diff --git a/.gitignore b/.gitignore
index 715eb30c..0d5ab6aa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -115,5 +115,10 @@ docs/cables/Figs
# Pycharm
.idea/
-# Other
+# ORBIT-specific
Analysis/
+examples/configs/array_system.yaml
+examples/configs/export_system.yaml
+examples/configs/mooring_system.yaml
+examples/configs/oss.yaml
+examples/configs/substructure.yaml
diff --git a/docs/_config.yml b/docs/_config.yml
index 72a493c5..58b9989f 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -18,7 +18,6 @@ execute:
- Thumbs.db
- DS_Store
- "**.ipynb_checkpoints"
- - "topical_guides/cost_curves.md"
# Define the name of the latex output file for PDF builds
latex:
diff --git a/docs/_toc.yml b/docs/_toc.yml
index eec00dd9..61d60338 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -25,6 +25,7 @@ parts:
- file: topical_guides/custom_array
- file: topical_guides/export_cable_system
- file: topical_guides/cable_installation
+ - file: topical_guides/cost_curves
- caption: API Reference
chapters:
- file: api/ProjectManager
diff --git a/docs/build_book.sh b/docs/build_book.sh
index e5c2d317..7e3d7583 100644
--- a/docs/build_book.sh
+++ b/docs/build_book.sh
@@ -1,4 +1,14 @@
rm -rf _build
-jupyter-book build .
-cp -f _build/jupyter_execute/tutorials/*.ipynb ../examples/
-cp -f _build/jupyter_execute/topical_guides/*.ipynb ../examples/
+jupyter-book build . > build_log.txt
+
+cp -f _build/jupyter_execute/**/*.ipynb ../examples/
+nb_file_names=$(find _build/jupyter_execute/**/*.ipynb -type f | awk -F/ 'BEGIN {ORS=" "} {print $NF}')
+
+cd ../examples
+python ../docs/clean_notebook.py $nb_file_names
+pre-commit run --files $nb_file_names
+
+cd ../docs
+printf "\nORBIT documentation built succesfully, and updated examples have been copied into ORBIT/examples/ and validated\n"
+tail -n 15 build_log.txt
+rm -rf build_log.txt
diff --git a/docs/clean_notebook.py b/docs/clean_notebook.py
new file mode 100644
index 00000000..baffe9cd
--- /dev/null
+++ b/docs/clean_notebook.py
@@ -0,0 +1,34 @@
+"""Removes all outputs and metadata in the Jupyter Notebook examples that
+cause painful diffs during code contribution and review time.
+"""
+
+import sys
+from pathlib import Path
+
+import nbformat as nbf
+
+
+def remove_extraneous(notebook_fn: str | Path):
+ """Delete outputs and metadata from Jupyter Notebook files."""
+ if isinstance(notebook_fn, str):
+ notebook_fn = Path(notebook_fn)
+ with notebook_fn.open("r", encoding="utf-8") as f:
+ notebook = nbf.read(f, as_version=4)
+
+ for cell in notebook["cells"]:
+ if cell["cell_type"] in ["code", "markdown"]:
+ cell["metadata"] = {}
+
+ with notebook_fn.open("w", encoding="utf-8") as f:
+ nbf.write(notebook, f)
+
+
+if __name__ == "__main__":
+ if len(sys.argv) < 2:
+ print("No files provided")
+ sys.exit(1)
+ for fn in sys.argv[1:]:
+ if not fn.endswith(".ipynb"):
+ print(f"Skipping non-notebook file: {fn}")
+ continue
+ remove_extraneous(fn)
diff --git a/docs/methods/CommonCost.md b/docs/methods/CommonCost.md
index ba19f30f..6b81f85c 100644
--- a/docs/methods/CommonCost.md
+++ b/docs/methods/CommonCost.md
@@ -19,7 +19,7 @@ and it shows the `Class`, `attribute_name`, `units`, and the cost value in 2024
future releases, these costs will adjust based on the market indices so any user
can be sure that the common costs in their model is not outdated.
-:::{figure} ../imagesimages/cost_by_procurement.png
+:::{figure} ../images/cost_by_procurement.png
:align: center
Cost by Procurement Year. Note that values are inflated from the procurement year USD to 2024 USD using CPI
diff --git a/docs/topical_guides/cost_curves.md b/docs/topical_guides/cost_curves.md
index 230bce02..84ac2729 100644
--- a/docs/topical_guides/cost_curves.md
+++ b/docs/topical_guides/cost_curves.md
@@ -6,7 +6,7 @@ jupytext:
format_version: 0.13
jupytext_version: 1.19.1
kernelspec:
- display_name: bos
+ display_name: Python 3 (ipykernel)
language: python
name: python3
---
@@ -14,29 +14,33 @@ kernelspec:
# Cost Curve Creator
This notebook enables fitting curves to ORBIT models to create a cost function that can be
-embedded in NRWAL.
-A variety of curve and surface fitting options are available, and new ones can be added easily.
-There are also tools for visualizing the ORBIT data and fitted curves.
+embedded in [NRWAL](https://github.com/NatLabRockies/NRWAL). A variety of curve and surface
+fitting options are available, and new ones can be added easily. There are also tools for
+visualizing the ORBIT data and fitted curves.
-## Dependencies
+## Extra Optional Dependencies
-- ORBIT
-- ipympl enables interactive matplotlib elements in jupyter notebooks via the `%matplotlib widget` magic command below
+- `ipympl` (`pip install ipympl`) enables interactive matplotlib elements in Jupyter notebooks via
+ the `%matplotlib widget` magic command below
## Instructions
Follow the steps below to configure and run the notebook.
-1. Create a basic ORBIT configuration file including at least the following sections:
-- site
-- turbine
-- plant
-
-2. Configure the notebook by setting the following variables in the "Configuration" section:
-- `BASE_CONFIG`: the ORBIT config file at a given path
-- `DEPTHS`: a list of water depths to use for cost curves
-- `MEAN_WIND_SPEED`: a list of mean wind speed to use for cost curves
-- Add any additional global parameter ranges
+1. Create a basic ORBIT configuration file including at least the following sections. As a note,
+ this example makes use of the
+ [`narwal.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs/narwal.yaml)
+ configuration file.
+* site
+* turbine
+* plant
+
+2. Configure the notebook by setting the following variables in the
+ [Configuration](#configuration) section:
+* `BASE_CONFIG`: the ORBIT config file at a given path
+* `DEPTHS`: a list of water depths to use for cost curves
+* `MEAN_WIND_SPEED`: a list of mean wind speed to use for cost curves
+* Add any additional global parameter ranges
3. Run the notebook to establish a first-pass fit for the ORBIT data. This will also plot the ORBIT
data and curves.
@@ -44,19 +48,21 @@ data and curves.
4. Refine the curve fits by swapping the curve-fit function from the options available in
the "Curve Fit Library" section
++++
+
## Practical Guidance
-This notebook specifically models spatially varying costs typically related to water depth
-but in some cases other variables are considered. The same methods could be used to model
-the cost relationship for other variables. The general workflow is to first create a parameterized
-ORBIT model and obtain the cost as a function of the variables of interest.
-Then, fit a curve or surface to the data by starting with the linear options.
-Plot the data and curve fits to evaluate whether the linear forms are sufficient.
-If not, move to the quadratic or higher order curve fits.
+This notebook specifically models spatially varying costs typically related to water depth but in
+some cases other variables are considered. The same methods could be used to model the cost
+relationship for other variables. The general workflow is to first create a parameterized ORBIT
+model and obtain the cost as a function of the variables of interest. Then, fit a curve or surface
+to the data by starting with the linear options. Plot the data and curve fits to evaluate whether
+the linear forms are sufficient. If not, move to the quadratic or higher order curve fits.
A class `CostFunction` is provided to simplify running the ORBIT parameterization, fit the
curves to the data, and visualize the results. An example is given below to instantiate the
class:
+
```python
cost_function = CostFunction(
config={"design_phases": ["MonopileDesign"]},
@@ -70,13 +76,14 @@ cost_function = CostFunction(
)
```
-The config parameter is a dictionary containing additional configuration parameters to add to
-the basic ORBIT configuration provided through the input file created in Step 1 in the instructions.
-Any parameters given in the `CostFunction` config will be added to the base configuration or
-overwritten if they already exist. The parameters dictionary contains the variables to be varied
-in the cost function via `ORBIT.ParametricManager`, and the results dictionary sets the results
-variables from ORBIT. Each of these dictionaries are passed directly to the
-`ORBIT.ParametricManager` class.
+The `config` parameter is a dictionary containing additional configuration parameters to add to
+the base ORBIT configuration provided through the input file created in Step 1 in the instructions.
+Any parameters given in the `CostFunction` `config` will be added to the base configuration or
+overwritten if they already exist.
+
+The `parameters` dictionary contains the variables to be varied in the cost function via
+ORBIT's `ParametricManager`, and the `results` dictionary sets the results variables from ORBIT.
+Each of these dictionaries are passed directly to the `ParametricManager` class.
The fitted curves are saved on the `CostFunction` object and multiple types can exist at the
same time. Two versions of one type cannot be saved at the same time. To create a curve fit,
@@ -90,6 +97,7 @@ Considerations:
- A new `CostFunction` instance should be created for each cost model.
### Plotting API for 2D vs 3D plots
+
The `CostFunction` class handles 2D and 3D data seamlessly by using the x and z parameters for 2D
and adding y for 3D. The appropriate matplotlib API is used depending if the data is 2D or 3D.
From the calling script, be sure to configure the Axes that is given to `CostFunction.plot` with
@@ -106,8 +114,7 @@ The following code block provides a template for creating a cost function for a
two independent parameters.
```python
-
-# Create the CostFunction object with the ORBIT configuration for the parameterization
+# 1. Create the CostFunction object with the ORBIT configuration for the parameterization
cost_function = CostFunction(
config={
"design_phases": ["Design"],
@@ -121,16 +128,16 @@ cost_function = CostFunction(
}
)
-# Run ORBIT via ORBIT.ParametricManager
+# 2. Run ORBIT via ORBIT.ParametricManager
cost_function.run()
-# Fit two curves (surfaces since there are two independent parameters) to the data.
+# 3. Fit two curves (surfaces since there are two independent parameters) to the data.
# After running the following two commands, the CostFunction object will have two related
# attributes that store the curve fits.
cost_function.fit_curve("linear_2d")
cost_function.fit_curve("quadratic_2d")
-# Plot the data and curves
+# 4. Plot the data and curves
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_title("Depth vs mean wind speed")
@@ -138,11 +145,11 @@ ax.set_xlabel("Depth (m)")
ax.set_ylabel("Mean wind speed (m/s)")
ax.set_zlabel("Cost ($)")
cost_function.plot(ax, plot_data=True)
-cost_function.plot(ax, plot_curves=["linear_1d", "quadratic_1d"]) # These curves must have been generated first
+cost_function.plot(ax, plot_curves=["linear_2d", "quadratic_2d"]) # These curves must have been generated first
# alternatively, the two lines above could be combined into a single line:
# cost_function.plot(ax, plot_data=True, plot_curves=["linear_1d", "quadratic_1d"])
-# Export the curve function to a NRWAL-compatible file
+# 5. Export the curve function to a NRWAL-compatible file
cost_function.export("design.yaml", "design_system")
```
@@ -150,29 +157,30 @@ cost_function.export("design.yaml", "design_system")
There are a number of curve fit options in the `CostFunction` class, and more can be added by
creating a new method and connecting it in some key places in the class.
+
First, create a new method on the `CostFunction` class that follows the naming convention of
-`{curve_type}_{dimension}` where `curve_type` is the name of the type of function like
-"exponential" or "linear" and `dimension` is the number of independent variables the curve.
-The function should return the fitted curve evaluated at the data points given to fit the curve.
-A generic function signature is given below:
+`{curve_type}_{dimension}` where `curve_type` is the name of the type of function like "exponential"
+or "linear" and `dimension` is the number of independent variables the curve. The function should
+return the fit curve evaluated at the data points given to fit the curve. A generic function
+signature is given below:
+
```python
class CostFunction:
def curvetype_dimension(self):
+ ...
- # Such as:
def linear_1d(self):
+ ...
```
To fit a curve to the data for one independent variable, it is recommended to use the
-`scipy.optimize.curve_fit` function via the `Curves` class.
-In general, a one-dimensional curve fit function will follow the form given below.
-By setting the curve fit function `f`, you define the shape of the curve and set
-the order of the coefficients in `self.coeffs` since they are returned in the order they are
-given in the function signature.
-The `Curves.polynomal_eval` function is available to easily evaluate polynomial curves, but other
-curve-types can be evaluated by simply plugging in the data points (`self.x`) to the fitted
-function.
+`scipy.optimize.curve_fit` function via the `Curves` class. In general, a one-dimensional curve fit
+function will follow the form given below. By setting the curve fit function `f`, you define the
+shape of the curve and set the order of the coefficients in `self.coeffs` since they are returned in
+the order they are given in the function signature. The `Curves.polynomal_eval` function is
+available to easily evaluate polynomial curves, but other curve-types can be evaluated by simply
+plugging in the data points (`self.x`) to the fit function.
```python
# Define a function for a prototype curve; this is where you define the shape of the curve
@@ -188,31 +196,32 @@ self.coeffs = Curves.fit(f, self.x, self.z)
self._linear_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)
```
-A two-dimensional curve (surface) fit will typically follow a similar process, as should below.
-For these types, it is recommended to use the `numpy.linalg.lstsq` function.
-First, reshape the data into a new array with each element containing the three-dimensional
-data points.
-Then, stack the data into a column matrix in the form of the equation that you're implementing.
-See the comments in the code block for more information.
-Evaluate the curve at the data points (`self.x`, `self.y`) by stating the form of the curve
-with the coefficients from the curve fit.
+A two-dimensional curve (surface) fit will typically follow a similar process, as shown below. For
+these types, it is recommended to use the `numpy.linalg.lstsq` function. First, reshape the data
+into a new array with each element containing the three-dimensional data points. Then, stack the
+data into a column matrix in the form of the equation that you're implementing. See the comments in
+the code block for more information. Evaluate the curve at the data points (`self.x`, `self.y`) by
+stating the form of the curve with the coefficients from the curve fit.
```python
- # Reshape the data into a new array with each element containing the three-dimensional data points
+ ...
+
+ # Reshape the data into a new array where each element is a three-dimensional data point
data_to_fit = np.array(list(zip(self.x, self.y, self.z)))
- # Stack the data into a column matrix in the form of the equation that you're implementing.
- # Here, the equation is z = ax + by + c and data_to_fit[:,0] are the x values,
- # data_to_fit[:,1] are the y values. The third column is all ones to account for the constant
- # term.
+ # Stack the data into a column matrix in the form of the equation that you're
+ # implementing. Here, the equation is z = ax + by + c and data_to_fit[:,0] are the x
+ # values, data_to_fit[:,1] are the y values. The third column is all ones to account
+ # for the constant term.
A = np.c_[
data_to_fit[:,0],
data_to_fit[:,1],
np.ones(data_to_fit.shape[0])
]
- # Fit the curve to the data; the data is the cost and these are always `self.z` which is data_to_fit[:,2]
- self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2])
+ # Fit the curve to the data; the data is the cost and these are always
+ # self.z which is data_to_fit[:,2]
+ self.coeffs, _, _, _ = linalg.lstsq(A, data_to_fit[:, 2])
# Evaluate it on the same points as the input data
self._linear_2d_curve = self.coeffs[0]*self.x + self.coeffs[1]*self.y + self.coeffs[2]
@@ -222,23 +231,33 @@ Finally, save the coefficients to `self.coeffs`, save the evaluated curve to
`self._{curve_type}_{dimension}_curve`, and add the corresponding if-statements
in `CostFunction.plot` and `CostFunction.export`.
+## Setup the Example
+
+First, we must import all the required libraries and ORBIT functionality to implement
+everything discussed up to this section.
+
```{code-cell} ipython3
-%matplotlib widget
+# NOTE: uncomment this line if using the interactive plotting functionality
+# %matplotlib widget
from copy import deepcopy
-import matplotlib.pyplot as plt
+from pathlib import Path
+
+import yaml
import numpy as np
import pandas as pd
+import matplotlib as mpl
+import matplotlib.pyplot as plt
from scipy import stats, optimize, linalg
-import yaml
-from ORBIT import (
- ParametricManager,
- load_config,
-)
+from ORBIT import ParametricManager, load_config
+
-import matplotlib as mpl
mpl.rcParams["figure.autolayout"] = True
+
+# Ensure the correct examples directory is used when running this in docs or in examples
+here = Path(".").resolve()
+example_dir = here.parents[1] / "examples" if here.stem == "topical_guides" else here
```
## Configuration
@@ -246,7 +265,7 @@ mpl.rcParams["figure.autolayout"] = True
Replace any of these throughout the notebook to customize a cost model parameterization.
```{code-cell} ipython3
-BASE_CONFIG = load_config("nrwal.yaml")
+BASE_CONFIG = load_config(example_dir / "configs/nrwal.yaml")
DEPTHS = [i for i in range(5, 60, 5)] # Ocean depth in meters
MEAN_WIND_SPEED = [i for i in range(2, 20, 2)] # Mean wind speed in m/s
@@ -279,13 +298,18 @@ class Curves():
This method evaluates a curve defined by a polynomial equation given a set of
coefficients and data points.
- Args:
- coeffs (list): A list of coefficients for the curve. The order of the
- coefficients should be from highest to lowest power.
- data_points (list): A list of data points at which to evaluate the curve.
-
- Returns:
- np.array: The curve evaluated at the given data points.
+ Parameters
+ ----------
+ coeffs : list
+ A list of coefficients for the curve. The order of the coefficients should
+ be from highest to lowest power.
+ data_points : list
+ A list of data points at which to evaluate the curve.
+
+ Returns
+ -------
+ np.array
+ The curve evaluated at the given data points.
"""
curve = np.zeros_like(data_points)
for i, dp in enumerate(data_points):
@@ -297,19 +321,25 @@ class Curves():
@staticmethod
def fit(func, x, y, fit_check=False):
- if x is pd.Series:
+ if isinstance(x, pd.Series):
x = x.to_numpy(dtype=np.float64)
- elif x is np.array:
+ elif isinstance(x, np.array):
x = x.astype(np.float64)
- if y is pd.Series:
+ else:
+ raise ValueError("`x` must be a pd.Series or np.ndarray.")
+
+ if isinstance(y, pd.Series):
y = y.to_numpy(dtype=np.float64)
- elif y is np.array:
+ elif isinstance(y, np.array):
y = y.astype(np.float64)
+ x = x.astype(np.float64)
+ else:
+ raise ValueError("`y` must be a pd.Series or np.ndarray.")
popt, pcov, nfodict, mesg, ier = optimize.curve_fit(func, x, y, full_output=True)
if fit_check:
- print(f"mesg: {mesg}")
+ print(f"message: {mesg}")
print(f"ier: {ier}")
print(f"Coefficients: {popt}")
# print(f"R-squared: {rvalue**2:.6f}")
@@ -320,25 +350,27 @@ class Curves():
```{code-cell} ipython3
class CostFunction():
"""
- This class is used to create the ORBIT parameterization, fit a curve, plot the curve, and
- export the function to NRWAL format. Parameterizations are limited to up to two independent
- variables.
+ Creates the ORBIT parameterization, curve fits, plots the curve, and exports
+ the function to a NRWAL format. Parameterizations are limited to up to two
+ independent variables.
"""
def __init__(self, config: dict, parameters: dict, results: dict):
"""
- On initialization, the config, parameters, and results dictionaries are prepared for
- use in ORBIT.ParametricManager. Additionally, the independent variables are extracted
- into x and y (for two-variable parameterizations) and z is extracted as the dependent
- variable. Whether the cost function is 3D or 2D is determined by the length of the
- parameters variable.
-
- Args:
- config (str): Configuration settings to added to the BASE_CONFIG or overwrite
- in the BASE_CONFIG. This must include the `design_phases` config.
- parameters (dict): Parameters to use with ORBIT.ParametricManager; maximum of two
- parameters are supported.
- results (dict): Results to use with ORBIT.ParametricManager; this must include only
- one variable.
+ Prepares the ``config``, ``parameters``, and ``results`` dictionaries for use
+ in ``ParametricManager``. Additionally, the independent variables are extracted
+ into ``x` and ``y`` (for two-variable parameterizations) and ``z`` is extracted
+ as the dependent variable. Whether the cost function is 3D or 2D is determined
+ by the length of theparameters variable.
+
+ Parameters
+ ----------
+ config : str
+ Configuration settings to added to the BASE_CONFIG or overwrite
+ in the ``BASE_CONFIG``. This must include the ``design_phases`` config.
+ parameters : dict
+ Parameters to use with ``ParametricManager``; maximum of two parameters.
+ results : dict
+ Results to use with ``ParametricManager``; this must include only one variable.
"""
self.is_3d = False
@@ -368,7 +400,11 @@ class CostFunction():
# results and postprocessing the data
self.parameters = deepcopy(self.parameters)
_vars = list(self.parameters.keys())
- self.x_variable = _vars.pop(0) # NOTE: This assumes the first parameter is site.depth; it's not critical to functionality but good to keep in mind
+
+ # NOTE: This assumes the first parameter is site.depth; it's not critical to
+ # functionality but good to keep in mind
+ self.x_variable = _vars.pop(0)
+
if len(_vars) == 1:
self.is_3d = True
self.y_variable = _vars.pop()
@@ -379,7 +415,9 @@ class CostFunction():
raise ValueError("This class is limited to results with one variable")
def run(self):
- self.parametric = ParametricManager(self.config, self.parameters, self.results, product=True)
+ self.parametric = ParametricManager(
+ self.config, self.parameters, self.results, product=True
+ )
self.parametric.run()
self.x = self.parametric.results[self.x_variable]
@@ -388,7 +426,9 @@ class CostFunction():
self.z = self.parametric.results[self.z_variable]
- ### --------- Curve fit functions --------- ###
+ # -------------------
+ # Curve fit functions
+ # -------------------
def linear_1d(self):
def f(x, a, b):
@@ -423,10 +463,16 @@ class CostFunction():
data_to_fit[:,1],
np.ones(data_to_fit.shape[0])
]
- self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2]) # coefficients
+ self.coeffs, _, _, _ = linalg.lstsq(A, data_to_fit[:, 2]) # coefficients
# Evaluate it on the same points as the input data
- self._linear_2d_curve = self.coeffs[0]*self.x + self.coeffs[1]*self.y + self.coeffs[2]
+ self._linear_2d_curve = (
+ self.coeffs[0]
+ * self.x
+ + self.coeffs[1]
+ * self.y
+ + self.coeffs[2]
+ )
def quadratic_2d(self):
data_to_fit = np.array(list(zip(self.x, self.y, self.z)))
@@ -434,36 +480,37 @@ class CostFunction():
# best-fit quadratic curve
A = np.c_[
np.ones(data_to_fit.shape[0]),
- data_to_fit[:,:2],
- np.prod(data_to_fit[:,:2], axis=1),
- data_to_fit[:,:2]**2
+ data_to_fit[:, :2],
+ np.prod(data_to_fit[:, :2], axis=1),
+ data_to_fit[:, :2]**2,
]
- self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2])
+ self.coeffs, _, _, _ = linalg.lstsq(A, data_to_fit[:, 2])
# Evaluate it on the same points as the input data
# This dot product is equivalent to the sum of the terms of the polynomial;
- # np.c_[] is used to concatenate the arrays into the correct form for the dot product
- # and C is the coefficients of the polynomial
+ # np.c_[] is used to concatenate the arrays into the correct form for the
+ # dot product and C is the coefficients of the polynomial
self._quadratic_2d_curve = np.dot(
np.c_[
np.ones(self.x.shape),
self.x,
self.y,
- self.x*self.y,
+ self.x * self.y,
self.x**2,
- self.y**2
+ self.y**2,
],
self.coeffs
).reshape(self.x.shape)
-
- ### --------- Plotting functions --------- ###
+ # ------------------
+ # Plotting functions
+ # ------------------
def plot(
self,
ax,
plot_data: bool = False,
- plot_curves: list[str] = []
+ plot_curves: list[str] = None,
):
if plot_data:
if self.is_3d:
@@ -471,47 +518,49 @@ class CostFunction():
else:
ax.scatter(self.x, self.z, label="Data")
+ if plot_curves is None:
+ plot_curves = []
for curve in plot_curves:
-
- if curve == "linear_1d":
- ax.plot(self.x, self._linear_1d_curve, label="Linear Fit")
-
- if curve == "quadratic_1d":
- ax.plot(self.x, self._quadratic_1d_curve, label="Quadratic Fit")
-
- if curve == "poly3_1d":
- ax.plot(self.x, self._poly3_1d_curve, label="Degree 3 Polynomial Fit")
-
- if curve == "linear_2d":
- ax.plot_surface(
- np.reshape(self.x, (len(DEPTHS), -1)),
- np.reshape(self.y, (len(DEPTHS), -1)),
- np.reshape(self._linear_2d_curve, (len(DEPTHS), -1)),
- alpha=0.3,
- label="Linear Fit"
- )
-
- if curve == "quadratic_2d":
- ax.plot_surface(
- np.reshape(self.x, (len(DEPTHS), -1)),
- np.reshape(self.y, (len(DEPTHS), -1)),
- np.reshape(self._quadratic_2d_curve, (len(DEPTHS), -1)),
- alpha=0.3,
- label="Quadratic Fit"
- )
-
-
- ### --------- Export functions --------- ###
-
- def export(self, filename: str, key: str, comments: str = ""):
+ match curve:
+ case "linear_1d":
+ ax.plot(self.x, self._linear_1d_curve, label="Linear Fit")
+ case "quadratic_1d":
+ ax.plot(self.x, self._quadratic_1d_curve, label="Quadratic Fit")
+ case "poly3_1d":
+ ax.plot(self.x, self._poly3_1d_curve, label="Degree 3 Polynomial Fit")
+ case "linear_2d":
+ ax.plot_surface(
+ np.reshape(self.x, (len(DEPTHS), -1)),
+ np.reshape(self.y, (len(DEPTHS), -1)),
+ np.reshape(self._linear_2d_curve, (len(DEPTHS), -1)),
+ alpha=0.3,
+ label="Linear Fit",
+ )
+ case "quadratic_2d":
+ ax.plot_surface(
+ np.reshape(self.x, (len(DEPTHS), -1)),
+ np.reshape(self.y, (len(DEPTHS), -1)),
+ np.reshape(self._quadratic_2d_curve, (len(DEPTHS), -1)),
+ alpha=0.3,
+ label="Quadratic Fit",
+ )
+
+ # ------------------
+ # Export functions
+ # ------------------
+
+ def export(self, filename: Path, key: str, comments: str = "") -> None:
"""
- This function writes the curve equation to a file for use in NRWAL.
-
- Args:
- filename (str): The file to write the curve equation to. If the file exists, the
- equation is appended to the end of the file.
- key (str): The key to use in the NRWAL file for the curve equation. In the key-value
- pair, this argument is the key and the value is the equation string.
+ Writes the curve equation to a file for use in NRWAL.
+
+ Parameters
+ ----------
+ filename : Path
+ The file to write the curve equation to. If the file exists, the
+ equation is appended to the end of the file.
+ key : str
+ The key to use in the NRWAL file for the curve equation. In the key-value
+ pair, this argument is the key and the value is the equation string.
"""
x_var = orbit_to_nrwal_params[self.x_variable]
@@ -523,6 +572,7 @@ class CostFunction():
if self._linear_1d_curve is not None:
# y = ax + b
equation_string = f"{F} * {S} + {F}".format(self.coeffs[0], x_var, self.coeffs[1])
+ equation_string = f"{F} * {S} + {F}".format(self.coeffs[0], x_var, self.coeffs[1])
if self._quadratic_1d_curve is not None:
# y = ax^2 + bx + c
@@ -567,7 +617,9 @@ class CostFunction():
# nrwal_dict = {self.config["design_phases"][0]: equation_string}
nrwal_dict = {key: equation_string}
- with open(filename, "a") as f:
+ if isinstance(filename, str):
+ filename = Path(filename).resolve()
+ with filename.open("a") as f:
f.write("\n")
if comments:
f.write(f"# {comments}\n")
@@ -579,8 +631,6 @@ class CostFunction():
# ORBIT Design Phase Cost Curves
-+++
-
## Monopile Substructure
Independent variables:
@@ -614,7 +664,7 @@ cost_function.plot(ax, plot_data=True)
cost_function.plot(ax, plot_curves=["linear_2d", "quadratic_2d"])
ax.legend()
-cost_function.export("substructure.yaml", "substructure_17MW")
+cost_function.export(example_dir / "configs/substructure.yaml", "substructure_15MW")
```
## Semi-Submersible Substructure
@@ -736,9 +786,9 @@ ax.set_ylabel("Cost ($)")
cost_semitaut.plot(ax, plot_data=True)
cost_semitaut.plot(ax, plot_curves=["linear_1d"])
-cost_catenary.export("mooring_system.yaml", "catenary")
-cost_tlp.export("mooring_system.yaml", "tlp")
-cost_semitaut.export("mooring_system.yaml", "semitaut")
+cost_catenary.export(example_dir / "configs/mooring_system.yaml", "catenary")
+cost_tlp.export(example_dir / "configs/mooring_system.yaml", "tlp")
+cost_semitaut.export(example_dir / "configs/mooring_system.yaml", "semitaut")
```
## Array System
@@ -909,7 +959,7 @@ cost_touchdown_cabledepth.plot(ax, plot_curves=["quadratic_2d"])
```
```{code-cell} ipython3
-cost_touchdown_cabledepth.export("array_system.yaml", "floating")
+cost_touchdown_cabledepth.export(example_dir / "configs/array_system.yaml", "floating")
```
## Export System
@@ -984,10 +1034,10 @@ multiline_comment = "\n# ".join([
"special because it's the only one that ",
"is like it is."
])
-cost_hvac.export("export_system.yaml", "floating_hvac", comments=multiline_comment)
+cost_hvac.export(example_dir / "configs/export_system.yaml", "floating_hvac", comments=multiline_comment)
singleline_comment = "HVDC export system"
-cost_hvdc.export("export_system.yaml", "floating_hvdc", comments=singleline_comment)
+cost_hvdc.export(example_dir / "configs/export_system.yaml", "floating_hvdc", comments=singleline_comment)
```
## Offshore Floating Substation
@@ -1018,5 +1068,5 @@ cost_function.plot(ax, plot_data=True)
cost_function.plot(ax, plot_curves=["linear_1d"])
ax.legend()
-cost_function.export("oss.yaml", "oss_substructure")
+cost_function.export(example_dir / "configs/oss.yaml", "oss_substructure")
```
diff --git a/examples/available_outputs.ipynb b/examples/available_outputs.ipynb
index ecc9506f..17dc6d54 100644
--- a/examples/available_outputs.ipynb
+++ b/examples/available_outputs.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "2ff27af5",
+ "id": "71bb88c1",
"metadata": {},
"source": [
"(outputs-tutorial)=\n",
@@ -23,7 +23,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "eb1ba216",
+ "id": "88bc2df7",
"metadata": {},
"outputs": [
{
@@ -43,6 +43,9 @@
"\n",
"from ORBIT import ProjectManager, load_config\n",
"\n",
+ "# Apply thousands separators and no decimals to floats\n",
+ "pd.options.display.float_format = '{:,.0f}'.format\n",
+ "\n",
"# Ensure the correct examples directory is used when running this in docs or in examples\n",
"here = Path(\".\").resolve()\n",
"example_dir = here.parents[1] / \"examples\" if here.stem == \"tutorials\" else here\n",
@@ -54,7 +57,7 @@
},
{
"cell_type": "markdown",
- "id": "1498686a",
+ "id": "caf5e2a3",
"metadata": {},
"source": [
"## Project Details\n",
@@ -68,7 +71,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "624e5aa6",
+ "id": "d00a78c2",
"metadata": {},
"outputs": [
{
@@ -142,7 +145,7 @@
},
{
"cell_type": "markdown",
- "id": "b9e74f9e",
+ "id": "d798d3db",
"metadata": {},
"source": [
"### Project Parameterizaions\n",
@@ -158,7 +161,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "321f305d",
+ "id": "3e1d137e",
"metadata": {},
"outputs": [
{
@@ -181,7 +184,7 @@
},
{
"cell_type": "markdown",
- "id": "109bd3e9",
+ "id": "583413a8",
"metadata": {},
"source": [
"### Event Timing\n",
@@ -194,7 +197,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "76e2354a",
+ "id": "0b35ac5d",
"metadata": {},
"outputs": [
{
@@ -213,7 +216,7 @@
},
{
"cell_type": "markdown",
- "id": "9c889ab7",
+ "id": "64f3a272",
"metadata": {},
"source": [
"## All Outputs At Once\n",
@@ -230,7 +233,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "03db516d",
+ "id": "622dae5c",
"metadata": {},
"outputs": [
{
@@ -351,7 +354,7 @@
},
{
"cell_type": "markdown",
- "id": "d4350ae5",
+ "id": "0a295cb7",
"metadata": {},
"source": [
"## CapEx\n",
@@ -375,7 +378,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "6d7b47f7",
+ "id": "d314feb0",
"metadata": {},
"outputs": [
{
@@ -394,7 +397,7 @@
},
{
"cell_type": "markdown",
- "id": "ec008904",
+ "id": "dae4f2d5",
"metadata": {},
"source": [
"### Categorical CapEx Breakdowns\n",
@@ -407,7 +410,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "001627ca",
+ "id": "b022c1a1",
"metadata": {},
"outputs": [
{
@@ -439,7 +442,7 @@
},
{
"cell_type": "markdown",
- "id": "a026b617",
+ "id": "0a5c4c0c",
"metadata": {},
"source": [
"Like in the previous examples, the `capex_breakdown_per_kw` will provide each category's associated\n",
@@ -449,7 +452,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "09ef6c92",
+ "id": "86087579",
"metadata": {},
"outputs": [
{
@@ -481,7 +484,7 @@
},
{
"cell_type": "markdown",
- "id": "cfb914e4",
+ "id": "854a9dc3",
"metadata": {},
"source": [
"### BOS CapEx\n",
@@ -493,7 +496,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "b7654950",
+ "id": "e02ff6e4",
"metadata": {},
"outputs": [
{
@@ -512,7 +515,7 @@
},
{
"cell_type": "markdown",
- "id": "ae9f4a41",
+ "id": "956fd69b",
"metadata": {},
"source": [
"### System CapEx\n",
@@ -527,7 +530,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "b85da225",
+ "id": "0de00ef2",
"metadata": {},
"outputs": [
{
@@ -546,7 +549,7 @@
},
{
"cell_type": "markdown",
- "id": "0c18adf3",
+ "id": "5253f59c",
"metadata": {},
"source": [
"To view the individual component system costs, users can inspect the `system_costs` dictionary where\n",
@@ -556,7 +559,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "8428f726",
+ "id": "f6b000f8",
"metadata": {},
"outputs": [
{
@@ -578,7 +581,7 @@
},
{
"cell_type": "markdown",
- "id": "889c09e9",
+ "id": "a48a3bf6",
"metadata": {},
"source": [
"### Installation Capex\n",
@@ -594,7 +597,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "58111600",
+ "id": "61506376",
"metadata": {},
"outputs": [
{
@@ -613,7 +616,7 @@
},
{
"cell_type": "markdown",
- "id": "f46e212f",
+ "id": "efc63a57",
"metadata": {},
"source": [
"To view the individual component installation costs, users can inspect the `installation_costs`\n",
@@ -624,7 +627,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "fc3bd0fa",
+ "id": "e2c57386",
"metadata": {},
"outputs": [
{
@@ -647,7 +650,7 @@
},
{
"cell_type": "markdown",
- "id": "c36d5c05",
+ "id": "8102094e",
"metadata": {},
"source": [
"### Turbine CapEx\n",
@@ -659,7 +662,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "037c1ac2",
+ "id": "96483c47",
"metadata": {},
"outputs": [
{
@@ -678,7 +681,7 @@
},
{
"cell_type": "markdown",
- "id": "b1121cc5",
+ "id": "abe5b7c2",
"metadata": {},
"source": [
"### Project CapEx\n",
@@ -693,7 +696,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "6db74098",
+ "id": "311d90fc",
"metadata": {},
"outputs": [
{
@@ -712,7 +715,7 @@
},
{
"cell_type": "markdown",
- "id": "bf83a824",
+ "id": "3ea8367a",
"metadata": {},
"source": [
"### Soft CapEx\n",
@@ -727,7 +730,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "baf878ec",
+ "id": "5cc775a5",
"metadata": {},
"outputs": [
{
@@ -746,7 +749,7 @@
},
{
"cell_type": "markdown",
- "id": "17743637",
+ "id": "ca2df6ba",
"metadata": {},
"source": [
"The soft CapEx can also be broken down using both the `soft_capex_breakdown` and the `capex_detailed_soft_capex_breakdown`, which also provide a capacity-noramlized variation by adding\n",
@@ -758,7 +761,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "12057ba4",
+ "id": "2b40f38d",
"metadata": {},
"outputs": [
{
@@ -782,7 +785,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "ea3fd05f",
+ "id": "75b589d2",
"metadata": {},
"outputs": [
{
@@ -819,7 +822,7 @@
},
{
"cell_type": "markdown",
- "id": "bbb1c9bb",
+ "id": "67f0d176",
"metadata": {},
"source": [
"The soft CapEx values are also available as independent values:\n",
@@ -835,7 +838,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "14cd601c",
+ "id": "3f073ee5",
"metadata": {},
"outputs": [
{
@@ -862,7 +865,7 @@
},
{
"cell_type": "markdown",
- "id": "dff9f663",
+ "id": "f28d9ad5",
"metadata": {},
"source": [
"### All Other CapEx Categories\n",
@@ -879,7 +882,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "faed7724",
+ "id": "6dda18bd",
"metadata": {},
"outputs": [
{
@@ -898,7 +901,7 @@
},
{
"cell_type": "markdown",
- "id": "dfae7916",
+ "id": "7201b4d4",
"metadata": {},
"source": [
"#### Onshore Substation CapEx\n",
@@ -909,7 +912,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "e2a55a2b",
+ "id": "e1b74645",
"metadata": {},
"outputs": [
{
@@ -928,7 +931,7 @@
},
{
"cell_type": "markdown",
- "id": "e4cb433a",
+ "id": "ef5198a6",
"metadata": {},
"source": [
"#### Overnight CapEx\n",
@@ -939,7 +942,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "id": "500d1108",
+ "id": "c33616fb",
"metadata": {},
"outputs": [
{
@@ -956,7 +959,7 @@
},
{
"cell_type": "markdown",
- "id": "33e2d0a4",
+ "id": "d5266e63",
"metadata": {},
"source": [
"## Logging\n",
@@ -974,7 +977,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "id": "c6ee40bf",
+ "id": "94789b34",
"metadata": {},
"outputs": [
{
@@ -1002,7 +1005,7 @@
},
{
"cell_type": "markdown",
- "id": "57ef770a",
+ "id": "df4b1233",
"metadata": {},
"source": [
"The `project_logs` provides a list of the when a component installation was completed using the\n",
@@ -1032,7 +1035,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "id": "d2d9e0d9",
+ "id": "b32ad896",
"metadata": {},
"outputs": [
{
@@ -1120,7 +1123,7 @@
},
{
"cell_type": "markdown",
- "id": "063c2b3b",
+ "id": "c6b258dd",
"metadata": {},
"source": [
"Below, we can see the installation timing is not quite realistic given the WTIV is used for the\n",
@@ -1132,7 +1135,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "8df9e57c",
+ "id": "c6cd0603",
"metadata": {},
"outputs": [
{
@@ -1170,7 +1173,7 @@
},
{
"cell_type": "markdown",
- "id": "ae9db786",
+ "id": "7e0bf8cc",
"metadata": {},
"source": [
"### Detailed Event Timing\n",
@@ -1183,7 +1186,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "e47033c0",
+ "id": "179a2eb1",
"metadata": {},
"outputs": [
{
@@ -1228,13 +1231,13 @@
"
\n",
"
\n",
"
0
\n",
- "
0.5
\n",
+ "
0
\n",
"
Array Cable Installation Vessel
\n",
"
Mobilize
\n",
- "
72.0
\n",
- "
361756.5
\n",
+ "
72
\n",
+ "
361,756
\n",
"
ACTION
\n",
- "
0.0
\n",
+ "
0
\n",
"
ArrayCableInstallation
\n",
"
NaN
\n",
"
NaN
\n",
@@ -1247,13 +1250,13 @@
"
\n",
"
\n",
"
1
\n",
- "
1.0
\n",
+ "
1
\n",
"
WTIV
\n",
"
Mobilize
\n",
- "
168.0
\n",
- "
2800000.0
\n",
+ "
168
\n",
+ "
2,800,000
\n",
"
ACTION
\n",
- "
0.0
\n",
+ "
0
\n",
"
MonopileInstallation
\n",
"
NaN
\n",
"
NaN
\n",
@@ -1266,13 +1269,13 @@
"
\n",
"
\n",
"
2
\n",
- "
0.5
\n",
+ "
0
\n",
"
Heavy Lift Vessel
\n",
"
Mobilize
\n",
- "
72.0
\n",
- "
936918.0
\n",
+ "
72
\n",
+ "
936,918
\n",
"
ACTION
\n",
- "
0.0
\n",
+ "
0
\n",
"
OffshoreSubstationInstallation
\n",
"
NaN
\n",
"
NaN
\n",
@@ -1285,13 +1288,13 @@
"
\n",
"
\n",
"
3
\n",
- "
0.5
\n",
+ "
0
\n",
"
Feeder 0
\n",
"
Mobilize
\n",
- "
72.0
\n",
- "
220858.5
\n",
+ "
72
\n",
+ "
220,858
\n",
"
ACTION
\n",
- "
0.0
\n",
+ "
0
\n",
"
OffshoreSubstationInstallation
\n",
"
NaN
\n",
"
NaN
\n",
@@ -1304,13 +1307,13 @@
"
\n",
"
\n",
"
4
\n",
- "
0.5
\n",
+ "
0
\n",
"
SPI Vessel
\n",
"
Mobilize
\n",
- "
72.0
\n",
- "
224860.5
\n",
+ "
72
\n",
+ "
224,860
\n",
"
ACTION
\n",
- "
0.0
\n",
+ "
0
\n",
"
ScourProtectionInstallation
\n",
"
NaN
\n",
"
NaN
\n",
@@ -1327,18 +1330,18 @@
],
"text/plain": [
" cost_multiplier agent action duration \\\n",
- "0 0.5 Array Cable Installation Vessel Mobilize 72.0 \n",
- "1 1.0 WTIV Mobilize 168.0 \n",
- "2 0.5 Heavy Lift Vessel Mobilize 72.0 \n",
- "3 0.5 Feeder 0 Mobilize 72.0 \n",
- "4 0.5 SPI Vessel Mobilize 72.0 \n",
+ "0 0 Array Cable Installation Vessel Mobilize 72 \n",
+ "1 1 WTIV Mobilize 168 \n",
+ "2 0 Heavy Lift Vessel Mobilize 72 \n",
+ "3 0 Feeder 0 Mobilize 72 \n",
+ "4 0 SPI Vessel Mobilize 72 \n",
"\n",
- " cost level time phase phase_name \\\n",
- "0 361756.5 ACTION 0.0 ArrayCableInstallation NaN \n",
- "1 2800000.0 ACTION 0.0 MonopileInstallation NaN \n",
- "2 936918.0 ACTION 0.0 OffshoreSubstationInstallation NaN \n",
- "3 220858.5 ACTION 0.0 OffshoreSubstationInstallation NaN \n",
- "4 224860.5 ACTION 0.0 ScourProtectionInstallation NaN \n",
+ " cost level time phase phase_name \\\n",
+ "0 361,756 ACTION 0 ArrayCableInstallation NaN \n",
+ "1 2,800,000 ACTION 0 MonopileInstallation NaN \n",
+ "2 936,918 ACTION 0 OffshoreSubstationInstallation NaN \n",
+ "3 220,858 ACTION 0 OffshoreSubstationInstallation NaN \n",
+ "4 224,860 ACTION 0 ScourProtectionInstallation NaN \n",
"\n",
" site_depth hub_height per_trip location max_waveheight max_windspeed \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
@@ -1367,7 +1370,7 @@
},
{
"cell_type": "markdown",
- "id": "9eb26864",
+ "id": "fc8fd3b7",
"metadata": {},
"source": [
"Using the data frame we can filter produce vessel timing summaries for a single phase or a single\n",
@@ -1382,116 +1385,146 @@
{
"cell_type": "code",
"execution_count": 27,
- "id": "1cc3660c",
+ "id": "dc0b2276",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
- "\n",
- "
\n",
+ ""
],
"text/plain": [
- ""
+ " monthly_opex monthly_expenses monthly_revenue cash_flow\n",
+ "0 0 46,066,982 0 -46,066,982\n",
+ "1 0 35,066,817 0 -35,066,817\n",
+ "2 0 33,022,562 0 -33,022,562\n",
+ "3 0 27,488,874 0 -27,488,874\n",
+ "4 4,500,000 30,140,361 8,409,600 -21,730,761\n",
+ "5 6,300,000 19,454,129 11,773,440 -7,680,689\n",
+ "6 7,200,000 15,594,619 13,455,360 -2,139,259\n",
+ "7 7,500,000 14,520,562 14,016,000 -504,562\n",
+ "8 7,500,000 8,078,377 14,016,000 5,937,623\n",
+ "9 7,500,000 7,500,000 14,016,000 6,516,000\n",
+ "10 7,500,000 7,500,000 14,016,000 6,516,000\n",
+ "11 7,500,000 7,500,000 14,016,000 6,516,000"
]
},
"execution_count": 29,
@@ -1686,7 +1742,7 @@
" pd.DataFrame(project.cash_flow.values(), columns=[\"cash_flow\"]),\n",
" ],\n",
" axis=1\n",
- ").head(12).style.format(\"{:,.2f}\")"
+ ").head(12)"
]
}
],
@@ -1719,61 +1775,61 @@
"source_map": [
12,
28,
- 44,
- 53,
- 55,
- 66,
- 71,
- 79,
+ 47,
+ 56,
+ 58,
+ 69,
+ 74,
82,
- 94,
- 96,
- 115,
+ 85,
+ 97,
+ 99,
118,
- 126,
+ 121,
129,
- 134,
+ 132,
137,
- 144,
+ 140,
147,
- 157,
+ 150,
160,
- 165,
+ 163,
168,
- 179,
+ 171,
182,
- 188,
+ 185,
191,
- 198,
+ 194,
201,
- 211,
+ 204,
214,
- 224,
+ 217,
227,
- 234,
- 239,
+ 230,
+ 237,
242,
- 253,
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- 271,
+ 245,
+ 256,
+ 263,
274,
- 280,
+ 277,
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- 289,
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+ 294,
+ 307,
+ 309,
+ 334,
+ 340,
+ 347,
+ 367,
375,
- 385,
- 395,
- 411,
- 413,
- 419
+ 378,
+ 388,
+ 396,
+ 412,
+ 414,
+ 420
]
},
"nbformat": 4,
diff --git a/examples/cable_installation.ipynb b/examples/cable_installation.ipynb
index daa0fb3e..cbe8946a 100644
--- a/examples/cable_installation.ipynb
+++ b/examples/cable_installation.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "63386374",
+ "id": "146ab067",
"metadata": {},
"source": [
"# Cable Laying and Burying\n",
@@ -15,7 +15,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "23051ed0",
+ "id": "827f7203",
"metadata": {},
"outputs": [],
"source": [
@@ -28,7 +28,7 @@
},
{
"cell_type": "markdown",
- "id": "20d3d8d3",
+ "id": "78b1baec",
"metadata": {},
"source": [
"Below, we set up a base configuration using an imagined cable and sections (25 each of 1km and 2km cable sections) designed for simplicity."
@@ -37,7 +37,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "359e9b09",
+ "id": "66223ce4",
"metadata": {},
"outputs": [],
"source": [
@@ -58,7 +58,7 @@
},
{
"cell_type": "markdown",
- "id": "41308710",
+ "id": "7ddd128c",
"metadata": {},
"source": [
"## Single Cable Laying and Burying Process\n",
@@ -71,7 +71,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "aada4950",
+ "id": "faf15b7c",
"metadata": {},
"outputs": [
{
@@ -92,7 +92,7 @@
},
{
"cell_type": "markdown",
- "id": "3b18166b",
+ "id": "e106d8b0",
"metadata": {},
"source": [
"## Separate Cable Laying and Burying Processes\n",
@@ -110,7 +110,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "bc638c30",
+ "id": "d6306178",
"metadata": {},
"outputs": [],
"source": [
@@ -124,7 +124,7 @@
},
{
"cell_type": "markdown",
- "id": "2750f34b",
+ "id": "5a241b8b",
"metadata": {},
"source": [
"## Including a Trenching Vessel\n",
@@ -138,7 +138,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "d4f637d7",
+ "id": "124af83a",
"metadata": {},
"outputs": [],
"source": [
@@ -154,7 +154,7 @@
},
{
"cell_type": "markdown",
- "id": "f9c69249",
+ "id": "e2984c17",
"metadata": {},
"source": [
"## Viewing the results\n",
@@ -166,7 +166,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "be805dfc",
+ "id": "cd9532be",
"metadata": {},
"outputs": [
{
@@ -391,7 +391,7 @@
},
{
"cell_type": "markdown",
- "id": "3a98b62e",
+ "id": "107b8bd2",
"metadata": {},
"source": [
"Now, we demonstrate the separate process by combining the separate laying and burying steps taken\n",
@@ -404,7 +404,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "b22592fe",
+ "id": "faa4519e",
"metadata": {},
"outputs": [
{
@@ -665,7 +665,7 @@
},
{
"cell_type": "markdown",
- "id": "df6233cd",
+ "id": "56292aae",
"metadata": {},
"source": [
"Similar to the above, when we add trenching as a separate step, we have three discrete stages to\n",
@@ -675,7 +675,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "cd44470c",
+ "id": "e989a1bb",
"metadata": {},
"outputs": [
{
diff --git a/examples/nrwal.yaml b/examples/configs/nrwal.yaml
similarity index 83%
rename from examples/nrwal.yaml
rename to examples/configs/nrwal.yaml
index 561dbfa7..aea91f31 100644
--- a/examples/nrwal.yaml
+++ b/examples/configs/nrwal.yaml
@@ -2,15 +2,15 @@
site:
depth: 30
distance: 100
- distance_to_landfall: 60
mean_windspeed: 9
-turbine: 17MW_low_SP
+turbine: 15MW_generic
plant:
layout: grid
num_turbines: 36
row_spacing: 7
substation_distance: 1
turbine_spacing: 7
+
# --- Additional Configs ---
OffshoreSubstationInstallation:
feeder: example_heavy_feeder
@@ -26,9 +26,10 @@ export_cable_install_vessel: example_cable_lay_vessel
export_system_design:
cables: XLPE_1000mm_220kV
percent_added_length: 0.05
-landfall:
- interconnection_distance: 3
- trench_length: 2
+ landfall:
+ interconnection_distance: 3
+ trench_length: 2
+ distance_to_landfall: 60
oss_install_vessel: example_heavy_lift_vessel
scour_protection_design:
@@ -40,7 +41,8 @@ port:
monthly_rate: 2000000.0
sub_assembly_lines: 1
turbine_assembly_cranes: 1
-# --- Don't specify these here since they're set in the curve generator ---
+
+# NOTE: Don't specify any of the below settings becacuse they're set in the curve generator
# design_phases:
# - MonopileDesign
# - ScourProtectionDesign
diff --git a/examples/cost_curves.ipynb b/examples/cost_curves.ipynb
index 8067103c..07b268e0 100644
--- a/examples/cost_curves.ipynb
+++ b/examples/cost_curves.ipynb
@@ -2,54 +2,65 @@
"cells": [
{
"cell_type": "markdown",
+ "id": "ec3350cd",
"metadata": {},
"source": [
"# Cost Curve Creator\n",
"\n",
"This notebook enables fitting curves to ORBIT models to create a cost function that can be\n",
- "embedded in NRWAL.\n",
- "A variety of curve and surface fitting options are available, and new ones can be added easily.\n",
- "There are also tools for visualizing the ORBIT data and fitted curves.\n",
+ "embedded in [NRWAL](https://github.com/NatLabRockies/NRWAL). A variety of curve and surface\n",
+ "fitting options are available, and new ones can be added easily. There are also tools for\n",
+ "visualizing the ORBIT data and fitted curves.\n",
"\n",
- "## Dependencies\n",
+ "## Extra Optional Dependencies\n",
"\n",
- "- ORBIT\n",
- "- ipympl enables interactive matplotlib elements in jupyter notebooks via the `%matplotlib widget` magic command below\n",
+ "- `ipympl` (`pip install ipympl`) enables interactive matplotlib elements in Jupyter notebooks via\n",
+ " the `%matplotlib widget` magic command below\n",
"\n",
"## Instructions\n",
"\n",
"Follow the steps below to configure and run the notebook.\n",
"\n",
- "1. Create a basic ORBIT configuration file including at least the following sections:\n",
- "- site\n",
- "- turbine\n",
- "- plant\n",
- "\n",
- "2. Configure the notebook by setting the following variables in the \"Configuration\" section:\n",
- "- `BASE_CONFIG`: the ORBIT config file at a given path\n",
- "- `DEPTHS`: a list of water depths to use for cost curves\n",
- "- `MEAN_WIND_SPEED`: a list of mean wind speed to use for cost curves\n",
- "- Add any additional global parameter ranges\n",
+ "1. Create a basic ORBIT configuration file including at least the following sections. As a note,\n",
+ " this example makes use of the\n",
+ " [`narwal.yaml`](https://github.com/NLRWindSystems/ORBIT/tree/main/examples/configs/narwal.yaml)\n",
+ " configuration file.\n",
+ "* site\n",
+ "* turbine\n",
+ "* plant\n",
+ "\n",
+ "2. Configure the notebook by setting the following variables in the\n",
+ " [Configuration](#configuration) section:\n",
+ "* `BASE_CONFIG`: the ORBIT config file at a given path\n",
+ "* `DEPTHS`: a list of water depths to use for cost curves\n",
+ "* `MEAN_WIND_SPEED`: a list of mean wind speed to use for cost curves\n",
+ "* Add any additional global parameter ranges\n",
"\n",
"3. Run the notebook to establish a first-pass fit for the ORBIT data. This will also plot the ORBIT\n",
"data and curves.\n",
"\n",
"4. Refine the curve fits by swapping the curve-fit function from the options available in\n",
- "the \"Curve Fit Library\" section\n",
- "\n",
+ "the \"Curve Fit Library\" section"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d863381b",
+ "metadata": {},
+ "source": [
"## Practical Guidance\n",
"\n",
- "This notebook specifically models spatially varying costs typically related to water depth\n",
- "but in some cases other variables are considered. The same methods could be used to model\n",
- "the cost relationship for other variables. The general workflow is to first create a parameterized\n",
- "ORBIT model and obtain the cost as a function of the variables of interest.\n",
- "Then, fit a curve or surface to the data by starting with the linear options.\n",
- "Plot the data and curve fits to evaluate whether the linear forms are sufficient.\n",
- "If not, move to the quadratic or higher order curve fits.\n",
+ "This notebook specifically models spatially varying costs typically related to water depth but in\n",
+ "some cases other variables are considered. The same methods could be used to model the cost\n",
+ "relationship for other variables. The general workflow is to first create a parameterized ORBIT\n",
+ "model and obtain the cost as a function of the variables of interest. Then, fit a curve or surface\n",
+ "to the data by starting with the linear options. Plot the data and curve fits to evaluate whether\n",
+ "the linear forms are sufficient. If not, move to the quadratic or higher order curve fits.\n",
"\n",
"A class `CostFunction` is provided to simplify running the ORBIT parameterization, fit the\n",
"curves to the data, and visualize the results. An example is given below to instantiate the\n",
"class:\n",
+ "\n",
"```python\n",
"cost_function = CostFunction(\n",
" config={\"design_phases\": [\"MonopileDesign\"]},\n",
@@ -63,13 +74,14 @@
")\n",
"```\n",
"\n",
- "The config parameter is a dictionary containing additional configuration parameters to add to\n",
- "the basic ORBIT configuration provided through the input file created in Step 1 in the instructions.\n",
- "Any parameters given in the `CostFunction` config will be added to the base configuration or\n",
- "overwritten if they already exist. The parameters dictionary contains the variables to be varied\n",
- "in the cost function via `ORBIT.ParametricManager`, and the results dictionary sets the results\n",
- "variables from ORBIT. Each of these dictionaries are passed directly to the\n",
- "`ORBIT.ParametricManager` class.\n",
+ "The `config` parameter is a dictionary containing additional configuration parameters to add to\n",
+ "the base ORBIT configuration provided through the input file created in Step 1 in the instructions.\n",
+ "Any parameters given in the `CostFunction` `config` will be added to the base configuration or\n",
+ "overwritten if they already exist.\n",
+ "\n",
+ "The `parameters` dictionary contains the variables to be varied in the cost function via\n",
+ "ORBIT's `ParametricManager`, and the `results` dictionary sets the results variables from ORBIT.\n",
+ "Each of these dictionaries are passed directly to the `ParametricManager` class.\n",
"\n",
"The fitted curves are saved on the `CostFunction` object and multiple types can exist at the\n",
"same time. Two versions of one type cannot be saved at the same time. To create a curve fit,\n",
@@ -83,6 +95,7 @@
"- A new `CostFunction` instance should be created for each cost model.\n",
"\n",
"### Plotting API for 2D vs 3D plots\n",
+ "\n",
"The `CostFunction` class handles 2D and 3D data seamlessly by using the x and z parameters for 2D\n",
"and adding y for 3D. The appropriate matplotlib API is used depending if the data is 2D or 3D.\n",
"From the calling script, be sure to configure the Axes that is given to `CostFunction.plot` with\n",
@@ -99,8 +112,7 @@
"two independent parameters.\n",
"\n",
"```python\n",
- "\n",
- "# Create the CostFunction object with the ORBIT configuration for the parameterization\n",
+ "# 1. Create the CostFunction object with the ORBIT configuration for the parameterization\n",
"cost_function = CostFunction(\n",
" config={\n",
" \"design_phases\": [\"Design\"],\n",
@@ -114,16 +126,16 @@
" }\n",
")\n",
"\n",
- "# Run ORBIT via ORBIT.ParametricManager\n",
+ "# 2. Run ORBIT via ORBIT.ParametricManager\n",
"cost_function.run()\n",
"\n",
- "# Fit two curves (surfaces since there are two independent parameters) to the data.\n",
+ "# 3. Fit two curves (surfaces since there are two independent parameters) to the data.\n",
"# After running the following two commands, the CostFunction object will have two related\n",
"# attributes that store the curve fits.\n",
"cost_function.fit_curve(\"linear_2d\")\n",
"cost_function.fit_curve(\"quadratic_2d\")\n",
"\n",
- "# Plot the data and curves\n",
+ "# 4. Plot the data and curves\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.set_title(\"Depth vs mean wind speed\")\n",
@@ -131,11 +143,11 @@
"ax.set_ylabel(\"Mean wind speed (m/s)\")\n",
"ax.set_zlabel(\"Cost ($)\")\n",
"cost_function.plot(ax, plot_data=True)\n",
- "cost_function.plot(ax, plot_curves=[\"linear_1d\", \"quadratic_1d\"]) # These curves must have been generated first\n",
+ "cost_function.plot(ax, plot_curves=[\"linear_2d\", \"quadratic_2d\"]) # These curves must have been generated first\n",
"# alternatively, the two lines above could be combined into a single line:\n",
"# cost_function.plot(ax, plot_data=True, plot_curves=[\"linear_1d\", \"quadratic_1d\"])\n",
"\n",
- "# Export the curve function to a NRWAL-compatible file\n",
+ "# 5. Export the curve function to a NRWAL-compatible file\n",
"cost_function.export(\"design.yaml\", \"design_system\")\n",
"```\n",
"\n",
@@ -143,29 +155,30 @@
"\n",
"There are a number of curve fit options in the `CostFunction` class, and more can be added by\n",
"creating a new method and connecting it in some key places in the class.\n",
+ "\n",
"First, create a new method on the `CostFunction` class that follows the naming convention of\n",
- "`{curve_type}_{dimension}` where `curve_type` is the name of the type of function like\n",
- "\"exponential\" or \"linear\" and `dimension` is the number of independent variables the curve.\n",
- "The function should return the fitted curve evaluated at the data points given to fit the curve.\n",
- "A generic function signature is given below:\n",
+ "`{curve_type}_{dimension}` where `curve_type` is the name of the type of function like \"exponential\"\n",
+ "or \"linear\" and `dimension` is the number of independent variables the curve. The function should\n",
+ "return the fit curve evaluated at the data points given to fit the curve. A generic function\n",
+ "signature is given below:\n",
+ "\n",
"```python\n",
"class CostFunction:\n",
"\n",
" def curvetype_dimension(self):\n",
+ " ...\n",
"\n",
- " # Such as:\n",
" def linear_1d(self):\n",
+ " ...\n",
"```\n",
"\n",
"To fit a curve to the data for one independent variable, it is recommended to use the\n",
- "`scipy.optimize.curve_fit` function via the `Curves` class.\n",
- "In general, a one-dimensional curve fit function will follow the form given below.\n",
- "By setting the curve fit function `f`, you define the shape of the curve and set\n",
- "the order of the coefficients in `self.coeffs` since they are returned in the order they are\n",
- "given in the function signature.\n",
- "The `Curves.polynomal_eval` function is available to easily evaluate polynomial curves, but other\n",
- "curve-types can be evaluated by simply plugging in the data points (`self.x`) to the fitted\n",
- "function.\n",
+ "`scipy.optimize.curve_fit` function via the `Curves` class. In general, a one-dimensional curve fit\n",
+ "function will follow the form given below. By setting the curve fit function `f`, you define the\n",
+ "shape of the curve and set the order of the coefficients in `self.coeffs` since they are returned in\n",
+ "the order they are given in the function signature. The `Curves.polynomal_eval` function is\n",
+ "available to easily evaluate polynomial curves, but other curve-types can be evaluated by simply\n",
+ "plugging in the data points (`self.x`) to the fit function.\n",
"\n",
"```python\n",
"# Define a function for a prototype curve; this is where you define the shape of the curve\n",
@@ -181,31 +194,32 @@
"self._linear_1d_curve = Curves.polynomial_eval(self.coeffs, self.x)\n",
"```\n",
"\n",
- "A two-dimensional curve (surface) fit will typically follow a similar process, as should below.\n",
- "For these types, it is recommended to use the `numpy.linalg.lstsq` function.\n",
- "First, reshape the data into a new array with each element containing the three-dimensional\n",
- "data points.\n",
- "Then, stack the data into a column matrix in the form of the equation that you're implementing.\n",
- "See the comments in the code block for more information.\n",
- "Evaluate the curve at the data points (`self.x`, `self.y`) by stating the form of the curve\n",
- "with the coefficients from the curve fit.\n",
+ "A two-dimensional curve (surface) fit will typically follow a similar process, as shown below. For\n",
+ "these types, it is recommended to use the `numpy.linalg.lstsq` function. First, reshape the data\n",
+ "into a new array with each element containing the three-dimensional data points. Then, stack the\n",
+ "data into a column matrix in the form of the equation that you're implementing. See the comments in\n",
+ "the code block for more information. Evaluate the curve at the data points (`self.x`, `self.y`) by\n",
+ "stating the form of the curve with the coefficients from the curve fit.\n",
"\n",
"```python\n",
- " # Reshape the data into a new array with each element containing the three-dimensional data points\n",
+ " ...\n",
+ "\n",
+ " # Reshape the data into a new array where each element is a three-dimensional data point\n",
" data_to_fit = np.array(list(zip(self.x, self.y, self.z)))\n",
"\n",
- " # Stack the data into a column matrix in the form of the equation that you're implementing.\n",
- " # Here, the equation is z = ax + by + c and data_to_fit[:,0] are the x values,\n",
- " # data_to_fit[:,1] are the y values. The third column is all ones to account for the constant\n",
- " # term.\n",
+ " # Stack the data into a column matrix in the form of the equation that you're\n",
+ " # implementing. Here, the equation is z = ax + by + c and data_to_fit[:,0] are the x\n",
+ " # values, data_to_fit[:,1] are the y values. The third column is all ones to account\n",
+ " # for the constant term.\n",
" A = np.c_[\n",
" data_to_fit[:,0],\n",
" data_to_fit[:,1],\n",
" np.ones(data_to_fit.shape[0])\n",
" ]\n",
"\n",
- " # Fit the curve to the data; the data is the cost and these are always `self.z` which is data_to_fit[:,2]\n",
- " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2])\n",
+ " # Fit the curve to the data; the data is the cost and these are always\n",
+ " # self.z which is data_to_fit[:,2]\n",
+ " self.coeffs, _, _, _ = linalg.lstsq(A, data_to_fit[:, 2])\n",
"\n",
" # Evaluate it on the same points as the input data\n",
" self._linear_2d_curve = self.coeffs[0]*self.x + self.coeffs[1]*self.y + self.coeffs[2]\n",
@@ -213,35 +227,47 @@
"\n",
"Finally, save the coefficients to `self.coeffs`, save the evaluated curve to\n",
"`self._{curve_type}_{dimension}_curve`, and add the corresponding if-statements\n",
- "in `CostFunction.plot` and `CostFunction.export`."
+ "in `CostFunction.plot` and `CostFunction.export`.\n",
+ "\n",
+ "## Setup the Example\n",
+ "\n",
+ "First, we must import all the required libraries and ORBIT functionality to implement\n",
+ "everything discussed up to this section."
]
},
{
"cell_type": "code",
"execution_count": 1,
+ "id": "af501f9a",
"metadata": {},
"outputs": [],
"source": [
- "%matplotlib widget\n",
+ "# NOTE: uncomment this line if using the interactive plotting functionality\n",
+ "# %matplotlib widget\n",
"\n",
"from copy import deepcopy\n",
- "import matplotlib.pyplot as plt\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import yaml\n",
"import numpy as np\n",
"import pandas as pd\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
"from scipy import stats, optimize, linalg\n",
- "import yaml\n",
"\n",
- "from ORBIT import (\n",
- " ParametricManager,\n",
- " load_config,\n",
- ")\n",
+ "from ORBIT import ParametricManager, load_config\n",
"\n",
- "import matplotlib as mpl\n",
- "mpl.rcParams[\"figure.autolayout\"] = True"
+ "\n",
+ "mpl.rcParams[\"figure.autolayout\"] = True\n",
+ "\n",
+ "# Ensure the correct examples directory is used when running this in docs or in examples\n",
+ "here = Path(\".\").resolve()\n",
+ "example_dir = here.parents[1] / \"examples\" if here.stem == \"topical_guides\" else here"
]
},
{
"cell_type": "markdown",
+ "id": "72f822c5",
"metadata": {},
"source": [
"## Configuration\n",
@@ -252,10 +278,11 @@
{
"cell_type": "code",
"execution_count": 2,
+ "id": "cc5371be",
"metadata": {},
"outputs": [],
"source": [
- "BASE_CONFIG = load_config(\"nrwal.yaml\")\n",
+ "BASE_CONFIG = load_config(example_dir / \"configs/nrwal.yaml\")\n",
"\n",
"DEPTHS = [i for i in range(5, 60, 5)] # Ocean depth in meters\n",
"MEAN_WIND_SPEED = [i for i in range(2, 20, 2)] # Mean wind speed in m/s"
@@ -264,6 +291,7 @@
{
"cell_type": "code",
"execution_count": 3,
+ "id": "91b814a1",
"metadata": {},
"outputs": [],
"source": [
@@ -279,6 +307,7 @@
},
{
"cell_type": "markdown",
+ "id": "02a7ca94",
"metadata": {},
"source": [
"## Curve Fit Library"
@@ -287,6 +316,7 @@
{
"cell_type": "code",
"execution_count": 4,
+ "id": "bef59908",
"metadata": {},
"outputs": [],
"source": [
@@ -303,13 +333,18 @@
" This method evaluates a curve defined by a polynomial equation given a set of\n",
" coefficients and data points.\n",
"\n",
- " Args:\n",
- " coeffs (list): A list of coefficients for the curve. The order of the\n",
- " coefficients should be from highest to lowest power.\n",
- " data_points (list): A list of data points at which to evaluate the curve.\n",
- "\n",
- " Returns:\n",
- " np.array: The curve evaluated at the given data points.\n",
+ " Parameters\n",
+ " ----------\n",
+ " coeffs : list\n",
+ " A list of coefficients for the curve. The order of the coefficients should\n",
+ " be from highest to lowest power.\n",
+ " data_points : list\n",
+ " A list of data points at which to evaluate the curve.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " np.array\n",
+ " The curve evaluated at the given data points.\n",
" \"\"\"\n",
" curve = np.zeros_like(data_points)\n",
" for i, dp in enumerate(data_points):\n",
@@ -321,53 +356,62 @@
"\n",
" @staticmethod\n",
" def fit(func, x, y, fit_check=False):\n",
- " if x is pd.Series:\n",
+ " if isinstance(x, pd.Series):\n",
" x = x.to_numpy(dtype=np.float64)\n",
- " elif x is np.array:\n",
+ " elif isinstance(x, np.array):\n",
" x = x.astype(np.float64)\n",
- " if y is pd.Series:\n",
+ " else:\n",
+ " raise ValueError(\"`x` must be a pd.Series or np.ndarray.\")\n",
+ "\n",
+ " if isinstance(y, pd.Series):\n",
" y = y.to_numpy(dtype=np.float64)\n",
- " elif y is np.array:\n",
+ " elif isinstance(y, np.array):\n",
" y = y.astype(np.float64)\n",
+ " x = x.astype(np.float64)\n",
+ " else:\n",
+ " raise ValueError(\"`y` must be a pd.Series or np.ndarray.\")\n",
"\n",
" popt, pcov, nfodict, mesg, ier = optimize.curve_fit(func, x, y, full_output=True)\n",
"\n",
" if fit_check:\n",
- " print(f\"mesg: {mesg}\")\n",
+ " print(f\"message: {mesg}\")\n",
" print(f\"ier: {ier}\")\n",
" print(f\"Coefficients: {popt}\")\n",
" # print(f\"R-squared: {rvalue**2:.6f}\")\n",
- " \n",
+ "\n",
" return popt"
]
},
{
"cell_type": "code",
"execution_count": 5,
+ "id": "1b8c255a",
"metadata": {},
"outputs": [],
"source": [
"class CostFunction():\n",
" \"\"\"\n",
- " This class is used to create the ORBIT parameterization, fit a curve, plot the curve, and\n",
- " export the function to NRWAL format. Parameterizations are limited to up to two independent\n",
- " variables.\n",
+ " Creates the ORBIT parameterization, curve fits, plots the curve, and exports\n",
+ " the function to a NRWAL format. Parameterizations are limited to up to two\n",
+ " independent variables.\n",
" \"\"\"\n",
" def __init__(self, config: dict, parameters: dict, results: dict):\n",
" \"\"\"\n",
- " On initialization, the config, parameters, and results dictionaries are prepared for\n",
- " use in ORBIT.ParametricManager. Additionally, the independent variables are extracted\n",
- " into x and y (for two-variable parameterizations) and z is extracted as the dependent\n",
- " variable. Whether the cost function is 3D or 2D is determined by the length of the\n",
- " parameters variable.\n",
- "\n",
- " Args:\n",
- " config (str): Configuration settings to added to the BASE_CONFIG or overwrite\n",
- " in the BASE_CONFIG. This must include the `design_phases` config.\n",
- " parameters (dict): Parameters to use with ORBIT.ParametricManager; maximum of two\n",
- " parameters are supported.\n",
- " results (dict): Results to use with ORBIT.ParametricManager; this must include only\n",
- " one variable.\n",
+ " Prepares the ``config``, ``parameters``, and ``results`` dictionaries for use\n",
+ " in ``ParametricManager``. Additionally, the independent variables are extracted\n",
+ " into ``x` and ``y`` (for two-variable parameterizations) and ``z`` is extracted\n",
+ " as the dependent variable. Whether the cost function is 3D or 2D is determined\n",
+ " by the length of theparameters variable.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " config : str\n",
+ " Configuration settings to added to the BASE_CONFIG or overwrite\n",
+ " in the ``BASE_CONFIG``. This must include the ``design_phases`` config.\n",
+ " parameters : dict\n",
+ " Parameters to use with ``ParametricManager``; maximum of two parameters.\n",
+ " results : dict\n",
+ " Results to use with ``ParametricManager``; this must include only one variable.\n",
" \"\"\"\n",
" self.is_3d = False\n",
"\n",
@@ -397,7 +441,11 @@
" # results and postprocessing the data\n",
" self.parameters = deepcopy(self.parameters)\n",
" _vars = list(self.parameters.keys())\n",
- " self.x_variable = _vars.pop(0) # NOTE: This assumes the first parameter is site.depth; it's not critical to functionality but good to keep in mind\n",
+ "\n",
+ " # NOTE: This assumes the first parameter is site.depth; it's not critical to\n",
+ " # functionality but good to keep in mind\n",
+ " self.x_variable = _vars.pop(0)\n",
+ "\n",
" if len(_vars) == 1:\n",
" self.is_3d = True\n",
" self.y_variable = _vars.pop()\n",
@@ -408,7 +456,9 @@
" raise ValueError(\"This class is limited to results with one variable\")\n",
"\n",
" def run(self):\n",
- " self.parametric = ParametricManager(self.config, self.parameters, self.results, product=True)\n",
+ " self.parametric = ParametricManager(\n",
+ " self.config, self.parameters, self.results, product=True\n",
+ " )\n",
" self.parametric.run()\n",
"\n",
" self.x = self.parametric.results[self.x_variable]\n",
@@ -417,7 +467,9 @@
" self.z = self.parametric.results[self.z_variable]\n",
"\n",
"\n",
- " ### --------- Curve fit functions --------- ###\n",
+ " # -------------------\n",
+ " # Curve fit functions\n",
+ " # -------------------\n",
"\n",
" def linear_1d(self):\n",
" def f(x, a, b):\n",
@@ -452,10 +504,16 @@
" data_to_fit[:,1],\n",
" np.ones(data_to_fit.shape[0])\n",
" ]\n",
- " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2]) # coefficients\n",
+ " self.coeffs, _, _, _ = linalg.lstsq(A, data_to_fit[:, 2]) # coefficients\n",
"\n",
" # Evaluate it on the same points as the input data\n",
- " self._linear_2d_curve = self.coeffs[0]*self.x + self.coeffs[1]*self.y + self.coeffs[2]\n",
+ " self._linear_2d_curve = (\n",
+ " self.coeffs[0]\n",
+ " * self.x\n",
+ " + self.coeffs[1]\n",
+ " * self.y\n",
+ " + self.coeffs[2]\n",
+ " )\n",
"\n",
" def quadratic_2d(self):\n",
" data_to_fit = np.array(list(zip(self.x, self.y, self.z)))\n",
@@ -463,36 +521,37 @@
" # best-fit quadratic curve\n",
" A = np.c_[\n",
" np.ones(data_to_fit.shape[0]),\n",
- " data_to_fit[:,:2],\n",
- " np.prod(data_to_fit[:,:2], axis=1),\n",
- " data_to_fit[:,:2]**2\n",
+ " data_to_fit[:, :2],\n",
+ " np.prod(data_to_fit[:, :2], axis=1),\n",
+ " data_to_fit[:, :2]**2,\n",
" ]\n",
- " self.coeffs,_,_,_ = linalg.lstsq(A, data_to_fit[:,2])\n",
+ " self.coeffs, _, _, _ = linalg.lstsq(A, data_to_fit[:, 2])\n",
"\n",
" # Evaluate it on the same points as the input data\n",
" # This dot product is equivalent to the sum of the terms of the polynomial;\n",
- " # np.c_[] is used to concatenate the arrays into the correct form for the dot product\n",
- " # and C is the coefficients of the polynomial\n",
+ " # np.c_[] is used to concatenate the arrays into the correct form for the\n",
+ " # dot product and C is the coefficients of the polynomial\n",
" self._quadratic_2d_curve = np.dot(\n",
" np.c_[\n",
" np.ones(self.x.shape),\n",
" self.x,\n",
" self.y,\n",
- " self.x*self.y,\n",
+ " self.x * self.y,\n",
" self.x**2,\n",
- " self.y**2\n",
+ " self.y**2,\n",
" ],\n",
" self.coeffs\n",
" ).reshape(self.x.shape)\n",
"\n",
- "\n",
- " ### --------- Plotting functions --------- ###\n",
+ " # ------------------\n",
+ " # Plotting functions\n",
+ " # ------------------\n",
"\n",
" def plot(\n",
" self,\n",
" ax,\n",
" plot_data: bool = False,\n",
- " plot_curves: list[str] = []\n",
+ " plot_curves: list[str] = None,\n",
" ):\n",
" if plot_data:\n",
" if self.is_3d:\n",
@@ -500,47 +559,49 @@
" else:\n",
" ax.scatter(self.x, self.z, label=\"Data\")\n",
"\n",
+ " if plot_curves is None:\n",
+ " plot_curves = []\n",
" for curve in plot_curves:\n",
- "\n",
- " if curve == \"linear_1d\":\n",
- " ax.plot(self.x, self._linear_1d_curve, label=\"Linear Fit\")\n",
- "\n",
- " if curve == \"quadratic_1d\":\n",
- " ax.plot(self.x, self._quadratic_1d_curve, label=\"Quadratic Fit\")\n",
- "\n",
- " if curve == \"poly3_1d\":\n",
- " ax.plot(self.x, self._poly3_1d_curve, label=\"Degree 3 Polynomial Fit\")\n",
- "\n",
- " if curve == \"linear_2d\":\n",
- " ax.plot_surface(\n",
- " np.reshape(self.x, (len(DEPTHS), -1)),\n",
- " np.reshape(self.y, (len(DEPTHS), -1)),\n",
- " np.reshape(self._linear_2d_curve, (len(DEPTHS), -1)),\n",
- " alpha=0.3,\n",
- " label=\"Linear Fit\"\n",
- " )\n",
- "\n",
- " if curve == \"quadratic_2d\":\n",
- " ax.plot_surface(\n",
- " np.reshape(self.x, (len(DEPTHS), -1)),\n",
- " np.reshape(self.y, (len(DEPTHS), -1)),\n",
- " np.reshape(self._quadratic_2d_curve, (len(DEPTHS), -1)),\n",
- " alpha=0.3,\n",
- " label=\"Quadratic Fit\"\n",
- " )\n",
- "\n",
- "\n",
- " ### --------- Export functions --------- ###\n",
- "\n",
- " def export(self, filename: str, key: str, comments: str = \"\"):\n",
+ " match curve:\n",
+ " case \"linear_1d\":\n",
+ " ax.plot(self.x, self._linear_1d_curve, label=\"Linear Fit\")\n",
+ " case \"quadratic_1d\":\n",
+ " ax.plot(self.x, self._quadratic_1d_curve, label=\"Quadratic Fit\")\n",
+ " case \"poly3_1d\":\n",
+ " ax.plot(self.x, self._poly3_1d_curve, label=\"Degree 3 Polynomial Fit\")\n",
+ " case \"linear_2d\":\n",
+ " ax.plot_surface(\n",
+ " np.reshape(self.x, (len(DEPTHS), -1)),\n",
+ " np.reshape(self.y, (len(DEPTHS), -1)),\n",
+ " np.reshape(self._linear_2d_curve, (len(DEPTHS), -1)),\n",
+ " alpha=0.3,\n",
+ " label=\"Linear Fit\",\n",
+ " )\n",
+ " case \"quadratic_2d\":\n",
+ " ax.plot_surface(\n",
+ " np.reshape(self.x, (len(DEPTHS), -1)),\n",
+ " np.reshape(self.y, (len(DEPTHS), -1)),\n",
+ " np.reshape(self._quadratic_2d_curve, (len(DEPTHS), -1)),\n",
+ " alpha=0.3,\n",
+ " label=\"Quadratic Fit\",\n",
+ " )\n",
+ "\n",
+ " # ------------------\n",
+ " # Export functions\n",
+ " # ------------------\n",
+ "\n",
+ " def export(self, filename: Path, key: str, comments: str = \"\") -> None:\n",
" \"\"\"\n",
- " This function writes the curve equation to a file for use in NRWAL.\n",
- "\n",
- " Args:\n",
- " filename (str): The file to write the curve equation to. If the file exists, the\n",
- " equation is appended to the end of the file.\n",
- " key (str): The key to use in the NRWAL file for the curve equation. In the key-value\n",
- " pair, this argument is the key and the value is the equation string.\n",
+ " Writes the curve equation to a file for use in NRWAL.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " filename : Path\n",
+ " The file to write the curve equation to. If the file exists, the\n",
+ " equation is appended to the end of the file.\n",
+ " key : str\n",
+ " The key to use in the NRWAL file for the curve equation. In the key-value\n",
+ " pair, this argument is the key and the value is the equation string.\n",
" \"\"\"\n",
"\n",
" x_var = orbit_to_nrwal_params[self.x_variable]\n",
@@ -552,6 +613,7 @@
" if self._linear_1d_curve is not None:\n",
" # y = ax + b\n",
" equation_string = f\"{F} * {S} + {F}\".format(self.coeffs[0], x_var, self.coeffs[1])\n",
+ " equation_string = f\"{F} * {S} + {F}\".format(self.coeffs[0], x_var, self.coeffs[1])\n",
"\n",
" if self._quadratic_1d_curve is not None:\n",
" # y = ax^2 + bx + c\n",
@@ -596,7 +658,9 @@
" # nrwal_dict = {self.config[\"design_phases\"][0]: equation_string}\n",
" nrwal_dict = {key: equation_string}\n",
"\n",
- " with open(filename, \"a\") as f:\n",
+ " if isinstance(filename, str):\n",
+ " filename = Path(filename).resolve()\n",
+ " with filename.open(\"a\") as f:\n",
" f.write(\"\\n\")\n",
" if comments:\n",
" f.write(f\"# {comments}\\n\")\n",
@@ -608,15 +672,11 @@
},
{
"cell_type": "markdown",
+ "id": "560266a2",
"metadata": {},
"source": [
- "# ORBIT Design Phase Cost Curves"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
+ "# ORBIT Design Phase Cost Curves\n",
+ "\n",
"## Monopile Substructure\n",
"\n",
"Independent variables:\n",
@@ -627,36 +687,29 @@
{
"cell_type": "code",
"execution_count": 6,
+ "id": "7dc932ae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "ORBIT library intialized at '/Users/rmudafor/Development/orbit/library'\n",
- "{'substructure_17MW': '-569653.7 * depth**2 + 12505.1 * depth * mean_windspeed + 545620.3 * mean_windspeed**2 + 6917.7 * depth + 235.1 * mean_windspeed + -15478.7'}\n"
+ "ORBIT library intialized at '/Users/rhammond/GitHub_Public/ORBIT/library'\n",
+ "{'substructure_15MW': '-620854.4 * depth**2 + 8899.8 * depth * mean_windspeed + 508191.7 * mean_windspeed**2 + 6938.1 * depth + 247.7 * mean_windspeed + -14812.4'}\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'substructure_15MW': '-620854.4 * depth**2 + 8899.8 * depth * mean_windspeed + 508191.7 * mean_windspeed**2 + 6938.1 * depth + 247.7 * mean_windspeed + -14812.4'}\n"
]
},
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "d67df42313104cf1a234f208e058429d",
- "version_major": 2,
- "version_minor": 0
- },
- "image/png": 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",
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",
"text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous \u2026"
+ ""
]
},
"metadata": {},
@@ -1030,18 +1044,19 @@
{
"cell_type": "code",
"execution_count": 10,
+ "id": "6e5f9ae7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:386\n",
+ "RuntimeWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/_cables.py:405\n",
"The iteration is not making good progress, as measured by the \n",
- " improvement from the last ten iterations.RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:386\n",
+ " improvement from the last ten iterations.RuntimeWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/_cables.py:405\n",
"The iteration is not making good progress, as measured by the \n",
- " improvement from the last ten iterations.RuntimeWarning: /Users/rmudafor/Development/orbit/ORBIT/phases/design/_cables.py:372\n",
- "overflow encountered in cosh"
+ " improvement from the last ten iterations.RuntimeWarning: /Users/rhammond/GitHub_Public/ORBIT/ORBIT/phases/design/_cables.py:391\n",
+ "overflow encountered in cosh\n"
]
},
{
@@ -1106,28 +1121,14 @@
{
"cell_type": "code",
"execution_count": 11,
+ "id": "abc5a865",
"metadata": {},
"outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "a228ee4b8be142bdb82efb0c18561d66",
- "version_major": 2,
- "version_minor": 0
- },
- "image/png": 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