def viz_zone_stat(input_shapefile,poly,uid):
try:
os.remove('ndvi_stats.html')
except OSError:
pass
palette.reverse()
output_notebook()
output_file('ndvi_stats1.html')
props = OrderedDict()
with fiona.open(input_shapefile, 'r') as polys:
for i in polys:
prop = OrderedDict()
prop[i['properties'].get(uid)] = {}
prop[i['properties'].get(uid)].update({ stat : val for stat , val in i['properties'].items()})
props.update(prop)
px = []
py = []
for i in poly:
x,y = i.exterior.xy
x=x.tolist()
y=y.tolist()
px.append(x)
py.append(y)
polyID = []
mndvi = []
std_ndvi= []
color_mapper = LogColorMapper(palette=palette)
for key in props:
polyID.append(props[key][uid])
mndvi.append(props[key]['mean'])
std_ndvi.append(props[key]['std'])
source = ColumnDataSource(data=OrderedDict(
tid=polyID,mndvi=mndvi,stdndvi=std_ndvi,x=px,y=py))
TOOLS = "pan,wheel_zoom,reset,hover,save"
TOOLTIPS=[
((uid), "@tid"),
("Mean NDVI", "@mndvi"),
("Std. NDVI", "@stdndvi")]
p = figure(plot_width=500, plot_height=500,title="Zonal Statistics")
#p.hover.point_policy = "follow_mouse"
"fill color", "$color[hex, swatch]:fill_color"
plots = p.patches('x', 'y', source=source,
fill_color={'field': 'mndvi', 'transform': color_mapper},
fill_alpha=0.7, line_color="black", line_width=0.5)
p.add_tools(HoverTool(tooltips=TOOLTIPS, mode='mouse'))
show(p)
def plot_bokeh_intensity_map(self, spatial_entity_to_coordinates,
spatial_entity_to_values):
entity_xs = []
entity_ys = []
entity_names = []
entity_rates = []
for name, coords in spatial_entity_to_coordinates.items():
xs = [i for i in coords[0]]
ys = [i for i in coords[1]]
try:
intensity_value = np.median(spatial_entity_to_values[name])
except:
intensity_value = 0.0
entity_xs.append(xs)
entity_ys.append(ys)
entity_names.append(name)
entity_rates.append(intensity_value)
palette.reverse()
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(
x=entity_xs,
y=entity_ys,
name=entity_names,
rate=entity_rates,
))
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="London Median House Prices, 2013-2014",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None)
p.grid.grid_line_color = None
p.patches('x',
'y',
source=source,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Name", "@name"),
("Price change rate)", "@rate%"),
("(Long, Lat)", "($x, $y)"),
]
show(p)
def hot_map_init(self, width=700, height=700, webgl=True):
self.districts = NYCGeoPolygon.load_districts()
rates = []
for district in self.districts:
x, y = district.xy()
self.districts_xs.append(x)
self.districts_ys.append(y)
self.districts_names.append(district.name)
rates.append(
self.data.pickups[district.index]) # default uses pickups
self.hot_map_source = ColumnDataSource(data=dict(
x=self.districts_xs,
y=self.districts_ys,
name=self.districts_names,
rate=rates,
))
palette.reverse()
color_mapper = LogColorMapper(palette=palette)
self.hot_map = figure(webgl=webgl,
plot_height=height,
plot_width=width,
tools='pan,wheel_zoom,box_zoom,reset,hover,save',
x_axis_location=None,
y_axis_location=None)
self.hot_map.grid.grid_line_color = None
self.hot_map.patches('x',
'y',
source=self.hot_map_source,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
self.hot_map.title.text = "%s %s/%s, %s" % \
(self.selected_type,
self.selected_year, self.selected_month,
NYCBorough.BOROUGHS[self.selected_borough])
hover = self.hot_map.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("District", "@name"),
("Trips", "@rate"),
("Coordinates", "($x, $y)"),
]
def plot(location, predictions, state_name):
from bokeh.sampledata.us_counties import data as counties
palette.reverse()
counties = {
code: county
for code, county in counties.items()
if county["state"] in [state_name]
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()
] #Make sure names match with data
county_rates = [unemployment[county_id]
for county_id in counties] #These would be the predictions
color_mapper = LogColorMapper(palette=palette)
data = dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=predictions,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="Vote Preference Predictions",
tools=TOOLS,
#x_axis_location=None, y_axis_location=None,tooltips=[("Name", "@name"), ("Unemployment rate)", "@rate%"), ("(Long, Lat)", "($x, $y)")])
x_axis_location=None,
y_axis_location=None,
tooltips=[("Name", "@name"), ("Yes vote (percent)", "@rate%")])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x',
'y',
source=data,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
show(p)
script, div = components(p)
return script, div
def choropleth(df):
palette.reverse()
if "HI" in states:
del states["HI"]
del states["AK"]
state_xs = [states[code]["lons"] for code in states]
state_ys = [states[code]["lats"] for code in states]
state_names = [name for name in states]
state_rates = [df[state] if state in df else 0 for state in states]
color_mapper = LogColorMapper(
palette="Viridis256", low=0, high=max(state_rates))
data = dict(
x=state_xs,
y=state_ys,
name=state_names,
rate=state_rates,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
width=850, height=500,
title="Location of Charities in Forward Investivation", tools=TOOLS,
x_axis_location=None, y_axis_location=None,
tooltips=[
("State", "@name"), ("Number of Charities", "@rate")
])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x', 'y', source=data,
fill_color={'field': 'rate', 'transform': color_mapper},
fill_alpha=0.7, line_color="black", line_width=0.5)
color_bar = ColorBar(
color_mapper=color_mapper,
ticker=FixedTicker(
ticks=[1, 10, 20, 50, 100, 500]),
label_standoff=8, border_line_color=None, location=(0, 0))
p.add_layout(color_bar, 'right')
show(p)
def gen_texas():
from bokeh.io import show
from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette.reverse()
counties = {
code: county for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="Texas Unemployment, 2009", tools=TOOLS,
x_axis_location=None, y_axis_location=None,
tooltips=[
("Name", "@name"), ("Unemployment rate)", "@rate%"), ("(Long, Lat)", "($x, $y)")
])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x', 'y', source=data,
fill_color={'field': 'rate', 'transform': color_mapper},
fill_alpha=0.7, line_color="white", line_width=0.5)
show(p)
class texas:
#https://bokeh.pydata.org/en/latest/docs/gallery/texas.html
palette.reverse()
counties = {
code: county for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="Texas Unemployment, 2009", tools=TOOLS,
x_axis_location=None, y_axis_location=None,
tooltips=[
("Name", "@name"), ("Unemployment rate)", "@rate%"), ("(Long, Lat)", "($x, $y)")
])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x', 'y', source=data,
fill_color={'field': 'rate', 'transform': color_mapper},
fill_alpha=0.7, line_color="white", line_width=0.5)
show(p)
from bokeh.io import show
from bokeh.models import (ColumnDataSource, HoverTool, LogColorMapper)
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
if __name__ == "__main__":
palette.reverse()
counties = {code: county \
for code, county in counties.items() \
if county["state"] == "tx" }
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(
x=county_xs, y=county_ys, name=county_names, rate=county_rates))
p = figure(title="Texas Unemployment, 2009",
tools="pan,wheel_zoom,reset,hover,save",
x_axis_location=None,
y_axis_location=None)
p.grid.grid_line_color = None
from bokeh.io import show
from bokeh.models import (
ColumnDataSource,
HoverTool,
LogColorMapper
)
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette.reverse()
counties = {
code: county for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
))
# In[2]:
from bokeh.io import show # importing show function to display the output
from bokeh.models import (
ColumnDataSource,
HoverTool, # hovertool helps the user to hover over the map and give details
LogColorMapper # fills the color
)
from bokeh.palettes import Viridis6 as palette # imports different palettes
from bokeh.plotting import figure # import different figures
from bokeh.sampledata.us_counties import data as counties # importing data of counties
from bokeh.sampledata.unemployment import data as unemployment # importing data of unemployment within counties
palette.reverse() # reverses colors of the palette
counties = {
code: county for code, county in counties.items() if county["state"] == "nj"
} # specify the coude for county state Nj
county_xs = [county["lons"] for county in counties.values()] # plotting x axis
county_ys = [county["lats"] for county in counties.values()] # plotting y axis
county_names = [county['name'] for county in counties.values()] # assigning names of county data
county_rates = [unemployment[county_id] for county_id in counties] # assigning unemployment rate of county
color_mapper = LogColorMapper(palette=palette) # using the color mapper funtion to fill the map according to the rates
source = ColumnDataSource(data=dict(
x=county_xs, # assigining x axis
y=county_ys, # assigining y axis
def map():
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette.reverse()
counties = {
code: county
for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
))
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="Texas Unemployment, 2009",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None)
p.grid.grid_line_color = None
p.patches('x',
'y',
source=source,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Name", "@name"),
("Unemployment rate)", "@rate%"),
("(Long, Lat)", "($x, $y)"),
]
#show(p)
# grab the static resources
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
# render template
script, div = components(p)
html = render_template(
'index.html',
plot_script=script,
plot_div=div,
js_resources=js_resources,
css_resources=css_resources,
)
return encode_utf8(html)
