From 3d7dc04e74f1f955c93455390e76c74fe42dd0b7 Mon Sep 17 00:00:00 2001 From: Tomas Stolker Date: Mon, 27 Jan 2020 17:43:29 +0100 Subject: [PATCH] Removed config_path from SpeciesInit, updated test cases and tutorials --- docs/configuration.rst | 6 +- docs/tutorials/atmospheric_models.ipynb | 9 ++- docs/tutorials/color_magnitude.ipynb | 17 +++--- docs/tutorials/data_model.ipynb | 15 +++-- docs/tutorials/fitting_photometry.ipynb | 2 +- docs/tutorials/flux_magnitude.ipynb | 9 ++- docs/tutorials/photometric_calibration.ipynb | 23 ++++---- docs/tutorials/running_species.ipynb | 8 ++- docs/tutorials/spectral_library.ipynb | 58 ++++++++++++-------- docs/tutorials/synthetic_photometry.ipynb | 9 ++- species/core/setup.py | 24 +++----- species/data/vega.py | 2 +- test/test_analysis/test_photometry.py | 2 +- test/test_read/test_calibration.py | 2 +- test/test_read/test_color.py | 2 +- test/test_read/test_filter.py | 2 +- test/test_read/test_isochrone.py | 2 +- test/test_read/test_model.py | 2 +- test/test_read/test_object.py | 2 +- test/test_read/test_planck.py | 2 +- test/test_read/test_spectrum.py | 2 +- 21 files changed, 113 insertions(+), 87 deletions(-) diff --git a/docs/configuration.rst b/docs/configuration.rst index 805fe6df..5cf4d261 100644 --- a/docs/configuration.rst +++ b/docs/configuration.rst @@ -21,11 +21,13 @@ In this case the database is stored in the working folder and an absolute path p >>> import os >>> os.getcwd() -A configuration file with default values is automatically created when `species` is initiated by running :class:`~species.core.setup.SpeciesInit` and no configuration file is present in the working folder, for example:: +A configuration file with default values is automatically created when `species` is initiated by running :class:`~species.core.setup.SpeciesInit` in case the configuration file is not present in the working folder. *species* is now initiated with: + +.. code-block:: python import species - species.SpeciesInit(config_path='./') + species.SpeciesInit() .. tip:: The same `data_folder` can be used in multiple configuration files. In this way, the data is only downloaded once and easily reused by a new instance of :class:`~species.core.setup.SpeciesInit`. Also the HDF5 database can be reused by simply including the same `database` in the configuration file. diff --git a/docs/tutorials/atmospheric_models.ipynb b/docs/tutorials/atmospheric_models.ipynb index 8c365bf1..b98b24b2 100644 --- a/docs/tutorials/atmospheric_models.ipynb +++ b/docs/tutorials/atmospheric_models.ipynb @@ -35,6 +35,9 @@ "text": [ "Initiating species v0.1.0... [DONE]\n", "Creating species_config.ini... [DONE]\n", + "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", + "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_database.hdf5... [DONE]\n", "Creating data folder... [DONE]\n" ] @@ -42,7 +45,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -51,7 +54,7 @@ } ], "source": [ - "species.SpeciesInit('./')" + "species.SpeciesInit()" ] }, { @@ -74,7 +77,7 @@ "text": [ "Downloading DRIFT-PHOENIX model spectra (151 MB)... [DONE]\n", "Unpacking DRIFT-PHOENIX model spectra... [DONE]\n", - "Adding DRIFT-PHOENIX model spectra... [DONE] \n" + "Adding DRIFT-PHOENIX model spectra... [DONE] \n" ] } ], diff --git a/docs/tutorials/color_magnitude.ipynb b/docs/tutorials/color_magnitude.ipynb index d05a34b7..7394b1aa 100644 --- a/docs/tutorials/color_magnitude.ipynb +++ b/docs/tutorials/color_magnitude.ipynb @@ -43,13 +43,16 @@ "text": [ "Initiating species v0.1.0... [DONE]\n", "Creating species_config.ini... [DONE]\n", + "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", + "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_database.hdf5... [DONE]\n", "Creating data folder... [DONE]\n" ] } ], "source": [ - "species.SpeciesInit('./')\n", + "species.SpeciesInit()\n", "database = species.Database()" ] }, @@ -274,10 +277,10 @@ "text": [ "Downloading AMES-Cond model spectra (823 MB)... [DONE]\n", "Unpacking AMES-Cond model spectra (823 MB)... [DONE]\n", - "Adding AMES-Cond model spectra... [DONE] \n", + "Adding AMES-Cond model spectra... [DONE] \n", "Downloading AMES-Dusty model spectra [Fe/H]=0.0 (106 MB)... [DONE]\n", "Unpacking AMES-Dusty model spectra [Fe/H]=0.0 (106 MB)... [DONE]\n", - "Adding AMES-Dusty model spectra... [DONE] \n" + "Adding AMES-Dusty model spectra... [DONE] \n" ] }, { @@ -589,13 +592,7 @@ "\t\t- Paranal: \n", "\t\t\t- NACO.Lp: \n", "\t\t\t- NACO.NB405: \n", - "\t\t\t- SPHERE.IRDIS_B_J: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\t\t\t- SPHERE.IRDIS_B_J: \n", "\t\t\t- SPHERE.IRDIS_D_H23_2: \n", "\t\t\t- SPHERE.IRDIS_D_H23_3: \n", "\t\t\t- SPHERE.IRDIS_D_K12_1: \n", diff --git a/docs/tutorials/data_model.ipynb b/docs/tutorials/data_model.ipynb index 7a7a8f85..ca1f1269 100644 --- a/docs/tutorials/data_model.ipynb +++ b/docs/tutorials/data_model.ipynb @@ -41,6 +41,9 @@ "text": [ "Initiating species v0.1.0... [DONE]\n", "Creating species_config.ini... [DONE]\n", + "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", + "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_database.hdf5... [DONE]\n", "Creating data folder... [DONE]\n" ] @@ -48,7 +51,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -57,7 +60,7 @@ } ], "source": [ - "species.SpeciesInit('./')" + "species.SpeciesInit()" ] }, { @@ -103,7 +106,7 @@ "text": [ "Downloading AMES-Cond model spectra (823 MB)... [DONE]\n", "Unpacking AMES-Cond model spectra (823 MB)... [DONE]\n", - "Adding AMES-Cond model spectra... [DONE] \n" + "Adding AMES-Cond model spectra... [DONE] \n" ] } ], @@ -468,6 +471,7 @@ "output_type": "stream", "text": [ "Opening ModelBox...\n", + "\n", "model = ames-cond\n", "type = None\n", "wavelength = [0.1 0.1001 0.1002001 ... 5.739604 5.7453437 5.751089 ]\n", @@ -499,11 +503,12 @@ "output_type": "stream", "text": [ "Opening ObjectBox...\n", + "\n", "name = PZ Tel B\n", - "filters = ('Gemini/NICI.ED286', 'Gemini/NIRI.H2S1v2-1-G0220', 'Paranal/NACO.H', 'Paranal/NACO.J', 'Paranal/NACO.Ks', 'Paranal/NACO.Lp', 'Paranal/NACO.Mp', 'Paranal/NACO.NB405', 'Paranal/SPHERE.IRDIS_D_H23_2', 'Paranal/SPHERE.IRDIS_D_H23_3', 'Paranal/SPHERE.IRDIS_D_K12_1', 'Paranal/SPHERE.IRDIS_D_K12_2', 'Paranal/SPHERE.ZIMPOL_I_PRIM', 'Paranal/SPHERE.ZIMPOL_R_PRIM')\n", + "filters = ['Gemini/NICI.ED286', 'Gemini/NIRI.H2S1v2-1-G0220', 'Paranal/NACO.H', 'Paranal/NACO.J', 'Paranal/NACO.Ks', 'Paranal/NACO.Lp', 'Paranal/NACO.Mp', 'Paranal/NACO.NB405', 'Paranal/SPHERE.IRDIS_D_H23_2', 'Paranal/SPHERE.IRDIS_D_H23_3', 'Paranal/SPHERE.IRDIS_D_K12_1', 'Paranal/SPHERE.IRDIS_D_K12_2', 'Paranal/SPHERE.ZIMPOL_I_PRIM', 'Paranal/SPHERE.ZIMPOL_R_PRIM']\n", "magnitude = {'Gemini/NICI.ED286': array([11.68, 0.14], dtype=float32), 'Gemini/NIRI.H2S1v2-1-G0220': array([11.39, 0.14], dtype=float32), 'Paranal/NACO.H': array([11.93, 0.14], dtype=float32), 'Paranal/NACO.J': array([12.47, 0.2 ], dtype=float32), 'Paranal/NACO.Ks': array([11.53, 0.07], dtype=float32), 'Paranal/NACO.Lp': array([11.04, 0.22], dtype=float32), 'Paranal/NACO.Mp': array([10.93, 0.03], dtype=float32), 'Paranal/NACO.NB405': array([10.94, 0.07], dtype=float32), 'Paranal/SPHERE.IRDIS_D_H23_2': array([11.78, 0.19], dtype=float32), 'Paranal/SPHERE.IRDIS_D_H23_3': array([11.65, 0.19], dtype=float32), 'Paranal/SPHERE.IRDIS_D_K12_1': array([11.56, 0.09], dtype=float32), 'Paranal/SPHERE.IRDIS_D_K12_2': array([11.29, 0.1 ], dtype=float32), 'Paranal/SPHERE.ZIMPOL_I_PRIM': array([15.16, 0.12], dtype=float32), 'Paranal/SPHERE.ZIMPOL_R_PRIM': array([17.84, 0.31], dtype=float32)}\n", "flux = {'Gemini/NICI.ED286': array([2.7825625e-14, 3.5979198e-15], dtype=float32), 'Gemini/NIRI.H2S1v2-1-G0220': array([1.0590437e-14, 1.3693687e-15], dtype=float32), 'Paranal/NACO.H': array([1.9687588e-14, 2.5456521e-15], dtype=float32), 'Paranal/NACO.J': array([3.1106848e-14, 5.7625536e-15], dtype=float32), 'Paranal/NACO.Ks': array([1.1241627e-14, 7.2527667e-16], dtype=float32), 'Paranal/NACO.Lp': array([2.0200626e-15, 4.1212692e-16], dtype=float32), 'Paranal/NACO.Mp': array([9.1877844e-16, 2.5390018e-17], dtype=float32), 'Paranal/NACO.NB405': array([1.6704631e-15, 1.0777336e-16], dtype=float32), 'Paranal/SPHERE.IRDIS_D_H23_2': array([2.5412999e-14, 4.4699182e-15], dtype=float32), 'Paranal/SPHERE.IRDIS_D_H23_3': array([2.4269955e-14, 4.2688669e-15], dtype=float32), 'Paranal/SPHERE.IRDIS_D_K12_1': array([1.1547812e-14, 9.5833011e-16], dtype=float32), 'Paranal/SPHERE.IRDIS_D_K12_2': array([1.1428121e-14, 1.0540576e-15], dtype=float32), 'Paranal/SPHERE.ZIMPOL_I_PRIM': array([1.0862880e-14, 1.2030557e-15], dtype=float32), 'Paranal/SPHERE.ZIMPOL_R_PRIM': array([1.8263311e-15, 5.2856905e-16], dtype=float32)}\n", - "distance = 47.13\n", + "distance = 47.130001068115234\n", "spectrum = None\n" ] } diff --git a/docs/tutorials/fitting_photometry.ipynb b/docs/tutorials/fitting_photometry.ipynb index 5fea1342..0914c3b9 100644 --- a/docs/tutorials/fitting_photometry.ipynb +++ b/docs/tutorials/fitting_photometry.ipynb @@ -44,7 +44,7 @@ } ], "source": [ - "species.SpeciesInit('./')" + "species.SpeciesInit()" ] }, { diff --git a/docs/tutorials/flux_magnitude.ipynb b/docs/tutorials/flux_magnitude.ipynb index e8b9fe00..99e7de31 100644 --- a/docs/tutorials/flux_magnitude.ipynb +++ b/docs/tutorials/flux_magnitude.ipynb @@ -18,6 +18,9 @@ "text": [ "Initiating species v0.1.0... [DONE]\n", "Creating species_config.ini... [DONE]\n", + "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", + "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_database.hdf5... [DONE]\n", "Creating data folder... [DONE]\n" ] @@ -25,7 +28,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -35,7 +38,7 @@ ], "source": [ "import species\n", - "species.SpeciesInit('./')" + "species.SpeciesInit()" ] }, { @@ -58,7 +61,7 @@ "text": [ "Adding filter: JWST/NIRCam.F115W... [DONE]\n", "Downloading Vega spectrum (270 kB)... [DONE]\n", - "Adding Vega spectrum... [DONE]\n", + "Adding Vega spectrum...[DONE]\n", "Flux density [W m-2 micron-1] = 4.15e-15 +/- 7.69e-16\n" ] } diff --git a/docs/tutorials/photometric_calibration.ipynb b/docs/tutorials/photometric_calibration.ipynb index 901fae18..e8d36edd 100644 --- a/docs/tutorials/photometric_calibration.ipynb +++ b/docs/tutorials/photometric_calibration.ipynb @@ -36,6 +36,9 @@ "text": [ "Initiating species v0.1.0... [DONE]\n", "Creating species_config.ini... [DONE]\n", + "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", + "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_database.hdf5... [DONE]\n", "Creating data folder... [DONE]\n" ] @@ -43,7 +46,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -52,7 +55,7 @@ } ], "source": [ - "species.SpeciesInit('./')" + "species.SpeciesInit()" ] }, { @@ -125,7 +128,7 @@ "text": [ "Adding filter: 2MASS/2MASS.J... [DONE]\n", "Downloading Vega spectrum (270 kB)... [DONE]\n", - "Adding Vega spectrum...[DONE]\n", + "Adding Vega spectrum... [DONE]\n", "Adding filter: 2MASS/2MASS.H... [DONE]\n", "Adding filter: 2MASS/2MASS.Ks... [DONE]\n", "Adding object: PZ Tel A... [DONE]\n" @@ -192,15 +195,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:05<00:00, 171.10it/s]\n" + "100%|██████████| 1000/1000 [00:06<00:00, 166.03it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Mean acceptance fraction: 0.786\n", - "Integrated autocorrelation time = [3818.13532212]\n" + "Mean acceptance fraction: 0.787\n", + "Integrated autocorrelation time = [3830.83832333]\n" ] } ], @@ -238,7 +241,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -280,7 +283,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKwAAADqCAYAAAA/MK6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAet0lEQVR4nO2deVgUV9bGXzYXIC5RA65goiiuqCgoQrOIgxpUXHAXzWQUt2fQuIyGUYJjEjULY5QoxtExcYlGGaIoRhxQMBIkGlcEjKOMyCYoCqjQcL4/+KiBkr0udHX1/T1PP9J1q855q3y7+tTtW7f0iIjA4WgJ+poWwOHUB25YjlbBDcvRKrhhOVoFNyxHq+CG5WgV3LA6zpo1a+Do6Ig5c+aguLi4Tu3iZXl5eRg2bBhMTU1x8+ZNYdtLly7B2dkZzs7OsLKywvLlyyXr5YbVEebNm/fasmvXriEtLQ0xMTHo3bs3fvjhh1rbq1pmbGyM8PBwTJkypdL2w4cPR3R0NKKjozFixAhMnDhR8n5INuyDBw8wYcIEODk5wc3NDc7OztixY4ekmIGBgTA3N0dAQAAAYOzYsYiOjpYqlSPi559/xujRowEAHh4euHjxYq3tVS0zMjJChw4dqs1TVFSE+Ph4ODo6StYs2bDz5s3DH/7wB1y4cAHnzp1DQEAAdu7cKSnm+vXr4eHhIbw/fPgwVCqVVKlMyc3NhZeXF0xMTGBhYYGDBw/WuP727dtha2uL5s2bv3a2q6kNABITE+Hq6orWrVujR48eCA0NrVd7dTx58gStWrUCALRu3Rq5ubm1tte2TVVERkbCzc0N+vrSv9AlR4iPj4ezs7Pw3tnZGbNmzZIathKtWrWCnp4e05hSWbJkCZo1a4bMzEwcOHAAixYtwq1bt6pdv1OnTvD398d7771Xrza1Wo0JEybg3XffRW5uLkJCQjB79mwkJyfX2p6amirUkBEREcLfRUVFAIA2bdrg2bNnAIC8vDy8+eablXJX1V7bNlVx9OhRTJ06tdb16gRJxNramnx8fCg/P7/K9rt375KHhwc5OTmRvb09rV+/noiIPvroI3JxcSEXFxcaN24cpaWlVdrOx8eHNmzYQFu2bCEzMzPasGEDERHt2rWLLCwsaNq0abRgwQIaNGgQjRkzhl68eCFse/nyZRoyZAiNGDGCfH19ycHBgXr16kVhYWFSd5eIiPLz88nIyIiSkpKEZbNnz6Y1a9bUuu2HH35IPj4+dW67ceMGmZiYUGlpqbDM3d2d/P3969ReTlU5r169SnPmzCEiok2bNtHBgwdrba9pGx8fH7px40alGEVFRdS3b18qKSmpcp/ri+Qz7FdffYUTJ06gc+fOmD9/Ps6fPy+0lZSUwNPTE9OnT8f58+cRERGBPXv2AADatm2Lc+fO4d///jemTJmCNWvWVBl/1apVlcqDBQsWYN68eYiJicGnn36KhIQEpKamCl+DRUVF8PLywgcffICLFy9i4cKFiIuLw1/+8heMHz/+tfh+fn7CmUf88vPzq1JTcnIyDA0NYWVlJSwbOHBgjWdYlhBRpavx+raXY2NjAzMzMzg6OuLWrVuYPHkyMjIysGHDhmrbq1oGlF1n/PTTT/jTn/6Effv2CTkiIyPh6urKpBwo3znJ5Ofn0+7du8nZ2Zn09PRowYIFREQUGxtLRkZGVFxcLKwbGxtLREShoaGkUqnI0dGRBg4cSNbW1pVilp9hxX8TEW3YsIG8vLyE99OmTaNNmzYREVF0dDQ1a9aM1Gq10N6/f3/au3cvi10lIqILFy6QmZlZpWUhISGkUqlq3ba+Z9iioiLq3r07bd68mYqKiujMmTNkZGREo0ePrlO70mBiexMTE7z//vuIiopCVFQUvvnmG9y7dw8PHz5E27ZtYWhoKKzr4OCAlJQUeHt7Y+vWrbhw4QKCgoJQWFhYr5zlhT8AtGjRQqjL0tPT0aZNGxgYGAjtdamz6oOpqalQx5Xz7NkzvPHGG0zzAICRkRH+9a9/ITw8HObm5vj888/h7e2NLl261KldaUg27KJFiyq9V6lUaNeuHfLy8tC1a1c8efIEarVaaE9MTMTPP/+MVq1aYejQoQBQZYd1Q+nYsSOePn1aKWdOTk616zekJLCysoJarUZKSoqw7Nq1a+jbty+z/ajIgAEDcP78eeTk5ODMmTO4d+8ehg0bVuf2mtC5Hw4iIyMRHx8vvD9//jz09fXRu3dv2NnZoUePHkKXT25uLry9vdG7d288efJEuNKNiIiQKkNg+PDheOutt/D9998DAH777bdKxhITFBQkdG6LX0FBQVVuY2JigkmTJmH9+vUoKCjAxYsXERYWhjlz5lSbR61W4+XLlygpKUFJSQlevnwpfKhqagOA69ev4+XLlygsLMRnn32G9PT0St1ftbVXhzb+cCC5hg0JCSEnJydydnYmR0dHUqlUdOnSJaG9Yi/ByJEjKTo6moiI/P39ycLCgsaPH08LFiyg5s2bC1efH330EZmZmZGFhQV9+umnwt/ffPMNHThwgCwsLMjMzIyCg4Np165dQvuBAweIiCg+Pp4GDx5MDg4O5OfnR46OjrRv3z6pu1qJnJwcmjBhAhkbG1PXrl2F3OV4eHgIdTVRWd0NoNKrvC6vqY2IaOXKldSmTRsyMTEhDw8PSklJqZSrtvbqCA4Opn/+859ERJSQkEBLliyptb2mbarqJSAievXqFVlbWzPpKWBy0SU3cnJyKr3v06cPnTp1SkNq5MumTZsoNDSUiIhSUlJoxowZtbbXtE11hg0PD6elS5cy0WxY/blXe5k9ezb279+P9u3b49dff0V6ejrs7Ow0LavJycjIwPTp019bfvjwYZibmzfpDwfz58+XujsAFDr4xd3dHR4eHlCpVFiyZAl++OEH5j0F2oC5uXmVtbm5uTkAYMSIEYiMjAQAnDlzBg4ODpW2r6q9tm3EFBcX4/Llyxg5ciSbnWJynuZoLStXrqSRI0fSzJkz6dWrV5Seni78GllVe3XLxowZQx07diR7e/tKfd6nTp2iZcuWMdOrR8Rv8+ZoD4osCTjKhRuWo1U0qJdAbkP9ONpJQ6pRfoblaBWSDEtlPzzU+Krreo3xakjusLAw6bkDlzLZbyla5HzcpcDPsCICAwM1LUFATlrkAjcsR6vghhXh7e2taQkCctIiFxrdsOW3W2iChuSeMWMGk9zVjaWtD1K0aNtxrysN+qWrvFtLagEtR2xtbZGQkCAtyMZlsA27JDkOEy0yRIp/eEnA0Sq4YUX06NFDNnFYaVESvCRoDDYuA/76laZVyBZeEjCk4hwImo7DSouS4IYV8fjxY9nEYaVFSXDDcrQKblgR9ZnW09LSEnp6eq+9EhISkJWV1aRadAVuWBGbNm2q87oPHjyocnCHra0t8vLyqjSznp4eLC0tmWvRFbhhRZw9e5ZJnJ49e1Y7WunBgwdNqkVJcMNytApuWBFffPGFbOKw0qIkuGFFpKenyyYOKy1KghtWxNatW2UTh5UWJcENy9EquGFFzJ07VzZxWGlREtywIsaMGSObOKy0KAluWBGs7jhgEYeVFiXBDcvRKrhhRfTr1082cVhpURJ8ALcE9PT0qj4GtQzgrnY7HYEP4GYIq2fasogjt+frygFuWBEFBQWyicNKi5LghuVoFdywIuLi4mQTh5UWJcENK2LVqlWyicNKi5LghhURExMj/F3dLTDlLwsLizrFYaGFU4Yin9PFivJbYFhjYWFR7SzmFhYWuH//PvOcSoEbVkRwcHCjx6nJkBWNzEqLkuAlgYjExETZxGGlRUlww4r46is2UwyxiMNKi5LghuVoFdywIhYsWCCbOKy0KAluWBHDhw+XTRxWWpQEN6wIVo9JZxGHlRYlwQ3L0Sq4YUUMHTpUNnFYaVESfAB3DTR4oLWEGbh1YXA3H8DNEDs7O9nEYaVFSXDDiigpKZFNHFZalAQ3rAgDAwPZxGGlRUlww4r45ZdfZBOHlRYlwQ0rYtGiRbKJw0qLkuCGFXH58mXZxGGlRUlww3K0Cm5YEXv37pVNHFZalAQ3rIhLly7JJg4rLUqCG1ZESEiIbOKw0qIkuGE5WgU3rIhly5bJJg4rLUqCG1aEtbW1bOKw0qIkuGFFLF68WDZxWGlREtywHK2CG1aEo6OjbOKw0qIk+ABuEWq1GoaGZRPiSBnArV77pRCnPlTMWVGLkuADuBlib28vmzistCgJ5X1864mlpeVrj4MvPwPUNDshRzPo/Bm2fIbC8peTk5Pwt5RZBE1MTCRrYxFDaeh8DdsoN/3xmxBrhNewDJk3b55G45TPHVvVy9LSkok2bYYbVsTNmzc1Guf+/ftCSTJkyJBK5Yq41tZFuGE5WgU3rIhDhw7JJg4rLUqCG1bE6dOnZROHlRYlwQ0rYv/+/bKJw0qLkuCG5WgV3LAi+IPl5A03rIiOHTvKJg4rLUqCG1bEihUrZBOHlRYlwQ3L0Sq4YUW4u7vLJg4rLUqCD34RDTbJz8+HqamptKAblyF/+SeS44i1KGVgDB/8whBnZ2fZxGGlRUlww3K0Cm5YEe3bt5dNHFZalASvYWU2gLsmeA3Lz7CvMX36dNnEYaVFSXDDirh7965s4rDSoiS4YTlaBTesiNDQUNnEYaVFSXDDiuB3HMgbblgRR44ckU0cVlqUBDcsR6vghhWxfv162cRhpUVJcMOKaNGihWzisNKiJLhhRaxbt042cVhpURLcsBytQicMa2lpWe18VeIpNT09PZnkZBGHlRYloRODX+ozaCQ7OxsdOnSQlnDjMmT7rpccR6ylqrlsK2JhYSFpitCmgg9+YciYMWNkE0cco+JEcVW9dGGyOG5YjlbBDSuiS5cusonDSouS4DVsY9BIA7hrQ1sGePMaliETJ06UTRxWWpQEN6yIhw8fyiYOKy1KghuWo1Vww4rgExrLG25YEcHBwbKJw0qLkuCGFXHixAnZxGGlRUlww3K0Cm5YER9//LFs4rDSoiS4YUW8fPlSNnFYaVES3LAiAgMDZROHlRYlwQ3L0Sq4YUV4e3vLJg4rLUqCG1bEjBkzZBOHlRYlwQ0rwsvLSzZxWGlREtywHK2CG1ZEjx49ZBOHlRYlwQdwNwZ8AHeN8AHcDPHw8JBNHFZalAQ3rIjHjx/LJg4rLUqCG1ZBWFhYVDthiKWlpablMcFQ0wJYUdMkE+LZXWoiOjqaiR4Wceobo6ZJNMrrRm1HMWfYBw8eVDvBRH1mQ9m0aRMTPSzisNKiJBRjWFacPXtWNnFYaVES3LAcrYIbVsQXX3whmzistCgJblgR6enpsonDSouS4IYVsXXrVtnEYaVFSXDDcrQKblgRc+fOlU0cVlqUBDesCDlPaMzhhn0NfseBvOGG5WgV3LAi+vXrJ5s4rLQoCcUM4JbV4GUNDeCuCTkdH50ZwF2f5201FJVKJZs4rLQAyhl6qFXDC8tHZDUmBQUFsonDSgugnKGHWnWG5XC4YUXExcXJJg4rLUqCG1bEqlWrZBOHlRYlwQ0rIiYmRjZxWGlREo1u2ICAgDqvW1MvQEN6AuqTmzWPHj3SWG6gfvvOugehMY97o/fD1qf/j3VfYUPixcfHY9iwYdISb1wGvfXbJe+LFC2sjmVD4tS2jc70wzYFiYmJmpYgICctcqHJDdsUnf9S+Oor+fxCJSctckFSScDhSIGXBBzF06CfZuUyiIKje/AzLEer4IblaBXcsJwmRWo5qVHDaqIWTk1Nxa1btwAApaWlTZ6/Ipq8FmjqfX/+/DkA6T9oNLlhU1NTcf78eQBNPwr+7t27GD58OObNmwcA0Ndv2t1PT0/H1atXcfv2bQBN2z2YmpqKU6dO4eTJkwDK9r2pjv2dO3fg7e2NxYsXA5C439SEvHz5kqZNm0bdu3en8+fPC8tLS0sbPXdKSgp5eHjQsWPHyNfXlw4ePNjoOSty+/ZtcnFxoXHjxpGTkxO5u7tTVlZWk+ROSkoie3t7GjVqFPXq1Yvee++9JslLVHbcHR0dac+ePXT8+PFKbQ35f29SwxIRffzxx9S1a1dydHSkH3/8UVjemKZNTk4mFxcXOnz4MBER7d69m/72t781Wj4xxcXFNGXKFDp69CgREd24cYMcHByoe/fuFBcX16i509LSyNPTkyIjI4mI6NtvvyVXV1d6/vy5sE5jHvu1a9dSSEgIERGVlJRQcnIynTt3rsE5m+w7kf7/60elUmHUqFGYPHkyPvvsM4SHhwMo+5pQq9XM8xYWFmLz5s2YNGkSpk2bBgBwdXXF0aNH8eOPPzLPVxWlpaXIy8uDubk5gLK7YWNjY2FtbY0pU6bg+vXrwnqs+e2332Bqago3NzcAwNixY2FoaIhbt25h//79yM/Pb9TSpLCwEAMGDAAATJ06FatXr4avry+srKxw+vRpvHr1ql7xmsyw5QfFxsYG+vr66NWrF1QqFT755BPExcUhNTUVERERKC4uZprX2NgYQ4cOFQ6aWq3G22+/jYCAACQkJECtVjf6BUizZs0waNAgbNmypdKMhOHh4ejTp48wYUZj1NRmZmZwcHDAs2fPAADHjh3D7du3sWfPHmzevBn9+/fHnTt3ADTORaCVlRV27tyJwMBADB48GKGhobh16xbs7OywbNkyXLt2rX65mZ3760BJSQkREQUEBNC1a9coJyeHtm7dSgMHDqR33nmHMjIymqSeJSK6cuUKeXp60qNHj4iI/deiWq0W9peIKCoqijw8PGjDhg2Uk5MjLC8sLCRbW1vasGFDo+XOzc2l0tJSUqvVtHPnTsrOzhbaXF1dyd7evtFy37hxg3x9fWnUqFEUGhpaad3x48eTg4NDveI32hn24cOHuHPnTqVPTvkZpHfv3ti7dy/efPNNWFhY4NmzZ2jXrh2Cg4OxevVqyWfZqnKLGTRoEGxsbLB27VoUFRUx/VpMSUnBnDlzMGnSJPj5+aGgoADOzs5wcHDATz/9hJCQEGRnZwMoO/vOnDkTRUVFjZI7Ly8Pbdu2BQAYGBhg4cKFaN++PQoLCwEAH330EYyNjZncoVsx9/Lly5Gfn49+/frBysoK8fHx2LFjBzIyMoT1N2/eDHNz83p9wzWKYYuKijBz5kwsXrxY6MIB/nfat7OzQ+vWrXHz5k3s3r0bO3bsQPfu3bFv3z74+PjAyMiIee6KlB+gRYsWoVOnTsjNzW1wPjF3796Fr68vBg8ejMmTJ+Pbb7/FH//4RwCAv78/XFxccPr0aWzatAn37t2DgYEBWrRogfT0dKjVaklfy1XlrtiVVDG2sbExgLIxt4aG0u/2F+fev38/3n//fQDA8uXLsXLlSmRlZSEgIAD/+c9/AJQNUC8sLKxfHSv1K6A6VqxYQQMHDiR3d3e6efPma+1eXl7UrVs3ioyMpMzMTHJ1daXbt283Se5yiouLafLkybRx40YmeV+9ekV+fn703XffCcuSkpKoS5cuFB4eLizbuXMnzZgxgzp06EAffPAB9e/fn+7cudNouSMiIoRlz58/pzNnztDt27dp37595ObmRomJiY2Wu+J+b9u2jSZOnEjt2rUjPz8/GjRoUL33u1EMm5iYSLt27SIiIk9PTxo9evRrxvn999/p2rVrRET04sULoZZsitxE/6tZc3Nz6ffff2eSu7S0lHx9femvf/0rEREVFRUREdGHH35IYWFhr6178uRJioqKonv37jVp7qCgIJo/fz55enpSUlJSk+YuLCykY8eO0ZkzZxp03JkatuKFS2ZmJhGVFeFjx44VznblBXn5v+U715S5y9dllbs8F1HZxdzZs2fp1atXQtuePXuEft+MjAyKjY2tdGHSlLl//fVXIiJ68uQJ5eXlNWnuixcvSt5vJjVseZdJxb7Ut956C6WlpTAwMEB4eDiMjIzg5+cn1JWZmZkAIKle1XTuivkNDAwAAAMHDoRKpUKzZs2EmrFt27bCRY6BgQEOHjyIhIQEjeQOCQlBQkIC2rRpg1atWjVp7gMHDkjeb8mGTUtLw/Tp0xEUFAQAMDQ0FC5q9PX1UVJSAgA4ceIEmjVrBn9/f2zduhWrV69Gfn6+RnKvWrVKcu6q8gNlH5zyD0J5z4OlpSVMTEwAANu3b8fXX38NMzMzjeQOCQlBhw4dNJKbxX5LNuzTp0/x4sULxMXFYceOHWVB9fUF4xgYGECtVkNfXx/h4eG4fv06goOD4e/vD1NTU63NXV1+PT2917pp9PX1oa+vj40bN2Lz5s2Ij4+XfMOlruaW3J9x584dWFpawtraGpGRkQCAJUuWCMbR19cXuk1SUlLw9ttvY9u2bejVq5fU1BrNXdf8RARDQ0N8+eWXKCgoQGxsLIYMGcJzNxRJFTARPX78mEpLS6mgoIA++eQT8vLyou3btwvtFYvse/fuCRdELNBk7vrkf/r0Kb377rt09epVnlsiBgES55Vp2bIl1Go1WrRogV69eiE7OxsxMTHIzs7GsGHDhA5rPT09tG3bVqhpWKDJ3PXJ36xZM3h5eaFr1648t0QaZNjS0lKhsNbT04OBgQGICCYmJujduzcyMzMRExMjPPo9KyuL2SQZmszdkPyZmZmwtLRk0iOhq7krUddTcVZWFqWkpFB6enql5RX7P8v/zsrKop07d1Lfvn3pnXfeoeTk5IZ+A2g8t9T8UjvmdTV3ddSplyA5ORmjRo3CunXr0L9/f6xdu1aYCrLib9Tln8AOHTqguLgYRITw8HD07NmzwR8oTeZmkd/KyornZkltjs7Pz6dJkybRoUOHiIjo8OHD5O3tTa6ursJwsYoXN+WfuH/84x81/o5fFzSZW9P5dTV3bdSpJJg7dy6dOHFCeB8fH08LFy4ke3t7OnnypLBc6iAKueXWdH5dzV0TNV50ERGKiooQGhqK9PR04dmnnTt3RteuXXHv3j388ssvsLGxQVJSEpydnWFjYyP5a1jTuTWdX1dz14UaDaunpwdDQ0N0794dvr6+MDY2xogRIwAA5ubmaN26NU6ePIlevXph1KhRMDExgZ2dHdq1aydZmCZzazq/ruauE3U9Fe/Zs4f09fUrdRATlQ0hmzFjBhE13t2Xmsyt6fy6mrs66twPO2DAALRr1w5+fn4AgP79+6Nly5Z4+PAhXrx4gdGjRzfa3ZeazK3p/Lqau1rq6/CwsDAyNzenCRMmkI+PDzk4ONCNGzca48Mkq9yazq+rucU0aAbu//73v0hKSkJmZiYcHBya9Fmlmsyt6fy6mrsiDTIsh6Mp+HSbHK2CG5ajVXDDcrQKbliOVsENy9EquGE5WgU3LEer4IblaBU6a9j8/HwsW7YMw4YNg7OzM+zs7ODt7Y0LFy5Iiuvn5wdzc3NUHKKxePFifP755xIVcwAG8xJoKytXrsTjx49x6dIlYcKN+fPn49SpU3Bycmpw3KCgIDx9+rTSsi1btrC/GU9H0dkzbGxsLFxcXIS5oQwNDbFu3ToMGjSIeS5TU1M0b96ceVxdRGcN+8477yAkJATJycnCMmtra+HBHY8ePcLUqVPh6OgIJycneHt7IzExEQAQEREBNzc3uLu7w9HRsdIcU2K+/vpr9O7dG87OzgCAs2fPwsbGBpaWlti2bRvc3d1hZWWF/fv3C9sQEQIDA9GnTx+4ubkhMDAQlpaWsLGxQURERCMcDS1CI2PEZEBycjL17duX9PT0yMHBgbZs2SLczlxSUkKDBw8mf39/Yf3FixfTl19+SURER44cEeZ0LS0tJZVKRRcuXBDW9fHxqfTMgr1795JKpRLeR0VFkZGRkTDZb0xMDLVq1UqYunLv3r3UsWNH4VkE33//Penp6VFUVBTz46Bt6OwZtmfPnrhx4wbOnj2LAQMGYOvWrejevTuOHz+O+Ph4XLlyRZhuHSib7n3s2LEAyp6Es27dOjg4OMDFxQV37txBXFxcvfIbGxsL8WxtbfHs2TNkZWUBAL777jtMmDAB7du3BwB4e3szn7VGW9FZwwJl9y+5ubkhODgYaWlpmDZtGlasWIHU1FQAqDQ1ZMeOHYV77ceNG4cOHTogNjYW0dHR8PDwqPdDLdq0aSP83aJFCwAQ5vpPS0t7bUrMJrtnSuborGFnzZqFvLw84b2RkRGmTp2Kp0+folu3bgD+N/ExAOTk5ODu3bvIzs5GSkoKPD09hdtDWD0BppzOnTsLZ9uK+Tk6bNi0tDRhblOg7IFzR44cwejRozFs2DAMHjwYwcHBQvuqVasQFxeHdu3aoX379kJ/bV5enjAjCivmzJmDsLAw4dFIR44cqfcTA5WKzvbD+vn5Yc+ePRg+fDiaN2+OgoIC2NraIigoCPr6+jhx4gT+/Oc/Y+TIkSgtLYWbmxtmz54NADh06BCWLl2KqKgoWFpaomfPnti3bx/MzMyQlJSEiIgItGjRAiYmJjA1NcXf//53ZGRkYNasWVi6dCn8/PyQkZGBCRMm4OjRoxg9ejQAYPr06Th06BDmzp2L+/fvQ6VSoVOnThgzZgw6duzY9Df8yRB+i4wMefXqFYqKivDGG28AKOvmMjU1xZUrV5hNxqyt6GxJIGeuXr0KX19f4f2hQ4fQrVs39OjRQ4Oq5IHOlgRyplu3bsjPz4e9vT2MjIzQsmVLHD9+XPhVTpfhJQFHq+AlAUer4IblaBXcsBytghuWo1Vww3K0Cm5Yjlbxf3sHre/UPPFVAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -321,7 +324,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Getting MCMC spectra: 100%|██████████| 30/30 [00:00<00:00, 1116.07it/s]\n" + "Getting MCMC spectra: 100%|██████████| 30/30 [00:00<00:00, 1190.47it/s]\n" ] } ], @@ -438,7 +441,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] diff --git a/docs/tutorials/running_species.ipynb b/docs/tutorials/running_species.ipynb index 8a26b41a..c66448f4 100644 --- a/docs/tutorials/running_species.ipynb +++ b/docs/tutorials/running_species.ipynb @@ -55,6 +55,9 @@ "text": [ "Initiating species v0.1.0... [DONE]\n", "Creating species_config.ini... [DONE]\n", + "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", + "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_database.hdf5... [DONE]\n", "Creating data folder... [DONE]\n" ] @@ -62,7 +65,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -71,7 +74,7 @@ } ], "source": [ - "species.SpeciesInit('./')" + "species.SpeciesInit()" ] }, { @@ -155,6 +158,7 @@ "Adding filter: Keck/NIRC2.Lp... [DONE]\n", "Adding filter: Keck/NIRC2.Ms... [DONE]\n", "Adding object: 51 Eri b... [DONE]\n", + "Adding filter: LCO/VisAO.Ys... [DONE]\n", "Adding filter: Paranal/NACO.J... [DONE]\n", "Adding filter: Gemini/NICI.ED286... [DONE]\n", "Adding filter: Paranal/NACO.H... [DONE]\n", diff --git a/docs/tutorials/spectral_library.ipynb b/docs/tutorials/spectral_library.ipynb index af597753..c6fc77fc 100644 --- a/docs/tutorials/spectral_library.ipynb +++ b/docs/tutorials/spectral_library.ipynb @@ -11,15 +11,6 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [ - "import species" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -27,6 +18,9 @@ "text": [ "Initiating species v0.1.0... [DONE]\n", "Creating species_config.ini... [DONE]\n", + "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", + "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_database.hdf5... [DONE]\n", "Creating data folder... [DONE]\n" ] @@ -34,21 +28,22 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "species.SpeciesInit('./')" + "import species\n", + "species.SpeciesInit()" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -67,7 +62,7 @@ "Downloading IRTF Spectral Library - L dwarfs (850 kB)... [DONE]\n", "Downloading IRTF Spectral Library - T dwarfs (100 kB)... [DONE]\n", "Unpacking IRTF Spectral Library... [DONE]\n", - "Adding IRTF Spectral Library... [DONE] \n" + "Adding IRTF Spectral Library... 2MASS J05591915-1404489 " ] }, { @@ -77,6 +72,23 @@ "/Users/tomasstolker/.pyenv/versions/3.6.0/envs/general3.6/lib/python3.6/site-packages/astroquery/simbad/core.py:138: UserWarning: Warning: The script line number 3 raised an error (recorded in the `errors` attribute of the result table): Identifier not found in the database : 2MASS J05591915-1404489\n", " (error.line, error.msg))\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Adding IRTF Spectral Library... SDSS J125453.90-012247.4 \r", + "Adding IRTF Spectral Library... [DONE] \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tomasstolker/applications/species/species/data/queries.py:209: UserWarning: No parallax was found for 2MASS J05591915-1404489 so storing a NaN value.\n", + " warnings.warn(f'No parallax was found for {target} so storing a NaN value.')\n" + ] } ], "source": [ @@ -86,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -123,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -170,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -195,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -205,7 +217,7 @@ "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/tutorials/synthetic_photometry.ipynb b/docs/tutorials/synthetic_photometry.ipynb index 0cdd3040..8082c203 100644 --- a/docs/tutorials/synthetic_photometry.ipynb +++ b/docs/tutorials/synthetic_photometry.ipynb @@ -29,6 +29,9 @@ "text": [ "Initiating species v0.1.0... [DONE]\n", "Creating species_config.ini... [DONE]\n", + "Database: /Users/tomasstolker/applications/species/docs/tutorials/species_database.hdf5\n", + "Data folder: /Users/tomasstolker/applications/species/docs/tutorials/data\n", + "Working folder: /Users/tomasstolker/applications/species/docs/tutorials\n", "Creating species_database.hdf5... [DONE]\n", "Creating data folder... [DONE]\n" ] @@ -36,7 +39,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -45,7 +48,7 @@ } ], "source": [ - "species.SpeciesInit('./')" + "species.SpeciesInit()" ] }, { @@ -80,7 +83,7 @@ "text": [ "Adding filter: JWST/NIRCam.F115W... [DONE]\n", "Downloading Vega spectrum (270 kB)... [DONE]\n", - "Adding Vega spectrum... [DONE]\n" + "Adding Vega spectrum...[DONE]\n" ] } ], diff --git a/species/core/setup.py b/species/core/setup.py index 1510a75d..f05639c2 100644 --- a/species/core/setup.py +++ b/species/core/setup.py @@ -16,14 +16,8 @@ class SpeciesInit: not present in the working folder, and creating the data folder for storage of input data. """ - def __init__(self, - config_path): + def __init__(self): """ - Parameters - ---------- - config_path : str - Location of the configuration file named *species_config.ini*. - Returns ------- NoneType @@ -32,9 +26,9 @@ def __init__(self, print(f'Initiating species v{species.__version__}...', end='') - self.config_path = config_path + working_folder = os.path.abspath(os.getcwd()) - config_file = os.path.join(self.config_path, 'species_config.ini') + config_file = os.path.join(working_folder, 'species_config.ini') print(' [DONE]') @@ -52,20 +46,20 @@ def __init__(self, config = configparser.ConfigParser() config.read_file(open(config_file)) - database_file = config['species']['database'] - data_folder = config['species']['data_folder'] + database_file = os.path.abspath(config['species']['database']) + data_folder = os.path.abspath(config['species']['data_folder']) + + print(f'Database: {database_file}') + print(f'Data folder: {data_folder}') + print(f'Working folder: {working_folder}') if not os.path.isfile(database_file): print('Creating species_database.hdf5...', end='') - h5_file = h5py.File(database_file, 'w') h5_file.close() - print(' [DONE]') if not os.path.exists(data_folder): print('Creating data folder...', end='') - os.makedirs(data_folder) - print(' [DONE]') diff --git a/species/data/vega.py b/species/data/vega.py index bc754db6..1c19e4f0 100644 --- a/species/data/vega.py +++ b/species/data/vega.py @@ -62,4 +62,4 @@ def add_vega(input_path, database): data=np.vstack((wavelength, flux, error_stat)), dtype='f') - print('[DONE]') + print(' [DONE]') diff --git a/test/test_analysis/test_photometry.py b/test/test_analysis/test_photometry.py index 8a82650f..dc776537 100644 --- a/test/test_analysis/test_photometry.py +++ b/test/test_analysis/test_photometry.py @@ -19,7 +19,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_synthetic_photometry(self): species.SyntheticPhotometry('MKO/NSFCam.J') diff --git a/test/test_read/test_calibration.py b/test/test_read/test_calibration.py index b2873d7a..bdb425f7 100644 --- a/test/test_read/test_calibration.py +++ b/test/test_read/test_calibration.py @@ -22,7 +22,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_read_calibration(self): database = species.Database() diff --git a/test/test_read/test_color.py b/test/test_read/test_color.py index d8695c70..a1604427 100644 --- a/test/test_read/test_color.py +++ b/test/test_read/test_color.py @@ -20,7 +20,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_read_color_magnitude(self): database = species.Database() diff --git a/test/test_read/test_filter.py b/test/test_read/test_filter.py index 4640303a..3c3fc4cd 100644 --- a/test/test_read/test_filter.py +++ b/test/test_read/test_filter.py @@ -21,7 +21,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_read_filter(self): read_filter = species.ReadFilter('MKO/NSFCam.H') diff --git a/test/test_read/test_isochrone.py b/test/test_read/test_isochrone.py index 44dac383..1a5643dc 100644 --- a/test/test_read/test_isochrone.py +++ b/test/test_read/test_isochrone.py @@ -28,7 +28,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_read_isochrone(self): database = species.Database() diff --git a/test/test_read/test_model.py b/test/test_read/test_model.py index 60fe408b..7560d641 100644 --- a/test/test_read/test_model.py +++ b/test/test_read/test_model.py @@ -21,7 +21,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_read_model(self): database = species.Database() diff --git a/test/test_read/test_object.py b/test/test_read/test_object.py index de8824f8..7661eb24 100644 --- a/test/test_read/test_object.py +++ b/test/test_read/test_object.py @@ -21,7 +21,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_read_object(self): database = species.Database() diff --git a/test/test_read/test_planck.py b/test/test_read/test_planck.py index d57900da..883c7652 100644 --- a/test/test_read/test_planck.py +++ b/test/test_read/test_planck.py @@ -21,7 +21,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_read_planck(self): read_planck = species.ReadPlanck(filter_name='MKO/NSFCam.J') diff --git a/test/test_read/test_spectrum.py b/test/test_read/test_spectrum.py index abcc7fa2..690ba6ef 100644 --- a/test/test_read/test_spectrum.py +++ b/test/test_read/test_spectrum.py @@ -22,7 +22,7 @@ def teardown_class(self): def test_species_init(self): test_util.create_config('./') - species.SpeciesInit('./') + species.SpeciesInit() def test_read_spectrum(self): database = species.Database()