def get_obs_frequency():
payload = request.get_json()
labels, thrWT, path, code, mode, cheaper = (
payload["labels"],
payload["thrWT"],
sanitize_path(payload["path"]),
payload["code"],
payload["mode"],
payload["cheaper"],
)
loader = load_point_data_by_path(path, cheaper=cheaper)
thrWT = [float(cell) for cell in thrWT]
series = pandas.Series(dict(zip(labels, thrWT)))
thrL, thrH = series.iloc[::2], series.iloc[1::2]
wt = WeatherType(
thrL=thrL, thrH=thrH, thrL_labels=labels[::2], thrH_labels=labels[1::2]
)
df, _ = wt.evaluate(
loader.error_type.name, "LonOBS", "LatOBS", "OBS", loader=loader
)
cv_map = wt.plot_maps(df, code, mode.lower())
return jsonify(cv_map)
def get_wt_histogram():
payload = request.get_json()
labels, thrWT, path, y_lim, bins, num_bins, cheaper = (
payload["labels"],
payload["thrWT"],
sanitize_path(payload["path"]),
payload["yLim"],
payload["bins"],
payload["numBins"],
payload["cheaper"],
)
loader = load_point_data_by_path(path, cheaper=cheaper)
thrWT = [float(cell) for cell in thrWT]
series = pandas.Series(dict(zip(labels, thrWT)))
thrL, thrH = series.iloc[::2], series.iloc[1::2]
bins = [float(each) for each in bins]
wt = WeatherType(
thrL=thrL, thrH=thrH, thrL_labels=labels[::2], thrH_labels=labels[1::2]
)
df, title_tokens = wt.evaluate(loader.error_type.name, loader=loader)
title = wrap_title(title=title_tokens, chunk_size=6)
error = df[loader.error_type.name]
plot = wt.plot(error, bins, title, y_lim=int(y_lim), num_bins=int(num_bins))
return jsonify({"histogram": plot})
def get_pdt_metadata():
payload = request.get_json()
path = sanitize_path(payload["path"])
loader = load_point_data_by_path(path)
return Response(json.dumps(loader.metadata), mimetype="application/json")
def save_wt_histograms():
payload = request.get_json()
labels, thrGridOut, path, y_lim, destination, bins, num_bins, cheaper = (
payload["labels"],
payload["thrGridOut"],
sanitize_path(payload["path"]),
payload["yLim"],
payload["destinationDir"],
payload["bins"],
payload["numBins"],
payload["cheaper"],
)
destination = sanitize_path(destination)
loader = load_point_data_by_path(path, cheaper=cheaper)
matrix = [[float(cell) for cell in row[1:]] for row in thrGridOut]
df = pandas.DataFrame.from_records(matrix, columns=labels)
bins = [float(each) for each in bins]
thrL_out, thrH_out = df.iloc[:, ::2], df.iloc[:, 1::2]
for idx in range(len(thrL_out)):
thrL = thrL_out.iloc[idx]
thrH = thrH_out.iloc[idx]
wt = WeatherType(
thrL=thrL, thrH=thrH, thrL_labels=labels[::2], thrH_labels=labels[1::2]
)
dataframe, title_tokens = wt.evaluate(loader.error_type.name, loader=loader)
title = wrap_title(title=title_tokens, chunk_size=6)
error = dataframe[loader.error_type.name]
wt_code = thrGridOut[idx][0]
wt.plot(
error,
bins,
title,
y_lim=int(y_lim),
num_bins=int(num_bins),
out_path=os.path.join(destination, f"WT_{wt_code}.png"),
)
return jsonify({"status": "success"})
def get_obs_metadata():
payload = request.get_json()
path = Path(sanitize_path(payload["path"]))
first_geo_file = next(path.glob("**/*.geo"))
try:
units = geopoints_loader.read_units(first_geo_file)
except ValueError:
units = None
return Response(json.dumps({"units": units}), mimetype="application/json")
def get_predictors():
payload = request.get_json()
path = sanitize_path(payload["path"])
codes = [
name
for name in os.listdir(path)
if os.path.isdir(os.path.join(path, name)) and not name.startswith(".")
]
# Warming up the LRU cache for fetching units
for code in codes:
get_metadata(os.path.join(path, code))
return Response(json.dumps(codes), mimetype="application/json")
def get_error_rep():
payload = request.get_json()
labels, matrix, path, numCols, cheaper, ranges = (
payload["labels"],
payload["matrix"],
sanitize_path(payload["path"]),
payload["numCols"],
payload["cheaper"],
payload["ranges"],
)
matrix = [[float(cell) for cell in row] for row in matrix]
df = pandas.DataFrame.from_records(matrix, columns=labels)
thrL, thrH = df.iloc[:, ::2], df.iloc[:, 1::2]
loader = load_point_data_by_path(path, cheaper=cheaper)
dt = DecisionTree(threshold_low=thrL, threshold_high=thrH, ranges=ranges)
rep = dt.cal_rep_error(loader, nBin=int(numCols))
s = StringIO()
rep.to_csv(s)
return jsonify(s.getvalue())
def get_pdt_statistics():
payload = request.get_json()
path = sanitize_path(payload["path"])
resp = postprocessing_svc.get_pdt_statistics(path)
return Response(json.dumps(resp), mimetype="application/json")
def get_breakpoints_suggestions():
payload = request.get_json()
labels, thrWT, path, predictor, num_bp, lower_bound, upper_bound, cheaper = (
payload["labels"],
payload["thrWT"],
sanitize_path(payload["path"]),
payload["predictor"],
int(payload["numBreakpoints"]),
payload.get("lowerBound"),
payload.get("upperBound"),
payload["cheaper"],
)
loader = load_point_data_by_path(path, cheaper=cheaper)
thrWT = [float(cell) for cell in thrWT]
series = pandas.Series(dict(zip(labels, thrWT)))
thrL, thrH = series.iloc[::2], series.iloc[1::2]
lower_bound = float(lower_bound) if lower_bound else -inf
upper_bound = float(upper_bound) if upper_bound else inf
wt = WeatherType(
thrL=thrL, thrH=thrH, thrL_labels=labels[::2], thrH_labels=labels[1::2]
)
df, title_tokens = wt.evaluate(loader.error_type.name, predictor, loader=loader)
title_tokens = [
f"({lower_bound} <= {predictor} < {upper_bound})"
if predictor in token
else token
for token in title_tokens
]
title_ks = wrap_title(title_tokens, chunk_size=4)
df_breakpoints, df_size = ks_test_engine(
df=df,
predictor_name=predictor,
error_name=loader.error_type.name,
breakpoints_num=num_bp,
lower_bound=lower_bound,
upper_bound=upper_bound,
)
plot = plot_ks_stats(
df=df_breakpoints,
node=title_ks + f"\n\nNo. of points: {df_size}",
predictor=predictor,
unit=loader.units["predictors"][predictor],
)
df_breakpoints = format_ks_stats(df_breakpoints)
return Response(
json.dumps(
{
"records": df_breakpoints.to_dict("records"),
"figure": plot,
"count": df_size,
}
),
mimetype="application/json",
)
def get_predictor_units():
payload = request.get_json()
path = sanitize_path(payload["path"])
metadata = get_metadata(path)
return Response(json.dumps(metadata), mimetype="application/json")
def save_operation():
payload = request.get_json()
labels = payload["labels"]
matrix = payload["matrix"]
ranges = payload["fieldRanges"]
pdt_path = sanitize_path(payload["pdtPath"])
mf_cols = payload["mfcols"]
cheaper = payload["cheaper"]
mode = payload["mode"]
output_path = Path(sanitize_path(payload["outPath"]))
if mode == "all":
version = payload["version"]
family = payload["family"]
accumulation = payload["accumulation"]
accumulation = f"{accumulation}h" if accumulation else ""
dataset_name = payload["datasetName"]
output_path = output_path / f"{family}{accumulation}{dataset_name}_{version}"
os.makedirs(output_path, exist_ok=True)
if mode in ["breakpoints", "all"]:
csv = payload["breakpointsCSV"]
path = output_path
if mode == "all":
path = path / "BP.csv"
with open(path, "w") as f:
f.write(csv)
if mode in ["mf", "all"]:
matrix = [[float(cell) for cell in row] for row in matrix]
df = pandas.DataFrame.from_records(matrix, columns=labels)
thrL, thrH = df.iloc[:, ::2], df.iloc[:, 1::2]
loader = load_point_data_by_path(pdt_path, cheaper=cheaper)
dt = DecisionTree(threshold_low=thrL, threshold_high=thrH, ranges=ranges)
rep = dt.cal_rep_error(loader, nBin=int(mf_cols))
path = output_path
if mode == "all":
path = path / f"{loader.error_type.name}.csv"
with open(path, "w") as f:
rep.to_csv(
f,
header=[str(i + 1) for i in range(int(mf_cols))],
index_label="WT Code",
)
if mode in ["wt", "all"]:
ylim = payload["yLim"]
bins = payload["bins"]
num_bins = payload["numBins"]
thrGridOut = payload["thrGridOut"]
matrix = [[float(cell) for cell in row[1:]] for row in thrGridOut]
df = pandas.DataFrame.from_records(matrix, columns=labels)
loader = load_point_data_by_path(pdt_path, cheaper=cheaper)
bins = [float(each) for each in bins]
thrL_out, thrH_out = df.iloc[:, ::2], df.iloc[:, 1::2]
path = output_path
if mode == "all":
path = path / "WTs"
os.makedirs(path, exist_ok=True)
for idx in range(len(thrL_out)):
thrL = thrL_out.iloc[idx]
thrH = thrH_out.iloc[idx]
wt = WeatherType(
thrL=thrL, thrH=thrH, thrL_labels=labels[::2], thrH_labels=labels[1::2]
)
dataframe, title_tokens = wt.evaluate(loader.error_type.name, loader=loader)
title = wrap_title(title=title_tokens, chunk_size=6)
error = dataframe[loader.error_type.name]
wt_code = thrGridOut[idx][0]
wt.plot(
error,
bins,
title,
y_lim=int(ylim),
num_bins=int(num_bins),
out_path=os.path.join(path, f"WT_{wt_code}.png"),
)
if mode in ["bias", "all"]:
thrGridOut = payload["thrGridOut"]
bins = payload["bins"]
num_bins = payload["numBins"]
bins = [float(each) for each in bins]
matrix = [[float(cell) for cell in row[1:]] for row in thrGridOut]
df = pandas.DataFrame.from_records(matrix, columns=labels)
loader = load_point_data_by_path(pdt_path, cheaper=cheaper)
thrL_out, thrH_out = df.iloc[:, ::2], df.iloc[:, 1::2]
path = output_path
if mode == "all":
path = path / "Bias.csv"
csv = []
for idx in range(len(thrL_out)):
thrL = thrL_out.iloc[idx]
thrH = thrH_out.iloc[idx]
wt = WeatherType(
thrL=thrL, thrH=thrH, thrL_labels=labels[::2], thrH_labels=labels[1::2]
)
dataframe, title_tokens = wt.evaluate(loader.error_type.name, loader=loader)
error = dataframe[loader.error_type.name]
discretized_error = wt.discretize_error(error=error, num_bins=int(num_bins))
bias = loader.error_type.bias(
error=discretized_error, low=bins[0], high=bins[-1]
)
bias = f"{bias:.2f}"
wt_code = thrGridOut[idx][0]
csv += [(wt_code, bias)]
pandas.DataFrame.from_records(csv, columns=["WT Code", "Bias"]).to_csv(
path, index=False
)
if mode == "all":
family = payload["family"]
version = payload["version"]
accumulation = payload["accumulation"]
accumulation = f", {accumulation}-hourly" if accumulation else ""
with open(output_path / "README.txt", "w") as f:
text = dedent(
f"""
ecPoint-{family}{accumulation}
Version: {version}
Timestamp: {datetime.now()}
"""
)
f.write(text.lstrip())
loader = load_point_data_by_path(pdt_path, cheaper=cheaper)
if pdt_path.endswith(".ascii"):
ext = "ascii"
elif pdt_path.endswith(".parquet"):
ext = "parquet"
else:
ext = "ascii"
exclude_cols = payload["excludePredictors"]
cols = [col for col in loader.columns if col not in exclude_cols]
loader.clone(*cols, path=output_path / f"PDT.{ext}")
return Response(json.dumps({}), mimetype="application/json")