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)
Exemple #2
0
    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)
Exemple #3
0
    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
Exemple #5
0
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)
Exemple #6
0
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)
Exemple #7
0
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
Exemple #9
0
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,
))
Exemple #10
0
# 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
Exemple #11
0
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)