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use USGS API to gather information on high water marks #20

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Mar 8, 2022
Merged

use USGS API to gather information on high water marks #20

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6c3b8cc
use USGS API to gather information on high water marks; also, separat…
zacharyburnett Mar 8, 2022
69cee00
fix usage of generics to make tests work on Python 3.6
zacharyburnett Mar 8, 2022
246a891
update reference files
zacharyburnett Mar 8, 2022
042290a
skip some tests for python 3.6
zacharyburnett Mar 8, 2022
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212 changes: 113 additions & 99 deletions README.md
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13 changes: 0 additions & 13 deletions stormevents/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1 @@
from stormevents.coops.tidalstations import (
COOPS_Query,
COOPS_Station,
coops_stations,
coops_stations_within_region,
)
from stormevents.nhc.storms import nhc_storms
from stormevents.nhc.track import VortexTrack
from stormevents.stormevent import StormEvent
from stormevents.usgs.highwatermarks import (
StormHighWaterMarks,
usgs_highwatermark_events,
usgs_highwatermark_storms,
)
2 changes: 1 addition & 1 deletion stormevents/coops/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
from stormevents.coops.tidalstations import (
COOP_VelocityType,
COOPS_Interval,
COOPS_Product,
coops_product_within_region,
Expand All @@ -11,4 +10,5 @@
COOPS_TidalDatum,
COOPS_TimeZone,
COOPS_Units,
COOPS_VelocityType,
)
65 changes: 32 additions & 33 deletions stormevents/coops/tidalstations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ class COOPS_TidalDatum(Enum):
STND = 'STND' # Station Datum


class COOP_VelocityType(Enum):
class COOPS_VelocityType(Enum):
SPEED_DIR = 'speed_dir' # Return results for speed and dirction
DEFAULT = 'default' # Return results for velocity major, mean flood direction and mean ebb dirction

Expand Down Expand Up @@ -177,7 +177,7 @@ def constituents(self) -> DataFrame:
constituents.set_index('#', inplace=True)
return constituents

def get(
def product(
self,
product: COOPS_Product,
start_date: datetime,
Expand All @@ -200,7 +200,7 @@ def get(
:return: data for the current station within the specified parameters

>>> station = COOPS_Station(8632200)
>>> station.get('water_level', start_date=datetime(2018, 9, 13), end_date=datetime(2018, 9, 16, 12))
>>> station.product('water_level', start_date=datetime(2018, 9, 13), end_date=datetime(2018, 9, 16, 12))
<xarray.Dataset>
Dimensions: (nos_id: 1, t: 841)
Coordinates:
Expand All @@ -218,7 +218,7 @@ def get(

if self.__query is None:
self.__query = COOPS_Query(
station=self,
station=self.nos_id,
product=product,
start_date=start_date,
end_date=end_date,
Expand All @@ -228,19 +228,19 @@ def get(
interval=interval,
)
else:
if start_date is None:
if start_date is not None:
self.__query.start_date = start_date
if end_date is None:
if end_date is not None:
self.__query.end_date = end_date
if product is None:
if product is not None:
self.__query.product = product
if datum is None:
if datum is not None:
self.__query.datum = datum
if units is None:
if units is not None:
self.__query.units = units
if time_zone is None:
if time_zone is not None:
self.__query.time_zone = time_zone
if interval is None:
if interval is not None:
self.__query.interval = interval

data = self.__query.data
Expand Down Expand Up @@ -302,9 +302,8 @@ def __init__(
COOPS_Query(1612480, datetime.datetime(2022, 1, 1, 0, 0), datetime.datetime(2022, 1, 3, 0, 0), 'water_level', 'MLLW', 'metric', 'gmt', 'h')
"""

if not isinstance(station, COOPS_Station):
station = COOPS_Station(station)
station = station.nos_id
if isinstance(station, COOPS_Station):
station = station.nos_id
if end_date is None:
end_date = datetime.today()
if datum is None:
Expand Down Expand Up @@ -429,17 +428,17 @@ def data(self) -> DataFrame:
>>> query.data
v s f q
t
2022-01-01 00:00:00 1.193 0.002 0,0,0,0 p
2022-01-01 00:06:00 1.180 0.002 1,0,0,0 p
2022-01-01 00:12:00 1.167 0.002 0,0,0,0 p
2022-01-01 00:18:00 1.156 0.003 1,0,0,0 p
2022-01-01 00:24:00 1.147 0.003 0,0,0,0 p
... ... ... ... ..
2022-01-02 23:36:00 1.229 0.004 1,0,0,0 p
2022-01-02 23:42:00 1.219 0.003 1,0,0,0 p
2022-01-02 23:48:00 1.223 0.004 1,0,0,0 p
2022-01-02 23:54:00 1.217 0.004 0,0,0,0 p
2022-01-03 00:00:00 1.207 0.002 0,0,0,0 p
2022-01-01 00:00:00 1.193 0.002 0,0,0,0 v
2022-01-01 00:06:00 1.180 0.002 0,0,0,0 v
2022-01-01 00:12:00 1.167 0.002 0,0,0,0 v
2022-01-01 00:18:00 1.156 0.003 0,0,0,0 v
2022-01-01 00:24:00 1.147 0.003 0,0,0,0 v
... ... ... ..
2022-01-02 23:36:00 1.229 0.004 0,0,0,0 v
2022-01-02 23:42:00 1.219 0.003 0,0,0,0 v
2022-01-02 23:48:00 1.223 0.004 0,0,0,0 v
2022-01-02 23:54:00 1.217 0.004 0,0,0,0 v
2022-01-03 00:00:00 1.207 0.002 0,0,0,0 v
[481 rows x 4 columns]
"""

Expand Down Expand Up @@ -568,7 +567,7 @@ def coops_stations_within_region(
:param station_type: one of ``current`` or ``historical``
:return: data frame of stations within the specified region

>>> from stormevents import VortexTrack
>>> from stormevents.nhc import VortexTrack
>>> from shapely import ops
>>> track = VortexTrack('florence2018', file_deck='b')
>>> combined_wind_swaths = ops.unary_union(list(track.wind_swaths(34).values()))
Expand Down Expand Up @@ -624,30 +623,30 @@ def coops_product_within_region(
:param station_type: either ``current`` or ``historical``
:return: array of data within the specified region

>>> from stormevents import VortexTrack
>>> from stormevents.nhc import VortexTrack
>>> from shapely import ops
>>> from datetime import datetime, timedelta
>>> track = VortexTrack('florence2018', file_deck='b')
>>> combined_wind_swaths = ops.unary_union(list(track.wind_swaths(34).values()))
>>> coops_product_within_region('water_level', region=combined_wind_swaths, start_date=datetime.now() - timedelta(hours=1), end_date=datetime.now())
<xarray.Dataset>
Dimensions: (nos_id: 10, t: 10)
Dimensions: (nos_id: 10, t: 11)
Coordinates:
* nos_id (nos_id) int64 8651370 8652587 8654467 ... 8662245 8665530 8670870
* t (t) datetime64[ns] 2022-02-23T08:06:00 ... 2022-02-23T09:00:00
* t (t) datetime64[ns] 2022-03-08T14:48:00 ... 2022-03-08T15:48:00
nws_id (nos_id) <U5 'DUKN7' 'ORIN7' 'HCGN7' ... 'NITS1' 'CHTS1' 'FPKG1'
x (nos_id) float64 -75.75 -75.56 -75.69 ... -79.19 -79.94 -80.88
y (nos_id) float64 36.19 35.78 35.22 34.72 ... 33.34 32.78 32.03
Data variables:
v (nos_id, t) float32 6.097 6.096 6.059 6.005 ... 2.39 2.324 2.336
s (nos_id, t) float32 0.07 0.052 0.054 0.063 ... 0.014 0.02 0.009
f (nos_id, t) object '1,0,0,0' '1,0,0,0' ... '0,0,0,0' '1,0,0,0'
v (nos_id, t) float32 6.256 6.304 6.361 6.375 ... 2.633 2.659 2.686
s (nos_id, t) float32 0.107 0.097 0.127 0.122 ... 0.005 0.004 0.004
f (nos_id, t) object '1,0,0,0' '1,0,0,0' ... '1,0,0,0' '1,0,0,0'
q (nos_id, t) object 'p' 'p' 'p' 'p' 'p' 'p' ... 'p' 'p' 'p' 'p' 'p'
"""

stations = coops_stations_within_region(region=region, station_type=station_type)
station_data = [
COOPS_Station(station).get(
COOPS_Station(station).product(
product=product,
start_date=start_date,
end_date=end_date,
Expand Down
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