def get_station_objects(start_year=1980, end_year=2010, sel_names=None):
# read ice depth values
df = get_obs_data()
lon_min, lon_max = -100, 0
lat_min, lat_max = 40, 90
nvals_min = 100
p = parser.parse(STATION_COORDS_FILE.open())
root = p.getroot()
station_elts = root.Document.Placemark
# select points based on the lat/lon limits?
stations = []
for el in station_elts:
lon, lat, _ = [float(c.strip()) for c in el.Point.coordinates.text.split(",")]
# Check if the station
if sel_names is not None:
is_ok = False
for sel_name in sel_names:
if sel_name.lower() in el.name.text.lower():
is_ok = True
break
if not is_ok:
continue
if (lon_min <= lon <= lon_max) and (lat_min <= lat <= lat_max):
print("{}: {}".format(el.name, el.Point.coordinates))
df_s = df.loc[df.station_name.str.lower().str.startswith(el.name.text.lower())]
df_s = df_s.loc[(df_s.year >= start_year) & (df_s.year <= end_year)]
if len(df_s) < nvals_min:
continue
print(len(df_s))
d_to_v = dict(zip(df_s["Date"][:], df_s["ice_depth"][:]))
# df_s.plot(x="Date", y="ice_depth")
# plt.title(el.name.text)
# plt.show()
# print(df_s.station_name)
stations.append(Station(st_id=df_s.station_name.iloc[0], lon=lon, lat=lat, date_to_value=d_to_v))
return stations
def load_stations_from_csv(
index_file="mh/obs_data/streamflow_data_organized/station_index.txt",
selected_ids=None):
res = []
data_dir = Path(index_file).parent
with open(index_file) as f:
# skip the first line
f.readline()
for line in f:
if line.strip() == "":
continue
toks = re.split("\s+", line)
st_id = toks[0].strip()
if (selected_ids is not None) and (st_id not in selected_ids):
continue
lon, lat, = [float(tok.strip()) for tok in toks[1:3]]
st_da = None
try:
st_da = float(toks[3].strip())
except Exception:
pass
st_name = " ".join(toks[5:]).split(",")[0]
s = Station(st_id=st_id, lon=lon, lat=lat, name=st_name)
s.source_data_units = toks[4].strip()
s.drainage_km2 = st_da
print(s)
ts = read_data_file_for_station(
s, data_file=Path(data_dir.joinpath("{}.csv".format(s.id))))
ts.dropna(inplace=True)
# if it is date do nothing
if hasattr(ts.iloc[0, 0], "year"):
pass
# convert to dates if it is just a year
elif isinstance(ts.iloc[0, 0], str):
date_format = None
# try different known date formats
for the_date_format in known_date_formats:
try:
datetime.strptime(ts.iloc[0, 0], the_date_format)
date_format = the_date_format
except Exception:
pass
if date_format is None:
raise Exception(
"Do not understand this date format: {}".format(
ts.iloc[0, 0]))
ts[0] = [
datetime.strptime(t, date_format) for t in ts.iloc[:, 0]
]
elif float(ts.iloc[
0, 0]).is_integer(): # in case we have only year values
ts[0] = [datetime(int(y), 6, 15) for y in ts.iloc[:, 0]]
else:
print(ts.iloc[0, 0])
raise Exception("Could not convert {} to a date".format(
ts.iloc[0, 0]))
print(ts.head())
# start - plot for debug
# fig = plt.figure()
# ax = plt.gca()
# ax.set_title(s.id)
# ts.plot(ax=ax, x=0, y=1)
# fig.autofmt_xdate()
#
# img_file = img_folder.joinpath("{}.png".format(s.id))
# fig.savefig(str(img_file))
# end - plot for debug
set_data_from_pandas_timeseries(ts, s, date_col=0)
res.append(s)
return res
