Outflows
CustomOutflows
¶
Create a simple outflow timeseries for GLM.
Generates an outflow timeseries in m^3/second between a given start and end datetime. The timeseries can be updated in two ways: 1. Providing a dictionary with specific datetimes and their corresponding outflows. 2. Specifying a fixed outflow value between two datetimes. The outflow timeseries can be returned as a pandas DataFrame or exported to a CSV file.
Attributes:
| Name | Type | Description |
|---|---|---|
start_datetime |
Union[str, Timestamp, datetime]
|
The start datetime of the outflow timeseries. Must be of type Timestamp, datetime, or a valid datetime string. |
end_datetime |
Union[str, Timestamp, datetime]
|
The end datetime of the outflow timeseries. Must be of type Timestamp, datetime, or a valid datetime string. |
frequency |
str
|
Frequency of the outflow timeseries. Must be either '24h' (daily) or '1h' (hourly). Default is '24h'. |
base_outflow |
Union[int, float]
|
Base flow of the outflow timeseries in m^3/day or m^3/hour depending
on |
Examples:
Initialise a daily outflow timeseries with a base outflow of 0.0 m^3/day:
>>> outflows = outflows.CustomOutflows(
... start_datetime="2020-01-01",
... end_datetime="2020-01-10",
... frequency="24h",
... base_outflow=0.0
... )
Update the timeseries with a dictionary of specific dates and their
corresponding outflows in m^3/day:
>>> outflows_dict = {
... "2020-01-02": 2, # 2m^3/day
... "2020-01-03": 4, # 4m^3/day
... "2020-01-04": 6 # 6m^3/day
... }
>>> outflows.set_on_datetime(outflows_dict)
Return and print the outflows timeseries as a pandas DataFrame. Note, the outflow units have been converted to m^3/second as expected by GLM:
>>> print(outflows.get_outflows())
time flow
0 2020-01-01 0.000000
1 2020-01-02 0.000023
2 2020-01-03 0.000046
3 2020-01-04 0.000069
4 2020-01-05 0.000000
5 2020-01-06 0.000000
6 2020-01-07 0.000000
7 2020-01-08 0.000000
8 2020-01-09 0.000000
9 2020-01-10 0.000000
Update the timeseries with a fixed outflow between two dates:
>>> outflows.set_over_datetime(
... from_datetime="2020-01-05",
... to_datetime = "2020-01-09",
... outflow = 5 # 5m^3/day
... )
Return and print the outflows timeseries as a pandas DataFrame:
>>> print(outflows.get_outflows())
time flow
0 2020-01-01 0.000000
1 2020-01-02 0.000023
2 2020-01-03 0.000046
3 2020-01-04 0.000069
4 2020-01-05 0.000058
5 2020-01-06 0.000058
6 2020-01-07 0.000058
7 2020-01-08 0.000058
8 2020-01-09 0.000058
9 2020-01-10 0.000000
Write the outflows to a CSV without the index:
get_outflows()
¶
Get the outflow timeseries.
Returns the outflow timeseries as a pandas DataFrame.
Examples:
set_on_datetime(datetime_outflows)
¶
Set the outflow volume for specific datetimes.
The outflow volume for specific datetimes can be set by providing a
dictionary with datetimes as keys and outflow volumes as values.
Outflow volumes will be treated as having the same units as the base
outflow (m^3/day or m^3/hour depending on frequency).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
datetime_outflows |
Dict[Union[str, pd.Timestamp, dt.datetime],
|
|
required |
Union |
Dictionary with valid datetimes as keys and outflow volumes as values. |
required |
Examples:
>>> from glmpy import outflows
>>> outflows = outflows.CustomOutflows(
... start_datetime="2020-01-01 00:00:00",
... end_datetime="2020-01-01 10:00:00",
... frequency="1h",
... base_outflow=0.0
... )
>>> outflows_dict = {
... "2020-01-01 01:00:00": 10,
... "2020-01-01 03:00:00": 12,
... "2020-01-01 05:00:00": 14
... }
>>> outflows.set_on_datetime(outflows_dict)
>>> print(outflows.get_outflows())
time flow
0 2020-01-01 00:00:00 0.000000
1 2020-01-01 01:00:00 0.002778
2 2020-01-01 02:00:00 0.000000
3 2020-01-01 03:00:00 0.003333
4 2020-01-01 04:00:00 0.000000
5 2020-01-01 05:00:00 0.003889
6 2020-01-01 06:00:00 0.000000
7 2020-01-01 07:00:00 0.000000
8 2020-01-01 08:00:00 0.000000
9 2020-01-01 09:00:00 0.000000
10 2020-01-01 10:00:00 0.000000
set_over_datetime(from_datetime, to_datetime, outflow)
¶
Set the outflow volume between two datetimes.
Outflow volumes between two datetimes can be set by providing a start datetime, end datetime, and an outflow volume. Outflows are updated on the start and end datetime.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
from_datetime |
Union[str, Timestamp, datetime]
|
The datetime to update the outflow timeseries from. |
required |
to_datetime |
Union[str, Timestamp, datetime]
|
The datetime to update the outflow timeseries to. |
required |
outflow |
Union[float, int]
|
The outflow volume to set between the |
required |
Examples:
>>> from glmpy import outflows
>>> outflows = outflows.CustomOutflows(
... start_datetime="2020-01-01 00:00:00",
... end_datetime="2020-01-01 10:00:00",
... frequency="1h",
... base_outflow=0.0
... )
>>> outflows.set_over_datetime(
... from_datetime="2020-01-01 01:00:00",
... to_datetime = "2020-01-01 09:00:00",
... outflow = 5
... )
>>> print(outflows.get_outflows())
time flow
0 2020-01-01 00:00:00 0.000000
1 2020-01-01 01:00:00 0.001389
2 2020-01-01 02:00:00 0.001389
3 2020-01-01 03:00:00 0.001389
4 2020-01-01 04:00:00 0.001389
5 2020-01-01 05:00:00 0.001389
6 2020-01-01 06:00:00 0.001389
7 2020-01-01 07:00:00 0.001389
8 2020-01-01 08:00:00 0.001389
9 2020-01-01 09:00:00 0.001389
10 2020-01-01 10:00:00 0.000000
write_outflows(file_path)
¶
Write the outflow timeseries to a CSV file.
The outflow timeseries can be written to a CSV file by providing a path to the CSV file. The index of the DataFrame is not included in the CSV file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
file_path |
str
|
Path to the CSV file to which the outflow timeseries will be written. |
required |
Examples: