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modules/mix-contribution-analysis/vendor/modules/charting/draw_waterfall.py
43.3 KB · Oct 6, 2026 · 06:02 UTC
"""Waterfall chart drawing utilities.
This module relies on **Polars**. When counting rows in a DataFrame or
LazyFrame, prefer using ``frame.height`` or ``get_row_count``.
"""
import copy
import math
import numpy as np
import plotly.graph_objects as go
import polars as pl
from plotly.subplots import make_subplots
from modules.charting.chart_helpers import (
get_pinhead_outliers,
set_up_tab_for_show_or_download_chart,
)
from modules.charting.chart_primitives import (
add_message_as_annotation,
add_sign_to_labels,
add_title_as_annotation,
check_if_plan_or_py,
divide_by_value_prefix,
enable_draw_shapes,
get_color_choice,
get_color_dictionary,
get_color_sequence,
get_user_message,
millify_dataframe,
reset_row_and_column_counters,
)
from modules.charting.draw_charts_utils import (
add_negative_outlier_pins_to_column,
add_percent_change_markers_to_column,
add_positive_outlier_pins_to_column,
get_text_template,
)
from modules.charting.draw_multitier import (
add_absolute_value_bars_to_multitier_column,
add_negative_outlier_pins_to_bar,
add_percent_change_markers_to_bar,
add_positive_outlier_pins_to_bar,
)
from modules.charting.make_titles import make_horizontal_waterfall_chart_title
from modules.charting.setup_fig import setup_fig_for_horizontal_waterfall_charts
from modules.charting.update_layouts import update_horizontal_waterfall_layout
from modules.data.misc_charts_data_prep import create_color_column
from modules.data.waterfall_data_prep import (
get_waterfall_number_format,
prepare_data_for_horizontal_waterfall_plot,
prepare_horizontal_waterfall_data_for_openAi,
)
from modules.utilities.config import (
get_config_params,
get_naming_params,
get_variance_aggregation_params,
)
from modules.utilities.helpers import (
drop_columns,
duplicate_dataframe,
get_periods_array,
unique,
)
from modules.utilities.utils import (
ensure_polars_df,
get_row_count,
get_schema_and_column_names,
is_valid_lazyframe,
)
def color_first_bar_vertical(df, fig, paramDict, chartDict, colorDict, run):
"""
colors first bar based on if planned or previous data
"""
namingParams = get_naming_params()
showInitialAndFinalValues = namingParams["showInitialAndFinalValues"]
varianceAmountName = namingParams["varianceAmountName"]
workColumn = namingParams["workColumn"]
drilldownReportRunName = namingParams["drilldownReportRunName"]
totalVarianceAggregation = namingParams["totalVarianceAggregation"]
isYearBeforePy = namingParams["isYearBeforePy"]
marginVarianceAggregation = namingParams["marginVarianceAggregation"]
varianceAggregation = namingParams["varianceAggregation"]
netOfDiscountAggregation = namingParams["netOfDiscountAggregation"]
initialAndFinalValuesCanBeShown = True
if (
chartDict[varianceAggregation]
not in [
totalVarianceAggregation,
netOfDiscountAggregation,
marginVarianceAggregation,
]
and drilldownReportRunName in run
):
initialAndFinalValuesCanBeShown = False
if (
showInitialAndFinalValues in chartDict
and chartDict[showInitialAndFinalValues]
and initialAndFinalValuesCanBeShown
):
# Retrieve first label/value in a Polars-friendly way (works for DataFrame or LazyFrame)
lf = df.lazy() if isinstance(df, pl.DataFrame) else df
_vals = lf.select(
pl.col(workColumn).first().alias("__first_label"),
pl.col(varianceAmountName).first().alias("__first_var"),
).collect(engine="streaming")
firstLabel = _vals["__first_label"][0]
firstVar = _vals["__first_var"][0]
isExpectedData, planName = check_if_plan_or_py([firstLabel])
firstBarColor, lineWidth, lineColor = set_semantic_bar_color(
isExpectedData, colorDict, paramDict
)
fig.add_shape(
type="rect",
fillcolor=firstBarColor,
opacity=1,
line_width=lineWidth,
line_color=lineColor,
y0=-0.4,
y1=0.4,
yref="y",
x0=0,
x1=firstVar,
xref="x",
row=1,
col=1,
)
return fig
def set_semantic_bar_color(isExpectedData, colorDict, paramDict):
namingParams = get_naming_params()
isYearBeforePy = namingParams["isYearBeforePy"]
if isExpectedData:
firstBarColor, lineWidth, lineColor = (
colorDict["whiteColor"],
0.5,
colorDict["lightGreyColor"],
)
elif isYearBeforePy in paramDict and paramDict[isYearBeforePy]:
firstBarColor, lineWidth, lineColor = (
colorDict["veryLightGreyColor"],
0.5,
colorDict["veryLightGreyColor"],
)
else:
firstBarColor, lineWidth, lineColor = (
colorDict["lightGreyColor"],
0.5,
colorDict["lightGreyColor"],
)
return firstBarColor, lineWidth, lineColor
def add_total_variance_arrow_horizontal(
df, fig, paramDict, chartDict, colorDict, run, metric, row, col
):
"""
we add the red or green arrow total variance annotation
"""
namingParams = get_naming_params()
configParams = get_config_params()
font = configParams[namingParams["fontChoice"]]
fontSize = configParams[namingParams["fontSizeText"]]
varianceAmountName = namingParams["varianceAmountName"]
showInitialAndFinalValues = namingParams["showInitialAndFinalValues"]
drilldownReportRunName = namingParams["drilldownReportRunName"]
fcName = namingParams["fcName"]
acName = namingParams["acName"]
firstBarColor, lineWidth, lineColor = (
colorDict["whiteColor"],
0.5,
colorDict["lightGreyColor"],
)
columns, schema = get_schema_and_column_names(df)
lf = df.lazy() if isinstance(df, pl.DataFrame) else df
values = lf.select(
pl.col(varianceAmountName).first().alias("_p0"),
pl.col(varianceAmountName).last().alias("_p1"),
).collect(engine="streaming")
periodZeroValue = values["_p0"][0]
periodOneValue = values["_p1"][0]
if fcName in columns:
sums = lf.select(
pl.col(acName).sum().alias("_ac_sum"),
pl.col(fcName).sum().alias("_fc_sum"),
).collect(engine="streaming")
periodOneValue = sums["_ac_sum"][0] + sums["_fc_sum"][0]
totalVarianceAggregation = namingParams["totalVarianceAggregation"]
marginVarianceAggregation = namingParams["marginVarianceAggregation"]
varianceAggregation = namingParams["varianceAggregation"]
discountName = namingParams["discountName"]
indirectCostsName = namingParams["indirectCostsName"]
deltaName = namingParams["deltaName"]
cogsName = namingParams["cogsName"]
reverseColorMetricsArray = [discountName, indirectCostsName, cogsName]
initialAndFinalValuesCanBeShown = True
if varianceAggregation in chartDict:
if (
chartDict[varianceAggregation]
not in [totalVarianceAggregation, marginVarianceAggregation]
and drilldownReportRunName in run
):
initialAndFinalValuesCanBeShown = False
if (
showInitialAndFinalValues in chartDict
and chartDict[showInitialAndFinalValues]
and initialAndFinalValuesCanBeShown
):
if metric in reverseColorMetricsArray:
if periodOneValue >= periodZeroValue:
arrowColor = colorDict["redColor"]
else:
arrowColor = colorDict["greenColor"]
else:
if periodOneValue >= periodZeroValue:
arrowColor = colorDict["greenColor"]
else:
arrowColor = colorDict["redColor"]
fig.add_shape(
type="line",
opacity=1,
line_width=lineWidth,
line_color=lineColor,
x0=-0.4,
x1=df.height,
xref="paper",
y0=periodZeroValue,
y1=periodZeroValue,
yref="y",
row=row,
col=col,
)
fig.add_shape(
type="line",
opacity=1,
line_width=lineWidth,
line_color=lineColor,
x0=df.height - 1,
x1=df.height,
xref="paper",
y0=periodOneValue,
y1=periodOneValue,
yref="y",
row=row,
col=col,
)
fig.add_shape(
type="line",
opacity=1,
line_width=5,
line_color=arrowColor,
x1=df.height,
x0=df.height,
xref="paper",
y1=periodZeroValue,
y0=periodOneValue,
yref="y",
row=row,
col=col,
)
if periodZeroValue != 0:
percentChange = ((periodOneValue - periodZeroValue) / periodZeroValue) * 100
difference = periodOneValue - periodZeroValue
difference = divide_by_value_prefix(difference, chartDict, False)
difference = deltaName + " " + str(difference)
if not math.isnan(percentChange):
percentChange = "<i>(" + str(int(round(percentChange, 0))) + "%)</i>"
else:
percentChange = ""
changevalue = difference + "<br>" + percentChange
fig.add_annotation(
showarrow=False,
text=changevalue,
align="center",
font=dict(
family=font,
size=fontSize,
),
yshift=-10,
xshift=20,
ax=df.height,
x=df.height,
xref="paper",
ay=periodZeroValue,
y=periodOneValue,
yref="y",
ayref="y",
row=row,
col=col,
)
else:
periodZeroValue = deltaName + " nan"
return fig
def color_first_bar_horizontal(df, fig, paramDict, chartDict, colorDict, run, row, col):
"""t
colors first bar based on if planned or previous data
"""
namingParams = get_naming_params()
showInitialAndFinalValues = namingParams["showInitialAndFinalValues"]
varianceAmountName = namingParams["varianceAmountName"]
workColumn = namingParams["workColumn"]
drilldownReportRunName = namingParams["drilldownReportRunName"]
totalVarianceAggregation = namingParams["totalVarianceAggregation"]
marginVarianceAggregation = namingParams["marginVarianceAggregation"]
varianceAggregation = namingParams["varianceAggregation"]
initialAndFinalValuesCanBeShown = True
if varianceAggregation in chartDict:
if (
chartDict[varianceAggregation]
not in [totalVarianceAggregation, marginVarianceAggregation]
and drilldownReportRunName in run
):
initialAndFinalValuesCanBeShown = False
if (
showInitialAndFinalValues in chartDict
and chartDict[showInitialAndFinalValues]
and initialAndFinalValuesCanBeShown
):
# Retrieve first label/value in a Polars-friendly way (works for DataFrame or LazyFrame)
lf = df.lazy() if isinstance(df, pl.DataFrame) else df
_vals = lf.select(
pl.col(workColumn).first().alias("__first_label"),
pl.col(varianceAmountName).first().alias("__first_var"),
).collect(engine="streaming")
firstLabel = _vals["__first_label"][0]
firstVar = _vals["__first_var"][0]
isExpectedData, planName = check_if_plan_or_py([firstLabel])
if isExpectedData:
firstBarColor, lineWidth, lineColor = (
colorDict["whiteColor"],
0.5,
colorDict["lightGreyColor"],
)
else:
firstBarColor, lineWidth, lineColor = (
colorDict["lightGreyColor"],
0.5,
colorDict["lightGreyColor"],
)
fig.add_shape(
type="rect",
fillcolor=firstBarColor,
opacity=1,
line_width=lineWidth,
line_color=lineColor,
x0=-0.4,
x1=0.4,
xref="x",
y0=0,
y1=firstVar,
yref="y",
row=row,
col=col,
)
return fig
def add_annotations_to_horizontal_waterfall_plot(
fig, dfCopy, metric, colorDict, chartDict, paramDict, row, col, plotWithPins
):
"""Plot a horizontal waterfall chart with annotations.
Row counts within this function rely on ``df.height`` to follow Polars
idioms.
"""
if isinstance(dfCopy, pl.LazyFrame):
dfCopy = dfCopy.collect()
namingParams = get_naming_params()
configParams = get_config_params()
periodsArray = configParams["periodsArray"]
runVariableDimensionalAnalysis = namingParams["runVariableDimensionalAnalysis"]
measureName = namingParams["measureName"]
varianceAmountName = namingParams["varianceAmountName"]
varianceTypeName = namingParams["varianceTypeName"]
workColumn = namingParams["workColumn"]
workColumnTwo = namingParams["workColumnTwo"]
processingChoice = namingParams["processingChoice"]
horizontalWaterfallChart = namingParams["horizontalWaterfallChart"]
totalVarianceAggregation = namingParams["totalVarianceAggregation"]
marginVarianceAggregation = namingParams["marginVarianceAggregation"]
varianceAggregation = namingParams["varianceAggregation"]
variancePercentChangeName = namingParams["variancePercentChangeName"]
marginVariance = namingParams["marginVariance"]
drilldownReportRunName = namingParams["drilldownReportRunName"]
separatorString = namingParams["separatorString"]
amountName = namingParams["monetaryLocalCurrencyName"]
marginName = namingParams["marginName"]
plotSmallMultiplesKey = namingParams["plotSmallMultiplesOtherCharts"]
varianceInPercent = namingParams["varianceInPercent"]
shareOfTotalMarket = namingParams["shareOfTotalMarket"]
selectedPeriods = namingParams["selectedPeriods"]
filterDates = namingParams["filterDates"]
dateName = namingParams["dateName"]
acName = namingParams["acName"]
pyName = namingParams["pyName"]
plName = namingParams["plName"]
fcName = namingParams["fcName"]
labelName = namingParams["labelName"]
discountName = namingParams["discountName"]
indirectCostsName = namingParams["indirectCostsName"]
cogsName = namingParams["cogsName"]
compareScenariosOrPeriods = namingParams["compareScenariosOrPeriods"]
compareScenarios = namingParams["compareScenarios"]
periodOrder = chartDict[selectedPeriods]
reverseColorMetricsArray = [discountName, indirectCostsName, cogsName]
if (
plotWithPins
or plotSmallMultiplesKey not in chartDict
or not chartDict[plotSmallMultiplesKey]
):
row, col = 2, 1
df = duplicate_dataframe(dfCopy)
orientation = "v"
columns, schema = get_schema_and_column_names(df)
if filterDates in chartDict and chartDict[filterDates]:
if fcName in columns:
yArray = [plName, fcName, acName]
else:
yArray = [plName, acName]
else:
yArray = [pyName, acName]
df = create_color_column(
df, metric, yArray, horizontalWaterfallChart, paramDict, chartDict
)
if isinstance(df, pl.LazyFrame):
df = df.collect()
colorSequenceArray, lineWidth = get_color_sequence(df, paramDict, chartDict)
colorChoice = get_color_choice(chartDict)
if metric in reverseColorMetricsArray:
decreasingColorDict = {"marker": {"color": colorDict["greenColor"]}}
increasingColorDict = {"marker": {"color": colorDict["redColor"]}}
else:
decreasingColorDict = {"marker": {"color": colorDict["redColor"]}}
increasingColorDict = {"marker": {"color": colorDict["greenColor"]}}
df, chartDict = add_sign_to_labels(
df, horizontalWaterfallChart, workColumnTwo, 1, False, chartDict
)
if (
compareScenariosOrPeriods in chartDict
and chartDict[compareScenariosOrPeriods] == compareScenarios
):
columns, schema = get_schema_and_column_names(df)
texttemplate, textformat = get_text_template(chartDict)
df = df.with_columns(
pl.when(pl.col(dateName) != "")
.then(pl.concat_str([pl.lit(" "), pl.col(workColumn)]))
.otherwise(pl.col(workColumn))
.alias(workColumn),
pl.when(pl.col(dateName) != "")
.then(pl.concat_str([pl.lit(" "), pl.col(dateName)]))
.otherwise(pl.col(dateName))
.alias(dateName),
)
fig.add_trace(
go.Waterfall(
orientation=orientation,
measure=df[measureName],
x=df[workColumn],
y=df[varianceAmountName],
textinfo="text",
text=df[labelName],
texttemplate=texttemplate,
decreasing=decreasingColorDict,
increasing=increasingColorDict,
totals={
"marker": {"color": colorSequenceArray[1]}
}, # colorDict["greyColor"]}},
connector={
"mode": "between",
"line": {"width": 1, "color": "rgb(169,169,169)", "dash": "solid"},
},
textposition="outside",
cliponaxis=False,
),
row=row,
col=col,
)
anchos = [0.68] * get_row_count(df)
periodOrder = [yArray[0], yArray[1]]
dfCopy = duplicate_dataframe(df)
last_idx = pl.len() - 1
dfCopy = (
dfCopy.with_row_index("_idx")
.with_columns(
pl.when((pl.col("_idx") == 0) | (pl.col("_idx") == last_idx))
.then(pl.lit(None))
.otherwise(pl.col(yArray[0]))
.alias(yArray[0]),
pl.when((pl.col("_idx") == 0) | (pl.col("_idx") == last_idx))
.then(pl.lit(None))
.otherwise(pl.col(yArray[1]))
.alias(yArray[1]),
)
.drop("_idx")
)
showAbsoluteValueBars = True
anchosPercent = [0.48 / 4] * get_row_count(dfCopy)
if (
plotWithPins
or plotSmallMultiplesKey not in chartDict
or not chartDict[plotSmallMultiplesKey]
):
df, largestArray, smallestArray, chartDict = get_pinhead_outliers(
dfCopy, chartDict
)
df = ensure_polars_df(df)
fig = add_percent_change_markers_to_column(fig, df, colorChoice, lineWidth, 24)
fig = add_positive_outlier_pins_to_column(fig, df, largestArray, colorDict, 1)
fig = add_negative_outlier_pins_to_column(fig, df, smallestArray, colorDict, 1)
fig = add_label_to_horizontal_waterflow(fig, 1)
else:
pass
colorSequenceArray, lineWidth = get_color_sequence(df, paramDict, chartDict)
constant = 24
offset = -0.2
fig, df, chartDict = add_absolute_value_bars_to_multitier_column(
fig,
df,
metric,
paramDict,
offset,
constant,
colorSequenceArray,
lineWidth,
row,
col,
chartDict,
)
if get_row_count(df) >= 12:
pass
fig = add_total_variance_arrow_horizontal(
df,
fig,
paramDict,
chartDict,
colorDict,
horizontalWaterfallChart,
metric,
row,
col,
)
fig = color_first_bar_horizontal(
df, fig, paramDict, chartDict, colorDict, horizontalWaterfallChart, row, col
)
return fig, chartDict
def adjust_horizontal_waterfall_plot(
fig, df, key, metric, title, height, width, paramDict, chartDict, plotWithPins
):
namingParams = get_naming_params()
configParams = get_config_params()
font = configParams[namingParams["fontChoice"]]
chosenChart = namingParams["chosenChart"]
chosenChart = chartDict[chosenChart]
fig = update_horizontal_waterfall_layout(
df, fig, height, width, paramDict, chartDict, plotWithPins
)
fig, message = get_user_message(
fig, chosenChart, metric, key, paramDict, chartDict, df, width, None
)
fig = add_message_as_annotation(
fig, message, None, chosenChart, chartDict, paramDict
)
fig = add_title_as_annotation(fig, title, chosenChart, chartDict)
fig.update_annotations(font=dict(size=10, family=font))
fig = enable_draw_shapes(fig)
return fig
def draw_horizontal_waterfall_chart(
dfCopy, chosenDimension, metricArray, repeatArray, paramDict, chartDict
):
"""Build and plot a horizontal waterfall chart.
Row counts and array lengths are computed using ``frame.height`` where
applicable to maintain Polars style.
"""
if isinstance(dfCopy, pl.LazyFrame):
dfCopy = dfCopy.collect()
namingParams = get_naming_params()
configParams = get_config_params()
fontSize = configParams[namingParams["fontSizeText"]]
font = configParams[namingParams["fontChoice"]]
trendComparisonByPeriodChart = namingParams["trendComparisonByPeriodChart"]
numberOfPlots = namingParams["numberOfPlots"]
chosenChart = namingParams["chosenChart"]
periodName = namingParams["periodName"]
plName = namingParams["plName"]
pyName = namingParams["pyName"]
acName = namingParams["acName"]
plotSmallMultiplesKey = namingParams["plotSmallMultiplesOtherCharts"]
chosenChart = chartDict[chosenChart]
configPlotlyDict = configParams["configPlotlyDict"]
configPlotlyDict = configPlotlyDict[chosenChart]
exportDataArray = []
colorDict = get_color_dictionary(chartDict)
numberOfMetrics = len(metricArray)
key = None
if is_valid_lazyframe(dfCopy):
repeatArrayToPlot = []
for element in repeatArray:
repeatArrayToPlot.append(element)
columns, schema = get_schema_and_column_names(dfCopy)
count, countRows, countCols = 1, 1, 1
plotWithPins = False
if chosenDimension == None and numberOfMetrics == 1:
plotWithPins = True
if plotSmallMultiplesKey in chartDict and chartDict[plotSmallMultiplesKey]:
fig, height, width, numberOfCols, numberOfRows = (
setup_fig_for_horizontal_waterfall_charts(
repeatArrayToPlot, chosenDimension, chartDict, plotWithPins
)
)
if chosenDimension in columns:
paramDict[numberOfPlots] = len(repeatArray)
# fullFig=False
# metricType=False
# same scale does not work here because Other Rank > is plotted as last
for column in repeatArray:
df = duplicate_dataframe(dfCopy)
periodsArray = get_periods_array(df)
df = df.filter(pl.col(chosenDimension) == column)
if plName in periodsArray:
pyName = plName
df = drop_columns(df, [chosenDimension])
for metric in metricArray:
if (
plotSmallMultiplesKey not in chartDict
or not chartDict[plotSmallMultiplesKey]
):
fig, height, width, numberOfCols, numberOfRows = (
setup_fig_for_horizontal_waterfall_charts(
repeatArrayToPlot,
chosenDimension,
chartDict,
plotWithPins,
)
)
df, paramDict = prepare_data_for_horizontal_waterfall_plot(
df, column, metric, paramDict, chartDict
)
if isinstance(df, pl.LazyFrame):
df = df.collect()
dfDim = duplicate_dataframe(df)
# Add chosenDimension as a new column (Polars) and place it first
dfDim = dfDim.with_columns(pl.lit(column).alias(chosenDimension))
cols, _ = get_schema_and_column_names(dfDim)
if cols and cols[0] != chosenDimension:
dfDim = dfDim.select(
[chosenDimension]
+ [c for c in cols if c != chosenDimension]
)
dfDim = prepare_horizontal_waterfall_data_for_openAi(
dfDim, chartDict
)
exportDataArray.append(dfDim)
fig, chartDict = add_annotations_to_horizontal_waterfall_plot(
fig,
df,
metric,
colorDict,
chartDict,
paramDict,
countRows,
countCols,
plotWithPins,
)
count, countRows, countCols, chartDict = (
reset_row_and_column_counters(
count,
countCols,
countRows,
numberOfCols,
numberOfRows,
chartDict,
)
)
if (
plotSmallMultiplesKey not in chartDict
or not chartDict[plotSmallMultiplesKey]
):
key = chosenDimension + column
titleColumn = chosenDimension + ": " + column
title, paramDict, chartDict = (
make_horizontal_waterfall_chart_title(
df,
chosenChart,
paramDict,
titleColumn,
metric,
chartDict,
pyName,
acName,
)
)
# fig,fullFig,metricType=keep_same_scale_for_all_plots(fig,metric,metricType,fullFig,"Y")
# same scale does not work here because Other Rank > is plotted as last)
fig = adjust_horizontal_waterfall_plot(
fig,
df,
key,
metric,
title,
height,
width,
paramDict,
chartDict,
plotWithPins,
)
df1 = duplicate_dataframe(df)
# Add chosenDimension as a new column (Polars) and place it first
df1 = df1.with_columns(pl.lit(column).alias(chosenDimension))
cols1, _ = get_schema_and_column_names(df1)
if cols1 and cols1[0] != chosenDimension:
df1 = df1.select(
[chosenDimension]
+ [c for c in cols1 if c != chosenDimension]
)
df1 = prepare_horizontal_waterfall_data_for_openAi(
df1, chartDict
)
paramDict = set_up_tab_for_show_or_download_chart(
df1,
fig,
configPlotlyDict,
chartDict,
title,
False,
None,
chosenDimension,
paramDict,
)
else:
paramDict[numberOfPlots] = len(metricArray)
periodsArray = dfCopy[periodName].unique().to_list()
if plName in periodsArray:
pyName = plName
for metric in metricArray:
df = duplicate_dataframe(dfCopy)
if (
plotSmallMultiplesKey not in chartDict
or not chartDict[plotSmallMultiplesKey]
or numberOfMetrics == 1
):
fig, height, width, numberOfCols, numberOfRows = (
setup_fig_for_horizontal_waterfall_charts(
repeatArrayToPlot, chosenDimension, chartDict, plotWithPins
)
)
df, paramDict = prepare_data_for_horizontal_waterfall_plot(
df, chosenDimension, metric, paramDict, chartDict
)
if isinstance(df, pl.LazyFrame):
df = df.collect()
fig, chartDict = add_annotations_to_horizontal_waterfall_plot(
fig,
df,
metric,
colorDict,
chartDict,
paramDict,
countRows,
countCols,
plotWithPins,
)
count, countRows, countCols, chartDict = reset_row_and_column_counters(
count, countCols, countRows, numberOfCols, numberOfRows, chartDict
)
fig.update_annotations(font=dict(size=fontSize, family=font))
if (
plotSmallMultiplesKey not in chartDict
or not chartDict[plotSmallMultiplesKey]
):
title, paramDict, chartDict = make_horizontal_waterfall_chart_title(
df,
chosenChart,
paramDict,
"",
metric,
chartDict,
pyName,
acName,
)
fig = adjust_horizontal_waterfall_plot(
fig,
df,
key,
metric,
title,
height,
width,
paramDict,
chartDict,
plotWithPins,
)
df1 = duplicate_dataframe(df)
df1 = prepare_horizontal_waterfall_data_for_openAi(df1, chartDict)
paramDict = set_up_tab_for_show_or_download_chart(
df1,
fig,
configPlotlyDict,
chartDict,
title,
False,
None,
chosenDimension,
paramDict,
)
if plotSmallMultiplesKey in chartDict and chartDict[plotSmallMultiplesKey]:
key = chosenDimension
title, paramDict, chartDict = make_horizontal_waterfall_chart_title(
df, chosenChart, paramDict, key, metric, chartDict, pyName, acName
)
fig = adjust_horizontal_waterfall_plot(
fig,
df,
key,
metric,
title,
height,
width,
paramDict,
chartDict,
plotWithPins,
)
if chosenDimension in columns and len(exportDataArray) > 1:
df1 = pl.concat(exportDataArray)
else:
df1 = duplicate_dataframe(df)
df1 = prepare_horizontal_waterfall_data_for_openAi(df1, chartDict)
paramDict = set_up_tab_for_show_or_download_chart(
df1,
fig,
configPlotlyDict,
chartDict,
title,
False,
None,
chosenDimension,
paramDict,
)
return paramDict
def draw_vertical_waterfall_chart(dfCopy, colorDict, paramDict, chartDict, run):
"""Plot a vertical waterfall chart.
Internally we use ``df.height`` when the number of rows is needed.
"""
namingParams = get_naming_params()
configParams = get_config_params()
varianceAggregationParams = get_variance_aggregation_params()
cogsAggregationArray = varianceAggregationParams[
namingParams["cogsAggregationArray"]
]
discountsAggregationArray = varianceAggregationParams[
namingParams["discountsAggregationArray"]
]
periodsArray = configParams["periodsArray"]
runVariableDimensionalAnalysis = namingParams["runVariableDimensionalAnalysis"]
measureName = namingParams["measureName"]
varianceAmountName = namingParams["varianceAmountName"]
varianceTypeName = namingParams["varianceTypeName"]
workColumn = namingParams["workColumn"]
workColumnTwo = namingParams["workColumnTwo"]
showInitialAndFinalValues = namingParams["showInitialAndFinalValues"]
processingChoice = namingParams["processingChoice"]
verticalWaterfallChart = namingParams["verticalWaterfallChart"]
totalVarianceAggregation = namingParams["totalVarianceAggregation"]
marginVarianceAggregation = namingParams["marginVarianceAggregation"]
netOfDiscountAggregation = namingParams["netOfDiscountAggregation"]
varianceAggregation = namingParams["varianceAggregation"]
priceAndUnitsAggregation = namingParams["priceAndUnitsAggregation"]
variancePercentChangeName = namingParams["variancePercentChangeName"]
marginVariance = namingParams["marginVariance"]
drilldownReportRunName = namingParams["drilldownReportRunName"]
separatorString = namingParams["separatorString"]
amountName = namingParams["monetaryLocalCurrencyName"]
marginName = namingParams["marginName"]
netOfDiscountName = namingParams["netOfDiscountName"]
plotSmallMultiples = namingParams["plotSmallMultiplesWaterfall"]
varianceInPercent = namingParams["varianceInPercent"]
shareOfTotalMarket = namingParams["shareOfTotalMarket"]
runOneDimensionalAnalysis = namingParams["runOneDimensionalAnalysis"]
metConditionValue = namingParams["metConditionValue"]
notMetConditionValue = namingParams["notMetConditionValue"]
mainDimension = namingParams["mainDimension"]
shareOfTotalMarket = namingParams["shareOfTotalMarket"]
deltaName = namingParams["deltaName"]
labelName = namingParams["labelName"]
df = duplicate_dataframe(dfCopy)
numberFormat, varianceSum = get_waterfall_number_format(df, run)
numberOfRows = 1
numberOfCols = 1
addTable = False
showItems = [""]
specs = [
[{"type": "waterfall"}],
]
columnWidths = [1]
orientation = "h"
showPercent = notMetConditionValue
if chartDict[varianceInPercent]:
showPercent = notMetConditionValue
elif chartDict[shareOfTotalMarket]:
showPercent = notMetConditionValue
elif plotSmallMultiples in chartDict and chartDict[plotSmallMultiples]:
showPercent = notMetConditionValue
elif (
chartDict[processingChoice] in [runOneDimensionalAnalysis]
and chartDict[varianceAggregation]
in [
totalVarianceAggregation,
netOfDiscountAggregation,
marginVarianceAggregation,
]
and mainDimension in chartDict
):
showPercent = metConditionValue
elif chartDict[processingChoice] in [runVariableDimensionalAnalysis] and chartDict[
varianceAggregation
] in [
totalVarianceAggregation,
netOfDiscountAggregation,
marginVarianceAggregation,
]:
showPercent = metConditionValue
if showPercent:
df = create_color_column(
df, None, None, verticalWaterfallChart, paramDict, chartDict
)
specs = [
[{"type": "waterfall"}, {}],
]
columnWidths = [0.75, 0.25]
numberOfCols = 2
showItems = ["", deltaName + "%"]
if addTable:
numberOfRows = 2
specs = [
[{"type": "waterfall"}],
[{"type": "table"}],
]
colorSequenceArray, lineWidth = get_color_sequence(df, paramDict, chartDict)
fig = make_subplots(
rows=numberOfRows,
cols=numberOfCols,
shared_xaxes=True,
horizontal_spacing=0.2,
specs=specs,
column_widths=columnWidths,
subplot_titles=showItems,
)
df, chartDict = add_sign_to_labels(
df, verticalWaterfallChart, workColumnTwo, 1, False, chartDict
)
fig.add_trace(
go.Waterfall(
orientation=orientation,
measure=df[measureName],
y=df[workColumn],
x=df[varianceAmountName],
textinfo="text",
text=df[labelName],
decreasing={"marker": {"color": colorDict["redColor"]}},
increasing={"marker": {"color": colorDict["greenColor"]}},
totals={
"marker": {"color": colorSequenceArray[1]}
}, # colorDict["greyColor"]}},
connector={
"mode": "between",
"line": {"width": 1, "color": "rgb(169,169,169)", "dash": "solid"},
},
textposition="outside",
cliponaxis=False,
),
row=1,
col=1,
)
initialAndFinalValuesCanBeShown = True
if (
chartDict[varianceAggregation]
not in [
totalVarianceAggregation,
netOfDiscountAggregation,
marginVarianceAggregation,
]
and drilldownReportRunName in run
):
initialAndFinalValuesCanBeShown = False
if not chartDict[varianceInPercent]:
if (
showInitialAndFinalValues in chartDict
and chartDict[showInitialAndFinalValues]
and initialAndFinalValuesCanBeShown
):
columns, schema = get_schema_and_column_names(df)
if chartDict[varianceAggregation] in cogsAggregationArray:
periodZeroValue = marginName + separatorString + periodsArray[0]
periodOneValue = marginName + separatorString + periodsArray[1]
elif chartDict[varianceAggregation] in discountsAggregationArray:
periodZeroValue = netOfDiscountName + separatorString + periodsArray[0]
periodOneValue = netOfDiscountName + separatorString + periodsArray[1]
else:
periodZeroValue = amountName + separatorString + periodsArray[0]
periodOneValue = amountName + separatorString + periodsArray[1]
periodOrder = [periodZeroValue, periodOneValue]
if periodZeroValue in columns and periodOneValue in columns:
last_idx = pl.len() - 1
df = (
df.with_row_index("_idx")
.with_columns(
pl.when((pl.col("_idx") == 0) | (pl.col("_idx") == last_idx))
.then(pl.lit(None))
.otherwise(pl.col(periodZeroValue))
.alias(periodZeroValue),
pl.when((pl.col("_idx") == 0) | (pl.col("_idx") == last_idx))
.then(pl.lit(None))
.otherwise(pl.col(periodOneValue))
.alias(periodOneValue),
)
.drop("_idx")
)
showAbsoluteValueBars = True
if (shareOfTotalMarket in chartDict and chartDict[shareOfTotalMarket]) or (
varianceInPercent in chartDict and chartDict[varianceInPercent]
):
showAbsoluteValueBars = False
if showAbsoluteValueBars and periodZeroValue in columns:
pass
fig = add_absolute_value_bars_to_vertical_waterfall(
fig,
df,
workColumn,
periodOrder,
lineWidth,
colorSequenceArray,
paramDict,
chartDict,
)
anchosPercent = [0.48 / 5] * get_row_count(df)
if drilldownReportRunName in run:
anchosPercent = [0.48 / 5] * get_row_count(df)
colorChoice = get_color_choice(chartDict)
if showPercent:
if chartDict[processingChoice] in [runOneDimensionalAnalysis]:
anchosPercent = [0.48 / 10] * get_row_count(df)
df, largestArray, smallestArray, myDict = get_pinhead_outliers(
df, chartDict
)
df = ensure_polars_df(df)
fig = add_percent_change_markers_to_bar(
fig, df, workColumn, colorChoice, anchosPercent, 2
)
fig = add_positive_outlier_pins_to_bar(fig, df, largestArray, colorDict, 2)
fig = add_negative_outlier_pins_to_bar(fig, df, smallestArray, colorDict, 2)
fig.update_yaxes(
showticklabels=False,
zeroline=False,
visible=False,
ticks="",
rangemode="tozero",
col=2,
)
else:
pass
return fig, numberFormat, chartDict
def add_label_to_horizontal_waterflow(fig, col):
namingParams = get_naming_params()
deltaName = namingParams["deltaName"]
align = "center"
yShift = 10
yref = "paper"
y = 0
xref = "x"
x = 0
ax = x
xShift = -22
fig.add_annotation(
text=deltaName + "%",
# font=dict(size=8,),
showarrow=False,
align=align,
yshift=yShift,
yref=yref,
y=y,
ax=ax,
x=x,
xref=xref,
xshift=xShift,
row=1,
col=col,
)
return fig
def add_absolute_value_bars_to_vertical_waterfall(
fig, df, column, periodOrder, lineWidth, colorSequenceArray, paramDict, chartDict
):
namingParams = get_naming_params()
fcName = namingParams["fcName"]
workColumn = namingParams["workColumn"]
labelName = namingParams["labelName"]
# texttemplate=" %{customdata:,.3s}"
# hovertemplate=' %{customdata:,.3s}
constant = 24
offset = -0.2
anchos = [0.68] * get_row_count(df)
colorDict = get_color_dictionary(chartDict)
columns, schema = get_schema_and_column_names(df)
if fcName in columns and workColumn in columns:
customdataActual = df[workColumn]
hovertemplate = ""
df, myDict = millify_dataframe(df, workColumn, None, labelName, chartDict)
else:
customdataActual = df[periodOrder[1]]
hovertemplate = " %{customdata:,.3s}"
df, myDict = millify_dataframe(df, periodOrder[1], None, labelName, chartDict)
fig.add_trace(
go.Bar(
y=df[column],
x=df[periodOrder[0]],
marker=dict(
color=colorSequenceArray[0],
line=dict(color=colorDict["lightGreyColor"], width=lineWidth),
),
customdata=df[periodOrder[0]],
hovertext=df[periodOrder[0]],
width=anchos,
name=periodOrder[0],
orientation="h",
showlegend=False,
),
row=1,
col=1,
)
fig.add_trace(
go.Bar(
y=df[column],
x=df[periodOrder[1]],
marker_color=colorSequenceArray[1],
offset=offset,
text=df[labelName],
hovertext=df[labelName],
textposition="outside",
width=anchos,
name=periodOrder[1],
cliponaxis=False,
showlegend=False,
orientation="h",
),
row=1,
col=1,
)
return fig
SHA-256: 2e4e39337e67efa4f5398b39fda99a096d8453ee7e68fb455f50039b5f2f567e