diff --git a/.github/workflows/generate_notebook_matrix.py b/.github/workflows/generate_notebook_matrix.py index 02f81a00a..966f921e6 100644 --- a/.github/workflows/generate_notebook_matrix.py +++ b/.github/workflows/generate_notebook_matrix.py @@ -14,14 +14,14 @@ "cdip_example.ipynb": 420, "Delft3D_example.ipynb": 180, "directional_waves.ipynb": 180, - "environmental_contours_example.ipynb": 360, - "extreme_response_contour_example.ipynb": 360, - "extreme_response_full_sea_state_example.ipynb": 360, - "extreme_response_MLER_example.ipynb": 360, + "environmental_contours_example.ipynb": 240, + "extreme_response_contour_example.ipynb": 240, + "extreme_response_full_sea_state_example.ipynb": 240, + "extreme_response_MLER_example.ipynb": 240, "loads_example.ipynb": 180, "metocean_example.ipynb": 180, "mooring_example.ipynb": 240, - "PacWave_resource_characterization_example.ipynb": 780, + "PacWave_resource_characterization_example.ipynb": 240, "power_example.ipynb": 180, "qc_example.ipynb": 180, "river_example.ipynb": 180, diff --git a/examples/PacWave_resource_characterization_example.ipynb b/examples/PacWave_resource_characterization_example.ipynb index 9f46be4b9..32bcc49fa 100644 --- a/examples/PacWave_resource_characterization_example.ipynb +++ b/examples/PacWave_resource_characterization_example.ipynb @@ -1119,12 +1119,19 @@ "\n", " Tz_list.append(resource.average_zero_crossing_period(year_data.T))\n", "\n", - "# Concatenate list of Series into a single DataFrame\n", + "# Concatenate each list of Series into a single Series\n", "Te = pd.concat(Te_list, axis=0)\n", "Tp = pd.concat(Tp_list, axis=0)\n", "Hm0 = pd.concat(Hm0_list, axis=0)\n", "J = pd.concat(J_list, axis=0)\n", "Tz = pd.concat(Tz_list, axis=0)\n", + "\n", + "# Name each Series and concat into a dataFrame\n", + "Te.name = 'Te'\n", + "Tp.name = 'Tp'\n", + "Hm0.name = 'Hm0'\n", + "J.name = 'J'\n", + "Tz.name = 'Tz'\n", "data = pd.concat([Hm0, Te, Tp, J, Tz], axis=1)\n", "\n", "# Calculate wave steepness\n", @@ -1590,7 +1597,7 @@ " J = []\n", " for i in range(len(result)):\n", " b = resource.jonswap_spectrum(f, result.Tp[i], result.Hm0[i])\n", - " J.extend([resource.energy_flux(b, h=399.0).values[0][0]])\n", + " J.extend([resource.energy_flux(b, h=399.0).item()])\n", "\n", " result[\"J\"] = J\n", " results[N] = result\n", @@ -1622,7 +1629,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/examples/cdip_example.ipynb b/examples/cdip_example.ipynb index 0f5028af2..698d794bd 100644 --- a/examples/cdip_example.ipynb +++ b/examples/cdip_example.ipynb @@ -576,7 +576,7 @@ "Hs = buoy_data[\"data\"][\"wave\"][\"waveHs\"]\n", "Tp = buoy_data[\"data\"][\"wave\"][\"waveTp\"]\n", "Dp = buoy_data[\"data\"][\"wave\"][\"waveDp\"]\n", - "buoy_name = buoy_data[\"data\"][\"wave\"].name\n", + "buoy_name = buoy_data[\"metadata\"][\"name\"]\n", "ax = graphics.plot_compendium(Hs, Tp, Dp, buoy_name)" ] }, @@ -590,7 +590,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -604,7 +604,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/examples/environmental_contours_example.ipynb b/examples/environmental_contours_example.ipynb index 9f5e72da8..c97f2a634 100644 --- a/examples/environmental_contours_example.ipynb +++ b/examples/environmental_contours_example.ipynb @@ -647,9 +647,13 @@ " Hm0_list.append(resource.significant_wave_height(year_data.T))\n", " Te_list.append(resource.energy_period(year_data.T))\n", "\n", - "# Concatenate list of Series into a single DataFrame\n", + "# Concatenate each list of Series into a single Series\n", "Te = pd.concat(Te_list, axis=0)\n", "Hm0 = pd.concat(Hm0_list, axis=0)\n", + "\n", + "# Name each Series and concat into a dataFrame\n", + "Te.name = 'Te'\n", + "Hm0.name = 'Hm0'\n", "Hm0_Te = pd.concat([Hm0, Te], axis=1)\n", "\n", "# Drop any NaNs created from the calculation of Hm0 or Te\n", @@ -800,7 +804,7 @@ "source": [ "## Resource Clusters\n", "\n", - "Often in resource characterization we want to pick a few representative sea state to run an alaysis. To do this with the resource data in python we reccomend using a Gaussian Mixture Model (a more generalized k-means clustering method). Using sckitlearn this is very straigth forward. We combine our Hm0 and Te data into an N x 2 numpy array. We specify our number of components (number of representative sea states) and then call the fit method on the data. Fianlly, using the methods `means_` and `weights` we can organize the results into an easily digestable table." + "Often in resource characterization we want to pick a few representative sea state to run an alaysis. To do this with the resource data in python we reccomend using a Gaussian Mixture Model (a more generalized k-means clustering method). Using sckitlearn this is very straight forward. We combine our Hm0 and Te data into an N x 2 numpy array. We specify our number of components (number of representative sea states) and then call the fit method on the data. Fianlly, using the methods `means_` and `weights` we can organize the results into an easily digestable table." ] }, { @@ -933,9 +937,13 @@ " Hm0_list.append(resource.significant_wave_height(year_data.T))\n", " Tp_list.append(resource.peak_period(year_data.T))\n", "\n", - "# Concatenate list of Series into a single DataFrame\n", + "# Concatenate each list of Series into a single Series\n", "Tp = pd.concat(Tp_list, axis=0)\n", "Hm0 = pd.concat(Hm0_list, axis=0)\n", + "\n", + "# Name each Series and concat into a dataFrame\n", + "Tp.name = 'Tp'\n", + "Hm0.name = 'Hm0'\n", "Hm0_Tp = pd.concat([Hm0, Tp], axis=1)\n", "\n", "# Drop any NaNs created from the calculation of Hm0 or Te\n", @@ -1116,7 +1124,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('.venv': venv)", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1130,7 +1138,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/examples/extreme_response_MLER_example.ipynb b/examples/extreme_response_MLER_example.ipynb index 2860c6ef2..be95fb280 100644 --- a/examples/extreme_response_MLER_example.ipynb +++ b/examples/extreme_response_MLER_example.ipynb @@ -197,11 +197,11 @@ "\n", "# generate wave number k\n", "k = resource.wave_number(wave_freq, 70)\n", - "k = k.fillna(0)\n", + "np.nan_to_num(k, 0)\n", "\n", "peakHeightDesired = Hs / 2 * 1.9\n", "mler_norm = extreme.mler_wave_amp_normalize(\n", - " peakHeightDesired, mler_data, sim, k.k.values\n", + " peakHeightDesired, mler_data, sim, k\n", ")" ] }, @@ -239,7 +239,7 @@ } ], "source": [ - "mler_ts = extreme.mler_export_time_series(RAO.values, mler_norm, sim, k.k.values)\n", + "mler_ts = extreme.mler_export_time_series(RAO.values, mler_norm, sim, k)\n", "mler_ts.plot(xlabel=\"Time (s)\", ylabel=\"[m] / [*]\", xlim=[-100, 100], grid=True)" ] }, @@ -256,7 +256,7 @@ "hash": "6acc4428af86beefd6565514d05fe9ce8e024621fafadd3627cdac7b7bd68bc4" }, "kernelspec": { - "display_name": "Python 3.8.10 64-bit ('MHKdev': conda)", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -270,10 +270,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "orig_nbformat": 4 + "version": "3.12.4" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/extreme_response_contour_example.ipynb b/examples/extreme_response_contour_example.ipynb index ae22dd57e..0ee20f4c9 100644 --- a/examples/extreme_response_contour_example.ipynb +++ b/examples/extreme_response_contour_example.ipynb @@ -72,9 +72,13 @@ " Hm0_list.append(resource.significant_wave_height(year_data.T))\n", " Te_list.append(resource.energy_period(year_data.T))\n", "\n", - "# Concatenate list of Series into a single DataFrame\n", + "# Concatenate each list of Series into a single Series\n", "Te = pd.concat(Te_list, axis=0)\n", "Hm0 = pd.concat(Hm0_list, axis=0)\n", + "\n", + "# Name each Series and concat into a dataFrame\n", + "Te.name = 'Te'\n", + "Hm0.name = 'Hm0'\n", "Hm0_Te = pd.concat([Hm0, Te], axis=1)\n", "\n", "# Drop any NaNs created from the calculation of Hm0 or Te\n", @@ -323,7 +327,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/examples/extreme_response_full_sea_state_example.ipynb b/examples/extreme_response_full_sea_state_example.ipynb index 4181e1bc3..76266e05e 100644 --- a/examples/extreme_response_full_sea_state_example.ipynb +++ b/examples/extreme_response_full_sea_state_example.ipynb @@ -75,9 +75,13 @@ " Hm0_list.append(resource.significant_wave_height(year_data.T))\n", " Te_list.append(resource.energy_period(year_data.T))\n", "\n", - "# Concatenate list of Series into a single DataFrame\n", + "# Concatenate each list of Series into a single Series\n", "Te = pd.concat(Te_list, axis=0)\n", "Hm0 = pd.concat(Hm0_list, axis=0)\n", + "\n", + "# Name each Series and concat into a dataFrame\n", + "Te.name = 'Te'\n", + "Hm0.name = 'Hm0'\n", "Hm0_Te = pd.concat([Hm0, Te], axis=1)\n", "\n", "# Drop any NaNs created from the calculation of Hm0 or Te\n", @@ -573,7 +577,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/examples/wave_example.ipynb b/examples/wave_example.ipynb index e03f81feb..2beb8de1c 100644 --- a/examples/wave_example.ipynb +++ b/examples/wave_example.ipynb @@ -1,2883 +1,2808 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MHKiT Wave Module \n", - "The following example runs an application of the [MHKiT wave module](https://mhkit-software.github.io/MHKiT/mhkit-python/api.wave.html) to 1) read in NDBC data, 2) compute metrics from spectral data, 3) generate a capture length matrix, 4) calculate MAEP, and 5) plot the matrices.\n", - "\n", - "Start by importing the necessary python packages and MHKiT module." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from mhkit import wave" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load NDBC Data \n", - "\n", - "We can use MHKiT to load data downloaded from https://www.ndbc.noaa.gov. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\sterl\\codes\\mhkit-python\\mhkit\\wave\\io\\ndbc.py:99: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " data = pd.read_csv(\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 2018-01-01 00:40:00 2018-01-01 01:40:00 2018-01-01 02:40:00 \\\n", - "0.0200 0.0 0.0 0.0 \n", - "0.0325 0.0 0.0 0.0 \n", - "0.0375 0.0 0.0 0.0 \n", - "0.0425 0.0 0.0 0.0 \n", - "0.0475 0.0 0.0 0.0 \n", - "\n", - " 2018-01-01 03:40:00 2018-01-01 04:40:00 2018-01-01 05:40:00 \\\n", - "0.0200 0.0 0.0 0.00 \n", - "0.0325 0.0 0.0 0.00 \n", - "0.0375 0.0 0.0 0.00 \n", - "0.0425 0.0 0.0 0.00 \n", - "0.0475 0.0 0.0 0.01 \n", - "\n", - " 2018-01-01 06:40:00 2018-01-01 07:40:00 2018-01-01 08:40:00 \\\n", - "0.0200 0.0 0.0 0.0 \n", - "0.0325 0.0 0.0 0.0 \n", - "0.0375 0.0 0.0 0.0 \n", - "0.0425 0.0 0.0 0.0 \n", - "0.0475 0.0 0.0 0.0 \n", - "\n", - " 2018-01-01 09:40:00 ... 2018-01-31 14:40:00 2018-01-31 15:40:00 \\\n", - "0.0200 0.0 ... 0.00 0.0 \n", - "0.0325 0.0 ... 0.00 0.0 \n", - "0.0375 0.0 ... 0.00 0.0 \n", - "0.0425 0.0 ... 0.00 0.0 \n", - "0.0475 0.0 ... 0.06 0.0 \n", - "\n", - " 2018-01-31 16:40:00 2018-01-31 17:40:00 2018-01-31 18:40:00 \\\n", - "0.0200 0.0 0.0 0.00 \n", - "0.0325 0.0 0.0 0.00 \n", - "0.0375 0.0 0.0 0.00 \n", - "0.0425 0.0 0.0 0.00 \n", - "0.0475 0.0 0.0 0.07 \n", - "\n", - " 2018-01-31 19:40:00 2018-01-31 20:40:00 2018-01-31 21:40:00 \\\n", - "0.0200 0.0 0.0 0.0 \n", - "0.0325 0.0 0.0 0.0 \n", - "0.0375 0.0 0.0 0.0 \n", - "0.0425 0.0 0.0 0.0 \n", - "0.0475 0.0 0.0 0.0 \n", - "\n", - " 2018-01-31 22:40:00 2018-01-31 23:40:00 \n", - "0.0200 0.0 0.0 \n", - "0.0325 0.0 0.0 \n", - "0.0375 0.0 0.0 \n", - "0.0425 0.0 0.0 \n", - "0.0475 0.0 0.0 \n", - "\n", - "[5 rows x 743 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Transpose raw NDBC data\n", - "ndbc_data = raw_ndbc_data.T\n", - "ndbc_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute Wave Metrics \n", - "We will now use MHKiT to compute the significant wave height, energy period, and energy flux. " - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MHKiT Wave Module \n", + "The following example runs an application of the [MHKiT wave module](https://mhkit-software.github.io/MHKiT/mhkit-python/api.wave.html) to 1) read in NDBC data, 2) compute metrics from spectral data, 3) generate a capture length matrix, 4) calculate MAEP, and 5) plot the matrices.\n", + "\n", + "Start by importing the necessary python packages and MHKiT module." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from mhkit import wave" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load NDBC Data \n", + "\n", + "We can use MHKiT to load data downloaded from https://www.ndbc.noaa.gov. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2018-01-01 00:40:007.458731
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" - ], - "text/plain": [ - " Te\n", - "index \n", - "2018-01-01 00:40:00 7.458731\n", - "2018-01-01 01:40:00 7.682413\n", - "2018-01-01 02:40:00 7.498263\n", - "2018-01-01 03:40:00 7.676198\n", - "2018-01-01 04:40:00 7.669476" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute the enegy periods from the NDBC spectra data\n", - "Te = wave.resource.energy_period(ndbc_data)\n", - "Te.head()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\sterl\\codes\\mhkit-python\\mhkit\\wave\\io\\ndbc.py:99: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " data = pd.read_csv(\n" + ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" ], - "source": [ - "# Compute the significant wave height from the NDBC spectra data\n", - "Hm0 = wave.resource.significant_wave_height(ndbc_data)\n", - "Hm0.head()" + "text/plain": [ + " 0.0200 0.0325 0.0375 0.0425 0.0475 0.0525 0.0575 \\\n", + "2018-01-01 00:40:00 0.0 0.0 0.0 0.0 0.0 0.03 0.04 \n", + "2018-01-01 01:40:00 0.0 0.0 0.0 0.0 0.0 0.02 0.06 \n", + "2018-01-01 02:40:00 0.0 0.0 0.0 0.0 0.0 0.00 0.08 \n", + "2018-01-01 03:40:00 0.0 0.0 0.0 0.0 0.0 0.00 0.13 \n", + "2018-01-01 04:40:00 0.0 0.0 0.0 0.0 0.0 0.03 0.14 \n", + "\n", + " 0.0625 0.0675 0.0725 ... 0.3300 0.3400 0.3500 \\\n", + "2018-01-01 00:40:00 0.09 0.22 0.22 ... 0.07 0.08 0.07 \n", + "2018-01-01 01:40:00 0.08 0.21 0.21 ... 0.07 0.07 0.08 \n", + "2018-01-01 02:40:00 0.07 0.14 0.32 ... 0.08 0.06 0.05 \n", + "2018-01-01 03:40:00 0.22 0.26 0.32 ... 0.05 0.06 0.05 \n", + "2018-01-01 04:40:00 0.17 0.37 0.40 ... 0.06 0.12 0.04 \n", + "\n", + " 0.3650 0.3850 0.4050 0.4250 0.4450 0.4650 0.4850 \n", + "2018-01-01 00:40:00 0.07 0.03 0.01 0.02 0.01 0.01 0.0 \n", + "2018-01-01 01:40:00 0.02 0.01 0.02 0.01 0.01 0.00 0.0 \n", + "2018-01-01 02:40:00 0.03 0.03 0.02 0.01 0.01 0.00 0.0 \n", + "2018-01-01 03:40:00 0.03 0.03 0.01 0.01 0.00 0.00 0.0 \n", + "2018-01-01 04:40:00 0.04 0.02 0.01 0.01 0.01 0.01 0.0 \n", + "\n", + "[5 rows x 47 columns]" ] - }, + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ndbc_data_file = \"data/wave/data.txt\"\n", + "\n", + "# ndbc.read_file outputs the NDBC file data into two variables.\n", + "# raw_ndbc_data is a pandas DataFrame containing the file data.\n", + "# meta contains the meta data, if available.\n", + "[raw_ndbc_data, meta] = wave.io.ndbc.read_file(ndbc_data_file)\n", + "raw_ndbc_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting DataFrame is spectra indexed (rows) by datetime with frequency as the columns. To use this data in MHKiT functions we must first transpose the DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" ], - "source": [ - "# Set water depth to 60 m\n", - "h = 60\n", - "\n", - "# Compute the energy flux from the NDBC spectra data and water depth\n", - "J = wave.resource.energy_flux(ndbc_data, h)\n", - "J.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conversion from DataFrames to Series\n", - "MHKiT functions typically take in Pandas Series so that the function knows exactly which column to use. Pandas Series are one-dimensional ndarrays with axis labels which are most commonly encountered when a single column is requested from a DataFrame (e.g. `ndbc_data['2018-01-01 01:40:00']` returns a Pandas Series from the Pandas DataFrame of all frequencies at the specified time). \n", - "\n", - "To the above results (energy Period, energy flux, and significant wave height) were returned as DataFrames. For DataFrames which only have one column the conversion from DataFrame to Series can accomplished using the `squeeze` method. Alternatively we can call on the column header. Both methods are demonstrated below:" + "text/plain": [ + " 2018-01-01 00:40:00 2018-01-01 01:40:00 2018-01-01 02:40:00 \\\n", + "0.0200 0.0 0.0 0.0 \n", + "0.0325 0.0 0.0 0.0 \n", + "0.0375 0.0 0.0 0.0 \n", + "0.0425 0.0 0.0 0.0 \n", + "0.0475 0.0 0.0 0.0 \n", + "\n", + " 2018-01-01 03:40:00 2018-01-01 04:40:00 2018-01-01 05:40:00 \\\n", + "0.0200 0.0 0.0 0.00 \n", + "0.0325 0.0 0.0 0.00 \n", + "0.0375 0.0 0.0 0.00 \n", + "0.0425 0.0 0.0 0.00 \n", + "0.0475 0.0 0.0 0.01 \n", + "\n", + " 2018-01-01 06:40:00 2018-01-01 07:40:00 2018-01-01 08:40:00 \\\n", + "0.0200 0.0 0.0 0.0 \n", + "0.0325 0.0 0.0 0.0 \n", + "0.0375 0.0 0.0 0.0 \n", + "0.0425 0.0 0.0 0.0 \n", + "0.0475 0.0 0.0 0.0 \n", + "\n", + " 2018-01-01 09:40:00 ... 2018-01-31 14:40:00 2018-01-31 15:40:00 \\\n", + "0.0200 0.0 ... 0.00 0.0 \n", + "0.0325 0.0 ... 0.00 0.0 \n", + "0.0375 0.0 ... 0.00 0.0 \n", + "0.0425 0.0 ... 0.00 0.0 \n", + "0.0475 0.0 ... 0.06 0.0 \n", + "\n", + " 2018-01-31 16:40:00 2018-01-31 17:40:00 2018-01-31 18:40:00 \\\n", + "0.0200 0.0 0.0 0.00 \n", + "0.0325 0.0 0.0 0.00 \n", + "0.0375 0.0 0.0 0.00 \n", + "0.0425 0.0 0.0 0.00 \n", + "0.0475 0.0 0.0 0.07 \n", + "\n", + " 2018-01-31 19:40:00 2018-01-31 20:40:00 2018-01-31 21:40:00 \\\n", + "0.0200 0.0 0.0 0.0 \n", + "0.0325 0.0 0.0 0.0 \n", + "0.0375 0.0 0.0 0.0 \n", + "0.0425 0.0 0.0 0.0 \n", + "0.0475 0.0 0.0 0.0 \n", + "\n", + " 2018-01-31 22:40:00 2018-01-31 23:40:00 \n", + "0.0200 0.0 0.0 \n", + "0.0325 0.0 0.0 \n", + "0.0375 0.0 0.0 \n", + "0.0425 0.0 0.0 \n", + "0.0475 0.0 0.0 \n", + "\n", + "[5 rows x 743 columns]" ] - }, + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Transpose raw NDBC data\n", + "ndbc_data = raw_ndbc_data.T\n", + "ndbc_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute Wave Metrics \n", + "We will now use MHKiT to compute the significant wave height, energy period, and energy flux. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "index\n", - "2018-01-01 00:40:00 7.458731\n", - "2018-01-01 01:40:00 7.682413\n", - "2018-01-01 02:40:00 7.498263\n", - "2018-01-01 03:40:00 7.676198\n", - "2018-01-01 04:40:00 7.669476\n", - "Name: Te, dtype: float64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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" ], - "source": [ - "# Convert the energy period DataFrame to a Series.\n", - "Te = Te.squeeze()\n", - "Te.head()" + "text/plain": [ + " Te\n", + "index \n", + "2018-01-01 00:40:00 7.458731\n", + "2018-01-01 01:40:00 7.682413\n", + "2018-01-01 02:40:00 7.498263\n", + "2018-01-01 03:40:00 7.676198\n", + "2018-01-01 04:40:00 7.669476" ] - }, + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute the enegy periods from the NDBC spectra data\n", + "Te = wave.resource.energy_period(ndbc_data)\n", + "Te.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index\n", - "2018-01-01 00:40:00 0.939574\n", - "2018-01-01 01:40:00 1.001399\n", - "2018-01-01 02:40:00 0.924770\n", - "2018-01-01 03:40:00 0.962497\n", - "2018-01-01 04:40:00 0.989949\n", - " ... \n", - "2018-01-31 19:40:00 2.650811\n", - "2018-01-31 20:40:00 3.086746\n", - "2018-01-31 21:40:00 2.650358\n", - "2018-01-31 22:40:00 2.941428\n", - "2018-01-31 23:40:00 2.895928\n", - "Name: Hm0, Length: 743, dtype: float64\n", - "index\n", - "2018-01-01 00:40:00 3354.825613\n", - "2018-01-01 01:40:00 3916.541523\n", - "2018-01-01 02:40:00 3278.298930\n", - "2018-01-01 03:40:00 3664.246679\n", - "2018-01-01 04:40:00 3867.014933\n", - " ... \n", - "2018-01-31 19:40:00 37596.750775\n", - "2018-01-31 20:40:00 52532.427635\n", - "2018-01-31 21:40:00 38153.227517\n", - "2018-01-31 22:40:00 50501.872907\n", - "2018-01-31 23:40:00 47070.874952\n", - "Name: J, Length: 743, dtype: float64\n" - ] - } + "data": { + "text/html": [ + "
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" ], - "source": [ - "# Alternatively, convert to Series by calling a specific column in the DataFrame\n", - "Hm0 = Hm0[\"Hm0\"]\n", - "print(Hm0)\n", - "\n", - "J = J[\"J\"]\n", - "print(J)" + "text/plain": [ + " Hm0\n", + "index \n", + "2018-01-01 00:40:00 0.939574\n", + "2018-01-01 01:40:00 1.001399\n", + "2018-01-01 02:40:00 0.924770\n", + "2018-01-01 03:40:00 0.962497\n", + "2018-01-01 04:40:00 0.989949" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Random Power Data\n", - "\n", - "For demonstration purposes, this example uses synthetic power data generated from statistical distributions. In a real application, the user would provide power values from a WEC. The data is stored in pandas Series, containing 743 points. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the random seed, to reproduce results\n", - "np.random.seed(1)\n", - "# Generate random power values\n", - "P = pd.Series(np.random.normal(200, 40, 743), index=J.index)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Capture Length Matrices\n", - "\n", - "The following operations create capture length matrices, as specified by the IEC/TS 62600-100. But first, we need to calculate capture length and define bin centers. The mean capture length matrix is printed below. Keep in mind that this data has been artificially generated, so it may not be representative of what a real-world scatter diagram would look like." - ] - }, + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute the significant wave height from the NDBC spectra data\n", + "Hm0 = wave.resource.significant_wave_height(ndbc_data)\n", + "Hm0.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" ], - "source": [ - "# Calculate capture length\n", - "L = wave.performance.capture_length(P, J)\n", - "\n", - "# Generate bins for Hm0 and Te, input format (start, stop, step_size)\n", - "Hm0_bins = np.arange(0, Hm0.values.max() + 0.5, 0.5)\n", - "Te_bins = np.arange(0, Te.values.max() + 1, 1)\n", - "\n", - "# Create capture length matrices using mean, standard deviation, count, min and max statistics\n", - "LM_mean = wave.performance.capture_length_matrix(Hm0, Te, L, \"mean\", Hm0_bins, Te_bins)\n", - "LM_std = wave.performance.capture_length_matrix(Hm0, Te, L, \"std\", Hm0_bins, Te_bins)\n", - "LM_count = wave.performance.capture_length_matrix(\n", - " Hm0, Te, L, \"count\", Hm0_bins, Te_bins\n", - ")\n", - "LM_min = wave.performance.capture_length_matrix(Hm0, Te, L, \"min\", Hm0_bins, Te_bins)\n", - "LM_max = wave.performance.capture_length_matrix(Hm0, Te, L, \"max\", Hm0_bins, Te_bins)\n", - "\n", - "# Show mean capture length matrix\n", - "LM_mean" + "text/plain": [ + " J\n", + "index \n", + "2018-01-01 00:40:00 3354.825613\n", + "2018-01-01 01:40:00 3916.541523\n", + "2018-01-01 02:40:00 3278.298930\n", + "2018-01-01 03:40:00 3664.246679\n", + "2018-01-01 04:40:00 3867.014933" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additional capture length matrices can be computed, for example, the frequency matrix is computed below." - ] - }, + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set water depth to 60 m\n", + "h = 60\n", + "\n", + "# Compute the energy flux from the NDBC spectra data and water depth\n", + "J = wave.resource.energy_flux(ndbc_data, h)\n", + "J.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note on data types\n", + "MHKiT functions typically allow Pandas Series, Pandas DataFrame, or xarray DataArray input. Multidimensional data (DataFrames and DataArrays) typically require an index or dimension name to specify the frequency or time dimension in question. If not supplied, the first dimension is assumed to be the relevant dimension.\n", + "\n", + "The above results (energy period, energy flux, and significant wave height) were returned as Pandas Series. 2D wave spectral data (frequency x time) was input and the frequency dimension was reduced leaving 1D, columnar data as the output. In Pandas, this is represented as a Series. If a DataArray with 3 or more dimensions was input, the output would be a DataArray with one fewer dimensions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Random Power Data\n", + "\n", + "For demonstration purposes, this example uses synthetic power data generated from statistical distributions. In a real application, the user would provide power values from a WEC. The data is stored in pandas Series, containing 743 points. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the random seed, to reproduce results\n", + "np.random.seed(1)\n", + "# Generate random power values\n", + "P = pd.Series(np.random.normal(200, 40, 743), index=J.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Capture Length Matrices\n", + "\n", + "The following operations create capture length matrices, as specified by the IEC/TS 62600-100. But first, we need to calculate capture length and define bin centers. The mean capture length matrix is printed below. Keep in mind that this data has been artificially generated, so it may not be representative of what a real-world scatter diagram would look like." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" ], - "source": [ - "# Create capture length matrices using frequency\n", - "LM_freq = wave.performance.capture_length_matrix(\n", - " Hm0, Te, L, \"frequency\", Hm0_bins, Te_bins\n", - ")\n", - "\n", - "# Show capture length matrix using frequency\n", - "LM_freq" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `capture_length_matrix` function can also be used as an arbitrary matrix generator. To do this, simply pass a different Series in the place of capture length (L). For example, while not specified by the IEC standards, if the user doesn't have the omnidirectional wave flux, the average power matrix could hypothetically be generated in the following manner." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Demonstration of arbitrary matrix generator\n", - "PM_mean_not_standard = wave.performance.capture_length_matrix(\n", - " Hm0, Te, P, \"mean\", Hm0_bins, Te_bins\n", - ")" + "text/plain": [ + "y_centers 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 \\\n", + "x_centers \n", + "0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "0.5 NaN NaN NaN NaN NaN NaN NaN 0.120286 0.053376 \n", + "1.0 NaN NaN NaN NaN NaN NaN 0.110686 0.068070 0.049452 \n", + "1.5 NaN NaN NaN NaN NaN NaN NaN 0.019749 0.018673 \n", + "2.0 NaN NaN NaN NaN NaN NaN NaN 0.013882 0.012547 \n", + "2.5 NaN NaN NaN NaN NaN NaN NaN NaN 0.007244 \n", + "3.0 NaN NaN NaN NaN NaN NaN NaN 0.004500 0.005660 \n", + "3.5 NaN NaN NaN NaN NaN NaN NaN NaN 0.003924 \n", + "4.0 NaN NaN NaN NaN NaN NaN NaN NaN 0.003185 \n", + "4.5 NaN NaN NaN NaN NaN NaN NaN NaN 0.002343 \n", + "5.0 NaN NaN NaN NaN NaN NaN NaN NaN 0.001913 \n", + "5.5 NaN NaN NaN NaN NaN NaN NaN NaN 0.002101 \n", + "6.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "6.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "7.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "7.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "8.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "8.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "9.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "9.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "10.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "10.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "y_centers 9.0 10.0 11.0 12.0 13.0 14.0 \\\n", + "x_centers \n", + "0.0 NaN NaN NaN NaN NaN NaN \n", + "0.5 NaN NaN NaN NaN NaN NaN \n", + "1.0 0.065912 NaN 0.056593 0.029950 0.017234 NaN \n", + "1.5 NaN NaN 0.012473 0.011205 0.012307 0.010432 \n", + "2.0 0.009672 0.008770 0.008585 0.007525 0.005272 0.007809 \n", + "2.5 0.006488 0.005788 0.005652 0.005180 0.004260 0.003623 \n", + "3.0 0.004691 0.004109 0.003952 0.003104 0.003408 0.002291 \n", + "3.5 0.003674 0.003020 0.002746 0.002247 0.002000 0.002257 \n", + "4.0 0.002513 0.002386 0.002147 0.002246 0.001605 0.001730 \n", + "4.5 0.002087 0.001919 0.001590 0.001438 NaN NaN \n", + "5.0 0.001720 0.001716 0.001411 0.001219 0.001345 NaN \n", + "5.5 0.001516 0.001331 0.000902 0.001033 NaN NaN \n", + "6.0 0.001097 0.000895 NaN 0.000858 0.000987 NaN \n", + "6.5 0.000837 0.001024 0.000419 NaN 0.000688 NaN \n", + "7.0 NaN NaN NaN 0.000461 0.000633 NaN \n", + "7.5 NaN 0.000553 NaN NaN 0.000312 0.000437 \n", + "8.0 NaN NaN NaN NaN NaN 0.000443 \n", + "8.5 NaN NaN NaN NaN NaN NaN \n", + "9.0 NaN NaN NaN NaN NaN NaN \n", + "9.5 NaN NaN NaN NaN NaN NaN \n", + "10.0 NaN NaN NaN NaN NaN NaN \n", + "10.5 NaN NaN NaN NaN NaN NaN \n", + "\n", + "y_centers 15.0 16.0 \n", + "x_centers \n", + "0.0 NaN NaN \n", + "0.5 NaN NaN \n", + "1.0 NaN NaN \n", + "1.5 NaN NaN \n", + "2.0 NaN NaN \n", + "2.5 0.004509 NaN \n", + "3.0 0.001792 NaN \n", + "3.5 0.002033 NaN \n", + "4.0 NaN NaN \n", + "4.5 NaN NaN \n", + "5.0 NaN NaN \n", + "5.5 NaN NaN \n", + "6.0 NaN NaN \n", + "6.5 NaN NaN \n", + "7.0 NaN NaN \n", + "7.5 NaN NaN \n", + "8.0 0.000351 NaN \n", + "8.5 0.000418 0.000405 \n", + "9.0 NaN NaN \n", + "9.5 0.000153 NaN \n", + "10.0 NaN 0.000281 \n", + "10.5 0.000204 0.000225 " ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `capture_length_matrix` function can also use a callable function as the statistic argument. For example, suppose that we wanted to generate a matrix with the variance of the capture length. We could achieve this by passing the NumPy variance function `np.var` into the `capture_length_matrix` function, as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Demonstration of passing a callable function to the matrix generator\n", - "LM_variance = wave.performance.capture_length_matrix(\n", - " Hm0, Te, L, np.var, Hm0_bins, Te_bins\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Power Matrices\n", - "As specified in IEC/TS 62600-100, the power matrix is generated from the capture length matrix and wave energy flux matrix, as shown below" - ] - }, + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate capture length\n", + "L = wave.performance.capture_length(P, J)\n", + "\n", + "# Generate bins for Hm0 and Te, input format (start, stop, step_size)\n", + "Hm0_bins = np.arange(0, Hm0.values.max() + 0.5, 0.5)\n", + "Te_bins = np.arange(0, Te.values.max() + 1, 1)\n", + "\n", + "# Create capture length matrices using mean, standard deviation, count, min and max statistics\n", + "LM_mean = wave.performance.capture_length_matrix(Hm0, Te, L, \"mean\", Hm0_bins, Te_bins)\n", + "LM_std = wave.performance.capture_length_matrix(Hm0, Te, L, \"std\", Hm0_bins, Te_bins)\n", + "LM_count = wave.performance.capture_length_matrix(\n", + " Hm0, Te, L, \"count\", Hm0_bins, Te_bins\n", + ")\n", + "LM_min = wave.performance.capture_length_matrix(Hm0, Te, L, \"min\", Hm0_bins, Te_bins)\n", + "LM_max = wave.performance.capture_length_matrix(Hm0, Te, L, \"max\", Hm0_bins, Te_bins)\n", + "\n", + "# Show mean capture length matrix\n", + "LM_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additional capture length matrices can be computed, for example, the frequency matrix is computed below." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" ], - "source": [ - "# Create wave energy flux matrix using mean\n", - "JM = wave.performance.wave_energy_flux_matrix(Hm0, Te, J, \"mean\", Hm0_bins, Te_bins)\n", - "\n", - "# Create power matrix using mean\n", - "PM_mean = wave.performance.power_matrix(LM_mean, JM)\n", - "\n", - "# Create power matrix using standard deviation\n", - "PM_std = wave.performance.power_matrix(LM_std, JM)\n", - "\n", - "# Show mean power matrix, round to 3 decimals\n", - "PM_mean.round(3)" + "text/plain": [ + "y_centers 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 \\\n", + "x_centers \n", + "0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.002692 0.001346 \n", + "1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.001346 0.006729 0.004038 \n", + "1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.005384 0.002692 \n", + "2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.002692 0.005384 \n", + "2.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.013459 \n", + "3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.001346 0.021534 \n", + "3.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.006729 \n", + "4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.009421 \n", + "4.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.016151 \n", + "5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.002692 \n", + "5.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.001346 \n", + "6.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "6.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "7.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "7.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "8.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "8.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "9.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "9.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "10.5 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.000000 \n", + "\n", + "y_centers 9.0 10.0 11.0 12.0 13.0 14.0 \\\n", + "x_centers \n", + "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "0.5 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1.0 0.001346 0.000000 0.002692 0.002692 0.001346 0.000000 \n", + "1.5 0.000000 0.000000 0.009421 0.004038 0.006729 0.005384 \n", + "2.0 0.018843 0.018843 0.029610 0.021534 0.001346 0.002692 \n", + "2.5 0.052490 0.055182 0.018843 0.025572 0.022880 0.005384 \n", + "3.0 0.044415 0.047106 0.020188 0.012113 0.010767 0.010767 \n", + "3.5 0.040377 0.029610 0.047106 0.004038 0.008075 0.004038 \n", + "4.0 0.017497 0.029610 0.040377 0.002692 0.004038 0.005384 \n", + "4.5 0.013459 0.017497 0.022880 0.012113 0.000000 0.000000 \n", + "5.0 0.008075 0.008075 0.010767 0.022880 0.001346 0.000000 \n", + "5.5 0.012113 0.006729 0.004038 0.014805 0.000000 0.000000 \n", + "6.0 0.002692 0.002692 0.000000 0.005384 0.001346 0.000000 \n", + "6.5 0.002692 0.002692 0.001346 0.000000 0.002692 0.000000 \n", + "7.0 0.000000 0.000000 0.000000 0.001346 0.004038 0.000000 \n", + "7.5 0.000000 0.001346 0.000000 0.000000 0.001346 0.008075 \n", + "8.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.002692 \n", + "8.5 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "9.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "9.5 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "10.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "10.5 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "\n", + "y_centers 15.0 16.0 \n", + "x_centers \n", + "0.0 0.000000 0.000000 \n", + "0.5 0.000000 0.000000 \n", + "1.0 0.000000 0.000000 \n", + "1.5 0.000000 0.000000 \n", + "2.0 0.000000 0.000000 \n", + "2.5 0.001346 0.000000 \n", + "3.0 0.001346 0.000000 \n", + "3.5 0.001346 0.000000 \n", + "4.0 0.000000 0.000000 \n", + "4.5 0.000000 0.000000 \n", + "5.0 0.000000 0.000000 \n", + "5.5 0.000000 0.000000 \n", + "6.0 0.000000 0.000000 \n", + "6.5 0.000000 0.000000 \n", + "7.0 0.000000 0.000000 \n", + "7.5 0.000000 0.000000 \n", + "8.0 0.002692 0.000000 \n", + "8.5 0.001346 0.001346 \n", + "9.0 0.000000 0.000000 \n", + "9.5 0.001346 0.000000 \n", + "10.0 0.000000 0.001346 \n", + "10.5 0.001346 0.001346 " ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate MAEP\n", - "There are two ways to calculate the mean annual energy production (MEAP). One is from capture length and wave energy flux matrices, the other is from time-series data, as shown below." - ] - }, + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create capture length matrices using frequency\n", + "LM_freq = wave.performance.capture_length_matrix(\n", + " Hm0, Te, L, \"frequency\", Hm0_bins, Te_bins\n", + ")\n", + "\n", + "# Show capture length matrix using frequency\n", + "LM_freq" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `capture_length_matrix` function can also be used as an arbitrary matrix generator. To do this, simply pass a different Series in the place of capture length (L). For example, while not specified by the IEC standards, if the user doesn't have the omnidirectional wave flux, the average power matrix could hypothetically be generated in the following manner." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Demonstration of arbitrary matrix generator\n", + "PM_mean_not_standard = wave.performance.capture_length_matrix(\n", + " Hm0, Te, P, \"mean\", Hm0_bins, Te_bins\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `capture_length_matrix` function can also use a callable function as the statistic argument. For example, suppose that we wanted to generate a matrix with the variance of the capture length. We could achieve this by passing the NumPy variance function `np.var` into the `capture_length_matrix` function, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Demonstration of passing a callable function to the matrix generator\n", + "LM_variance = wave.performance.capture_length_matrix(\n", + " Hm0, Te, L, np.var, Hm0_bins, Te_bins\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Power Matrices\n", + "As specified in IEC/TS 62600-100, the power matrix is generated from the capture length matrix and wave energy flux matrix, as shown below" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MAEP from timeseries = 1767087.5275863332\n", - "MAEP from matrices = 1781210.865283919\n" - ] - } + "data": { + "text/html": [ + "
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" ], - "source": [ - "# Calcaulte maep from timeseries\n", - "maep_timeseries = wave.performance.mean_annual_energy_production_timeseries(L, J)\n", - "print(\"MAEP from timeseries = \", maep_timeseries)\n", - "\n", - "# Calcaulte maep from matrix \n", - "# See Issue #339\n", - "# maep_matrix = wave.performance.mean_annual_energy_production_matrix(\n", - "# LM_mean, JM, LM_freq\n", - "# )\n", - "\n", - "T = 8766 # Average length of a year (h)\n", - "maep_matrix = T * np.nansum(LM_mean * JM * LM_freq)\n", - "\n", - "print(\"MAEP from matrices = \", maep_matrix)" + "text/plain": [ + "y_centers 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 \\\n", + "x_centers \n", + "0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "0.5 NaN NaN NaN NaN NaN NaN NaN 224.996 117.594 \n", + "1.0 NaN NaN NaN NaN NaN NaN 212.762 202.713 188.707 \n", + "1.5 NaN NaN NaN NaN NaN NaN NaN 176.402 199.802 \n", + "2.0 NaN NaN NaN NaN NaN NaN NaN 203.667 216.857 \n", + "2.5 NaN NaN NaN NaN NaN NaN NaN NaN 193.397 \n", + "3.0 NaN NaN NaN NaN NaN NaN NaN 170.739 216.459 \n", + "3.5 NaN NaN NaN NaN NaN NaN NaN NaN 194.894 \n", + "4.0 NaN NaN NaN NaN NaN NaN NaN NaN 217.289 \n", + "4.5 NaN NaN NaN NaN NaN NaN NaN NaN 197.994 \n", + "5.0 NaN NaN NaN NaN NaN NaN NaN NaN 198.149 \n", + "5.5 NaN NaN NaN NaN NaN NaN NaN NaN 249.158 \n", + "6.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "6.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "7.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "7.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "8.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "8.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "9.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "9.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "10.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "10.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "y_centers 9.0 10.0 11.0 12.0 13.0 14.0 15.0 \\\n", + "x_centers \n", + "0.0 NaN NaN NaN NaN NaN NaN NaN \n", + "0.5 NaN NaN NaN NaN NaN NaN NaN \n", + "1.0 187.103 NaN 213.926 174.154 164.886 NaN NaN \n", + "1.5 NaN NaN 201.883 191.598 221.705 190.124 NaN \n", + "2.0 192.965 201.633 216.268 209.634 162.569 232.530 NaN \n", + "2.5 203.529 196.907 212.883 211.277 202.760 199.263 272.421 \n", + "3.0 197.484 200.895 212.107 193.837 222.185 169.497 122.296 \n", + "3.5 214.108 202.725 206.901 184.099 186.077 221.659 186.201 \n", + "4.0 189.403 201.362 207.532 207.971 172.771 213.854 NaN \n", + "4.5 194.238 205.559 203.195 197.980 NaN NaN NaN \n", + "5.0 196.527 222.219 215.221 204.002 254.004 NaN NaN \n", + "5.5 212.561 212.734 168.655 208.220 NaN NaN NaN \n", + "6.0 182.314 159.418 NaN 208.418 241.347 NaN NaN \n", + "6.5 164.712 233.890 110.517 NaN 207.919 NaN NaN \n", + "7.0 NaN NaN NaN 155.691 229.022 NaN NaN \n", + "7.5 NaN 166.855 NaN NaN 128.897 198.053 NaN \n", + "8.0 NaN NaN NaN NaN NaN 230.281 184.510 \n", + "8.5 NaN NaN NaN NaN NaN NaN 248.338 \n", + "9.0 NaN NaN NaN NaN NaN NaN NaN \n", + "9.5 NaN NaN NaN NaN NaN NaN 116.230 \n", + "10.0 NaN NaN NaN NaN NaN NaN NaN \n", + "10.5 NaN NaN NaN NaN NaN NaN 190.849 \n", + "\n", + "y_centers 16.0 \n", + "x_centers \n", + "0.0 NaN \n", + "0.5 NaN \n", + "1.0 NaN \n", + "1.5 NaN \n", + "2.0 NaN \n", + "2.5 NaN \n", + "3.0 NaN \n", + "3.5 NaN \n", + "4.0 NaN \n", + "4.5 NaN \n", + "5.0 NaN \n", + "5.5 NaN \n", + "6.0 NaN \n", + "6.5 NaN \n", + "7.0 NaN \n", + "7.5 NaN \n", + "8.0 NaN \n", + "8.5 264.534 \n", + "9.0 NaN \n", + "9.5 NaN \n", + "10.0 244.634 \n", + "10.5 212.411 " ] - }, + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create wave energy flux matrix using mean\n", + "JM = wave.performance.wave_energy_flux_matrix(Hm0, Te, J, \"mean\", Hm0_bins, Te_bins)\n", + "\n", + "# Create power matrix using mean\n", + "PM_mean = wave.performance.power_matrix(LM_mean, JM)\n", + "\n", + "# Create power matrix using standard deviation\n", + "PM_std = wave.performance.power_matrix(LM_std, JM)\n", + "\n", + "# Show mean power matrix, round to 3 decimals\n", + "PM_mean.round(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate MAEP\n", + "There are two ways to calculate the mean annual energy production (MEAP). One is from capture length and wave energy flux matrices, the other is from time-series data, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Graphics\n", - "The graphics function `plot_matrix` can be used to visualize results. It is important to note that the plotting function assumes the step size between bins to be linear." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "MAEP from timeseries = 1767087.5275863332\n", + "MAEP from matrices = 1781210.865283919\n" + ] + } + ], + "source": [ + "# Calcaulte maep from timeseries\n", + "maep_timeseries = wave.performance.mean_annual_energy_production_timeseries(L, J)\n", + "print(\"MAEP from timeseries = \", maep_timeseries)\n", + "\n", + "# Calcaulte maep from matrix \n", + "# See Issue #339\n", + "# maep_matrix = wave.performance.mean_annual_energy_production_matrix(\n", + "# LM_mean, JM, LM_freq\n", + "# )\n", + "\n", + "T = 8766 # Average length of a year (h)\n", + "maep_matrix = T * np.nansum(LM_mean * JM * LM_freq)\n", + "\n", + "print(\"MAEP from matrices = \", maep_matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graphics\n", + "The graphics function `plot_matrix` can be used to visualize results. It is important to note that the plotting function assumes the step size between bins to be linear." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the capture length mean matrix\n", - "ax = wave.graphics.plot_matrix(LM_mean)" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the capture length mean matrix\n", + "ax = wave.graphics.plot_matrix(LM_mean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plotting function only requires the matrix as input, but the function can also take several other arguments.\n", + "The list of optional arguments is: `xlabel, ylabel, zlabel, show_values, and ax`. The following uses these optional arguments. The matplotlib package is imported to define an axis with a larger figure size." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plotting function only requires the matrix as input, but the function can also take several other arguments.\n", - "The list of optional arguments is: `xlabel, ylabel, zlabel, show_values, and ax`. The following uses these optional arguments. The matplotlib package is imported to define an axis with a larger figure size." + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Customize the matrix plot\n", - "import matplotlib.pylab as plt\n", - "\n", - "plt.figure(figsize=(6, 6))\n", - "ax = plt.gca()\n", - "wave.graphics.plot_matrix(\n", - " PM_mean,\n", - " xlabel=\"Te (s)\",\n", - " ylabel=\"Hm0 (m)\",\n", - " zlabel=\"Mean Power (kW)\",\n", - " show_values=False,\n", - " ax=ax,\n", - ")" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } + ], + "source": [ + "# Customize the matrix plot\n", + "import matplotlib.pylab as plt\n", + "\n", + "plt.figure(figsize=(6, 6))\n", + "ax = plt.gca()\n", + "wave.graphics.plot_matrix(\n", + " PM_mean,\n", + " xlabel=\"Te (s)\",\n", + " ylabel=\"Hm0 (m)\",\n", + " zlabel=\"Mean Power (kW)\",\n", + " show_values=False,\n", + " ax=ax,\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 4 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/mhkit/loads/extreme/mler.py b/mhkit/loads/extreme/mler.py index 5aa174d26..f77f7d883 100644 --- a/mhkit/loads/extreme/mler.py +++ b/mhkit/loads/extreme/mler.py @@ -61,9 +61,9 @@ def _calculate_spectral_values( spectrum_r = np.abs(rao_array) ** 2 * (2 * wave_spectrum) # Calculate spectral moments - m_0 = frequency_moment(pd.Series(spectrum_r, index=freq_hz), 0).iloc[0, 0] - m_1 = frequency_moment(pd.Series(spectrum_r, index=freq_hz), 1).iloc[0, 0] - m_2 = frequency_moment(pd.Series(spectrum_r, index=freq_hz), 2).iloc[0, 0] + m_0 = frequency_moment(pd.Series(spectrum_r, index=freq_hz), 0).item() + m_1 = frequency_moment(pd.Series(spectrum_r, index=freq_hz), 1).item() + m_2 = frequency_moment(pd.Series(spectrum_r, index=freq_hz), 2).item() # Calculate coefficient A_{R,n} coeff_a_rn = ( diff --git a/mhkit/tests/loads/test_loads.py b/mhkit/tests/loads/test_loads.py index 8c119a38e..633763f83 100644 --- a/mhkit/tests/loads/test_loads.py +++ b/mhkit/tests/loads/test_loads.py @@ -422,10 +422,8 @@ def test_mler_wave_amp_normalize(self): mler["WaveSpectrum"] = self.mler["Res_Spec"].values mler["Phase"] = self.mler["phase"].values k = resource.wave_number(wave_freq, 70) - k = k.fillna(0) - mler_norm = loads.extreme.mler_wave_amp_normalize( - 4.5 * 1.9, mler, self.sim, k.k.values - ) + k = np.nan_to_num(k, nan=0) + mler_norm = loads.extreme.mler_wave_amp_normalize(4.5 * 1.9, mler, self.sim, k) mler_norm.reset_index(drop=True, inplace=True) assert_series_equal( @@ -442,11 +440,9 @@ def test_mler_export_time_series(self): mler["WaveSpectrum"] = self.mler["Norm_Spec"].values mler["Phase"] = self.mler["phase"].values k = resource.wave_number(wave_freq, 70) - k = k.fillna(0) + np.nan_to_num(k, 0) RAO = self.mler["RAO"].astype(complex) - mler_ts = loads.extreme.mler_export_time_series( - RAO.values, mler, self.sim, k.k.values - ) + mler_ts = loads.extreme.mler_export_time_series(RAO.values, mler, self.sim, k) mler_ts.index.name = None # compatibility with old data assert_frame_equal(self.mler_ts, mler_ts, atol=0.0001) diff --git a/mhkit/tests/wave/test_performance.py b/mhkit/tests/wave/test_performance.py index b8fce7cb8..a12c8050c 100644 --- a/mhkit/tests/wave/test_performance.py +++ b/mhkit/tests/wave/test_performance.py @@ -126,6 +126,7 @@ def test_powerperformance_workflow(self): if isfile(filename): os.remove(filename) P = pd.Series(np.random.normal(200, 40, 743), index=self.S.columns) + P.index.name = "variable" statistic = ["mean"] savepath = plotdir show_values = True diff --git a/mhkit/tests/wave/test_resource_metrics.py b/mhkit/tests/wave/test_resource_metrics.py index 9cdf589fc..3df52fcd1 100644 --- a/mhkit/tests/wave/test_resource_metrics.py +++ b/mhkit/tests/wave/test_resource_metrics.py @@ -95,10 +95,11 @@ def test_kfromw(self): expected = self.valdata1[i]["k"] k = wave.resource.wave_number(f, h, rho) - calculated = k.loc[:, "k"].values + calculated = k error = ((expected - calculated) ** 2).sum() # SSE self.assertLess(error, 1e-6) + self.assertIsInstance(calculated, type(f)) def test_kfromw_one_freq(self): g = 9.81 @@ -106,21 +107,26 @@ def test_kfromw_one_freq(self): h = 1e9 w = np.pi * 2 * f # deep water dispersion expected = w**2 / g - calculated = wave.resource.wave_number(f=f, h=h, g=g).values[0][0] + calculated = wave.resource.wave_number(f=f, h=h, g=g).item() error = np.abs(expected - calculated) self.assertLess(error, 1e-6) + self.assertIsInstance(calculated, type(f)) def test_wave_length(self): k_array = np.asarray([1.0, 2.0, 10.0, 3.0]) - k_int = int(k_array[0]) k_float = k_array[0] k_df = pd.DataFrame(k_array, index=[1, 2, 3, 4]) k_series = k_df[0] - for l in [k_array, k_int, k_float, k_df, k_series]: - l_calculated = wave.resource.wave_length(l) - self.assertTrue(np.all(2.0 * np.pi / l == l_calculated)) + for k in [k_int]: + l_calculated = wave.resource.wave_length(k) + self.assertTrue(np.all(2.0 * np.pi / k == l_calculated)) + + for k in [k_array, k_float, k_df, k_series]: + l_calculated = wave.resource.wave_length(k) + self.assertTrue(np.all(2.0 * np.pi / k == l_calculated)) + self.assertIsInstance(l_calculated, type(k)) def test_depth_regime(self): h = 10 @@ -144,11 +150,13 @@ def test_depth_regime(self): for l in [l_array, l_series, l_da, l_ds]: calculated = wave.resource.depth_regime(l, h) self.assertTrue(np.all(expected == calculated)) + self.assertIsInstance(calculated, type(l)) # special formatting for pd.DataFrame for l in [l_df]: calculated = wave.resource.depth_regime(l, h) self.assertTrue(np.all(expected == calculated[0])) + self.assertIsInstance(calculated, type(l)) def test_wave_celerity(self): # Depth regime ratio @@ -218,7 +226,7 @@ def test_moments(self): calculated = wave.resource.frequency_moment( S, int(m), frequency_bins=f_bins - ).iloc[0, 0] + ).item() error = np.abs(expected - calculated) / expected self.assertLess(error, 0.01) @@ -234,11 +242,11 @@ def test_energy_period_to_peak_period(self): for g in gamma: for T in Te: Tp = wave.resource.energy_period_to_peak_period(T, g) + self.assertIsInstance(Tp, type(T)) f = np.linspace(1 / (10 * Tp), 3 / Tp, 100) S = wave.resource.jonswap_spectrum(f, Tp, Hs, g) - - Te_calc = wave.resource.energy_period(S).values[0][0] + Te_calc = wave.resource.energy_period(S).item() error = np.abs(T - Te_calc) / Te_calc self.assertLess(error, 0.01) @@ -259,16 +267,16 @@ def test_metrics(self): expected = data["metrics"]["Hm0"] calculated = wave.resource.significant_wave_height( S, frequency_bins=f_bins - ).iloc[0, 0] + ) error = np.abs(expected - calculated) / expected # print('Hm0', expected, calculated, error) self.assertLess(error, 0.01) # Te expected = data["metrics"]["Te"] - calculated = wave.resource.energy_period(S, frequency_bins=f_bins).iloc[ - 0, 0 - ] + calculated = wave.resource.energy_period( + S, frequency_bins=f_bins + ).item() error = np.abs(expected - calculated) / expected # print('Te', expected, calculated, error) self.assertLess(error, 0.01) @@ -277,7 +285,7 @@ def test_metrics(self): expected = data["metrics"]["T0"] calculated = wave.resource.average_zero_crossing_period( S, frequency_bins=f_bins - ).iloc[0, 0] + ).item() error = np.abs(expected - calculated) / expected # print('T0', expected, calculated, error) self.assertLess(error, 0.01) @@ -289,7 +297,7 @@ def test_metrics(self): S, # Tc = Tavg**2 frequency_bins=f_bins, - ).iloc[0, 0] + ).item() ** 2 ) error = np.abs(expected - calculated) / expected @@ -300,14 +308,14 @@ def test_metrics(self): expected = np.sqrt(data["metrics"]["Tm"]) calculated = wave.resource.average_wave_period( S, frequency_bins=f_bins - ).iloc[0, 0] + ).item() error = np.abs(expected - calculated) / expected # print('Tm', expected, calculated, error) self.assertLess(error, 0.01) # Tp expected = data["metrics"]["Tp"] - calculated = wave.resource.peak_period(S).iloc[0, 0] + calculated = wave.resource.peak_period(S) error = np.abs(expected - calculated) / expected # print('Tp', expected, calculated, error) self.assertLess(error, 0.001) @@ -316,7 +324,7 @@ def test_metrics(self): expected = data["metrics"]["e"] calculated = wave.resource.spectral_bandwidth( S, frequency_bins=f_bins - ).iloc[0, 0] + ).item() error = np.abs(expected - calculated) / expected # print('e', expected, calculated, error) self.assertLess(error, 0.001) @@ -325,8 +333,8 @@ def test_metrics(self): if file_i != "CDiP": for i, j in zip(data["h"], data["J"]): expected = data["J"][j] - calculated = wave.resource.energy_flux(S, i) - error = np.abs(expected - calculated.values) / expected + calculated = wave.resource.energy_flux(S, i).item() + error = np.abs(expected - calculated) / expected self.assertLess(error, 0.1) # v @@ -335,14 +343,14 @@ def test_metrics(self): expected = data["metrics"]["v"] calculated = wave.resource.spectral_width( S, frequency_bins=f_bins - ).iloc[0, 0] + ).item() error = np.abs(expected - calculated) / expected self.assertLess(error, 0.01) if file_i == "MC": expected = data["metrics"]["v"] # testing that default uniform frequency bin widths works - calculated = wave.resource.spectral_width(S).iloc[0, 0] + calculated = wave.resource.spectral_width(S).item() error = np.abs(expected - calculated) / expected self.assertLess(error, 0.01) diff --git a/mhkit/tests/wave/test_resource_spectrum.py b/mhkit/tests/wave/test_resource_spectrum.py index 5ac06cb29..e52f53bf7 100644 --- a/mhkit/tests/wave/test_resource_spectrum.py +++ b/mhkit/tests/wave/test_resource_spectrum.py @@ -36,8 +36,8 @@ def tearDownClass(self): def test_pierson_moskowitz_spectrum(self): S = wave.resource.pierson_moskowitz_spectrum(self.f, self.Tp, self.Hs) - Hm0 = wave.resource.significant_wave_height(S).iloc[0, 0] - Tp0 = wave.resource.peak_period(S).iloc[0, 0] + Hm0 = wave.resource.significant_wave_height(S).item() + Tp0 = wave.resource.peak_period(S).item() errorHm0 = np.abs(self.Tp - Tp0) / self.Tp errorTp0 = np.abs(self.Hs - Hm0) / self.Hs @@ -60,8 +60,8 @@ def test_pierson_moskowitz_spectrum_zero_freq(self): def test_jonswap_spectrum(self): S = wave.resource.jonswap_spectrum(self.f, self.Tp, self.Hs) - Hm0 = wave.resource.significant_wave_height(S).iloc[0, 0] - Tp0 = wave.resource.peak_period(S).iloc[0, 0] + Hm0 = wave.resource.significant_wave_height(S).item() + Tp0 = wave.resource.peak_period(S).item() errorHm0 = np.abs(self.Tp - Tp0) / self.Tp errorTp0 = np.abs(self.Hs - Hm0) / self.Hs @@ -122,14 +122,14 @@ def test_surface_elevation_moments(self): eta, 1 / dt, len(eta.values), detrend=False, window="boxcar", noverlap=0 ) - m0 = wave.resource.frequency_moment(S, 0).m0.values[0] - m0n = wave.resource.frequency_moment(Sn, 0).m0.values[0] + m0 = wave.resource.frequency_moment(S, 0).item() + m0n = wave.resource.frequency_moment(Sn, 0).item() errorm0 = np.abs((m0 - m0n) / m0) self.assertLess(errorm0, 0.01) - m1 = wave.resource.frequency_moment(S, 1).m1.values[0] - m1n = wave.resource.frequency_moment(Sn, 1).m1.values[0] + m1 = wave.resource.frequency_moment(S, 1).item() + m1n = wave.resource.frequency_moment(Sn, 1).item() errorm1 = np.abs((m1 - m1n) / m1) self.assertLess(errorm1, 0.01) @@ -149,6 +149,39 @@ def test_surface_elevation_rmse(self): self.assertLess(rmse_sum, 0.02) + def test_elevation_spectrum_multiple_variables(self): + time = np.linspace(0, 100, 1000) + eta1 = np.sin(2 * np.pi * 0.1 * time) + eta2 = np.sin(2 * np.pi * 0.2 * time) + eta3 = np.sin(2 * np.pi * 0.3 * time) + + eta_dataset = xr.Dataset( + { + "eta1": (["time"], eta1), + "eta2": (["time"], eta2), + "eta3": (["time"], eta3), + }, + coords={"time": time}, + ) + + sample_rate = 10 + nnft = 256 + + spectra = wave.resource.elevation_spectrum(eta_dataset, sample_rate, nnft) + + # For each variable, find the frequency at which the spectrum has its maximum value + for var_name, expected_peak_freq in [ + ("eta1", 0.117), + ("eta2", 0.2), + ("eta3", 0.3125), + ]: + spec_values = spectra[var_name].values + peak_index = np.argmax(spec_values) + peak_freq = spectra.index[peak_index] + + # Assert that the peak frequency is close to the expected frequency + self.assertAlmostEqual(peak_freq, expected_peak_freq, places=2) + def test_mhkit_spectrum_without_frequency_index_name_defined(self): S = wave.resource.jonswap_spectrum(self.f, self.Tp, self.Hs) S.index.name = None @@ -168,7 +201,7 @@ def test_user_spectrum_without_frequency_index_name_defined(self): expected_magnitude = [-0.983917, 1.274248, -2.129812] - assert_allclose(result["magnitude"], expected_magnitude, atol=1e-6) + assert_allclose(result.values[:, 0], expected_magnitude, atol=1e-6) def test_ifft_sum_of_sines(self): S = wave.resource.jonswap_spectrum(self.f, self.Tp, self.Hs) diff --git a/mhkit/utils/type_handling.py b/mhkit/utils/type_handling.py index 1b06c7d12..634bebeaf 100644 --- a/mhkit/utils/type_handling.py +++ b/mhkit/utils/type_handling.py @@ -160,27 +160,25 @@ def convert_to_dataarray(data, name="data"): # iloc returns a Series with one value as expected. data = data.iloc[:, 0] else: - index = data.index.values - columns = data.columns.values - data = xr.DataArray( - data=data.T, - dims=("variable", "index"), - coords={"variable": columns, "index": index}, - ) + # With this conversion, dataframe columns always become "dim_1". + # Rename to "variable" to match how multiple Dataset variables get converted into a DataArray dimension + data = xr.DataArray(data) + if data.dims[1] == "dim_1": + # Slight chance there is already a name for the columns + data = data.rename({"dim_1": "variable"}) # Checks xr.Dataset input and converts to xr.DataArray if possible if isinstance(data, xr.Dataset): keys = list(data.keys()) if len(keys) == 1: - # if only one variable, remove the "variable" dimension and rename the DataArray to simplify - data = data.to_array() - data = data.sel(variable=keys[0]) - data.name = keys[0] - data.drop_vars("variable") + # if only one variable, select that variable so reduce the Dataset to a DataArray + data = data[keys[0]] else: # Allow multiple variables if they have the same dimensions if all([data[keys[0]].dims == data[key].dims for key in keys]): - data = data.to_array() + data = ( + data.to_array().T + ) # transpose so that the new "variable dimension" is the last dimension (matches DataFrame to DataArray behavior) else: raise ValueError( "Multivariate Datasets can only be input if all variables have the same dimensions." diff --git a/mhkit/wave/performance.py b/mhkit/wave/performance.py index 4d137dd48..160918cc0 100644 --- a/mhkit/wave/performance.py +++ b/mhkit/wave/performance.py @@ -6,7 +6,7 @@ from mhkit import wave import matplotlib.pylab as plt from os.path import join -from mhkit.utils import convert_to_dataarray, convert_to_dataset +from mhkit.utils import convert_to_dataarray def capture_length(P, J, to_pandas=True): @@ -246,22 +246,22 @@ def power_matrix(LM, JM): Parameters ------------ - LM: pandas DataFrame or xarray Dataset + LM: pandas DataFrame, xarray DataArray, or xarray Dataset Capture length matrix - JM: pandas DataFrame or xarray Dataset + JM: pandas DataFrame, xarray DataArray, or xarray Dataset Wave energy flux matrix Returns --------- - PM: pandas DataFrame or xarray Dataset + PM: pandas DataFrame, xarray DataArray, or xarray Dataset Power matrix """ - if not isinstance(LM, (pd.DataFrame, xr.Dataset)): + if not isinstance(LM, (pd.DataFrame, xr.DataArray, xr.Dataset)): raise TypeError( f"LM must be of type pd.DataFrame or xr.Dataset. Got: {type(LM)}" ) - if not isinstance(JM, (pd.DataFrame, xr.Dataset)): + if not isinstance(JM, (pd.DataFrame, xr.DataArray, xr.Dataset)): raise TypeError( f"JM must be of type pd.DataFrame or xr.Dataset. Got: {type(JM)}" ) @@ -306,11 +306,11 @@ def mean_annual_energy_production_matrix(LM, JM, frequency): Parameters ------------ - LM: pandas DataFrame or xarray Dataset + LM: pandas DataFrame, xarray DataArray, or xarray Dataset Capture length - JM: pandas DataFrame or xarray Dataset + JM: pandas DataFrame, xarray DataArray, or xarray Dataset Wave energy flux - frequency: pandas DataFrame or xarray Dataset + frequency: pandas DataFrame, xarray DataArray, or xarray Dataset Data frequency for each bin Returns @@ -393,7 +393,7 @@ def power_performance_workflow( maep_matrix: float Mean annual energy production """ - S = convert_to_dataset(S) + S = convert_to_dataarray(S) if not isinstance(h, (int, float)): raise TypeError(f"h must be of type int or float. Got: {type(h)}") P = convert_to_dataarray(P) @@ -408,19 +408,16 @@ def power_performance_workflow( # Compute the enegy periods from the spectra data Te = wave.resource.energy_period(S, frequency_bins=frequency_bins, to_pandas=False) - Te = Te["Te"] # Compute the significant wave height from the NDBC spectra data Hm0 = wave.resource.significant_wave_height( S, frequency_bins=frequency_bins, to_pandas=False ) - Hm0 = Hm0["Hm0"] # Compute the energy flux from spectra data and water depth J = wave.resource.energy_flux( S, h, deep=deep, rho=rho, g=g, ratio=ratio, to_pandas=False ) - J = J["J"] # Calculate capture length from power and energy flux L = wave.performance.capture_length(P, J, to_pandas=False) diff --git a/mhkit/wave/resource.py b/mhkit/wave/resource.py index 0cfe28e06..488df50c2 100644 --- a/mhkit/wave/resource.py +++ b/mhkit/wave/resource.py @@ -1,5 +1,4 @@ import warnings - from scipy.optimize import fsolve as _fsolve from scipy import signal as _signal import pandas as pd @@ -47,14 +46,14 @@ def elevation_spectrum( Returns --------- - S: pandas DataFrame or xr.Dataset - Spectral density [m^2/Hz] indexed by frequency [Hz] + S: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset + Spectral density [m^2/Hz] indexed by frequency [Hz]. """ # TODO: Add confidence intervals, equal energy frequency spacing, and NDBC # frequency spacing # TODO: may need to raise an error for the length of nnft- signal.welch breaks when nfft is too short - eta = convert_to_dataset(eta) + eta = convert_to_dataset(eta, "eta") if not isinstance(sample_rate, (float, int)): raise TypeError( f"sample_rate must be of type int or float. Got: {type(sample_rate)}" @@ -84,18 +83,18 @@ def elevation_spectrum( if not np.allclose(time.diff(dim=time_dimension)[1:], delta_t): raise ValueError( "Time bins are not evenly spaced. Create a constant " - + "temporal spacing for eta." + + f"temporal spacing for eta." ) S = xr.Dataset() for var in eta.data_vars: - data = eta[var] + eta_subset = eta[var] if detrend: - data = _signal.detrend( - data.dropna(dim=time_dimension), axis=-1, type="linear", bp=0 + eta_subset = _signal.detrend( + eta_subset.dropna(dim=time_dimension), axis=-1, type="linear", bp=0 ) [f, wave_spec_measured] = _signal.welch( - data, + eta_subset, fs=sample_rate, window=window, nperseg=nnft, @@ -106,7 +105,7 @@ def elevation_spectrum( S = S.assign_coords({"Frequency": f}) if to_pandas: - S = S.to_dataframe() + S = S.to_pandas() return S @@ -275,24 +274,24 @@ def surface_elevation( method: str (optional) Method used to calculate the surface elevation. 'ifft' (Inverse Fast Fourier Transform) used by default if the - given frequency_bins==None. + given frequency_bins==None or is evenly spaced. 'sum_of_sines' explicitly sums each frequency component - and used by default if frequency_bins are provided. + and used by default if uneven frequency_bins are provided. The 'ifft' method is significantly faster. frequency_dimension: string (optional) Name of the xarray dimension corresponding to frequency. If not supplied, - defaults to the first dimension. Does not affect pandas input. + defaults to the first dimension (the index for pandas input). to_pandas: bool (optional) Flag to output pandas instead of xarray. Default = True. Returns --------- eta: pandas DataFrame or xarray Dataset - Wave surface elevation [m] indexed by time [s] + Wave surface elevation [m] indexed by time [s]. """ + S = convert_to_dataset(S, "S") time_index = to_numeric_array(time_index, "time_index") - S = convert_to_dataset(S) if not isinstance(seed, (type(None), int)): raise TypeError(f"If specified, seed must be of type int. Got: {type(seed)}") if not isinstance(phases, type(None)): @@ -304,11 +303,18 @@ def surface_elevation( if frequency_dimension == "": frequency_dimension = list(S.coords)[0] - elif frequency_dimension not in list(S.dims): + # frequency_dimension given, but not in list of possible dimensions raise ValueError( f"frequency_dimension is not a dimension of S ({list(S.dims)}). Got: {frequency_dimension}." ) + frequency_axis = list(S.dims).index(frequency_dimension) + + # Create dimensions and coordinates for the new dataset (frequency becomes time) + new_dims = list(S.dims) + new_dims[frequency_axis] = "Time" + new_coords = S.sum(dim=frequency_dimension).coords + new_coords = new_coords.assign({"Time": time_index}) f = S[frequency_dimension] if not isinstance(frequency_bins, (type(None), np.ndarray)): @@ -322,40 +328,39 @@ def surface_elevation( if frequency_bins is not None: if not frequency_bins.squeeze().shape == f.shape: raise ValueError( - "shape of frequency_bins must match shape of the frequency dimension of S" + "shape of frequency_bins must only contain 1 column and match the shape of the frequency dimension of S" ) + delta_f = frequency_bins + delta_f_even = np.allclose(frequency_bins, frequency_bins[0]) + if delta_f_even: + # reduce delta_f to a scalar if it is uniform + delta_f = delta_f[0].item() + else: + delta_f = f.values[1] - f.values[0] + delta_f_even = np.allclose(f.diff(dim=frequency_dimension)[1:], delta_f) if phases is not None: - if not list(phases.data_vars) == list(S.data_vars): - raise ValueError("phases must have the same variable names as S") for var in phases.data_vars: if not phases[var].shape == S[var].shape: raise ValueError( "shape of variables in phases must match shape of variables in S" ) - if method is not None: - if not (method == "ifft" or method == "sum_of_sines"): - raise ValueError(f"Method must be 'ifft' or 'sum_of_sines'. Got: {method}") - if method == "ifft": + # ifft method must have a zero frequency and evenly spaced frequency bins if not f[0] == 0: warnings.warn( f"ifft method must have zero frequency defined. Lowest frequency is: {f[0].values}. Setting method to less efficient `sum_of_sines` method." ) method = "sum_of_sines" - - if frequency_bins is None: - delta_f = f.values[1] - f.values[0] - if not np.allclose(f.diff(dim=frequency_dimension)[1:], delta_f): - raise ValueError( - "Frequency bins are not evenly spaced. " - + "Define 'frequency_bins' or create a constant " - + "frequency spacing for S." + if not delta_f_even: + warnings.warn( + f"ifft method must have evenly spaced frequency bins. Setting method to less efficient `sum_of_sines` method." ) + method = "sum_of_sines" + elif method == "sum_of_sines": + # For sum of sines, does not matter if there is a zero frequency or if frequency bins are evenly spaced + pass else: - if not len(frequency_bins.squeeze().shape) == 1: - raise ValueError("frequency_bins must only contain 1 column") - delta_f = frequency_bins - method = "sum_of_sines" + raise ValueError(f"Method must be 'ifft' or 'sum_of_sines'. Got: {method}") omega = xr.DataArray( data=2 * np.pi * f, dims=frequency_dimension, coords={frequency_dimension: f} @@ -363,10 +368,13 @@ def surface_elevation( eta = xr.Dataset() for var in S.data_vars: + S_subset = S[var] if phases is None: np.random.seed(seed) phase = xr.DataArray( - data=2 * np.pi * np.random.rand(S[var].size), + data=2 + * np.pi + * np.random.random_sample(S_subset[frequency_dimension].shape), dims=frequency_dimension, coords={frequency_dimension: f}, ) @@ -374,18 +382,16 @@ def surface_elevation( phase = phases[var] # Wave amplitude times delta f - A = 2 * S[var] - A = A * delta_f - A = np.sqrt(A) + A = np.sqrt(2 * S_subset * delta_f) if method == "ifft": A_cmplx = A * (np.cos(phase) + 1j * np.sin(phase)) - eta_tmp = np.fft.irfft( - 0.5 * A_cmplx.values * time_index.size, time_index.size - ) - eta[var] = xr.DataArray( - data=eta_tmp, dims="Time", coords={"Time": time_index} + eta_tmp = np.fft.irfftn( + 0.5 * A_cmplx * time_index.size, + list(time_index.shape), + axes=[frequency_axis], ) + eta[var] = xr.DataArray(data=eta_tmp, dims=new_dims, coords=new_coords) elif method == "sum_of_sines": # Product of omega and time @@ -402,7 +408,9 @@ def surface_elevation( eta[var] = (C * A).sum(dim=frequency_dimension) if to_pandas: - eta = eta.to_dataframe() + eta = eta.to_pandas() + if isinstance(eta, (pd.Series, pd.DataFrame, xr.DataArray)): + eta.name = "eta" return eta @@ -430,7 +438,7 @@ def frequency_moment(S, N, frequency_bins=None, frequency_dimension="", to_panda m: pandas DataFrame or xarray Dataset Nth Frequency Moment indexed by S.columns """ - S = convert_to_dataset(S) + S = convert_to_dataarray(S) if not isinstance(N, int): raise TypeError(f"N must be of type int. Got: {type(N)}") if not isinstance(to_pandas, bool): @@ -464,10 +472,10 @@ def frequency_moment(S, N, frequency_bins=None, frequency_dimension="", to_panda m = S * fn * delta_f m = m.sum(dim=frequency_dimension) - m = _transform_dataset(m, "m" + str(N)) - if to_pandas: - m = m.to_dataframe() + m = m.to_pandas() + if isinstance(m, (pd.Series, pd.DataFrame, xr.DataArray)): + m.name = "m" + str(N) return m @@ -495,7 +503,7 @@ def significant_wave_height( Hm0: pandas DataFrame or xarray Dataset Significant wave height [m] index by S.columns """ - S = convert_to_dataset(S) + S = convert_to_dataarray(S) if not isinstance(to_pandas, bool): raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}") @@ -506,11 +514,13 @@ def significant_wave_height( frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m0": "Hm0"}) + ) Hm0 = 4 * np.sqrt(m0) if to_pandas: - Hm0 = Hm0.to_dataframe() + Hm0 = Hm0.to_pandas() + if isinstance(Hm0, (pd.Series, pd.DataFrame, xr.DataArray)): + Hm0.name = "Hm0" return Hm0 @@ -538,7 +548,6 @@ def average_zero_crossing_period( Tz: pandas DataFrame or xarray Dataset Average zero crossing period [s] indexed by S.columns """ - S = convert_to_dataset(S) if not isinstance(to_pandas, bool): raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}") @@ -549,19 +558,20 @@ def average_zero_crossing_period( frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m0": "Tz"}) + ) m2 = frequency_moment( S, 2, frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m2": "Tz"}) - + ) Tz = np.sqrt(m0 / m2) if to_pandas: - Tz = Tz.to_dataframe() + Tz = Tz.to_pandas() + if isinstance(Tz, (pd.Series, pd.DataFrame, xr.DataArray)): + Tz.name = "Tz" return Tz @@ -590,7 +600,6 @@ def average_crest_period( Average wave period [s] indexed by S.columns """ - S = convert_to_dataset(S) if not isinstance(to_pandas, bool): raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}") @@ -600,19 +609,21 @@ def average_crest_period( frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m2": "Tavg"}) + ) m4 = frequency_moment( S, 4, frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m4": "Tavg"}) + ) Tavg = np.sqrt(m2 / m4) if to_pandas: - Tavg = Tavg.to_dataframe() + Tavg = Tavg.to_pandas() + if isinstance(Tavg, (pd.Series, pd.DataFrame, xr.DataArray)): + Tavg.name = "Tavg" return Tavg @@ -638,7 +649,6 @@ def average_wave_period(S, frequency_dimension="", frequency_bins=None, to_panda Tm: pandas DataFrame or xarray Dataset Mean wave period [s] indexed by S.columns """ - S = convert_to_dataset(S) if not isinstance(to_pandas, bool): raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}") @@ -648,19 +658,21 @@ def average_wave_period(S, frequency_dimension="", frequency_bins=None, to_panda frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m0": "Tm"}) + ) m1 = frequency_moment( S, 1, frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m1": "Tm"}) + ) Tm = np.sqrt(m0 / m1) if to_pandas: - Tm = Tm.to_dataframe() + Tm = Tm.to_pandas() + if isinstance(Tm, (pd.Series, pd.DataFrame, xr.DataArray)): + Tm.name = "Tm" return Tm @@ -684,7 +696,7 @@ def peak_period(S, frequency_dimension="", to_pandas=True): Tp: pandas DataFrame or xarray Dataset Wave peak period [s] indexed by S.columns """ - S = convert_to_dataset(S) + S = convert_to_dataarray(S) if not isinstance(to_pandas, bool): raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}") @@ -699,10 +711,10 @@ def peak_period(S, frequency_dimension="", to_pandas=True): fp = S.idxmax(dim=frequency_dimension) # Hz Tp = 1 / fp - Tp = _transform_dataset(Tp, "Tp") - if to_pandas: - Tp = Tp.to_dataframe() + Tp = Tp.to_pandas() + if isinstance(Tp, (pd.Series, pd.DataFrame, xr.DataArray)): + Tp.name = "Tp" return Tp @@ -728,7 +740,7 @@ def energy_period(S, frequency_dimension="", frequency_bins=None, to_pandas=True Te: pandas DataFrame or xarray Dataset Wave energy period [s] indexed by S.columns """ - S = convert_to_dataset(S) + S = convert_to_dataarray(S) if not isinstance(to_pandas, bool): raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}") @@ -738,20 +750,22 @@ def energy_period(S, frequency_dimension="", frequency_bins=None, to_pandas=True frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m-1": "Te"}) + ) m0 = frequency_moment( S, 0, frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m0": "Te"}) + ) # Eq 13 in IEC 62600-101 Te = mn1 / m0 if to_pandas: - Te = Te.to_dataframe() + Te = Te.to_pandas() + if isinstance(Te, (pd.Series, pd.DataFrame, xr.DataArray)): + Te.name = "Te" return Te @@ -777,7 +791,7 @@ def spectral_bandwidth(S, frequency_dimension="", frequency_bins=None, to_pandas e: pandas DataFrame or xarray Dataset Spectral bandwidth [s] indexed by S.columns """ - S = convert_to_dataset(S) + S = convert_to_dataarray(S) if not isinstance(to_pandas, bool): raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}") @@ -787,26 +801,28 @@ def spectral_bandwidth(S, frequency_dimension="", frequency_bins=None, to_pandas frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m2": "e"}) + ) m0 = frequency_moment( S, 0, frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m0": "e"}) + ) m4 = frequency_moment( S, 4, frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m4": "e"}) + ) e = np.sqrt(1 - (m2**2) / (m0 / m4)) if to_pandas: - e = e.to_dataframe() + e = e.to_pandas() + if isinstance(e, (pd.Series, pd.DataFrame, xr.DataArray)): + e.name = "e" return e @@ -832,7 +848,6 @@ def spectral_width(S, frequency_dimension="", frequency_bins=None, to_pandas=Tru v: pandas DataFrame or xarray Dataset Spectral width [m] indexed by S.columns """ - S = convert_to_dataset(S) if not isinstance(to_pandas, bool): raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}") @@ -842,27 +857,29 @@ def spectral_width(S, frequency_dimension="", frequency_bins=None, to_pandas=Tru frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m-2": "v"}) + ) m0 = frequency_moment( S, 0, frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m0": "v"}) + ) mn1 = frequency_moment( S, -1, frequency_bins=frequency_bins, frequency_dimension=frequency_dimension, to_pandas=False, - ).rename({"m-1": "v"}) + ) # Eq 16 in IEC 62600-101 v = np.sqrt((m0 * mn2 / np.power(mn1, 2)) - 1) if to_pandas: - v = v.to_dataframe() + v = v.to_pandas() + if isinstance(v, (pd.Series, pd.DataFrame, xr.DataArray)): + v.name = "v" return v @@ -909,7 +926,7 @@ def energy_flux( J: pandas DataFrame or xarray Dataset Omni-directional wave energy flux [W/m] indexed by S.columns """ - S = convert_to_dataset(S) + S = convert_to_dataarray(S) if not isinstance(h, (int, float)): raise TypeError(f"h must be of type int or float. Got: {type(h)}") if not isinstance(deep, bool): @@ -933,8 +950,8 @@ def energy_flux( if deep: # Eq 8 in IEC 62600-100, deep water simplification - Te = energy_period(S, to_pandas=False).rename({"Te": "J"}) - Hm0 = significant_wave_height(S, to_pandas=False).rename({"Hm0": "J"}) + Te = energy_period(S, to_pandas=False) + Hm0 = significant_wave_height(S, to_pandas=False) coeff = rho * (g**2) / (64 * np.pi) @@ -945,9 +962,7 @@ def energy_flux( k = wave_number(f, h, rho, g, to_pandas=False) # wave celerity (group velocity) - Cg = wave_celerity(k, h, g, depth_check=True, ratio=ratio, to_pandas=False)[ - "Cg" - ] + Cg = wave_celerity(k, h, g, depth_check=True, ratio=ratio, to_pandas=False) # Calculating the wave energy flux, Eq 9 in IEC 62600-101 delta_f = f.diff(dim=frequency_dimension) @@ -958,10 +973,11 @@ def energy_flux( CgSdelF = S * delta_f * Cg J = rho * g * CgSdelF.sum(dim=frequency_dimension) - J = _transform_dataset(J, "J") if to_pandas: - J = J.to_dataframe() + J = J.to_pandas() + if isinstance(J, (pd.Series, pd.DataFrame, xr.DataArray)): + J.name = "J" return J @@ -997,8 +1013,8 @@ def energy_period_to_peak_period(Te, gamma): factor = 0.8255 + 0.03852 * gamma - 0.005537 * gamma**2 + 0.0003154 * gamma**3 Tp = Te / factor - if isinstance(Tp, xr.Dataset): - Tp.rename({"Te": "Tp"}) + if isinstance(Tp, (pd.Series, pd.DataFrame, xr.DataArray)): + Tp.name = "Tp" return Tp @@ -1091,10 +1107,10 @@ def wave_celerity( ) Cg.name = "Cg" - Cg = Cg.to_dataset() - if to_pandas: - Cg = Cg.to_dataframe() + Cg = Cg.to_pandas() + if isinstance(Cg, (pd.Series, pd.DataFrame, xr.DataArray)): + Cg.name = "Cg" return Cg @@ -1122,6 +1138,8 @@ def wave_length(k): ) l = 2 * np.pi / k + if isinstance(l, (pd.Series, pd.DataFrame, xr.DataArray)): + l.name = "l" return l @@ -1135,7 +1153,7 @@ def wave_number(f, h, rho=1025, g=9.80665, to_pandas=True): Parameters ----------- - f: int, float, numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset + f: int, float, numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray Frequency [Hz] h: float Water depth [m] @@ -1153,7 +1171,6 @@ def wave_number(f, h, rho=1025, g=9.80665, to_pandas=True): """ if isinstance(f, (int, float)): f = np.asarray([f]) - f = convert_to_dataarray(f) if not isinstance(h, (int, float)): raise TypeError(f"h must be of type int or float. Got: {type(h)}") if not isinstance(rho, (int, float)): @@ -1182,12 +1199,10 @@ def func(kk): if not ier == 1: raise ValueError("Wave number not found. " + mesg) k0[mask] = k + k = k0 - k0.name = "k" - k = k0.to_dataset() - - if to_pandas: - k = k.to_dataframe() + if isinstance(k, (pd.Series, pd.DataFrame, xr.DataArray)): + k.name = "k" return k @@ -1228,16 +1243,7 @@ def depth_regime(l, h, ratio=2): raise TypeError(f"h must be of type int or float. Got: {type(h)}") depth_reg = h / l > ratio + if isinstance(depth_reg, (pd.Series, pd.DataFrame, xr.DataArray)): + depth_reg.name = "depth_reg" return depth_reg - - -def _transform_dataset(data, name): - # Converting data from a Dataset into a DataArray will turn the variables - # columns into a 'variable' dimension. - # Converting it back to a dataset will keep this concise variable dimension - # but in the expected xr.Dataset/pd.DataFrame format - data = data.to_array() - data = convert_to_dataset(data, name=name) - data = data.rename({"variable": "index"}) - return data