# Each tab is drawn by one script
from scripts.histogram import histogram_tab
from scripts.table import table_tab
from scripts.time import time_tab
# Using included state data from Bokeh for map
# from bokeh.sampledata.us_states import data as states
# output_file("nichd.html")
# Read data into dataframes
trials = pd.read_csv(join(dirname(__file__), 'data', 'SearchResults.csv'),
index_col=0).dropna(subset=['Phases', 'Enrollment'])
# Formatted Flight Delay Data for map
# map_data = pd.read_csv(join(dirname(__file__), 'data', 'flights_map.csv'),
# header=[0,1], index_col=0)
# Create each of the tabs
tab1 = histogram_tab(trials)
# tab2 = density_tab(flights)
tab3 = table_tab(trials)
tab4 = time_tab(trials)
# tab5 = route_tb(flights)
# Put all the tabs into one application
tabs = Tabs(tabs=[tab1, tab3, tab4])
# Put the tabs in the current document for display
curdoc().add_root(tabs)
import pandas as pd
from bokeh.io import curdoc
from bokeh.models.widgets import Tabs
from scripts.histogram import histogram_tab
import json
with open('config.json') as fp:
config = json.load(fp)
file_name = config['file_path']
df = pd.read_csv(file_name)
tab1 = histogram_tab(df)
tabs = Tabs(tabs=[tab1])
curdoc().add_root(tabs)
from scripts.density import density_tab
from scripts.table import table_tab
from scripts.draw_map import map_tab
from scripts.routes import route_tab
# Using included state data from Bokeh for map
from bokeh.sampledata.us_states import data as states
# Read data into dataframes
flights = pd.read_csv(join(dirname(__file__), 'data', 'flights.csv'),
index_col=0).dropna()
# Formatted Flight Delay Data for map
map_data = pd.read_csv(join(dirname(__file__), 'data', 'flights_map.csv'),
header=[0,1], index_col=0)
# Create each of the tabs
tab1 = histogram_tab(flights)
tab2 = density_tab(flights)
tab3 = table_tab(flights)
tab4 = map_tab(map_data, states)
tab5 = route_tab(flights)
# Put all the tabs into one application
tabs = Tabs(tabs = [tab1, tab2, tab3, tab4, tab5])
# Put the tabs in the current document for display
curdoc().add_root(tabs)
from bokeh.io import curdoc
from bokeh.models.widgets import Tabs
from bokeh.plotting import output_file, show
# Each tab is drawn by one script
from scripts.histogram import histogram_tab
from scripts.density import density_tab
from scripts.table import table_tab
from scripts.plot import plot_tab
# Read data into dataframes
df_avgs_w_l = pd.read_pickle('df_avgs_w_l')
df_avgs_w_l['rpi'] = pd.to_numeric(df_avgs_w_l['rpi'], errors='coerce')
# Create each of the tabs
tab1 = histogram_tab(df_avgs_w_l)
tab2 = density_tab(df_avgs_w_l)
tab3 = table_tab(df_avgs_w_l)
tab4 = plot_tab(df_avgs_w_l)
# tab5 = route_tb(flights)
# Put all the tabs into one application
tabs = Tabs(tabs=[tab1, tab2, tab3, tab4])
# Put the tabs in the current document for display
curdoc().add_root(tabs)
output_file('plots.html')
show(tabs)
# Each tab is drawn by one script
from scripts.histogram import histogram_tab
# Using included state data from Bokeh for map
from bokeh.sampledata.us_states import data as states
# read and clean data
df = pd.read_csv(
'bokeh_app/data/African_Studies_Journal_Review_Project_Database.csv',
header=0,
encoding='unicode_escape')
cleaned_df = df.iloc[:, [0] + list(range(19, 140))]
cleaned_df = cleaned_df.dropna()
cleaned_df.iloc[:,
range(21, 115)] = cleaned_df.iloc[:,
range(21, 115)].astype(int)
data = cleaned_df.iloc[:, [0, 7] + list(range(21, 115))]
# Create each of the tabs
tab1 = histogram_tab(data)
# tab2 = density_tab(flights)
# tab3 = table_tab(data)
# tab4 = map_tab(map_data, states)
# tab5 = route_tab(flights)
# Put all the tabs into one application
tabs = Tabs(tabs=[tab1])
# Put the tabs in the current document for display
curdoc().add_root(tabs)
'mean',
'count':
'mean',
'income':
'mean',
'weight':
'mean',
'looped':
'mean'
})
featlist = grouplabels.columns[2:]
#print(featlist)
# Create each of the tabs
# histogram plot of features
tab1 = histogram_tab(groupaddr, featlist)
#time plot
tab2 = timeplot_tab(grouplabels, lstlabels[1:])
# Put all the tabs into one application
#tabs = Tabs(tabs =tab1)
tabs = Tabs(tabs=[tab1, tab2])
# Put the tabs in the current document for display
curdoc().add_root(tabs)
# p = figure(plot_width=1800, plot_height=750, x_axis_type="datetime")
# p.title.text = 'Click on legend entries to mute the corresponding lines'
# for label, color in zip(lstlabels[1:],Spectral9):
# df=grouplabels[grouplabels['label']==label].sort_values(by=['date'])
