def makeTable(df, width, ID1, ID2):
'''
df: DataFrame, which contain the cartographical information
width: float/int, giving the width of the table
ID1: str, str provides the the ID of the first dataset
ID2: str or None, use None if no second ID is provided, str provides the the ID of the second dataset
'''
table = PreText(text="", width=width)
if not ID2:
table.text = str(df.loc[[ID1]].T)
else:
table.text = str(df.loc[ID1:ID2].T)
return table
def stock2(ticker1, ticker2):
pretext = PreText(text="", width=500)
df = get_data(ticker1, ticker2)
source = ColumnDataSource(data=df)
source.tags = ['main_source']
p = figure(
title="%s vs %s" % (ticker1, ticker2),
plot_width=400, plot_height=400,
tools="pan,wheel_zoom,box_select,reset",
title_text_font_size="10pt",
)
p.circle(ticker1 + "_returns", ticker2 + "_returns",
size=2,
nonselection_alpha=0.02,
source=source
)
stats = df.describe()
pretext.text = str(stats)
hist1 = hist_plot(df, ticker1)
hist2 = hist_plot(df, ticker2)
line1 = line_plot(ticker1, source)
line2 = line_plot(ticker2, source, line1.x_range)
return dict(scatterplot=p,
statstext=pretext,
hist1=hist1,
hist2=hist2,
line1=line1,
line2=line2)
def stock(ticker1, ticker2):
pretext = PreText(text="", width=500)
df = get_data(ticker1, ticker2)
source = ColumnDataSource(data=df)
source.tags = ['main_source']
p = figure(
title="%s vs %s" % (ticker1, ticker2),
plot_width=400, plot_height=400,
tools="pan,wheel_zoom,box_select,reset",
title_text_font_size="10pt",
)
p.circle(ticker1 + "_returns", ticker2 + "_returns",
size=2,
nonselection_alpha=0.02,
source=source
)
stats = df.describe()
pretext.text = str(stats)
row1 = HBox(children=[p, pretext])
hist1 = hist_plot(df, ticker1)
hist2 = hist_plot(df, ticker2)
row2 = HBox(children=[hist1, hist2])
line1 = line_plot(ticker1, source)
line2 = line_plot(ticker2, source, line1.x_range)
output = VBox(children=[row1, row2, line1, line2])
return output
def stock2(ticker1, ticker2):
pretext = PreText(text="", width=500)
df = get_data(ticker1, ticker2)
source = ColumnDataSource(data=df)
source.tags = ['main_source']
p = figure(
title="%s vs %s" % (ticker1, ticker2),
plot_width=400,
plot_height=400,
tools="pan,wheel_zoom,box_select,reset",
title_text_font_size="10pt",
)
p.circle(ticker1 + "_returns",
ticker2 + "_returns",
size=2,
nonselection_alpha=0.02,
source=source)
stats = df.describe()
pretext.text = str(stats)
hist1 = hist_plot(df, ticker1)
hist2 = hist_plot(df, ticker2)
line1 = line_plot(ticker1, source)
line2 = line_plot(ticker2, source, line1.x_range)
return dict(scatterplot=p,
statstext=pretext,
hist1=hist1,
hist2=hist2,
line1=line1,
line2=line2)
def stock(ticker1, ticker2):
pretext = PreText(text="", width=500)
df = get_data(ticker1, ticker2)
source = ColumnDataSource(data=df)
source.tags = ['main_source']
p = figure(
title="%s vs %s" % (ticker1, ticker2),
plot_width=400,
plot_height=400,
tools="pan,wheel_zoom,box_select,reset",
title_text_font_size="10pt",
)
p.circle(ticker1 + "_returns",
ticker2 + "_returns",
size=2,
nonselection_alpha=0.02,
source=source)
stats = df.describe()
pretext.text = str(stats)
row1 = HBox(children=[p, pretext])
hist1 = hist_plot(df, ticker1)
hist2 = hist_plot(df, ticker2)
row2 = HBox(children=[hist1, hist2])
line1 = line_plot(ticker1, source)
line2 = line_plot(ticker2, source, line1.x_range)
output = VBox(children=[row1, row2, line1, line2])
return output
x='ds',
y='trend',
line_width=1,
alpha=0.5,
color='firebrick',
line_dash="6 4")
# ogarka
p.xgrid.grid_line_color = None
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.yaxis.formatter = NumeralTickFormatter(format="0,0")
stats = PreText(text='', height=500, width=600)
prep = pd.DataFrame(forecast_filtered_2, columns=['ds', 'yhat'])
stats.text = str(prep[prep['ds'] >= '2018-03-14'])
# main_row = row(p, stats)
# series = row(p, stats)
# layout = column(main_row, series)
curdoc().add_root(row(p, stats))
curdoc().title = "Prognoza GMV - Zestawy"
# show(p)
# # wydruk sum dla prognozy i zapis do pliku .csv
# print(forecast_filtered_2[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])
# forecast_filtered_2[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].to_csv('output_20180302.csv', sep=',')
# print('mid: ' + str(round(sum(forecast_filtered_2['yhat']) + 10329770)))
# print('low: ' + str(round(sum(forecast_filtered_2['yhat_lower']) + 10329770)))
ticker1.on_change('value', ticker1_change)
X_Metadata_broad_sample=ticker1.value.split('__')[0]
X_Metadata_mmoles_per_liter=ticker1.value.split('__')[1].split(' ')[0]
df_to_disp=meta_lincs2[(meta_lincs2["Metadata_broad_sample"]==X_Metadata_broad_sample) &\
(meta_lincs2["Metadata_mmoles_per_liter"]==float(X_Metadata_mmoles_per_liter))].reset_index(drop=True)
fS=create_figure(df_to_disp)
stats2_df=drug_list_rank[(drug_list_rank["Metadata_broad_sample"]==X_Metadata_broad_sample) &\
(drug_list_rank["Metadata_mmoles_per_liter"]==float(X_Metadata_mmoles_per_liter))].T
stats2.text = str(stats2_df)
# set up layout
# fS,notes=create_figure()
# fS=[]
# series = row(children=[ticker1]+[fS])
series = row(children=[column(children=[ticker1,stats2])]+[fS])
layoutt = column(series)
# initialize
# update()
curdoc().add_root(layoutt)
# curdoc().title = "Rosetta Eval"
question_2 = Div(text='<h2>Are there any noteworthy differences/anomalies ' +
'in the top 200 universities of the world?</h2>',
width=1000,
height=100)
# on change switch values
continent_select.on_change('value', drop_change)
correlation_select.on_change('value', correlation_change)
pyramid_year_select.on_change('value', year_change)
pyramid_xaxis_slider.on_change('value', year_change)
histogram_year_select.on_change('value', hist_year_change)
map_dropdown.on_change('value', map_handler)
gdp_correlation_select.on_change('value', gdp_correlation_change)
# gets static plot and table string from helper.py
static_col, static_table_data = helper.bar_chart_continent_split()
static_table.text = str(static_table_data)
# lays out the widgets and columss
non_static_row = row(column_bar_split, table)
static_row = row(static_col, static_table)
map_column = column(map_dropdown, world_map_div)
main_column = column(continent_select, non_static_row, static_row)
main_column = column(question_1, main_column)
correlation_column = column(correlation_select, correlation)
pyramid_plot = gridplot([[pyramid_left, pyramid_right]], border_space=0)
pyramid_plot_slider = row(pyramid_plot, pyramid_xaxis_slider)
pyramid_column = column(pyramid_year_select, pyramid_plot_slider)
pyramid_row = row(pyramid_column)
correlations_200 = row(correlation_column, pyramid_row)
correlations_200 = column(question_2, correlations_200)
