def dashboard(request):
state_xs = [state["lons"] for state in states.values()]
state_ys = [state["lats"] for state in states.values()]
state_names = [state for state in states.keys()]
color_mapper = LogColorMapper(palette=palette)
rate_dict = {}
for name in state_names:
rate_dict[name] = 0
rate_values = Lead.objects.values('state').annotate(dcount=Count('state'))
for i in rate_values:
if i['state'] != None:
rate_dict[i['state']] = i['dcount']
print(rate_dict)
#rate = [float(random.randrange(1, 10)) for _ in range(0, 51)]
rate = list(rate_dict.values())
data = dict(
x=state_xs,
y=state_ys,
name=state_names,
rate=rate,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="Number of Leads in Each State",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None,
tooltips=[("Name", "@name"), ("Count", "@rate"),
("(Long, Lat)", "($x, $y)")],
x_range=(-130, -60),
y_range=(20, 60))
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.legend.location = 'top_right'
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 render(request, 'bokeh/dashboard.html', {
'the_script': script,
'the_div': div
})
def create_us_state_map(scores):
from bokeh.sampledata.us_states import data as states
states = {
code: states
for code, states in states.items() if code not in ['AK', 'HI']
}
state_xs = [state["lons"] for state in states.values()]
state_ys = [state["lats"] for state in states.values()]
teal_palette = [
'#ffffff', '#e0f2f1', '#b2dfdb', '#80cbc4', '#4db6ac', '#26a69a',
'#009688', '#00897b', '#00796b', '#00695c'
]
state_names = [state['name'] for state in states.values()]
state_scores = [
scores[code] if code in scores.keys() else 0 for code in states.keys()
]
color_mapper = LogColorMapper(palette=teal_palette,
low=0.01,
high=max(scores.values()))
data = dict(
x=state_xs,
y=state_ys,
name=state_names,
rate=state_scores,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="NLP Ranking Scores Across U.S. States",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None,
sizing_mode="scale_width",
plot_width=1100,
plot_height=700,
tooltips=[("State", "@name"), ("Score", "@rate{0,0.00}")])
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)
return p
["state"] not in ["ak", "hi", "pr", "gu", "vi", "mp", "as"]
]
county_colors = []
for county_id in us_counties:
if us_counties[county_id]["state"] in [
"ak", "hi", "pr", "gu", "vi", "mp", "as"
]:
continue
try:
rate = unemployment[county_id]
idx = min(int(rate / 2), 5)
except KeyError:
county_colors.append("black")
state_names = [state['name'] for state in states.values()]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(x=state_xs, y=state_ys, name=state_names))
TOOLS = "pan,wheel_zoom,reset,hover,save"
"""year_slider = Slider(start = 2009, end=2017,value=1,step=1,title="Year")
show(widgetbox(year_slider))"""
output_file("choropleth.html", title="choropleth.py example")
p = figure(title="US Unemployment 2009",
tools=TOOLS,
toolbar_location="left",
plot_width=1100,
plot_height=700)
from bokeh.models import HoverTool, HBox, VBox, Slider, Toggle
from bokeh.plotting import figure, show, ColumnDataSource
from bokeh.sampledata.us_states import data as states
from bokeh.palettes import Purples9
states = {
code: state
for code, state in states.items() if code not in ['HI', 'AK']
}
def gen_initial_rate(y):
return min(np.random.choice([15, 40]) + np.random.uniform(-10, 10), 100)
state_xs = [state['lons'] for state in states.values()]
state_ys = [state['lats'] for state in states.values()]
colors = Purples9[::-1]
names = [state['name'] for state in states.values()]
initial_rates = [gen_initial_rate(1) for _ in states.values()]
state_colors = [colors[int(rate / 20)] for rate in initial_rates]
source = ColumnDataSource(data=dict(
x=state_xs, y=state_ys, color=state_colors, name=names,
rate=initial_rates))
TOOLS = ['hover']
p = figure(title='Algorithms Deployed, Iteration 0',
tools=TOOLS,
from bokeh.plotting import figure, show, ColumnDataSource
from bokeh.sampledata.us_states import data as states
from bokeh.palettes import Purples9
states = {
code: state for code, state in states.items() if
code not in ['HI', 'AK']
}
def gen_initial_rate(y):
return min(
np.random.choice([15, 40]) + np.random.uniform(-10, 10),
100
)
state_xs = [state['lons'] for state in states.values()]
state_ys = [state['lats'] for state in states.values()]
colors = Purples9[::-1]
names = [state['name'] for state in states.values()]
initial_rates = [gen_initial_rate(1) for _ in states.values()]
state_colors = [colors[int(rate / 20)] for rate in initial_rates]
source = ColumnDataSource(data=dict(
x=state_xs,
y=state_ys,
color=state_colors,
name=names,
rate=initial_rates
))
def q3(filedir, outputdir, n_iters=10000):
ec = pd.read_csv(filedir + 'electoralCollege.csv')
pw = pd.read_csv(filedir + 'probWin.csv')
tot = pd.merge(ec, pw, on='State')
tot['ProbWin'] = tot['ProbWin'].str.replace('%', "")
p1_win = []
p2_win = []
for i in tot['ProbWin']:
p1 = float(int(i) / 100)
p2 = 1 - p1
p1_win.append(p1)
p2_win.append(p2)
tot['P1 Win'] = p1_win
tot['P2 Win'] = p2_win
tot.drop('ProbWin', axis=1)
# coerce each column in data frame to a list
state_name = list(tot['State'])
state_votes = list(tot['Votes'])
state_p1_probs = list(tot['P1 Win'])
state_p2_probs = list(tot['P2 Win'])
# initialize a counter to calculate how many times each party wins
p1s_sims = 0
p2s_sims = 0
for x in range(n_iters):
p1s = []
p2s = []
for y in range(len(tot)):
win = np.random.choice(['p1', 'p2'], p=[p1_win[y], p2_win[y]])
if win == 'p1':
p1s.append(tot['Votes'][y])
else:
p2s.append(tot['Votes'][y])
if sum(p1s) > 270:
p1s_sims += 1
else:
p2s_sims += 1
# set up the map boundaries for each state
state_xs = [
states[code]["lons"] for code in states if code not in ['HI', 'AK']
]
state_ys = [
states[code]["lats"] for code in states if code not in ['HI', 'AK']
]
# delete outer states in order to keep the map well aligned
if 'HI' and 'AK' in states.keys():
del states['HI']
del states['AK']
s = tot.iloc[:, 0].values
# set dictionary values for proportions each state votes for winning party
props = dict()
for j in s:
pwin = np.random.choice(['p1', 'p2'],
p=[(p1s_sims / n_iters), (p2s_sims / n_iters)])
if (pwin == ['p1']):
props[j] = props.get(j, 0) + float(
tot.loc[tot['State'] == j]['P1 Win'])
else:
props[j] = props.get(j, 0) + float(
tot.loc[tot['State'] == j]['P2 Win'])
data = list(states.values())
final_graph = []
# set up data in the structure of holoviews
for x in data:
x['Votes'] = tot.loc[tot['State'] == str(x['name'])]['Votes']
x['Party 1'] = str(
int(
float(tot.loc[tot['State'] == str(x['name'])]['P1 Win']) *
100)) + "%"
x['Party 2'] = str(
int(
float(tot.loc[tot['State'] == str(x['name'])]['P2 Win']) *
100)) + "%"
x['Proportions'] = props.get(str(x['name']))
final_graph.append(x)
choropleth = hv.Polygons(
final_graph, ['lons', 'lats'],
[('name', 'State'), ('Votes', 'Votes'),
('Party 1', 'Probability of Party 1 Winning'),
('Party 2', 'Probability of Party 2 Winning'),
('Proportions', 'Proportion Voted for Winning Party')])
ch = choropleth.options(logz=True,
tools=['hover'],
xaxis=None,
yaxis=None,
show_grid=False,
show_frame=False,
width=575,
height=400,
colorbar=False,
line_color='black',
color_index='Proportions',
cmap="RdBu")
hv.save(ch, "jvasanda_hw6_q3.html")
from readData import *
data = createJsonObject("crimeData.json")
year = 2010
del states["HI"]
del states["AK"]
state_xs = [states[code]["lons"] for code in states]
state_ys = [states[code]["lats"] for code in states]
colors = ["#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043", "#490303", "#000000"]
state_names = [state['name'] for state in states.values()]
state_colors = []
state_rates = []
for state_id in states:
try:
rate = getDataForYear(data, year, state_id)
state_rates.append(rate)
rate = math.log(rate)
idx = int(rate/2)
state_colors.append(colors[idx])
except KeyError:
state_colors.append("black")
geom = affinity.rotate(geom, 30)
f['geometry'] = mapping(geom)
return json.dumps(geojson)
geojson = get_geojson()
[x for x in geojson]
from bokeh.models import HoverTool
from bokeh.plotting import figure, show, output_file, ColumnDataSource
from bokeh.sampledata.us_states import data as states
from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.models import GeoJSONDataSource
states.pop('DC')
state_xs = [state["lons"] for state in states.values()]
state_ys = [state["lats"] for state in states.values()]
colors = ["#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043"]
sorter = pd.DataFrame(columns=['wookie'])
for idx, name in enumerate(states):
sorter.loc[idx] = [states[name]['name']]
final = pd.merge(sorter, final, left_on='wookie',
right_on='State').drop('wookie', axis=1)
assert [states[name]['name'] for name in states] == final.State.tolist()
###############################################################################
from bokeh.sampledata.sample_geojson import geojson
state_names = final.State.tolist()
## Choropleth
choro = figure(title="Composite Reimbursement by State 2018 (%)",
plot_height=575,
plot_width=1000,
tools=" wheel_zoom, reset, save")
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]
## Source data
state_names = [state['name'] for state in states.values()]
state_cc = [state['ccName'] for state in states.values()]
state_rates = [state['reimbursement'] for state in states.values()]
palette.reverse()
color_mapper = LogColorMapper(palette=palette)
choro_source = ColumnDataSource(data=dict(
x=state_xs,
y=state_ys,
name=state_names,
cost_center=state_cc,
rate=state_rates,
def make_dataset(self):
per_capita = self.per_capita.active == 1
data_type = self.data_getter.labels[self.data_getter.active].lower()
date = self.date.value
data = np.empty(len(US_STATES))
if data_type in ("cases", "deaths"):
if not per_capita:
dt_label = data_type
label = f"Total New {data_type.title()}"
else:
dt_label = f"{data_type}_pc"
label = f"New {data_type.title()} per 100,000"
subset = GH_STATES_DATA.loc[GH_STATES_DATA["date"] == date, :]
for i, (abbrv, state) in enumerate(US_STATES.items()):
state_name = state["name"]
value = subset.loc[subset["state"] == state_name,
f"avg_{dt_label}"]
if not value.empty and not np.isnan(value.values[0]):
data[i] = max(0, value.values[0])
else:
data[i] = 0
maxval = GH_STATES_DATA.loc[:, f"avg_{dt_label}"].max()
elif data_type == "positivity":
label = "Positivity (%)"
subset = TRACKING_DATA.loc[TRACKING_DATA["datetime"] == date,
("state", "positivity")]
for i, (abbrv, state) in enumerate(US_STATES.items()):
value = subset.loc[subset["state"] == abbrv.upper(),
"positivity"]
if not value.empty and not np.isnan(value.values[0]):
data[i] = max(0, value.values[0])
else:
data[i] = 0
maxval = TRACKING_DATA.loc[:, "positivity"].max()
interp = (
compute_log_palette # if logarithmic else compute_linear_palette
)
color_data = {
"color":
[interp(PALETTE, maxval / 256, maxval, val) for val in data],
"value": data,
"state": [state["name"] for state in US_STATES.values()],
"lons": [],
"lats": [],
}
for state in US_STATES.values():
color_data["lons"].append(state["lons"])
color_data["lats"].append(state["lats"])
return label, maxval, ColumnDataSource(color_data)
fit = np.polyfit(years, df2.loc[state, dtype], 1)
state_data[dtype] = 2018 * fit[0] + fit[1]
data_2018[n] = state_data
dfs[2018] = pd.DataFrame(data_2018).T
df = pd.concat(dfs, names=['year'], sort=False)
# Selection box
slider_left = Slider(start=2013, end=2018, value=2013, step=1, title='Comparison Year Left')
slider_right = Slider(start=2013, end=2018, value=2014, step=1, title='Comparison Year Right')
state_xs = [state["lons"] for state in states.values()]
state_ys = [state["lats"] for state in states.values()]
state_names = [state['name'] for state in states.values()]
# get color palletes
Blues9.reverse()
diffs = RdBu11.copy()
diffs_prcp = RdBu11.copy()
diffs_prcp.reverse()
# diffs.reverse()
temp_cmap = LinearColorMapper(palette=RdBu11, low=df['tmax'].min(), high=df['tmax'].max())
diff_cmap = LinearColorMapper(palette=diffs, low=-25, high=25)
diff_cmap_prcp = LinearColorMapper(palette=diffs_prcp, low=-25, high=25)
prcp_cmap = LinearColorMapper(palette=Blues9, low=0, high=df['prcp'].max())
aqi_cmap = LinearColorMapper(palette=BrBG11, low=df['Median AQI'].min(), high=df['Median AQI'].max())