(page.driver, slider, -70)
page.eval_custom_action()
results = page.results
new = results['data']['val']
assert new[0] == '2017-08-03'
def add_slider(self):
"""Build slider
"""
self.controls['slider'] = DateSlider(start=self.meta['dates'][-1].date(),
end=self.meta['dates'][0].date(),
value=self.meta['dates'][0].date(),
width=self.plot.plot_width-40-84,
title='Reported Date')
_callback = CustomJS(args=dict(source=self.srcs['counties'],
date=self.controls['slider']),
code="""
// javascript code
var data = source.data;
var cur_day = data['day'];
// from DateSlider
var day = Math.floor((date.end - date.value)/(1000*60*60*24));
// create column names
var ci = 'c'.concat(day.toString());
var di = 'd'.concat(day.toString());
var mi = 'm'.concat(day.toString());
// change data
if (cur_day[0] != day){
for (var i=0; i < cur_day.length; i++){
data['c'][i] = data[ci][i];
data['d'][i] = data[di][i];
data['m'][i] = data[mi][i];
cur_day[0] = day;
}
}
source.change.emit();
""")
self.controls['slider'].js_on_change('value', _callback)
log.debug('slider added')
def test_server_callback_value_vs_value_throttled(
self, bokeh_server_page: BokehServerPage) -> None:
junk = dict(v=0, vt=0)
slider = DateSlider(start=start, end=end, value=value, width=300)
def modify_doc(doc):
plot = Plot(height=400,
width=400,
x_range=Range1d(0, 1),
y_range=Range1d(0, 1),
min_border=0)
def cbv(attr, old, new):
junk['v'] += 1
def cbvt(attr, old, new):
junk['vt'] += 1
slider.on_change('value', cbv)
slider.on_change('value_throttled', cbvt)
doc.add_root(column(slider, plot))
page = bokeh_server_page(modify_doc)
drag_slider(page.driver, slider, 30, release=False)
sleep(1) # noUiSlider does a transition that takes some time
drag_slider(page.driver, slider, 30, release=False)
sleep(1) # noUiSlider does a transition that takes some time
drag_slider(page.driver, slider, 30, release=False)
sleep(1) # noUiSlider does a transition that takes some time
drag_slider(page.driver, slider, 30, release=True)
sleep(1) # noUiSlider does a transition that takes some time
assert junk['v'] == 4
assert junk['vt'] == 1
# Define a function to update color mapper used in both patches and colorbar
def update_bar(new):
for i, d in enumerate(labels):
if i == new:
color_bar.color_mapper = mappers[d]["transform"]
color_bar.title = d
cantons.glyph.fill_color = mappers[d]
buttons.on_click(update_bar)
# T2.5 Add a dateslider to control which per capita daily new cases information to display
# Define a dateslider using maximum and mimimum dates, set value to be the latest date
timeslider = DateSlider(title='Date',
start=dates.min(),
end=dates.max(),
value=dates.max())
# Complete the callback function
# Hints:
# convert the timestamp value from the slider to datetime and format it in the form of '%Y-%m-%d'
# update columns 'size', 'dnc' with the column named '%Y-%m-%d' in merged
# update geosource with new merged
def callback(attr, old, new):
# Convert timestamp to datetime
# https://stackoverflow.com/questions/9744775/how-to-convert-integer-timestamp-to-python-datetime
date = datetime.fromtimestamp(new / 1e3)
i = date.strftime('%Y-%m-%d')
merged.size = merged[i] * 1e5 / 5 + 10
fill_color={
'field': 'temp_anomalies',
'transform': color_mapper
},
fill_alpha=0.5,
line_color=None)
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=12,
border_line_color=None,
location=(0, 0))
p.add_layout(color_bar, 'right')
# Make a slider object: slider
slider = DateSlider(title="Date",
value=min(climate_data.index),
start=min(climate_data.index),
end=max(climate_data.index),
step=1,
width=900)
# Attach the callback to the 'value' property of slider
slider.on_change('value', update_plot)
# Make a row layout of widgetbox(slider) and plot and add it to the current document
layout = column(widgetbox(slider, sizing_mode='scale_width'), p)
# Add the plot to the current document and add a title
curdoc().add_root(layout)
curdoc().title = 'Global Warming'
max(df_map[plot_var[3]]),
max(df_map[plot_var[4]]),
max(df_map[plot_var[5]]),
max(df_map[plot_var[6]]),
max(df_map[plot_var[7]]),
max(df_map[plot_var[8]]),
max(df_map[plot_var[9]]),
max(df_map[plot_var[10]]),
max(df_map[plot_var[11]])
]
# Make a selection of the date to plot
slider = DateSlider(title='Date',
start=first_dt,
end=last_dt,
step=1,
value=last_dt,
height=20,
margin=(20, 50, 20, 50),
sizing_mode="stretch_width")
slider.on_change('value_throttled', update_map)
# Make a span to show current date in plots
dt_span = Span(location=slider.value_as_date,
dimension='height',
line_color='red',
line_dash='solid',
line_width=2)
# Update timeseries plots based on selection
source_map.selected.on_change('indices', update_plot)
def create_html_page(html_page_name, df):
del states["HI"]
del states["AK"]
EXCLUDED = ("ak", "hi", "pr", "gu", "vi", "mp", "as")
state_xs = [states[code]["lons"] for code in states]
state_ys = [states[code]["lats"] for code in states]
county_xs = [
counties[code]["lons"] for code in counties
if counties[code]["state"] not in EXCLUDED
]
county_ys = [
counties[code]["lats"] for code in counties
if counties[code]["state"] not in EXCLUDED
]
county_names = [
counties[code]["name"] for code in counties
if counties[code]["state"] not in EXCLUDED
]
col_names = list(df.columns.values)
len_col = len(col_names)
last_day_str = col_names[len_col - 1]
last_day = datetime.strptime(last_day_str, '%Y-%m-%d %H:%M:%S').date()
first_day_str = col_names[4] # first 4 colums contain names etc
first_day = datetime.strptime(first_day_str, '%Y-%m-%d %H:%M:%S').date()
a_dict = create_dict_forJS(df)
source_new = ColumnDataSource(data=a_dict)
source_visible = ColumnDataSource(
data={
'x': county_xs,
'y': county_ys,
'name': county_names,
'rate': a_dict[str(last_day)]
})
'''
c_dict = create_colors_forJS(df)
hex_color = rgb_to_hex((255, 255, 255))
source_visible = ColumnDataSource(data={'x': county_xs, 'y': county_ys,
'name': county_names, 'rate': a_dict[str(last_day)],
'color': [hex_color]})
'''
# Define a sequential multi-hue color palette.
palette = brewer['YlGnBu'][6]
palette = palette[::-1]
# TODO: get the high value from median?
color_mapper = LinearColorMapper(palette=palette, low=0, high=100)
# TODO: add agenda?
TOOLS = "pan,zoom_in,zoom_out,wheel_zoom,box_zoom,reset,hover,save"
p = figure(title="US density",
toolbar_location="right",
output_backend="webgl",
plot_width=1100,
plot_height=700,
tools=TOOLS,
tooltips=[("County", "@name"), ("Confirmed cases", "@rate"),
("(Long, Lat)", "($x, $y)")])
# hide grid and axes
p.axis.visible = None
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
# p.patches(county_xs, county_ys,
# fill_alpha=0.7,
# line_color="white", line_width=0.5)
p.patches(state_xs,
state_ys,
fill_alpha=0.7,
line_color="white",
line_width=2,
line_alpha=0.3)
p.patches(
'x',
'y',
source=source_visible,
fill_color={
'field': 'rate',
'transform': color_mapper
},
#fill_color='color',
fill_alpha=0.7,
line_color="white",
line_width=0.2)
date_slider = DateSlider(title="Date:",
start=first_day,
end=last_day,
value=last_day,
step=1)
callback = CustomJS(args=dict(source=source_new, ts=source_visible),
code="""
var data=ts.data;
var data1=source.data;
var f=cb_obj.value; //this is the selection value of slider
var event = new Date(f);
var date_selected = event.toISOString().substring(0,10); // converting date from python to JS
//ts.data['color'] = '#ffffff' //color_dict.data[date_selected];
data['rate']=data1[date_selected];
ts.change.emit();
""")
date_slider.js_on_change('value', callback)
layout = row(column(date_slider), p)
output_file(html_page_name, title="Interactive USA density map")
show(layout)
def bkapp_page():
###-----------------------------------------------------------------------###
###------------------------PREPARING DATA---------------------------------###
### This section contains getting and preparing data for this plot------ ###
###-----------------------------------------------------------------------###
### Load dataset rat interventions
bait_interventions_to_save = pd.read_pickle('development/bait_interventions_to_save_pitch_night.pickle')
### Load indices of rows for each month for bait dataset
indices_bait_df = pd.read_pickle('development/indices_bait_df_pitch_night.pickle') #has 2 columns: 'from_index' and 'to_index'
### Load dataset rat sighting
dataset_sightings_locations_to_save = pd.read_pickle('development/dataset_sightings_locations_to_save_pitch_night.pickle')
### Load indices of rows for each month for sightings dataset
indices_sight_df = pd.read_pickle('development/indices_sight_df_pitch_night.pickle') #has 2 columns: 'from_index' and 'to_index'
### Load dataset by zipcodes
by_zipcodes_df = pd.read_pickle('development/dataset_by_zipcodes_pitch_night.pickle')
### PREDICTIONS datasets
df = pd.read_pickle('development/locations_prediction_df_pitch_night.pickle')
prophet_fit_all_nyc = pd.read_pickle('development/prophet_fit_all_nyc_pitch_night.pickle')
prophet_prediction_all_nyc = pd.read_pickle('development/prophet_prediction_all_nyc_pitch_night.pickle')
### Read the nyc map data
nyc_df = pd.read_pickle('development/nyc_converted_to_save.pickle')
nyc_by_zips = pd.read_pickle('development/dataset_map_by_zipcodes_pitch_night.pickle')
zip_hm = pd.read_pickle('development/zip_heatmaps_pitch_night.pickle')
### Read existing year-month strings in dataset
with open('development/timepoints_pitch_night.pickle', "rb") as f:
timepoints = pickle.load(f)
timepoints = timepoints[:-1]
### list of zipcodes in dataset
with open('development/all_zips.pickle', "rb") as f:
all_zips = pickle.load(f)
### Read predicted months
with open('development/predicted_months_pitch_night.pickle', "rb") as f:
predicted_months = pickle.load(f)
### prepare data for bokeh
bait_source = ColumnDataSource(bait_interventions_to_save)
indices_bait_source = ColumnDataSource(indices_bait_df)
sight_source = ColumnDataSource(dataset_sightings_locations_to_save)
indices_sight_source = ColumnDataSource(indices_sight_df)
nyc_source = ColumnDataSource(nyc_df)
timepoints_cds = ColumnDataSource(pd.DataFrame({'timepoints':timepoints}))
predicted_months_cds = ColumnDataSource(pd.DataFrame({'predicted_months':predicted_months}))
by_zipcodes_source = ColumnDataSource(by_zipcodes_df)
nyc_by_zips_source = ColumnDataSource(nyc_by_zips)
zip_hm_first = ColumnDataSource(zip_hm.loc[:,['ZIPCODE','x','y','sightings']])
zip_hm_original = ColumnDataSource(zip_hm)
### bokeh data source for initial plot rendered:
first_source_bait = ColumnDataSource(bait_interventions_to_save.iloc[indices_bait_df['from_index'][51]:indices_bait_df['to_index'][51],:])
first_source_sight = ColumnDataSource(dataset_sightings_locations_to_save.iloc[indices_sight_df['from_index'][51]:indices_sight_df['to_index'][51],:])
###-----------------------------------------------------------------------###
###----------------------GRAPHICAL USER INTERFACE-------------------------###
### This code defines the Bokeh controls that are used for the user ###
### interface. ---------------------------------------------------------- ###
###-----------------------------------------------------------------------###
### Initialize plot figure
p = figure(x_range=(-74.2, -73.7), y_range=(40.53, 40.915), tools= 'box_zoom,pan,save,reset', active_drag="box_zoom",
min_border_right = 40, min_border_top = 5, min_border_bottom = 5, border_fill_color = "black",
background_fill_color = "black", toolbar_location="left")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.axis.visible = False
p.outline_line_color = "black"
###-----------------------------------------------------------------------###
###------------------------PREDICTED locations----------------------------###
###-------------------------heatmap---------------------------------------###
###-----------------------------------------------------------------------###
colors = ['#000000'] + brewer["Reds"][9]
binsize = 0.5/80
hm_source = ColumnDataSource(pd.read_pickle('development/df_mock_pitch_practice.pickle'))
# hm_source = ColumnDataSource(df)
hm_source_original = ColumnDataSource(df)
## not nomalized count
mapper = LinearColorMapper(palette=colors, low=df.rat_count.min(), high=df.rat_count.max())
prediction_location = p.rect(x="level_0", y="level_1", width=binsize, height=binsize,
source=hm_source,
fill_color={'field': 'start', 'transform': mapper},
line_color=None)
###-----------------------------------------------------------------------###
###---------------------------NYC map-------------------------------------###
###------------------------and events from data---------------------------###
###-----------------------------------------------------------------------###
### Add nyc map
p.patches('x', 'y', source=nyc_source, color='black', line_width=1, fill_color={'field': 'colors'}, fill_alpha = 0.4)
### Add my bait points
baits = p.circle('LONGITUDE', 'LATITUDE', source=first_source_bait, fill_color='#4dc6e0', line_color = '#4dc6e0', line_width=3, line_alpha=0.6, legend="Rat Interventions")
### Add my sights points
sights = p.circle('LONGITUDE', 'LATITUDE', source=first_source_sight, fill_color = '#d96c82',line_color = '#d96c82',line_width=3, line_alpha=0.6, legend="Rat Sightings")
p.legend.location = "top_left"
p.legend.label_text_color = 'white'
p.legend.border_line_color = "white"
p.legend.background_fill_color = "black"
### Add slider
date_slider = DateSlider(title="Date", start=dt.date(2010, 1, 1), end=dt.date(2018, 9, 1),value=dt.date(2014, 4, 1), step=1, format = "%B %Y")
### Add hovers
bait_hover = HoverTool(tooltips = """
<div>
<div>
<span style="font-size: 14px; font-weight:bold; color: #00BFFF">Location:</span> <span style="font-size: 15px; color: #000000">@HOUSE_NUMBER @STREET_NAME</span><br>
<span style="font-size: 14px; font-weight:bold; color: #00BFFF;">Zip Code:</span> <span style="font-size: 15px; color: #000000"> @ZIP_CODE </span><br>
<span style="font-size: 14px; font-weight:bold; color: #00BFFF;">Intervention Date: </span> <span style="font-size: 15px; color: #000000">@Inspection_Date</span><br>
<span style="font-size: 14px; font-weight:bold; color: #00BFFF;">Intervention Type: </span> <span style="font-size: 15px; color: #000000">@RESULT</span>
</div>
</div>
""", renderers=[baits])
p.add_tools(bait_hover)
sight_hover = HoverTool(tooltips = """
<div>
<div>
<span style="font-size: 14px; font-weight:bold; color: #F08080">Location:</span> <span style="font-size: 15px; color: #000000">@ADDRESS</span><br>
<span style="font-size: 14px; font-weight:bold; color: #F08080;">Zip Code:</span> <span style="font-size: 15px; color: #000000"> @ZIP_CODE </span><br>
<span style="font-size: 14px; font-weight:bold; color: #F08080;">Rat Sighting Date: </span> <span style="font-size: 15px; color: #000000">@Sighting_Date</span>
</div>
</div>
""", renderers=[sights])
p.add_tools(sight_hover)
prediction_hover = HoverTool(tooltips = """
<div>
<div>
<span style="font-size: 14px; font-weight:bold; color: #F08080">Longitude:</span> <span style="font-size: 15px; color: #000000">@level_0</span><br>
<span style="font-size: 14px; font-weight:bold; color: #F08080;">Latitude:</span> <span style="font-size: 15px; color: #000000"> @level_1 </span><br>
<span style="font-size: 14px; font-weight:bold; color: #F08080;">Predicted monthly sightings: </span> <span style="font-size: 15px; color: #000000">@start</span>
</div>
</div>
""", renderers=[prediction_location])
p.add_tools(prediction_hover)
### Add a Zip Code selection option
zip_select = Select(title="Selected Zipcode:", value="all zipcodes", options= all_zips)
###-----------------------------------------------------------------------###
###------------------------PLOT of whole----------------------------------###
###----------------------city sightings numbers---------------------------###
###-----------------------------------------------------------------------###
fit_source = ColumnDataSource(prophet_fit_all_nyc)
prediction_source = ColumnDataSource(prophet_prediction_all_nyc)
p_right = figure(title = 'CITY-WIDE MONTHLY PREDICTIONS',tools= 'box_zoom,pan,save,reset', min_border_top = 250, min_border_left = 100, border_fill_color = "black",
background_fill_color = "black", width = 600, height = 550, active_drag="box_zoom", x_axis_type="datetime")
# interval shading glyph:
lowerband = prophet_prediction_all_nyc['yhat_lower'].values
upperband = prophet_prediction_all_nyc['yhat_upper'].values
band_x = np.append(prophet_prediction_all_nyc['ds'].values, prophet_prediction_all_nyc['ds'].values[::-1])
band_y = np.append(lowerband, upperband[::-1])
p_right.patch(band_x, band_y, color='white', fill_alpha=0.5, alpha = 0.5)
p_right.line(x = 'ds', y = 'y', source = fit_source, color = '#d96c82', line_width=2.6, legend = 'monthly rat sightings')
p_right.circle(x = 'ds', y = 'y', source = fit_source, color = '#d96c82', size = 7, alpha = 0.5, legend = 'monthly rat sightings')
p_right.line(x = 'ds', y = 'yhat', source = prophet_fit_all_nyc, line_width=2, color = 'white', legend = 'FBprophet fit/prediction')
p_right.circle(x = 'ds', y = 'yhat', source = prophet_fit_all_nyc, color = 'white', size = 5, alpha = 0.5, legend = 'FBprophet fit/prediction')
p_right.line(x = 'ds', y = 'yhat', source = prophet_prediction_all_nyc, line_width=2, color = 'white', line_dash="4 4")
p_right.circle(x = 'ds', y = 'yhat', source = prophet_prediction_all_nyc, size = 5, color = 'white', alpha = 0.5, line_dash="4 4")
p_right.line([prophet_fit_all_nyc.iloc[-1,0], prophet_prediction_all_nyc.iloc[0,0]],
[prophet_fit_all_nyc.iloc[-1,2], prophet_prediction_all_nyc.iloc[0,1]], line_dash="4 4",
line_width=2, color='white')
p_right.legend.location = "top_left"
p_right.xaxis.major_label_text_font_size = "14pt"
p_right.yaxis.major_label_text_font_size = "14pt"
p_right.title.text_font_size = '16pt'
p_right.legend.label_text_font_size = '9pt'
p_right.legend.location = "top_left"
p_right.xaxis.axis_label = 'Date'
p_right.yaxis.axis_label = 'monthly rat sightings'
p_right.xaxis.axis_label_text_font_size = "14pt"
p_right.yaxis.axis_label_text_font_size = "14pt"
p_right.xaxis.axis_label_text_color = '#909090'
p_right.xaxis.axis_line_color = '#909090'
p_right.xaxis.major_label_text_color = '#909090'
p_right.yaxis.axis_label_text_color = '#909090'
p_right.yaxis.axis_line_color = '#909090'
p_right.yaxis.major_label_text_color = '#909090'
p_right.title.text_color = '#909090'
p_right.legend.label_text_color = '#909090'
p_right.legend.border_line_color = "#909090"
p_right.outline_line_color = "#909090"
p_right.legend.background_fill_color = "black"
###-----------------------------------------------------------------------###
###----------------------------CALLBACKS----------------------------------###
### This section defines the behavior of the GUI as the user interacts ###
### with the controls. --------------------------------------------------###
###-----------------------------------------------------------------------###
### Slider callback function
callback = CustomJS(args=dict(date_slider = date_slider, zip_select = zip_select, first_source_bait = first_source_bait, original_source_bait = bait_source, bait_indices = indices_bait_source, first_source_sight = first_source_sight, original_source_sight = sight_source, hm_source_original = hm_source_original, hm_source = hm_source, sight_indices = indices_sight_source, timepoints_cds = timepoints_cds, predicted_months_cds = predicted_months_cds), code="""
var date_slider = new Date(date_slider.value);
var timepoints_cds = timepoints_cds.data;
var predicted_months_cds = predicted_months_cds.data;
var zip_selected = parseFloat(zip_select.value);
var data_bait = first_source_bait.data;
var whole_data_bait = original_source_bait.data;
var bait_indices = bait_indices.data;
var data_sight = first_source_sight.data;
var whole_data_sight = original_source_sight.data;
var sight_indices = sight_indices.data;
var data_hm = hm_source.data;
var data_hm_original = hm_source_original.data;
const monthNames = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"];
var year_month = (date_slider.getUTCFullYear()).toString() +'-'+ monthNames[date_slider.getUTCMonth()];
console.log(year_month)
var g = timepoints_cds['timepoints'].indexOf(year_month)
var test = 0;
data_hm['start'] = [];
data_hm['level_0'] = [];
data_hm['level_1'] = [];
if(predicted_months_cds['predicted_months'].indexOf(year_month) >= 0 ) {
for (k = 0; k < 80*80; k++) {
data_hm['start'].push(data_hm_original['predicted_'+ year_month][k])
data_hm['level_0'].push(data_hm_original['level_0'][k])
data_hm['level_1'].push(data_hm_original['level_1'][k])
test = k;
}
}
console.log(data_hm['start'][test])
data_bait['LONGITUDE'] = []
data_bait['LATITUDE'] = []
data_bait['HOUSE_NUMBER'] = []
data_bait['STREET_NAME'] = []
data_bait['ZIP_CODE'] = []
data_bait['Inspection_Date'] = []
data_bait['RESULT'] = []
for (i = bait_indices['from_index'][g]; i < bait_indices['to_index'][g] + 1; i++) {
if(whole_data_bait['ZIP_CODE'][i] == zip_selected || zip_selected == "all zipcodes" || isNaN(zip_selected)) {
data_bait['LONGITUDE'].push(whole_data_bait['LONGITUDE'][i])
data_bait['LATITUDE'].push(whole_data_bait['LATITUDE'][i])
data_bait['HOUSE_NUMBER'].push(whole_data_bait['HOUSE_NUMBER'][i])
data_bait['STREET_NAME'].push(whole_data_bait['STREET_NAME'][i])
data_bait['ZIP_CODE'].push(whole_data_bait['ZIP_CODE'][i])
data_bait['Inspection_Date'].push(whole_data_bait['Inspection_Date'][i])
data_bait['RESULT'].push(whole_data_bait['RESULT'][i])
}
}
data_sight['LONGITUDE'] = []
data_sight['LATITUDE'] = []
data_sight['ADDRESS'] = []
data_sight['ZIP_CODE'] = []
data_sight['Sighting_Date'] = []
for (j = sight_indices['from_index'][g]; j < sight_indices['to_index'][g] + 1; j++) {
if(whole_data_sight['ZIP_CODE'][j] == zip_selected || zip_selected == "all zipcodes" || isNaN(zip_selected)) {
data_sight['LONGITUDE'].push(whole_data_sight['LONGITUDE'][j])
data_sight['LATITUDE'].push(whole_data_sight['LATITUDE'][j])
data_sight['ADDRESS'].push(whole_data_sight['ADDRESS'][j])
data_sight['ZIP_CODE'].push(whole_data_sight['ZIP_CODE'][j])
data_sight['Sighting_Date'].push(whole_data_sight['Sighting_Date'][j])
}
}
hm_source.change.emit();
first_source_sight.change.emit();
first_source_bait.change.emit();
""")
### Zip code select callback function
zip_callback = CustomJS(args=dict(zip_select = zip_select, nyc_source = nyc_source, date_slider = date_slider, first_source_bait = first_source_bait, original_source_bait = bait_source, bait_indices = indices_bait_source, first_source_sight = first_source_sight, original_source_sight = sight_source, sight_indices = indices_sight_source, timepoints_cds = timepoints_cds), code="""
var zip_selected = parseFloat(zip_select.value);
var date_slider = new Date(date_slider.value);
var timepoints_cds = timepoints_cds.data;
var nyc_source_data = nyc_source.data;
var zip_color_selected = "black";
if (zip_selected == "all zipcodes" || isNaN(zip_selected)) {
zip_color_selected = "white";
}
var zip_color_rest = "white";
var data_bait = first_source_bait.data;
var whole_data_bait = original_source_bait.data;
var bait_indices = bait_indices.data;
var data_sight = first_source_sight.data;
var whole_data_sight = original_source_sight.data;
var sight_indices = sight_indices.data;
nyc_source_data['colors'] = []
for (i = 0; i <nyc_source_data['ZIPCODE'].length; i++) {
if (nyc_source_data['ZIPCODE'][i] == zip_selected || zip_selected == "all zipcodes" || isNaN(zip_selected)) {
nyc_source_data['colors'].push(zip_color_selected);
} else {
nyc_source_data['colors'].push(zip_color_rest);
}
}
const monthNames = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"];
var year_month = (date_slider.getUTCFullYear()).toString() +'-'+ monthNames[date_slider.getUTCMonth()];
console.log(year_month)
console.log(zip_selected)
var g = timepoints_cds['timepoints'].indexOf(year_month)
data_bait['LONGITUDE'] = []
data_bait['LATITUDE'] = []
data_bait['HOUSE_NUMBER'] = []
data_bait['STREET_NAME'] = []
data_bait['ZIP_CODE'] = []
data_bait['Inspection_Date'] = []
data_bait['RESULT'] = []
for (i = bait_indices['from_index'][g]; i < bait_indices['to_index'][g] + 1; i++) {
if(whole_data_bait['ZIP_CODE'][i] == zip_selected || zip_selected == "all zipcodes" || isNaN(zip_selected)) {
data_bait['LONGITUDE'].push(whole_data_bait['LONGITUDE'][i])
data_bait['LATITUDE'].push(whole_data_bait['LATITUDE'][i])
data_bait['HOUSE_NUMBER'].push(whole_data_bait['HOUSE_NUMBER'][i])
data_bait['STREET_NAME'].push(whole_data_bait['STREET_NAME'][i])
data_bait['ZIP_CODE'].push(whole_data_bait['ZIP_CODE'][i])
data_bait['Inspection_Date'].push(whole_data_bait['Inspection_Date'][i])
data_bait['RESULT'].push(whole_data_bait['RESULT'][i])
}
}
data_sight['LONGITUDE'] = []
data_sight['LATITUDE'] = []
data_sight['ADDRESS'] = []
data_sight['ZIP_CODE'] = []
data_sight['Sighting_Date'] = []
for (j = sight_indices['from_index'][g]; j < sight_indices['to_index'][g] + 1; j++) {
if(whole_data_sight['ZIP_CODE'][j] == zip_selected || zip_selected == "all zipcodes" || isNaN(zip_selected)) {
data_sight['LONGITUDE'].push(whole_data_sight['LONGITUDE'][j])
data_sight['LATITUDE'].push(whole_data_sight['LATITUDE'][j])
data_sight['ADDRESS'].push(whole_data_sight['ADDRESS'][j])
data_sight['ZIP_CODE'].push(whole_data_sight['ZIP_CODE'][j])
data_sight['Sighting_Date'].push(whole_data_sight['Sighting_Date'][j])
}
}
first_source_sight.change.emit();
first_source_bait.change.emit();
nyc_source.change.emit();
""")
zip_select.js_on_change('value', zip_callback)
date_slider.js_on_change('value', callback)
layout = row(column(row(date_slider, zip_select), p), p_right)
# layout = gridplot([[p, p_right]])
the_script, the_div = components(layout)
###-----------------------------------------------------------------------###
###-----------------------------------------------------------------------###
###-----------------------BY ZIPCODES PLOT--------------------------------###
###-----------------------------------------------------------------------###
###-----------------------------------------------------------------------###
colors = ['#f27991', '#f493a7', '#f7aebd',
'#fce4e9'] + ['#FFFFFF'] + ['#ddf8fd','#ccf4fd', '#bbf1fc','#aaedfc','#99eafb','#88e6fa', '#77e3fa',
'#66dff9', '#56dcf9']
mapper = LinearColorMapper(palette=colors, low=by_zipcodes_df.dev.min(), high=by_zipcodes_df.dev.max())
color_bar = ColorBar(color_mapper=mapper, location=(0, 0), label_standoff=10,
major_label_text_font_size='14pt', major_label_text_color = '#909090',
background_fill_color = 'black', scale_alpha = 0.7)
mp = figure(x_range=(-74.2, -73.7), y_range=(40.53, 40.915), width = 600, height =630,
tools= 'box_zoom,pan,save,reset', active_drag="box_zoom", background_fill_color = "black",
min_border_right = 40, min_border_top = 5, min_border_bottom = 5, border_fill_color = "black")
mp.xgrid.grid_line_color = None
mp.ygrid.grid_line_color = None
mp.axis.visible = False
mp.outline_line_color = "black"
zips = mp.patches('x', 'y', source=nyc_by_zips_source, color='black', line_width=1,
fill_color={'field': 'dev', 'transform': mapper}, alpha = 0.7)
zips_hover = HoverTool(tooltips = """
<div>
<div>
<span style="font-size: 14px; font-weight:bold; color: #000000;">Zip Code: </span> <span style="font-size: 15px; color: #000000">@ZIPCODE</span><br>
<span style="font-size: 14px; font-weight:bold; color: #000000">Average number of rat sightings per year:</span> <span style="font-size: 15px; color: #000000">@sightings</span><br>
<span style="font-size: 14px; font-weight:bold; color: #000000;">Average number of interventions per year:</span> <span style="font-size: 15px; color: #000000"> @interventions </span><br>
<span style="font-size: 14px; font-weight:bold; color: #000000;">Number of interventions above expectation:</span> <span style="font-size: 15px; color: #000000"> @dev </span>
</div>
</div>
""", renderers=[zips])
mp.add_tools(zips_hover)
p_zips = figure(title = 'Sightings and interventions by zipcode',tools= 'box_zoom,pan,save,reset',
active_drag="box_zoom", background_fill_color = "black",
min_border_right = 40, min_border_top = 5, min_border_bottom = 5, border_fill_color = "black")
points = p_zips.circle(x = 'sightings', y = 'interventions', source = by_zipcodes_source, size=12,
fill_color={'field': 'dev', 'transform': mapper}, alpha = 0.7, line_color = 'black')
p_zips.line(x = 'sightings', y = 'lin_fit', source = by_zipcodes_source, color = 'white')
p_zips.add_layout(color_bar, 'left')
points_hover = HoverTool(tooltips = """
<div>
<div>
<span style="font-size: 14px; font-weight:bold; color: #000000;">Zip Code: </span> <span style="font-size: 15px; color: #000000">@ZIP_CODE</span><br>
<span style="font-size: 14px; font-weight:bold; color: #000000">Average number of rat sightings per year:</span> <span style="font-size: 15px; color: #000000">@sightings</span><br>
<span style="font-size: 14px; font-weight:bold; color: #000000;">Average number of interventions per year:</span> <span style="font-size: 15px; color: #000000"> @interventions </span><br>
<span style="font-size: 14px; font-weight:bold; color: #000000;">Number of interventions above expectation:</span> <span style="font-size: 15px; color: #000000"> @dev </span>
</div>
</div>
""", renderers=[points])
p_zips.add_tools(points_hover)
p_zips.xaxis.major_label_text_font_size = "14pt"
p_zips.yaxis.major_label_text_font_size = "14pt"
p_zips.title.text_font_size = '16pt'
p_zips.xaxis.axis_label = 'average number of rat sightings per year'
p_zips.yaxis.axis_label = 'average number of interventions per year'
p_zips.xaxis.axis_label_text_font_size = "14pt"
p_zips.yaxis.axis_label_text_font_size = "14pt"
p_zips.xaxis.axis_label_text_color = '#909090'
p_zips.xaxis.axis_line_color = '#909090'
p_zips.xaxis.major_label_text_color = '#909090'
p_zips.yaxis.axis_label_text_color = '#909090'
p_zips.yaxis.axis_line_color = '#909090'
p_zips.yaxis.major_label_text_color = '#909090'
p_zips.title.text_color = '#909090'
layout_zips = row(mp,p_zips)
the_script_zips, the_div_zips = components(layout_zips)
###-----------------------------------------------------------------------###
###-----------------------------------------------------------------------###
###--------------------------HEATMAPS PLOT--------------------------------###
###------------------------- BY ZIPCODE----------------------------------###
###-----------------------------------------------------------------------###
colors_hm = ['#fdf1f4', '#fce4e9', '#fbd6de', '#f9c9d3', '#f8bcc8',
'#f7aebd', '#f5a1b2','#f493a7', '#f3869c', '#f27991']
mapper_hm = LinearColorMapper(palette=colors_hm, low=0, high=50)
hm_color_bar = ColorBar(color_mapper=mapper_hm, location=(0, 0), label_standoff=10,
major_label_text_font_size='14pt', major_label_text_color = '#909090',
background_fill_color = 'black', scale_alpha = 0.7)
heatmap = figure(x_range=(-74.2, -73.7), y_range=(40.53, 40.915), width = 500, height =500,
tools= 'box_zoom,pan,save,reset', active_drag="box_zoom", background_fill_color = "black",
min_border_top = 5, min_border_bottom = 5, border_fill_color = "black",
toolbar_location="left")
heatmap.xgrid.grid_line_color = None
heatmap.ygrid.grid_line_color = None
heatmap.axis.visible = False
heatmap.outline_line_color = "black"
zips_hm = heatmap.patches('x', 'y', source=zip_hm_first, color='black', line_width=1,
fill_color={'field': 'sightings', 'transform': mapper_hm}, alpha = 0.7)
zips_hm_hover = HoverTool(tooltips = """
<div>
<div>
<span style="font-size: 14px; font-weight:bold; color: #000000;">Zip Code: </span> <span style="font-size: 15px; color: #000000">@ZIPCODE</span><br>
<span style="font-size: 14px; font-weight:bold; color: #000000">Number of sightings this month:</span> <span style="font-size: 15px; color: #000000">@sightings</span><br>
</div>
</div>
""", renderers=[zips_hm])
heatmap.add_tools(zips_hm_hover)
dummy = figure(height=500, width=150, toolbar_location=None, min_border=0, outline_line_color=None,
background_fill_color = "black",
min_border_top = 5, min_border_bottom = 5, border_fill_color = "black")
dummy.add_layout(hm_color_bar, 'right')
### Add slider
date_slider_hm = DateSlider(title="Date", start=dt.date(2010, 1, 1), end=dt.date(2018, 4, 1),value=dt.date(2014, 7, 1), step=1, format = "%B %Y")
### Slider callback function
hm_callback = CustomJS(args=dict(date_slider = date_slider_hm,
zip_hm_first = zip_hm_first, zip_hm_original = zip_hm_original),
code="""
var date_slider = new Date(date_slider.value);
var data_hm = zip_hm_first.data;
var data_hm_original = zip_hm_original.data;
const monthNames = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"];
var year_month = (date_slider.getUTCFullYear()).toString() +'-'+ monthNames[date_slider.getUTCMonth()];
console.log(year_month)
var test = 0;
data_hm['sightings'] = [];
for (k = 0; k < 263; k++) {
data_hm['sightings'].push(data_hm_original[year_month][k])
}
zip_hm_first.change.emit();
""")
date_slider_hm.js_on_change('value', hm_callback)
layout_hm = column(date_slider_hm,row(heatmap, dummy))
the_script_hm, the_div_hm = components(layout_hm)
return render_template('myindex-pitch-night.html', div = the_div, script=the_script,
the_script_zips = the_script_zips, the_div_zips = the_div_zips,
the_script_hm = the_script_hm, the_div_hm = the_div_hm)
def bokehTile(tileFile,
jsonFile,
TT=[0, 0, 0],
DD=[2019, 10, 1],
dynamic=False,
plotTitle=''):
citls, h = fitsio.read(tileFile, header=True)
w = (np.where(citls['IN_DESI'] == 1)[0])
inci = citls[w]
if jsonFile is not None:
with open(jsonFile, "r") as read_file:
data = json.load(read_file)
## Coloring scheme
palette = ['green', 'red', 'white']
dye = []
for tile in citls['TILEID']:
rang = 2 # 'orange'
if jsonFile is not None:
if str(tile) in data:
rang = 0 # 'green' #green default
if len(data[str(tile)]
['unassigned']) > 0: # not assigned (red)
rang = 1 # 'red' #'red'
if (0 in data[str(tile)]['gfa_stars_percam']):
print(data[str(tile)]['gfa_stars_percam'])
rang = 1 # 'cyan'
else:
rang = 0 # green if qa.json is not provided
dye.append(rang)
dye = np.asarray(dye)
w = (np.where(dye < 2)[0])
citls = citls[w]
dye = dye[w]
mapper = linear_cmap(field_name='DYE', palette=palette, low=0, high=2)
#########################################################
TOOLS = [
'pan', 'tap', 'wheel_zoom', 'box_zoom', 'reset', 'save', 'box_select'
]
obsTime = dt(DD[0], DD[1], DD[2], TT[0], TT[1], TT[2])
# print(get_kp_twilights(TT,DD))
if plotTitle == '' or plotTitle is None:
PTITLE = ''
else:
PTITLE = 'Program: ' + plotTitle
p = figure(tools=TOOLS,
toolbar_location="right",
plot_width=800,
plot_height=450,
title=PTITLE,
active_drag='box_select') # str(DD[1])+" - 2019")
p.title.text_font_size = '16pt'
p.title.text_color = 'black'
p.grid.grid_line_color = "gainsboro"
############################### adding ecliptic plane+ hour grid ####################3
add_plane(p, color='red', plane='ecliptic', projection='equatorial')
tiledata = dict(
RA=citls['RA'],
DEC=citls['DEC'],
TILEID=citls['TILEID'],
BRIGHTRA=citls['BRIGHTRA'][:, 0],
BRIGHTDEC=citls['BRIGHTDEC'][:, 0],
BRIGHTVTMAG=citls['BRIGHTVTMAG'][:, 0],
EBV_MED=np.round(citls['EBV_MED'], 3),
STAR_DENSITY=citls['STAR_DENSITY'],
DYE=dye,
program=citls['PROGRAM'],
selected=np.ones(len(citls), dtype=bool),
)
for colname in ['STAR_DENSITY', 'EBV_MED']:
if colname in citls.dtype.names:
tiledata[colname] = citls[colname]
tiles = ColumnDataSource(data=tiledata)
colformat = bktables.NumberFormatter(format='0,0.00')
columns = [
bktables.TableColumn(field='TILEID', title='TILEID', width=80),
bktables.TableColumn(field='RA', title='RA', formatter=colformat),
bktables.TableColumn(field='DEC', title='DEC', formatter=colformat),
]
for colname in ['STAR_DENSITY', 'EBV_MED']:
if colname in tiledata:
columns.append(
bktables.TableColumn(field=colname,
title=colname,
formatter=colformat))
columns.append(bktables.TableColumn(field='selected', title='Selected'))
tiletable = bktables.DataTable(columns=columns, source=tiles, width=800)
tiles.selected.js_on_change(
'indices',
CustomJS(args=dict(s1=tiles),
code="""
var inds = cb_obj.indices;
var d1 = s1.data;
for (var i=0; i<d1['selected'].length; i++) {
d1['selected'][i] = false;
}
for (var i = 0; i < inds.length; i++) {
d1['selected'][inds[i]] = true;
}
s1.change.emit();
"""))
render = p.circle(
'RA',
'DEC',
source=tiles,
size=9,
line_color='chocolate',
color=mapper,
alpha=0.4,
hover_color='orange',
hover_alpha=1,
hover_line_color='red',
# set visual properties for selected glyphs
selection_fill_color='orange',
selection_line_color='white',
# set visual properties for non-selected glyphs
nonselection_fill_alpha=0.4,
nonselection_fill_color=mapper)
p.xaxis.axis_label = 'RA [deg]'
p.yaxis.axis_label = 'Dec. [deg]'
p.xaxis.axis_label_text_font_size = "14pt"
p.yaxis.axis_label_text_font_size = "14pt"
p.grid.grid_line_color = "gainsboro"
p.yaxis.major_label_text_font_size = "12pt"
p.xaxis.major_label_text_font_size = "12pt"
p.x_range = Range1d(360, 0)
p.y_range = Range1d(-40, 95)
p.toolbar.logo = None
p.toolbar_location = None
# mytext = Label(x=180, y=-35, text="S", text_color='gray', text_font_size='12pt') ; p.add_layout(mytext)
# mytext = Label(x=180, y=88, text="N", text_color='gray', text_font_size='12pt') ; p.add_layout(mytext)
# mytext = Label(x=350, y=45, text="E", text_color='gray', text_font_size='12pt', angle=np.pi/2) ; p.add_layout(mytext)
# mytext = Label(x=4, y=45, text="W", text_color='gray', text_font_size='12pt', angle=np.pi/2) ; p.add_layout(mytext)
## Javascript code to open up custom html pages, once user click on a tile
code = """
var index_selected = source.selected['1d']['indices'][0];
var tileID = source.data['TILEID'][index_selected];
if (tileID!==undefined) {
var win = window.open("http://www.astro.utah.edu/~u6022465/cmx/ALL_SKY/dr8/allSKY_ci_tiles/sub_pages/tile-"+tileID+".html", " ");
try {win.focus();} catch (e){} }
"""
taptool = p.select(type=TapTool)
taptool.callback = CustomJS(args=dict(source=tiles), code=code)
## The html code for the hover window that contain tile infrormation
ttp = """
<div>
<div>
<span style="font-size: 14px; color: blue;">Tile ID:</span>
<span style="font-size: 14px; font-weight: bold;">@TILEID{int}</span>
</div>
<div>
<span style="font-size: 14px; color: blue;">RA:</span>
<span style="font-size: 14px; font-weight: bold;">@RA</span>
</div>
<div>
<span style="font-size: 14px; color: blue;">Dec:</span>
<span style="font-size: 14px; font-weight: bold;">@DEC</span>
</div>
<div>
<span style="font-size: 14px; color: blue;">EBV_MED:</span>
<span style="font-size: 14px; font-weight: bold;">@EBV_MED{0.000}</span>
</div>
<div>
<span style="font-size: 14px; color: blue;">STAR_DENSITY:</span>
<span style="font-size: 14px; font-weight: bold;">@STAR_DENSITY{0}</span>
</div>
<div>
<span style="font-size: 14px; color: blue;">BRIGHTEST_STAR_VTMAG:</span>
<span style="font-size: 14px; font-weight: bold;">@BRIGHTVTMAG</span>
</div>
<div>
<span style="font-size: 14px; color: blue;">BRIGHTEST_STAR_LOC:</span>
<span style="font-size: 14px; font-weight: bold;">(@BRIGHTRA, @BRIGHTDEC)</span>
</div>
</div>
"""
hover = HoverTool(tooltips=ttp, renderers=[render])
hover.point_policy = 'snap_to_data'
hover.line_policy = 'nearest'
# hover.mode='vline'
p.add_tools(hover)
cross = CrosshairTool()
# cross.dimensions='height'
cross.line_alpha = 0.3
cross.line_color = 'gray'
p.add_tools(cross)
# Setting the second y axis range name and range
p.extra_y_ranges = {"foo": p.y_range}
p.extra_x_ranges = {"joo": p.x_range}
# Adding the second axis to the plot.
p.add_layout(LinearAxis(y_range_name="foo"), 'right')
p.add_layout(LinearAxis(x_range_name="joo"), 'above')
p.xaxis.major_label_text_font_size = "12pt"
p.yaxis.major_label_text_font_size = "12pt"
if dynamic:
# twilight_source = get_kp_twilights(TT,DD) # evening and morning twilights at every TT and DD
circleSource_1 = skyCircle(TT, DD, 1.5)
p.circle('RA', 'DEC', source=circleSource_1, size=1.5, color='black')
circleSource_2 = skyCircle(TT, DD, 2.0)
p.circle('RA', 'DEC', source=circleSource_2, size=0.5, color='gray')
else:
circleSource = skyCircle(TT, DD, 1.5)
p.circle('RA', 'DEC', source=circleSource, size=1.5, color=None)
### Dealing with the Moon and Jupiter
inFile = 'moonLoc_jupLoc_fracPhase.csv' # 'moon_loc_jup_loc_fracPhase_namePhase.csv'
tbl_moon_jup = np.genfromtxt(inFile,
delimiter=',',
filling_values=-1,
names=True,
dtype=None) # , dtype=np.float)
loc = EarthLocation.of_site('Kitt Peak')
kp_lat = 31, 57, 48
kp_lon = -111, 36, 00
mooninfo_obj = pylunar.MoonInfo((kp_lat), (kp_lon))
m_ra, m_dec, frac_phase, name_phase = moonLoc(TT, DD, loc, mooninfo_obj)
j_ra, j_dec = jupLoc(TT, DD, loc)
#moonSource = ColumnDataSource({"moon_RAS": tbl_moon_jup['moon_ra'], "moon_DECS": tbl_moon_jup['moon_dec'],
# "Phase_frac": tbl_moon_jup['moon_phase_frac']})
moonSource = ColumnDataSource({
"moon_RAS":
tbl_moon_jup['moon_ra'],
"moon_DECS":
tbl_moon_jup['moon_dec'],
"Phase_frac":
np.round(100 * tbl_moon_jup['moon_phase_frac'])
})
####moon_RADEC = ColumnDataSource({"moon_ra": [m_ra.deg], "moon_dec": [m_dec.deg], "phase_frac": [frac_phase]})
moon_RADEC_ = ColumnDataSource({
"moon_ra": [m_ra.deg - 360],
"moon_dec": [m_dec.deg],
"phase_frac": [frac_phase]
})
moon_RADEC = ColumnDataSource({
"moon_ra": [m_ra.deg],
"moon_dec": [m_dec.deg],
"phase_frac": [frac_phase]
})
render_moon = p.circle('moon_ra',
'moon_dec',
source=moon_RADEC,
size=170,
color='cyan',
alpha=0.2)
render_moon = p.circle('moon_ra',
'moon_dec',
source=moon_RADEC,
size=4,
color='blue')
render_moon = p.circle('moon_ra',
'moon_dec',
source=moon_RADEC_,
size=170,
color='cyan',
alpha=0.2)
render_moon = p.circle('moon_ra',
'moon_dec',
source=moon_RADEC_,
size=4,
color='blue')
jupSource = ColumnDataSource({
"jup_RAS": tbl_moon_jup['jup_ra'],
"jup_DECS": tbl_moon_jup['jup_dec']
})
jup_RADEC = ColumnDataSource({
"jup_ra": [j_ra.deg],
"jup_dec": [j_dec.deg]
})
twilight = get_kp_twilights(
TT, DD) # evening and morning twilights at every TT and DD
twilight_source = ColumnDataSource({
"eve_twilight": [twilight[0]],
"mor_twilight": [twilight[1]]
})
render_jup = p.circle('jup_ra',
'jup_dec',
source=jup_RADEC,
size=5,
color='blue')
render_jup = p.circle('jup_ra',
'jup_dec',
source=jup_RADEC,
size=4,
color='gold')
from bokeh.models.glyphs import Text
TXTsrc = ColumnDataSource(
dict(x=[350],
y=[85],
text=['Moon Phase: ' + "%.0f" % (frac_phase * 100) + "%"]))
glyph = Text(x="x", y="y", text="text", angle=0, text_color="black")
p.add_glyph(TXTsrc, glyph)
TXTsrc_moon = ColumnDataSource(
dict(x=[m_ra.deg + 10], y=[m_dec.deg - 10], text=['Moon']))
glyph = Text(x="x",
y="y",
text="text",
angle=0,
text_color="blue",
text_alpha=0.3,
text_font_size='10pt')
p.add_glyph(TXTsrc_moon, glyph)
TXTsrc_jup = ColumnDataSource(
dict(x=[j_ra.deg + 5], y=[j_dec.deg - 8], text=['Jup.']))
glyph = Text(x="x",
y="y",
text="text",
angle=0,
text_color="black",
text_alpha=0.3,
text_font_size='10pt')
p.add_glyph(TXTsrc_jup, glyph)
callback = CustomJS(args=dict(source_sky1=circleSource_1,
source_sky2=circleSource_2,
source_moon=moonSource,
source_moon_RADEC=moon_RADEC,
source_moon_RADEC_=moon_RADEC_,
source_jup=jupSource,
source_jup_RADEC=jup_RADEC,
sourceTXT=TXTsrc,
sourceTXTmoon=TXTsrc_moon,
sourceTXTjup=TXTsrc_jup),
code="""
// First set times as if they were UTC
var t = new Date(time_slider.value);
var d = new Date(date_slider.value);
var data1 = source_sky1.data;
var ra_1 = data1['RA'];
var ra0_1 = data1['RA0'];
var data2 = source_sky2.data;
var ra_2 = data2['RA'];
var ra0_2 = data2['RA0'];
var data_moon = source_moon.data;
var ras_moon = data_moon['moon_RAS'];
var decs_moon = data_moon['moon_DECS'];
var phase_frac = data_moon['Phase_frac'];
var moonRADEC = source_moon_RADEC.data;
var moon_ra = moonRADEC['moon_ra'];
var moon_dec = moonRADEC['moon_dec'];
var moonRADEC_ = source_moon_RADEC_.data;
var moon_ra_ = moonRADEC_['moon_ra'];
var moon_dec_ = moonRADEC_['moon_dec'];
var data_jup = source_jup.data;
var ras_jup = data_jup['jup_RAS'];
var decs_jup = data_jup['jup_DECS'];
var jupRADEC = source_jup_RADEC.data;
var jup_ra = jupRADEC['jup_ra'];
var jup_dec = jupRADEC['jup_dec'];
var Hour = t.getUTCHours();
var Day = d.getDate();
var Month = d.getMonth();
var Year = new Array(31,28,31,30,31,30,31,31,30,31,30,31);
var all_FULdays = 0;
for (var i = 0; i < Month; i++)
all_FULdays=all_FULdays+Year[i];
all_FULdays = all_FULdays + (Day-1);
if (Hour<12) all_FULdays=all_FULdays+1;
var all_minutes = all_FULdays*24+Hour;
if (all_minutes<8800) {
moon_ra[0] = ras_moon[all_minutes];
moon_dec[0] = decs_moon[all_minutes];
moon_ra_[0] = ras_moon[all_minutes]-360.;
moon_dec_[0] = decs_moon[all_minutes];
}
var jupTXTdata = sourceTXTjup.data;
var x_jup = jupTXTdata['x'];
var y_jup = jupTXTdata['y'];
var text_jup = jupTXTdata['text'];
if (all_minutes<8800) {
jup_ra[0] = ras_jup[all_minutes];
jup_dec[0] = decs_jup[all_minutes];
x_jup[0] = jup_ra[0]+5;
y_jup[0] = jup_dec[0]-8;
}
if (t.getUTCHours() < 12) {
d.setTime(date_slider.value + 24*3600*1000);
} else {
d.setTime(date_slider.value);
}
d.setUTCHours(t.getUTCHours());
d.setUTCMinutes(t.getUTCMinutes());
d.setUTCSeconds(0);
// Correct to KPNO local time
// d object still thinks in UTC, which is 7 hours ahead of KPNO
d.setTime(d.getTime() + 7*3600*1000);
// noon UT on 2000-01-01
var reftime = new Date();
reftime.setUTCFullYear(2000);
reftime.setUTCMonth(0); // Months are 0-11 (!)
reftime.setUTCDate(1); // Days are 1-31 (!)
reftime.setUTCHours(12);
reftime.setUTCMinutes(0);
reftime.setUTCSeconds(0);
// time difference in days (starting from milliseconds)
var dt = (d.getTime() - reftime.getTime()) / (24*3600*1000);
// Convert to LST
var mayall_longitude_degrees = -(111 + 35/60. + 59.6/3600);
var LST_hours = ((18.697374558 + 24.06570982441908 * dt) + mayall_longitude_degrees/15) % 24;
var LST_degrees = LST_hours * 15;
for (var i = 0; i < ra_1.length; i++) {
ra_1[i] = (ra0_1[i] + LST_degrees) % 360;
}
for (var i = 0; i < ra_2.length; i++) {
ra_2[i] = (ra0_2[i] + LST_degrees) % 360;
}
//// Here we gtake care of the moon phasde text
var TXTdata = sourceTXT.data;
var x = TXTdata['x'];
var y = TXTdata['y'];
var text = TXTdata['text'];
var moonTXTdata = sourceTXTmoon.data;
var x_moon = moonTXTdata['x'];
var y_moon = moonTXTdata['y'];
var text_moon = moonTXTdata['text'];
// x[0] = 1;
// y[0] = 40;
if (all_minutes<8800) {
text[0] = 'Moon Phase: ' + phase_frac[all_minutes]+'%';
x_moon[0] = moon_ra[0]+10;
y_moon[0] = moon_dec[0]-10;
}
sourceTXT.change.emit();
/////////////////////////////// Moon phase code ends.
source_sky1.change.emit();
source_sky2.change.emit();
//source_moon_RADEC.change.emit();
//source_moon_RADEC_.change.emit();
//source_jup_RADEC.change.emit();
sourceTXTmoon.change.emit();
sourceTXTjup.change.emit();
//alert(d);
""")
if dynamic:
### TIME
Timeslider = DateSlider(start=dt(2019, 9, 1, 16, 0, 0),
end=dt(2019, 9, 2, 8, 0, 0),
value=dt(2019, 9, 1, 16, 0, 0),
step=1,
title="KPNO local time(hh:mm)",
format="%H:%M",
width=800)
## DATE
Dateslider = DateSlider(start=dt(2019, 9, 1, 16, 0, 0),
end=dt(2020, 8, 31, 8, 0, 0),
value=dt(2019, 10, 1, 16, 0, 0),
step=1,
title="Date of sunset(4pm-8am)",
format="%B:%d",
width=800)
callback.args['time_slider'] = Timeslider
callback.args['date_slider'] = Dateslider
Dateslider.js_on_change('value', callback)
Timeslider.js_on_change('value', callback)
layout = column(p, Dateslider, Timeslider, tiletable)
# show(p)
return layout
return p
def update_plot(attr, old, new):
selectedDate = str(datepicker.value_as_date)
new_data = filtered_data(selectedDate)
new_data['date'] = new_data['date'].to_string()
new_json = new_data.to_json()
geosource.geojson = new_json
#Datepicker
datepicker = DateSlider(start="2020-12-13",
end=current_date,
step=1,
value=current_date,
width=200,
align="center",
title="Currently Displaying")
datepicker.on_change('value', update_plot)
# Last Updated on Current Date Text
update_text = "Last Updated on " + str(current_date)
p.add_layout(Title(text=update_text, align="center", text_alpha=0.5), "below")
#Authorship
p.add_layout(Title(text="Made by Frank Hoang", align="center", text_alpha=0.5),
"below")
# Make a column layout of widgetbox(slider) and plot, and add it to the current document
layout = Column(datepicker, p)
def direction_plot(dataframe: object, ti: str) -> object:
"""
:param dataframe: pandas dataframe
:param ti: string defining 'week', 'month'...
:return:
"""
# use data in percent => transform db_data to percent
df = (dataframe.transpose().iloc[2:, 0:] / dataframe['sum']) * 100
df.columns = dataframe['tstamp']
db_datadictstr = {
str(int(time.mktime(item.timetuple()) * 1000)): list(df[item])
for item in df.columns
}
maxlist = df.max(1)
hist = df.mean(1)
# maxhist = sorted(maxlist)[-3]
maxhist = mean(
maxlist
) * 0.8 # don't use real max of dataset, too many discordant values
sumhist = sum(hist)
start = [-radians((i * 10) - 85) for i in list(range(0, 36))]
end = [-radians((i * 10) - 75) for i in list(range(0, 36))]
pdstart = [-radians((i * 10) - 95) for i in list(range(0, 36))]
pdend = start
# pdend = [-radians((i * 10) - 85) for i in list(range(0, 36))]
labeltext = ("Selection of " + ti + "ly histograms")
titletext = (ti + 'ly median and sum of all histograms').capitalize()
# need two different sources for callback in Bokeh
pdsource = ColumnDataSource(
data=dict(radius=hist, start=pdstart, end=pdend))
jssource = ColumnDataSource(data=db_datadictstr)
mainplot = figure(title=titletext,
plot_width=400,
plot_height=400,
x_axis_type=None,
y_axis_type=None,
tools="save",
min_border=0,
outline_line_color=None)
mainplot.title.text_font_size = "14pt"
# simple rose plot
mainplot.wedge(radius=hist,
start_angle=start,
end_angle=end,
x=0,
y=0,
direction='clock',
line_color='blue',
fill_color='lightblue',
alpha=0.5,
legend_label='Whole dataset')
# plot connected to slider
mainplot.wedge(radius='radius',
start_angle='start',
end_angle='end',
source=pdsource,
x=0,
y=0,
alpha=0.5,
direction='clock',
line_color='darkred',
fill_color='lightsalmon',
legend_label=labeltext)
# create slider
day = 1000 * 3600 * 24
stepsize = day
if ti == 'week':
stepsize = day
elif ti == 'month':
stepsize = 7 * day
elif ti == 'year':
stepsize = 30 * day
slider = DateSlider(start=min(df.columns),
end=max(df.columns),
value=min(df.columns),
step=stepsize,
title="date within histogram")
callback = CustomJS(args=dict(source=pdsource, data=jssource, slid=slider),
code="""
const S = slid.value;
let radius = source.data.radius;
const radii = Object.keys(data.data)
let slidestop = radii.reduce(function(prev, curr) {
(Math.abs(curr - S) < Math.abs(prev - S) ? curr : prev)
return (Math.abs(curr - S) < Math.abs(prev - S) ? curr : prev);
});
source.data.radius = data.data[slidestop]
source.change.emit();
""")
slider.js_on_change('value', callback)
# create range slider
rslider = DateRangeSlider(start=min(df.columns),
end=max(df.columns),
value=(min(df.columns), max(df.columns)),
step=stepsize,
title="Data within date range from ")
rcallback = CustomJS(args=dict(source=pdsource,
data=jssource,
rslid=rslider),
code="""
const smin = rslid.value[0]
const smax = rslid.value[1]
let radius = source.data.radius;
const radii = Object.keys(data.data)
let lstop = radii.reduce(function(prev, curr) {
return (Math.abs(curr - smin) < Math.abs(prev - smin) ? curr : prev);
});
let rstop = radii.reduceRight(function(prev, curr) {
return (Math.abs(curr - smax) < Math.abs(prev - smax) ? curr : prev);
});
let keylist = [];
for (let k in data.data) keylist.push(k);
let fromkey = keylist.indexOf(lstop);
let tokey = keylist.indexOf(rstop);
let rangekeys = keylist.slice(fromkey, tokey)
var dataavg = Array(36).fill(0)
var count = 0;
for (let k of rangekeys) {
dataavg = dataavg.map(function (num, idx) {return num + data.data[k][idx];});
count += 1
}
dataavg = dataavg.map(function (num, idx) {return num/count;});
source.data.radius = dataavg;
source.change.emit();
""")
rslider.js_on_change('value', rcallback)
# create grid
rund_perc = ceil(maxhist / sumhist * 100)
labels = list(range(0, rund_perc, 2))
labels.append(rund_perc)
rad_pos = [i * sumhist / 100 for i in labels]
out_rim = rad_pos[-1]
label_pos = [sqrt(((i - 1)**2) / 2) for i in rad_pos]
mainplot.text(label_pos[1:],
label_pos[1:], [str(r) + ' %' for r in labels[1:]],
text_font_size="10pt",
text_align="left",
text_baseline="top")
for rad in rad_pos:
mainplot.circle(x=0,
y=0,
radius=rad,
fill_color=None,
line_color='grey',
line_width=0.5,
line_alpha=0.8)
diagonal = sqrt((out_rim**2) / 2)
mainplot.multi_line(xs=[[diagonal, -diagonal], [-diagonal, diagonal],
[-out_rim, out_rim], [0, 0]],
ys=[[diagonal, -diagonal], [diagonal, -diagonal],
[0, 0], [-out_rim, out_rim]],
line_color="grey",
line_width=0.5,
line_alpha=0.8)
mainplot.x_range = Range1d(-out_rim * 1.1, out_rim * 1.1)
mainplot.y_range = Range1d(-out_rim * 1.1, out_rim * 1.1)
mainplot.legend.location = "top_left"
mainplot.legend.click_policy = "hide"
# plot bars for the number of values in each group as secondary 'by' plot
mapper = linear_cmap(field_name='count',
palette=Oranges9,
low=0,
high=max(dataframe['sum']))
bin_width = df.columns[0] - df.columns[1]
source = ColumnDataSource({
'date': dataframe['tstamp'],
'count': dataframe['sum']
})
distriplot = distribution_plot(source, mapper, bin_width,
'Number of values per cluster', 400)
script, div = components(column(distriplot, mainplot, slider, rslider),
wrap_script=False)
return {'div': div, 'script': script}
completions = ["aaa", "aab", "aac", "baa", "caa"] autocomplete_input = AutocompleteInput(placeholder="Enter value (auto-complete) ...", completions=completions) text_area = TextAreaInput(placeholder="Enter text ...", cols=20, rows=10, value="uuu") select = Select(options=["Option 1", "Option 2", "Option 3"]) multi_select = MultiSelect(options=["Option %d" % (i+1) for i in range(16)], size=6) multi_choice = MultiChoice(options=["Option %d" % (i+1) for i in range(16)]) slider = Slider(value=10, start=0, end=100, step=0.5) range_slider = RangeSlider(value=[10, 90], start=0, end=100, step=0.5) date_slider = DateSlider(value=date(2016, 1, 1), start=date(2015, 1, 1), end=date(2017, 12, 31)) date_range_slider = DateRangeSlider(value=(date(2016, 1, 1), date(2016, 12, 31)), start=date(2015, 1, 1), end=date(2017, 12, 31)) spinner = Spinner(value=100) color_picker = ColorPicker(color="red", title="Choose color:") date_picker = DatePicker(value=date(2017, 8, 1)) paragraph = Paragraph(text="some text") div = Div(text="some <b>text</b>") pre_text = PreText(text="some text")
def update_date(attr, old, new):
yr = date_slider.value
new_data = json_data(yr)
geosource.geojson = new_data
p.title.text = 'covid 19 deaths, %s' %yr
def update_color(attr, old, new):
color_mapper.high = color_slider.value
merged = read_all_data()
geosource = GeoJSONDataSource(geojson = json_data(str(date(2020, 4,25))))
palette = brewer['YlGnBu'][8]
palette = palette[::-1]
color_cap_deaths = round(np.nanmax(merged["Deaths"]) + 500, -3)
color_mapper = LinearColorMapper(palette = palette, low = 0, high = color_cap_deaths, nan_color = '#d9d9d9')
hover = HoverTool(tooltips = [ ('Country/region','@country'),('deaths', '@Deaths')])
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 900, height = 20, border_line_color=None,location = (0,0), orientation = 'horizontal')
p = figure(title = 'Covid 19 deaths', plot_height = 900 , plot_width = 1600, tools = [BoxZoomTool(), ResetTool(), hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.patches('xs','ys', source = geosource,fill_color = {'field' :'Deaths', 'transform' : color_mapper}, line_color = 'black', line_width = 0.25, fill_alpha = 1)
p.add_layout(color_bar, 'below')
date_slider = DateSlider(title="Date Range: ", start=date(2020, 1, 31), end=date(2020, 4, 25), value=date(2020, 4, 25), step=1)
date_slider.on_change('value', update_date)
color_slider = Slider(title="Max value", start=0, end=color_cap_deaths, value=color_cap_deaths, step=100)
color_slider.on_change('value', update_color)
layout = column(p,widgetbox(date_slider), widgetbox(color_slider))
curdoc().add_root(layout)
show(layout)
def build_time_evolution_tab():
# Importing geographical shapefile
s3_root = 'https://covid19-bokeh-app.s3.eu-west-2.amazonaws.com'
geo_data_gdf = gpd.read_file(f'{s3_root}/data/_geo_data/ne_50m_land.zip')
geosource = GeoJSONDataSource(geojson=geo_data_gdf.to_json())
# Importing geo-evolutions cases/deaths data
time_evol_df = pd.read_csv(f'{s3_root}/data/geo_time_evolution.csv')
# Selecting earliest snapshot
time_evol_df.date = pd.to_datetime(time_evol_df.date, format="%Y-%m-%d")
snapshot_df = time_evol_df[time_evol_df.date == min(time_evol_df.date)]
global_by_day_df = pd.read_csv(f'{s3_root}/data/global_by_day.csv')
global_by_day_df.date = pd.to_datetime(global_by_day_df.date,
format="%Y-%m-%d")
global_totals_df = global_by_day_df.loc[global_by_day_df.date == min(
time_evol_df.date)]
global_cases = int(global_totals_df.iloc[0]['cases'])
global_deaths = int(global_totals_df.iloc[0]['deaths'])
# Applying bubble-size mapping
bubble_size = snapshot_df['cases'].apply(lambda x: 0.5 * math.log(x, 1.1)
if x > 0 else 0)
snapshot_df = snapshot_df.assign(size=bubble_size.values)
# Creating ColumnDataSource for visualisation
cases_cds = ColumnDataSource(snapshot_df)
# Adding figure and geographical patches from shapefile
geo_plot = figure(plot_height=450,
plot_width=720,
x_range=(-180, 180),
y_range=(-90, 90),
name="time_evolution_geo_plot",
sizing_mode="scale_width")
geo_patches = geo_plot.patches('xs', 'ys', source=geosource, alpha=0.5)
# Adding circle glyph to create bubble plot
cases_circles = geo_plot.circle(x='long',
y='lat',
size='size',
source=cases_cds,
color='red',
alpha=0.2)
# Adding hover tool
hover = HoverTool(tooltips=[('Country/Region', '@region'),
('Province/State', '@province'),
('Cases', '@cases')],
renderers=[cases_circles])
geo_plot.add_tools(hover)
# Adding hbar
countries_df = snapshot_df.loc[:, ['date', 'region', 'cases']]
countries_df.rename(columns={"cases": "value"}, inplace=True)
countries_df = countries_df.groupby(['date', 'region']).sum().reset_index()
countries_df.sort_values(by='value', ascending=False, inplace=True)
countries_df = countries_df.reset_index(drop=True)
countries_cds = ColumnDataSource(countries_df)
hbar_plot = figure(plot_height=450,
plot_width=475,
y_range=(10.5, -0.5),
x_range=(0, countries_df.value.max() * 1.2),
name="time_evolution_hbar_plot",
sizing_mode="scale_width")
hbar = hbar_plot.hbar(left=0,
right='value',
y='index',
source=countries_cds,
height=0.5,
line_color='white')
labels = LabelSet(x='value',
y='index',
text='region',
text_font_size='10pt',
text_color='white',
x_offset=5,
y_offset=0,
source=countries_cds,
level='glyph',
render_mode='canvas')
hbar_plot.add_layout(labels)
# Adding hover tool
hover_hbar = HoverTool(tooltips=[('Country/Region', '@region'),
('Cases', '@value')],
renderers=[hbar])
hbar_plot.add_tools(hover_hbar)
# Adding callback for updating data
def data_view_callback(attr, old, new):
"""Callback function to update data source:
- Updates source data to selected data view
(cases/deaths/new cases/new deaths)
and selected snapshot date on date slider.
- Updates HoverTool to reflect data view change
- Updates Divs for total cases/deaths
"""
# Determine data view selection
if cases_deaths_button.active == 0:
data_view = "cases"
elif cases_deaths_button.active == 1:
data_view = "deaths"
if total_new_button.active == 1:
data_view = f"new_{data_view}"
# Determine date selection
slider_date = date_slider.value_as_datetime.date()
# Filter data for selected date
snapshot_df = time_evol_df[time_evol_df.date == pd.Timestamp(
slider_date)]
# Map bubble size on selected data view
bubble_size = snapshot_df[data_view].apply(
lambda x: 0.5 * math.log(x, 1.1) if x > 0 else 0)
snapshot_df = snapshot_df.assign(size=bubble_size.values)
cases_cds.data = snapshot_df
hover.tooltips = [('Country/Region', '@region'),
('Province/State', '@province'),
(data_view.replace('_',
' ').title(), f'@{data_view}')]
# Update hbar data
countries_df = snapshot_df.loc[:, ['date', 'region', data_view]]
countries_df.rename(columns={data_view: "value"}, inplace=True)
countries_df = countries_df.groupby(['date',
'region']).sum().reset_index()
countries_df.sort_values(by="value", ascending=False, inplace=True)
countries_df = countries_df.reset_index(drop=True)
countries_cds.data = countries_df
hbar_plot.x_range.end = countries_df.value.max() * 1.2
hover_hbar.tooltips = [('Country/Region', '@region'),
(data_view.replace('_', ' ').title(), '@value')]
# Filter for totals
global_totals_df = global_by_day_df.loc[(
global_by_day_df.date == pd.Timestamp(slider_date))]
global_cases = int(global_totals_df.iloc[0]['cases'])
global_deaths = int(global_totals_df.iloc[0]['deaths'])
cases_div.text = (f'<h3 class="card-text">' f'{global_cases:,}</h3>')
deaths_div.text = (f'<h3 class="card-text">' f'{global_deaths:,}</h3>')
# Adding Date slider
date_range = [
pd.Timestamp(date_val) for date_val in time_evol_df.date.unique()
]
date_slider = DateSlider(title="Date",
start=min(date_range),
end=max(date_range),
value=min(date_range),
sizing_mode="scale_width")
date_slider.on_change('value', data_view_callback)
# Adding Cases/Deaths toggle
cases_deaths_button = RadioButtonGroup(labels=["Cases", "Deaths"],
active=0,
sizing_mode="scale_width")
cases_deaths_button.on_change('active', data_view_callback)
# Adding Total/New toggle
total_new_button = RadioButtonGroup(labels=["Total", "New"],
active=0,
sizing_mode="scale_width")
total_new_button.on_change('active', data_view_callback)
# Adding callback for zooming into a selected continent
def continent_zoom_callback(attr, old, new):
continent_map_ref = {
# Worldwide
0: {
'x_range': [-200, 200],
'y_range': [-100, 100]
},
# Europe
1: {
'x_range': [-30, 50],
'y_range': [30, 70]
},
# North America
2: {
'x_range': [-175, -15],
'y_range': [0, 80]
},
# South America
3: {
'x_range': [-140, 10],
'y_range': [-60, 15]
},
# Africa
4: {
'x_range': [-55, 105],
'y_range': [-40, 40]
},
# Asia
5: {
'x_range': [40, 140],
'y_range': [-5, 45]
},
# Oceania
6: {
'x_range': [80, 200],
'y_range': [-55, 5]
}
}
map_ref = continent_map_ref[continent_button.active]
geo_plot.x_range.start = map_ref['x_range'][0]
geo_plot.x_range.end = map_ref['x_range'][1]
geo_plot.y_range.start = map_ref['y_range'][0]
geo_plot.y_range.end = map_ref['y_range'][1]
# Adding continent toggle
continent_button = RadioGroup(labels=[
"Worldwide", "Europe", "North America", "South America", "Africa",
"Asia", "Oceania"
],
active=0)
continent_button.on_change('active', continent_zoom_callback)
# Adding animation with Play/Pause button
callback_id = None
def animate():
def animate_update():
date = date_slider.value_as_datetime.date() + timedelta(days=1)
date = pd.Timestamp(date)
if date >= max(date_range):
date = min(date_range)
date_slider.value = date
global callback_id
if play_button.label == '► Play':
play_button.label = '❚❚ Pause'
callback_id = curdoc().add_periodic_callback(animate_update, 300)
else:
play_button.label = '► Play'
curdoc().remove_periodic_callback(callback_id)
play_button = Button(label='► Play', width=60, button_type="success")
play_button.on_click(animate)
# Adding Cases/Deaths count
cases_div = Div(text=f'<h3 class="card-text">'
f'{global_cases:,}</h3>',
sizing_mode="scale_width",
name="cases_div")
deaths_div = Div(text=f'<h3 class="card-text">'
f'{global_deaths:,}</h3>',
sizing_mode="scale_width",
name="deaths_div")
# Defining layout of tab
widgets = widgetbox(date_slider,
cases_deaths_button,
total_new_button,
play_button,
continent_button,
name="time_evolution_widgetbox",
sizing_mode="scale_width")
# Add the plot to the current document
curdoc().add_root(geo_plot)
curdoc().add_root(hbar_plot)
curdoc().add_root(widgets)
curdoc().add_root(cases_div)
curdoc().add_root(deaths_div)
def create_world_map_tab():
"Factory for creating first tab of app."
## Data Sources
source_df, source_CDS = get_time_series_confirmed_data()
## Map
color_mapper = log_cmap(field_name='number',
palette=Spectral6,
low=1,
high=1e6)
TOOLTIPS = [('Region', '@full_name'), ('Num. Cases', '@number{0,0}')]
map_figure = figure(title='Confirmed COVID-19 Cases by Region',
tooltips=TOOLTIPS,
x_range=(-16697923.62, 18924313),
y_range=(-8399737.89, 8399737.89),
x_axis_type='mercator',
y_axis_type='mercator',
active_scroll='wheel_zoom')
tile_provider = get_provider(CARTODBPOSITRON)
map_figure.add_tile(tile_provider)
map_figure.circle(x='web_mercator_x',
y='web_mercator_y',
source=source_CDS,
size='sizes',
color=color_mapper)
## Colorbar
color_bar = ColorBar(title='Num. Cases',
title_standoff=20,
color_mapper=color_mapper['transform'],
label_standoff=20,
width=8,
location=(0, 0),
ticker=LogTicker())
color_bar.formatter.use_scientific = False
map_figure.add_layout(color_bar, 'right')
## Slider
def slider_callback(attr, old, new):
delta = datetime.timedelta(milliseconds=new)
date = datetime.date(1970, 1, 1) + delta
date_string = date.strftime('%Y-%m-%d')
try:
source_CDS.data['number'] = source_df[date_string]
source_CDS.data['sizes'] = source_df[date_string].apply(scale)
except KeyError:
pass
slider = DateSlider(title='Date',
start=START_DATE,
end=datetime.date.today(),
step=1,
value=START_DATE)
slider.on_change('value', slider_callback)
## Data Table
columns = [
TableColumn(field='full_name', title='Region'),
TableColumn(field='number', title='Cases')
]
data_table = DataTable(
source=source_CDS,
columns=columns,
)
## Cancel Selection Button
def cancel_selection_callback():
source_CDS.selected.indices = []
cancel_selection_button = Button(label='Clear Selection',
button_type='warning')
cancel_selection_button.on_click(cancel_selection_callback)
child = row([
column([slider, map_figure]),
column([data_table, cancel_selection_button]),
])
return Panel(child=child, title='World Map')
def create_html_page(html_page_name, df):
del states["HI"]
del states["AK"]
EXCLUDED = ("ak", "hi", "pr", "gu", "vi", "mp", "as")
state_xs = [states[code]["lons"] for code in states]
state_ys = [states[code]["lats"] for code in states]
county_xs = [
counties[code]["lons"] for code in counties
if counties[code]["state"] not in EXCLUDED
]
county_ys = [
counties[code]["lats"] for code in counties
if counties[code]["state"] not in EXCLUDED
]
county_names = [
counties[code]["name"] for code in counties
if counties[code]["state"] not in EXCLUDED
]
county_rates = []
col_names = list(df.columns.values)
len_col = len(col_names)
for county_id in counties:
if counties[county_id]["state"] in EXCLUDED:
continue
fips = str(county_id[0]).zfill(2) + str(county_id[1]).zfill(3)
# len_col - 1 = its the last day that we have
rate = df.loc[df['FIPS'] == fips][col_names[len_col - 1]].values
if len(rate) == 0:
county_rates.append(0.0)
else:
county_rates.append(rate[0])
source_visible = ColumnDataSource(data={
'x': county_xs,
'y': county_ys,
'name': county_names,
'rate': county_rates
})
source_new = create_dict_forJS(df)
print(np.shape(source_new), type(source_new))
# Define a sequential multi-hue color palette.
#palette = brewer['YlGnBu'][9]
palette = Magma256
# palette = big_palette(200, bokeh.palettes.plasma)
palette = palette[::-1]
color_mapper = LinearColorMapper(palette=palette, low=0, high=100)
#color_mapper = LogColorMapper(palette=palette)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="US density",
toolbar_location="left",
plot_width=1100,
plot_height=700,
tools=TOOLS,
tooltips=[("County", "@name"), ("Infection rate", "@rate"),
("(Long, Lat)", "($x, $y)")])
# hide grid and axes
p.axis.visible = None
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches(
county_xs,
county_ys,
# fill_alpha=0.7,
line_color="white",
line_width=0.5)
p.patches(state_xs,
state_ys,
fill_alpha=0.0,
line_color="#884444",
line_width=2,
line_alpha=0.3)
p.patches('x',
'y',
source=source_visible,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
# Make a slider object: slider
last_day_str = col_names[len_col - 1]
last_day = datetime.strptime(
last_day_str, '%Y-%m-%d %H:%M:%S').date() # + timedelta(days=1)
first_day_str = col_names[4] # first 4 colums contain names etc
first_day = datetime.strptime(first_day_str, '%Y-%m-%d %H:%M:%S').date()
print(last_day)
date_slider = DateSlider(title="Date:",
start=first_day,
end=last_day,
value=last_day,
step=1)
callback = CustomJS(args=dict(source=source_new, ts=source_visible),
code="""
var data=ts.data;
var rate=data['rate'];
var name=data['name'];
var data1=source.data;
var f=cb_obj.value; //this is the selection value of slider
const event = new Date(f);
var date_selected = event.toISOString().substring(0,10); // converting date from python to JS
//console.log(typeof data1[0]);
data['rate']=data1[date_selected];
ts.change.emit();
""")
date_slider.js_on_change('value', callback)
#layout = column(p, widgetbox(slider),widgetbox(date_slider))
layout = column(p, date_slider)
output_file(html_page_name, title="Interactive USA density map")
show(layout)
def create_us_map_tab():
"Factory for creating second tab of app: US Only Data"
## Data Sources
source_df_confirmed, source_CDS = get_time_series_confirmed_US_data()
source_CDS.data['number_per_capita'] = source_df_confirmed[
START_DATE_STRING] / source_df_confirmed['population']
## Map
color_mapper = log_cmap(field_name='number',
palette=Spectral6,
low=1,
high=1e6)
TOOLTIPS = [('County/Region', '@county'), ('State/Province', '@region'),
('Population', '@population'), ('Cases', '@number'),
('Cases Per Capita', '@number_per_capita')]
map_figure = figure(
title='Confirmed COVID-19 Cases in the United States',
tooltips=TOOLTIPS,
x_range=(-18367715, -6901808.43),
y_range=(0, 13377019.78),
x_axis_type='mercator',
y_axis_type='mercator',
active_scroll='wheel_zoom',
)
tile_provider = get_provider(CARTODBPOSITRON)
map_figure.add_tile(tile_provider)
map_figure.circle(x='web_mercator_x',
y='web_mercator_y',
source=source_CDS,
size='sizes',
color=color_mapper)
## Colorbar
color_bar = ColorBar(title='Num. Cases',
title_standoff=20,
color_mapper=color_mapper['transform'],
label_standoff=20,
width=8,
location=(0, 0),
ticker=LogTicker())
color_bar.formatter.use_scientific = False
map_figure.add_layout(color_bar, 'right')
## Slider
def slider_callback(attr, old, new):
delta = datetime.timedelta(milliseconds=new)
date = datetime.date(1970, 1, 1) + delta
date_string = date.strftime('%Y-%m-%d')
try:
source_CDS.data['number'] = source_df_confirmed[date_string]
source_CDS.data['sizes'] = source_df_confirmed[date_string].apply(
scale)
source_CDS.data['number_per_capita'] = source_df_confirmed[
date_string] / source_df_confirmed['population']
except KeyError:
pass
slider = DateSlider(title='Date',
start=START_DATE,
end=datetime.date.today(),
step=1,
value=START_DATE)
slider.on_change('value', slider_callback)
## Data Table
columns = [
TableColumn(field='county', title='County/Region'),
TableColumn(field='region', title='State/Province'),
TableColumn(field='population', title='Population'),
TableColumn(field='number', title='Cases'),
TableColumn(field='number_per_capita', title='Cases Per Capita'),
]
data_table = DataTable(
source=source_CDS,
columns=columns,
)
## Cancel Selection Button
def cancel_selection_callback():
source_CDS.selected.indices = []
cancel_selection_button = Button(label='Clear Selection',
button_type='warning')
cancel_selection_button.on_click(cancel_selection_callback)
child = row([
column([slider, map_figure]),
column([data_table, cancel_selection_button])
])
return Panel(child=child, title='United States Map')
