sequent-peak-algorithm

Python implementation of the Sequent Peak Algorithm for the design and simulation of water reservoirs in hydrology.

Installation with requirements

pip install sequent-peak-algorithm==0.0.5

Usage example

# 0. Create random input data
q_in = np.random.rand(100) * 10
q_out = np.random.rand(100) * 10

# 1. Run sequent peak algorithm
res_cap = spa.spa(q_in=q_in, q_out=q_out)
print("Capacity: ", res_cap.capacity)

# 2. Plot results from sequent peak algorithm
fig_cap = spa.spa_plot(res_cap)
plt.savefig("example_2_spa.png", dpi=300)

# 3. Run storage simulation and explore results
res_sim = spa.sim(
    q_in=q_in,
    q_out=q_out,
    initial_storage=0.0,
    capacity=res_cap.capacity
)

# Plot results from storage simulation
fig_sim = spa.sim_plot(res_sim)
plt.savefig("example_2_sim.png", dpi=300)

For further explanations see the examples/ folder.

Sequent Peak Algorithm Visualization

Simulation Visualization

Documentation

spa

Parameters

• q_in: list[float] : discharge input values
• q_out: list[float]: discharge output values

Returns

• Result: collections.namedtuple: Result of the sequent peak algorithm with the following attributes:
  • q_in: list[float]: discharge input values
  • q_out: list[float]: discharge output values
  • storage: list[float]: storage values, calculated with _storage
  • cumulative_storage: list[float]: cumulative storage values, calculated with _cumulative_storage
  • max_vals: list[float]: discharge values of the maxima, calculated with _maxima
  • max_indices: list[int]: indices of the maxima, calculated with _maxima
  • min_vals: list[float]: discharge values of the minima, calculated with _minima
  • min_indices: list[int]: indices of the minima, calculated with _minima
  • capacity: list[float]: capacity, calculated with _capacity
  • capacity_indices: list[int]: indices of the capacity (multiple positions possible), calculated with _capacity
  • capacity_max_vals: list[float]: preceeding maxima of the capacity, calculated with _capacity
  • capacity_max_indices: list[int]: indices of the preceeding maxima of the capacity, calculated with _capacity
  • capacity_min_vals: list[float]: succeeding minima of the capacity, calculated with _capacity
  • capacity_min_indices: list[int]: indices of the succeeding minima of the capacity, calculated with _capacity

sim

Parameters

• q_in: list[float] : discharge input values
• q_out: list[float]: discharge output values
• initial_storage: float: initial storage value, chosen by the user
• capacity: float: capacity values, calculated with spa or chosen by the user

Returns

• Result: collections.namedtuple: Result of the sequent peak algorithm with the following attributes:
  • q_in: list[float] : discharge input values
  • q_out: list[float]: discharge output values
  • initial_storage: float: initial storage value, chosen by the user
  • capacity: list[float]: capacity values, calculated with spa or chosen by the user
  • q_out_real: list[float]: real discharge output accounting for the capacity (deficit and overflow situations)
  • storage: list[float]: storage values
  • deficit: list[float]: deficit values
  • overflow: list[float]: overflow values

spa_plot

Parameters

• Result: collections.namedtuple: Result of the sequent peak algorithm

Returns

• fig: matplotlib.figure.Figure: Figure object of the plot

sim_plot

Parameters

• Result: collections.namedtuple: Result of the storage simulation

Returns

• fig: matplotlib.figure.Figure: Figure object of the plot