FLASC v1.5

.github/ISSUE_TEMPLATE/bug_report.yaml

@@ -80,4 +80,4 @@ body:
 
 
   validations:
-    required: false
+    required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -2,4 +2,4 @@ blank_issues_enabled: false
 contact_links:
   - name: GitHub Discussions
     url: https://github.com/NREL/flasc/discussions/new
-    about: Discuss potential issues or ideas here prior to opening a GitHub Issue
+    about: Discuss potential issues or ideas here prior to opening a GitHub Issue

.github/ISSUE_TEMPLATE/documentation.yaml

@@ -2,7 +2,7 @@ name: Documentation
 description: Report incorrect documentation or describe new documentation
 title: "[Documentation]: <title>"
 labels: "Type: Documentation"
-body: 
+body:
   - type: textarea
     id: doc-issue
     attributes:
@@ -18,4 +18,4 @@ body:
       label: URL
       description: If this issue references online documentation, enter the complete url here.
       placeholder:
-        https://nrel.github.io/flasc//<full link here>.html
+        https://nrel.github.io/flasc//<full link here>.html

.github/ISSUE_TEMPLATE/feature_request.yaml

@@ -2,7 +2,7 @@ name: Feature
 description: Submit a detailed feature request
 title: "[Feature]: <title>"
 labels: 'Type: Enhancement'
-body: 
+body:
   - type: textarea
     id: feature-description
     attributes:
@@ -17,4 +17,4 @@ body:
     id: relevant-links
     attributes:
       label: Related URLs
-      description: If this feature request is derived from a GitHub Issue or Discussion, enter the url here.
+      description: If this feature request is derived from a GitHub Issue or Discussion, enter the url here.

.github/PULL_REQUEST_TEMPLATE.md

@@ -22,4 +22,4 @@
     - [ ] docs/source/conf.py
     - [ ] flasc/VERSION
 - [ ] Create a tag in the NREL/FLASC repository
--->
+-->

.github/workflows/continuous-integration-workflow.yaml

@@ -22,6 +22,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install -e ".[develop]"
+    - uses: pre-commit/action@v3.0.0
     - name: Run tests and collect coverage
       run: |
         # -rA displays the captured output for all tests after they're run
@@ -35,11 +36,10 @@ jobs:
         pip install pytest
         pip install pytest-cov
         pytest --cov=./ --cov-report=xml tests/
-    - name: Upload coverage to Codecov  
+    - name: Upload coverage to Codecov
       if: ${{ env.CODECOV_TOKEN }}  # Don't attempt to upload if the codecov token is not configured
       uses: codecov/codecov-action@v3
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
         files: ./coverage.xml
         fail_ci_if_error: true
-

.gitignore

@@ -27,4 +27,4 @@ _build
 docs/build
 SMARTEOLE_WakeSteering_ReadMe.xlsx
 SMARTEOLE_WakeSteering_Map.pdf
-SMARTEOLE-WFC-open-dataset.zip
+SMARTEOLE-WFC-open-dataset.zip

.pre-commit-config.yaml

@@ -0,0 +1,26 @@
+
+repos:
+
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.4.0
+  hooks:
+  - id: trailing-whitespace
+  - id: end-of-file-fixer
+  - id: check-executables-have-shebangs
+  - id: check-yaml
+    args: [--unsafe]
+  - id: check-merge-conflict
+  - id: check-symlinks
+  - id: mixed-line-ending
+
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.1.7
+  hooks:
+    # Run the linter.
+    - id: ruff
+      types_or: [ python, pyi, jupyter ]
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format
+      types_or: [ python, pyi, jupyter ]

README.md

@@ -5,12 +5,12 @@
 
 ## Description
 
-FLASC provides a **rich suite of analysis tools** for SCADA data filtering, analysis, 
-wind farm model validation, field experiment design, and field experiment monitoring. 
+FLASC provides a **rich suite of analysis tools** for SCADA data filtering, analysis,
+wind farm model validation, field experiment design, and field experiment monitoring.
 
 The repository is centrally built around NRELs in-house ``FLORIS`` wind farm model, available at
-**https://github.com/nrel/floris**. FLASC also largely relies on the ``energy ratio``, among others, 
-to quantify wake losses in synthetic and historical data, to perform turbine northing calibrations, 
+**https://github.com/nrel/floris**. FLASC also largely relies on the ``energy ratio``, among others,
+to quantify wake losses in synthetic and historical data, to perform turbine northing calibrations,
 and model parameter estimation.
 
 For technical questions or concerns, please email paul.fleming@nrel.gov.
@@ -27,7 +27,9 @@ For technical questions or concerns, please email paul.fleming@nrel.gov.
 We recommend installing this repository in a separate virtual environment.
 After creating a new virtual environment, clone this repository to your local
 system and install it locally using ``pip``. The command for this is ``pip install -e flasc``.
-
+
+If installing for develop, follow the developer [install instructions](https://nrel.github.io/flasc/installation.html)
+
 ## Documentation
 
 Documentation is provided via the included examples folders as well as [online documentation](https://nrel.github.io/flasc/).

docs/data_processing.rst

@@ -79,7 +79,7 @@ This function also deals with 360-degree wrapping for nacelle headings and
 wind direction measurements, as specified by the ``circular_cols`` option.
 
 After each turbine's measurements are sampled onto a common time vector, they
-can straightforwardly be appended into a single, wide-formatted table 
+can straightforwardly be appended into a single, wide-formatted table
 (which in Python is a ``pd.DataFrame``). A common way to format a long table
 into a wide table, assuming they all share the same ``date`` vector, is:
 
@@ -98,7 +98,7 @@ Data filtering
 ==============
 
 With the SCADA data now in a wide-formatted Pandas DataFrame, the flasc
-data filtering tools can readily be applied. The files in 
+data filtering tools can readily be applied. The files in
 ``examples_artifical_data/raw_data_processing`` demonstrate how the SCADA data files are
 processed.
 
@@ -109,7 +109,7 @@ a_00_initial_download.py
 The first script, ``a_00_initial_download.py``, simply demonstrates how the raw data is imported. This
 basically compromises of the previous two steps, being data downloading and
 formatting it into a wide table format. Data is typically saved within flasc
-using the 
+using the
 `feather format <https://arrow.apache.org/docs/python/feather.html>`_, which
 is known for its excellent IO speed and its efficient storage, being often a
 factor 10 smaller than a similar .csv file.
@@ -317,7 +317,7 @@ turbine 3 and a comparable signal of the met mast. Here, that signal is the
 wind direction, which should be comparable between the two turbines.
 Note that we have not yet calibrated the turbine's wind direction to
 true north, so we may have an offset anywhere between -180 and +180 deg
-compared to the met mast signal. The algorithm in 
+compared to the met mast signal. The algorithm in
 ``a_06a_determine_timeshift_datasources.py`` accommodates for that by
 calculating and subtracting the mean difference in wind directions over
 the entire time period. The assign the measurement(s) to compare in
@@ -338,7 +338,7 @@ a_06b_apply_timeshift_dfs.py (optional)
 +++++++++++++++++++++++++++++++++++++++
 The optimal time shift found in ``a_06a_determine_timeshift_datasources.py``
 is used in this script to shift the time vector of one of the two dataframes
-so that they are synchronized with the other dataframe. 
+so that they are synchronized with the other dataframe.
 
 +++++++++++++++++++++++++++++++++++++
 a_07a_estimate_wd_bias_per_turbine.py
@@ -379,7 +379,7 @@ the average difference between the wind direction signals of the calibrated
 turbine and any other (uncalibrated) turbine, and that becomes the bias
 correction that the turbine needs. We can determine the shift between
 two turbine's wind direction signals using the ``match_y_curves_by_offset``
-function, as demonstrated in the example script 
+function, as demonstrated in the example script
 ``a_07a_estimate_wd_bias_per_turbine.py``.
 
 For more information how the subtleties about calculating the energy ratio,
@@ -414,4 +414,4 @@ the example and documentation surrounding the energy ratio analysis suite.
    :align: center
 
 
-.. seealso:: `Return to table of contents <index.html>`_ 
+.. seealso:: `Return to table of contents <index.html>`_

docs/energy_ratio.md

@@ -22,4 +22,4 @@ Key syntax for computing energy ratios is provided in the examples:
  Within the set of analysis of the smarteole dataset are also exmaples of using flasc's energy ratio calculations.  See for example:
 
  - [Baseline Energy Ratio Analysis](https://github.com/NREL/flasc/blob/main/examples_smarteole/05_baseline_energy_ratio_analysis.ipynb)
- - [Wake Steering Energy Ratio Analysis](https://github.com/NREL/flasc/blob/main/examples_smarteole/06_wake_steering_energy_ratio_analysis.ipynb)
+ - [Wake Steering Energy Ratio Analysis](https://github.com/NREL/flasc/blob/main/examples_smarteole/06_wake_steering_energy_ratio_analysis.ipynb)

docs/getting_started.rst

@@ -41,7 +41,7 @@ clock or timezone.
 Raw data processing
 ===================
 Once the artificial dataset has been generated, it should be filtered and
-postprocessed using the steps described in 
+postprocessed using the steps described in
 `Raw data processing <data_processing.html>`_. This will filter the SCADA data
 for measurement outliers and deal with northing calibration.
 
@@ -53,4 +53,4 @@ analyses. See `Data analysis <data_analysis.html>`_ for examples on how to
 derive useful information from the data, such as for model tuning and model
 validation.
 
-.. seealso:: `Return to table of contents <index.html>`_ 
+.. seealso:: `Return to table of contents <index.html>`_

docs/index.md

@@ -4,8 +4,8 @@
 
 Welcome to the documentation of the NREL FLASC repository!
 
-FLASC provides a rich suite of analysis tools for SCADA data filtering & 
-analysis, wind farm model validation, field experiment design, and field 
+FLASC provides a rich suite of analysis tools for SCADA data filtering &
+analysis, wind farm model validation, field experiment design, and field
 experiment monitoring. The repository is centrally built around NRELs
 in-house [FLORIS](https://github.com/NREL/floris/discussions/)
  wake modeling utility.
@@ -14,4 +14,4 @@ losses in synthetic and historical data, to perform turbine northing
 calibrations, and for model parameter estimation.
 
 The FLASC repository is intended as a community driven toolbox, available on
-its [GitHub Repository](https://github.com/NREL/flasc).
+its [GitHub Repository](https://github.com/NREL/flasc).

docs/licensing.rst

@@ -25,4 +25,4 @@ For more information, contact the NREL FLASC team at
     limitations under the License.
 
 
-.. seealso:: `Return to table of contents <index.html>`_ 
+.. seealso:: `Return to table of contents <index.html>`_

docs/overview.md

@@ -1,7 +1,7 @@
 # Overview
 
-FLASC provides a rich suite of analysis tools for SCADA data filtering & 
-analysis, wind farm model validation, field experiment design, and field 
+FLASC provides a rich suite of analysis tools for SCADA data filtering &
+analysis, wind farm model validation, field experiment design, and field
 experiment monitoring. The repository is centrally built around NRELs
 in-house [floris](https://github.com/nrel/floris) wake modeling utility.
 FLASC also largely relies on the energy ratio to, among others, quantify wake

docs/references.bib

@@ -10,8 +10,8 @@ @article{Doekemeijer2022a
 	note = "In review",
 }
 
-@article{Bay2022a, 
-	author={C J Bay and P A Fleming and B M Doekemeijer and J King and M Churchfield and R Mudafort}, 
+@article{Bay2022a,
+	author={C J Bay and P A Fleming and B M Doekemeijer and J King and M Churchfield and R Mudafort},
 	TITLE = {Addressing Deep Array Effects and Impacts to Wake Steering with the Cumulative-Curl Wake Model},
 	JOURNAL = {Wind Energy Science Discussions},
 	VOLUME = {2022},
@@ -20,4 +20,4 @@ @article{Bay2022a
 	__URL = {https://wes.copernicus.org/preprints/wes-2021-32/},
 	__DOI = {10.5194/wes-2021-32},
 	note = {In preparation},
-}
+}

docs/total_uplift.md

@@ -2,4 +2,4 @@
 
 Total uplift in energy production can be computed from one dataframe to another.  It uses a similar input as the [energy ratio](energy_ratio) but returns a single value representing the total uplift, rather then binned by wind direction.
 
-Currently the main example usage of the total uplift function is at the end of [smarteole example 06](https://github.com/NREL/flasc/blob/main/examples_smarteole/06_wake_steering_energy_ratio_analysis.ipynb)
+Currently the main example usage of the total uplift function is at the end of [smarteole example 06](https://github.com/NREL/flasc/blob/main/examples_smarteole/06_wake_steering_energy_ratio_analysis.ipynb)

examples_artificial_data/00_setup_floris_model/00_visualize_layout.py

@@ -11,72 +11,65 @@
 # the License.
 
 
-import os
 import matplotlib.pyplot as plt
 import numpy as np
 
+from flasc.utilities_examples import load_floris_artificial as load_floris
 from flasc.visualization import (
     plot_floris_layout,
     plot_layout_only,
     plot_layout_with_waking_directions,
-    shade_region
+    shade_region,
 )
 
-from flasc.utilities_examples import load_floris_artificial as load_floris
-
-
 # Example demonstrates some methods for visualizing the layout of the farm
 # represented within the FLORIS interface
 
 
 if __name__ == "__main__":
-
     # Set up FLORIS interface
-    print('Initializing the FLORIS object for our demo wind farm')
+    print("Initializing the FLORIS object for our demo wind farm")
     fi, _ = load_floris()
 
     # Defines alternative names for each turbine with 1-index
-    turbine_names = ['Turbine-%d' % (t + 1) for t in range(len(fi.layout_x))]
-
+    turbine_names = ["Turbine-%d" % (t + 1) for t in range(len(fi.layout_x))]
 
     # Plot using default 0-indexed labels (includes power/thrust curve)
-    plot_floris_layout(fi, plot_terrain=False) 
+    plot_floris_layout(fi, plot_terrain=False)
 
     # Plot using default given 1-indexed labels (includes power/thrust curve)
-    plot_floris_layout(fi, plot_terrain=False, turbine_names=turbine_names) 
+    plot_floris_layout(fi, plot_terrain=False, turbine_names=turbine_names)
 
     # Plot only the layout with default options
     plot_layout_only(fi)
 
     # Plot only the layout with custom options
-    plot_layout_only(fi,
-        {
-            'turbine_names':turbine_names,
-            'color':'g'
-        }
-    )
+    plot_layout_only(fi, {"turbine_names": turbine_names, "color": "g"})
 
     # Plot layout with wake directions and inter-turbine distances labeled
     plot_layout_with_waking_directions(fi)
 
     # Plot layout with wake directions and inter-turbine distances labeled
     # (using custom options)
-    plot_layout_with_waking_directions(fi,
-        limit_num = 3, # limit to 3 lines per turbine
-        layout_plotting_dict = {'turbine_names':turbine_names, 
-                                'turbine_indices':range(2,len(fi.layout_x))},
-        wake_plotting_dict={'color':'r'}
+    plot_layout_with_waking_directions(
+        fi,
+        limit_num=3,  # limit to 3 lines per turbine
+        layout_plotting_dict={
+            "turbine_names": turbine_names,
+            "turbine_indices": range(2, len(fi.layout_x)),
+        },
+        wake_plotting_dict={"color": "r"},
     )
 
     # Demonstrate shading of an arbitrary region
     points_for_demo = np.array([[600, 0], [1400, 0], [1200, 1000]])
     ax = plot_layout_only(fi)
     shade_region(
-        points_for_demo, 
+        points_for_demo,
         show_points=True,
-        plotting_dict_region={"color":"blue", "label":"Example region"},
-        plotting_dict_points={"color":"blue", "marker":"+", "s":50},
-        ax=ax
+        plotting_dict_region={"color": "blue", "label": "Example region"},
+        plotting_dict_points={"color": "blue", "marker": "+", "s": 50},
+        ax=ax,
     )
 
     plt.show()