From d1cab1ba5781477808e85f68ef5c33041113ca52 Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Wed, 8 Oct 2025 13:35:39 -0700 Subject: [PATCH 01/20] switch to uv --- .github/workflows/python-package.yml | 35 +- .pre-commit-config.yaml | 34 +- calibration_test.ipynb | 23 +- docs/source/conf.py | 2 +- docs/source/thresh_plot.py | 12 +- examples/Hough_transform.ipynb | 132 +- examples/Synthetic.ipynb | 183 +- examples/Untitled.ipynb | 100 +- .../Worked_example_with_ground_truth.ipynb | 127 +- examples/example_gemini_gmos_longslit.py | 12 +- ...ample_gemini_gmos_longslit_manual_atlas.py | 16 +- examples/example_gtc_osiris.py | 4 +- examples/example_int_ids.py | 8 +- examples/example_keck_deimos_830g.py | 4 +- examples/example_lt_sprat_xe.py | 8 +- examples/example_lt_sprat_xe_manual_atlas.py | 12 +- examples/example_ntt_efosc_grism11.py | 4 +- examples/example_wht_isis_r300r.py | 12 +- examples/monotonic_polyfit.ipynb | 168 +- pyproject.toml | 74 +- readthedocs.yml | 2 +- requirements.txt | 5 - setup.py | 6 - src/rascal/__init__.py | 8 +- src/rascal/arc_lines/convolve_lines.py | 4 +- src/rascal/atlas.py | 51 +- src/rascal/calibrator.py | 272 +-- src/rascal/houghtransform.py | 88 +- src/rascal/plotting.py | 124 +- src/rascal/synthetic.py | 33 +- src/rascal/util.py | 68 +- test/__init__.py | 0 test/refit_pfit.py | 4 +- test/test_effective_pixel.py | 12 +- test/test_hough_transform.py | 76 +- test/test_lt_sprat_manual_atlas.py | 36 +- test/test_plotting.py | 8 +- test/test_polynomial_fit.py | 24 +- test/test_synthetic_calibration.py | 16 +- test/test_util.py | 16 +- test/test_xe_calibration.py | 15 +- uv.lock | 1765 +++++++++++++++++ 42 files changed, 2494 insertions(+), 1109 deletions(-) delete mode 100644 requirements.txt delete mode 100644 setup.py create mode 100644 test/__init__.py create mode 100644 uv.lock diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index eb24b1c..d204cc7 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -16,31 +16,28 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] + python-version: ["3.10", "3.11", "3.12", "3.13"] steps: - - uses: actions/checkout@v2 - - uses: s-weigand/setup-conda@v1 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + - uses: actions/checkout@v5 + + - name: Install uv + uses: astral-sh/setup-uv@v6 with: - python-version: ${{ matrix.python-version }} + python-version: ${{ matrix.python-version }} + + - name: Setup venv + run: uv python install + - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install flake8 coverage psutil requests kaleido - python -m pip install -r requirements.txt - python -m pip install -r test-requirements.txt - python -m pip install -e . - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + run: uv sync --all-extras --dev + + - name: Lint with ruff using uv + run: uv run ruff check + - name: Test with pytest run: | - coverage run -m pytest --disable-warnings -p no:warnings test + uv run pytest -v --disable-warnings -p no:warnings test - name: Generate Coveralls reports if: startsWith(matrix.os, 'ubuntu') uses: AndreMiras/coveralls-python-action@develop diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0a5e51b..987e6e2 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,12 +1,4 @@ repos: -- repo: https://github.com/psf/black - rev: 23.1.0 - hooks: - - id: black - language_version: python3 - args: # arguments to configure black - - --line-length=79 - - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.4.0 hooks: @@ -23,20 +15,12 @@ repos: - id: debug-statements - id: requirements-txt-fixer -- repo: local - hooks: - - id: remarklint - name: remarklint - language: node - entry: remark - types: [markdown] - args: ["--frail", "--quiet"] - additional_dependencies: - [ - remark, - remark-lint, - remark-cli, - remark-preset-lint-recommended, - remark-lint-list-item-indent, - remark-lint-no-undefined-references, - ] +- repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.14.0 + hooks: + # Run the linter. + - id: ruff-check + args: [ --fix ] + # Run the formatter. + - id: ruff-format \ No newline at end of file diff --git a/calibration_test.ipynb b/calibration_test.ipynb index a1e7866..d67f35f 100644 --- a/calibration_test.ipynb +++ b/calibration_test.ipynb @@ -8,7 +8,6 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import astropy.units as u\n", "from astropy.io import fits\n", "from scipy.signal import find_peaks\n", "from rascal.calibrator import Calibrator\n", @@ -51,19 +50,16 @@ } ], "source": [ - "spectrum = np.median(fits.open('./examples/v_a_20190516_55_1_0_1.fits')[0].data[110:120], axis=0)\n", + "spectrum = np.median(fits.open(\"./examples/v_a_20190516_55_1_0_1.fits\")[0].data[110:120], axis=0)\n", "print(spectrum.shape)\n", "\n", - "resampled_flux = resample(spectrum-spectrum.min(), len(spectrum)*4)\n", + "resampled_flux = resample(spectrum - spectrum.min(), len(spectrum) * 4)\n", "peaks, _ = find_peaks(resampled_flux, prominence=150, distance=20, threshold=None)\n", "peaks = peaks / 4.0\n", "\n", - "plt.figure(figsize=(18,3))\n", + "plt.figure(figsize=(18, 3))\n", "plt.plot(spectrum)\n", - "plt.vlines(peaks,\n", - " spectrum[peaks.astype('int')],\n", - " spectrum.max(),\n", - " colors='C1')" + "plt.vlines(peaks, spectrum[peaks.astype(\"int\")], spectrum.max(), colors=\"C1\")" ] }, { @@ -72,10 +68,7 @@ "metadata": {}, "outputs": [], "source": [ - "c = Calibrator(peaks, elements=[\"Xe_strong\"],\n", - " min_wavelength=3400.,\n", - " max_wavelength=8100.,\n", - " range_tolerance=200)\n", + "c = Calibrator(peaks, elements=[\"Xe_strong\"], min_wavelength=3400.0, max_wavelength=8100.0, range_tolerance=200)\n", "c.set_fit_constraints(n_pix=len(spectrum))" ] }, @@ -155,10 +148,10 @@ } ], "source": [ - "peaks, wavelengths, lamps = np.loadtxt(\"./examples/ground_truth_v_a.csv\", delimiter=',', dtype='U').T\n", + "peaks, wavelengths, lamps = np.loadtxt(\"./examples/ground_truth_v_a.csv\", delimiter=\",\", dtype=\"U\").T\n", "\n", - "peaks = peaks.astype('float32')\n", - "wavelengths = wavelengths.astype('float32')\n", + "peaks = peaks.astype(\"float32\")\n", + "wavelengths = wavelengths.astype(\"float32\")\n", "\n", "x, y = c._combine_linear_estimates(c.candidates)\n", "plt.scatter(x, y, s=20, label=\"Candidate match\")\n", diff --git a/docs/source/conf.py b/docs/source/conf.py index 2b45191..55564bb 100755 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -92,6 +92,6 @@ html_theme = "sphinx_rtd_theme" html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # html_static_path = ["_static"] - except: + except: #noqa E722 html_theme = "alabaster" # html_static_path = ["_static"] diff --git a/docs/source/thresh_plot.py b/docs/source/thresh_plot.py index f5af254..d80921c 100755 --- a/docs/source/thresh_plot.py +++ b/docs/source/thresh_plot.py @@ -6,9 +6,7 @@ # data point pix = np.arange(0, 1001, 50) wave = 4000.0 + pix**0.63437 * 50.0 -plt.scatter( - pix, wave, label="Solution", c="midnightblue", s=10, marker="x", zorder=15 -) +plt.scatter(pix, wave, label="Solution", c="midnightblue", s=10, marker="x", zorder=15) # minmax & range tolerance wave_min = 4000.0 @@ -29,9 +27,7 @@ lw=1, c="orangered", ) -plt.plot( - [pix[0], pix[-1]], [wave_min, wave_min], c="orangered", lw=1, label="" -) +plt.plot([pix[0], pix[-1]], [wave_min, wave_min], c="orangered", lw=1, label="") plt.plot( [pix[0], pix[-1]], [wave_max - range_tolerance, wave_max - range_tolerance], @@ -47,9 +43,7 @@ lw=1, c="orangered", ) -plt.plot( - [pix[0], pix[-1]], [wave_max, wave_max], c="orangered", lw=1, label="" -) +plt.plot([pix[0], pix[-1]], [wave_max, wave_max], c="orangered", lw=1, label="") plt.annotate( s="", diff --git a/examples/Hough_transform.ipynb b/examples/Hough_transform.ipynb index b65f781..f2b72fb 100644 --- a/examples/Hough_transform.ipynb +++ b/examples/Hough_transform.ipynb @@ -7,6 +7,7 @@ "outputs": [], "source": [ "import sys\n", + "\n", "sys.path.append(\"..\")" ] }, @@ -27,7 +28,6 @@ } ], "source": [ - "import matplotlib\n", "import numpy as np\n", "from scipy.signal import find_peaks\n", "from astropy.io import fits\n", @@ -87,24 +87,24 @@ ], "source": [ "# Load the 2D spectrum\n", - "spectrum_image = fits.open('data_lt_sprat/v_a_20190516_55_1_0_1.fits')[0].data\n", + "spectrum_image = fits.open(\"data_lt_sprat/v_a_20190516_55_1_0_1.fits\")[0].data\n", "\n", "# Collapsing into a 1D spectrum\n", "spectrum = np.median(spectrum_image[110:130], axis=0)[110:]\n", "\n", "# Make the plot\n", - "plt.figure(1, figsize=(12,4))\n", - "plt.imshow(spectrum_image[:,110:], origin='lower')\n", - "plt.xlabel('x pixel (Spectral Direction)')\n", - "plt.ylabel('y pixel (Spatial Direction)')\n", + "plt.figure(1, figsize=(12, 4))\n", + "plt.imshow(spectrum_image[:, 110:], origin=\"lower\")\n", + "plt.xlabel(\"x pixel (Spectral Direction)\")\n", + "plt.ylabel(\"y pixel (Spatial Direction)\")\n", "plt.tight_layout()\n", "\n", - "plt.figure(2, figsize=(12,4))\n", + "plt.figure(2, figsize=(12, 4))\n", "plt.plot(spectrum)\n", "plt.grid()\n", "plt.xlim(0, 914)\n", - "plt.xlabel('x pixel (Spectral Direction)')\n", - "plt.ylabel('Intensity')\n", + "plt.xlabel(\"x pixel (Spectral Direction)\")\n", + "plt.ylabel(\"Intensity\")\n", "plt.tight_layout()" ] }, @@ -131,8 +131,7 @@ ], "source": [ "pix_scale = 9.2\n", - "peaks, _ = find_peaks(\n", - " spectrum, distance=3., prominence=np.percentile(spectrum, 20))\n", + "peaks, _ = find_peaks(spectrum, distance=3.0, prominence=np.percentile(spectrum, 20))\n", "\n", "print(peaks)" ] @@ -156,15 +155,15 @@ } ], "source": [ - "#NB The small peak on the left is at 4500, the peak on the far right is at \n", + "# NB The small peak on the left is at 4500, the peak on the far right is at\n", "\n", - "plt.figure(1, figsize=(12,8))\n", + "plt.figure(1, figsize=(12, 8))\n", "plt.plot(spectrum)\n", "plt.vlines(peaks, 0, max(spectrum), alpha=0.3)\n", - "plt.xlim(0,910)\n", - "plt.ylim(0,15000)\n", - "plt.xlabel('x pixel')\n", - "plt.ylabel('Total ADU')\n", + "plt.xlim(0, 910)\n", + "plt.ylim(0, 15000)\n", + "plt.xlabel(\"x pixel\")\n", + "plt.ylabel(\"Total ADU\")\n", "plt.tight_layout()" ] }, @@ -190,7 +189,7 @@ "metadata": {}, "outputs": [], "source": [ - "c = Calibrator(peaks, len(spectrum), min_wavelength=4000., max_wavelength=8000., plotting_library='plotly')\n", + "c = Calibrator(peaks, len(spectrum), min_wavelength=4000.0, max_wavelength=8000.0, plotting_library=\"plotly\")\n", "c.add_atlas([\"Xe\"])\n", "c.set_fit_constraints(range_tolerance=500)" ] @@ -208,12 +207,12 @@ "metadata": {}, "outputs": [], "source": [ - "num_slopes = [10,50,100,500,1000,5000,10000]\n", + "num_slopes = [10, 50, 100, 500, 1000, 5000, 10000]\n", "\n", "accumulators = {}\n", "\n", "for n in num_slopes:\n", - " accumulators[n] = c._hough_points(c.pairs[:,0], c.pairs[:,1], num_slopes=n)" + " accumulators[n] = c._hough_points(c.pairs[:, 0], c.pairs[:, 1], num_slopes=n)" ] }, { @@ -242,17 +241,17 @@ } ], "source": [ - "fig, axs = plt.subplots(2, 2, sharex='col', sharey='row', gridspec_kw={'hspace': 0, 'wspace': 0}, figsize=(12,8))\n", + "fig, axs = plt.subplots(2, 2, sharex=\"col\", sharey=\"row\", gridspec_kw={\"hspace\": 0, \"wspace\": 0}, figsize=(12, 8))\n", "(ax1, ax2), (ax3, ax4) = axs\n", "ax1.scatter(*accumulators[10].T, s=1, alpha=0.1)\n", "ax2.scatter(*accumulators[50].T, s=1, alpha=0.1)\n", "ax3.scatter(*accumulators[100].T, s=1, alpha=0.05)\n", "ax4.scatter(*accumulators[500].T, s=1, alpha=0.01)\n", "\n", - "ax1.set_ylabel('Intercept Wavelength')\n", - "ax3.set_ylabel('Intercept Wavelength')\n", - "ax3.set_xlabel('Gradient')\n", - "ax4.set_xlabel('Gradient')\n", + "ax1.set_ylabel(\"Intercept Wavelength\")\n", + "ax3.set_ylabel(\"Intercept Wavelength\")\n", + "ax3.set_xlabel(\"Gradient\")\n", + "ax4.set_xlabel(\"Gradient\")\n", "plt.tight_layout()" ] }, @@ -328,14 +327,12 @@ "outputs": [], "source": [ "def plot_accumulator(c, a, nbins=50):\n", - " hist, xedge, yedge = np.histogram2d(a[:, 1],\n", - " a[:, 0],\n", - " bins=(nbins, nbins),\n", - " range=((c.min_intercept, c.max_intercept),\n", - " (c.min_slope, c.max_slope)))\n", - " \n", + " hist, xedge, yedge = np.histogram2d(\n", + " a[:, 1], a[:, 0], bins=(nbins, nbins), range=((c.min_intercept, c.max_intercept), (c.min_slope, c.max_slope))\n", + " )\n", + "\n", " # left, right, bottom, top\n", - " plt.imshow(hist, extent=[c.min_slope, c.max_slope, c.min_intercept, c.max_intercept], aspect='auto', origin='lower')" + " plt.imshow(hist, extent=[c.min_slope, c.max_slope, c.min_intercept, c.max_intercept], aspect=\"auto\", origin=\"lower\")" ] }, { @@ -364,7 +361,7 @@ } ], "source": [ - "plt.figure(1, figsize=(12,8))\n", + "plt.figure(1, figsize=(12, 8))\n", "\n", "plt.subplot(221)\n", "plot_accumulator(c, accumulators[50], nbins=25)\n", @@ -409,7 +406,7 @@ } ], "source": [ - "plt.figure(1, figsize=(12,8))\n", + "plt.figure(1, figsize=(12, 8))\n", "\n", "plt.subplot(221)\n", "plot_accumulator(c, accumulators[100], nbins=25)\n", @@ -454,7 +451,7 @@ } ], "source": [ - "plt.figure(1, figsize=(12,8))\n", + "plt.figure(1, figsize=(12, 8))\n", "\n", "plt.subplot(221)\n", "plot_accumulator(c, accumulators[1000], nbins=25)\n", @@ -499,7 +496,7 @@ } ], "source": [ - "plt.figure(1, figsize=(12,8))\n", + "plt.figure(1, figsize=(12, 8))\n", "\n", "plt.subplot(221)\n", "plot_accumulator(c, accumulators[10000], nbins=25)\n", @@ -576,29 +573,32 @@ } ], "source": [ - "plt.figure(10, figsize=(12,8))\n", + "plt.figure(10, figsize=(12, 8))\n", "\n", - "slope_range = c.max_slope-c.min_slope\n", + "slope_range = c.max_slope - c.min_slope\n", "\n", "for ns in tqdm(num_slopes):\n", " x = []\n", " y = []\n", - " for nb in np.linspace(10,10000):\n", - " \n", + " for nb in np.linspace(10, 10000):\n", " if nb > ns:\n", " continue\n", - " \n", + "\n", " a = accumulators[ns]\n", - " hist, xedge, yedge = np.histogram2d(a[:, 0],\n", - " a[:, 1],\n", - " bins=(int(nb), int(nb)),\n", - " range=((c.min_slope, c.max_slope),\n", - " (c.min_intercept, c.max_intercept)))\n", - " \n", + " hist, xedge, yedge = np.histogram2d(\n", + " a[:, 0], a[:, 1], bins=(int(nb), int(nb)), range=((c.min_slope, c.max_slope), (c.min_intercept, c.max_intercept))\n", + " )\n", + "\n", " x.append(nb)\n", - " y.append(hist.max()/hist[hist > 0].mean())\n", - " \n", - " plt.plot(x, y, label=\"slope_res:{} A/px\".format(round(slope_range/ns, 4), ns))\n", + " y.append(hist.max() / hist[hist > 0].mean())\n", + "\n", + " plt.plot(\n", + " x,\n", + " y,\n", + " label=\"slope_res:{} A/px\".format(\n", + " round(slope_range / ns, 4),\n", + " ),\n", + " )\n", "\n", "plt.legend(title=\"Slope resolution\")\n", "plt.xlabel(\"Number of bins\")\n", @@ -663,18 +663,18 @@ " # Get the top 5 lines and matched wavelengths\n", " slope, intercept = line\n", " peaks, wavelengths = c._get_candidate_points_linear(slope, intercept, thresh=30)\n", - " x = np.arange(100,900)\n", - " y = x*slope + intercept\n", - " \n", + " x = np.arange(100, 900)\n", + " y = x * slope + intercept\n", + "\n", " # Plot\n", - " plt.figure(1, figsize=(12,8))\n", + " plt.figure(1, figsize=(12, 8))\n", " plt.scatter(peaks, wavelengths, s=10)\n", " plt.plot(x, y)\n", " plt.grid()\n", - " plt.xlim(100,900)\n", - " plt.ylim(4000,8500)\n", - " plt.xlabel('x pixel')\n", - " plt.ylabel(r'Wavelength / ${\\AA}$')\n", + " plt.xlim(100, 900)\n", + " plt.ylim(4000, 8500)\n", + " plt.xlabel(\"x pixel\")\n", + " plt.ylabel(r\"Wavelength / ${\\AA}$\")\n", " plt.title(\"Fit threshold of 30A\")\n", " plt.tight_layout()" ] @@ -702,18 +702,18 @@ " # Get the top 5 lines and matched wavelengths\n", " slope, intercept = line\n", " peaks, wavelengths = c._get_candidate_points_linear(slope, intercept, thresh=1)\n", - " x = np.arange(100,900)\n", - " y = x*slope + intercept\n", - " \n", + " x = np.arange(100, 900)\n", + " y = x * slope + intercept\n", + "\n", " # Plot\n", - " plt.figure(1, figsize=(12,8))\n", + " plt.figure(1, figsize=(12, 8))\n", " plt.scatter(peaks, wavelengths, s=10)\n", " plt.plot(x, y)\n", " plt.grid()\n", - " plt.xlim(100,900)\n", - " plt.ylim(4000,8500)\n", - " plt.xlabel('x pixel')\n", - " plt.ylabel(r'Wavelength / ${\\AA}$')\n", + " plt.xlim(100, 900)\n", + " plt.ylim(4000, 8500)\n", + " plt.xlabel(\"x pixel\")\n", + " plt.ylabel(r\"Wavelength / ${\\AA}$\")\n", " plt.title(\"Fit threshold of 1A\")\n", " plt.tight_layout()" ] diff --git a/examples/Synthetic.ipynb b/examples/Synthetic.ipynb index 5db4555..78bb32e 100644 --- a/examples/Synthetic.ipynb +++ b/examples/Synthetic.ipynb @@ -26,12 +26,9 @@ "from rascal import util\n", "\n", "import matplotlib.pyplot as plt\n", - "from tqdm.autonotebook import tqdm\n", "\n", - "import numpy as np\n", "\n", - "from rascal.calibrator import Calibrator\n", - "from rascal.util import refine_peaks\n" + "from rascal.calibrator import Calibrator" ] }, { @@ -55,6 +52,7 @@ "source": [ "from scipy import signal\n", "\n", + "\n", "def gaus(x, a, b, x0, sigma):\n", " \"\"\"\n", " Simple Gaussian function.\n", @@ -76,33 +74,37 @@ " -------\n", " Array or float of same type as input (x).\n", " \"\"\"\n", - " return a * np.exp(-(x - x0)**2 / (2 * sigma**2)) + b\n", + " return a * np.exp(-((x - x0) ** 2) / (2 * sigma**2)) + b\n", "\n", "\n", "min_wavelength = 4000\n", "max_wavelength = 8000\n", "num_pix = 1024\n", "\n", - "elements1 = 'Hg'\n", - "elements2 = 'Ar'\n", - "nist1 = util.load_calibration_lines(elements1,\n", - " min_atlas_wavelength=min_wavelength,\n", - " max_atlas_wavelength=max_wavelength,\n", - " min_intensity=10.,\n", - " min_distance=5.,\n", - " vacuum=False)\n", - "nist2 = util.load_calibration_lines(elements2,\n", - " min_atlas_wavelength=min_wavelength,\n", - " max_atlas_wavelength=max_wavelength,\n", - " min_intensity=10.,\n", - " min_distance=5.,\n", - " vacuum=False)\n", + "elements1 = \"Hg\"\n", + "elements2 = \"Ar\"\n", + "nist1 = util.load_calibration_lines(\n", + " elements1,\n", + " min_atlas_wavelength=min_wavelength,\n", + " max_atlas_wavelength=max_wavelength,\n", + " min_intensity=10.0,\n", + " min_distance=5.0,\n", + " vacuum=False,\n", + ")\n", + "nist2 = util.load_calibration_lines(\n", + " elements2,\n", + " min_atlas_wavelength=min_wavelength,\n", + " max_atlas_wavelength=max_wavelength,\n", + " min_intensity=10.0,\n", + " min_distance=5.0,\n", + " vacuum=False,\n", + ")\n", "nist_element1 = nist1[0]\n", - "nist_wavelength1 = nist1[1].astype('float')\n", - "nist_intensity1 = nist1[2].astype('float')\n", + "nist_wavelength1 = nist1[1].astype(\"float\")\n", + "nist_intensity1 = nist1[2].astype(\"float\")\n", "nist_element2 = nist2[0]\n", - "nist_wavelength2 = nist2[1].astype('float')\n", - "nist_intensity2 = nist2[2].astype('float')\n", + "nist_wavelength2 = nist2[1].astype(\"float\")\n", + "nist_intensity2 = nist2[2].astype(\"float\")\n", "\n", "nist_element = np.concatenate((nist_element1, nist_element2))\n", "nist_wavelength = np.concatenate((nist_wavelength1, nist_wavelength2))\n", @@ -112,9 +114,7 @@ "xe_i = nist_intensity\n", "\n", "# Generate the equally spaced-wavelength array, and the corresponding intensity\n", - "wavelength = np.around(np.arange(min_wavelength,\n", - " max_wavelength + 0.01, 0.01),\n", - " decimals=2)\n", + "wavelength = np.around(np.arange(min_wavelength, max_wavelength + 0.01, 0.01), decimals=2)\n", "intensity = np.zeros_like(wavelength)\n", "intensity[np.where(np.isin(wavelength, xe_w))] = xe_i\n", "\n", @@ -125,23 +125,23 @@ "# Nyquist sampling rate (2.3) for CCD at seeing of 1 arcsec\n", "sigma = R * 2.3 * 1.0\n", "x = np.arange(-100, 100.01, 0.01)\n", - "gaussian = gaus(x, a=1., b=0., x0=0., sigma=sigma)\n", + "gaussian = gaus(x, a=1.0, b=0.0, x0=0.0, sigma=sigma)\n", "\n", "# Convolve to simulate the arc spectrum\n", - "model_spectrum = signal.convolve(intensity, gaussian, 'same')\n", + "model_spectrum = signal.convolve(intensity, gaussian, \"same\")\n", "model_spectrum = signal.resample(model_spectrum, num_pix)\n", "model_wavelength = np.linspace(min_wavelength, max_wavelength, num_pix)\n", "\n", "plt.figure(1, figsize=(8, 8))\n", - "plt.plot(wavelength, intensity, color='grey', label='NIST values')\n", - "plt.plot(model_wavelength, model_spectrum, label='Convolved Arc')\n", - "plt.xlabel('Vacuum Wavelength / A')\n", - "plt.ylabel('NIST intensity')\n", + "plt.plot(wavelength, intensity, color=\"grey\", label=\"NIST values\")\n", + "plt.plot(model_wavelength, model_spectrum, label=\"Convolved Arc\")\n", + "plt.xlabel(\"Vacuum Wavelength / A\")\n", + "plt.ylabel(\"NIST intensity\")\n", "plt.grid()\n", "plt.xlim(3800, 8200)\n", "plt.ylim(0, max(intensity))\n", "plt.legend()\n", - "plt.tight_layout()\n" + "plt.tight_layout()" ] }, { @@ -151,7 +151,7 @@ "outputs": [], "source": [ "class SyntheticSpectrum:\n", - " def __init__(self, coefficients, model_type='cubic', degree=None):\n", + " def __init__(self, coefficients, model_type=\"cubic\", degree=None):\n", " \"\"\"\n", " Creates a synthetic spectrum generator which, given a suitable model,\n", " outputs the expected pixel locations of input wavelengths.\n", @@ -175,16 +175,15 @@ " # Angstrom\n", " self.min_wavelength = 2000\n", " self.max_wavelength = 12000\n", - " \n", + "\n", " self.coefficients = coefficients\n", "\n", " # Model to fit\n", - " if model_type == 'quadratic':\n", + " if model_type == \"quadratic\":\n", " self.model = models.quadratic(self.coefficients)\n", - " elif model_type == 'cubic':\n", + " elif model_type == \"cubic\":\n", " self.model = models.cubic(self.coefficients)\n", - " elif model_type == 'poly':\n", - "\n", + " elif model_type == \"poly\":\n", " if degree is None:\n", " raise ValueError(\"You should specify a polynomial degree.\")\n", "\n", @@ -215,24 +214,24 @@ "\n", "\n", "class RandomSyntheticSpectrum(SyntheticSpectrum):\n", - "\n", - " def __init__(self, num_pixels=1024, min_wavelength=4000, max_wavelength=8000, endpoint_jitter=100, model_type='poly', degree=1):\n", - "\n", + " def __init__(\n", + " self, num_pixels=1024, min_wavelength=4000, max_wavelength=8000, endpoint_jitter=100, model_type=\"poly\", degree=1\n", + " ):\n", " # This is the maximum and minimum wavelength of the sensor\n", - " min_wavelength = min_wavelength +endpoint_jitter*(-1 + 2*random.random())\n", - " max_wavelength += endpoint_jitter*(-1 + 2*random.random())\n", + " min_wavelength = min_wavelength + endpoint_jitter * (-1 + 2 * random.random())\n", + " max_wavelength += endpoint_jitter * (-1 + 2 * random.random())\n", " self.num_pixels = 1024\n", - " \n", + "\n", " x0 = min_wavelength\n", - " x1 = (max_wavelength - x0)/num_pixels\n", - " \n", + " x1 = (max_wavelength - x0) / num_pixels\n", + "\n", " coefficients = [x0, x1]\n", - " \n", + "\n", " if degree >= 2:\n", - " coefficients.append(0.002*random.random())\n", - " \n", + " coefficients.append(0.002 * random.random())\n", + "\n", " if degree >= 3:\n", - " coefficients.append(-0.001+0.002*random.random())\n", + " coefficients.append(-0.001 + 0.002 * random.random())\n", "\n", " coefficients.reverse()\n", "\n", @@ -240,15 +239,17 @@ " self.set_wavelength_limit(x0, max_wavelength)\n", "\n", " def add_atlas(self, elements, n_lines=30, min_intensity=1000, min_distance=10):\n", - " lines = util.load_calibration_lines(elements,\n", - " min_atlas_wavelength=self.min_wavelength,\n", - " max_atlas_wavelength=self.max_wavelength,\n", - " min_intensity=min_intensity,\n", - " min_distance=min_distance,\n", - " vacuum=False)\n", - " \n", + " lines = util.load_calibration_lines(\n", + " elements,\n", + " min_atlas_wavelength=self.min_wavelength,\n", + " max_atlas_wavelength=self.max_wavelength,\n", + " min_intensity=min_intensity,\n", + " min_distance=min_distance,\n", + " vacuum=False,\n", + " )\n", + "\n", " idx = sorted(random.sample(range(len(lines[0])), min(len(lines[0]), n_lines)))\n", - " \n", + "\n", " self.elements = lines[0][idx]\n", " self.wavelengths = lines[1][idx]\n", " self.intensities = lines[2][idx]" @@ -260,37 +261,40 @@ "metadata": {}, "outputs": [], "source": [ - "def test_fit(spectrum, uncertainty = 0, range_tolerance=500, elements=['Hg', 'Ar']):\n", + "def test_fit(spectrum, uncertainty=0, range_tolerance=500, elements=[\"Hg\", \"Ar\"]):\n", " c = Calibrator(pixs)\n", - " \n", - " c.set_calibrator_properties(num_pix=spectrum.num_pixels, plotting_library='matplotlib',\n", - " log_level='info') \n", - "\n", - " c.set_hough_properties(num_slopes=5000,\n", - " xbins=100,\n", - " ybins=100,\n", - " min_wavelength=spectrum.min_wavelength,\n", - " max_wavelength=spectrum.max_wavelength,\n", - " range_tolerance=range_tolerance,\n", - " linearity_tolerance=100)\n", - "\n", - " #c.add_atlas(elements, min_atlas_wavelength=spectrum.min_wavelength - uncertainty,\n", + "\n", + " c.set_calibrator_properties(num_pix=spectrum.num_pixels, plotting_library=\"matplotlib\", log_level=\"info\")\n", + "\n", + " c.set_hough_properties(\n", + " num_slopes=5000,\n", + " xbins=100,\n", + " ybins=100,\n", + " min_wavelength=spectrum.min_wavelength,\n", + " max_wavelength=spectrum.max_wavelength,\n", + " range_tolerance=range_tolerance,\n", + " linearity_tolerance=100,\n", + " )\n", + "\n", + " # c.add_atlas(elements, min_atlas_wavelength=spectrum.min_wavelength - uncertainty,\n", " # max_atlas_wavelength=spectrum.max_wavelength + uncertainty)\n", - " \n", - " c.add_user_atlas(['Hg']*len(spectrum.wavelengths), spectrum.wavelengths)\n", - " \n", + "\n", + " c.add_user_atlas([\"Hg\"] * len(spectrum.wavelengths), spectrum.wavelengths)\n", + "\n", " print(c.atlas)\n", "\n", - " c.set_ransac_properties(sample_size=5,\n", - " top_n_candidate=5,\n", - " linear=True,\n", - " filter_close=True,\n", - " ransac_tolerance=5,\n", - " candidate_weighted=True,\n", - " hough_weight=1.0)\n", + " c.set_ransac_properties(\n", + " sample_size=5,\n", + " top_n_candidate=5,\n", + " linear=True,\n", + " filter_close=True,\n", + " ransac_tolerance=5,\n", + " candidate_weighted=True,\n", + " hough_weight=1.0,\n", + " )\n", "\n", " c.do_hough_transform()\n", - " \n", + "\n", " return c" ] }, @@ -354,19 +358,14 @@ "min_wavelength = 4000\n", "max_wavelength = 8000\n", "endpoint_jitter = 0\n", - "elements = ['Hg', 'Ar']\n", + "elements = [\"Hg\", \"Ar\"]\n", "poly_degree = 1\n", "num_pixels = 1024\n", "\n", "\"\"\"\n", "Generate spectrum + ground truth\n", "\"\"\"\n", - "spectrum = RandomSyntheticSpectrum(num_pixels,\n", - " min_wavelength,\n", - " max_wavelength,\n", - " endpoint_jitter,\n", - " 'poly',\n", - " poly_degree)\n", + "spectrum = RandomSyntheticSpectrum(num_pixels, min_wavelength, max_wavelength, endpoint_jitter, \"poly\", poly_degree)\n", "spectrum.add_atlas(elements)\n", "\n", "wavs = spectrum.wavelengths\n", @@ -446,7 +445,7 @@ " if len(atlas_idx) == 0:\n", " print(\"\\tLine is not in atlas.\")\n", " else:\n", - " idxs = np.argwhere(np.abs(candidates[:,1] - arc) < 1e-5)\n", + " idxs = np.argwhere(np.abs(candidates[:, 1] - arc) < 1e-5)\n", "\n", " if len(idxs) == 0:\n", " print(\"\\tIn atlas, but not a candidate\")\n", diff --git a/examples/Untitled.ipynb b/examples/Untitled.ipynb index 7b8f80e..b1a114b 100644 --- a/examples/Untitled.ipynb +++ b/examples/Untitled.ipynb @@ -2273,24 +2273,18 @@ } ], "source": [ - "import os\n", - "import logging\n", - "\n", "from astropy.io import fits\n", "from matplotlib import pyplot as plt\n", "\n", "import numpy as np\n", - "import pytest\n", "from scipy.signal import find_peaks\n", - "from tqdm.autonotebook import tqdm\n", "\n", "from rascal.calibrator import Calibrator\n", - "from rascal import models\n", "from rascal import util\n", "\n", "\n", "# Load the LT SPRAT data\n", - "spectrum2D = fits.open('data_lt_sprat/v_a_20190516_57_1_0_1.fits')[0].data\n", + "spectrum2D = fits.open(\"data_lt_sprat/v_a_20190516_57_1_0_1.fits\")[0].data\n", "# Collapse into 1D spectrum between row 110 and 120\n", "spectrum = np.median(spectrum2D[110:120], axis=0)\n", "# Identify the peaks\n", @@ -2298,31 +2292,57 @@ "peaks = util.refine_peaks(spectrum, peaks, window_width=5)\n", "\n", "# Initialise the calibrator\n", - "c = Calibrator(peaks, num_pix=1024, min_wavelength=3500., max_wavelength=8000.)\n", - "c.set_fit_constraints(num_slopes=5000,\n", - " range_tolerance=500.,\n", - " xbins=100,\n", - " ybins=100)\n", + "c = Calibrator(peaks, num_pix=1024, min_wavelength=3500.0, max_wavelength=8000.0)\n", + "c.set_fit_constraints(num_slopes=5000, range_tolerance=500.0, xbins=100, ybins=100)\n", "# blend: 4829.71, 4844.33\n", "# blend: 5566.62, 5581.88\n", "# blend: 6261.212, 6265.302\n", "# blend: 6872.11, 6882.16\n", "# blend: 7283.961, 7285.301\n", "# blend: 7316.272, 7321.452\n", - "atlas = [4193.5, 4385.77,\n", - " 4500.98, 4524.68, 4582.75, 4624.28, 4671.23, 4697.02, 4734.15, 4807.02, 4921.48,\n", + "atlas = [\n", + " 4193.5,\n", + " 4385.77,\n", + " 4500.98,\n", + " 4524.68,\n", + " 4582.75,\n", + " 4624.28,\n", + " 4671.23,\n", + " 4697.02,\n", + " 4734.15,\n", + " 4807.02,\n", + " 4921.48,\n", " 5028.28,\n", - " 5618.88, 5823.89, 5893.29, 5934.17,\n", - " 6182.42, 6318.06, 6472.841,\n", - " 6595.56, 6668.92, 6728.01, 6827.32, 6976.18,\n", - " 7119.60, 7257.9, 7393.8,\n", - " 7584.68, 7642.02, 7740.31, 7802.65, 7887.40, 7967.34, 8057.258]\n", - "element = ['Xe'] * len(atlas)\n", + " 5618.88,\n", + " 5823.89,\n", + " 5893.29,\n", + " 5934.17,\n", + " 6182.42,\n", + " 6318.06,\n", + " 6472.841,\n", + " 6595.56,\n", + " 6668.92,\n", + " 6728.01,\n", + " 6827.32,\n", + " 6976.18,\n", + " 7119.60,\n", + " 7257.9,\n", + " 7393.8,\n", + " 7584.68,\n", + " 7642.02,\n", + " 7740.31,\n", + " 7802.65,\n", + " 7887.40,\n", + " 7967.34,\n", + " 8057.258,\n", + "]\n", + "element = [\"Xe\"] * len(atlas)\n", "\n", "c.load_user_atlas(element, atlas)\n", "\n", "c.set_peaks(constrain_poly=True)\n", "\n", + "\n", "def run_sprat_calibration(polydeg, peaks):\n", " # Run the wavelength calibration\n", " best_p, rms, residual, peak_utilisation = c.fit(max_tries=10000)\n", @@ -2330,20 +2350,22 @@ " best_p, x_fit, y_fit, residual, peak_utilisation = c.match_peaks(\n", " best_p,\n", " delta=best_p[:1] * 0.001,\n", - " tolerance=10.,\n", + " tolerance=10.0,\n", " convergence=1e-10,\n", - " method='Nelder-Mead',\n", + " method=\"Nelder-Mead\",\n", " robust_refit=True,\n", - " polydeg=polydeg)\n", + " polydeg=polydeg,\n", + " )\n", " # Second set is to refine all the coefficients\n", " best_p, x_fit, y_fit, residual, peak_utilisation = c.match_peaks(\n", " best_p,\n", " delta=best_p * 0.001,\n", - " tolerance=10.,\n", + " tolerance=10.0,\n", " convergence=1e-10,\n", - " method='Nelder-Mead',\n", + " method=\"Nelder-Mead\",\n", " robust_refit=True,\n", - " polydeg=polydeg)\n", + " polydeg=polydeg,\n", + " )\n", " wave = np.polynomial.polynomial.polyval(peaks, best_p)\n", " return best_p, residual, peak_utilisation, rms, wave\n", "\n", @@ -2366,14 +2388,14 @@ " c0[i], c1[i], c2[i], c3[i], c4[i] = best_p\n", "\n", "\n", - "#sort by the wavelength fitted to the largest pixel value\n", - "mask_sorted = np.argsort(waves[:,-1])\n", + "# sort by the wavelength fitted to the largest pixel value\n", + "mask_sorted = np.argsort(waves[:, -1])\n", "\n", "plt.figure(1)\n", "plt.clf()\n", "plt.scatter(c0[mask_sorted], c1[mask_sorted], c=np.arange(n), s=5)\n", - "plt.xlabel('c0')\n", - "plt.ylabel('c1')\n", + "plt.xlabel(\"c0\")\n", + "plt.ylabel(\"c1\")\n", "plt.xlim(min(c0), max(c0))\n", "plt.ylim(min(c1), max(c1))\n", "plt.colorbar()\n", @@ -2383,8 +2405,8 @@ "plt.figure(2)\n", "plt.clf()\n", "plt.scatter(c1[mask_sorted], c2[mask_sorted], c=np.arange(n), s=5)\n", - "plt.xlabel('c1')\n", - "plt.ylabel('c2')\n", + "plt.xlabel(\"c1\")\n", + "plt.ylabel(\"c2\")\n", "plt.xlim(min(c1), max(c1))\n", "plt.ylim(min(c2), max(c2))\n", "plt.colorbar()\n", @@ -2394,8 +2416,8 @@ "plt.figure(3)\n", "plt.clf()\n", "plt.scatter(c2[mask_sorted], c3[mask_sorted], c=np.arange(n), s=5)\n", - "plt.xlabel('c2')\n", - "plt.ylabel('c3')\n", + "plt.xlabel(\"c2\")\n", + "plt.ylabel(\"c3\")\n", "plt.xlim(min(c2), max(c2))\n", "plt.ylim(min(c3), max(c3))\n", "plt.colorbar()\n", @@ -2405,8 +2427,8 @@ "plt.figure(4)\n", "plt.clf()\n", "plt.scatter(c3[mask_sorted], c4[mask_sorted], c=np.arange(n), s=5)\n", - "plt.xlabel('c3')\n", - "plt.ylabel('c4')\n", + "plt.xlabel(\"c3\")\n", + "plt.ylabel(\"c4\")\n", "plt.xlim(min(c3), max(c3))\n", "plt.ylim(min(c4), max(c4))\n", "plt.colorbar()\n", @@ -2416,11 +2438,11 @@ "plt.figure(5)\n", "plt.clf()\n", "for i in range(len(peaks)):\n", - " plt.plot(waves[mask_sorted][:,i], color='black', lw=1)\n", + " plt.plot(waves[mask_sorted][:, i], color=\"black\", lw=1)\n", "\n", - "plt.title('hand-picked lines')\n", + "plt.title(\"hand-picked lines\")\n", "plt.grid()\n", - "plt.tight_layout()\n" + "plt.tight_layout()" ] }, { diff --git a/examples/Worked_example_with_ground_truth.ipynb b/examples/Worked_example_with_ground_truth.ipynb index 07ac94f..1d70ced 100644 --- a/examples/Worked_example_with_ground_truth.ipynb +++ b/examples/Worked_example_with_ground_truth.ipynb @@ -14,9 +14,9 @@ "outputs": [], "source": [ "import sys\n", + "\n", "sys.path.append(\"..\")\n", "\n", - "import matplotlib\n", "%matplotlib inline" ] }, @@ -41,7 +41,6 @@ "from astropy.io import fits\n", "from scipy.signal import find_peaks\n", "from matplotlib import pyplot as plt\n", - "from tqdm.autonotebook import tqdm\n", "\n", "from rascal.calibrator import Calibrator\n", "from rascal import models" @@ -53,13 +52,15 @@ "metadata": {}, "outputs": [], "source": [ - "params = {'legend.fontsize': 20,\n", - " 'figure.figsize': (10,10),\n", - " 'axes.labelsize': 20,\n", - " 'axes.titlesize': 20,\n", - " 'xtick.labelsize': 20,\n", - " 'ytick.labelsize': 20,\n", - " 'axes.titlepad': 28}\n", + "params = {\n", + " \"legend.fontsize\": 20,\n", + " \"figure.figsize\": (10, 10),\n", + " \"axes.labelsize\": 20,\n", + " \"axes.titlesize\": 20,\n", + " \"xtick.labelsize\": 20,\n", + " \"ytick.labelsize\": 20,\n", + " \"axes.titlepad\": 28,\n", + "}\n", "plt.rcParams.update(params)" ] }, @@ -83,10 +84,10 @@ "metadata": {}, "outputs": [], "source": [ - "peaks, wavelengths, lamps = np.loadtxt(\"data_lt_sprat/ground_truth_v_a.csv\", delimiter=',', dtype='U').T\n", + "peaks, wavelengths, lamps = np.loadtxt(\"data_lt_sprat/ground_truth_v_a.csv\", delimiter=\",\", dtype=\"U\").T\n", "\n", - "peaks = peaks.astype('float32')\n", - "wavelengths = wavelengths.astype('float32')\n", + "peaks = peaks.astype(\"float32\")\n", + "wavelengths = wavelengths.astype(\"float32\")\n", "lamps = np.unique(lamps)" ] }, @@ -143,7 +144,7 @@ ], "source": [ "for p, w in zip(peaks, wavelengths):\n", - " print(\"Pixel position and Best-fit wavelength of the peaks: \" + str(int(p)) + ' matched to ' + str(w))" + " print(\"Pixel position and Best-fit wavelength of the peaks: \" + str(int(p)) + \" matched to \" + str(w))" ] }, { @@ -171,10 +172,7 @@ "source": [ "c = Calibrator(peaks)\n", "c.set_calibrator_properties(num_pix=1024)\n", - "c.set_hough_properties(min_wavelength=3400.,\n", - " max_wavelength=8000.,\n", - " xbins=100,\n", - " ybins=100)\n", + "c.set_hough_properties(min_wavelength=3400.0, max_wavelength=8000.0, xbins=100, ybins=100)\n", "c.add_atlas(elements=lamps)" ] }, @@ -200,7 +198,7 @@ ], "source": [ "c_true, m_true = models.robust_polyfit(peaks, wavelengths, 1)\n", - "print('Gradient = ' + str(m_true) + ' and intercept = ' + str(c_true))" + "print(\"Gradient = \" + str(m_true) + \" and intercept = \" + str(c_true))" ] }, { @@ -235,13 +233,13 @@ "\n", "# Plot\n", "ax.scatter(*c.pairs.T, s=4)\n", - "ax.scatter(peaks, wavelengths, c='red', s=80, marker='*', label=\"True match\", zorder=20)\n", - "ax.plot(peaks, wavelengths, c='black', zorder=10)\n", + "ax.scatter(peaks, wavelengths, c=\"red\", s=80, marker=\"*\", label=\"True match\", zorder=20)\n", + "ax.plot(peaks, wavelengths, c=\"black\", zorder=10)\n", "\n", "# Set the format\n", "plt.xlabel(\"Peak location (px)\")\n", "plt.ylabel(\"Xe emission line (NIST, $\\AA$)\")\n", - "plt.ylim(3800,8750)\n", + "plt.ylim(3800, 8750)\n", "plt.grid()\n", "plt.legend()\n", "plt.tight_layout()" @@ -287,18 +285,18 @@ "\n", "# Plot\n", "ax.scatter(*c.pairs.T, s=4)\n", - "ax.scatter(peaks, wavelengths, c='red', s=80, marker='*', label=\"True match\", zorder=20)\n", - "ax.plot(peaks, wavelengths, c='black', zorder=10)\n", - "ax.scatter(most_common_peaks, most_common_wavelengths, alpha=0.4, c='black', linestyle='dashed')\n", + "ax.scatter(peaks, wavelengths, c=\"red\", s=80, marker=\"*\", label=\"True match\", zorder=20)\n", + "ax.plot(peaks, wavelengths, c=\"black\", zorder=10)\n", + "ax.scatter(most_common_peaks, most_common_wavelengths, alpha=0.4, c=\"black\", linestyle=\"dashed\")\n", "\n", "# Set the format\n", "ax.set_xlabel(\"Peak location (px)\")\n", "ax.set_ylabel(\"Xe emission line (NIST, $\\AA$)\")\n", - "ax.set_ylim(3800,8750)\n", + "ax.set_ylim(3800, 8750)\n", "ax.grid()\n", "ax.legend()\n", "\n", - "fig.tight_layout()\n" + "fig.tight_layout()" ] }, { @@ -336,9 +334,9 @@ "most_common_m, most_common_intercept = hexplot._offsets[np.argsort(-hexplot._A)[:20]].T\n", "# Add circles\n", "for m, intercept in zip(most_common_m, most_common_intercept):\n", - " ax.scatter(m, intercept, s=200, edgecolors='red', facecolors='None', linewidths=2)\n", + " ax.scatter(m, intercept, s=200, edgecolors=\"red\", facecolors=\"None\", linewidths=2)\n", "\n", - "ax.scatter(m_true, c_true, s=200, edgecolors='white', facecolors='None', linewidths=2)\n", + "ax.scatter(m_true, c_true, s=200, edgecolors=\"white\", facecolors=\"None\", linewidths=2)\n", "\n", "# Add colour bar\n", "cb = fig.colorbar(hexplot)\n", @@ -347,8 +345,8 @@ "# Set the format\n", "ax.set_xlabel(\"Dispersion ($\\AA$ / px)\")\n", "ax.set_ylabel(\"Intercept ($\\AA$)\")\n", - "ax.set_xlim(4., 6.)\n", - "ax.set_ylim(3000., 3800.)\n", + "ax.set_xlim(4.0, 6.0)\n", + "ax.set_ylim(3000.0, 3800.0)\n", "fig.tight_layout()" ] }, @@ -397,37 +395,36 @@ } ], "source": [ - "fig = plt.figure(figsize=(10,10))\n", + "fig = plt.figure(figsize=(10, 10))\n", "ax = fig.add_subplot(111)\n", "\n", "i = 0\n", - "c.candidate_thresh=15\n", + "c.candidate_thresh = 15\n", "c._get_candidate_points_linear()\n", "\n", "most_common_peaks, most_common_wavelengths = c._get_most_common_candidates(c.candidates, top_n_candidate=20, weighted=True)\n", "\n", - "'''\n", + "\"\"\"\n", "for l in c.hough_lines:\n", " #title(\"{} {}\".format(*l))\n", " f = models.polynomial(l, degree=1)\n", " ax.plot(x, f(x), alpha=0.2, c='black')\n", - "'''\n", + "\"\"\"\n", + "\n", + "ax.scatter(most_common_peaks, most_common_wavelengths, c=\"blue\", s=20, alpha=0.3)\n", + "ax.scatter(most_common_peaks[0], most_common_wavelengths[0], c=\"blue\", s=20, label=\"Candidate match\", alpha=1)\n", + "\n", + "ax.scatter(peaks, wavelengths, c=\"red\", s=80, marker=\"*\", label=\"True match\", zorder=20)\n", "\n", - "ax.scatter(most_common_peaks, most_common_wavelengths, c='blue', s=20, alpha=0.3)\n", - "ax.scatter(most_common_peaks[0], most_common_wavelengths[0], c='blue', s=20, label=\"Candidate match\", alpha=1)\n", - " \n", - "ax.scatter(peaks, wavelengths, c='red', s=80, marker='*', label=\"True match\", zorder=20)\n", - " \n", "# Set the format\n", "ax.set_xlabel(\"Peak location (px)\", fontsize=20)\n", "ax.set_ylabel(\"Catalogue emission line ($\\AA$)\", fontsize=20)\n", - "ax.set_ylim(3800,8750)\n", + "ax.set_ylim(3800, 8750)\n", "ax.legend()\n", "ax.grid()\n", "fig.tight_layout()\n", "\n", "\n", - "\n", "x = np.linspace(min(peaks), max(peaks))\n", "\n", "# Set the plotting space\n", @@ -436,11 +433,11 @@ "\n", "# Plot\n", "ax.scatter(*c.pairs.T, s=4)\n", - "ax.scatter(peaks, wavelengths, c='red', s=80, marker='*', label=\"True match\", zorder=20)\n", - "ax.plot(peaks, wavelengths, c='black', zorder=10)\n", + "ax.scatter(peaks, wavelengths, c=\"red\", s=80, marker=\"*\", label=\"True match\", zorder=20)\n", + "ax.plot(peaks, wavelengths, c=\"black\", zorder=10)\n", "\n", "for p, a in zip(most_common_peaks, most_common_wavelengths):\n", - " ax.plot(p, a, alpha=0.4, c='black', linestyle='dashed')\n" + " ax.plot(p, a, alpha=0.4, c=\"black\", linestyle=\"dashed\")" ] }, { @@ -473,14 +470,14 @@ "for i, l in enumerate([lines[0], lines[13]]):\n", " candidate_peaks, candidate_wavelengths = c._get_candidate_points_linear(l[0], l[1], thresh=15)\n", "\n", - " fig = plt.figure(i, figsize=(10,10))\n", + " fig = plt.figure(i, figsize=(10, 10))\n", " ax = fig.add_subplot(111)\n", "\n", " f = models.polynomial(l, degree=1)\n", - " ax.plot(x, f(x), alpha=0.2, c='black', label=\"Linear fit\")\n", - " \n", - " ax.scatter(candidate_peaks, candidate_wavelengths, c='blue', s=20, label=\"Candidate match\")\n", - " ax.scatter(peaks, wavelengths, c='red',s=20, label=\"Actual match\")\n", + " ax.plot(x, f(x), alpha=0.2, c=\"black\", label=\"Linear fit\")\n", + "\n", + " ax.scatter(candidate_peaks, candidate_wavelengths, c=\"blue\", s=20, label=\"Candidate match\")\n", + " ax.scatter(peaks, wavelengths, c=\"red\", s=20, label=\"Actual match\")\n", "\n", " ax.set_xlabel(\"Peak location (px)\")\n", " ax.set_ylabel(\"Catalogue emission line ($\\AA$)\")\n", @@ -508,7 +505,7 @@ "out_p, out_w = c._combine_linear_estimates(candidates, top_n=5)\n", "\n", "ax.scatter(out_p, out_w)\n", - "ax.scatter(peaks, wavelengths, c='red', s=100, marker='+', label=\"Actual match\")\n", + "ax.scatter(peaks, wavelengths, c=\"red\", s=100, marker=\"+\", label=\"Actual match\")\n", "ax.set_xlabel(\"Peak location (px)\")\n", "ax.set_ylabel(\"Catalogue emission line ($\\AA$)\")\n", "ax.grid()\n", @@ -540,7 +537,17 @@ "outputs": [], "source": [ "p_coeff, _, _, _, _ = c._solve_candidate_ransac(\n", - " x=out_p, y=out_w, polydeg=4, sample_size=5, thresh=7, max_tries=1e4, coeff=None, brute_force=False, progress=True, filter_close=True)" + " x=out_p,\n", + " y=out_w,\n", + " polydeg=4,\n", + " sample_size=5,\n", + " thresh=7,\n", + " max_tries=1e4,\n", + " coeff=None,\n", + " brute_force=False,\n", + " progress=True,\n", + " filter_close=True,\n", + ")" ] }, { @@ -569,12 +576,12 @@ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "\n", - "ax.scatter(peaks, wavelengths-y, s=20)\n", + "ax.scatter(peaks, wavelengths - y, s=20)\n", "ax.hlines(0, xmin=200, xmax=1000, ls=\"--\")\n", "ax.set_xlim(200, 1000)\n", "ax.set_xlabel(\"Peak location (px)\")\n", "ax.set_ylabel(\"Residual ($\\AA$)\")\n", - "ax.grid()\n" + "ax.grid()" ] }, { @@ -590,15 +597,15 @@ "metadata": {}, "outputs": [], "source": [ - "spectrum = np.median(fits.open('data_lt_sprat/v_a_20190516_55_1_0_1.fits')[0].data[110:120], axis=0)\n", + "spectrum = np.median(fits.open(\"data_lt_sprat/v_a_20190516_55_1_0_1.fits\")[0].data[110:120], axis=0)\n", "peaks, _ = find_peaks(spectrum, height=500, distance=5, threshold=None)\n", "\n", "plt.figure()\n", - "plt.plot(spectrum/spectrum.max())\n", - "plt.title('Number of pixels: ' + str(spectrum.shape[0]))\n", + "plt.plot(spectrum / spectrum.max())\n", + "plt.title(\"Number of pixels: \" + str(spectrum.shape[0]))\n", "plt.xlabel(\"Pixel (Spectral Direction)\")\n", "plt.ylabel(\"Normalised Count\")\n", - "plt.xlim(0,1024)\n", + "plt.xlim(0, 1024)\n", "plt.grid()\n", "plt.tight_layout()" ] @@ -626,11 +633,11 @@ "outputs": [], "source": [ "# Open a different Arc file taken on the same night\n", - "spectrum_actual = np.median(fits.open('data_lt_sprat/v_a_20190516_57_1_0_1.fits')[0].data[110:120], axis=0)\n", + "spectrum_actual = np.median(fits.open(\"data_lt_sprat/v_a_20190516_57_1_0_1.fits\")[0].data[110:120], axis=0)\n", "peaks_actual, _ = find_peaks(spectrum_actual, height=500, distance=5, threshold=None)\n", "\n", - "c_actual = Calibrator(peaks_actual, num_pix=1024, min_wavelength=3800., max_wavelength=8200.)\n", - "c_actual.set_fit_constraints(num_slopes=5000, range_tolerance=500., xbins=100, ybins=100)\n", + "c_actual = Calibrator(peaks_actual, num_pix=1024, min_wavelength=3800.0, max_wavelength=8200.0)\n", + "c_actual.set_fit_constraints(num_slopes=5000, range_tolerance=500.0, xbins=100, ybins=100)\n", "c_actual.add_atlas(elements=lamps)\n", "\n", "p_coeff_actual, _, _, _ = c_actual.fit(max_tries=1e4)\n", diff --git a/examples/example_gemini_gmos_longslit.py b/examples/example_gemini_gmos_longslit.py index 8675a7e..4486f60 100644 --- a/examples/example_gemini_gmos_longslit.py +++ b/examples/example_gemini_gmos_longslit.py @@ -19,17 +19,13 @@ # Load the GMOS data base_dir = os.path.dirname(__file__) -spectrum2D = fits.open( - os.path.join(base_dir, "data_gemini_gmos/N20181115S0215_flattened.fits") -)[0].data +spectrum2D = fits.open(os.path.join(base_dir, "data_gemini_gmos/N20181115S0215_flattened.fits"))[0].data # Collapse into 1D spectrum between row 300 and 310 spectrum = np.median(spectrum2D[300:310], axis=0)[::-1] # Identify the peaks -peaks, _ = find_peaks( - spectrum, height=1000, prominence=500, distance=5, threshold=None -) +peaks, _ = find_peaks(spectrum, height=1000, prominence=500, distance=5, threshold=None) peaks = util.refine_peaks(spectrum, peaks, window_width=3) peaks_shifted = rawpix_to_pix_itp(peaks) @@ -73,9 +69,7 @@ ) = c.fit(max_tries=1000, fit_deg=4) # Plot the solution -c.plot_fit( - best_p, spectrum, plot_atlas=True, log_spectrum=False, tolerance=5.0 -) +c.plot_fit(best_p, spectrum, plot_atlas=True, log_spectrum=False, tolerance=5.0) # Show the parameter space for searching possible solution c.plot_search_space() diff --git a/examples/example_gemini_gmos_longslit_manual_atlas.py b/examples/example_gemini_gmos_longslit_manual_atlas.py index 7f611f8..e478d8f 100644 --- a/examples/example_gemini_gmos_longslit_manual_atlas.py +++ b/examples/example_gemini_gmos_longslit_manual_atlas.py @@ -20,9 +20,7 @@ # Load the LT SPRAT data base_dir = os.path.dirname(__file__) -spectrum2D = fits.open( - os.path.join(base_dir, "data_gemini_gmos/N20181115S0215_flattened.fits") -)[0].data +spectrum2D = fits.open(os.path.join(base_dir, "data_gemini_gmos/N20181115S0215_flattened.fits"))[0].data plt.ion() plt.figure(1, figsize=(10, 4)) @@ -36,9 +34,7 @@ spectrum = np.median(spectrum2D[300:310], axis=0)[::-1] # Identify the peaks -peaks, _ = find_peaks( - spectrum, height=1000, prominence=500, distance=5, threshold=None -) +peaks, _ = find_peaks(spectrum, height=1000, prominence=500, distance=5, threshold=None) peaks = util.refine_peaks(spectrum, peaks, window_width=5) peaks_shifted = rawpix_to_pix_itp(peaks) @@ -46,9 +42,7 @@ # Initialise the calibrator c = Calibrator(peaks_shifted, spectrum=spectrum) c.set_calibrator_properties(pixel_list=pixels) -c.plot_arc( - pixels, save_fig="png", filename="output/gemini-gmosls-arc-spectrum" -) +c.plot_arc(pixels, save_fig="png", filename="output/gemini-gmosls-arc-spectrum") c.set_hough_properties( num_slopes=5000, range_tolerance=500.0, @@ -147,9 +141,7 @@ ) # Show the parameter space for searching possible solution -c.plot_search_space( - save_fig="png", filename="output/gemini-gmosls-search-space" -) +c.plot_search_space(save_fig="png", filename="output/gemini-gmosls-search-space") print("Stdev error: {} A".format(residual.std())) print("Peaks utilisation rate: {}%".format(peak_utilisation * 100)) diff --git a/examples/example_gtc_osiris.py b/examples/example_gtc_osiris.py index 77c49b7..11779a7 100644 --- a/examples/example_gtc_osiris.py +++ b/examples/example_gtc_osiris.py @@ -52,9 +52,7 @@ spectrum = np.median(data.data.T[550:570], axis=0) # Identify the arc lines -peaks, _ = find_peaks( - spectrum, height=1250, prominence=20, distance=3, threshold=None -) +peaks, _ = find_peaks(spectrum, height=1250, prominence=20, distance=3, threshold=None) peaks_refined = refine_peaks(spectrum, peaks, window_width=3) c = Calibrator(peaks_refined, spectrum=spectrum) diff --git a/examples/example_int_ids.py b/examples/example_int_ids.py index b15de13..cca15d5 100644 --- a/examples/example_int_ids.py +++ b/examples/example_int_ids.py @@ -10,9 +10,7 @@ # Load the LT SPRAT data base_dir = os.path.dirname(os.path.abspath(__file__)) -spectrum2D = fits.open( - os.path.join(base_dir, "data_int_ids/int20180101_01355922.fits.fz") -)[1].data +spectrum2D = fits.open(os.path.join(base_dir, "data_int_ids/int20180101_01355922.fits.fz"))[1].data # Collapse into 1D spectrum between row 110 and 120 spectrum = np.flip(spectrum2D.mean(1), 0) @@ -57,9 +55,7 @@ ) = c.fit(max_tries=100, fit_deg=4) # Plot the solution -c.plot_fit( - best_p, spectrum, plot_atlas=True, log_spectrum=False, tolerance=5.0 -) +c.plot_fit(best_p, spectrum, plot_atlas=True, log_spectrum=False, tolerance=5.0) # Show the parameter space for searching possible solution c.plot_search_space() diff --git a/examples/example_keck_deimos_830g.py b/examples/example_keck_deimos_830g.py index d649797..3265ba4 100644 --- a/examples/example_keck_deimos_830g.py +++ b/examples/example_keck_deimos_830g.py @@ -8,9 +8,7 @@ from rascal.util import refine_peaks # Load the 1D Spectrum from Pypeit -spectrum_json = json.load( - open("data_keck_deimos/keck_deimos_830g_l_PYPIT.json") -) +spectrum_json = json.load(open("data_keck_deimos/keck_deimos_830g_l_PYPIT.json")) spectrum = np.array(spectrum_json["spec"]) # Identify the arc lines diff --git a/examples/example_lt_sprat_xe.py b/examples/example_lt_sprat_xe.py index 745e759..970c5d0 100644 --- a/examples/example_lt_sprat_xe.py +++ b/examples/example_lt_sprat_xe.py @@ -10,9 +10,7 @@ # Load the LT SPRAT data base_dir = os.path.dirname(__file__) -fits_file = fits.open( - os.path.join(base_dir, "data_lt_sprat/v_a_20190516_57_1_0_1.fits") -)[0] +fits_file = fits.open(os.path.join(base_dir, "data_lt_sprat/v_a_20190516_57_1_0_1.fits"))[0] spectrum2D = fits_file.data @@ -67,9 +65,7 @@ ) = c.fit(max_tries=500) # Plot the solution -c.plot_fit( - best_p, spectrum, plot_atlas=True, log_spectrum=False, tolerance=5.0 -) +c.plot_fit(best_p, spectrum, plot_atlas=True, log_spectrum=False, tolerance=5.0) # Show the parameter space for searching possible solution c.plot_search_space() diff --git a/examples/example_lt_sprat_xe_manual_atlas.py b/examples/example_lt_sprat_xe_manual_atlas.py index e7ccdca..5242a67 100644 --- a/examples/example_lt_sprat_xe_manual_atlas.py +++ b/examples/example_lt_sprat_xe_manual_atlas.py @@ -11,9 +11,7 @@ # Load the LT SPRAT data base_dir = os.path.dirname(__file__) -fits_file = fits.open( - os.path.join(base_dir, "data_lt_sprat/v_a_20190516_57_1_0_1.fits") -)[0] +fits_file = fits.open(os.path.join(base_dir, "data_lt_sprat/v_a_20190516_57_1_0_1.fits"))[0] spectrum2D = fits_file.data @@ -33,9 +31,7 @@ relative_humidity = fits_file.header["REFHUMID"] # Identify the peaks -peaks, _ = find_peaks( - spectrum, height=300, prominence=150, distance=5, threshold=None -) +peaks, _ = find_peaks(spectrum, height=300, prominence=150, distance=5, threshold=None) peaks = util.refine_peaks(spectrum, peaks, window_width=5) # Initialise the calibrator @@ -120,9 +116,7 @@ ) = c.fit(max_tries=1000) # Plot the solution -c.plot_fit( - best_p, spectrum, plot_atlas=True, log_spectrum=False, tolerance=5.0 -) +c.plot_fit(best_p, spectrum, plot_atlas=True, log_spectrum=False, tolerance=5.0) # Show the parameter space for searching possible solution c.plot_search_space() diff --git a/examples/example_ntt_efosc_grism11.py b/examples/example_ntt_efosc_grism11.py index 2211898..dd1085b 100644 --- a/examples/example_ntt_efosc_grism11.py +++ b/examples/example_ntt_efosc_grism11.py @@ -15,9 +15,7 @@ peaks_refined = refine_peaks(spectrum, peaks, window_width=3) c = Calibrator(peaks_refined, spectrum) -c.set_calibrator_properties( - num_pix=len(spectrum), plotting_library="matplotlib", log_level="info" -) +c.set_calibrator_properties(num_pix=len(spectrum), plotting_library="matplotlib", log_level="info") c.plot_arc() diff --git a/examples/example_wht_isis_r300r.py b/examples/example_wht_isis_r300r.py index a123dc2..20398f3 100644 --- a/examples/example_wht_isis_r300r.py +++ b/examples/example_wht_isis_r300r.py @@ -11,9 +11,7 @@ # Load the LT SPRAT data base_dir = os.path.dirname(__file__) -spectrum2D = fits.open( - os.path.join(base_dir, "data_wht_isis/r2701004_red_arc.fit") -)[1].data.T +spectrum2D = fits.open(os.path.join(base_dir, "data_wht_isis/r2701004_red_arc.fit"))[1].data.T # Collapse into 1D spectrum between row 500 and 520 spectrum = np.median(spectrum2D[500:520], axis=0) @@ -27,16 +25,12 @@ plt.savefig("output/wht-isis-arc-image.png") # Identify the peaks -peaks, _ = find_peaks( - spectrum, height=500, prominence=100, distance=5, threshold=None -) +peaks, _ = find_peaks(spectrum, height=500, prominence=100, distance=5, threshold=None) peaks = util.refine_peaks(spectrum, peaks, window_width=3) # Initialise the calibrator c = Calibrator(peaks, spectrum=spectrum) -c.plot_arc( - log_spectrum=True, save_fig="png", filename="output/wht-isis-arc-spectrum" -) +c.plot_arc(log_spectrum=True, save_fig="png", filename="output/wht-isis-arc-spectrum") c.set_hough_properties( num_slopes=10000, xbins=500, diff --git a/examples/monotonic_polyfit.ipynb b/examples/monotonic_polyfit.ipynb index 6255d0c..03dd459 100644 --- a/examples/monotonic_polyfit.ipynb +++ b/examples/monotonic_polyfit.ipynb @@ -16,7 +16,6 @@ "outputs": [], "source": [ "import numpy as np\n", - "import scipy\n", "import math\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import minimize" @@ -30,58 +29,62 @@ "source": [ "# Residual sum of squares\n", "\n", + "\n", "def make_rss(x, y, alpha=1, w=None):\n", " \"\"\"\n", " Construct the objective function (residual sum of squares)\n", " \"\"\"\n", - " \n", + "\n", " if w is None:\n", " w = np.ones_like(x)\n", - " \n", - " def rss(p0): \n", + "\n", + " def rss(p0):\n", " fit = eval_pol_sos(x, p0, alpha)\n", - " return np.sum(w*(y - fit)**2) + np.sum(p0)**2\n", - " \n", + " return np.sum(w * (y - fit) ** 2) + np.sum(p0) ** 2\n", + "\n", " return rss\n", "\n", + "\n", "def eval_pol_sos(x, p0, alpha=1):\n", " \"\"\"\n", " Evaluate the poylnomial with coefficients p0\n", - " \n", + "\n", " \"\"\"\n", " beta = eval_coef_sos(p0, alpha)\n", " return eval_pol(x, beta)\n", "\n", + "\n", "def eval_coef_sos(p0, alpha=1):\n", " \"\"\"\n", " Convert objective function coefficients to beta notation (split polynomial)\n", - " \n", + "\n", " \"\"\"\n", " assert len(p0) > 3\n", - " \n", + "\n", " delta = p0[0]\n", - " degree = (len(p0)-1)//2\n", - " \n", + " degree = (len(p0) - 1) // 2\n", + "\n", " if degree == 0:\n", - " res = [delta, alpha*p0[1]]\n", - " else: \n", - " beta_1 = p0[1:degree+1]\n", - " beta_2 = p0[degree+1:]\n", - " \n", + " res = [delta, alpha * p0[1]]\n", + " else:\n", + " beta_1 = p0[1 : degree + 1]\n", + " beta_2 = p0[degree + 1 :]\n", + "\n", " assert len(beta_1) == len(beta_2)\n", - " \n", + "\n", " res = np.convolve(beta_1, beta_1[::-1], mode=\"same\")\n", " res += np.convolve(beta_2, beta_2[::-1], mode=\"same\")\n", - " \n", - " res = alpha*res/np.arange(1,len(res)+1)\n", - " \n", + "\n", + " res = alpha * res / np.arange(1, len(res) + 1)\n", + "\n", " return res\n", "\n", + "\n", "def eval_pol(x, p0):\n", " res = 0\n", " for p in p0[::-1]:\n", - " res = res*x + p\n", - " \n", + " res = res * x + p\n", + "\n", " return res" ] }, @@ -95,92 +98,93 @@ " \"\"\"\n", " Returns the gradient of the objective function\n", " for (x, y) with respect to parameters theta\n", - " \n", + "\n", " \"\"\"\n", - " \n", + "\n", " if w is None:\n", " w = np.ones_like(x)\n", - " \n", + "\n", " def grad(p0):\n", " grad = eval_grad_pol_sos(x, p0, alpha)\n", " fit = eval_pol_sos(x, p0)\n", - " temp = -2*w*(y - fit)\n", - " \n", - " return np.sum(grad*temp.reshape((-1,1)), axis=0)\n", - " \n", + " temp = -2 * w * (y - fit)\n", + "\n", + " return np.sum(grad * temp.reshape((-1, 1)), axis=0)\n", + "\n", " return grad\n", "\n", + "\n", "def eval_grad_pol_sos(x, p0, alpha=1):\n", " \"\"\"\n", " Computes the gradient vector\n", - " \n", + "\n", " \"\"\"\n", " coeff = eval_grad_coeff_sos(p0, alpha)\n", - " res = np.zeros((len(x), len(coeff)+1))\n", - " \n", + " res = np.zeros((len(x), len(coeff) + 1))\n", + "\n", " # Gradient with respect to delta is always 1\n", - " res[:,0] = 1\n", - " \n", + " res[:, 0] = 1\n", + "\n", " for i, c in enumerate(coeff):\n", " pc = c\n", - " order = len(c)-1\n", - " tmp = pc[order]*x\n", - " \n", - " for j in range(order,0):\n", - " tmp = (tmp + pc[j])*x\n", - " \n", + " order = len(c) - 1\n", + " tmp = pc[order] * x\n", + "\n", + " for j in range(order, 0):\n", + " tmp = (tmp + pc[j]) * x\n", + "\n", " # Gradient with respect to theta_i\n", - " res[:, i+1] = tmp\n", - " \n", + " res[:, i + 1] = tmp\n", + "\n", " return res\n", - " \n", + "\n", + "\n", "def eval_grad_coeff_sos(p0, alpha=1):\n", " \"\"\"\n", " Calculate dp/dtheta\n", - " \n", + "\n", " \"\"\"\n", " delta = p0[0]\n", - " degree = (len(p0)-1)//2\n", + " degree = (len(p0) - 1) // 2\n", + "\n", + " res = [0] * 2 * degree\n", + "\n", + " res[0] = p0[1 : 1 + degree] # Beta 1\n", + " res[degree] = p0[1 + degree :] # Beta 2\n", "\n", - " res = [0]*2*degree\n", - " \n", - " res[0] = p0[1:1+degree] # Beta 1\n", - " res[degree] = p0[1+degree:] # Beta 2\n", - " \n", " if degree > 1:\n", " for i in range(1, degree):\n", - " res[i] = [0] + res[i-1]\n", - " res[i+degree] = [0] + res[degree+i-1]\n", + " res[i] = [0] + res[i - 1]\n", + " res[i + degree] = [0] + res[degree + i - 1]\n", "\n", " for i in range(len(res)):\n", - " res[i] = 2*alpha*np.array(res[i])/np.arange(1, len(res[i])+1)\n", + " res[i] = 2 * alpha * np.array(res[i]) / np.arange(1, len(res[i]) + 1)\n", "\n", " return res\n", "\n", "\n", "def get_jac(x):\n", - " \n", " def jac(args, alpha=1):\n", - " \"\"\"\n", - " nb alpha = 1 for monotone increasing\n", - " in the paper degree = ql\n", - " \"\"\"\n", + " \"\"\"\n", + " nb alpha = 1 for monotone increasing\n", + " in the paper degree = ql\n", + " \"\"\"\n", "\n", - " # define args = delta, beta_1, beta_2\n", - " delta = args[0]\n", - " degree = (len(args)-1)//2\n", + " # define args = delta, beta_1, beta_2\n", + " delta = args[0]\n", + " degree = (len(args) - 1) // 2\n", "\n", - " grad_out = [np.ones_like(x)]\n", + " grad_out = [np.ones_like(x)]\n", "\n", - " for l in [1,2]:\n", - " for j in range(degree):\n", - " _grad = np.zeros_like(x, dtype='float64')\n", - " for k in range(degree):\n", - " pow_x = np.power(x, (j+k+1)) / (k+j+1)\n", - " _grad += args[l*k]*pow_x\n", - " grad_out.append(2*alpha*_grad)\n", + " for l in [1, 2]:\n", + " for j in range(degree):\n", + " _grad = np.zeros_like(x, dtype=\"float64\")\n", + " for k in range(degree):\n", + " pow_x = np.power(x, (j + k + 1)) / (k + j + 1)\n", + " _grad += args[l * k] * pow_x\n", + " grad_out.append(2 * alpha * _grad)\n", "\n", - " return np.array(grad_out)\n", + " return np.array(grad_out)\n", "\n", " return jac" ] @@ -218,10 +222,10 @@ "degree = 4\n", "\n", "a = 20\n", - "b = 2*np.random.random()\n", - "c = 3*np.random.random()\n", + "b = 2 * np.random.random()\n", + "c = 3 * np.random.random()\n", "\n", - "y = a + b*x + c*x**2 + 25*np.random.random((len(x)))\n", + "y = a + b * x + c * x**2 + 25 * np.random.random((len(x)))\n", "\n", "norm_x = x / x.std()\n", "norm_y = y / y.std()\n", @@ -229,13 +233,13 @@ "objective_func = make_rss(norm_x, norm_y)\n", "grad = make_grad(norm_x, norm_y)\n", "\n", - "p0 = np.ones(2*degree+1)\n", + "p0 = np.ones(2 * degree + 1)\n", "p0[0] = 0.5\n", "\n", - "res = minimize(objective_func, method='Newton-CG', x0=p0, jac=grad)\n", + "res = minimize(objective_func, method=\"Newton-CG\", x0=p0, jac=grad)\n", "\n", - "plt.plot(norm_x, norm_y, \".\", label='data')\n", - "plt.plot(norm_x, eval_pol_sos(norm_x, res.x), label='fit')\n", + "plt.plot(norm_x, norm_y, \".\", label=\"data\")\n", + "plt.plot(norm_x, eval_pol_sos(norm_x, res.x), label=\"fit\")\n", "plt.legend()" ] }, @@ -268,7 +272,7 @@ } ], "source": [ - "delta = 0 # zero\n", + "delta = 0 # zero\n", "c10 = 0\n", "c11 = math.sqrt(2)\n", "c21 = 0\n", @@ -279,10 +283,10 @@ "grad = make_grad(x, y)\n", "rss = make_rss(x, y)\n", "\n", - "plt.figure(figsize=(10,10))\n", - "plt.plot(x, y, label='data')\n", - "plt.plot(x, eval_pol_sos(x, p0), label='fit')\n", - "plt.plot((y-eval_pol_sos(x, p0))**2, label='res')\n", + "plt.figure(figsize=(10, 10))\n", + "plt.plot(x, y, label=\"data\")\n", + "plt.plot(x, eval_pol_sos(x, p0), label=\"fit\")\n", + "plt.plot((y - eval_pol_sos(x, p0)) ** 2, label=\"res\")\n", "plt.legend()" ] }, diff --git a/pyproject.toml b/pyproject.toml index ed8ce04..17f5e73 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,71 @@ -[build-system] -requires = ["poetry>=1.1", "setuptools >= 60.6.0", "wheel"] -build-backend = "setuptools.build_meta" +[project] +name = "rascal" +version = "0.4.0" +description = "A library for automated spectrometer wavelength calibration." +readme = "README.md" +requires-python = ">=3.10" +license = {text = "BSD-3-Clause"} +authors = [ + {name = "Josh Veitch-Michaelis", email = "j.veitchmichaelis@gmail.com"}, + {name = "Marco C Lam", email = "lam@mail.tau.ac.il"} +] +maintainers = [ + {name = "Josh Veitch-Michaelis", email = "j.veitchmichaelis@gmail.com"}, + {name = "Marco C Lam", email = "lam@mail.tau.ac.il"} +] + +urls = { Homepage = "https://rascal.readthedocs.io/en/latest/?badge=latest", Download = "https://github.com/jveitchmichaelis/rascal" } + +classifiers = [ + "License :: OSI Approved :: BSD License", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Operating System :: OS Independent" +] +dependencies = [ + "kaleido>=1.1.0", + "matplotlib>=3.10.6", + "numpy>=2.2.6", + "pynverse>=0.1.4.6", + "scipy>=1.15.3", + "tqdm>=4.67.1", +] + +[tool.setuptools] +package-dir = {"" = "src"} +zip-safe = false +include-package-data = true + +[tool.ruff] +line-length = 127 +lint.select = ["E", "F"] +lint.ignore = ["E741"] +exclude = ["*.ipynb"] +fix = false + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.coverage.run] +relative_files = true + +[dependency-groups] +dev = [ + "astropy>=6.1.7", + "coverage>=7.10.7", + "plotly>=6.3.1", + "pre-commit>=4.3.0", + "psutil>=7.1.0", + "pytest>=8.4.2", + "pytest-cov>=7.0.0", + "requests>=2.32.5", + "ruff>=0.14.0", +] + +[tool.pytest.ini_options] +pythonpath = "src" +addopts = [ + "--import-mode=importlib", +] \ No newline at end of file diff --git a/readthedocs.yml b/readthedocs.yml index ca05ddc..c52c133 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -1,5 +1,5 @@ python: - version: 3.7 + version: 3.13 install: - requirements: docs/requirements.txt - method: pip diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 0407efa..0000000 --- a/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -matplotlib>=3.0.3 -numpy>=1.16,<1.24 -pynverse>=0.1.4 -scipy>=1.3.3 -tqdm>=4.48.0 diff --git a/setup.py b/setup.py deleted file mode 100644 index 8c9122f..0000000 --- a/setup.py +++ /dev/null @@ -1,6 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -from setuptools import setup - -setup() diff --git a/src/rascal/__init__.py b/src/rascal/__init__.py index 5d1c48b..2388c01 100644 --- a/src/rascal/__init__.py +++ b/src/rascal/__init__.py @@ -1,12 +1,12 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -from pkg_resources import get_distribution, DistributionNotFound +from importlib.metadata import version, PackageNotFoundError try: - __version__ = get_distribution(__name__).version -except DistributionNotFound: - pass # package is not installed + __version__ = version(__name__) +except PackageNotFoundError: + __version__ = "unknown" from . import calibrator from . import models diff --git a/src/rascal/arc_lines/convolve_lines.py b/src/rascal/arc_lines/convolve_lines.py index a74c08c..165c7f2 100644 --- a/src/rascal/arc_lines/convolve_lines.py +++ b/src/rascal/arc_lines/convolve_lines.py @@ -37,9 +37,7 @@ def gaus(x, a, b, x0, sigma): xe_i = nist_intensity[xe_idx] # Generate the equally spaced-wavelength array, and the corresponding intensity -wavelength = np.around( - np.arange(min(xe_w) - 500.0, max(xe_w) + 500.01, 0.01), decimals=2 -) +wavelength = np.around(np.arange(min(xe_w) - 500.0, max(xe_w) + 500.01, 0.01), decimals=2) intensity = np.zeros_like(wavelength) intensity[np.where(np.isin(wavelength, xe_w))] = xe_i diff --git a/src/rascal/atlas.py b/src/rascal/atlas.py index a352e8d..8798759 100644 --- a/src/rascal/atlas.py +++ b/src/rascal/atlas.py @@ -152,26 +152,16 @@ def add( """ if min_atlas_wavelength is None: - min_atlas_wavelength = ( - self.min_atlas_wavelength - self.range_tolerance - ) + min_atlas_wavelength = self.min_atlas_wavelength - self.range_tolerance if max_atlas_wavelength is None: - max_atlas_wavelength = ( - self.max_atlas_wavelength + self.range_tolerance - ) + max_atlas_wavelength = self.max_atlas_wavelength + self.range_tolerance if not np.isfinite(min_atlas_wavelength): - raise ValueError( - "min_atlas_wavelength has to be finite or None. " - "{} is given.".format(min_atlas_wavelength) - ) + raise ValueError("min_atlas_wavelength has to be finite or None. {} is given.".format(min_atlas_wavelength)) if not np.isfinite(max_atlas_wavelength): - raise ValueError( - "max_atlas_wavelength has to be finite or None. " - "{} is given.".format(max_atlas_wavelength) - ) + raise ValueError("max_atlas_wavelength has to be finite or None. {} is given.".format(max_atlas_wavelength)) if isinstance(elements, str): elements = [elements] @@ -196,12 +186,8 @@ def add( brightest_n_lines=brightest_n_lines, ) - for element, line, intensity in list( - zip(atlas_elements_tmp, atlas_tmp, atlas_intensities_tmp) - ): - self.atlas_lines.append( - AtlasLine(line, element, intensity, "NIST") - ) + for element, line, intensity in list(zip(atlas_elements_tmp, atlas_tmp, atlas_intensities_tmp)): + self.atlas_lines.append(AtlasLine(line, element, intensity, "NIST")) def add_user_atlas( self, @@ -259,24 +245,16 @@ def add_user_atlas( if not isinstance(intensities, list): intensities = list(intensities) - assert len(elements) == len(wavelengths), ValueError( - "Input elements and wavelengths have different length." - ) - assert len(elements) == len(intensities), ValueError( - "Input elements and intensities have different length." - ) + assert len(elements) == len(wavelengths), ValueError("Input elements and wavelengths have different length.") + assert len(elements) == len(intensities), ValueError("Input elements and intensities have different length.") if vacuum: - wavelengths = vacuum_to_air_wavelength( - wavelengths, temperature, pressure, relative_humidity - ) + wavelengths = vacuum_to_air_wavelength(wavelengths, temperature, pressure, relative_humidity) self.min_atlas_wavelength = min(wavelengths) self.max_atlas_wavelength = max(wavelengths) - for element, line, intensity in list( - zip(elements, wavelengths, intensities) - ): + for element, line, intensity in list(zip(elements, wavelengths, intensities)): self.atlas_lines.append(AtlasLine(line, element, intensity)) def get_lines(self): @@ -335,14 +313,7 @@ def list(self): """ for line in self.atlas_lines: - print( - "Element " - + str(line.element) - + " at " - + str(line.wavelength) - + " with intensity " - + str(line.intensity) - ) + print("Element " + str(line.element) + " at " + str(line.wavelength) + " with intensity " + str(line.intensity)) def clear(self): """ diff --git a/src/rascal/calibrator.py b/src/rascal/calibrator.py index 0673c22..33ebf7b 100644 --- a/src/rascal/calibrator.py +++ b/src/rascal/calibrator.py @@ -100,10 +100,7 @@ def _generate_pairs(self): """ - pairs = [ - pair - for pair in itertools.product(self.peaks, self.atlas.get_lines()) - ] + pairs = [pair for pair in itertools.product(self.peaks, self.atlas.get_lines())] if self.constrain_poly: # Remove pairs outside polygon @@ -113,15 +110,11 @@ def _generate_pairs(self): (0, self.min_intercept - self.candidate_tolerance), ( self.pixel_list.max(), - self.max_wavelength - - self.range_tolerance - - self.candidate_tolerance, + self.max_wavelength - self.range_tolerance - self.candidate_tolerance, ), ( self.pixel_list.max(), - self.max_wavelength - + self.range_tolerance - + self.candidate_tolerance, + self.max_wavelength + self.range_tolerance + self.candidate_tolerance, ), ] ) @@ -151,9 +144,7 @@ def _merge_candidates(self, candidates): return np.sort(np.array(merged)) - def _get_most_common_candidates( - self, candidates, top_n_candidate, weighted - ): + def _get_most_common_candidates(self, candidates, top_n_candidate, weighted): """ Takes a number of candidate pair sets and returns the most common pair for each wavelength @@ -199,9 +190,7 @@ def _get_most_common_candidates( else: counts = np.ones_like(probabilities[idx]) - n = int( - min(top_n_candidate, len(np.unique(wavelengths_matched))) - ) + n = int(min(top_n_candidate, len(np.unique(wavelengths_matched)))) unique_wavelengths = np.unique(wavelengths_matched) aggregated_count = np.zeros_like(unique_wavelengths) @@ -210,9 +199,7 @@ def _get_most_common_candidates( aggregated_count[j] = np.sum(counts[idx_j]) out_peaks.extend([peak] * n) - out_wavelengths.extend( - wavelengths_matched[np.argsort(-aggregated_count)[:n]] - ) + out_wavelengths.extend(wavelengths_matched[np.argsort(-aggregated_count)[:n]]) return out_peaks, out_wavelengths @@ -256,9 +243,7 @@ def _get_candidate_points_linear(self, candidate_tolerance): (self.range_tolerance + self.linearity_tolerance) * 1.1775, ) - self.candidates.append( - (self.pairs[:, 0][mask], actual[mask], weight) - ) + self.candidates.append((self.pairs[:, 0][mask], actual[mask], weight)) def _get_candidate_points_poly(self, candidate_tolerance): """ @@ -295,10 +280,7 @@ def _get_candidate_points_poly(self, candidate_tolerance): n = len(self.hough_lines) - delta = ( - np.random.random(n) * self.range_tolerance * 2.0 - - self.range_tolerance - ) + delta = np.random.random(n) * self.range_tolerance * 2.0 - self.range_tolerance for d in delta: # predicted wavelength @@ -307,12 +289,8 @@ def _get_candidate_points_poly(self, candidate_tolerance): mask = diff < candidate_tolerance if np.sum(mask) > 0: - weight = gauss( - actual[mask], 1.0, predicted[mask], self.range_tolerance - ) - self.candidates.append( - [self.peaks[mask], actual[mask], weight] - ) + weight = gauss(actual[mask], 1.0, predicted[mask], self.range_tolerance) + self.candidates.append([self.peaks[mask], actual[mask], weight]) def _match_bijective(self, candidates, peaks, fit_coeff): """ @@ -456,9 +434,7 @@ def _solve_candidate_ransac( # Filter close wavelengths if self.filter_close: unique_y = np.unique(y) - idx = np.argwhere( - unique_y[1:] - unique_y[0:-1] < 3 * self.ransac_tolerance - ) + idx = np.argwhere(unique_y[1:] - unique_y[0:-1] < 3 * self.ransac_tolerance) separation_mask = np.argwhere((y == unique_y[idx]).sum(0) == 0) y = y[separation_mask].flatten() x = x[separation_mask].flatten() @@ -535,9 +511,7 @@ def _solve_candidate_ransac( else: # Pick some random peaks - x_hat = np.random.choice( - peaks, self.sample_size, replace=False, p=prob - ) + x_hat = np.random.choice(peaks, self.sample_size, replace=False, p=prob) y_hat = [] # Pick a random wavelength for this x @@ -555,10 +529,7 @@ def _solve_candidate_ransac( y_hat.append(y_temp) else: - self.logger.debug( - "Not possible to draw a unique " - "set of atlas wavelengths." - ) + self.logger.debug("Not possible to draw a unique set of atlas wavelengths.") stop_n_candidateow = True break @@ -575,9 +546,7 @@ def _solve_candidate_ransac( fit_coeffs = self.polyfit(x_hat, y_hat, self.fit_deg) # Check the intercept. - if (fit_coeffs[0] < self.min_intercept) | ( - fit_coeffs[0] > self.max_intercept - ): + if (fit_coeffs[0] < self.min_intercept) | (fit_coeffs[0] > self.max_intercept): self.logger.debug("Intercept exceeds bounds.") continue @@ -586,23 +555,12 @@ def _solve_candidate_ransac( pix_max = peaks[-1] + np.ptp(peaks) * 0.2 self.logger.debug((pix_min, pix_max)) - if not np.all( - np.diff( - self.polyval( - np.arange(pix_min, pix_max, 1), fit_coeffs - ) - ) - > 0 - ): - self.logger.debug( - "Solution is not monotonically increasing." - ) + if not np.all(np.diff(self.polyval(np.arange(pix_min, pix_max, 1), fit_coeffs)) > 0): + self.logger.debug("Solution is not monotonically increasing.") continue # Compute error and filter out many-to-one matches - err, matched_x, matched_y = self._match_bijective( - candidates, peaks, fit_coeffs - ) + err, matched_x, matched_y = self._match_bijective(candidates, peaks, fit_coeffs) if len(matched_x) == 0: continue @@ -612,25 +570,17 @@ def _solve_candidate_ransac( # use the Hough space density as weights for the cost function wave = self.polyval(self.pixel_list, fit_coeffs) - gradient = self.polyval( - self.pixel_list, _derivative(fit_coeffs) - ) + gradient = self.polyval(self.pixel_list, _derivative(fit_coeffs)) intercept = wave - gradient * self.pixel_list # modified cost function weighted by the Hough space density if (self.hough_weight is not None) & (twoditp is not None): - weight = self.hough_weight * np.sum( - twoditp(intercept, gradient, grid=False) - ) + weight = self.hough_weight * np.sum(twoditp(intercept, gradient, grid=False)) else: weight = 1.0 - cost = ( - sum(err) - / (len(err) - len(fit_coeffs) + 1) - / (weight + 1e-9) - ) + cost = sum(err) / (len(err) - len(fit_coeffs) + 1) / (weight + 1e-9) # If this is potentially a new best fit, then handle that first if cost <= best_cost: @@ -643,38 +593,26 @@ def _solve_candidate_ransac( matched_atlas = matched_y[mask] if len(matched_peaks) <= self.fit_deg: - self.logger.debug( - "Too few good candidates for fitting." - ) + self.logger.debug("Too few good candidates for fitting.") continue # Now we do a robust fit try: - coeffs = models.robust_polyfit( - matched_peaks, matched_atlas, self.fit_deg - ) + coeffs = models.robust_polyfit(matched_peaks, matched_atlas, self.fit_deg) except np.linalg.LinAlgError: - self.logger.warning( - "Linear algebra error in robust fit" - ) + self.logger.warning("Linear algebra error in robust fit") continue # Get the residual of the fit - residual = ( - self.polyval(matched_peaks, coeffs) - matched_atlas - ) - residual[ - np.abs(residual) > self.ransac_tolerance - ] = self.ransac_tolerance + residual = self.polyval(matched_peaks, coeffs) - matched_atlas + residual[np.abs(residual) > self.ransac_tolerance] = self.ransac_tolerance rms_residual = np.sqrt(np.mean(residual**2)) # Make sure that we don't accept fits with zero error if rms_residual < self.minimum_fit_error: - self.logger.debug( - "Fit error too small, " "{:1.2f}.".format(best_err) - ) + self.logger.debug("Fit error too small, {:1.2f}.".format(best_err)) continue @@ -683,17 +621,13 @@ def _solve_candidate_ransac( if n_inliers < self.minimum_matches: self.logger.debug( - "Not enough matched peaks for valid solution, " - "user specified {}.".format(self.minimum_matches) + "Not enough matched peaks for valid solution, user specified {}.".format(self.minimum_matches) ) continue - if n_inliers < self.minimum_peak_utilisation * len( - self.peaks - ): + if n_inliers < self.minimum_peak_utilisation * len(self.peaks): self.logger.debug( - "Not enough matched peaks for valid solution, " - "user specified {:1.2f} %.".format( + "Not enough matched peaks for valid solution, user specified {:1.2f} %.".format( 100 * self.minimum_matches ) ) @@ -709,23 +643,12 @@ def _solve_candidate_ransac( self.matched_atlas = list(copy.deepcopy(matched_atlas)) # Sanity check that matching peaks/atlas lines are 1:1 - assert len(np.unique(self.matched_peaks)) == len( - self.matched_peaks - ) - assert len(np.unique(self.matched_atlas)) == len( - self.matched_atlas - ) - assert len(np.unique(self.matched_atlas)) == len( - np.unique(self.matched_peaks) - ) + assert len(np.unique(self.matched_peaks)) == len(self.matched_peaks) + assert len(np.unique(self.matched_atlas)) == len(self.matched_atlas) + assert len(np.unique(self.matched_atlas)) == len(np.unique(self.matched_peaks)) if progress: - sampler_list.set_description( - "Most inliers: {:d}, " - "best error: {:1.4f}".format( - best_inliers, best_err - ) - ) + sampler_list.set_description("Most inliers: {:d}, best error: {:1.4f}".format(best_inliers, best_err)) # Break early if all peaks are matched if best_inliers == len(peaks): @@ -807,15 +730,11 @@ def _adjust_polyfit(self, delta, fit, tolerance, min_frac): if len(x_matched) < len(self.peaks) * min_frac: return np.inf - if not np.all( - np.diff(self.polyval(np.sort(self.pixel_list), fit_new)) > 0 - ): + if not np.all(np.diff(self.polyval(np.sort(self.pixel_list), fit_new)) > 0): self.logger.info("not monotonic") return np.inf - lsq = ( - np.sum((y_matched - self.polyval(x_matched, fit_new)) ** 2.0) / dof - ) + lsq = np.sum((y_matched - self.polyval(x_matched, fit_new)) ** 2.0) / dof return lsq @@ -897,8 +816,7 @@ def set_calibrator_properties( self.log_level = level formatter = logging.Formatter( - "[%(asctime)s] %(levelname)s [%(filename)s:%(lineno)d] " - "%(message)s", + "[%(asctime)s] %(levelname)s [%(filename)s:%(lineno)d] %(message)s", datefmt="%a, %d %b %Y %H:%M:%S", ) @@ -918,9 +836,7 @@ def set_calibrator_properties( except Exception as e: self.logger.warning(e) self.logger.warning( - "Neither num_pix nor spectrum is given, " - "it uses 1.1 times max(peaks) as the " - "maximum pixel value." + "Neither num_pix nor spectrum is given, it uses 1.1 times max(peaks) as the maximum pixel value." ) self.num_pix = 1.1 * max(self.peaks) @@ -1090,29 +1006,13 @@ def set_hough_properties( self.max_intercept = self.min_wavelength + self.range_tolerance self.min_slope = ( - ( - self.max_wavelength - - self.range_tolerance - - self.linearity_tolerance - ) - - ( - self.min_intercept - + self.range_tolerance - + self.linearity_tolerance - ) + (self.max_wavelength - self.range_tolerance - self.linearity_tolerance) + - (self.min_intercept + self.range_tolerance + self.linearity_tolerance) ) / np.ptp(self.pixel_list) self.max_slope = ( - ( - self.max_wavelength - + self.range_tolerance - + self.linearity_tolerance - ) - - ( - self.min_intercept - - self.range_tolerance - - self.linearity_tolerance - ) + (self.max_wavelength + self.range_tolerance + self.linearity_tolerance) + - (self.min_intercept - self.range_tolerance - self.linearity_tolerance) ) / np.ptp(self.pixel_list) if self.atlas is not None: @@ -1259,10 +1159,7 @@ def set_ransac_properties( # Set the minimum utilisation required if minimum_peak_utilisation is not None: - assert ( - minimum_peak_utilisation >= 0 - and minimum_peak_utilisation <= 1.0 - ) + assert minimum_peak_utilisation >= 0 and minimum_peak_utilisation <= 1.0 self.minimum_peak_utilisation = minimum_peak_utilisation elif self.minimum_peak_utilisation is None: @@ -1296,10 +1193,7 @@ def add_atlas( temperature=273.15, relative_humidity=0.0, ): - self.logger.warning( - "Using add_atlas is now deprecated. " - "Please use the new Atlas class." - ) + self.logger.warning("Using add_atlas is now deprecated. Please use the new Atlas class.") if min_atlas_wavelength is None: min_atlas_wavelength = self.min_wavelength - self.range_tolerance @@ -1373,10 +1267,7 @@ def add_user_atlas( candidate_tolerance=10, constrain_poly=False, ): - self.logger.warning( - "Using add_user_atlas is now deprecated. " - "Please use the new Atlas class." - ) + self.logger.warning("Using add_user_atlas is now deprecated. Please use the new Atlas class.") if self.atlas is None: self.atlas = Atlas() @@ -1423,11 +1314,11 @@ def set_atlas(self, atlas, candidate_tolerance=10.0, constrain_poly=False): self._generate_pairs() def do_hough_transform(self, brute_force=False): - if self.pairs == []: + if len(self.pairs) == 0: logging.warning("pairs list is empty. Try generating now.") self._generate_pairs() - if self.pairs == []: + if len(self.pairs) == 0: logging.error("pairs list is still empty.") # Generate the hough_points from the pairs @@ -1439,13 +1330,9 @@ def do_hough_transform(self, brute_force=False): ) if brute_force: - self.ht.generate_hough_points_brute_force( - self.pairs[:, 0], self.pairs[:, 1] - ) + self.ht.generate_hough_points_brute_force(self.pairs[:, 0], self.pairs[:, 1]) else: - self.ht.generate_hough_points( - self.pairs[:, 0], self.pairs[:, 1], num_slopes=self.num_slopes - ) + self.ht.generate_hough_points(self.pairs[:, 0], self.pairs[:, 1], num_slopes=self.num_slopes) self.ht.bin_hough_points(self.xbins, self.ybins) self.hough_points = self.ht.hough_points @@ -1532,18 +1419,13 @@ def set_known_pairs(self, pix=(), wave=()): wave = np.asarray(wave, dtype="float").reshape(-1) assert pix.size == wave.size, ValueError( - "Please check the length of the input arrays. pix has size {} " - "and wave has size {}.".format(pix.size, wave.size) + "Please check the length of the input arrays. pix has size {} and wave has size {}.".format(pix.size, wave.size) ) - if not all( - isinstance(p, (float, int)) & (not np.isnan(p)) for p in pix - ): + if not all(isinstance(p, (float, int)) & (not np.isnan(p)) for p in pix): raise ValueError("All pix elements have to be numeric.") - if not all( - isinstance(w, (float, int)) & (not np.isnan(w)) for w in wave - ): + if not all(isinstance(w, (float, int)) & (not np.isnan(w)) for w in wave): raise ValueError("All wave elements have to be numeric.") self.pix_known = pix @@ -1631,9 +1513,7 @@ def fit( self.polyval = np.polynomial.chebyshev.chebval else: - raise ValueError( - "fit_type must be: (1) poly, (2) legendre or (3) chebyshev" - ) + raise ValueError("fit_type must be: (1) poly, (2) legendre or (3) chebyshev") # Reduce sample_size if it is larger than the number of atlas available if self.sample_size > len(self.atlas): @@ -1692,9 +1572,7 @@ def fit( self.logger.warning("Invalid fit") if rms > self.fit_tolerance: - self.logger.warning( - "RMS too large {} > {}".format(rms, self.fit_tolerance) - ) + self.logger.warning("RMS too large {} > {}".format(rms, self.fit_tolerance)) assert fit_coeff is not None, "Couldn't fit" @@ -1814,10 +1692,7 @@ def match_peaks( fit_coeff_new[i] += d if np.any(np.isnan(fit_coeff_new)): - self.logger.warning( - "_adjust_polyfit() returns None. " - "Input solution is returned." - ) + self.logger.warning("_adjust_polyfit() returns None. Input solution is returned.") return fit_coeff, None, None, None, None, None, None matched_peaks = [] @@ -1876,9 +1751,7 @@ def match_peaks( candidates.append(new_candidates) if len(candidates) > 1: - self.logger.info( - "More than one match solution found, checking permutations." - ) + self.logger.info("More than one match solution found, checking permutations.") self.matched_peaks = np.array(copy.deepcopy(matched_peaks)) @@ -1905,9 +1778,7 @@ def match_peaks( assert self.matched_atlas is not None assert self.residuals is not None - self.rms = np.sqrt( - np.nansum(self.residuals**2.0) / len(self.residuals) - ) + self.rms = np.sqrt(np.nansum(self.residuals**2.0) / len(self.residuals)) self.peak_utilisation = len(self.matched_peaks) / len(self.peaks) self.atlas_utilisation = len(self.matched_atlas) / len(self.atlas) @@ -1920,10 +1791,7 @@ def match_peaks( ) if np.any(np.isnan(self.fit_coeff)): - self.logger.warning( - "robust_polyfit() returns None. " - "Input solution is returned." - ) + self.logger.warning("robust_polyfit() returns None. Input solution is returned.") return ( fit_coeff, self.matched_peaks, @@ -1935,12 +1803,8 @@ def match_peaks( ) else: - self.residuals = self.matched_atlas - self.polyval( - self.matched_peaks, self.fit_coeff - ) - self.rms = np.sqrt( - np.nansum(self.residuals**2.0) / len(self.residuals) - ) + self.residuals = self.matched_atlas - self.polyval(self.matched_peaks, self.fit_coeff) + self.rms = np.sqrt(np.nansum(self.residuals**2.0) / len(self.residuals)) else: self.fit_coeff = fit_coeff_new @@ -1970,15 +1834,9 @@ def get_pix_wave_pairs(self): pw_pairs = [] - for i, (p, w) in enumerate( - zip(self.matched_peaks, self.matched_atlas) - ): + for i, (p, w) in enumerate(zip(self.matched_peaks, self.matched_atlas)): pw_pairs.append((i, p, w)) - self.logger.info( - "Position {}: pixel {} is matched to wavelength {}".format( - i, p, w - ) - ) + self.logger.info("Position {}: pixel {} is matched to wavelength {}".format(i, p, w)) return pw_pairs @@ -2025,9 +1883,7 @@ def remove_pix_wave_pair(self, arg): self.matched_peaks = matched_peaks self.matched_atlas = matched_atlas - def manual_refit( - self, matched_peaks=None, matched_atlas=None, degree=None, x0=None - ): + def manual_refit(self, matched_peaks=None, matched_atlas=None, degree=None, x0=None): """ Perform a refinement of the matched peaks and atlas lines. @@ -2111,9 +1967,7 @@ def manual_refit( self.matched_peaks = copy.deepcopy(matched_peaks) self.matched_atlas = copy.deepcopy(matched_atlas) self.residuals = y - self.polyval(x, fit_coeff_new) - self.rms = np.sqrt( - np.nansum(self.residuals**2.0) / len(self.residuals) - ) + self.rms = np.sqrt(np.nansum(self.residuals**2.0) / len(self.residuals)) return ( self.fit_coeff, diff --git a/src/rascal/houghtransform.py b/src/rascal/houghtransform.py index 41a2699..8cb3cc6 100644 --- a/src/rascal/houghtransform.py +++ b/src/rascal/houghtransform.py @@ -19,9 +19,7 @@ def __init__(self): self.min_intercept = None self.max_intercept = None - def set_constraints( - self, min_slope, max_slope, min_intercept, max_intercept - ): + def set_constraints(self, min_slope, max_slope, min_intercept, max_intercept): """ Define the minimum and maximum of the intercepts (wavelength) and gradients (wavelength/pixel) that Hough pairs will be generated. @@ -39,18 +37,10 @@ def set_constraints( """ - assert np.isfinite(min_slope), ( - "min_slope has to be finite, %s is given " % min_slope - ) - assert np.isfinite(max_slope), ( - "max_slope has to be finite, %s is given " % max_slope - ) - assert np.isfinite(min_intercept), ( - "min_intercept has to be finite, %s is given " % min_intercept - ) - assert np.isfinite(max_intercept), ( - "max_intercept has to be finite, %s is given " % max_intercept - ) + assert np.isfinite(min_slope), "min_slope has to be finite, %s is given " % min_slope + assert np.isfinite(max_slope), "max_slope has to be finite, %s is given " % max_slope + assert np.isfinite(min_intercept), "min_intercept has to be finite, %s is given " % min_intercept + assert np.isfinite(max_intercept), "max_intercept has to be finite, %s is given " % max_intercept self.min_slope = min_slope self.max_slope = max_slope @@ -82,9 +72,7 @@ def generate_hough_points(self, x, y, num_slopes): gradients = np.concatenate(np.column_stack([slopes] * len(x))) # Apply boundaries - mask = (self.min_intercept <= intercepts) & ( - intercepts <= self.max_intercept - ) + mask = (self.min_intercept <= intercepts) & (intercepts <= self.max_intercept) intercepts = intercepts[mask] gradients = gradients[mask] @@ -188,11 +176,7 @@ def bin_hough_points(self, xbins, ybins): # Get the line fit_coeffients from the promising bins in the # histogram - self.hist_sorted_arg = np.dstack( - np.unravel_index( - np.argsort(self.hist.ravel())[::-1], self.hist.shape - ) - )[0] + self.hist_sorted_arg = np.dstack(np.unravel_index(np.argsort(self.hist.ravel())[::-1], self.hist.shape))[0] xbin_width = (self.xedges[1] - self.xedges[0]) / 2 ybin_width = (self.yedges[1] - self.yedges[0]) / 2 @@ -241,19 +225,19 @@ def save( fileformat_split = fileformat.split(delimiter) if "npy" in fileformat_split: - output_npy = [] - - output_npy.append(self.hough_points) - output_npy.append(self.hist) - output_npy.append(self.xedges) - output_npy.append(self.yedges) - output_npy.append([self.min_slope]) - output_npy.append([self.max_slope]) - output_npy.append([self.min_intercept]) - output_npy.append([self.max_intercept]) + output_dict = { + "hough_points": self.hough_points, + "hist": self.hist, + "xedges": self.xedges, + "yedges": self.yedges, + "min_slope": self.min_slope, + "max_slope": self.max_slope, + "min_intercept": self.min_intercept, + "max_intercept": self.max_intercept, + } if to_disk: - np.save(filename + ".npy", output_npy) + np.save(filename, output_dict) if "json" in fileformat_split: output_json = {} @@ -272,18 +256,14 @@ def save( json.dump(output_json, f) if not to_disk: - if ("npy" in fileformat_split) and ( - "json" not in fileformat_split - ): - return output_npy - - elif ("npy" not in fileformat_split) and ( - "json" in fileformat_split - ): + if ("npy" in fileformat_split) and ("json" not in fileformat_split): + return output_dict + + elif ("npy" not in fileformat_split) and ("json" in fileformat_split): return output_json elif ("npy" in fileformat_split) and ("json" in fileformat_split): - return output_npy, output_json + return output_dict, output_json else: return None @@ -307,16 +287,16 @@ def load(self, filename="hough_transform", filetype="npy"): if filename[-4:] != ".npy": filename += ".npy" - input_npy = np.load(filename, allow_pickle=True) + input_npy = np.load(filename, allow_pickle=True).item() - self.hough_points = input_npy[0] - self.hist = input_npy[1].astype("float") - self.xedges = input_npy[2].astype("float") - self.yedges = input_npy[3].astype("float") - self.min_slope = float(input_npy[4][0]) - self.max_slope = float(input_npy[5][0]) - self.min_intercept = float(input_npy[6][0]) - self.max_intercept = float(input_npy[7][0]) + self.hough_points = input_npy["hough_points"] + self.hist = input_npy["hist"].astype("float") + self.xedges = input_npy["xedges"].astype("float") + self.yedges = input_npy["yedges"].astype("float") + self.min_slope = float(input_npy["min_slope"]) + self.max_slope = float(input_npy["max_slope"]) + self.min_intercept = float(input_npy["min_intercept"]) + self.max_intercept = float(input_npy["max_intercept"]) elif filetype == "json": if filename[-5:] != ".json": @@ -334,6 +314,4 @@ def load(self, filename="hough_transform", filetype="npy"): self.max_intercept = float(input_json["max_intercept"]) else: - raise ValueError( - "Unknown filetype %s, it has to be npy or json" % filetype - ) + raise ValueError("Unknown filetype %s, it has to be npy or json" % filetype) diff --git a/src/rascal/plotting.py b/src/rascal/plotting.py index 96695d0..d6fe17c 100644 --- a/src/rascal/plotting.py +++ b/src/rascal/plotting.py @@ -161,11 +161,7 @@ def plot_calibration_lines( i = np.zeros_like(w) for e in elements: - i[ - np.isin( - w, np.around(wavelength_list[element_list == e], decimals=3) - ) - ] += intensity_list[element_list == e] + i[np.isin(w, np.around(wavelength_list[element_list == e], decimals=3))] += intensity_list[element_list == e] # Convolve to simulate the arc spectrum model_spectrum = signal.convolve(i, gaussian, mode="same") @@ -179,9 +175,7 @@ def plot_calibration_lines( intensity_list = intensity_list[intensity_mask] element_list = element_list[intensity_mask] - distance_mask = filter_separation( - wavelength_list, min_separation=min_distance - ) + distance_mask = filter_separation(wavelength_list, min_separation=min_distance) wavelength_list = wavelength_list[distance_mask] intensity_list = intensity_list[distance_mask] element_list = element_list[distance_mask] @@ -315,22 +309,16 @@ def plot_search_space( # Get the search space boundaries x = calibrator.pixel_list - m_1 = ( - calibrator.max_wavelength - calibrator.min_wavelength - ) / calibrator.pixel_list.max() + m_1 = (calibrator.max_wavelength - calibrator.min_wavelength) / calibrator.pixel_list.max() y_1 = m_1 * x + calibrator.min_wavelength m_2 = ( - calibrator.max_wavelength - + calibrator.range_tolerance - - (calibrator.min_wavelength + calibrator.range_tolerance) + calibrator.max_wavelength + calibrator.range_tolerance - (calibrator.min_wavelength + calibrator.range_tolerance) ) / calibrator.pixel_list.max() y_2 = m_2 * x + calibrator.min_wavelength + calibrator.range_tolerance m_3 = ( - calibrator.max_wavelength - - calibrator.range_tolerance - - (calibrator.min_wavelength - calibrator.range_tolerance) + calibrator.max_wavelength - calibrator.range_tolerance - (calibrator.min_wavelength - calibrator.range_tolerance) ) / calibrator.pixel_list.max() y_3 = m_3 * x + (calibrator.min_wavelength - calibrator.range_tolerance) @@ -340,9 +328,7 @@ def plot_search_space( fig = plt.figure(figsize=(10, 10)) # Plot all-pairs - plt.scatter( - *calibrator.pairs.T, alpha=0.2, color="C0", label="All pairs" - ) + plt.scatter(*calibrator.pairs.T, alpha=0.2, color="C0", label="All pairs") plt.scatter( calibrator._merge_candidates(calibrator.candidates)[:, 0], @@ -414,9 +400,7 @@ def plot_search_space( # (last pixel, maximum wavelength), and the two lines defining the # tolerance region. plt.plot(x, y_1, label="Linear Fit", color="C3") - plt.plot( - x, y_2, linestyle="dashed", label="Tolerance Region", color="C3" - ) + plt.plot(x, y_2, linestyle="dashed", label="Tolerance Region", color="C3") plt.plot(x, y_3, linestyle="dashed", color="C3") if fit_coeff is not None: @@ -476,9 +460,7 @@ def plot_search_space( y=calibrator.pairs[:, 1], mode="markers", name="All Pairs", - marker=dict( - color=pio.templates["CN"].layout.colorway[0], opacity=0.2 - ), + marker=dict(color=pio.templates["CN"].layout.colorway[0], opacity=0.2), ) ) @@ -488,9 +470,7 @@ def plot_search_space( y=calibrator._merge_candidates(calibrator.candidates)[:, 1], mode="markers", name="Candidate Pairs", - marker=dict( - color=pio.templates["CN"].layout.colorway[1], opacity=0.2 - ), + marker=dict(color=pio.templates["CN"].layout.colorway[1], opacity=0.2), ) ) fig.add_trace( @@ -627,10 +607,8 @@ def plot_search_space( yaxis=dict( title="Pixel", range=[ - calibrator.min_wavelength - - calibrator.range_tolerance * 1.1, - calibrator.max_wavelength - + calibrator.range_tolerance * 1.1, + calibrator.min_wavelength - calibrator.range_tolerance * 1.1, + calibrator.max_wavelength + calibrator.range_tolerance * 1.1, ], showgrid=True, ), @@ -736,9 +714,7 @@ def plot_fit( except Exception as e: calibrator.logger.error(e) - calibrator.logger.error( - "Spectrum is not provided, it cannot be " "plotted." - ) + calibrator.logger.error("Spectrum is not provided, it cannot be plotted.") if spectrum is not None: if log_spectrum: @@ -760,9 +736,7 @@ def plot_fit( if calibrator.plot_with_matplotlib: _import_matplotlib() - fig, (ax1, ax2, ax3) = plt.subplots( - nrows=3, sharex=True, gridspec_kw={"hspace": 0.0}, figsize=(15, 9) - ) + fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, gridspec_kw={"hspace": 0.0}, figsize=(15, 9)) fig.tight_layout() # Plot fitted spectrum @@ -770,9 +744,7 @@ def plot_fit( ax1.plot(wave, spectrum, label="Arc Spectrum") ax1.vlines( calibrator.polyval(calibrator.peaks, fit_coeff), - np.array(spectrum)[ - calibrator.pix_to_rawpix(calibrator.peaks).astype("int") - ], + np.array(spectrum)[calibrator.pix_to_rawpix(calibrator.peaks).astype("int")], vline_max, linestyles="dashed", colors="C1", @@ -806,9 +778,7 @@ def plot_fit( fitted_peaks.append(p) fitted_diff.append(diff[idx]) - calibrator.logger.info( - "- matched to {} A".format(calibrator.atlas.get_lines()[idx]) - ) + calibrator.logger.info("- matched to {} A".format(calibrator.atlas.get_lines()[idx])) if spectrum is not None: if first_one: @@ -870,9 +840,7 @@ def plot_fit( color="k", label="RMS", ) - ax2.hlines( - -rms, wave.min(), wave.max(), linestyles="dashed", color="k" - ) + ax2.hlines(-rms, wave.min(), wave.max(), linestyles="dashed", color="k") ax2.grid(linestyle=":") ax2.set_ylabel("Residual / A") ax2.legend() @@ -960,9 +928,7 @@ def plot_fit( y0=0, x1=x, y1=spec_max, - line=dict( - color=pio.templates["CN"].layout.colorway[1], width=1 - ), + line=dict(color=pio.templates["CN"].layout.colorway[1], width=1), ) diff = calibrator.atlas.get_lines() - x @@ -974,15 +940,9 @@ def plot_fit( if np.abs(diff[idx]) < tolerance: fitted_peaks.append(p) if spectrum is not None: - fitted_peaks_adu.append( - spectrum[int(calibrator.pix_to_rawpix(p))] - ) + fitted_peaks_adu.append(spectrum[int(calibrator.pix_to_rawpix(p))]) fitted_diff.append(diff[idx]) - calibrator.logger.info( - "- matched to {} A".format( - calibrator.atlas.get_lines()[idx] - ) - ) + calibrator.logger.info("- matched to {} A".format(calibrator.atlas.get_lines()[idx])) x_fitted = calibrator.polyval(fitted_peaks, fit_coeff) @@ -1014,9 +974,7 @@ def plot_fit( x=[wave.min(), wave.max()], y=[0, 0], mode="lines", - line=dict( - color=pio.templates["CN"].layout.colorway[0], dash="dash" - ), + line=dict(color=pio.templates["CN"].layout.colorway[0], dash="dash"), yaxis="y2", showlegend=False, ) @@ -1146,12 +1104,8 @@ def plot_fit( return fig.to_json() else: - assert calibrator.matplotlib_imported, ( - "matplotlib package not available. " + "Plot cannot be generated." - ) - assert calibrator.plotly_imported, ( - "plotly package is not available. " + "Plot cannot be generated." - ) + assert calibrator.matplotlib_imported, "matplotlib package not available. " + "Plot cannot be generated." + assert calibrator.plotly_imported, "plotly package is not available. " + "Plot cannot be generated." def plot_arc( @@ -1218,9 +1172,7 @@ def plot_arc( np.log10(calibrator.spectrum / calibrator.spectrum.max()), label="Arc Spectrum", ) - plt.vlines( - calibrator.peaks, -2, 0, label="Detected Peaks", color="C1" - ) + plt.vlines(calibrator.peaks, -2, 0, label="Detected Peaks", color="C1") plt.ylabel("log(Normalised Count)") plt.ylim(-2, 0) else: @@ -1276,21 +1228,13 @@ def plot_arc( fig.add_trace( go.Scatter( x=list(pixel_list), - y=list( - np.log10( - calibrator.spectrum / calibrator.spectrum.max() - ) - ), + y=list(np.log10(calibrator.spectrum / calibrator.spectrum.max())), mode="lines", name="Arc", ) ) - xmin = min( - np.log10(calibrator.spectrum / calibrator.spectrum.max()) - ) - xmax = max( - np.log10(calibrator.spectrum / calibrator.spectrum.max()) - ) + xmin = min(np.log10(calibrator.spectrum / calibrator.spectrum.max())) + xmax = max(np.log10(calibrator.spectrum / calibrator.spectrum.max())) else: # Plot all-pairs @@ -1315,16 +1259,12 @@ def plot_arc( y0=0, x1=i, y1=1.05, - line=dict( - color=pio.templates["CN"].layout.colorway[1], width=1 - ), + line=dict(color=pio.templates["CN"].layout.colorway[1], width=1), ) fig.update_layout( autosize=True, - yaxis=dict( - title="Normalised Count", range=[xmin, xmax], showgrid=True - ), + yaxis=dict(title="Normalised Count", range=[xmin, xmax], showgrid=True), xaxis=dict( title="Pixel", zeroline=False, @@ -1337,13 +1277,9 @@ def plot_arc( width=1000, ) - fig.update_xaxes( - showline=True, linewidth=1, linecolor="black", mirror=True - ) + fig.update_xaxes(showline=True, linewidth=1, linecolor="black", mirror=True) - fig.update_yaxes( - showline=True, linewidth=1, linecolor="black", mirror=True - ) + fig.update_yaxes(showline=True, linewidth=1, linecolor="black", mirror=True) if save_fig: fig_type = fig_type.split("+") diff --git a/src/rascal/synthetic.py b/src/rascal/synthetic.py index 64c159d..7c3ea87 100644 --- a/src/rascal/synthetic.py +++ b/src/rascal/synthetic.py @@ -4,9 +4,7 @@ class SyntheticSpectrum: - def __init__( - self, coefficients=None, min_wavelength=200.0, max_wavelength=1200.0 - ): + def __init__(self, coefficients=None, min_wavelength=200.0, max_wavelength=1200.0): """ Creates a synthetic spectrum generator which, given a suitable model, outputs the expected pixel locations of input wavelengths. It is @@ -47,14 +45,8 @@ def set_wavelength_limit(self, min_wavelength=None, max_wavelength=None): Set a wavelength filter for the 'get_pixels' function. """ - if ( - not isinstance(min_wavelength, float) - and min_wavelength is not None - ): - raise TypeError( - "Please provide a numeric value or None to " - "retain the min_wavelength." - ) + if not isinstance(min_wavelength, float) and min_wavelength is not None: + raise TypeError("Please provide a numeric value or None to retain the min_wavelength.") else: # Placeholder Min/Max @@ -64,14 +56,8 @@ def set_wavelength_limit(self, min_wavelength=None, max_wavelength=None): else: new_min_wavelength = self.min_wavelength - if ( - not isinstance(max_wavelength, float) - and max_wavelength is not None - ): - raise TypeError( - "Please provide a numeric value or None to " - "retain the max_wavelength." - ) + if not isinstance(max_wavelength, float) and max_wavelength is not None: + raise TypeError("Please provide a numeric value or None to retain the max_wavelength.") else: if max_wavelength is not None: @@ -86,10 +72,7 @@ def set_wavelength_limit(self, min_wavelength=None, max_wavelength=None): self.max_wavelength = new_max_wavelength else: - raise RuntimeError( - "Minimum wavelength cannot be larger than " - "the maximum wavelength." - ) + raise RuntimeError("Minimum wavelength cannot be larger than the maximum wavelength.") def get_pixels(self, wavelengths): """ @@ -109,9 +92,7 @@ def get_pixels(self, wavelengths): # Linear function y = mx + c elif self.degree == 1: # x = (y - c) / m - pixels = (wavelengths - self.coefficients[0]) / self.coefficients[ - 1 - ] + pixels = (wavelengths - self.coefficients[0]) / self.coefficients[1] else: pixels = pynverse.inversefunc(self.model, wavelengths) diff --git a/src/rascal/util.py b/src/rascal/util.py index ac340b0..12094d9 100644 --- a/src/rascal/util.py +++ b/src/rascal/util.py @@ -2,7 +2,7 @@ import numpy as np from scipy.optimize import curve_fit -import pkg_resources +from importlib.resources import files def get_vapour_pressure(temperature): @@ -36,9 +36,7 @@ def get_vapour_partial_pressure(relative_humidity, vapour_pressure): return partial_pressure -def edlen_refraction( - wavelengths, temperature, pressure, vapour_partial_pressure -): +def edlen_refraction(wavelengths, temperature, pressure, vapour_partial_pressure): """ Appendix A.IV of https://emtoolbox.nist.gov/Wavelength/Documentation.asp @@ -70,19 +68,11 @@ def edlen_refraction( n_s = 1.0 + 1e-8 * (A + B / (130 - S) + C / (38.9 - S)) X = (1.0 + 1e-8 * (E - F * t) * pressure) / (1.0 + G * t) n_tp = 1.0 + pressure * (n_s - 1.0) * X / D - n = ( - n_tp - - 1e-10 - * (292.75 / T) - * (3.7345 - 0.0401 * S) - * vapour_partial_pressure - ) + n = n_tp - 1e-10 * (292.75 / T) * (3.7345 - 0.0401 * S) * vapour_partial_pressure return n -def vacuum_to_air_wavelength( - wavelengths, temperature=273.15, pressure=101325, relative_humidity=0 -): +def vacuum_to_air_wavelength(wavelengths, temperature=273.15, pressure=101325, relative_humidity=0): """ The conversion follows the Modified Edlén Equations @@ -115,17 +105,11 @@ def vacuum_to_air_wavelength( t = temperature - 273.15 vapour_pressure = get_vapour_pressure(t) - vapour_partial_pressure = get_vapour_partial_pressure( - relative_humidity, vapour_pressure - ) - return np.array(wavelengths) / edlen_refraction( - wavelengths, t, pressure, vapour_partial_pressure - ) + vapour_partial_pressure = get_vapour_partial_pressure(relative_humidity, vapour_pressure) + return np.array(wavelengths) / edlen_refraction(wavelengths, t, pressure, vapour_partial_pressure) -def air_to_vacuum_wavelength( - wavelengths, temperature=273.15, pressure=101325, relative_humidity=0 -): +def air_to_vacuum_wavelength(wavelengths, temperature=273.15, pressure=101325, relative_humidity=0): """ The conversion follows the Modified Edlén Equations https://emtoolbox.nist.gov/Wavelength/Documentation.asp @@ -154,12 +138,8 @@ def air_to_vacuum_wavelength( # get_vapour_pressure takes temperature in Celcius vapour_pressure = get_vapour_pressure(t) - vapour_partial_pressure = get_vapour_partial_pressure( - relative_humidity, vapour_pressure - ) - return np.array(wavelengths) * edlen_refraction( - wavelengths, t, pressure, vapour_partial_pressure - ) + vapour_partial_pressure = get_vapour_partial_pressure(relative_humidity, vapour_pressure) + return np.array(wavelengths) * edlen_refraction(wavelengths, t, pressure, vapour_partial_pressure) def filter_wavelengths(lines, min_atlas_wavelength, max_atlas_wavelength): @@ -188,9 +168,7 @@ def filter_wavelengths(lines, min_atlas_wavelength, max_atlas_wavelength): _, index, _ = np.unique(wavelengths, return_counts=True, return_index=True) - wavelength_mask = (wavelengths[index] >= min_atlas_wavelength) & ( - wavelengths[index] <= max_atlas_wavelength - ) + wavelength_mask = (wavelengths[index] >= min_atlas_wavelength) & (wavelengths[index] <= max_atlas_wavelength) return lines[index][wavelength_mask] @@ -355,16 +333,12 @@ def load_calibration_lines( # Element, wavelength, intensity if isinstance(linelist, str): if linelist.lower() == "nist": - file_path = pkg_resources.resource_filename( - "rascal", "arc_lines/nist_clean.csv" - ) + file_path = files("rascal").joinpath("arc_lines/nist_clean.csv") lines = np.loadtxt(file_path, delimiter=",", dtype=">U12") elif os.path.exists(linelist): lines = np.loadtxt(linelist, delimiter=",", dtype=">U12") else: - raise ValueError( - f"Unknown string is provided as linelist: {linelist}." - ) + raise ValueError(f"Unknown string is provided as linelist: {linelist}.") else: raise ValueError("Please provide a valid format of line list.") @@ -382,9 +356,7 @@ def load_calibration_lines( ) # Filter wavelengths - lines = filter_wavelengths( - lines, min_atlas_wavelength, max_atlas_wavelength - ) + lines = filter_wavelengths(lines, min_atlas_wavelength, max_atlas_wavelength) # Filter intensities if isinstance(min_intensity, (float, int, list, np.ndarray)): @@ -397,9 +369,7 @@ def load_calibration_lines( intensity_list = lines[:, 2][intensity_mask].astype("float64") if brightest_n_lines is not None: - to_keep = np.argsort(np.array(intensity_list))[::-1][ - :brightest_n_lines - ] + to_keep = np.argsort(np.array(intensity_list))[::-1][:brightest_n_lines] element_list = element_list[to_keep] intensity_list = intensity_list[to_keep] wavelength_list = wavelength_list[to_keep] @@ -416,9 +386,7 @@ def load_calibration_lines( # Vacuum to air conversion if not vacuum: - wavelength_list = vacuum_to_air_wavelength( - wavelength_list, temperature, pressure, relative_humidity - ) + wavelength_list = vacuum_to_air_wavelength(wavelength_list, temperature, pressure, relative_humidity) return element_list, wavelength_list, intensity_list @@ -480,11 +448,7 @@ def refine_peaks(spectrum, peaks, window_width=10, distance=None): length = len(spectrum) for peak in peaks: - y = spectrum[ - max(0, int(peak) - window_width) : min( - int(peak) + window_width, length - ) - ] + y = spectrum[max(0, int(peak) - window_width) : min(int(peak) + window_width, length)] y /= np.nanmax(y) x = np.arange(len(y)) diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/refit_pfit.py b/test/refit_pfit.py index 466f610..1640a9d 100644 --- a/test/refit_pfit.py +++ b/test/refit_pfit.py @@ -32,9 +32,7 @@ def adjust_pfit_coefficient(delta, x, y, pfit): delta_trial = [0.0, 0.0, 0.0, 0.0] -delta = minimize( - adjust_pfit_coefficient, delta_trial, args=(x_shifted, y_shifted, pfit) -).x +delta = minimize(adjust_pfit_coefficient, delta_trial, args=(x_shifted, y_shifted, pfit)).x pfit_new = pfit.copy() for i, d in enumerate(delta): pfit_new[i] += d diff --git a/test/test_effective_pixel.py b/test/test_effective_pixel.py index 275d197..9072bf0 100644 --- a/test/test_effective_pixel.py +++ b/test/test_effective_pixel.py @@ -23,9 +23,7 @@ def test_providing_effective_pixel_not_affecting_fit(mock_show): peaks, waves = s.get_pixels(np.linspace(200, 1200, num=25)) effective_pixel = np.arange(987).astype("int") - effective_pixel[len(effective_pixel) // 2 :] = ( - effective_pixel[len(effective_pixel) // 2 :] + 53.37 - ) + effective_pixel[len(effective_pixel) // 2 :] = effective_pixel[len(effective_pixel) // 2 :] + 53.37 assert len(peaks) > 0 @@ -39,9 +37,7 @@ def test_providing_effective_pixel_not_affecting_fit(mock_show): c.set_calibrator_properties(pixel_list=effective_pixel) # Setup the Hough transform parameters - c.set_hough_properties( - range_tolerance=100.0, min_wavelength=100.0, max_wavelength=1300.0 - ) + c.set_hough_properties(range_tolerance=100.0, min_wavelength=100.0, max_wavelength=1300.0) c.set_ransac_properties(linear=False, minimum_fit_error=1e-14) @@ -51,9 +47,7 @@ def test_providing_effective_pixel_not_affecting_fit(mock_show): assert len(c.atlas.atlas_lines) > 0 # And let's try and fit... - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit( - max_tries=500, fit_coeff=best_p - ) + best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=500, fit_coeff=best_p) c.plot_fit() diff --git a/test/test_hough_transform.py b/test/test_hough_transform.py index 6f47f3b..36ccdd0 100644 --- a/test/test_hough_transform.py +++ b/test/test_hough_transform.py @@ -6,40 +6,38 @@ HERE = os.path.dirname(os.path.realpath(__file__)) -ht = HoughTransform() -ht.set_constraints( - min_slope=1, max_slope=4, min_intercept=3000.0, max_intercept=5000.0 -) -ht.generate_hough_points( - x=(np.random.random(1000) * 3 + 1), - y=(np.random.random(1000) * 2000 + 3000), - num_slopes=1000, -) -ht.bin_hough_points(xbins=100, ybins=100) - - -def test_save_as_npy(): + +@pytest.fixture(scope="module") +def ht(): + ht = HoughTransform() + ht.set_constraints(min_slope=1, max_slope=4, min_intercept=3000.0, max_intercept=5000.0) + ht.generate_hough_points( + x=(np.random.random(1000) * 3 + 1), + y=(np.random.random(1000) * 2000 + 3000), + num_slopes=1000, + ) + ht.bin_hough_points(xbins=100, ybins=100) + return ht + + +def test_save_as_npy(ht): ht.save( filename=os.path.join(HERE, "test_output", "test_hough_transform_npy"), fileformat="npy", ) -def test_save_as_json(): +def test_save_as_json(ht): ht.save( - filename=os.path.join( - HERE, "test_output", "test_hough_transform_json" - ), + filename=os.path.join(HERE, "test_output", "test_hough_transform_json"), fileformat="json", ) -def test_load_npy(): +def test_load_npy(ht): ht_loaded = HoughTransform() ht_loaded.load( - filename=os.path.join( - HERE, "test_output", "test_hough_transform_npy.npy" - ), + filename=os.path.join(HERE, "test_output", "test_hough_transform_npy.npy"), filetype="npy", ) @@ -53,12 +51,10 @@ def test_load_npy(): assert ht_loaded.max_intercept == ht.max_intercept -def test_load_json(): +def test_load_json(ht): ht_loaded = HoughTransform() ht_loaded.load( - filename=os.path.join( - HERE, "test_output", "test_hough_transform_json.json" - ), + filename=os.path.join(HERE, "test_output", "test_hough_transform_json.json"), filetype="json", ) @@ -72,18 +68,16 @@ def test_load_json(): assert ht_loaded.max_intercept == ht.max_intercept -@pytest.mark.xfail() def test_load_fail(): ht_fail = HoughTransform() - ht_fail.load( - filename=os.path.join( - HERE, "test_output", "test_hough_transform_json.json" - ), - filetype="lalala", - ) + with pytest.raises(ValueError): + ht_fail.load( + filename=os.path.join(HERE, "test_output", "test_hough_transform_json.json"), + filetype="lalala", + ) -def test_ht_not_saved_to_disk(): +def test_ht_not_saved_to_disk(ht): a = ht.save( filename=os.path.join(HERE, "test_output", "test_hough_transform_npy"), fileformat="npy", @@ -103,11 +97,9 @@ def test_ht_not_saved_to_disk(): assert b == d -def test_extending_ht(): +def test_extending_ht(ht): ht2 = HoughTransform() - ht2.set_constraints( - min_slope=1, max_slope=4, min_intercept=7000.0, max_intercept=9000.0 - ) + ht2.set_constraints(min_slope=1, max_slope=4, min_intercept=7000.0, max_intercept=9000.0) ht2.generate_hough_points( x=(np.random.random(1000) * 3 + 1), y=(np.random.random(1000) * 2000 + 7000), @@ -126,15 +118,11 @@ def test_extending_ht(): @pytest.mark.xfail() -def test_extending_ht_expect_fail(): +def test_extending_ht_expect_fail(ht): ht.add_hough_points(np.ones(100)) def test_loading_ht_into_calibrator(): c = Calibrator(np.arange(10)) - c.load_hough_transform( - os.path.join(HERE, "test_output", "test_hough_transform_npy") - ) - c.save_hough_transform( - os.path.join(HERE, "test_output", "test_hough_transform_npy_2") - ) + c.load_hough_transform(os.path.join(HERE, "test_output", "test_hough_transform_npy")) + c.save_hough_transform(os.path.join(HERE, "test_output", "test_hough_transform_npy_2")) diff --git a/test/test_lt_sprat_manual_atlas.py b/test/test_lt_sprat_manual_atlas.py index 3b0c66a..a0b47cc 100644 --- a/test/test_lt_sprat_manual_atlas.py +++ b/test/test_lt_sprat_manual_atlas.py @@ -51,9 +51,7 @@ def test_sprat_manual_atlas(): log_spectrum=True, fig_type="png+html", save_fig=True, - filename=os.path.join( - HERE, "test_output", "test_lt_sprat_arc_matplotlib" - ), + filename=os.path.join(HERE, "test_output", "test_lt_sprat_arc_matplotlib"), ) c.set_hough_properties( @@ -117,16 +115,12 @@ def test_sprat_manual_atlas(): ) c.set_atlas(a, constrain_poly=True) - c.set_ransac_properties( - sample_size=5, top_n_candidate=5, filter_close=True - ) + c.set_ransac_properties(sample_size=5, top_n_candidate=5, filter_close=True) c.do_hough_transform(brute_force=True) # Run the wavelength calibration - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit( - max_tries=250 - ) + best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=250) # Plot the solution c.plot_fit( @@ -159,9 +153,7 @@ def test_sprat_manual_atlas(): display=False, fig_type="png+html", save_fig=True, - filename=os.path.join( - HERE, "test_output", "test_lt_sprat_fit_matplotlib" - ), + filename=os.path.join(HERE, "test_output", "test_lt_sprat_fit_matplotlib"), tolerance=5.0, ) @@ -170,9 +162,7 @@ def test_sprat_manual_atlas(): display=False, fig_type="png+html", save_fig=True, - filename=os.path.join( - HERE, "test_output", "test_lt_sprat_search_space_matplotlib" - ), + filename=os.path.join(HERE, "test_output", "test_lt_sprat_search_space_matplotlib"), ) print("Stdev error: {} A".format(residual.std())) @@ -192,9 +182,7 @@ def test_sprat_manual_atlas(): display=False, save_fig=True, fig_type="png+html", - filename=os.path.join( - HERE, "test_output", "test_lt_sprat_arc_log_plotly" - ), + filename=os.path.join(HERE, "test_output", "test_lt_sprat_arc_log_plotly"), ) # Plot the solution @@ -255,9 +243,7 @@ def test_sprat_manual_atlas(): save_fig=True, fig_type="png+html", display=False, - filename=os.path.join( - HERE, "test_output", "test_lt_sprat_fit_log_plotly" - ), + filename=os.path.join(HERE, "test_output", "test_lt_sprat_fit_log_plotly"), return_jsonstring=True, ) # Plot the solution @@ -270,9 +256,7 @@ def test_sprat_manual_atlas(): save_fig=True, fig_type="png+html", display=False, - filename=os.path.join( - HERE, "test_output", "test_lt_sprat_fit_log_plotly" - ), + filename=os.path.join(HERE, "test_output", "test_lt_sprat_fit_log_plotly"), ) # Show the parameter space for searching possible solution @@ -280,8 +264,6 @@ def test_sprat_manual_atlas(): save_fig=True, fig_type="png+html", display=False, - filename=os.path.join( - HERE, "test_output", "test_lt_sprat_search_space_plotly" - ), + filename=os.path.join(HERE, "test_output", "test_lt_sprat_search_space_plotly"), return_jsonstring=True, ) diff --git a/test/test_plotting.py b/test/test_plotting.py index adab3b1..808a941 100644 --- a/test/test_plotting.py +++ b/test/test_plotting.py @@ -35,9 +35,7 @@ def test_save_plot_calibration_lines_1_element(): label=True, display=False, save_fig=True, - filename=os.path.join( - base_dir, "test_output", "example_CuNeAr_calibration_lines" - ), + filename=os.path.join(base_dir, "test_output", "example_CuNeAr_calibration_lines"), fig_kwarg={"figsize": (30, 8)}, ) @@ -52,8 +50,6 @@ def test_save_plot_calibration_lines_3_elements(): label=False, display=False, save_fig=True, - filename=os.path.join( - base_dir, "test_output", "example_CuNeAr_calibration_lines" - ), + filename=os.path.join(base_dir, "test_output", "example_CuNeAr_calibration_lines"), fig_kwarg={"figsize": (30, 8)}, ) diff --git a/test/test_polynomial_fit.py b/test/test_polynomial_fit.py index 8e5c952..56b1156 100644 --- a/test/test_polynomial_fit.py +++ b/test/test_polynomial_fit.py @@ -234,9 +234,7 @@ def test_quadratic_fit(): min_wavelength=3000.0, max_wavelength=8000.0, ) - a.add_user_atlas( - elements=elements_quadratic, wavelengths=wavelengths_quadratic - ) + a.add_user_atlas(elements=elements_quadratic, wavelengths=wavelengths_quadratic) c.set_atlas(a) c.set_ransac_properties(minimum_matches=20, minimum_fit_error=1e-12) c.do_hough_transform(brute_force=False) @@ -250,9 +248,7 @@ def test_quadratic_fit(): residual, peak_utilisation, atlas_utilisation, - ) = c.fit( - max_tries=2000, fit_tolerance=5.0, candidate_tolerance=2.0, fit_deg=2 - ) + ) = c.fit(max_tries=2000, fit_tolerance=5.0, candidate_tolerance=2.0, fit_deg=2) # Refine solution ( best_p_robust, @@ -284,13 +280,9 @@ def test_quadratic_fit_legendre(): min_wavelength=3000.0, max_wavelength=8000.0, ) - a.add_user_atlas( - elements=elements_quadratic, wavelengths=wavelengths_quadratic - ) + a.add_user_atlas(elements=elements_quadratic, wavelengths=wavelengths_quadratic) c.set_atlas(a) - c.set_ransac_properties( - sample_size=10, minimum_matches=20, minimum_fit_error=1e-12 - ) + c.set_ransac_properties(sample_size=10, minimum_matches=20, minimum_fit_error=1e-12) c.do_hough_transform(brute_force=False) # Run the wavelength calibration @@ -331,13 +323,9 @@ def test_quadratic_fit_chebyshev(): min_wavelength=3000.0, max_wavelength=8000.0, ) - a.add_user_atlas( - elements=elements_quadratic, wavelengths=wavelengths_quadratic - ) + a.add_user_atlas(elements=elements_quadratic, wavelengths=wavelengths_quadratic) c.set_atlas(a) - c.set_ransac_properties( - sample_size=10, minimum_matches=20, minimum_fit_error=1e-12 - ) + c.set_ransac_properties(sample_size=10, minimum_matches=20, minimum_fit_error=1e-12) c.do_hough_transform(brute_force=False) # Run the wavelength calibration diff --git a/test/test_synthetic_calibration.py b/test/test_synthetic_calibration.py index bd68bee..a921874 100644 --- a/test/test_synthetic_calibration.py +++ b/test/test_synthetic_calibration.py @@ -29,9 +29,7 @@ def test_default(): c.set_calibrator_properties(num_pix=768) # Setup the Hough transform parameters - c.set_hough_properties( - range_tolerance=100.0, min_wavelength=100.0, max_wavelength=1500.0 - ) + c.set_hough_properties(range_tolerance=100.0, min_wavelength=100.0, max_wavelength=1500.0) # Add our fake lines as the atlas a.add_user_atlas(elements=["Test"] * len(waves), wavelengths=waves) @@ -40,9 +38,7 @@ def test_default(): assert len(c.atlas.atlas_lines) > 0 # And let's try and fit... - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit( - max_tries=500 - ) + best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=500) ( best_p, @@ -87,9 +83,7 @@ def test_get_candidate_points_poly(): c.set_calibrator_properties(num_pix=768) # Setup the Hough transform parameters - c.set_hough_properties( - range_tolerance=100.0, min_wavelength=100.0, max_wavelength=1500.0 - ) + c.set_hough_properties(range_tolerance=100.0, min_wavelength=100.0, max_wavelength=1500.0) c.set_ransac_properties(linear=False, minimum_fit_error=1e-12) @@ -99,9 +93,7 @@ def test_get_candidate_points_poly(): assert len(c.atlas.atlas_lines) > 0 # And let's try and fit... - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit( - max_tries=500, fit_coeff=best_p - ) + best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=500, fit_coeff=best_p) ( best_p, diff --git a/test/test_util.py b/test/test_util.py index 7bbdcf1..cc6ce7b 100644 --- a/test/test_util.py +++ b/test/test_util.py @@ -11,9 +11,7 @@ relative_humidity = np.array([0, 0, 0, 0, 0, 25, 50, 75]) # These values are from 1996 # http://jupiter.chem.uoa.gr/thanost/papers/papers4/Metrol_30(1993)155.pdf -elden = np.array( - [21459.0, 26826.2, 32193.8, 27776.1, 25938.5, 26804.6, 26783.4, 26761.9] -) +elden = np.array([21459.0, 26826.2, 32193.8, 27776.1, 25938.5, 26804.6, 26783.4, 26761.9]) def test_edlen_refractive_index(): @@ -23,18 +21,14 @@ def test_edlen_refractive_index(): 6330.0, t, p, - util.get_vapour_partial_pressure( - h, util.get_vapour_pressure(t) - ), + util.get_vapour_partial_pressure(h, util.get_vapour_pressure(t)), ) - 1 ) * 1e8 # We accept large errors because the set of coefficients for the # Edlen equations changes all the time and we onlyl need 2 S.F. # accuracy - assert np.isclose( - nm1e8, e, rtol=0.1, atol=1000 - ), "{} istaed of {}.".format(nm1e8, e) + assert np.isclose(nm1e8, e, rtol=0.1, atol=1000), "{} istaed of {}.".format(nm1e8, e) def test_vacuum_to_air_wavelength(): @@ -74,9 +68,7 @@ def test_vacuum_to_air_wavelength(): ) assert np.isclose( wave_air, - util.vacuum_to_air_wavelength( - wave_vacuum, temperature=288.15, pressure=101325 - ), + util.vacuum_to_air_wavelength(wave_vacuum, temperature=288.15, pressure=101325), atol=0.1, rtol=0.01, ).all() diff --git a/test/test_xe_calibration.py b/test/test_xe_calibration.py index 7046336..7ce543a 100644 --- a/test/test_xe_calibration.py +++ b/test/test_xe_calibration.py @@ -56,11 +56,7 @@ def run_sprat_calibration(fit_deg): # Load the LT SPRAT data base_dir = os.path.dirname(__file__) - spectrum2D = fits.open( - os.path.join( - base_dir, "..", "examples/data_lt_sprat/v_a_20190516_57_1_0_1.fits" - ) - )[0].data + spectrum2D = fits.open(os.path.join(base_dir, "..", "examples/data_lt_sprat/v_a_20190516_57_1_0_1.fits"))[0].data # Collapse into 1D spectrum between row 110 and 120 spectrum = np.median(spectrum2D[110:120], axis=0) @@ -93,9 +89,7 @@ def run_sprat_calibration(fit_deg): c.atlas.list() # Run the wavelength calibration - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit( - max_tries=200, fit_deg=fit_deg - ) + best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=200, fit_deg=fit_deg) # Refine solution ( @@ -115,10 +109,7 @@ def run_sprat_calibration(fit_deg): def test_sprat_calibration(): - logger.info( - "Test if LT/SPRAT Xe calibration return the order of " - "polynomial properly." - ) + logger.info("Test if LT/SPRAT Xe calibration return the order of polynomial properly.") for i in range(3, 6): best_p, _, _, _, _ = run_sprat_calibration(fit_deg=i) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..35733f7 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1765 @@ +version = 1 +revision = 2 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.11'", + "python_full_version < 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b/src/rascal/calibrator.py @@ -1314,11 +1314,11 @@ def set_atlas(self, atlas, candidate_tolerance=10.0, constrain_poly=False): self._generate_pairs() def do_hough_transform(self, brute_force=False): - if len(self.pairs) == 0: + if len(list(self.pairs)) == 0: logging.warning("pairs list is empty. Try generating now.") self._generate_pairs() - if len(self.pairs) == 0: + if len(list(self.pairs)) == 0: logging.error("pairs list is still empty.") # Generate the hough_points from the pairs From f990a7664292c4a63e692de2c670aed2589b1907 Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Fri, 10 Oct 2025 14:58:32 -0700 Subject: [PATCH 03/20] Update calibrator.py --- src/rascal/calibrator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/rascal/calibrator.py b/src/rascal/calibrator.py index 086947c..31eecd2 100644 --- a/src/rascal/calibrator.py +++ b/src/rascal/calibrator.py @@ -1574,7 +1574,7 @@ def fit( if rms > self.fit_tolerance: self.logger.warning("RMS too large {} > {}".format(rms, self.fit_tolerance)) - assert fit_coeff is not None, "Couldn't fit" + assert fit_coeff is not None, f"Couldn't fit with {len(self.pairs)} pair(s) / {len(self.peaks)} peaks / {len(self.atlas)} lines." self.fit_coeff = fit_coeff self.rms = rms From 9c0f93cee44d5a2ab28bab7d26c70ba25c7ce2dc Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Fri, 10 Oct 2025 15:01:26 -0700 Subject: [PATCH 04/20] Update python-package.yml --- .github/workflows/python-package.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index d204cc7..730a46b 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -33,7 +33,7 @@ jobs: run: uv sync --all-extras --dev - name: Lint with ruff using uv - run: uv run ruff check + run: uv run ruff check --fix - name: Test with pytest run: | From 7c74b66de69e22cfd9cce7b0072801a8f1c41d95 Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Fri, 10 Oct 2025 15:02:58 -0700 Subject: [PATCH 05/20] Update python-package.yml --- .github/workflows/python-package.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 730a46b..61765ca 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -32,9 +32,6 @@ jobs: - name: Install dependencies run: uv sync --all-extras --dev - - name: Lint with ruff using uv - run: uv run ruff check --fix - - name: Test with pytest run: | uv run pytest -v --disable-warnings -p no:warnings test From f86111aec935b4efce1e036ddfe848e2b50510aa Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sat, 18 Oct 2025 11:51:14 +0100 Subject: [PATCH 06/20] perhaps a wrong variable name? --- src/rascal/calibrator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/rascal/calibrator.py b/src/rascal/calibrator.py index 31eecd2..a2419e6 100644 --- a/src/rascal/calibrator.py +++ b/src/rascal/calibrator.py @@ -598,14 +598,14 @@ def _solve_candidate_ransac( # Now we do a robust fit try: - coeffs = models.robust_polyfit(matched_peaks, matched_atlas, self.fit_deg) + fit_coeffs = models.robust_polyfit(matched_peaks, matched_atlas, self.fit_deg) except np.linalg.LinAlgError: self.logger.warning("Linear algebra error in robust fit") continue # Get the residual of the fit - residual = self.polyval(matched_peaks, coeffs) - matched_atlas + residual = self.polyval(matched_peaks, fit_coeffs) - matched_atlas residual[np.abs(residual) > self.ransac_tolerance] = self.ransac_tolerance rms_residual = np.sqrt(np.mean(residual**2)) @@ -636,7 +636,7 @@ def _solve_candidate_ransac( # If the best fit is accepted, update the lists best_cost = cost best_inliers = n_inliers - best_p = coeffs + best_p = fit_coeffs best_err = rms_residual best_residual = residual self.matched_peaks = list(copy.deepcopy(matched_peaks)) From 260d09ecc0424cbdb2a63b0ef33f6e01682fd74f Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sat, 18 Oct 2025 12:34:09 +0100 Subject: [PATCH 07/20] more logging --- src/rascal/calibrator.py | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/src/rascal/calibrator.py b/src/rascal/calibrator.py index a2419e6..b3fbaf1 100644 --- a/src/rascal/calibrator.py +++ b/src/rascal/calibrator.py @@ -581,6 +581,9 @@ def _solve_candidate_ransac( weight = 1.0 cost = sum(err) / (len(err) - len(fit_coeffs) + 1) / (weight + 1e-9) + self.logger.info( + f"Cost: {cost:1.4f}, Weight: {weight:1.4f}, fit_coeffs: {fit_coeffs}" + ) # If this is potentially a new best fit, then handle that first if cost <= best_cost: @@ -604,6 +607,8 @@ def _solve_candidate_ransac( self.logger.warning("Linear algebra error in robust fit") continue + self.logger.info(f"Robust fit_coeffs: {fit_coeffs}") + # Get the residual of the fit residual = self.polyval(matched_peaks, fit_coeffs) - matched_atlas residual[np.abs(residual) > self.ransac_tolerance] = self.ransac_tolerance @@ -621,7 +626,9 @@ def _solve_candidate_ransac( if n_inliers < self.minimum_matches: self.logger.debug( - "Not enough matched peaks for valid solution, user specified {}.".format(self.minimum_matches) + "Not enough matched peaks for valid solution, user specified {}.".format( + self.minimum_matches + ) ) continue @@ -648,7 +655,9 @@ def _solve_candidate_ransac( assert len(np.unique(self.matched_atlas)) == len(np.unique(self.matched_peaks)) if progress: - sampler_list.set_description("Most inliers: {:d}, best error: {:1.4f}".format(best_inliers, best_err)) + sampler_list.set_description( + "Most inliers: {:d}, best error: {:1.4f}".format(best_inliers, best_err) + ) # Break early if all peaks are matched if best_inliers == len(peaks): @@ -1419,7 +1428,9 @@ def set_known_pairs(self, pix=(), wave=()): wave = np.asarray(wave, dtype="float").reshape(-1) assert pix.size == wave.size, ValueError( - "Please check the length of the input arrays. pix has size {} and wave has size {}.".format(pix.size, wave.size) + "Please check the length of the input arrays. pix has size {} and wave has size {}.".format( + pix.size, wave.size + ) ) if not all(isinstance(p, (float, int)) & (not np.isnan(p)) for p in pix): @@ -1574,7 +1585,9 @@ def fit( if rms > self.fit_tolerance: self.logger.warning("RMS too large {} > {}".format(rms, self.fit_tolerance)) - assert fit_coeff is not None, f"Couldn't fit with {len(self.pairs)} pair(s) / {len(self.peaks)} peaks / {len(self.atlas)} lines." + assert ( + fit_coeff is not None + ), f"Couldn't fit with {len(self.pairs)} pair(s) / {len(self.peaks)} peaks / {len(self.atlas)} lines." self.fit_coeff = fit_coeff self.rms = rms From 6caf8f6420bc5e0bd6ceaadb4f36ae4808c7beec Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sat, 18 Oct 2025 13:05:49 +0100 Subject: [PATCH 08/20] removed test requirement file --- test-requirements.txt | 4 ---- 1 file changed, 4 deletions(-) delete mode 100644 test-requirements.txt diff --git a/test-requirements.txt b/test-requirements.txt deleted file mode 100644 index 64dc7df..0000000 --- a/test-requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -astropy>=4.0 -plotly>=4.0 -pytest>=5.3 -pytest-cov>=2.8 From d2ea73f0463ac600a674bf9cfe384c5caa29db7b Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sat, 18 Oct 2025 13:12:59 +0100 Subject: [PATCH 09/20] updated setup files --- pyproject.toml | 4 ++-- setup.cfg | 33 +++++++++++++++++++-------------- 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 17f5e73..5732685 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,11 +7,11 @@ requires-python = ">=3.10" license = {text = "BSD-3-Clause"} authors = [ {name = "Josh Veitch-Michaelis", email = "j.veitchmichaelis@gmail.com"}, - {name = "Marco C Lam", email = "lam@mail.tau.ac.il"} + {name = "Marco C Lam", email = "mlam@roe.ac.uk"} ] maintainers = [ {name = "Josh Veitch-Michaelis", email = "j.veitchmichaelis@gmail.com"}, - {name = "Marco C Lam", email = "lam@mail.tau.ac.il"} + {name = "Marco C Lam", email = "mlam@roe.ac.uk"} ] urls = { Homepage = "https://rascal.readthedocs.io/en/latest/?badge=latest", Download = "https://github.com/jveitchmichaelis/rascal" } diff --git a/setup.cfg b/setup.cfg index 20c16b3..2f55dd5 100644 --- a/setup.cfg +++ b/setup.cfg @@ -8,19 +8,18 @@ long_description = file: README.md long_description_content_type = text/markdown readme = README.md author = Josh Veitch-Michaelis, Marco C Lam -author_email = j.veitchmichaelis@gmail.com, lam@mail.tau.ac.il +author_email = j.veitchmichaelis@gmail.com, mlam@roe.ac.uk maintainer = Josh Veitch-Michaelis, Marco C Lam -maintainer_email = j.veitchmichaelis@gmail.com, lam@mail.tau.ac.il +maintainer_email = j.veitchmichaelis@gmail.com, mlam@roe.ac.uk url = https://rascal.readthedocs.io/en/latest/?badge=latest download_url = https://github.com/jveitchmichaelis/rascal classifiers = License :: OSI Approved :: BSD License Programming Language :: Python :: 3 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.11 + Programming Language :: Python :: 3.12 + Programming Language :: Python :: 3.13 Operating System :: OS Independent [options] @@ -31,16 +30,22 @@ packages = find: include_package_data = True python_requires = >=3.6 install_requires = - numpy>=1.16,<1.24 - scipy>=1.3.3 - pynverse>=0.1.4 - matplotlib>=3.0.3 - tqdm>=4.48.0 + numpy>=2.2.6 + scipy>=1.15.3 + pynverse>=0.1.4.6 + matplotlib>=3.10.6 + tqdm>=4.67.1 + kaleido>=1.1.0 setup_requires = - astropy>=4.0 - plotly>=4.0 - pytest>=5.3 - pytest-cov>=2.8 + astropy>=6.1.7 + coverage>=7.10.7 + plotly>=6.3.1 + pre-commit>=4.3.0 + psutil>=7.1.0 + pytest>=8.4.2 + pytest-cov>=7.0.0 + requests>=2.32.5 + ruff>=0.14.0 [coverage:run] relative_files = True From f67ce3b0ecb33389cd929c034ff15b739fb5ffdf Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 00:22:45 +0100 Subject: [PATCH 10/20] try boosting numpy version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 5732685..d7dcd55 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,7 @@ classifiers = [ dependencies = [ "kaleido>=1.1.0", "matplotlib>=3.10.6", - "numpy>=2.2.6", + "numpy>=2.3.4", "pynverse>=0.1.4.6", "scipy>=1.15.3", "tqdm>=4.67.1", From 9a2139dd367aca2497b36e5b67ae25e4f22b96ca Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 00:27:20 +0100 Subject: [PATCH 11/20] revert numpy version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index d7dcd55..5732685 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,7 @@ classifiers = [ dependencies = [ "kaleido>=1.1.0", "matplotlib>=3.10.6", - "numpy>=2.3.4", + "numpy>=2.2.6", "pynverse>=0.1.4.6", "scipy>=1.15.3", "tqdm>=4.67.1", From 3f2a7d8778e32887cc887851fae466665a358a21 Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 00:54:00 +0100 Subject: [PATCH 12/20] changed all debug and info message to warning to debug github actions --- src/rascal/calibrator.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/src/rascal/calibrator.py b/src/rascal/calibrator.py index b3fbaf1..976d1fa 100644 --- a/src/rascal/calibrator.py +++ b/src/rascal/calibrator.py @@ -543,7 +543,10 @@ def _solve_candidate_ransac( # Try to fit the data. # This doesn't need to be robust, it's an exact fit. + self.logger.warning(f"x_hat: {x_hat}") + self.logger.warning(f"x_hat: {y_hat}") fit_coeffs = self.polyfit(x_hat, y_hat, self.fit_deg) + self.logger.warning(f"fit_coeffs: {fit_coeffs}") # Check the intercept. if (fit_coeffs[0] < self.min_intercept) | (fit_coeffs[0] > self.max_intercept): @@ -553,10 +556,10 @@ def _solve_candidate_ransac( # Check monotonicity. pix_min = peaks[0] - np.ptp(peaks) * 0.2 pix_max = peaks[-1] + np.ptp(peaks) * 0.2 - self.logger.debug((pix_min, pix_max)) + self.logger.warning((pix_min, pix_max)) if not np.all(np.diff(self.polyval(np.arange(pix_min, pix_max, 1), fit_coeffs)) > 0): - self.logger.debug("Solution is not monotonically increasing.") + self.logger.warning("Solution is not monotonically increasing.") continue # Compute error and filter out many-to-one matches @@ -581,7 +584,7 @@ def _solve_candidate_ransac( weight = 1.0 cost = sum(err) / (len(err) - len(fit_coeffs) + 1) / (weight + 1e-9) - self.logger.info( + self.logger.warning( f"Cost: {cost:1.4f}, Weight: {weight:1.4f}, fit_coeffs: {fit_coeffs}" ) @@ -607,7 +610,7 @@ def _solve_candidate_ransac( self.logger.warning("Linear algebra error in robust fit") continue - self.logger.info(f"Robust fit_coeffs: {fit_coeffs}") + self.logger.warning(f"Robust fit_coeffs: {fit_coeffs}") # Get the residual of the fit residual = self.polyval(matched_peaks, fit_coeffs) - matched_atlas @@ -617,7 +620,7 @@ def _solve_candidate_ransac( # Make sure that we don't accept fits with zero error if rms_residual < self.minimum_fit_error: - self.logger.debug("Fit error too small, {:1.2f}.".format(best_err)) + self.logger.warning("Fit error too small, {:1.2f}.".format(best_err)) continue @@ -625,7 +628,7 @@ def _solve_candidate_ransac( # constraints if n_inliers < self.minimum_matches: - self.logger.debug( + self.logger.warning( "Not enough matched peaks for valid solution, user specified {}.".format( self.minimum_matches ) @@ -633,7 +636,7 @@ def _solve_candidate_ransac( continue if n_inliers < self.minimum_peak_utilisation * len(self.peaks): - self.logger.debug( + self.logger.warning( "Not enough matched peaks for valid solution, user specified {:1.2f} %.".format( 100 * self.minimum_matches ) From 501fc285467cc4ef16c22e02b31332aa500d5ade Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 01:01:14 +0100 Subject: [PATCH 13/20] try adding some minimal noise to the synthetic spectrum --- test/test_synthetic_calibration.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/test/test_synthetic_calibration.py b/test/test_synthetic_calibration.py index a921874..7127ca6 100644 --- a/test/test_synthetic_calibration.py +++ b/test/test_synthetic_calibration.py @@ -71,6 +71,8 @@ def test_get_candidate_points_poly(): waves = np.linspace(200, 1200, num=25) peaks, waves = s.get_pixels(waves) + # add some minimal noise + peaks = np.random.normal(peaks, 0.01) assert len(peaks) > 0 # Set up the calibrator with the pixel values of our From 6764b4b0adb5fb69d9046cf98d7d1d8acaac2c28 Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 01:07:34 +0100 Subject: [PATCH 14/20] turning debug and info messages back to debug/info --- src/rascal/calibrator.py | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/src/rascal/calibrator.py b/src/rascal/calibrator.py index 976d1fa..b3fbaf1 100644 --- a/src/rascal/calibrator.py +++ b/src/rascal/calibrator.py @@ -543,10 +543,7 @@ def _solve_candidate_ransac( # Try to fit the data. # This doesn't need to be robust, it's an exact fit. - self.logger.warning(f"x_hat: {x_hat}") - self.logger.warning(f"x_hat: {y_hat}") fit_coeffs = self.polyfit(x_hat, y_hat, self.fit_deg) - self.logger.warning(f"fit_coeffs: {fit_coeffs}") # Check the intercept. if (fit_coeffs[0] < self.min_intercept) | (fit_coeffs[0] > self.max_intercept): @@ -556,10 +553,10 @@ def _solve_candidate_ransac( # Check monotonicity. pix_min = peaks[0] - np.ptp(peaks) * 0.2 pix_max = peaks[-1] + np.ptp(peaks) * 0.2 - self.logger.warning((pix_min, pix_max)) + self.logger.debug((pix_min, pix_max)) if not np.all(np.diff(self.polyval(np.arange(pix_min, pix_max, 1), fit_coeffs)) > 0): - self.logger.warning("Solution is not monotonically increasing.") + self.logger.debug("Solution is not monotonically increasing.") continue # Compute error and filter out many-to-one matches @@ -584,7 +581,7 @@ def _solve_candidate_ransac( weight = 1.0 cost = sum(err) / (len(err) - len(fit_coeffs) + 1) / (weight + 1e-9) - self.logger.warning( + self.logger.info( f"Cost: {cost:1.4f}, Weight: {weight:1.4f}, fit_coeffs: {fit_coeffs}" ) @@ -610,7 +607,7 @@ def _solve_candidate_ransac( self.logger.warning("Linear algebra error in robust fit") continue - self.logger.warning(f"Robust fit_coeffs: {fit_coeffs}") + self.logger.info(f"Robust fit_coeffs: {fit_coeffs}") # Get the residual of the fit residual = self.polyval(matched_peaks, fit_coeffs) - matched_atlas @@ -620,7 +617,7 @@ def _solve_candidate_ransac( # Make sure that we don't accept fits with zero error if rms_residual < self.minimum_fit_error: - self.logger.warning("Fit error too small, {:1.2f}.".format(best_err)) + self.logger.debug("Fit error too small, {:1.2f}.".format(best_err)) continue @@ -628,7 +625,7 @@ def _solve_candidate_ransac( # constraints if n_inliers < self.minimum_matches: - self.logger.warning( + self.logger.debug( "Not enough matched peaks for valid solution, user specified {}.".format( self.minimum_matches ) @@ -636,7 +633,7 @@ def _solve_candidate_ransac( continue if n_inliers < self.minimum_peak_utilisation * len(self.peaks): - self.logger.warning( + self.logger.debug( "Not enough matched peaks for valid solution, user specified {:1.2f} %.".format( 100 * self.minimum_matches ) From d11084c20b5de455c21f8fe0132309a4dec5f019 Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 13:49:41 +0100 Subject: [PATCH 15/20] try combining uv and coverage --- .github/workflows/python-package.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 61765ca..e92dc41 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -34,7 +34,7 @@ jobs: - name: Test with pytest run: | - uv run pytest -v --disable-warnings -p no:warnings test + uv run coverage run pytest -v --disable-warnings -p no:warnings test - name: Generate Coveralls reports if: startsWith(matrix.os, 'ubuntu') uses: AndreMiras/coveralls-python-action@develop From 2b7808bbc0bf5d79c3c6ad7fb1c16d1c94459347 Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 13:54:52 +0100 Subject: [PATCH 16/20] try again --- .github/workflows/python-package.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index e92dc41..c4a5d74 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -34,7 +34,7 @@ jobs: - name: Test with pytest run: | - uv run coverage run pytest -v --disable-warnings -p no:warnings test + uv run bash -c "coverage run pytest -v --disable-warnings -p no:warnings test" - name: Generate Coveralls reports if: startsWith(matrix.os, 'ubuntu') uses: AndreMiras/coveralls-python-action@develop From 1ab293551f40cc81af7e3ac2aeb66195e251ce0a Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 14:11:44 +0100 Subject: [PATCH 17/20] try again --- .github/workflows/python-package.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index c4a5d74..8949199 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -34,7 +34,7 @@ jobs: - name: Test with pytest run: | - uv run bash -c "coverage run pytest -v --disable-warnings -p no:warnings test" + uv run python -m coverage run -m pytest -v --disable-warnings -p no:warnings test - name: Generate Coveralls reports if: startsWith(matrix.os, 'ubuntu') uses: AndreMiras/coveralls-python-action@develop From c526554c8bdfb80e81c65cc0c6ed367ef01c0eba Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 14:36:18 +0100 Subject: [PATCH 18/20] make codacy happy --- test/test_effective_pixel.py | 17 +- test/test_lt_sprat_manual_atlas.py | 2 +- test/test_synthetic_calibration.py | 26 ++- test/test_xe_calibration.py | 9 +- test/unit/test_atlas.py | 252 +++++++++++++++++++++++++++++ 5 files changed, 273 insertions(+), 33 deletions(-) create mode 100644 test/unit/test_atlas.py diff --git a/test/test_effective_pixel.py b/test/test_effective_pixel.py index 9072bf0..432c2e6 100644 --- a/test/test_effective_pixel.py +++ b/test/test_effective_pixel.py @@ -23,7 +23,7 @@ def test_providing_effective_pixel_not_affecting_fit(mock_show): peaks, waves = s.get_pixels(np.linspace(200, 1200, num=25)) effective_pixel = np.arange(987).astype("int") - effective_pixel[len(effective_pixel) // 2 :] = effective_pixel[len(effective_pixel) // 2 :] + 53.37 + effective_pixel[len(effective_pixel) // 2:] = effective_pixel[len(effective_pixel) // 2:] + 53.37 assert len(peaks) > 0 @@ -47,24 +47,21 @@ def test_providing_effective_pixel_not_affecting_fit(mock_show): assert len(c.atlas.atlas_lines) > 0 # And let's try and fit... - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=500, fit_coeff=best_p) + best_p, _, _, _, _, _, _ = c.fit(max_tries=500, fit_coeff=best_p) c.plot_fit() ( - best_p, - x_fit, - y_fit, + _, + _, + _, rms, - residual, + _, peak_utilisation, atlas_utilisation, ) = c.match_peaks(best_p, refine=True, robust_refit=True) - fit_diff = c.polyval(x_fit, best_p) - y_fit - rms = np.sqrt(np.sum(fit_diff**2 / len(x_fit))) - assert peak_utilisation > 0.7 assert atlas_utilisation > 0.0 assert rms < 5.0 - assert np.in1d(c.matched_peaks, c.peaks).all() + assert np.isin(c.matched_peaks, c.peaks).all() diff --git a/test/test_lt_sprat_manual_atlas.py b/test/test_lt_sprat_manual_atlas.py index a0b47cc..6fb8f3a 100644 --- a/test/test_lt_sprat_manual_atlas.py +++ b/test/test_lt_sprat_manual_atlas.py @@ -120,7 +120,7 @@ def test_sprat_manual_atlas(): c.do_hough_transform(brute_force=True) # Run the wavelength calibration - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=250) + best_p, _, _, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=250) # Plot the solution c.plot_fit( diff --git a/test/test_synthetic_calibration.py b/test/test_synthetic_calibration.py index 7127ca6..4f52e64 100644 --- a/test/test_synthetic_calibration.py +++ b/test/test_synthetic_calibration.py @@ -38,21 +38,18 @@ def test_default(): assert len(c.atlas.atlas_lines) > 0 # And let's try and fit... - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=500) + best_p, _, _, _, _, _, _ = c.fit(max_tries=500) ( - best_p, - x_fit, - y_fit, + _, + _, + _, rms, - residual, + _, peak_utilisation, atlas_utilisation, ) = c.match_peaks(best_p, refine=False, robust_refit=True) - fit_diff = c.polyval(x_fit, best_p) - y_fit - rms = np.sqrt(np.sum(fit_diff**2 / len(x_fit))) - assert peak_utilisation > 0.7 assert atlas_utilisation > 0.0 assert rms < 5.0 @@ -95,21 +92,18 @@ def test_get_candidate_points_poly(): assert len(c.atlas.atlas_lines) > 0 # And let's try and fit... - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=500, fit_coeff=best_p) + best_p, _, _, _, _, _, _ = c.fit(max_tries=500, fit_coeff=best_p) ( - best_p, - x_fit, - y_fit, + _, + _, + _, rms, - residual, + _, peak_utilisation, atlas_utilisation, ) = c.match_peaks(best_p, refine=True, robust_refit=True) - fit_diff = c.polyval(x_fit, best_p) - y_fit - rms = np.sqrt(np.sum(fit_diff**2 / len(x_fit))) - assert peak_utilisation > 0.7 assert atlas_utilisation > 0.0 assert rms < 5.0 diff --git a/test/test_xe_calibration.py b/test/test_xe_calibration.py index 7ce543a..5294c23 100644 --- a/test/test_xe_calibration.py +++ b/test/test_xe_calibration.py @@ -89,22 +89,19 @@ def run_sprat_calibration(fit_deg): c.atlas.list() # Run the wavelength calibration - best_p, x, y, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=200, fit_deg=fit_deg) + best_p, _, _, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=200, fit_deg=fit_deg) # Refine solution ( best_p, - x_fit, - y_fit, + _, + _, rms, residual, peak_utilisation, atlas_utilisation, ) = c.match_peaks(best_p, refine=False, robust_refit=True) - fit_diff = c.polyval(x_fit, best_p) - y_fit - rms = np.sqrt(np.sum(fit_diff**2 / len(x_fit))) - return best_p, residual, peak_utilisation, atlas_utilisation, rms diff --git a/test/unit/test_atlas.py b/test/unit/test_atlas.py new file mode 100644 index 0000000..60ce27f --- /dev/null +++ b/test/unit/test_atlas.py @@ -0,0 +1,252 @@ +import importlib.resources as import_resources +import logging +import os +import time +from functools import partialmethod +from itertools import combinations + +import numpy as np +import pytest + +# Suppress tqdm output +from tqdm import tqdm + +from rascal.atlas import Atlas +from rascal.calibrator import Calibrator +from rascal.util import get_elements + +tqdm.__init__ = partialmethod(tqdm.__init__, disable=True) + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger() + +HERE = os.path.dirname(os.path.realpath(__file__)) + + +@pytest.fixture +def elements(): + elements = get_elements() + return elements + + +def test_load_nist_single(elements): + + min_wavelength = 3000 + max_wavelength = 8000 + + for element in elements: + time_start = time.perf_counter() + + nist_atlas = Atlas( + element=element, + source="nist", + min_wavelength=min_wavelength, + max_wavelength=max_wavelength, + brightest_n_lines=None, + ) + + for line in nist_atlas.get_lines(): + assert line > ( + min_wavelength - nist_atlas.range_tolerance + ), "Line is less than min wavelength" + assert line < ( + max_wavelength + nist_atlas.range_tolerance + ), "Line is greater than max wavelength" + + time_end = time.perf_counter() + elapsed = time_end - time_start + assert elapsed < 0.5 + + +def test_load_with_min_intensity(): + + min_intensity = 10 + + nist_atlas = Atlas( + element="Xe", + source="nist", + min_wavelength=4000, + max_wavelength=8000, + min_intensity=min_intensity, + brightest_n_lines=None, + ) + + for i in nist_atlas.get_intensities(): + assert i >= min_intensity, "Line intensity is below minimum" + + +""" +# TODO: Move to Atlas collection +def test_load_nist_all(elements): + + collection = AtlasCo + nist_atlas = Atlas( + element=elements, + line_list="nist", + min_wavelength=0, + max_wavelength=8000, + ) +""" + + +def test_check_nonzero_length_common_elements_single(): + + for element in ["Xe", "Kr", "Ar", "Ne", "Cu"]: + nist_atlas = Atlas( + element=element, + source="nist", + min_wavelength=4000, + max_wavelength=8000, + ) + + assert len(nist_atlas) > 0 + + +def test_load_single_line(): + user_atlas = Atlas( + element="Test", + source="manual", + wavelengths=[5.0], + min_wavelength=0, + max_wavelength=10, + ) + cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) + + +def test_load_mutliple_lines(): + user_atlas = Atlas( + element="Test", + source="manual", + wavelengths=np.arange(10), + min_wavelength=0, + max_wavelength=10, + ) + cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) + + +def test_setting_a_known_pair(): + user_atlas = Atlas( + element="Test", + source="manual", + wavelengths=np.arange(10), + min_wavelength=0, + max_wavelength=10, + ) + logger.info("Testing adding a known pair.") + cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) + cal.set_known_pairs(123, 456) + assert cal.pix_known == 123 + assert cal.wave_known == 456 + + +def test_setting_known_pairs(): + user_atlas = Atlas( + element="Test", + source="manual", + wavelengths=np.arange(10), + min_wavelength=0, + max_wavelength=10, + ) + logger.info("Testing adding known pairs.") + cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) + cal.set_known_pairs([123, 234], [456, 567]) + assert len(cal.pix_known) == 2 + assert len(cal.wave_known) == 2 + + +@pytest.mark.xfail() +def test_setting_a_none_to_known_pairs_expect_fail(): + user_atlas = Atlas( + element="Test", + source="manual", + wavelengths=np.arange(10), + min_wavelength=0, + max_wavelength=10, + ) + logger.info("Testing adding None as known pairs.") + cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) + cal.set_known_pairs([1.0], [None]) + + +@pytest.mark.xfail() +def test_setting_nones_to_known_pairs_expect_fail(): + user_atlas = Atlas( + element="Test", + source="manual", + wavelengths=np.arange(10), + min_wavelength=0, + max_wavelength=10, + ) + logger.info("Testing adding None as known pairs.") + cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) + cal.set_known_pairs([None], [None]) + + +element_list = ["Hg", "Ar", "Xe", "Kr"] +user_atlas = Atlas( + element="Test", + source="manual", + wavelengths=np.arange(10), + min_wavelength=0, + max_wavelength=10, +) + +cal = Calibrator(np.arange(10), user_atlas.atlas_lines) + +""" +def test_get_summary_executive(): + cal.atlas_summary(mode="executive") + + +def test_get_summary_full(): + cal.atlas_summary(mode="full") + + +def test_get_summary_executive_return_string(): + something = cal.atlas_summary(mode="executive", return_string=True) + assert type(something) == str + + +def test_get_summary_full_return_string(): + something = cal.atlas_summary(mode="full", return_string=True) + assert type(something) == str + + +def test_save_executive_summary(): + filepath = os.path.join( + HERE, "test_output", "test_save_atlas_executive_summary.txt" + ) + cal.save_atlas_summary( + mode="executive", + filename=filepath, + ) + assert os.path.exists(filepath) + os.remove(filepath) + + +def test_save_full_summary(): + filepath = os.path.join( + HERE, "test_output", "test_save_atlas_full_summary.txt" + ) + cal.save_atlas_summary( + mode="full", + filename=filepath, + ) + assert os.path.exists(filepath) + os.remove(filepath) + + + +def test_save_executive_summary_default(): + output_path = cal.save_atlas_summary( + mode="executive", + ) + os.remove(output_path) + + +def test_save_full_summary_default(): + output_path = cal.save_atlas_summary( + mode="full", + ) + os.remove(output_path) +""" From 8bdeaec783d0d35b57d13e06b28cb3c6b1118813 Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 14:49:33 +0100 Subject: [PATCH 19/20] remove unintended file addition --- test/unit/test_atlas.py | 252 ---------------------------------------- 1 file changed, 252 deletions(-) delete mode 100644 test/unit/test_atlas.py diff --git a/test/unit/test_atlas.py b/test/unit/test_atlas.py deleted file mode 100644 index 60ce27f..0000000 --- a/test/unit/test_atlas.py +++ /dev/null @@ -1,252 +0,0 @@ -import importlib.resources as import_resources -import logging -import os -import time -from functools import partialmethod -from itertools import combinations - -import numpy as np -import pytest - -# Suppress tqdm output -from tqdm import tqdm - -from rascal.atlas import Atlas -from rascal.calibrator import Calibrator -from rascal.util import get_elements - -tqdm.__init__ = partialmethod(tqdm.__init__, disable=True) - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger() - -HERE = os.path.dirname(os.path.realpath(__file__)) - - -@pytest.fixture -def elements(): - elements = get_elements() - return elements - - -def test_load_nist_single(elements): - - min_wavelength = 3000 - max_wavelength = 8000 - - for element in elements: - time_start = time.perf_counter() - - nist_atlas = Atlas( - element=element, - source="nist", - min_wavelength=min_wavelength, - max_wavelength=max_wavelength, - brightest_n_lines=None, - ) - - for line in nist_atlas.get_lines(): - assert line > ( - min_wavelength - nist_atlas.range_tolerance - ), "Line is less than min wavelength" - assert line < ( - max_wavelength + nist_atlas.range_tolerance - ), "Line is greater than max wavelength" - - time_end = time.perf_counter() - elapsed = time_end - time_start - assert elapsed < 0.5 - - -def test_load_with_min_intensity(): - - min_intensity = 10 - - nist_atlas = Atlas( - element="Xe", - source="nist", - min_wavelength=4000, - max_wavelength=8000, - min_intensity=min_intensity, - brightest_n_lines=None, - ) - - for i in nist_atlas.get_intensities(): - assert i >= min_intensity, "Line intensity is below minimum" - - -""" -# TODO: Move to Atlas collection -def test_load_nist_all(elements): - - collection = AtlasCo - nist_atlas = Atlas( - element=elements, - line_list="nist", - min_wavelength=0, - max_wavelength=8000, - ) -""" - - -def test_check_nonzero_length_common_elements_single(): - - for element in ["Xe", "Kr", "Ar", "Ne", "Cu"]: - nist_atlas = Atlas( - element=element, - source="nist", - min_wavelength=4000, - max_wavelength=8000, - ) - - assert len(nist_atlas) > 0 - - -def test_load_single_line(): - user_atlas = Atlas( - element="Test", - source="manual", - wavelengths=[5.0], - min_wavelength=0, - max_wavelength=10, - ) - cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) - - -def test_load_mutliple_lines(): - user_atlas = Atlas( - element="Test", - source="manual", - wavelengths=np.arange(10), - min_wavelength=0, - max_wavelength=10, - ) - cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) - - -def test_setting_a_known_pair(): - user_atlas = Atlas( - element="Test", - source="manual", - wavelengths=np.arange(10), - min_wavelength=0, - max_wavelength=10, - ) - logger.info("Testing adding a known pair.") - cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) - cal.set_known_pairs(123, 456) - assert cal.pix_known == 123 - assert cal.wave_known == 456 - - -def test_setting_known_pairs(): - user_atlas = Atlas( - element="Test", - source="manual", - wavelengths=np.arange(10), - min_wavelength=0, - max_wavelength=10, - ) - logger.info("Testing adding known pairs.") - cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) - cal.set_known_pairs([123, 234], [456, 567]) - assert len(cal.pix_known) == 2 - assert len(cal.wave_known) == 2 - - -@pytest.mark.xfail() -def test_setting_a_none_to_known_pairs_expect_fail(): - user_atlas = Atlas( - element="Test", - source="manual", - wavelengths=np.arange(10), - min_wavelength=0, - max_wavelength=10, - ) - logger.info("Testing adding None as known pairs.") - cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) - cal.set_known_pairs([1.0], [None]) - - -@pytest.mark.xfail() -def test_setting_nones_to_known_pairs_expect_fail(): - user_atlas = Atlas( - element="Test", - source="manual", - wavelengths=np.arange(10), - min_wavelength=0, - max_wavelength=10, - ) - logger.info("Testing adding None as known pairs.") - cal = Calibrator(peaks=np.arange(10), atlas_lines=user_atlas.atlas_lines) - cal.set_known_pairs([None], [None]) - - -element_list = ["Hg", "Ar", "Xe", "Kr"] -user_atlas = Atlas( - element="Test", - source="manual", - wavelengths=np.arange(10), - min_wavelength=0, - max_wavelength=10, -) - -cal = Calibrator(np.arange(10), user_atlas.atlas_lines) - -""" -def test_get_summary_executive(): - cal.atlas_summary(mode="executive") - - -def test_get_summary_full(): - cal.atlas_summary(mode="full") - - -def test_get_summary_executive_return_string(): - something = cal.atlas_summary(mode="executive", return_string=True) - assert type(something) == str - - -def test_get_summary_full_return_string(): - something = cal.atlas_summary(mode="full", return_string=True) - assert type(something) == str - - -def test_save_executive_summary(): - filepath = os.path.join( - HERE, "test_output", "test_save_atlas_executive_summary.txt" - ) - cal.save_atlas_summary( - mode="executive", - filename=filepath, - ) - assert os.path.exists(filepath) - os.remove(filepath) - - -def test_save_full_summary(): - filepath = os.path.join( - HERE, "test_output", "test_save_atlas_full_summary.txt" - ) - cal.save_atlas_summary( - mode="full", - filename=filepath, - ) - assert os.path.exists(filepath) - os.remove(filepath) - - - -def test_save_executive_summary_default(): - output_path = cal.save_atlas_summary( - mode="executive", - ) - os.remove(output_path) - - -def test_save_full_summary_default(): - output_path = cal.save_atlas_summary( - mode="full", - ) - os.remove(output_path) -""" From ac7bec992786618e1f01b7b7d6a07788e6f86d8c Mon Sep 17 00:00:00 2001 From: cylammarco Date: Sun, 19 Oct 2025 15:28:02 +0100 Subject: [PATCH 20/20] more codacy fix --- test/test_lt_sprat_manual_atlas.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/test/test_lt_sprat_manual_atlas.py b/test/test_lt_sprat_manual_atlas.py index 6fb8f3a..48483ef 100644 --- a/test/test_lt_sprat_manual_atlas.py +++ b/test/test_lt_sprat_manual_atlas.py @@ -120,7 +120,7 @@ def test_sprat_manual_atlas(): c.do_hough_transform(brute_force=True) # Run the wavelength calibration - best_p, _, _, rms, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=250) + best_p, _, _, _, residual, peak_utilisation, atlas_utilisation = c.fit(max_tries=250) # Plot the solution c.plot_fit( @@ -137,9 +137,9 @@ def test_sprat_manual_atlas(): ( fit_coeff_new, - peak_matched, - atlas_matched, - rms, + _, + _, + _, residual, peak_utilisation, atlas_utilisation,