plans.hydrology.core

{Short module description (1-3 sentences)} todo docstring

Features

todo docstring

• {feature 1}

• {feature 2}

• {feature 3}

• {etc}

Overview

todo docstring {Overview description}

Examples

todo docstring {Examples in rST}

Print a message

# print message
print("Hello world!")
# [Output] >> 'Hello world!'

Functions

compute_decay(s, dt, k)
compute_flow(flow_pot, flow_cap)

Classes

Model([name, alias])

The core Model base object.

plans.hydrology.core.compute_decay(s, dt, k)

plans.hydrology.core.compute_flow(flow_pot, flow_cap)

class plans.hydrology.core.Model(name='MyModel', alias='HM01')

Bases: DataSet

The core Model base object. Expected to hold one pandas.DataFrame as simulation data and a dictionary as parameters. This is a dummy class to be developed downstream.

__init__(name='MyModel', alias='HM01')

Initialize the DataSet object.

Parameters:

• name (str) – unique object name

• alias (str) – unique object alias. If None, it takes the first and last characters from name

_set_fields()

Set fields names. Expected to increment superior methods.

_set_model_vars()

_set_model_params()

Internal method for setting up model parameters data

Returns:

None

Return type:

None

_set_view_specs()

Set view specifications. Expected to increment superior methods.

Returns:

None

Return type:

None

get_vars()

get_params()

get_metadata()

Get a dictionary with object metadata. Expected to increment superior methods.

Note

Metadata does not necessarily inclue all object attributes.

Returns:

dictionary with all metadata

Return type:

dict

setter(dict_setter)

Set selected attributes based on an incoming dictionary. This is calling the superior method using load_data=False.

Parameters:

dict_setter (dict) – incoming dictionary with attribute values

Returns:

None

Return type:

None

update()

Refresh all mutable attributes based on data (includins paths). Base method. This is overwrinting the superior method.

Returns:

None

Return type:

None

update_dt()

Update Time Step value, units and tag to match the model reference time parameter (like k)

Returns:

None

Return type:

None

load_params()

Load parameter data

Returns:

None

Return type:

None

load_data()

Load simulation data. Expected to overwrite superior methods.

Returns:

None

Return type:

None

load()

Load parameters and data

Returns:

None

Return type:

None

setup()

Set model simulation. Expected to be incremented downstream.

Warning

This method overwrites model data.

Returns:

None

Return type:

None

solve()

Solve the model for boundary and initial conditions by numerical methods.

Warning

This method overwrites model data.

Returns:

None

Return type:

None

evaluate()

Evaluate model.

Returns:

None

Return type:

None

run(setup_model=True)

Simulate model (full procedure).

Parameters:

setup_model (bool) – flag for setting up data (default=True)

Returns:

None

Return type:

None

export(folder, filename)

Export object resources

Parameters:

• folder (str) – path to folder

• filename (str) – file name without extension

Returns:

None

Return type:

None

save(folder)

Save to sourced files is not allowed for Model() family. Use .export() instead. This is overwriting superior methods.

Returns:

None

Return type:

None

view(show=True)

Get the basic visualization.

Parameters:

show (bool) – option for showing instead of saving.

Note

Uses values in the view_specs() attribute for plotting.

static get_timestep_factor(from_unit: str, to_unit: str) → float

Calculates the conversion factor between two time units.

For instance, to find out how many days are in a given number of ‘10min’ intervals, this function provides the multiplier.

Parameters:

• from_unit (str) – The starting time unit (e.g., ‘10min’, ’15s’).

• to_unit (str) – The desired time unit (e.g., ‘days’, ‘min’).

Returns:

The factor to convert from the starting to the desired time unit.

Return type:

float

static get_timestep_series(start_time: str, end_time: str, time_unit: str) → pandas.DatetimeIndex

Generates a time series of timestamps.

Creates a sequence of timestamps between a start and end time, with a specified frequency.

Parameters:

• start_time (str) – The starting timestamp for the series (e.g., ‘2024-01-01’, ‘2024-01-01 00:00:00’).

• end_time (str) – The ending timestamp for the series (e.g., ‘2024-01-10’, ‘2024-01-10 23:59:59’).

• time_unit (str) – The frequency or interval between timestamps (e.g., ‘D’ for day, ‘H’ for hour, ‘10min’).

Returns:

A pandas DatetimeIndex representing the generated time series.

Return type:

pd.DatetimeIndex

static get_gaussian_signal(value_max, size, sigma=50, position_factor=5, reverse=False)

Generates a vector of normally (gaussian) distributed values. Useful for testing inputs inflows.

Parameters:

• value_max (float) – actual maximum value in the vector

• size (int) – size of vector (time series size)

• sigma (float) – standard deviation for gaussian (normal) distribution.

• position_factor (float) – where to place the peak in the vector

• reverse (bool) – boolean to reverse position in order

Returns:

vector of values

Return type:

Numpy array