def calculate_assumed_expenses(client,
project,
expected_actuals_df,
level,
parent: dict = None):
progress = collection.get_document("Data",
client=client,
project=project,
data_set="progress")
n_houses = float(progress.get("totaal"))
if parent:
parent_value = (parent.get("value")
if parent.get("value") != "has" else "hpend")
else:
parent_value = None
progress_percent = {
k if k != "hpend" else "has": float(v) / n_houses
for k, v in progress.items()
if k in ([parent_value]
if parent else ["schouwen", "montage", "civiel", "hpend"])
}
if parent:
assumed_expenses_df = (pd.Series({
v[level]: v["bedrag"] * progress_percent.get(
"has" if parent_value == "hpend" else parent_value, 0)
for k, v in expected_actuals_df.iterrows()
}).dropna().to_frame().reset_index())
else:
assumed_expenses_df = ((
expected_actuals_df.set_index(level).bedrag *
pd.Series(progress_percent)).dropna().to_frame().reset_index())
assumed_expenses_df.columns = [level, "bedrag"]
return assumed_expenses_df
def financial_storage(dropdown_selection, client=client):
if dropdown_selection:
warnings = [
# dbc.Alert("Operationele voortgang voor civiel word berekend o.b.v. het status_civiel veld"
# " i.p.v. de opleverstatus", color="info")
]
finances = collection.get_document(collection="Finance",
project=dropdown_selection,
client=client)
finance_document_names = {
"expected_actuals": "Prognose einde werk",
"budget": "Begroting",
"actuals": "Realisatie",
}
missing_document_consequence = {
"expected_actuals":
"De begroting word nu als referentie gebruikt."
"De prognose einde werk wordt gelijkgesteld aan de begroting."
}
for document, value in finances.items():
if not value:
warnings.append(
dbc.Alert(
f"{dropdown_selection} heeft geen {finance_document_names[document]}. "
f"{missing_document_consequence.get(document, '')}",
color="warning",
))
if document == "expected_actuals":
finances[document] = finances["budget"]
return [warnings]
return [None]
def budget_bar_category(dropdown_selection, project, client=client):
data = collection.get_document(collection="Finance",
project=dropdown_selection,
client=client)
if data:
fig = calculate_figure(client, project, data, "categorie")
return [fig]
return [no_graph("Barchart")]
def pie_chart(client, key="overview"):
if key == "overview":
fig = collection.get_document(collection="Data",
client=client,
graph_name="reden_na_overview")["figure"]
else:
fig = collection.get_document(
collection="Data",
client=client,
graph_name="reden_na_projects",
project="pie_na_" + key,
)["figure"]
data = fig["data"]
trace = go.Pie(data)
layout = fig["layout"]
data = [trace]
fig = go.Figure(data, layout=layout)
return fig
def load_year_dropdown(dummy_data, reset, client=client):
return [
[
{"label": year, "value": year}
for year in collection.get_document(
collection="Data", client=client, graph_name="List_of_years"
)
],
str(datetime.now().year),
]
def fetch_data_for_status_redenna_piechart(project_name, client, click_filter=None):
counts = pd.DataFrame(
collection.get_document(
collection="Indicators",
graph_name="ActualStatusBarChartIndicator",
project=project_name,
client=client,
)
)
if not counts.empty:
clusters = config.client_config[client]["clusters_reden_na"]
cluster_types = pd.CategoricalDtype(
categories=list(clusters.keys()), ordered=True
)
counts["cluster_redenna"] = counts["cluster_redenna"].astype(cluster_types)
mask = pd.Series([True]).repeat(len(counts.index)).values
if click_filter:
for col, value in click_filter.items():
mask = mask & (counts[col] == value)
cols_filter = list(click_filter.keys())
else:
cols_filter = []
cols = list(dict.fromkeys(cols_filter + ["cluster_redenna"])) + ["laagbouw"]
result = counts[mask][cols + ["count"]].groupby(cols).sum().reset_index()
total = (
result.groupby("cluster_redenna")
.sum()
.reset_index()[["cluster_redenna", "count"]]
)
laagbouw = (
result[result.laagbouw]
.groupby("cluster_redenna")
.sum()
.reset_index()[["cluster_redenna", "count"]]
)
hoogbouw = (
result[~result.laagbouw]
.groupby("cluster_redenna")
.sum()
.reset_index()[["cluster_redenna", "count"]]
)
else:
total = pd.DataFrame()
laagbouw = pd.DataFrame()
hoogbouw = pd.DataFrame()
return total, laagbouw, hoogbouw
def get_search_bar(client, project):
return [
html.Div(
[
dcc.Dropdown(
id=f"project-dropdown-{client}",
options=collection.get_document(
collection="Data",
client=client,
graph_name="project_names").get("filters"),
value=project if project else None,
placeholder="Select a project",
)
],
className="two-third column",
),
html.Div(
children=html.Div(
children=[
dcc.Dropdown(
id=f"year-dropdown-{client}",
placeholder="Select a year",
clearable=False,
),
],
className="column",
style={"width": "77px"},
),
className="container-display",
),
html.Div([
dbc.Button(
"Reset overzicht",
id=f"overview-reset-{client}",
n_clicks=0,
style={
"margin-left": "10px",
"margin-right": "55px",
"background-color": colors["vwt_blue"],
},
)
]),
html.Div(
[
dbc.Button(
"Terug naar overzicht alle projecten",
id="overzicht-button-" + client,
style={"background-color": colors["vwt_blue"]},
)
],
className="one-third column",
),
]
def clickbar_lb(drop_selectie, mask_all):
fig = collection.get_document(
collection="Data",
graph_name="status_bar_chart",
project=drop_selectie,
filter=mask_all,
)["bar"]
bar = {}
for key in fig:
if "LB" in key:
bar[key] = [int(fig[key])]
labels = ["Schouwen", "BIS", "Montage-lasAP", "Montage-lasDP", "HAS"]
barLB1HC = dict(
x=labels,
y=bar["SchouwenLB1"] + bar["BISLB1"] + bar["Montage-lasAPLB1"] +
bar["Montage-lasDPLB1"] + bar["HASLB1"],
name="Opgeleverd HC",
type="bar",
marker=dict(color=colors["green"]),
)
barLB1HP = dict(
x=labels,
y=[0] + [0] + [0] + [0] + bar["HASLB1HP"],
name="Opgeleverd zonder HC",
type="bar",
marker=dict(color=colors["yellow"]),
)
barLB0 = dict(
x=labels,
y=bar["SchouwenLB0"] + bar["BISLB0"] + bar["Montage-lasAPLB0"] +
bar["Montage-lasDPLB0"] + bar["HASLB0"],
name="Niet opgeleverd",
type="bar",
marker=dict(color=colors["red"]),
)
fig = dict(
data=[barLB1HC, barLB1HP, barLB0],
layout=dict(
barmode="stack",
clickmode="event+select",
showlegend=True,
height=350,
plot_bgcolor=colors["plot_bgcolor"],
paper_bgcolor=colors["paper_bgcolor"],
title={
"text":
"Status oplevering per fase (LB)<br>[selectie resets na 3x klikken]:",
"x": 0.5,
},
yaxis={"title": "[aantal woningen]"},
),
)
return fig
def budget_bar_sub_category(click, project, client=client):
ctx = dash.callback_context
if "budget-bar-category" in [
x["prop_id"].rpartition("-")[0] for x in ctx.triggered
]:
for point in click.get("points", []):
data = collection.get_document(collection="Finance",
project=project,
client=client)
if data:
parent = dict(level="categorie", value=point.get("label"))
fig = calculate_figure(client, project, data,
"sub_categorie", parent)
return [fig, point.get("label")]
break
return [no_graph(text="Geen selectie"), ""]
def fetch_data_productionstatus(project, client, freq, phase_name, unit_type=""):
indicator_values = {}
timeseries = {}
name_indicator = {
"target": "Target",
"poc_verwacht": "Gerealiseerd & verwacht verloop",
"poc_ideal": "Gerealiseerd & ideaal verloop",
"work_stock": "Totale productie voorafgaande fase",
"work_stock_amount": "Hoeveelheid Werkvoorraad",
}
line_graph_bool = False
for key in [
"target",
"work_stock",
"poc_verwacht",
"poc_ideal",
"work_stock_amount",
]:
indicator_dict = collection.get_document(
collection="Lines",
line=key + "_indicator" + unit_type,
project=project,
client=client,
phase=phase_name,
)
if indicator_dict:
line_graph_bool = True
indicator_values[name_indicator[key]] = int(indicator_dict["next_" + freq])
timeseries[name_indicator[key]] = pd.Series(
indicator_dict["series_" + freq]
)
else:
indicator_values[name_indicator[key]] = 0
timeseries[name_indicator[key]] = pd.Series()
if indicator_values["Hoeveelheid Werkvoorraad"] < 0:
indicator_values["Hoeveelheid Werkvoorraad"] = 0
if phase_name == "geulen":
indicator_values["Hoeveelheid Werkvoorraad"] = None
del timeseries["Hoeveelheid Werkvoorraad"]
return indicator_values, timeseries, line_graph_bool
def info_table():
document = collection.get_document(collection="Graphs",
client="kpn",
graph_name="info_table")
df = pd.read_json(document["table"], orient="records")
df = df[document["col"]]
fig = dash_table.DataTable(
columns=[{
"name": i,
"id": i
} for i in df.columns],
data=df.to_dict("rows"),
filter_action="native",
sort_action="native",
style_table={"overflowX": "auto"},
style_header=table_styles["header"],
style_cell=table_styles["cell"]["action"],
style_filter=table_styles["filter"],
css=[{
"selector": "table",
"rule": "width: 100%;"
}],
)
return fig
def load_dropdown(dummy_data, client=client):
return [
collection.get_document(
collection="Data", client=client, graph_name="project_names"
)["filters"]
]
def progress_over_time(click, project, client=client):
ctx = dash.callback_context
if "budget-bar-category" in [
x["prop_id"].rpartition("-")[0] for x in ctx.triggered
]:
for point in click.get("points", []):
finance_data = collection.get_document(collection="Finance",
project=project,
client=client)
if finance_data:
parent = dict(level="categorie", value=point.get("label"))
actuals_df = pd.DataFrame(
finance_data.get("actuals", {
parent.get("level"): [],
"bedrag": []
}))
actuals_df = actuals_df[actuals_df[parent.get("level")] ==
parent.get("value")]
time_series = (actuals_df.groupby("vastlegdatum")
["bedrag"].sum().sort_index().cumsum())
if "expected_actuals" in finance_data:
expected_cost = pd.DataFrame(
finance_data.get(
"expected_actuals",
{
parent.get("level"): [],
"bedrag": []
},
))
else:
expected_cost = pd.DataFrame(
finance_data.get("budget", {
parent.get("level"): [],
"bedrag": []
}))
expected_cost = expected_cost[expected_cost[parent.get(
"level")] == parent.get("value")].bedrag.sum()
traces = [
go.Scatter(
x=time_series.index,
y=time_series,
mode="lines+markers",
name="Financieel",
line=dict(color=config.colors_vwt["vwt_blue"]),
)
]
if parent.get("value") in [
"has", "civiel", "montage", "schouwen"
]:
progress_data = collection.get_document(
collection="Data",
project=project,
client=client,
data_set="progress_over_time",
)
traces.append(
get_progress_scatter(
expected_cost,
progress_data,
parent.get("value"),
color=config.colors_vwt["darkgray"],
))
fig = go.Figure(data=traces)
fig.update_layout(
height=500,
paper_bgcolor=config.colors_vwt["paper_bgcolor"],
plot_bgcolor=config.colors_vwt["plot_bgcolor"],
)
return [fig, parent.get("value")]
break
return [no_graph(text="Geen selectie"), ""]
def fetch_data_for_status_barchart(project_name, client, click_filter=None):
counts = pd.DataFrame(
collection.get_document(
collection="Indicators",
graph_name="ActualStatusBarChartIndicator",
project=project_name,
client=client,
)
)
categories = [
"schouw_status",
"bis_status",
"lasAP_status",
"lasDP_status",
"HAS_status",
]
phase_category = pd.CategoricalDtype(categories=categories)
status_category = pd.CategoricalDtype(
categories=[
"niet_opgeleverd",
"ingeplanned",
"opgeleverd",
"opgeleverd_zonder_hc",
]
)
if not counts.empty:
mask = pd.Series([True]).repeat(len(counts.index)).values
if click_filter:
for col, value in click_filter.items():
mask = mask & (counts[col] == value)
cols_filter = list(click_filter.keys())
else:
cols_filter = []
laagbouw_matrix = []
hoogbouw_matrix = []
for category in categories:
cols = list(dict.fromkeys(cols_filter + [category])) + ["laagbouw"]
result = counts[mask][cols + ["count"]].groupby(cols).sum().reset_index()
lb = result[result.laagbouw][[category, "count"]]
for i, row in lb.iterrows():
laagbouw_matrix.append([category] + row.values.tolist())
hb = result[~result.laagbouw][[category, "count"]]
for i, row in hb.iterrows():
hoogbouw_matrix.append([category] + row.values.tolist())
lb_df = pd.DataFrame(laagbouw_matrix, columns=["phase", "status", "count"])
lb_df["status"] = lb_df.status.astype(status_category)
lb_df["phase"] = lb_df.phase.astype(phase_category)
lb_df = lb_df.groupby(by=["phase", "status"]).sum().reset_index()
if lb_df.empty:
lb_df["count"] = 0
else:
lb_df["count"] = lb_df["count"].fillna(0)
data_laagbouw = lb_df
hb_df = pd.DataFrame(hoogbouw_matrix, columns=["phase", "status", "count"])
hb_df["status"] = hb_df.status.astype(status_category)
hb_df["phase"] = hb_df.phase.astype(phase_category)
hb_df = hb_df.groupby(by=["phase", "status"]).sum().reset_index()
if hb_df.empty:
hb_df["count"] = 0
else:
hb_df["count"] = hb_df["count"].fillna(0)
data_hoogbouw = hb_df
else:
data_laagbouw = pd.DataFrame()
data_hoogbouw = pd.DataFrame()
return data_laagbouw, data_hoogbouw
def fetch_data_for_project_info_table(client):
return collection.get_document(
collection="ProjectInfo", graph_name="project_dates", client=client
)
