"""function to assembly the chart"""

print('starting the plot...')

# specify the output file name

output_file("movigrama_chart.html")

# force to show only one plot when multiples executions of the code occur

# otherwise the plots will append each time one new calling is done

reset_output()

# create ColumnDataSource objects directly from Pandas data frames

source = ColumnDataSource(df)

# use the column DT as index

df.set_index('DT', inplace=True)

###########################################################################

#

# Movigrama Plot

#

###########################################################################

# build figure of the plot

p = figure(x_axis_type='datetime',

x_axis_label='days of moviment',

y_axis_label='unities movimented',

plot_width=1230,

plot_height=500,

active_scroll='wheel_zoom')

# TODO Specify X range (not all plots have 365 days of moviment)

# build the Stock Level bar

r1 = p.vbar(x='DT',

bottom=0,

top='STOCK',

width=pd.Timedelta(days=1),

fill_alpha=0.4,

color='paleturquoise',

source=source)

# build the OUT bar

p.vbar(x='DT',

bottom=0,

top='SOMA_SAI',

width=pd.Timedelta(days=1),

fill_alpha=0.8,

color='crimson',

source=source)

# build the IN bar

p.vbar(x='DT',

bottom=0,

top='SOMA_ENTRA',

width=pd.Timedelta(days=1),

fill_alpha=0.8,

color='seagreen',

source=source)

# edit title

# adds warehouse title

p.add_layout(Title(text=complements['warehouse'],

text_font='helvetica',

text_font_size='10pt',

text_color='orangered',

text_alpha=0.5,

align='center',

text_font_style="italic"), 'above')

# adds product title

p.add_layout(Title(text=complements['product'],

text_font='helvetica',

text_font_size='10pt',

text_color='orangered',

text_alpha=0.5,

align='center',

text_font_style="italic"), 'above')

# adds main title

p.add_layout(Title(text='Movigrama Endicon',

text_font='helvetica',

text_font_size='16pt',

text_color='orangered',

text_alpha=0.9,

align='center',

text_font_style="bold"), 'above')

# adds horizontal line

hline = Span(location=0,

line_alpha=0.4,

dimension='width',

line_color='gray',

line_width=3)

p.renderers.extend([hline])

# adapt the range to the plot

p.x_range.range_padding = 0.1

p.y_range.range_padding = 0.1

# format the plot's outline

p.outline_line_width = 4

p.outline_line_alpha = 0.1

p.outline_line_color = 'orangered'

# format major labels

p.axis.major_label_text_color = 'gray'

p.axis.major_label_text_font_style = 'bold'

# format labels

p.axis.axis_label_text_color = 'gray'

p.axis.axis_label_text_font_style = 'bold'

# p.xgrid.grid_line_color = None # disable vertical bars

# p.ygrid.grid_line_color = None # disable horizontal bars

# change placement of minor and major ticks in the plot

p.axis.major_tick_out = 10

p.axis.minor_tick_in = -3

p.axis.minor_tick_out = 6

p.axis.minor_tick_line_color = 'gray'

# format properly the X datetime axis

p.xaxis.formatter = DatetimeTickFormatter(

days=['%d/%m'],

months=['%m/%Y'],

years=['%Y'])

# iniciate hover object

hover = HoverTool()

hover.mode = "vline" # activate hover by vertical line

hover.tooltips = [("SUM-IN", "@SOMA_ENTRA"),

("SUM-OUT", "@SOMA_SAI"),

("COUNT-IN", "@TRANSACT_ENTRA"),

("COUNT-OUT", "@TRANSACT_SAI"),

("STOCK", "@STOCK"),

("DT", "@DT{%d/%m/%Y}")]

# use 'datetime' formatter for 'DT' field

hover.formatters = {"DT": 'datetime'}

hover.renderers = [r1] # display tolltip only to one render

p.add_tools(hover)

###########################################################################

#

# Demand analysis

#

###########################################################################

# change to positive values

df['out_invert'] = df['SOMA_SAI']*-1

# moving average with n=30 days

df['MA30'] = df['out_invert'].rolling(30).mean().round(0)

# moving standard deviation with n=30 days

df['MA30_std'] = df['out_invert'].rolling(30).std().round(0)

# lower control limit for 1 sigma deviation

df['lcl_1sigma'] = (df['MA30'] - df['MA30_std'])

# upper control limit for 1 sigma deviation

df['ucl_1sigma'] = (df['MA30'] + df['MA30_std'])

source = ColumnDataSource(df)

p1 = figure(plot_width=1230,

plot_height=500,

x_range=p.x_range,

x_axis_type="datetime",

active_scroll='wheel_zoom')

# build the Sum_out bar

r1 = p1.vbar(x='DT',

top='out_invert',

width=pd.Timedelta(days=1),

color='darkred',

line_color='salmon',

fill_alpha=0.4,

source=source)

# build the moving average line

p1.line(x='DT',

y='MA30',

source=source)

# build the confidence interval

band = Band(base='DT',

lower='lcl_1sigma',

upper='ucl_1sigma',

source=source,

level='underlay',

fill_alpha=1.0,

line_width=1,

line_color='black')

p1.renderers.extend([band])

# adds title

p1.add_layout(Title(text='Demand Variability',

text_font='helvetica',

text_font_size='16pt',

text_color='orangered',

text_alpha=0.5,

align='center',

text_font_style="bold"), 'above')

# adds horizontal line

hline = Span(location=0,

line_alpha=0.4,

dimension='width',

line_color='gray',

line_width=3)

p1.renderers.extend([hline])

# format the plot's outline

p1.outline_line_width = 4

p1.outline_line_alpha = 0.1

p1.outline_line_color = 'orangered'

# format major labels

p1.axis.major_label_text_color = 'gray'

p1.axis.major_label_text_font_style = 'bold'

# format labels

p1.axis.axis_label_text_color = 'gray'

p1.axis.axis_label_text_font_style = 'bold'

# change placement of minor and major ticks in the plot

p1.axis.major_tick_out = 10

p1.axis.minor_tick_in = -3

p1.axis.minor_tick_out = 6

p1.axis.minor_tick_line_color = 'gray'

# format properly the X datetime axis

p1.xaxis.formatter = DatetimeTickFormatter(days=['%d/%m'],

months=['%m/%Y'],

years=['%Y'])

# iniciate hover object

hover = HoverTool()

hover.mode = "vline" # activate hover by vertical line

hover.tooltips = [("DEMAND", '@out_invert'),

("UCL 1σ", "@ucl_1sigma"),

("LCL 1σ", "@lcl_1sigma"),

("M AVG 30d", "@MA30"),

("DT", "@DT{%d/%m/%Y}")]

# use 'datetime' formatter for 'DT' field

hover.formatters = {"DT": 'datetime'}

hover.renderers = [r1] # display tolltip only to one render

p1.add_tools(hover)

###########################################################################

#

# Demand groupped by month

#

###########################################################################

resample_M = df.iloc[:, 0:6].resample('M').sum() # resample to month

# create column date as string

resample_M['date'] = resample_M.index.strftime('%b/%y').values

# moving average with n=3 months

resample_M['MA3'] = resample_M['out_invert'].rolling(3).mean()

resample_M['MA3'] = np.ceil(resample_M.MA3) # round up the column MA3

# resample to month with mean

resample_M['mean'] = np.ceil(resample_M['out_invert'].mean())

# resample to month with standard deviation

resample_M['std'] = np.ceil(resample_M['out_invert'].std())

# moving standard deviation with n=30 days

resample_M['MA3_std'] = np.ceil(resample_M['out_invert'].rolling(3).std())

# lower control limit for 1 sigma deviation

resample_M['lcl_1sigma'] = resample_M['MA3'] - resample_M['MA3_std']

# upper control limit for 1 sigma deviation

resample_M['ucl_1sigma'] = resample_M['MA3'] + resample_M['MA3_std']

source = ColumnDataSource(resample_M)

p2 = figure(plot_width=1230,

plot_height=500,

x_range=FactorRange(factors=list(resample_M.date)),

title='demand groupped by month')

colors = factor_cmap('date',

palette=Category20_20,

factors=list(resample_M.date))

p2.vbar(x='date',

top='out_invert',

width=0.8,

fill_color=colors,

fill_alpha=0.8,

source=source,

legend=value('OUT'))

p2.line(x='date',

y='MA3',

color='red',

line_width=3,

line_dash='dotted',

source=source,

legend=value('MA3'))

p2.line(x='date',

y='mean',

color='blue',

line_width=3,

line_dash='dotted',

source=source,

legend=value('mean'))

band = Band(base='date',

lower='lcl_1sigma',

upper='ucl_1sigma',

source=source,

level='underlay',

fill_alpha=1.0,

line_width=1,

line_color='black')

labels1 = LabelSet(x='date',

y='MA3',

text='MA3',

level='glyph',

y_offset=5,

source=source,

render_mode='canvas',

text_font_size="8pt",

text_color='darkred')

labels2 = LabelSet(x='date',

y='out_invert',

text='out_invert',

level='glyph',

y_offset=5,

source=source,

render_mode='canvas',

text_font_size="8pt",

text_color='gray')

low_box = BoxAnnotation(top=resample_M['mean'].iloc[0]-resample_M['std'].iloc[0], # analysis:ignore

fill_alpha=0.1,

fill_color='red')

mid_box = BoxAnnotation(bottom=resample_M['mean'].iloc[0]-resample_M['std'].iloc[0], # analysis:ignore

top=resample_M['mean'].iloc[0]+resample_M['std'].iloc[0], # analysis:ignore

fill_alpha=0.1, fill_color='green')

high_box = BoxAnnotation(bottom=resample_M['mean'].iloc[0]+resample_M['std'].iloc[0], # analysis:ignore

fill_alpha=0.1,

fill_color='red')

p2.renderers.extend([band, labels1, labels2, low_box, mid_box, high_box])

p2.legend.click_policy = "hide"

p2.legend.background_fill_alpha = 0.4

p2.add_layout(Title(text='Demand Grouped by Month',

text_font='helvetica',

text_font_size='16pt',

text_color='orangered',

text_alpha=0.5,

align='center',

text_font_style="bold"), 'above')

# adds horizontal line

hline = Span(location=0,

line_alpha=0.4,

dimension='width',

line_color='gray',

line_width=3)

p2.renderers.extend([hline])

# format the plot's outline

p2.outline_line_width = 4

p2.outline_line_alpha = 0.1

p2.outline_line_color = 'orangered'

# format major labels

p2.axis.major_label_text_color = 'gray'

p2.axis.major_label_text_font_style = 'bold'

# format labels

p2.axis.axis_label_text_color = 'gray'

p2.axis.axis_label_text_font_style = 'bold'

# change placement of minor and major ticks in the plot

p2.axis.major_tick_out = 10

p2.axis.minor_tick_in = -3

p2.axis.minor_tick_out = 6

p2.axis.minor_tick_line_color = 'gray'

# iniciate hover object

# TODO develop hoverTool

# hover = HoverTool()

# hover.mode = "vline" # activate hover by vertical line

# hover.tooltips = [("SUM-IN", "@SOMA_ENTRA"),

# ("SUM-OUT", "@SOMA_SAI"),

# ("COUNT-IN", "@TRANSACT_ENTRA"),

# ("COUNT-OUT", "@TRANSACT_SAI"),

# ("STOCK", "@STOCK")]

# hover.renderers = [r1] # display tolltip only to one render

# p2.add_tools(hover)

###########################################################################

#

# Plot figures

#

###########################################################################

# put the results in a column and show

show(column(p, p1, p2))

# show(p) # plot action