Update newest black formatting

Author: jmcvey3

mhkit/acoustics/__init__.py

@@ -1,17 +1,17 @@
 """
 The passive acoustics module provides a set of functions
-for analyzing and visualizing passive acoustic monitoring 
+for analyzing and visualizing passive acoustic monitoring
 data deployed in water bodies. This package reads in raw
-*.wav* files and conducts basic acoustics analysis and 
+*.wav* files and conducts basic acoustics analysis and
 visualization.
 
 To start using the module, import it directly from MHKiT:
 ``from mhkit import acoustics``. The analysis functions
-are available directly from the main import, while the 
-I/O and graphics submodules are available from 
+are available directly from the main import, while the
+I/O and graphics submodules are available from
 ``acoustics.io`` and  ``acoustics.graphics``, respectively.
-The base functions are intended to be used on top of the I/O submodule, and 
-include functionality to calibrate data, create spectral densities, sound 
+The base functions are intended to be used on top of the I/O submodule, and
+include functionality to calibrate data, create spectral densities, sound
 pressure levels, and time or band aggregate spectral data.
 """
 

mhkit/acoustics/analysis.py

@@ -337,7 +337,7 @@ def sound_pressure_spectral_density_level(spsd: xr.DataArray) -> xr.DataArray:
 
 
 def _validate_method(
-    method: Union[str, Dict[str, Union[float, int]]]
+    method: Union[str, Dict[str, Union[float, int]]],
 ) -> Tuple[str, Optional[Union[float, int]]]:
     """
     Validates the 'method' parameter and returns the method name and its argument (if any)

mhkit/acoustics/graphics.py

@@ -1,6 +1,6 @@
 """
-This submodule provides essential plotting functions for visualizing passive acoustics 
-data. The functions allow for customizable plotting of sound pressure spectral density 
+This submodule provides essential plotting functions for visualizing passive acoustics
+data. The functions allow for customizable plotting of sound pressure spectral density
 levels across time and frequency dimensions.
 
 Each plotting function leverages the flexibility of Matplotlib, allowing for passthrough
@@ -11,12 +11,12 @@
 -------------
 1. **plot_spectrogram**:
 
-   - Generates a spectrogram plot from sound pressure spectral density level data, 
+   - Generates a spectrogram plot from sound pressure spectral density level data,
      with a logarithmic frequency scale by default for improved readability of acoustic data.
 
 2. **plot_spectra**:
 
-   - Produces a spectral density plot with a log-transformed x-axis, allowing for clear 
+   - Produces a spectral density plot with a log-transformed x-axis, allowing for clear
      visualization of spectral density across frequency bands.
 """
 

mhkit/acoustics/io.py

@@ -1,7 +1,7 @@
 """
 This submodule provides input/output functions for passive acoustics data,
 focusing on hydrophone recordings stored in WAV files. The main functionality
-includes reading and processing hydrophone data from various manufacturers 
+includes reading and processing hydrophone data from various manufacturers
 and exporting audio files for easy playback and analysis.
 
 Supported Hydrophone Models
@@ -14,28 +14,28 @@
 
 1. **Data Reading**:
 
-   - `read_hydrophone`: Main function to read a WAV file from a hydrophone and 
-     convert it to either a voltage or pressure time series, depending on the 
+   - `read_hydrophone`: Main function to read a WAV file from a hydrophone and
+     convert it to either a voltage or pressure time series, depending on the
      availability of sensitivity data.
 
-   - `read_soundtrap`: Wrapper for reading Ocean Instruments SoundTrap hydrophone 
+   - `read_soundtrap`: Wrapper for reading Ocean Instruments SoundTrap hydrophone
      files, automatically using appropriate metadata.
 
-   - `read_iclisten`: Wrapper for reading Ocean Sonics icListen hydrophone files, 
-     including metadata processing to apply hydrophone sensitivity for direct 
+   - `read_iclisten`: Wrapper for reading Ocean Sonics icListen hydrophone files,
+     including metadata processing to apply hydrophone sensitivity for direct
      sound pressure calculation.
 
 2. **Audio Export**:
 
-   - `export_audio`: Converts processed sound pressure data back into a WAV file 
+   - `export_audio`: Converts processed sound pressure data back into a WAV file
      format, with optional gain adjustment to improve playback quality.
 
 3. **Data Extraction**:
 
-   - `_read_wav_metadata`: Extracts metadata from a WAV file, including bit depth 
+   - `_read_wav_metadata`: Extracts metadata from a WAV file, including bit depth
      and other header information.
 
-   - `_calculate_voltage_and_time`: Converts raw WAV data into voltage values and 
+   - `_calculate_voltage_and_time`: Converts raw WAV data into voltage values and
      generates a time index based on the sampling frequency.
 """
 

mhkit/dolfyn/adv/clean.py

@@ -1,5 +1,4 @@
-"""Module containing functions to clean data
-"""
+"""Module containing functions to clean data"""
 
 import warnings
 import numpy as np

mhkit/loads/__init__.py

@@ -1,7 +1,7 @@
 """
 The `loads` package of the MHKiT (Marine and Hydrokinetic Toolkit) library
 provides tools and functionalities for analyzing and visualizing loads data
-from marine and hydrokinetic (MHK) devices. This package is designed to 
+from marine and hydrokinetic (MHK) devices. This package is designed to
 assist engineers, researchers, and analysts in understanding the forces and
 stresses applied to MHK devices under various operational and environmental
 conditions.

mhkit/loads/extreme/__init__.py

@@ -3,7 +3,7 @@
 and wave data statistics.
 
 It includes methods for calculating peaks over threshold, estimating
-short-term extreme distributions,and performing wave amplitude 
+short-term extreme distributions,and performing wave amplitude
 normalization for most likely extreme response analysis.
 """
 

mhkit/loads/extreme/extremes.py

@@ -1,29 +1,29 @@
 """
 This module provides functionality for estimating the short-term and
-long-term extreme distributions of responses in a time series. It 
-includes methods for analyzing peaks, block maxima, and applying 
-statistical distributions to model extreme events. The module supports 
-various methods for short-term extreme estimation, including peaks 
-fitting with Weibull, tail fitting, peaks over threshold, and block 
-maxima methods with GEV (Generalized Extreme Value) and Gumbel 
-distributions. Additionally, it offers functionality to approximate 
-the long-term extreme distribution by weighting short-term extremes 
+long-term extreme distributions of responses in a time series. It
+includes methods for analyzing peaks, block maxima, and applying
+statistical distributions to model extreme events. The module supports
+various methods for short-term extreme estimation, including peaks
+fitting with Weibull, tail fitting, peaks over threshold, and block
+maxima methods with GEV (Generalized Extreme Value) and Gumbel
+distributions. Additionally, it offers functionality to approximate
+the long-term extreme distribution by weighting short-term extremes
 across different sea states.
 
 Functions:
-- ste_peaks: Estimates the short-term extreme distribution from peaks 
+- ste_peaks: Estimates the short-term extreme distribution from peaks
     distribution using specified statistical methods.
 - block_maxima: Finds the block maxima in a time-series data to be used
     in block maxima methods.
-- ste_block_maxima_gev: Approximates the short-term extreme distribution 
+- ste_block_maxima_gev: Approximates the short-term extreme distribution
     using the block maxima method with the GEV distribution.
-- ste_block_maxima_gumbel: Approximates the short-term extreme 
+- ste_block_maxima_gumbel: Approximates the short-term extreme
     distribution using the block maxima method with the Gumbel distribution.
-- ste: Alias for `short_term_extreme`, facilitating easier access to the 
+- ste: Alias for `short_term_extreme`, facilitating easier access to the
     primary functionality of estimating short-term extremes.
-- short_term_extreme: Core function to approximate the short-term extreme 
+- short_term_extreme: Core function to approximate the short-term extreme
     distribution from a time series using chosen methods.
-- full_seastate_long_term_extreme: Combines short-term extreme 
+- full_seastate_long_term_extreme: Combines short-term extreme
     distributions using weights to estimate the long-term extreme distribution.
 """
 

mhkit/loads/extreme/mler.py

@@ -1,16 +1,16 @@
 """
-This module provides functionalities to calculate and analyze Most 
+This module provides functionalities to calculate and analyze Most
 Likely Extreme Response (MLER) coefficients for wave energy converter
 design and risk assessment. It includes functions to:
 
   - Calculate MLER coefficients (`mler_coefficients`) from a sea state
     spectrum and a response Amplitude Response Operator (ARO).
   - Define and manipulate simulation parameters (`mler_simulation`) used
     across various MLER analyses.
-  - Renormalize the incoming amplitude of the MLER wave 
+  - Renormalize the incoming amplitude of the MLER wave
     (`mler_wave_amp_normalize`) to match the desired peak height for more
     accurate modeling and analysis.
-  - Export the wave amplitude time series (`mler_export_time_series`) 
+  - Export the wave amplitude time series (`mler_export_time_series`)
     based on the calculated MLER coefficients for further analysis or
     visualization.
 """

mhkit/loads/extreme/peaks.py

@@ -1,15 +1,15 @@
 """
 This module provides utilities for analyzing wave data, specifically
 for identifying significant wave heights and estimating wave peak
-distributions using statistical methods. 
+distributions using statistical methods.
 
 Functions:
-- _calculate_window_size: Calculates the window size for peak 
+- _calculate_window_size: Calculates the window size for peak
   independence using the auto-correlation function of wave peaks.
-- _peaks_over_threshold: Identifies peaks over a specified 
+- _peaks_over_threshold: Identifies peaks over a specified
   threshold and returns independent storm peak values adjusted by
   the threshold.
-- global_peaks: Identifies global peaks in a zero-centered 
+- global_peaks: Identifies global peaks in a zero-centered
   response time-series based on consecutive zero up-crossings.
 - number_of_short_term_peaks: Estimates the number of peaks within a
  specified short-term period.
@@ -20,13 +20,13 @@
 - automatic_hs_threshold: Determines the best significant wave height
  threshold for the peaks-over-threshold method.
 - peaks_distribution_peaks_over_threshold: Estimates the peaks
- distribution using the peaks over threshold method by fitting a 
+ distribution using the peaks over threshold method by fitting a
  generalized Pareto distribution.
 
 References:
-- Neary, V. S., S. Ahn, B. E. Seng, M. N. Allahdadi, T. Wang, Z. Yang, 
- and R. He (2020). "Characterization of Extreme Wave Conditions for 
- Wave Energy Converter Design and Project Risk Assessment.” J. Mar. 
+- Neary, V. S., S. Ahn, B. E. Seng, M. N. Allahdadi, T. Wang, Z. Yang,
+ and R. He (2020). "Characterization of Extreme Wave Conditions for
+ Wave Energy Converter Design and Project Risk Assessment.” J. Mar.
  Sci. Eng. 2020, 8(4), 289; https://doi.org/10.3390/jmse8040289.
 
 """

mhkit/loads/extreme/sample.py

@@ -2,10 +2,10 @@
 This module provides statistical analysis tools for extreme value
 analysis in environmental and engineering applications. It focuses on
 estimating values corresponding to specific return periods based on
-the statistical distribution of observed or simulated data. 
+the statistical distribution of observed or simulated data.
 
 Functionality:
-- return_year_value: Calculates the value from a given distribution 
+- return_year_value: Calculates the value from a given distribution
  corresponding to a specified return year. This function is particularly
  useful for determining design values for engineering structures or for
  risk assessment in environmental studies.

mhkit/loads/general.py

@@ -2,7 +2,7 @@
 This module provides tools for analyzing and processing data signals
 related to turbine blade performance and fatigue analysis. It implements
 methodologies based on standards such as IEC TS 62600-3:2020 ED1,
-incorporating statistical binning, moment calculations, and fatigue 
+incorporating statistical binning, moment calculations, and fatigue
 damage estimation using the rainflow counting algorithm. Key
 functionalities include:
 
@@ -11,8 +11,8 @@
       for each bin, following IEC TS 62600-3:2020 ED1 guidelines. It supports
       output in both pandas DataFrame and xarray Dataset formats.
 
-    - `blade_moments`: Calculates the flapwise and edgewise moments of turbine 
-      blades using derived calibration coefficients and raw strain signals. 
+    - `blade_moments`: Calculates the flapwise and edgewise moments of turbine
+      blades using derived calibration coefficients and raw strain signals.
       This function is crucial for understanding the loading and performance
       characteristics of turbine blades.
 

mhkit/loads/graphics.py

@@ -1,6 +1,6 @@
 """
 This module provides functionalities for plotting statistical data
-related to a given variable or dataset. 
+related to a given variable or dataset.
 
     - `plot_statistics` is designed to plot raw statistical measures
       (mean, maximum, minimum, and optional standard deviation) of a
@@ -9,8 +9,8 @@
 
     - `plot_bin_statistics` extends these capabilities to binned data,
       offering a way to visualize binned statistics (mean, maximum, minimum)
-      along with their respective standard deviations. This function also 
-      supports label and title customization, as well as saving the plot to 
+      along with their respective standard deviations. This function also
+      supports label and title customization, as well as saving the plot to
       a specified path.
 """
 

mhkit/mooring/graphics.py

@@ -1,20 +1,20 @@
 """
-This module provides a function for creating animated visualizations of a 
-MoorDyn node position dataset using the matplotlib animation API. 
+This module provides a function for creating animated visualizations of a
+MoorDyn node position dataset using the matplotlib animation API.
 
-It includes the main function `animate`, which creates either 2D or 3D 
-animations depending on the input parameters. 
+It includes the main function `animate`, which creates either 2D or 3D
+animations depending on the input parameters.
 
-In the animations, the position of nodes in the MoorDyn dataset are plotted 
-over time, allowing the user to visualize how these positions change. 
+In the animations, the position of nodes in the MoorDyn dataset are plotted
+over time, allowing the user to visualize how these positions change.
 
-This module also includes several helper functions that are used by 
-`animate` to validate inputs, generate lists of nodes along each axis, 
-calculate plot limits, and set labels and titles for plots. 
+This module also includes several helper functions that are used by
+`animate` to validate inputs, generate lists of nodes along each axis,
+calculate plot limits, and set labels and titles for plots.
 
-The user can specify various parameters for the animation such as the 
-dimension (2D or 3D), the axes to plot along, the plot limits for each 
-axis, the interval between frames, whether the animation repeats, and the 
+The user can specify various parameters for the animation such as the
+dimension (2D or 3D), the axes to plot along, the plot limits for each
+axis, the interval between frames, whether the animation repeats, and the
 labels and title for the plot.
 
 Requires:

mhkit/mooring/io.py

@@ -2,12 +2,12 @@
 This module provides functions to read and parse MoorDyn output files.
 
 The main function read_moordyn takes as input the path to a MoorDyn output file and optionally
-the path to a MoorDyn input file. It reads the data from the output file, stores it in an 
-xarray dataset, and then if provided, parses the input file for additional metadata to store 
+the path to a MoorDyn input file. It reads the data from the output file, stores it in an
+xarray dataset, and then if provided, parses the input file for additional metadata to store
 as attributes in the dataset.
 
-The helper function _moordyn_input is used to parse the MoorDyn output file. It loops through 
-each line in the output file, parses various sets of properties and parameters, and stores 
+The helper function _moordyn_input is used to parse the MoorDyn output file. It loops through
+each line in the output file, parses various sets of properties and parameters, and stores
 them as attributes in the provided dataset.
 
 Typical usage example:

mhkit/power/characteristics.py

@@ -1,21 +1,21 @@
 """
-This module contains functions for calculating electrical power metrics from 
-measured voltage and current data. It supports both direct current (DC) and 
-alternating current (AC) calculations, including instantaneous frequency 
-analysis for AC signals and power calculations for three-phase AC systems. 
-The calculations can accommodate both line-to-neutral and line-to-line voltage 
-measurements and offer flexibility in output formats, allowing results to be 
+This module contains functions for calculating electrical power metrics from
+measured voltage and current data. It supports both direct current (DC) and
+alternating current (AC) calculations, including instantaneous frequency
+analysis for AC signals and power calculations for three-phase AC systems.
+The calculations can accommodate both line-to-neutral and line-to-line voltage
+measurements and offer flexibility in output formats, allowing results to be
 saved as either pandas DataFrames or xarray Datasets.
 
 Functions:
     instantaneous_frequency: Calculates the instantaneous frequency of a measured
     voltage signal over time.
-    
+
     dc_power: Computes the DC power from voltage and current measurements, providing
     both individual channel outputs and a gross power calculation.
-    
+
     ac_power_three_phase: Calculates the magnitude of active AC power for three-phase
-    systems, considering the power factor and voltage measurement configuration 
+    systems, considering the power factor and voltage measurement configuration
     (line-to-neutral or line-to-line).
 """