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Merged
msweier merged 15 commits into from
Aug 5, 2025
Merged

Add location and timeseries group example #160

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4810bbe
add location_group_df_to_json
msweier Jul 16, 2025
4babc96
add cascade delete
msweier Jul 16, 2025
f38ca6b
add cwms group example notebook
msweier Jul 16, 2025
440a473
bump version to 0.8.1
msweier Jul 16, 2025
0feb614
Merge branch 'main' into location_groups
msweier Jul 25, 2025
af804ca
add test
msweier Jul 28, 2025
93e8de7
add location and ts group tests
msweier Jul 28, 2025
b4bb252
fix import order
msweier Jul 28, 2025
fa5b88e
add cascade delete and switch office
msweier Jul 29, 2025
238e1d7
fix formatting
msweier Jul 29, 2025
65d6bf1
fix import order
msweier Jul 29, 2025
68739e6
change to base location
msweier Jul 29, 2025
ff2fc37
fix sort
msweier Jul 29, 2025
a7327f8
update ts group to use module for locs and ts
msweier Aug 5, 2025
6d9193d
update loc group to use module for tests
msweier Aug 5, 2025
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86 changes: 85 additions & 1 deletion cwms/locations/location_groups.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,84 @@ def get_location_groups(
return Data(response)


def location_group_df_to_json(
data: pd.DataFrame,
group_id: str,
group_office_id: str,
category_office_id: str,
category_id: str,
) -> JSON:
"""
Converts a dataframe to a json dictionary in the correct format.

Parameters
----------
data: pd.DataFrame
Dataframe containing timeseries information.
group_id: str
The group ID for the timeseries.
office_id: str
The ID of the office associated with the specified timeseries.
category_id: str
The ID of the category associated with the group

Returns
-------
JSON
JSON dictionary of the timeseries data.
"""
df = data.copy()
required_columns = ["office-id", "location-id"]
optional_columns = ["alias-id", "attribute", "ref-location-id"]
for column in required_columns:
if column not in df.columns:
raise TypeError(
f"{column} is a required column in data when posting as a dataframe"
)

if df[required_columns].isnull().any().any():
raise ValueError(
f"Null/NaN values found in required columns: {required_columns}. "
)

# Fill optional columns with default values if missing
if "alias-id" not in df.columns:
df["alias-id"] = None
if "attribute" not in df.columns:
df["attribute"] = 0

# Replace NaN with None for optional columns
for column in optional_columns:
if column in df.columns:
data[column] = df[column].where(pd.notnull(df[column]), None)

# Build the list of time-series entries
assigned_locs = df.apply(
lambda entry: {
"office-id": entry["office-id"],
"location-id": entry["location-id"],
"alias-id": entry["alias-id"],
"attribute": entry["attribute"],
**(
{"ref-location-id": entry["ref-location-id"]}
if "ref-location-id" in entry and pd.notna(entry["ref-location-id"])
else {}
),
},
axis=1,
).tolist()

# Construct the final JSON dictionary
json_dict = {
"office-id": group_office_id,
"id": group_id,
"location-category": {"office-id": category_office_id, "id": category_id},
"assigned-locations": assigned_locs,
}

return json_dict


def store_location_groups(data: JSON) -> None:
"""
Create new Location Group
Expand Down Expand Up @@ -140,7 +218,12 @@ def update_location_group(
api.patch(endpoint=endpoint, data=data, params=params, api_version=1)


def delete_location_group(group_id: str, category_id: str, office_id: str) -> None:
def delete_location_group(
group_id: str,
category_id: str,
office_id: str,
cascade_delete: Optional[bool] = False,
) -> None:
"""Deletes requested time series group

Parameters
Expand All @@ -161,6 +244,7 @@ def delete_location_group(group_id: str, category_id: str, office_id: str) -> No
params = {
"office": office_id,
"category-id": category_id,
"cascade-delete": cascade_delete,
}

return api.delete(endpoint, params=params, api_version=1)
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