def hotmap(datetime, room, query):
"""Generates a heatmap bokeh model"""
#IMPORTANT: this query returns M-F values
# get a df with date and count values, for a given room and date
week_counts = pd.read_sql_query(query.format(date=datetime, room=room),
connectDB())
headcount = list(week_counts['count'])
headcount = [linear_predictor(x)
for x in headcount] # predict on all supplied values
bins = list(week_counts['count'])
# bins = [tertiary_classifier(x) for x in bins] # predict on all supplied values
tert_dictionary = {
'Empty': 0,
'Medium': 0.5,
'High': 1
} # map to int as only ints can go in heatmap
# bins = [tert_dictionary[x] for x in bins] # map with listcomp
# 5 * 9 = 45, so all elements need to have 45 values. Days and hours are just multipled out.
data = {
'days':
['fri'] * 9 + ['thu'] * 9 + ['wed'] * 9 + ['tue'] * 9 + ['mon'] * 9,
'occupancy': headcount,
'hours': ['9', '10', '11', '12', '13', '14', '15', '16', '17'] * 5
}
week_counts['headcount'] = week_counts['count'].apply(linear_predictor)
week_counts['bins'] = week_counts['count'].apply(tertiary_classifier)
source = ColumnDataSource(ColumnDataSource.from_df(week_counts))
# hover = HoverTool()
#
# tooltips = [("headcount", "@headcount")]
#
# tools = [hover]
# colors = ['#1abc9c', '#ec583a', '#474e5d'] # set colors; currently not shading on continuous features
hm = HeatMap(data,
x='hours',
y='days',
values='occupancy',
title='Occupancy in room over the week'.format(room),
stat=None,
palette=palette,
tools=None,
hover_tool=True)
legend = Legend(location=(0, -30))
hm.add_layout(legend, 'right')
all_plots = gridplot([[hm]])
return all_plots
from bokeh.charts import HeatMap, bins, output_file, show
from bokeh.palettes import RdBu, magma
from bokeh.models import ColumnDataSource, LabelSet, Label
output_file("index.html", title="BTC premia")
dateToDisplay = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
custompalette = ['#00814c', '#1ebd7c', '#26cc88', '#3ee09e', '#71ffc5', 'grey','#fcffc8', '#ffeb66', '#ffca49', '#ff9600', '#d00031']
hm1 = HeatMap(prem_last, x='from', y='to', values='diff', title=dateToDisplay+' Premia Last Closing (USD). diff = from - to, % = (from - to)/to', stat=None, palette=custompalette, legend=False)
source1 = ColumnDataSource(data=prem_last)
#labels1a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-10, y_offset=-10, render_mode='canvas', source=source1, text_font_size='9pt')
labels1b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-10, y_offset=-10, render_mode='canvas', source=source1, text_font_size='9pt')
#hm1.add_layout(labels1a)
hm1.add_layout(labels1b)
hm2 = HeatMap(prem_7day, x='from', y='to', values='diff', title=dateToDisplay+' Premia 7 days average (USD)', stat=None, palette=custompalette, legend=False)
source2 = ColumnDataSource(data=prem_7day)
#labels2a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-10, y_offset=-10, render_mode='canvas', source=source2, text_font_size='9pt')
labels2b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-10, y_offset=-10, render_mode='canvas', source=source2, text_font_size='9pt')
#hm2.add_layout(labels2a)
hm2.add_layout(labels2b)
hm3 = HeatMap(prem_30day, x='from', y='to', values='diff', title=dateToDisplay+' Premia 30 days average (USD)', stat=None, palette=custompalette, legend=False)
source3 = ColumnDataSource(data=prem_30day)
#labels3a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-10, y_offset=-10, render_mode='canvas', source=source3, text_font_size='9pt')
labels3b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-10, y_offset=-10, render_mode='canvas', source=source3, text_font_size='9pt')
#hm3.add_layout(labels3a)
hm3.add_layout(labels3b)
prem_7day = calcMatrixHeatMap(rolling_7day)
prem_30day = calcMatrixHeatMap(rolling_30day)
from bokeh.layouts import row
from bokeh.plotting import figure, show, output_file
from bokeh.charts import HeatMap, bins, output_file, show
from bokeh.palettes import RdBu, magma
from bokeh.models import ColumnDataSource, LabelSet, Label
output_file("ccy-premium.html", title="BTC premia")
hm1 = HeatMap(prem_last, x='from', y='to', values='diff', title='Premia Last Closing (USD). diff = from - to, % = (from - to)/to', stat=None, palette=['green', 'gray', 'red'], legend=False)
source1 = ColumnDataSource(data=prem_last)
labels1a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-15, y_offset=-10, render_mode='canvas', source=source1)
labels1b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-15, y_offset=-30, render_mode='canvas', source=source1)
hm1.add_layout(labels1a)
hm1.add_layout(labels1b)
hm2 = HeatMap(prem_7day, x='from', y='to', values='diff', title='Premia 7 days average (USD)', stat=None, palette=['green', 'gray', 'red'], legend=False)
source2 = ColumnDataSource(data=prem_7day)
labels2a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-15, y_offset=-10, render_mode='canvas', source=source2)
labels2b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-15, y_offset=-30, render_mode='canvas', source=source2)
hm2.add_layout(labels2a)
hm2.add_layout(labels2b)
hm3 = HeatMap(prem_30day, x='from', y='to', values='diff', title='Premia 30 days average (USD)', stat=None, palette=['green', 'gray', 'red'], legend=False)
source3 = ColumnDataSource(data=prem_30day)
labels3a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-15, y_offset=-10, render_mode='canvas', source=source3)
labels3b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-15, y_offset=-30, render_mode='canvas', source=source3)
hm3.add_layout(labels3a)
hm3.add_layout(labels3b)
# hm10 = HeatMap(unempl, x='Year', y='Month', values='Unemployment', stat=None,
# sort_dim={'x': False}, width=900, plot_height=500)
TOOLS = [BoxSelectTool(), HoverTool()]
hm11 = HeatMap(test, x='region', y='area', values='data', legend=False, stat=None, palette=GnRd9, width = 500, plot_height=500, title="Actlab Region Detection Test", tools=TOOLS)
hm12 = Bar(data_bar, values='dist', label='region', legend=False, title='Error Distance for Respective Regions', width = 800, plot_height = 500)
hm13 = BoxPlot(data_bar, values='dist', label='region', legend=False, title='Error Distance Boxplot', width = 300, plot_height=300)
#hm11.legend.location = 'right'
# data1 = [int(x) for x in data]
# print data1
# hover = HoverTool(tooltips=[
# ("error", "@data1")])
# hm11.add_tools(hover)
hm11.add_layout(labels)
hm11.add_layout(labels_dev)
hm12.add_layout(label_reg)
# output_file("heatmap.html")
output_file("Bar.html")
hm11.axis.visible = False
# show(hm11)
# show(hm12)
# show(hm13)
show(column(
gridplot(hm11, hm12,
ncols=1, plot_width=400, plot_height=400)
))
def bokeh_heatmap(index=['Date', 'Representative']):
#return three retention matrices
percent_MRR, actual_MRR, contract_MRR = pivot_tables(combo=generate_data(
n_sample=100, periods=30),
index=index)
#reformat data to column sparse matrix and place into dictionary
x = []
y = []
vals = []
[(y.append(i[0]), x.append(i[1]), vals.append(v))
for i, v in np.ndenumerate(percent_MRR)]
data = {'x': x, 'y': y, 'vals': vals}
#heatmap instantiate
hm = HeatMap(data=data,
x='x',
y='y',
values='vals',
stat=None,
title='Revenue Retention by {}'.format(','.join(index)),
ygrid=True,
xgrid=True,
xlabel='months',
ylabel='contract start',
yscale='categorical',
palette=brewer['RdYlGn'][11],
title_text_font_size='20pt')
#add value labels to heat map
# create formatted column of values for annotation labels
data['labels'] = [
'{:.0%}'.format(round(i, 2)) if i >= 0 else '' for i in data['vals']
]
#dropping vals which are not json compliant
del data['vals']
source = ColumnDataSource(data=data)
labels = LabelSet(x='x',
y='y',
text='labels',
level='glyph',
x_offset=-2,
y_offset=-4,
source=source,
render_mode='canvas',
text_font_size='7pt',
text_color='white')
hm.add_layout(labels)
# customize y-axis text
# new_index=actual_MRR.sum(axis=1).reset_index()
# new_index.rename(columns={0: 'Actual MRR'}, inplace=True)
# new_index['Contract MRR']=contract_MRR.sum(axis=1).reset_index()[0]
y_label_dict = {}
for index, groups in enumerate(list(percent_MRR.index)):
y_label_dict[index] = '-'.join(groups)
#reverse y-axis order
hm.y_range.start = max(y_label_dict.keys())
hm.y_range.end = min(y_label_dict.keys())
hm.x_range = Range1d(0, 12)
#generate javascript code to reformat y-axis
hm.yaxis.formatter = FuncTickFormatter(code="""var labels = {};
return labels[tick];
""".format(y_label_dict))
# fix ticks to display all values
hm.yaxis[0].ticker = FixedTicker(ticks=list(y_label_dict.keys()))
total_contract_mrr = contract_MRR.sum(axis=1)
total_actual_mrr = actual_MRR.sum(axis=1)
yaxis2_text = pd.concat([total_actual_mrr, total_contract_mrr], axis=1)
yaxis2_text.reset_index(drop=True, inplace=True)
yaxis2_text.columns = ['Actual MRR', 'Contract MRR']
yaxis2_text['text'] = yaxis2_text.apply(
lambda row: '{:,.0f}/{:,.0f} \t ({:.0%})'.format(
row['Actual MRR'], row['Contract MRR'], row['Actual MRR'] / row[
'Contract MRR']),
axis=1)
yaxis2_text = yaxis2_text['text'].to_dict()
#add right hand y-axis for additional information
top = max(yaxis2_text.keys())
bottom = min(yaxis2_text.keys())
print(top, bottom)
hm.extra_y_ranges = {'mrr': Range1d(top, bottom)}
hm.add_layout(LinearAxis(y_range_name="mrr"), 'right')
hm.yaxis[1].formatter = FuncTickFormatter(code="""var labels = {};
return labels[tick];
""".format(yaxis2_text))
hm.yaxis[1].ticker = FixedTicker(ticks=list(yaxis2_text.keys()))
hm.yaxis[1].axis_label = 'Actual and Contract MRR'
hm.yaxis[1].major_label_standoff = 30
# #help(hm.legend)
# legend_items=list(hm.legend)[0].items
#
hm.legend.location = 'bottom_right'
# exit()
output_file('interactive.html')
show(hm)
from bokeh.layouts import row
from bokeh.plotting import figure, show, output_file
from bokeh.charts import HeatMap, bins, output_file, show
from bokeh.palettes import RdBu, magma
from bokeh.models import ColumnDataSource, LabelSet, Label
output_file("ccy-premium.html", title="BTC premia")
dateToDisplay = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
hm1 = HeatMap(prem_last, x='from', y='to', values='diff', title=dateToDisplay+' Premia Last Minute Closing (USD). diff = from - to, % = (from - to)/to', stat=None, palette=['green', 'gray', 'red'], legend=False)
source1 = ColumnDataSource(data=prem_last)
labels1a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-15, y_offset=-10, render_mode='canvas', source=source1)
labels1b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-15, y_offset=-30, render_mode='canvas', source=source1)
hm1.add_layout(labels1a)
hm1.add_layout(labels1b)
hm2 = HeatMap(prem_7day, x='from', y='to', values='diff', title=dateToDisplay+' Premia Last 10 minutes average (USD)', stat=None, palette=['green', 'gray', 'red'], legend=False)
source2 = ColumnDataSource(data=prem_7day)
labels2a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-15, y_offset=-10, render_mode='canvas', source=source2)
labels2b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-15, y_offset=-30, render_mode='canvas', source=source2)
hm2.add_layout(labels2a)
hm2.add_layout(labels2b)
hm3 = HeatMap(prem_30day, x='from', y='to', values='diff', title=dateToDisplay+' Premia last 60 minutes average (USD)', stat=None, palette=['green', 'gray', 'red'], legend=False)
source3 = ColumnDataSource(data=prem_30day)
labels3a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-15, y_offset=-10, render_mode='canvas', source=source3)
labels3b = LabelSet(x='from', y='to', text='percent', level='glyph', x_offset=-15, y_offset=-30, render_mode='canvas', source=source3)
hm3.add_layout(labels3a)
hm3.add_layout(labels3b)
stat=None,
palette=custompalette,
legend=False)
source1 = ColumnDataSource(data=prem_last)
#labels1a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-10, y_offset=-10, render_mode='canvas', source=source1, text_font_size='9pt')
labels1b = LabelSet(x='from',
y='to',
text='percent',
level='glyph',
x_offset=-10,
y_offset=-10,
render_mode='canvas',
source=source1,
text_font_size='9pt')
#hm1.add_layout(labels1a)
hm1.add_layout(labels1b)
hm2 = HeatMap(prem_7day,
x='from',
y='to',
values='diff',
title=dateToDisplay + ' Premia 7 days average (USD)',
stat=None,
palette=custompalette,
legend=False)
source2 = ColumnDataSource(data=prem_7day)
#labels2a = LabelSet(x='from', y='to', text='formatted', level='glyph', x_offset=-10, y_offset=-10, render_mode='canvas', source=source2, text_font_size='9pt')
labels2b = LabelSet(x='from',
y='to',
text='percent',
level='glyph',
legend=False,
title='Nlsql_y2 Latitude Boxplot',
width=550,
plot_height=600)
hm24 = BoxPlot(box_y2lng,
values='Error Distance',
label='Devices',
legend=False,
title='Nlsql_y2 Longtitude Boxplot',
width=550,
plot_height=600)
refresh = Button(label="Refresh", button_type="success")
refresh.on_click(callback)
hm11.add_layout(labels_cal)
hm11.add_layout(labels_dev1)
hm14.add_layout(labels_bary)
hm14.add_layout(labels_dev2)
hm15.add_layout(labels_oys)
hm15.add_layout(labels_dev3)
hm16.add_layout(labels_y2)
hm16.add_layout(labels_dev4)
# output_file("Heatmap.html")
hm11.axis.visible = False
hm14.axis.visible = False
hm15.axis.visible = False
hm16.axis.visible = False
widgetrow = row(select1, refresh)
