def scatter():
scatter = vincent.Scatter(data.multi_iter2,
width=WIDTH,
height=HEIGHT,
iter_idx='index')
scatter.axis_titles(x='Index', y='Data Value')
scatter.legend(title='Categories')
return scatter.to_json()
def test_datetimeandserial(self):
'''Test pandas serialization and datetime parsing'''
rng = pd.date_range('1/1/2013', periods=30, freq='D')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
scatter = vincent.Scatter()
scatter.tabular_data(ts)
timevalue = time.mktime(ts.index[0].timetuple()) * 1000
assert scatter.data[0]['values'][0]['x'] == timevalue
def add_air_station_marker_with_graph(self, data, *args, **kwargs):
"""Esta función se encarga de agregar graficos al los marcadores existentes"""
locations = data.iloc[:, [1, 2]]
station_names = data.iloc[:, [3]]
station_ids = list(pd.unique(data.id))
station_groups = data.groupby('id')
for station_id in station_ids:
filtered_data = station_groups.get_group(station_id)
print('***** data get group')
print(type(filtered_data))
print(filtered_data.head())
plot_data = filtered_data.groupby(
['magnitude', 'year', 'month', 'day']).agg({
'value': 'mean'
}).reset_index()
print('***** data plot data')
print(type(plot_data))
print(plot_data.head())
x = [int(hour) for hour in list(plot_data['day'])]
y = [int(value) for value in list(plot_data['value'])]
print(x)
print(y)
xy_values = {
'x': x,
'y': y,
}
scatter_chart = vincent.Scatter(xy_values,
iter_idx='x',
width=600,
height=300)
scatter_chart.axis_titles(x='Día',
y='Promedio Dióxido de Nitrogeno día')
popup_scatter_plot = folium.Popup(max_width=900).add_child(
folium.Vega(scatter_chart, height=350, width=700))
air_quality_station = [
filtered_data.iloc[0, 1], filtered_data.iloc[0, 2]
]
print(air_quality_station)
station_name = [filtered_data.iloc[0, 3]]
print(station_name)
folium.Marker(
location=air_quality_station,
tooltip=folium.Tooltip(
f'Estación: {station_name[0]}<br>Latitud: {round(air_quality_station[0], 4)}<br>Longitud: {round(air_quality_station[1], 4)}'
),
popup=popup_scatter_plot,
icon=folium.CustomIcon(icon_image='icons/forecast.png',
icon_size=(40, 40))).add_to(self._map)
def draw_markers(self, data, coordinates, style, label, mplobj=None):
import vincent # only import if VincentRenderer is used
if coordinates != 'data':
warnings.warn("Only data coordinates supported. Skipping this")
markerdata = {'x': data[:, 0],
'y': data[:, 1]}
markers = vincent.Scatter(markerdata, iter_idx='x',
width=self.figwidth, height=self.figheight)
# TODO: respect the other style settings
markers.scales['color'].range = [style['facecolor']]
if self.chart is None:
self.chart = markers
else:
warnings.warn("Multiple plot elements not yet supported")
def plotrwresult(G):
visits = [G.node[node]['visits'] for node in G.nodes_iter()]
norm_visits = np.array(visits) / float(G.graph['total_visits'])
deaths = [G.node[node]['deaths'] for node in G.nodes_iter()]
norm_deaths = np.array(deaths) / float(sum(deaths))
if G.is_directed():
degrees = [G.in_degree(node) for node in G.nodes_iter()]
norm_degrees = np.array(degrees) / float(G.size())
else:
degrees = [G.degree(node) for node in G.nodes_iter()]
aorm_degrees = np.array(degrees) / float(2 * G.size())
multi_iter1 = {
'index': range(G.order()),
'Visits': norm_visits,
'Deaths': norm_deaths,
'Degree': norm_degrees
}
line = vincent.Scatter(multi_iter1, iter_idx='index')
line.axis_titles(x='Vertex', y='Juice')
line.legend(title='Results')
line.width = 400
line.height = 300
line.marks[0].marks[0].properties.enter.opacity = vincent.ValueRef(value=1)
line.marks[0].marks[0].properties.update = vincent.PropertySet()
line.marks[0].marks[0].properties.update.size = vincent.ValueRef(value=100)
line.marks[0].marks[0].properties.hover = vincent.PropertySet()
line.marks[0].marks[0].properties.hover.size = vincent.ValueRef(value=200)
line.marks[0].marks[0].properties.update.size = vincent.ValueRef(value=100)
line.marks[0].marks[0].properties.hover = vincent.PropertySet()
line.marks[0].marks[0].properties.hover.size = vincent.ValueRef(value=200)
line.scales['shape'] = vincent.Scale(
name='shape',
type='ordinal',
domain=vincent.DataRef(data='table', field='data.col'),
range=["square", "circle", "triangle-down", "triangle-up"])
line.marks[0].marks[0].properties.enter.shape = vincent.ValueRef(
scale="shape", field="data.col")
line.legends[0].shape = "shape"
return line
def test_datetimeandserial(self):
'''Test pandas serialization and datetime parsing'''
import pandas.io.data as web
all_data = {}
for ticker in ['AAPL', 'GOOG']:
all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2004',
'1/1/2006')
price = pd.DataFrame(
{tic: data['Adj Close']
for tic, data in all_data.iteritems()})
scatter = vincent.Scatter()
scatter.tabular_data(price, columns=['AAPL', 'GOOG'])
assert scatter.data[0]['values'][0]['x'] == 10.49
nt.assert_is_none(scatter.data[0]['values'][0]['y'])
line = vincent.Line()
line.tabular_data(price, columns=['AAPL'])
assert line.data[0]['values'][0]['x'] == 1073030400000
line = vincent.Line(multi_iter1, iter_idx='index') line.axis_titles(x='Index', y='Value') line.legend(title='Categories') line.display() # <codecell> line = vincent.Line(price[['GOOG', 'AAPL']]) line.axis_titles(x='Date', y='Price') line.legend(title='GOOG vs AAPL') line.display() # <codecell> scatter = vincent.Scatter(df_1) scatter.axis_titles(x='Index', y='Data Value') scatter.legend(title='Categories') scatter.colors(brew='Set3') scatter.display() # <codecell> stacked = vincent.StackedArea(df_1) stacked.axis_titles(x='Index', y='Value') stacked.legend(title='Categories') stacked.colors(brew='Spectral') stacked.display() # <codecell>
# -*- coding: utf-8 -*-
'''
Builds a Vega grammar specification from vincent.Scatter()
'''
import vincent
import random
vis = vincent.Scatter()
vis.tabular_data([random.randint(10, 100) for x in range(0, 201, 1)])
#Generate both the Vega JSON and a data JSON.
path = r'/vega.json'
vis.to_json(path, split_data=True, html=True)
#Make the visualization wider, and add a hover to the points
vis.update_vis(width=800)
vis + ({
'fill': {
'value': '#2a3140'
},
'size': {
'value': '100'
}
}, 'marks', 0, 'properties', 'update')
vis + ({
'fill': {
'value': '#a63737'
m.save(os.path.join('recursos', 'dibujar.html'))
#######################################
import json
import numpy as np
import vincent
N = 100
multi_iter2 = {
'x': [4, 5, 6],
'y': [1, 2, 3],
}
scatter = vincent.Scatter(multi_iter2, iter_idx='x', height=100, width=200)
data = json.loads(scatter.to_json())
m = folium.Map([-34.969672, -71.230373], zoom_start=12)
mk = features.Marker([-34.969672, -71.230373])
p = folium.Popup('Hello')
v = features.Vega(data, width='100%', height='100%')
mk.add_child(p)
p.add_child(v)
m.add_child(mk)
m.save(os.path.join('recursos', 'mapaGrafico.html'))
#################################
import json
import numpy as np
import folium
from folium import Vega
import vincent
scatter_points = {
'x': np.random.uniform(size=(100, )),
'y': np.random.uniform(size=(100, )),
}
# Let's create the vincent chart.
scatter_chart = vincent.Scatter(scatter_points,
iter_idx='x',
width=600,
height=300)
# Let's convert it to JSON.
scatter_json = scatter_chart.to_json()
# Let's convert it to dict.
scatter_dict = json.loads(scatter_json)
m = folium.Map([43, -100], zoom_start=4)
popup = folium.Popup()
folium.Vega(scatter_chart, height=350, width=650).add_to(popup)
folium.Marker([30, -120], popup=popup).add_to(m)
# Let's create a Vega popup based on scatter_json.
