diff --git a/metrics/index.py b/metrics/index.py
index 21e37e8..b6e351b 100644
--- a/metrics/index.py
+++ b/metrics/index.py
@@ -42,6 +42,8 @@
COLOR_ONE = "#ccecef"
COLOR_TWO = "#fc9cac"
+EURinSEK = 11.43
+
# Configure tabs to show in the UI
SHOWTAB_BILLABLE = False
SHOWTAB_COMPARISON = True
@@ -620,6 +622,149 @@ def determine_color(row):
return [figure_allocated]
+def figureAverageRate(crew_df: pd.DataFrame, allocations_df: pd.DataFrame, supplementary_data: SupplementaryData):
+ staff = crew_df[lambda df: (df["Consulting Hours"] == 0)]
+ consultants = crew_df[lambda df: (df["Consulting Hours"] > 0)]
+
+ consultant_count = consultants.index.size
+ sharedCosts = staff["Total cost"].sum() / consultant_count
+
+ # Here we have some constants in place to approximate working time in a year.
+ # 52 / 46 approximates a 46 working-week year, with a salary that covers the unworked time
+ # 22 is a typical work month, and 0.8 is our standard allocation amount
+ consultants["Cost to cover"] = consultants["Total cost"].apply(lambda x: (x + sharedCosts) * (52 / 47))
+ consultants["Monthly Hours"] = consultants["Consulting Hours"].apply(lambda hours: hours * 22 * 0.8)
+ consultants["Hour Rate EUR"] = consultants["Cost to cover"] / consultants["Monthly Hours"]
+ consultants["Hour Rate SEK"] = consultants["Hour Rate EUR"] * EURinSEK
+
+ avEUR = consultants["Hour Rate EUR"].mean()
+ avSEK = consultants["Hour Rate SEK"].mean()
+
+ period_names = ["Now", "Next Month", "2 Months"]
+ period_dates = [TODAY, TODAY + pd.offsets.MonthBegin(), TODAY + pd.offsets.MonthBegin(2)]
+ consultants["Period"] = [period_names for _ in range(len(consultants))]
+ consultants = consultants.explode("Period")
+
+ def generateAllocationSlice(day, period):
+ current_allocations = allocations_df[
+ lambda df: (df["Start"] <= day) & (df["Stop"] >= day) & (df["Unconfirmed"] == False)
+ ]
+ current_allocations["Key"] = current_allocations["JiraID"]
+ current_allocations = current_allocations[["User", "Allocation", "Key"]]
+ current_allocations = current_allocations.merge(supplementary_data.rates, on=["Key", "User"], how="left")
+ current_allocations["Current Rate EUR"] = current_allocations.apply(
+ lambda x: x["Rate"] / (EURinSEK if x["Currency"] == "SEK" else 1) * x["Allocation"] / 0.8, axis=1
+ )
+ current_allocations = current_allocations[["User", "Allocation", "Current Rate EUR"]]
+ current_allocations = (
+ current_allocations.groupby(["User"])
+ .agg(
+ {
+ "Allocation": "sum",
+ "Current Rate EUR": "sum",
+ }
+ )
+ .reset_index()
+ )
+ current_allocations["Period"] = [period for _ in range(len(current_allocations))]
+ return current_allocations
+
+ periods = []
+ for i in range(len(period_dates)):
+ periods.append(generateAllocationSlice(period_dates[i], period_names[i]))
+
+ consultants = consultants.sort_values(by=["Hour Rate EUR"])
+ figure = px.bar(
+ consultants,
+ x="User",
+ y=["Hour Rate EUR", "Hour Rate SEK"],
+ facet_row="variable",
+ facet_col="Period",
+ category_orders={"Period": period_names},
+ )
+ figure.update_yaxes(matches=None)
+
+ figure.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
+
+ for i in range(len(periods)):
+ period = periods[i]
+ well_allocated = period[lambda df: (df["Allocation"] >= 0.5)]
+
+ avEUR_current = well_allocated["Current Rate EUR"].mean()
+ avSEK_current = avEUR_current * EURinSEK
+
+ avEUR_common = period["Current Rate EUR"].sum() / consultant_count
+
+ avEUR_target = ((avEUR * consultant_count) - well_allocated["Current Rate EUR"].sum()) / (
+ consultant_count - well_allocated.index.size
+ )
+ avSEK_target = avEUR_target * EURinSEK
+
+ positions = ["bottom left", "top right"]
+ if avEUR_target < avEUR:
+ positions = ["top left", "bottom right"]
+
+ figure.add_hline(
+ y=avEUR_current,
+ line_dash="dash",
+ line_color="blue",
+ row=0,
+ col=i + 1,
+ annotation_text=f"Allocated: {avEUR_current:.2f} €",
+ annotation_position="bottom right",
+ )
+ figure.add_hline(
+ y=avSEK_current,
+ line_dash="dash",
+ line_color="red",
+ row=1,
+ col=i + 1,
+ annotation_text=f"Allocated: {avSEK_current:.2f} SEK",
+ annotation_position="bottom right",
+ )
+
+ figure.add_hline(y=avEUR_common, line_color="blue", row=0, col=i + 1)
+ figure.add_hline(y=avEUR_common * EURinSEK, line_color="red", row=1, col=i + 1)
+
+ figure.add_hline(
+ y=avEUR,
+ line_dash="dot",
+ row=0,
+ col=i + 1,
+ annotation_text=f"Average: {avEUR:.2f} €",
+ annotation_position=positions[0],
+ )
+ figure.add_hline(
+ y=avSEK,
+ line_dash="dot",
+ row=1,
+ col=i + 1,
+ annotation_text=f"Average: {avSEK:.2f} SEK",
+ annotation_position=positions[0],
+ )
+
+ figure.add_hline(
+ y=avEUR_target,
+ line_dash="dot",
+ row=0,
+ col=i + 1,
+ annotation_text=f"Target Avg Rate of New Contracts: {avEUR_target:.2f} €",
+ annotation_position=positions[1],
+ )
+ figure.add_hline(
+ y=avSEK_target,
+ line_dash="dot",
+ row=1,
+ col=i + 1,
+ annotation_text=f"Target Avg Rate of New Contracts: {avSEK_target:.2f} SEK",
+ annotation_position=positions[1],
+ )
+
+ # TODO: Individual's current hourly rates as lines
+
+ return figure
+
+
# =========================================================
# Figure: Remaining Runway
# =========================================================
@@ -686,7 +831,7 @@ def figureRunway(
# I'm sure there's a nice clever one-liner to do this. I'm apparently not that clever.
rate = 0
for _, raterow in supplemental.rates[lambda df: (df["Key"] == task_id) & (df["User"] == user)].iterrows():
- rate = raterow["Rate"] / (11.43 if raterow["Currency"] == "SEK" else 1) # TODO: Constant or helper for SEK
+ rate = raterow["Rate"] / (EURinSEK if raterow["Currency"] == "SEK" else 1)
break
prevm = start
@@ -1007,6 +1152,12 @@ def figureEggBaskets(data, supplementary_data):
main_list.append(figure_allocations)
delta("Allocations building")
+# Break-Even Average Rage
+# Requires Notion Allocations & Crew DBs, and rates file
+if not allocations_df.empty and not crew_df.empty and not supplementary_data.rates.empty:
+ main_list.append(figureAverageRate(crew_df, allocations_df, supplementary_data))
+delta("Break-even rate building")
+
# Runway
# Requires rates file, financials, crew, and allocations
if "Real_income" in supplementary_data.costs and not crew_df.empty and not allocations_df.empty:
diff --git a/metrics/notion.py b/metrics/notion.py
index 896dab0..e271391 100644
--- a/metrics/notion.py
+++ b/metrics/notion.py
@@ -91,16 +91,17 @@ def __init__(self, token: Optional[str] = None, database_id: str = "") -> None:
def get_crew(self) -> None:
result_dict = self.fetch_data(self.database_id).json()
- data = pd.DataFrame(columns=["User", "Total cost"])
+ data = pd.DataFrame(columns=["User", "Total cost", "Consulting Hours"])
for item in result_dict["results"]:
user = item["properties"]["Person"]["people"][0]["name"]
+ hours = item["properties"]["Consulting Hours"]["number"]
currency = item["properties"]["Currency"]["select"]["name"]
cost = item["properties"]["Total Cost"]["number"] / (
11.43 if currency == "SEK" else 1
) # TODO: constant or helper method for SEK to EUR
- data.loc[-1] = [user, cost]
+ data.loc[-1] = [user, cost, hours]
data.index = data.index + 1
self.data = data.sort_values(by=["User"])