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Implement hydroviz webapp-specific endpoint based on new water temperature endpoints #729

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Merged
BobTorgerson merged 1 commit into from
Jul 1, 2026
Merged

Implement hydroviz webapp-specific endpoint based on new water temperature endpoints #729

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196 changes: 196 additions & 0 deletions routes/arctic_hydrology.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1008,9 +1008,13 @@ def run_get_arctic_hydrology_hydroviz(stream_id):
"hydrograph": ...,
"id": ...,
"monthly_flow": ...,
"monthly_temperature": ...,
"max_flow_dates": ...,
"max_temp_dates": ...,
"name": ...,
"stats": ...,
"wt_hydrograph": ...,
"wt_stats": ...,
"summary": ...
}
Unlike the CONUS hydroviz, projected data is not nested by scenario since the Arctic
Expand All @@ -1024,8 +1028,14 @@ def run_get_arctic_hydrology_hydroviz(stream_id):
if isinstance(stats_response, tuple):
return stats_response

# fetch wt_stats with default source (gcm_diff_applied_to_cheng)
wt_stats_response = run_get_arctic_hydrology_wt_stats_data(stream_id)
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if isinstance(wt_stats_response, tuple):
return wt_stats_response
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cstephen marked this conversation as resolved.

try:
stats = stats_response.get_json()
wt_stats = wt_stats_response.get_json()

# Fetch original_gcm stats to get PGW models (absent from gcm_diff_applied_to_cheng).
# The describe call is a duplicate of what run_get_arctic_hydrology_stats_data already did,
Expand All @@ -1051,6 +1061,28 @@ def run_get_arctic_hydrology_hydroviz(stream_id):
if chart_era not in stats["data"].get(model, {}):
stats["data"][model] = model_data

# Fetch original_gcm wt_stats to get PGW models (absent from gcm_diff_applied_to_cheng).
wt_stats_decode_dict = asyncio.run(
get_decode_dicts_from_axis_attributes(coverages["wt_stats"])
)[0]
pgw_wt_ds = asyncio.run(
fetch_hydro_data(
coverages["wt_stats"],
stream_id,
source=stat_source_encodings["original_gcm"],
)
)[0]
for dim, mapping in wt_stats_decode_dict.items():
if dim == "source":
continue
pgw_wt_ds = pgw_wt_ds.assign_coords(
{dim: [mapping[int(v)] for v in pgw_wt_ds[dim].values]}
)
pgw_wt_stats = package_stats_data(stream_id, pgw_wt_ds)
for model, model_data in pgw_wt_stats["data"].items():
if chart_era not in wt_stats["data"].get(model, {}):
wt_stats["data"][model] = model_data

# Fetch and decode climatology once; compute both Cheng-adjusted and original_gcm
# versions in memory rather than making two network calls (the doy_climatology coverage
# has no source dimension, so both versions start from the same raw data).
Expand Down Expand Up @@ -1081,6 +1113,34 @@ def run_get_arctic_hydrology_hydroviz(stream_id):

climatology_data = cheng_adjusted

# Fetch and decode water temperature climatology
wt_datasets = asyncio.run(
fetch_hydro_data(coverages["doy_wt_climatology"], stream_id)
)
wt_decode_dicts = asyncio.run(
get_decode_dicts_from_axis_attributes(coverages["doy_wt_climatology"])
)

wt_decoded_datasets = []
for ds, decode_dict in zip(wt_datasets, wt_decode_dicts):
for dim, mapping in decode_dict.items():
ds = ds.assign_coords({dim: [mapping[int(v)] for v in ds[dim].values]})
wt_decoded_datasets.append(ds)

wt_raw_data_dict = package_hydrograph_data(stream_id, wt_decoded_datasets)

# Build Cheng-adjusted version for water temperature
wt_cheng_adjusted = calculate_and_apply_gcm_diffs_to_cheng_wt_climatology(
copy.deepcopy(wt_raw_data_dict["data"])
)

# Fill in PGW models (absent from wt_cheng_adjusted) using their original_gcm values
for model, model_data in wt_raw_data_dict["data"].items():
if model not in wt_cheng_adjusted:
wt_cheng_adjusted[model] = model_data

wt_climatology_data = wt_cheng_adjusted

historical_climatology = climatology_data["historical"][historical_era]
historical_stats = stats["data"]["historical"]

Expand All @@ -1096,6 +1156,22 @@ def run_get_arctic_hydrology_hydroviz(stream_id):
if model != "historical" and chart_era in model_data
}

# Water temperature data
wt_historical_climatology = wt_climatology_data["historical"][historical_era]
wt_historical_stats = wt_stats["data"]["historical"]

# Non-"historical" models at the future era only for water temperature
wt_projected_climatology = {
model: model_data
for model, model_data in wt_climatology_data.items()
if model != "historical" and chart_era in model_data
}
wt_projected_stats = {
model: model_data
for model, model_data in wt_stats["data"].items()
if model != "historical" and chart_era in model_data
}

########## Populate arrays for hydrograph. ##########

hydrograph = {
Expand Down Expand Up @@ -1212,6 +1288,122 @@ def run_get_arctic_hydrology_hydroviz(stream_id):
"max": round(max(values), 3),
}

########## Populate arrays for water temperature hydrograph. ##########

wt_hydrograph = {
"historical": {
"doy_min": [x["doy_min"] for x in wt_historical_climatology],
"doy_mean": [x["doy_mean"] for x in wt_historical_climatology],
"doy_max": [x["doy_max"] for x in wt_historical_climatology],
},
"projected": {
chart_era: {
"doy_min_min": [],
"doy_mean_min": [],
"doy_mean_mean": [],
"doy_mean_max": [],
"doy_max_max": [],
}
},
}

for i in range(366):
wt_doy_mins = []
wt_doy_means = []
wt_doy_maxes = []
for model_data in wt_projected_climatology.values():
wt_doy_mins.append(model_data[chart_era][i]["doy_min"])
wt_doy_means.append(model_data[chart_era][i]["doy_mean"])
wt_doy_maxes.append(model_data[chart_era][i]["doy_max"])

wt_hydrograph["projected"][chart_era]["doy_min_min"].append(
round(min(wt_doy_mins), 3)
)
wt_hydrograph["projected"][chart_era]["doy_mean_min"].append(
round(min(wt_doy_means), 3)
)
wt_hydrograph["projected"][chart_era]["doy_mean_mean"].append(
round(statistics.mean(wt_doy_means), 3)
)
wt_hydrograph["projected"][chart_era]["doy_mean_max"].append(
round(max(wt_doy_means), 3)
)
wt_hydrograph["projected"][chart_era]["doy_max_max"].append(
round(max(wt_doy_maxes), 3)
)

########## Populate arrays for monthly modeled water temperature chart. ##########

monthly_temperature_keys = [
"wt_mean_jan",
"wt_mean_feb",
"wt_mean_mar",
"wt_mean_apr",
"wt_mean_may",
"wt_mean_jun",
"wt_mean_jul",
"wt_mean_aug",
"wt_mean_sep",
"wt_mean_oct",
"wt_mean_nov",
"wt_mean_dec",
]

monthly_temperature = {
"historical": {},
"projected": {chart_era: {}},
}

for key in monthly_temperature_keys:
monthly_temperature["historical"][key] = wt_historical_stats[historical_era][key]

for model_stats in wt_projected_stats.values():
for key in monthly_temperature_keys:
if key not in monthly_temperature["projected"][chart_era]:
monthly_temperature["projected"][chart_era][key] = []
monthly_temperature["projected"][chart_era][key].append(
model_stats[chart_era][key]
)

########## Populate arrays for max temperature date chart. ##########

max_temp_dates = {
"historical": {
"temperature": wt_historical_stats[historical_era].get("wt_ann_max_temp_mean"),
"date": wt_historical_stats[historical_era].get("wt_ann_max_temp_doy_mean"),
},
"projected": {chart_era: {"temperature": [], "date": []}},
}

for model_stats in wt_projected_stats.values():
max_temp_dates["projected"][chart_era]["temperature"].append(
model_stats[chart_era].get("wt_ann_max_temp_mean")
)
max_temp_dates["projected"][chart_era]["date"].append(
model_stats[chart_era].get("wt_ann_max_temp_doy_mean")
)

########## Calculate water temperature stats for the stats table. ##########

wt_table_stats = {
"historical": wt_historical_stats,
"projected": {chart_era: {}},
}

wt_stat_arrays = {}
for model_stats in wt_projected_stats.values():
for stat, val in model_stats[chart_era].items():
if stat not in wt_stat_arrays:
wt_stat_arrays[stat] = []
wt_stat_arrays[stat].append(val)

for stat, values in wt_stat_arrays.items():
wt_table_stats["projected"][chart_era][stat] = {
"min": round(min(values), 3),
"median": round(statistics.median(values), 3),
"max": round(max(values), 3),
}

response = {
"gauge_id": stats.get("gauge_id"),
"huc8": stats.get("watershed"),
Expand All @@ -1220,8 +1412,12 @@ def run_get_arctic_hydrology_hydroviz(stream_id):
"id": stats["id"],
"name": stats.get("name"),
"monthly_flow": monthly_flow,
"monthly_temperature": monthly_temperature,
"max_flow_dates": max_flow_dates,
"max_temp_dates": max_temp_dates,
"stats": table_stats,
"wt_hydrograph": wt_hydrograph,
"wt_stats": wt_table_stats,
"summary": stats.get("summary"),
}

Expand Down
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