diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index a01a98fa..39be9ff9 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -14,7 +14,7 @@ on: jobs: tests_master: - name: Test against ScopeSim master + name: Test against ScopeSim release runs-on: ${{ matrix.os }} # Run if our target is IRDB master, or when this is ran manually. if: github.base_ref == 'master' || github.base_ref == '' @@ -22,31 +22,30 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest] - python-version: ['3.9', '3.10', '3.11', '3.12'] + python-version: ['3.10', '3.11', '3.12'] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.github_actions.txt - pip uninstall -y scopesim scopesim_templates - pip install git+https://github.com/AstarVienna/ScopeSim.git@master + pip uninstall -y scopesim_templates pip install git+https://github.com/AstarVienna/ScopeSim_Templates.git - name: Run Pytest run: pytest - name: Store badge report files - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: badge-report path: _REPORTS tests_devmaster: - name: Test against ScopeSim dev_master + name: Test against ScopeSim main runs-on: ${{ matrix.os }} # Run if our target is IRDB dev_master, or when this is ran manually. if: github.base_ref == 'dev_master' || github.base_ref == '' @@ -54,12 +53,12 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest] - python-version: ['3.9', '3.10', '3.11', '3.12'] + python-version: ['3.10', '3.11', '3.12'] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies @@ -67,7 +66,7 @@ jobs: python -m pip install --upgrade pip pip install -r requirements.github_actions.txt pip uninstall -y scopesim scopesim_templates - pip install git+https://github.com/AstarVienna/ScopeSim.git@dev_master + pip install git+https://github.com/AstarVienna/ScopeSim.git pip install git+https://github.com/AstarVienna/ScopeSim_Templates.git - name: Run Pytest run: pytest @@ -84,9 +83,9 @@ jobs: # https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows if: github.ref == 'dev_master' || github.ref == 'master' || github.base_ref == '' steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: # No matrix is used since this is a time-consuming tosk. python-version: 3.11 @@ -97,7 +96,7 @@ jobs: python -m pip install --upgrade pip pip install -r requirements.github_actions.txt pip uninstall -y scopesim scopesim_templates - pip install git+https://github.com/AstarVienna/ScopeSim.git@dev_master + pip install git+https://github.com/AstarVienna/ScopeSim.git pip install git+https://github.com/AstarVienna/ScopeSim_Templates.git - name: Run Notebooks env: @@ -125,9 +124,9 @@ jobs: # https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows if: github.ref == 'dev_master' || github.ref == 'master' || github.base_ref == '' steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: # No matrix is used since this is a time-consuming tosk. python-version: 3.11 @@ -138,7 +137,7 @@ jobs: python -m pip install --upgrade pip pip install -r requirements.github_actions.txt pip uninstall -y scopesim scopesim_templates - pip install git+https://github.com/AstarVienna/ScopeSim.git@dev_master + pip install git+https://github.com/AstarVienna/ScopeSim.git pip install git+https://github.com/AstarVienna/ScopeSim_Templates.git - name: Run Notebooks env: diff --git a/ELT/tests/test_TER_properties.py b/ELT/tests/test_TER_properties.py index 4b4f8740..0f9824a7 100644 --- a/ELT/tests/test_TER_properties.py +++ b/ELT/tests/test_TER_properties.py @@ -20,6 +20,7 @@ def test_eso_vs_scopesim_throughput(): + rc.__currsys__["!TEL.temperature"] = 7 slist = sim.effects.SurfaceList(filename="LIST_mirrors_ELT.tbl") wave = np.linspace(0.3, 2.5, 100) * u.um if PLOTS: diff --git a/HAWKI/default.yaml b/HAWKI/default.yaml index 1ca10c33..cf68a5a1 100644 --- a/HAWKI/default.yaml +++ b/HAWKI/default.yaml @@ -3,6 +3,7 @@ object : configuration alias : OBS name : HAWKI_default_configuration description : default parameters needed for a HAWKI simulation +status: experimental date_modified: 2022-02-22 changes: - 2022-02-22 (MV) formatting diff --git a/HAWKI/test_hawki/test_full_package_hawki.py b/HAWKI/test_hawki/test_full_package_hawki.py index 3cd7f7de..82cd96c8 100644 --- a/HAWKI/test_hawki/test_full_package_hawki.py +++ b/HAWKI/test_hawki/test_full_package_hawki.py @@ -126,8 +126,8 @@ def test_works_seamlessly_for_hawki_package(self, capsys): # test assert there are 4 detectors, each 2048x2048 pixels hdu = opt.readout()[0] - assert len(opt.detector_arrays[0].detectors) == 4 - for detector in opt.detector_arrays[0].detectors: + assert len(opt.detector_managers[0]) == 4 + for detector in opt.detector_managers[0]: assert detector.hdu.header["NAXIS1"] == 2048 assert detector.hdu.header["NAXIS2"] == 2048 diff --git a/LFOA/default.yaml b/LFOA/default.yaml index 4178b75c..1637c06d 100644 --- a/LFOA/default.yaml +++ b/LFOA/default.yaml @@ -3,6 +3,7 @@ object : configuration alias : OBS name : LFOA_default_configuration description : default parameters needed for a LFOA simulation +status: experimental packages : - LFOA diff --git a/METIS/METIS_DET_IFU.yaml b/METIS/METIS_DET_IFU.yaml index ee5df3ba..d17f1e11 100644 --- a/METIS/METIS_DET_IFU.yaml +++ b/METIS/METIS_DET_IFU.yaml @@ -71,3 +71,9 @@ effects: kwargs: noise_std: "!DET.readout_noise" n_channels: 32 + + - name: quantization + description: Turn photon count into integers + class: Quantization + kwargs: + dtype: uint16 diff --git a/METIS/METIS_DET_IMG_LM.yaml b/METIS/METIS_DET_IMG_LM.yaml index d42e2405..0a7724e2 100644 --- a/METIS/METIS_DET_IMG_LM.yaml +++ b/METIS/METIS_DET_IMG_LM.yaml @@ -90,3 +90,9 @@ effects: kwargs: noise_std: "!DET.readout_noise" n_channels: 32 + + - name: quantization + description: Turn photon count into integers + class: Quantization + kwargs: + dtype: uint16 diff --git a/METIS/METIS_DET_IMG_N_Aquarius.yaml b/METIS/METIS_DET_IMG_N_Aquarius.yaml index 4f219458..cbff95de 100644 --- a/METIS/METIS_DET_IMG_N_Aquarius.yaml +++ b/METIS/METIS_DET_IMG_N_Aquarius.yaml @@ -99,3 +99,9 @@ effects: chop_offsets: "!OBS.chop_offsets" nod_offsets: "!OBS.nod_offsets" pixel_scale: "!INST.pixel_scale" + + - name: quantization + description: Turn photon count into integers + class: Quantization + kwargs: + dtype: uint16 diff --git a/METIS/METIS_DET_IMG_N_GeoSnap.yaml b/METIS/METIS_DET_IMG_N_GeoSnap.yaml index 5d6ca376..907b7f6d 100644 --- a/METIS/METIS_DET_IMG_N_GeoSnap.yaml +++ b/METIS/METIS_DET_IMG_N_GeoSnap.yaml @@ -102,3 +102,9 @@ effects: chop_offsets: "!OBS.chop_offsets" nod_offsets: "!OBS.nod_offsets" pixel_scale: "!INST.pixel_scale" + + - name: quantization + description: Turn photon count into integers + class: Quantization + kwargs: + dtype: uint16 diff --git a/METIS/TRACE_LSS_L.fits b/METIS/TRACE_LSS_L.fits index 819561d1..20ddb418 100644 Binary files a/METIS/TRACE_LSS_L.fits and b/METIS/TRACE_LSS_L.fits differ diff --git a/METIS/TRACE_LSS_L_old.fits b/METIS/TRACE_LSS_L_old.fits new file mode 100644 index 00000000..819561d1 --- /dev/null +++ b/METIS/TRACE_LSS_L_old.fits @@ -0,0 +1 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T FILETYPE= 'Spectral Layout Definition' AUTHOR = 'Oliver Czoske' DATE = '2023-05-31' SOURCE = 'Conchi Cardenas' ORIGDATE= '2021-02-15' DESCRIPT= 'METIS LSS-L' STATUS = 'Zemax ray tracing' ETYPE = 'SLITTRAC' ECAT = 1 EDATA = 2 DATE_CRE= '2021-04-01' DATE_MOD= '2023-05-31' HISTORY 2021-04-22 (OC) simpler format for data extensions HISTORY 2023-05-31 (OC) add dispersion direction to extensions END XTENSION= 'TABLE ' / ASCII table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 41 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 4 / number of table fields TTYPE1 = 'description' TFORM1 = 'A11 ' TBCOL1 = 1 TTYPE2 = 'extension_id' TFORM2 = 'I10 ' TBCOL2 = 12 TTYPE3 = 'aperture_id' TFORM3 = 'I10 ' TBCOL3 = 22 TTYPE4 = 'image_plane_id' TFORM4 = 'I10 ' TBCOL4 = 32 EXTNAME = 'TOC ' / extension name END METIS LSS L 2 0 0 XTENSION= 'TABLE ' / ASCII table extension BITPIX = 8 / array data type NAXIS 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'Spectral Layout Definition' AUTHOR = 'Oliver Czoske' DATE = '2023-05-31' SOURCE = 'Conchi Cardenas' ORIGDATE= '2021-02-15' DESCRIPT= 'METIS LSS-M' STATUS = 'Zemax ray tracing' ETYPE = 'SLITTRAC' ECAT = 1 EDATA = 2 DATE_CRE= '2021-04-01' DATE_MOD= '2023-05-31' HISTORY 2021-04-22 (OC) simpler format for data extensions HISTORY 2023-05-31 (OC) add dispersion direction to extensions END XTENSION= 'TABLE ' / ASCII table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 41 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 4 / number of table fields TTYPE1 = 'description' TFORM1 = 'A11 ' TBCOL1 = 1 TTYPE2 = 'extension_id' TFORM2 = 'I10 ' TBCOL2 = 12 TTYPE3 = 'aperture_id' TFORM3 = 'I10 ' TBCOL3 = 22 TTYPE4 = 'image_plane_id' TFORM4 = 'I10 ' TBCOL4 = 32 EXTNAME = 'TOC ' / extension name END METIS LSS M 2 0 0 XTENSION= 'TABLE ' / ASCII table extension BITPIX = 8 / array 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+SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T FILETYPE= 'Spectral Layout Definition' AUTHOR = 'Oliver Czoske' DATE = '2023-05-31' SOURCE = 'Conchi Cardenas' ORIGDATE= '2021-02-15' DESCRIPT= 'METIS LSS-N' STATUS = 'Zemax ray tracing' ETYPE = 'SLITTRAC' ECAT = 1 EDATA = 2 DATE_CRE= '2021-04-01' DATE_MOD= '2023-05-31' HISTORY 2021-04-22 (OC) simpler format for data extensions HISTORY 2023-05-31 (OC) add dispersion direction to extensions END XTENSION= 'TABLE ' / ASCII table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 41 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 4 / number of table fields TTYPE1 = 'description' TFORM1 = 'A11 ' TBCOL1 = 1 TTYPE2 = 'extension_id' TFORM2 = 'I10 ' TBCOL2 = 12 TTYPE3 = 'aperture_id' TFORM3 = 'I10 ' TBCOL3 = 22 TTYPE4 = 'image_plane_id' TFORM4 = 'I10 ' 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1.3294000E+01 1.3500000E+01 5.0000000E+00 -1.2381000E+01 1.6867000E+01 1.3500000E+01 4.0000000E+00 -9.7170000E+00 1.6884000E+01 1.3500000E+01 3.0000000E+00 -7.0540000E+00 1.6897000E+01 1.3500000E+01 2.0000000E+00 -4.3910000E+00 1.6905000E+01 1.3500000E+01 1.0000000E+00 -1.7300000E+00 1.6910000E+01 1.3500000E+01 0.0000000E+00 9.3100000E-01 1.6911000E+01 1.3500000E+01 -1.0000000E+00 3.5910000E+00 1.6908000E+01 1.3500000E+01 -2.0000000E+00 6.2500000E+00 1.6900000E+01 1.3500000E+01 -3.0000000E+00 8.9070000E+00 1.6889000E+01 1.3500000E+01 -4.0000000E+00 1.1565000E+01 1.6874000E+01 1.3500000E+01 -5.0000000E+00 1.4221000E+01 1.6855000E+01 \ No newline at end of file diff --git a/METIS/code/2023-06-20-spectra_Grism-L_band.dat b/METIS/code/2023-06-20-spectra_Grism-L_band.dat new file mode 100644 index 00000000..96ca461d --- /dev/null +++ b/METIS/code/2023-06-20-spectra_Grism-L_band.dat @@ -0,0 +1,141 @@ +# author : Conchi Cardenas Vazquez +# date_created : 2023-06-20 +# source : Final LSS design for manufacturing +# wavelength_unit : um +# xi_unit : arcsec +# x_unit : mm +# y_unit : mm +# +# comments : +# - WARNING : Origin of coordinates at the detector: Detector sensitive surface, bottom-left corner. +# - lambda: 11 points +# - Field of View on-sky: 11 points +# - conf#2 +# - L-band +# - spectra lenght = 1760 px +# - Field of View on-sky: s [arcsec], with respect to the optical axis. +# - Slit @CFO FP2 on-axis. +# - Coords at the detector: x, y [mm] +# +wavelength xi x y +2.90 5 34.999538 33.745033 +2.90 4 31.685914 33.727450 +2.90 3 28.371944 33.713754 +2.90 2 25.057988 33.703953 +2.90 1 21.744409 33.698053 +2.90 0 18.431569 33.696056 +2.90 -1 15.119834 33.697959 +2.90 -2 11.809567 33.703757 +2.90 -3 8.501130 33.713441 +2.90 -4 5.194883 33.726997 +2.90 -5 1.891181 33.744408 +3.03 5 34.994055 30.515995 +3.03 4 31.681510 30.498397 +3.03 3 28.368629 30.484687 +3.03 2 25.055769 30.474876 +3.03 1 21.743290 30.468969 +3.03 0 18.431554 30.466970 +3.03 -1 15.120922 30.468875 +3.03 -2 11.811755 30.474679 +3.03 -3 8.504413 30.484373 +3.03 -4 5.199252 30.497942 +3.03 -5 1.896626 30.515369 +3.16 5 34.988580 27.305202 +3.16 4 31.677111 27.287605 +3.16 3 28.365316 27.273895 +3.16 2 25.053551 27.264083 +3.16 1 21.742172 27.258176 +3.16 0 18.431539 27.256176 +3.16 -1 15.122009 27.258082 +3.16 -2 11.813942 27.263886 +3.16 -3 8.507693 27.273580 +3.16 -4 5.203617 27.287149 +3.16 -5 1.902064 27.304575 +3.29 5 34.983130 24.110293 +3.29 4 31.672729 24.092716 +3.29 3 28.362016 24.079020 +3.29 2 25.051341 24.069217 +3.29 1 21.741059 24.063315 +3.29 0 18.431524 24.061317 +3.29 -1 15.123093 24.063221 +3.29 -2 11.816121 24.069020 +3.29 -3 8.510962 24.078705 +3.29 -4 5.207965 24.092260 +3.29 -5 1.907478 24.109667 +3.42 5 34.977720 20.929352 +3.42 4 31.668379 20.911812 +3.42 3 28.358739 20.898144 +3.42 2 25.049146 20.888361 +3.42 1 21.739953 20.882471 +3.42 0 18.431510 20.880476 +3.42 -1 15.124171 20.882376 +3.42 -2 11.818287 20.888164 +3.42 -3 8.514208 20.897829 +3.42 -4 5.212282 20.911357 +3.42 -5 1.912853 20.928726 +3.55 5 34.972370 17.760795 +3.55 4 31.664076 17.743311 +3.55 3 28.355496 17.729686 +3.55 2 25.046974 17.719932 +3.55 1 21.738859 17.714060 +3.55 0 18.431497 17.712071 +3.55 -1 15.125239 17.713965 +3.55 -2 11.820431 17.719736 +3.55 -3 8.517422 17.729372 +3.55 -4 5.216555 17.742857 +3.55 -5 1.918170 17.760170 +3.68 5 34.967098 14.603291 +3.68 4 31.659833 14.585883 +3.68 3 28.352298 14.572316 +3.68 2 25.044833 14.562603 +3.68 1 21.737780 14.556754 +3.68 0 18.431485 14.554774 +3.68 -1 15.126292 14.556660 +3.68 -2 11.822547 14.562407 +3.68 -3 8.520592 14.572002 +3.68 -4 5.220768 14.585430 +3.68 -5 1.923410 14.602669 +3.81 5 34.961925 11.455706 +3.81 4 31.655669 11.438394 +3.81 3 28.349158 11.424899 +3.81 2 25.042729 11.415238 +3.81 1 21.736720 11.409421 +3.81 0 18.431473 11.407450 +3.81 -1 15.127328 11.409327 +3.81 -2 11.824626 11.415043 +3.81 -3 8.523706 11.424588 +3.81 -4 5.224904 11.437943 +3.81 -5 1.928552 11.455087 +3.94 5 34.956871 8.317054 +3.94 4 31.651598 8.299858 +3.94 3 28.346088 8.286453 +3.94 2 25.040672 8.276855 +3.94 1 21.735684 8.271075 +3.94 0 18.431462 8.269118 +3.94 -1 15.128342 8.270982 +3.94 -2 11.826660 8.276661 +3.94 -3 8.526752 8.286143 +3.94 -4 5.228948 8.299410 +3.94 -5 1.933577 8.316439 +4.07 5 34.951959 5.186466 +4.07 4 31.647640 5.169408 +4.07 3 28.343101 5.156108 +4.07 2 25.038670 5.146585 +4.07 1 21.734676 5.140850 +4.07 0 18.431452 5.138908 +4.07 -1 15.129330 5.140758 +4.07 -2 11.828641 5.146393 +4.07 -3 8.529716 5.155801 +4.07 -4 5.232882 5.168964 +4.07 -5 1.938462 5.185856 +4.20 5 34.947210 2.063164 +4.20 4 31.643811 2.046264 +4.20 3 28.340211 2.033088 +4.20 2 25.036733 2.023652 +4.20 1 21.733701 2.017969 +4.20 0 18.431443 2.016044 +4.20 -1 15.130287 2.017877 +4.20 -2 11.830559 2.023461 +4.20 -3 8.532585 2.032783 +4.20 -4 5.236688 2.045824 +4.20 -5 1.943185 2.062559 diff --git a/METIS/code/2023-06-20-spectra_Grism-M_band.dat b/METIS/code/2023-06-20-spectra_Grism-M_band.dat new file mode 100644 index 00000000..541afaed --- /dev/null +++ b/METIS/code/2023-06-20-spectra_Grism-M_band.dat @@ -0,0 +1,141 @@ +# author : Conchi Cardenas Vazquez +# date_created : 2023-06-20 +# source : Final LSS design for manufacturing +# wavelength_unit : um +# xi_unit : arcsec +# x_unit : mm +# y_unit : mm +# +# comments : +# - WARNING : Origin of coordinates at the detector: Detector sensitive surface, bottom-left corner. +# - lambda: 11 points +# - Field of View on-sky: 11 points +# - conf#3 +# - M-band +# - spectra lenght = 1680 px +# - Field of View on-sky: s [arcsec], with respect to the optical axis. +# - Slit @CFO FP2: on-axis. +# - Coords at the detector: x, y [mm] +# +wavelength xi x y +4.50 5 34.998332 33.031880 +4.50 4 31.684942 33.011828 +4.50 3 28.371211 32.996210 +4.50 2 25.057496 32.985035 +4.50 1 21.744160 32.978308 +4.50 0 18.431566 32.976031 +4.50 -1 15.120076 32.978201 +4.50 -2 11.810052 32.984811 +4.50 -3 8.501857 32.995852 +4.50 -4 5.195847 33.011310 +4.50 -5 1.892379 33.031167 +4.57 5 34.993143 29.979960 +4.57 4 31.680774 29.959901 +4.57 3 28.368072 29.944276 +4.57 2 25.055395 29.933096 +4.57 1 21.743101 29.926366 +4.57 0 18.431551 29.924087 +4.57 -1 15.121105 29.926258 +4.57 -2 11.812123 29.932872 +4.57 -3 8.504964 29.943918 +4.57 -4 5.199983 29.959382 +4.57 -5 1.897532 29.979246 +4.64 5 34.987948 26.934743 +4.64 4 31.676599 26.914692 +4.64 3 28.364928 26.899072 +4.64 2 25.053290 26.887895 +4.64 1 21.742041 26.881167 +4.64 0 18.431537 26.878889 +4.64 -1 15.122137 26.881059 +4.64 -2 11.814199 26.887671 +4.64 -3 8.508077 26.898714 +4.64 -4 5.204125 26.914173 +4.64 -5 1.902692 26.934028 +4.71 5 34.982763 23.896042 +4.71 4 31.672431 23.876015 +4.71 3 28.361789 23.860414 +4.71 2 25.051188 23.849249 +4.71 1 21.740981 23.842527 +4.71 0 18.431523 23.840251 +4.71 -1 15.123169 23.842419 +4.71 -2 11.816272 23.849024 +4.71 -3 8.511186 23.860055 +4.71 -4 5.208261 23.875496 +4.71 -5 1.907842 23.895328 +4.78 5 34.977607 20.863664 +4.78 4 31.668285 20.843678 +4.78 3 28.358666 20.828107 +4.78 2 25.049096 20.816963 +4.78 1 21.739928 20.810254 +4.78 0 18.431510 20.807982 +4.78 -1 15.124196 20.810146 +4.78 -2 11.818336 20.816738 +4.78 -3 8.514281 20.827748 +4.78 -4 5.212376 20.843159 +4.78 -5 1.912966 20.862951 +4.85 5 34.972496 17.837403 +4.85 4 31.664174 17.817475 +4.85 3 28.355568 17.801948 +4.85 2 25.047022 17.790835 +4.85 1 21.738882 17.784144 +4.85 0 18.431497 17.781878 +4.85 -1 15.125216 17.784036 +4.85 -2 11.820385 17.790611 +4.85 -3 8.517351 17.801590 +4.85 -4 5.216457 17.816957 +4.85 -5 1.918044 17.836691 +4.92 5 34.967450 14.817045 +4.92 4 31.660113 14.797193 +4.92 3 28.352507 14.781723 +4.92 2 25.044972 14.770651 +4.92 1 21.737850 14.763984 +4.92 0 18.431485 14.761727 +4.92 -1 15.126224 14.763877 +4.92 -2 11.822410 14.770427 +4.92 -3 8.520385 14.781366 +4.92 -4 5.220489 14.796677 +4.92 -5 1.923060 14.816336 +4.99 5 34.962487 11.802366 +4.99 4 31.656118 11.782607 +4.99 3 28.349495 11.767210 +4.99 2 25.042954 11.756188 +4.99 1 21.736833 11.749552 +4.99 0 18.431474 11.747304 +4.99 -1 15.127217 11.749445 +4.99 -2 11.824404 11.755965 +4.99 -3 8.523372 11.766854 +4.99 -4 5.224457 11.782094 +4.99 -5 1.927993 11.801660 +5.06 5 34.957628 8.793131 +5.06 4 31.652205 8.773484 +5.06 3 28.346543 8.758173 +5.06 2 25.040976 8.747212 +5.06 1 21.735837 8.740612 +5.06 0 18.431464 8.738377 +5.06 -1 15.128192 8.740506 +5.06 -2 11.826359 8.746991 +5.06 -3 8.526300 8.757819 +5.06 -4 5.228345 8.772973 +5.06 -5 1.932824 8.792428 +5.13 5 34.952891 5.789092 +5.13 4 31.648388 5.769577 +5.13 3 28.343664 5.754367 +5.13 2 25.039046 5.743478 +5.13 1 21.734866 5.736922 +5.13 0 18.431454 5.734701 +5.13 -1 15.129144 5.736816 +5.13 -2 11.828268 5.743258 +5.13 -3 8.529157 5.754016 +5.13 -4 5.232138 5.769069 +5.13 -5 1.937534 5.788394 +5.20 5 34.948299 2.789993 +5.20 4 31.644686 2.770630 +5.20 3 28.340870 2.755537 +5.20 2 25.037173 2.744731 +5.20 1 21.733922 2.738224 +5.20 0 18.431445 2.736020 +5.20 -1 15.130069 2.738119 +5.20 -2 11.830123 2.744513 +5.20 -3 8.531931 2.755188 +5.20 -4 5.235818 2.770126 +5.20 -5 1.942102 2.789301 diff --git a/METIS/code/2023-06-20-spectra_Grism-N_band.dat b/METIS/code/2023-06-20-spectra_Grism-N_band.dat new file mode 100644 index 00000000..19c04252 --- /dev/null +++ b/METIS/code/2023-06-20-spectra_Grism-N_band.dat @@ -0,0 +1,141 @@ +# author : Conchi Cardenas Vazquez +# date_created : 2023-06-20 +# source : Final LSS design for manufacturing +# wave_unit : um +# xi_unit : arcsec +# x_unit : mm +# y_unit : mm +# +# comments : +# - WARNING : Origin of coordinates at the detector: Detector sensitive surface, bottom-left corner. +# - lambda: 11 points +# - Field of View on-sky: 11 points +# - conf#5 +# - N-band +# - spectra lenght = 1862 px +# - Field of View on-sky: s [arcsec], with respect to the optical axis. +# - Slit @CFO FP2: on-axis. +# - Coords at the detector: x, y [mm] +# +wavelength xi x y +7.60 5 5.050256 36.194583 +7.60 4 7.727612 36.173205 +7.60 3 10.404596 36.156533 +7.60 2 13.081082 36.144593 +7.60 1 15.756942 36.137400 +7.60 0 18.432042 36.134966 +7.60 -1 21.106248 36.137288 +7.60 -2 23.779428 36.144357 +7.60 -3 26.451448 36.156157 +7.60 -4 29.122180 36.172661 +7.60 -5 31.791501 36.193832 +8.18 5 5.058012 32.824603 +8.18 4 7.733884 32.803345 +8.18 3 10.409341 32.786763 +8.18 2 13.084270 32.774886 +8.18 1 15.758551 32.767732 +8.18 0 18.432063 32.765309 +8.18 -1 21.104682 32.767619 +8.18 -2 23.776284 32.774651 +8.18 -3 26.446748 32.786387 +8.18 -4 29.115956 32.802801 +8.18 -5 31.783794 32.823855 +8.76 5 5.065699 29.460637 +8.76 4 7.740103 29.439542 +8.76 3 10.414049 29.423085 +8.76 2 13.087433 29.411297 +8.76 1 15.760148 29.404195 +8.76 0 18.432084 29.401790 +8.76 -1 21.103127 29.404082 +8.76 -2 23.773164 29.411062 +8.76 -3 26.442085 29.422711 +8.76 -4 29.109783 29.439000 +8.76 -5 31.776156 29.459893 +9.34 5 5.073256 26.101630 +9.34 4 7.746220 26.080742 +9.34 3 10.418680 26.064446 +9.34 2 13.090546 26.052771 +9.34 1 15.761721 26.045738 +9.34 0 18.432104 26.043356 +9.34 -1 21.101595 26.045626 +9.34 -2 23.770093 26.052538 +9.34 -3 26.437497 26.064074 +9.34 -4 29.103712 26.080205 +9.34 -5 31.768647 26.100892 +9.92 5 5.080619 22.746734 +9.92 4 7.752184 22.726100 +9.92 3 10.423199 22.710000 +9.92 2 13.093585 22.698465 +9.92 1 15.763255 22.691515 +9.92 0 18.432124 22.689161 +9.92 -1 21.100100 22.691404 +9.92 -2 23.767094 22.698234 +9.92 -3 26.433019 22.709632 +9.92 -4 29.097791 22.725568 +9.92 -5 31.761329 22.746004 +10.50 5 5.087725 19.395242 +10.50 4 7.757946 19.374908 +10.50 3 10.427567 19.359040 +10.50 2 13.096523 19.347669 +10.50 1 15.764740 19.340818 +10.50 0 18.432142 19.338498 +10.50 -1 21.098653 19.340709 +10.50 -2 23.764194 19.347441 +10.50 -3 26.428691 19.358676 +10.50 -4 29.092070 19.374383 +10.50 -5 31.754266 19.394521 +11.08 5 5.094509 16.046540 +11.08 4 7.763452 16.026553 +11.08 3 10.431745 16.010954 +11.08 2 13.099335 15.999775 +11.08 1 15.766160 15.993038 +11.08 0 18.432159 15.990757 +11.08 -1 21.097267 15.992931 +11.08 -2 23.761418 15.999551 +11.08 -3 26.424549 16.010596 +11.08 -4 29.086602 16.026037 +11.08 -5 31.747522 16.045831 +11.66 5 5.100907 12.700087 +11.66 4 7.768651 12.680496 +11.66 3 10.435694 12.665202 +11.66 2 13.101994 12.654241 +11.66 1 15.767504 12.647635 +11.66 0 18.432175 12.645397 +11.66 -1 21.095955 12.647529 +11.66 -2 23.758791 12.654021 +11.66 -3 26.420634 12.664851 +11.66 -4 29.081438 12.679990 +11.66 -5 31.741161 12.699393 +12.24 5 5.106851 9.355396 +12.24 4 7.773490 9.336247 +12.24 3 10.439374 9.321297 +12.24 2 13.104474 9.310580 +12.24 1 15.768758 9.304122 +12.24 0 18.432189 9.301934 +12.24 -1 21.094729 9.304018 +12.24 -2 23.756340 9.310366 +12.24 -3 26.416984 9.320955 +12.24 -4 29.076630 9.335753 +12.24 -5 31.735248 9.354718 +12.82 5 5.112276 6.012023 +12.82 4 7.777916 5.993366 +12.82 3 10.442745 5.978797 +12.82 2 13.106748 5.968352 +12.82 1 15.769908 5.962056 +12.82 0 18.432201 5.959924 +12.82 -1 21.093603 5.961956 +12.82 -2 23.754090 5.968143 +12.82 -3 26.413639 5.978464 +12.82 -4 29.072230 5.992886 +12.82 -5 31.729851 6.011364 +13.4 5 5.117115 2.669565 +13.4 4 7.781875 2.651449 +13.4 3 10.445766 2.637298 +13.4 2 13.108790 2.627152 +13.4 1 15.770941 2.621037 +13.4 0 18.432211 2.618965 +13.4 -1 21.092590 2.620939 +13.4 -2 23.752068 2.626950 +13.4 -3 26.410638 2.636976 +13.4 -4 29.068293 2.650983 +13.4 -5 31.725035 2.668927 diff --git a/METIS/code/trace_lss_dat_to_fits.py b/METIS/code/trace_lss_dat_to_fits.py new file mode 100644 index 00000000..91cb4f65 --- /dev/null +++ b/METIS/code/trace_lss_dat_to_fits.py @@ -0,0 +1,85 @@ +from astropy import units as u +from astropy.table import Table +from astropy.io import fits, ascii +import matplotlib.pyplot as plt + +from pathlib import Path +from os import path as pth +from glob import glob +import numpy as np + + +def make_pri_hdu(): + pri_hdu = fits.PrimaryHDU() + pri_hdu.header["ECAT"] = 1 + pri_hdu.header["EDATA"] = 2 + + meta = {"author": "Kieran Leschinski", + "source": "Conchi Cardenas Vazquez", + "descript": "METIS L-band LSS Spectral Trace. Final version", + "date-cre": "2023-06-20", + "date-mod": "2024-04-08", + "status": "Ready for Manufacturing" + } + pri_hdu.header.update(meta) + + return pri_hdu + + +def make_cat_hdu(file_names): + trace_names = [path.stem for path in file_names] + cat_table = Table(data=[trace_names, + list(2 + np.arange(len(trace_names))), + [0]*len(trace_names), + [0]*len(trace_names) + ], + names=["description", "extension_id", + "aperture_id", "image_plane_id"]) + cat_hdu = fits.table_to_hdu(cat_table) + cat_hdu.header["EXTNAME"] = "TOC" + + return cat_hdu + + +def make_spec_trace_hdu(file_paths): + # NOTE : Data is delivered with x,y=(0,0) being the bottom-left corner + # need to change this to x,y=(0,0) at pixel coord (1024,1024) + + pixel_size = 0.018 # mm + dx, dy = [pixel_size * 1024] * 2 + + trace_hdus = [] + for path in file_paths: + data = ascii.read(path.name, fast_reader=True) + for i, unit_str in enumerate(["um", "arcsec", "mm", "mm"]): + data.columns[i].unit = u.Unit(unit_str) + data["x"] -= dx + data["y"] -= dy + table_hdu = fits.table_to_hdu(data) + table_hdu.header["EXTNAME"] = path.stem + table_hdu.header["DISPDIR"] = "y" + + trace_hdus += [table_hdu] + + # plt.scatter(data["x"], data["y"], c=data["wavelength"]) + # plt.show() + + return trace_hdus + + +def do_main(filter_name = "L"): + dir_path = Path(".") + filenames = [fn for fn in dir_path.glob(f"*{filter_name}_band.dat")] + + pri_hdu = make_pri_hdu() + cat_hdu = make_cat_hdu(filenames) + trace_hdus = make_spec_trace_hdu(filenames) + + trace_hdulist = fits.HDUList([pri_hdu] + [cat_hdu] + trace_hdus) + trace_hdulist.writeto(f"TRACE_LSS_{filter_name}.fits", overwrite=True) + + +if __name__ == '__main__': + do_main("L") + do_main("M") + do_main("N") diff --git a/METIS/default.yaml b/METIS/default.yaml index 25ec32c4..57523c2b 100644 --- a/METIS/default.yaml +++ b/METIS/default.yaml @@ -5,6 +5,7 @@ object: configuration alias: OBS name: METIS_default_configuration description: default parameters needed for a METIS simulation +status: development date_modified: 2022-03-14 changes: - 2021-12-16 (OC) chopnod defaults to perpendicular @@ -64,6 +65,7 @@ mode_yamls: alias: OBS name: img_lm description: "METIS LM-band imaging" + status: development yamls: - METIS_IMG_LM.yaml - METIS_DET_IMG_LM.yaml @@ -79,6 +81,7 @@ mode_yamls: alias: OBS name: img_n description: "METIS N-band imaging" + status: development yamls: - METIS_IMG_N.yaml - METIS_DET_IMG_N_GeoSnap.yaml @@ -96,13 +99,14 @@ mode_yamls: alias: OBS name: lss_l description: "METIS L-band slit spectroscopy" + status: development yamls: - METIS_IMG_LM.yaml - METIS_LSS.yaml - METIS_DET_IMG_LM.yaml properties: psf_file: PSF_LM_9mag_06seeing.fits - trace_file: TRACE_LSS_L_sci.fits + trace_file: TRACE_LSS_L.fits efficiency_file: TER_grating_L.fits slit: C-38_1 adc: const_90 @@ -114,13 +118,14 @@ mode_yamls: alias: OBS name: lss_m description: "METIS M-band slit spectroscopy" + status: development yamls: - METIS_IMG_LM.yaml - METIS_LSS.yaml - METIS_DET_IMG_LM.yaml properties: psf_file: PSF_LM_9mag_06seeing.fits - trace_file: TRACE_LSS_M_sci.fits + trace_file: TRACE_LSS_M.fits efficiency_file: TER_grating_M.fits slit: C-38_1 adc: const_90 @@ -132,13 +137,14 @@ mode_yamls: alias: OBS name: lss_n description: "METIS N-band slit spectroscopy" + status: development yamls: - METIS_IMG_N.yaml - METIS_LSS.yaml - METIS_DET_IMG_N_GeoSnap.yaml properties: psf_file: PSF_N_9mag_06seeing.fits - trace_file: TRACE_LSS_N_sci.fits + trace_file: TRACE_LSS_N.fits efficiency_file: TER_grating_N.fits slit: D-57_1 adc: false @@ -150,6 +156,7 @@ mode_yamls: alias: OBS name: lms description: "METIS LM-band integral-field spectroscopy, nominal mode" + status: experimental yamls: - METIS_LMS.yaml - METIS_DET_IFU.yaml @@ -165,6 +172,7 @@ mode_yamls: alias: OBS name: lms_extended description: "METIS LM-band integral-field spectroscopy, extended mode" + status: experimental yamls: - METIS_LMS_EXT.yaml - METIS_DET_IFU.yaml diff --git a/METIS/docs/example_notebooks/IFU_Template.ipynb b/METIS/docs/example_notebooks/IFU_Template.ipynb new file mode 100644 index 00000000..a7153cf3 --- /dev/null +++ b/METIS/docs/example_notebooks/IFU_Template.ipynb @@ -0,0 +1,1211 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f51b9870", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy import units as u\n", + "from astropy.modeling.models import BlackBody\n", + "from scipy.interpolate import interp1d" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c09323fa", + "metadata": {}, + "outputs": [], + "source": [ + "def donut_array_ellipse(a1, b1, ecc, inc, width, height):\n", + " array = np.zeros((height, width), dtype=np.float32)\n", + " \n", + " x0 = width // 2\n", + " y0 = height // 2\n", + " \n", + " # Outer ellipse\n", + " inc_rad = np.radians(inc)\n", + " cos_inc = np.cos(inc_rad)\n", + " sin_inc = np.sin(inc_rad)\n", + " \n", + " for y in range(height):\n", + " for x in range(width):\n", + " norm_x = (x - x0) / a1\n", + " norm_y = (y - y0) / b1\n", + " transformed_x = norm_x * cos_inc - norm_y * sin_inc\n", + " transformed_y = norm_x * sin_inc + norm_y * cos_inc\n", + " if transformed_x**2 / (1 -ecc**2) + transformed_y**2 <= 1:\n", + " array[y, x] = 1\n", + " \n", + " ratio = b1 / a1\n", + " a2 = a1 * ratio\n", + " b2 = b1 * ratio\n", + " \n", + " for y in range(height):\n", + " for x in range(width):\n", + " norm_x = (x - x0) / a2\n", + " norm_y = (y - y0) / b2\n", + " transformed_x = norm_x * cos_inc - norm_y * sin_inc\n", + " transformed_y = norm_x * sin_inc + norm_y * cos_inc\n", + " if transformed_x**2 / (1 -ecc**2) + transformed_y**2 <= 1:\n", + " array[y, x] = 0\n", + " \n", + " return array" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0fda5400", + "metadata": {}, + "outputs": [], + "source": [ + "draw_donut_ellipse = donut_array_ellipse(800, 500, 0.8, 30, 2000, 2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6ec704ed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.imshow(draw_donut_ellipse, cmap='hot', origin='lower')\n", + "plt.title('Ellipse Donut')\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b112ae67", + "metadata": {}, + "outputs": [], + "source": [ + "def donut_array_ellipse_flux(a1, b1, ecc, inc, width, height):\n", + " array = np.zeros((height, width), dtype=np.float32)\n", + " \n", + " x0 = width // 2\n", + " y0 = height // 2\n", + " \n", + " # Outer ellipse\n", + " inc_rad = np.radians(inc)\n", + " cos_inc = np.cos(inc_rad)\n", + " sin_inc = np.sin(inc_rad)\n", + " \n", + " for y in range(height):\n", + " for x in range(width):\n", + " norm_x = (x - x0) / a1\n", + " norm_y = (y - y0) / b1\n", + " transformed_x = norm_x * cos_inc - norm_y * sin_inc\n", + " transformed_y = norm_x * sin_inc + norm_y * cos_inc\n", + " if transformed_x**2 / (1 -ecc**2) + transformed_y**2 <= 1:\n", + " \n", + " dist = np.sqrt((x - x0)**2+ (y - y0)**2)\n", + " \n", + " intensity = 50 + 200 * np.exp(-dist**2 / (2 * (0.1 * width)**2))\n", + " \n", + " array[y, x] = intensity\n", + " \n", + " ratio = b1 / a1\n", + " a2 = a1 * ratio\n", + " b2 = b1 * ratio\n", + " \n", + " for y in range(height):\n", + " for x in range(width):\n", + " norm_x = (x - x0) / a2\n", + " norm_y = (y - y0) / b2\n", + " transformed_x = norm_x * cos_inc - norm_y * sin_inc\n", + " transformed_y = norm_x * sin_inc + norm_y * cos_inc\n", + " if transformed_x**2 / (1 -ecc**2) + transformed_y**2 <= 1:\n", + " array[y, x] = 0\n", + " \n", + " return array" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "90e0e8c2", + "metadata": {}, + "outputs": [], + "source": [ + "draw_donut_ellipse_flux = donut_array_ellipse_flux(800, 500, 0.8, 30, 2000, 2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "77ae9b86", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.imshow(draw_donut_ellipse_flux, cmap='hot', origin='lower')\n", + "plt.title('Ellipse Donut with Flux variation')\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "333dddd1", + "metadata": {}, + "outputs": [], + "source": [ + "def donut_array_ellipse_bb(a1, b1, ecc, inc, temp_disk, width, height):\n", + " array = np.zeros((height, width), dtype=np.float32)\n", + " \n", + " x0 = width // 2\n", + " y0 = height // 2\n", + " \n", + " # Outer ellipse\n", + " inc_rad = np.radians(inc)\n", + " cos_inc = np.cos(inc_rad)\n", + " sin_inc = np.sin(inc_rad)\n", + " \n", + " temp_disk = temp_disk * u.K\n", + " wav = np.linspace(1e-6, 100e-6, 1000) * u.meter\n", + " bb_disk = BlackBody(temperature=temp_disk)\n", + " fd_disk = bb_disk(wav)\n", + " interp_fd_disk = interp1d(np.arange(len(fd_disk)), fd_disk)(np.linspace(0, len(fd_disk) - 1, num=array.shape[0]))\n", + " \n", + " for y in range(height):\n", + " for x in range(width):\n", + " norm_x = (x - x0) / a1\n", + " norm_y = (y - y0) / b1\n", + " transformed_x = norm_x * cos_inc - norm_y * sin_inc\n", + " transformed_y = norm_x * sin_inc + norm_y * cos_inc\n", + " if transformed_x**2 / (1 -ecc**2) + transformed_y**2 <= 1:\n", + " \n", + " dist = np.sqrt((x - x0)**2+ (y - y0)**2)\n", + " \n", + " intensity = 50 + 200 * np.exp(-dist**2 / (2 * (0.1 * width)**2))\n", + " \n", + " array[y, x] = intensity + interp_fd_disk[y]\n", + " \n", + " ratio = b1 / a1\n", + " a2 = a1 * ratio\n", + " b2 = b1 * ratio\n", + " \n", + " for y in range(height):\n", + " for x in range(width):\n", + " norm_x = (x - x0) / a2\n", + " norm_y = (y - y0) / b2\n", + " transformed_x = norm_x * cos_inc - norm_y * sin_inc\n", + " transformed_y = norm_x * sin_inc + norm_y * cos_inc\n", + " if transformed_x**2 / (1 -ecc**2) + transformed_y**2 <= 1:\n", + " array[y, x] = 0\n", + " \n", + " return array" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "42392aba", + "metadata": {}, + "outputs": [], + "source": [ + "draw_donut_ellipse_bb = donut_array_ellipse_bb(800, 500, 0.8, 30, 500, 2000, 2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "de614f0d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.imshow(draw_donut_ellipse_bb, cmap='hot', origin='lower')\n", + "plt.title('Ellipse Donut with Spectrum')\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0f8ea1aa", + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'donut_ellipse.fits'\n", + "hdu = fits.PrimaryHDU(draw_donut_ellipse_bb)\n", + "hdu.writeto(filename, overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c950323b", + "metadata": {}, + "outputs": [], + "source": [ + "from scopesim.source import source_templates as sim_tp" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e3ed29e9", + "metadata": {}, + "outputs": [], + "source": [ + "src_star = sim_tp.star(flux=10*u.ABmag)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ff638457", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import datetime\n", + " \n", + "import shutil\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LogNorm\n", + "from astropy import units as u\n", + "from astropy.io import fits\n", + "from astropy.wcs import WCS\n", + "\n", + "import scopesim as sim\n", + "import scopesim_templates as sim_tp" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "424feba1", + "metadata": {}, + "outputs": [], + "source": [ + "hdul = fits.open('donut_ellipse.fits') #Donut\n", + "hdul[0].header[\"CDELT1\"] = (0.0057 / 3600) / 10 #CD1_1 \n", + "hdul[0].header[\"CDELT2\"] = (0.0057 / 3600) / 10 #CD2_2 #für metis 0.0057 / 3600\n", + "hdul[0].header[\"CRVAL1\"] = 0\n", + "hdul[0].header[\"CRVAL2\"] = 0\n", + "hdul[0].header[\"CRPIX1\"] = 1000.5 #(Naxis +1) /2\n", + "hdul[0].header[\"CRPIX2\"] = 1000.5\n", + "hdul[0].header[\"CUNIT1\"] = \"deg\"\n", + "hdul[0].header[\"CUNIT2\"] = \"deg\"\n", + "\n", + "#hdul[0].data -= 0.9 * np.median(hdul[0].data)\n", + "src_disk = sim.Source(image_hdu=hdul[0], flux=1e-6*u.Jy)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fb3a19a8", + "metadata": {}, + "outputs": [], + "source": [ + "src = src_star + src_disk" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2a50d522", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "src.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e34db6b4", + "metadata": {}, + "outputs": [], + "source": [ + "cmd_l = sim.UserCommands(use_instrument='METIS', set_modes=['img_lm'], \n", + " properties={\"!OBS.exptime\": 3600})\n", + "metis = sim.OpticalTrain(cmd_l)\n", + "metis['skycalc_atmosphere'].include=False\n", + "metis['detector_linearity'].include=False" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2471b1cc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " FOVs: 0%| | 0/1 [00:00" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AMAzZnqnKfvaqXJPymI7m5mY1Nzc73l5TUxNz/bnnntOcOXP0t3/7tzH7R48ePeTYiPfee0+bNm3Stm3bNH36dEnSww8/rJkzZ2r37t06//zzU202AAAAgCzxdEzHgQMH9MILL2jx4sVDbtuwYYMqKyt14YUXauXKlTp69Gj0tq1btyoYDEYDDkmaMWOGgsGgtmzZYvtcPT096urqitkAAAAAZJ+ns1c99thjKisr07XXXhuz/6abbtL48eNVU1OjnTt3atWqVfrd736n1tZWSVJnZ6eqqqqGPF5VVZU6Ozttn2vNmjW6++673X8RAAAAKGDMXuUGT4OOX/ziF7rppps0cuTImP1LliyJXm5oaNCECRM0bdo07dixQ1OmTJEUHpBuZRiG7X5JWrVqlZYvXx693tXVpfr6ejdeBgAAAIBh8CzoeO2117R792499dRTCY+dMmWKiouLtWfPHk2ZMkU1NTU6cODAkOM+/vhjVVdX2z5GaWmpSktLh91uAAAAAO7ybEzH+vXrNXXqVE2ePDnhsbt27VJfX59qa2slSTNnzlQoFNKbb74ZPeaNN95QKBTSrFmzvGoyAAAAYNGv7M9WZd4KpLyqu7tb77//fvR6R0eH2tvbVVFRobPPPltSuLTp3/7t3/TP//zPQ+7/pz/9SRs2bNDVV1+tyspKvfvuu1qxYoUuuugiffnLX5YkXXDBBZo7d66WLFkSnUr3lltu0bx585i5CgAAAMgxKWc63nrrLV100UW66KKLJEnLly/XRRddpH/8x3+MHrNx40YZhqFvfvObQ+5fUlKi//t//6+uuuoqnX/++brjjjvU1NSkzZs3q6ioKHrchg0bNGnSJDU1NampqUlf+MIX9MQTT6TzGgEAAABkUcAwDD8Nx3dNV1eXgsGgRkmyH3oOAACAbDIkHZcUCoVUXl6e7ebEiPQlQ6F6lZd7uspESrq6BhQM7vPlOYvHP2cQAAAAQF4i6AAAAADgKU/X6QAAAABy20n563f6gWw3IC1+OoMAAAAA8hBBBwAAAABPUV4FAAAAOKK8yg1+OoMAAAAA8hBBBwAAAABPUV4FAAAAOKK8yg1+OoMAAAAA8hBBBwAAAABPUV4FAAAAOOqXv0qajGw3IC1kOgAAAAB4iqADAAAAgKcorwIAAAAcnZQUyHYjTCivAgAAAIAhCDoAAAAAeIqgAwAAAHB00oeb1NjYqIkTJ2rdunUevnb3MKYDAAAAyDFtbW0qLy/PdjOSRqYDAAAAgKcIOgAAAAB4ivIqAAAAwBFT5rqBTAcAAAAATxF0AAAAAPAU5VUAAACAE2PAXxVNfmpLCsh0AAAAAPAUQQcAAAAAT1FeBQAAADgZGNz8wk9tSQGZDgAAAACeIugAAAAA4CnKqwAAAAAn/YObX/ipLSkg0wEAAADAUwQdAAAAADxFeRUAAADghPIqV5DpAAAAAOApgg4AAAAAnqK8CgAAAHDC4oCuINMBAAAAwFMEHQAAAAA8RXkVAAAA4ITZq1xBpgMAAACApwg6AAAAAHiK8ioAAADACbNXuYJMBwAAAABPEXQAAAAA8BTlVQAAAICTAflrxijKqwAAAABgKIIOAAAAAJ6ivAoAAABwwuKAriDTAQAAAMBTBB0AAAAAPEV5FQAAAOCExQFdQaYDAAAAgKcIOgAAAAB4ivIqAAAAwAmzV7mCTAcAAAAATxF0AAAAAPAU5VUAAACAE8qrXEGmAwAAAMgxjY2NmjhxotatW5ftpiSFTAcAAACQY9ra2lReXp7tZiSNoAMAAABwwuKArqC8CgAAAICnCDoAAAAAeIryKgAAAMAJs1e5IuVMx6uvvqprrrlGdXV1CgQCevbZZ2Nuv/nmmxUIBGK2GTNmxBzT09OjpUuXqrKyUmPGjNH8+fP14Ycfxhxz+PBhtbS0KBgMKhgMqqWlRUeOHEn5BQIAAADIrpSDjmPHjmny5Mlau3at4zFz587V/v37o9uLL74Yc/uyZcv0zDPPaOPGjXr99dfV3d2tefPmqb//VOi2cOFCtbe3a9OmTdq0aZPa29vV0tKSanMBAAAAZFnK5VXNzc1qbm6Oe0xpaalqampsbwuFQlq/fr2eeOIJXXHFFZKkJ598UvX19dq8ebOuuuoqvffee9q0aZO2bdum6dOnS5IefvhhzZw5U7t379b555+farMBAACA1Bny14xRRrYbkB5PBpK//PLLqqqq0nnnnaclS5bo4MGD0du2b9+uvr4+NTU1RffV1dWpoaFBW7ZskSRt3bpVwWAwGnBI0owZMxQMBqPHAAAAAMgNrg8kb25u1vXXX69x48apo6NDd911ly677DJt375dpaWl6uzsVElJic4444yY+1VXV6uzs1OS1NnZqaqqqiGPXVVVFT3GqqenRz09PdHrXV1dLr4qAAAAAOlyPehYsGBB9HJDQ4OmTZumcePG6YUXXtC1117reD/DMBQIBKLXzZedjjFbs2aN7r777mG0HAAAALBg9ipXeL5OR21trcaNG6c9e/ZIkmpqatTb26vDhw/HHHfw4EFVV1dHjzlw4MCQx/r444+jx1itWrVKoVAouu3bt8/lVwIAAAAgHZ4HHYcOHdK+fftUW1srSZo6daqKi4vV2toaPWb//v3auXOnZs2aJUmaOXOmQqGQ3nzzzegxb7zxhkKhUPQYq9LSUpWXl8dsAAAAALIv5fKq7u5uvf/++9HrHR0dam9vV0VFhSoqKrR69Wpdd911qq2t1V/+8hf98Ic/VGVlpb7xjW9IkoLBoBYvXqwVK1Zo7Nixqqio0MqVKzVp0qTobFYXXHCB5s6dqyVLluihhx6SJN1yyy2aN28eM1cBAAAgcyivckXKQcdbb72lOXPmRK8vX75ckrRo0SI9+OCDeuedd/T444/ryJEjqq2t1Zw5c/TUU0+prKwsep/7779fI0aM0A033KDjx4/r8ssv16OPPqqioqLoMRs2bNAdd9wRneVq/vz5cdcGAQAAAOBPAcMwcnS23/i6uroUDAY1SpL90HMAAABkkyHpuMLruPmtND7Slwxtk8o/l+3WnNLVLQVn+POcxeP67FUAAABA3hiQvxYH9FNbUuD5QHIAAAAAhY2gAwAAAICnKK8CAAAAnDB7lSvIdAAAAADwFEEHAAAAAE9RXgUAAAA4obzKFWQ6AAAAAHiKoAMAAACApyivAgAAAJywOKAryHQAQJ4qynYDAAAYRNABAHkqR8caAgDyEOVVAJCnIpkOgg8AGIYB+euDlPIqAEC6vC6FotQKAJBNZDoAwKesgUKyP7QVmY71049zAIDCRdABAD4QCQ7sMhL9pv3mgMLMer8im/1O9wUAxMHsVa4g6ACALLOOvYgEGeYAwXpbOgg4AADZwpgOAPAJu+yE3e1mqQQSjOsAAGQLmQ4AyKJ4gYD5Nqfyq1QCCTIdAJCGfvnrA9RPbUkBQQcAZFg6GQc3shRMoQsAyBbKqwAgw7Ld6afMCgCQaWQ6AKBA2A1QBwAkQHmVK8h0AECBIMMBAMgWMh0AkEF0/AEAhYigAwAyKN4igAAAH2JxQFdQXgUAGUbAAQAoNAQdAJBBfgg4/NAGAEBhobwKADLEL539HJ34BACyg9mrXEGmAwAAAICnCDoAAAAAeIryKgAoMCwQCAApoLzKFWQ6AKAA+WV8CQCgMBB0AEABytEfygAAOYryKgDIADILAJCjDPlrQT4j2w1ID5kOAMgAP2UW/NQWAEBhIOgAAAAA4CmCDgAoMJR6AUAK+n24SWpsbNTEiRO1bt067167ixjTAQAZQEcfAOCmtrY2lZeXZ7sZSSPTAQAFiCAIAJBJZDoAAAAAJwPy1+xVfmpLCsh0AECBItsBAMgUgg4AcFkudOZNYxEBAPAc5VUA4LJ+hQMPOvUAkAf89iuNn9qSAjIdAOCBSOAh5UbmAwAALxF0AIBH+i1//SSZQIhgCQDgFsqrAMAF1nKqXCivsmszAMCC8ipXEHQAQIrMnfXIZafvAL935M3t67fZBwCAGwg6ACBF/Q6XpdgOe6513iPtJfgAALiNoAMAEnAqlYrst3bWzftyjTWgSvTaE+0DgJzH4oCuIOgAgAScOtJ2HXQpdwMOyb7cyrzfbnC89TYAAKwIOgAgCYkGXedyoOEk2SCKYAMAkAhBBwCkyG4gubXUKtek0nan10rwASAvMXuVK1inAwAScBq7YQ4+cjngkFILOCJ/zd/DTuM+AACQCDoAIEa8jrI52CiyuVxorOfBKkd/jAMAeIDyKgAwccpiRPY5KZTAw66sTKZ9dscAQE4bkL8+zHJ09ioyHQAKnlPAUMhZDCdO2Q2772M/fUcDALKLoANAwUvUYU6m5KrQWTMeBGsAADPKqwAUtGQXuYuXDUFi8Wb8AgBfY3FAV5DpAJD3kgkMrCVDBBPJcwocrDNdWW8j4ACAwkGmA0Deizedq7WMKtenvs2GeDNXxRtoDgAoHAQdAAqeXccYwxMvk0HwASCnsDigKyivAlAQnH6NJ8jIjhz9zgQApImgA0DecppFiXEb2ZXMuicAgPxCeRWAvBVvBiqyHN5zKluznncGmgPwNWavcgWZDgA5K1HQYM1o+K0sF/ZZJ4JBAMg/KQcdr776qq655hrV1dUpEAjo2Wefjd7W19en73//+5o0aZLGjBmjuro6/f3f/70++uijmMeYPXu2AoFAzHbjjTfGHHP48GG1tLQoGAwqGAyqpaVFR44cSetFAshPTgv4WVfNNgcblFb5Q7/lr/U2gkMAyC8pBx3Hjh3T5MmTtXbt2iG3ffbZZ9qxY4fuuusu7dixQ08//bT++Mc/av78+UOOXbJkifbv3x/dHnrooZjbFy5cqPb2dm3atEmbNm1Se3u7WlpaUm0ugAIRr/PKCtn+45ThKLJc5t8NQNb1+3DLQSmP6WhublZzc7PtbcFgUK2trTH7/uf//J/60pe+pL179+rss8+O7h89erRqampsH+e9997Tpk2btG3bNk2fPl2S9PDDD2vmzJnavXu3zj///FSbDSCPJVv/TwfW/xKNtWGsBwDkJs/HdIRCIQUCAZ1++ukx+zds2KDKykpdeOGFWrlypY4ePRq9bevWrQoGg9GAQ5JmzJihYDCoLVu22D5PT0+Purq6YjYAhcNu9Wt+Kc9d1vKrXFjbg/caADjzdPaqEydO6Ac/+IEWLlyo8vLy6P6bbrpJ48ePV01NjXbu3KlVq1bpd7/7XTRL0tnZqaqqqiGPV1VVpc7OTtvnWrNmje6++25vXggAX+BX7vyXbJYj2+8Fu5nQeG8CecpvJU1+aksKPAs6+vr6dOONN2pgYEAPPPBAzG1LliyJXm5oaNCECRM0bdo07dixQ1OmTJEkBQKBIY9pGIbtfklatWqVli9fHr3e1dWl+vp6N14KgAQy1eGKNwWu+Tq/OOc+u8yV+Xo2vnOdsi0EHACQmCflVX19fbrhhhvU0dGh1tbWmCyHnSlTpqi4uFh79uyRJNXU1OjAgQNDjvv4449VXV1t+xilpaUqLy+P2QB4z67j71VZk920qn77AcpNp1ku231gO+2zbrnMrrOfzcAy3oKTAAB7rn8XRQKOPXv2aPPmzRo7dmzC++zatUt9fX2qra2VJM2cOVOhUEhvvvlm9Jg33nhDoVBIs2bNcrvJAIbBOn7CqRZ/OJwyGubr+RJ4WAOFyOXI2lR2wUQ+BRjxZPrfuUiJg+gcn0wGQDIGfLjloJTLq7q7u/X+++9Hr3d0dKi9vV0VFRWqq6vT3/3d32nHjh36j//4D/X390fHYFRUVKikpER/+tOftGHDBl199dWqrKzUu+++qxUrVuiiiy7Sl7/8ZUnSBRdcoLlz52rJkiXRqXRvueUWzZs3j5mrgCyLdPycOmHJLvaWaifNWm5jfZ5c/6U5UbCQz8FEPNYxHObyKi8Gl+f6+wgA/CpgGIaRyh1efvllzZkzZ8j+RYsWafXq1Ro/frzt/V566SXNnj1b+/bt03/6T/9JO3fuVHd3t+rr6/XVr35VP/rRj1RRURE9/tNPP9Udd9yh559/XpI0f/58rV27dsgsWE66uroUDAY1SpL9KBAAbnCrk+Y0XiPROI5cl41gIkd/JIuyBr1uBR2pvrfIbgDDZ0g6rvBsp34rjY/0JUP3SuWjst2aU7qOS8Hv+fOcxZNy0JErCDoA73jd8Xeq3Tf/0p0PwUc2sxe5Gnh4keFI571EwAG4IyeCjp9K5SOz3ZpTuk5IwR/485zFU6gZewApsnb+++Ncd+u58mmsRoRfxl5k+/nTZbeCebwSu3hlgOlOeJBv70kAyARP1+kAkD8SdbTczjxYAw67cRy5Jlc7+n6WaHyRX2a8AoBCR9ABICl2WYdIh8+rzlyiwei5xI8BR6RNuVpqZWW3ernb2TKyHEAB8tuMUX5qSwr8+D0IwGecylbysfzJC37/oPVLyVc6zGN8rGM9zJmx4QSt1mmgAQCpI9MBICG7zpab63Dkq1zsxOciL6bOtSLgAIDhIegAkBaCDWfZCDasJUVmqXSYc7nkyov3JMEGAN+tAOqntqSAoAMAXOJlsJFsh9qpFC7V76jIKuiFym58iN11M6fzTBkiABB0AEhDvF/VC02kc+5FwOHmuaXjmxy7yRLSva+U39M/A0AqCDoApMzrWatyxWmWv27w8pym0vGNBFKFlu1wWozSepvTfZ0CcQIOIIdRXuUKxjkCSIm1U2b+i/QMZ6G6dJ4rGV4EVLnK+m8Tb3asQg/EAcAJ3ycAEkrmF95C4fb0srlw7nJ1Ot1kxfsRM165FUE3ACSP8iqgQNmt1mztPEX20dFyv9OdC8GGld05yIfyq3grmtvtS2UsU6H9PwHyEosDuiKff7wCYCPeIPB4U61ay0cKaUxHvn1Quvnvlm/nJhECDgBID5kOoMDYZS2slyXnX3mdbstHXnWoC+X85SP+7QAgPQQdACQNLa8y/6LrtOBcPnfA8iHgyMRK3VLstME5mvV3FRkOIM8we5UrCDoADFlLIFHZldO+fOHHkqHhnO9MrBFhne2qEIOPHO0HAEBGEHQAiAk0Uh0omy8yEWikek5zdXFAAg4AgBVBBwBJQwMPZEemFgdkhWz3cB6BPEd5lSsIOgDEKLQsR6ZKqVKZljXXFdIYjxz97geAjPNj6TKALCm0NTjy/QPQ7vVlKsjJ94DDbz98AoDfkekAUJCyHXCk2/lPtjOfzMBur0usEp3jXA1KCDaAAmPIXx9YRrYbkB6CDgC2zLX/+SaTAcdwz59dW+MFHk6vzY+ZB3Nb/dY2q0jZIQEHAKSHoANAwfBLsJEoEEmmncN5LX4cUO7HoMjML+cJAHIVQQeAuMwdrVzNevgl2HDiZfvMnWWnjn2mFhFMxG+BR7bPBwCf8NsgLj+1JQUEHQCSlmiV8nyU6DWm+tlvfTyvA47I32Ta6Yfgwy+BR45+pwOAbxF0AEhJLo31SLdDn8prG855SKd9yQQQ8dqUTKferVIicztSebxsBx4EHADgPoIOACnJldr2TAQcw3nsRO2zjrtwepxUnjuVf7d0/p0TjWPx8/vGz20DkGUD8kcKNsJPbUkBQQeAlJjX8siFbEcqsvV6MrVwYKoDyBOVW6XavlQCj0xnO/xQWgYA+YygA0Da/DjIPN2SpUwyt9HL53aakjbVDn2ybXS7457pwIOAAwC8Q9ABIG94HXCkcmwqA7dTkerigF6Kl6HxewfemqljHQ4Ajpi9yhUEHQBckUsDzCO8HDDudHyiYMApqDjN5nKi4yLt6Dc97nDX6MjGv68XGY9cep8CQD4g6ABQkDI1Q5WZXVlVpENtF1Sk8nhWRZbL5sAjFalM8ZvMY6ca6HidsbG2hWwHAHiDoAOAZzI52DyVzqmbAUeqnWK7Gazc7lgn8/rMmY8Ip2xVMu1LdYyIXzr21rb4pV0AfITyKlcQdABwlbnjmqmFBL36NdyrgGM463PYDdZONPuVdbyCU6ffaUpfp8cfzveeHwKPyHmh1AoAvJeJsYYACpAfZ7aSUp+JyUky62xYt9NM97O7vSjObYpzjNNzW19LpA3xXlNks2u3031zdMr4Iecu20EQAKSisbFREydO1Lp167LdlKSQ6QDgOvOv2Lm4nke6AUeilcDNx9gdm+hcWe+baPC+ecC5XWbBbtC53X47yazxkWudeKfMEIAC59PFAdva2lReXp7dtqSAoAOAZzLRaXN7LIebJVV2nfrhlFhF7hf57kt2hiy78RtO94lXSpXOIPR47wE/dOytJWdF8ke7ACDfEHQAcIW502Y3diAXMh7DCTgS3dcu4LDeJ9nF9ZIp7bJjF3jYlWFF9Fv+Wve7IdsrgTOQHAAyg6ADgCucOqjxyomGy80sh9sBh3Ugtjng8PKcxGuTeepcp0HjUmxGo99mn1W8qXiTzRpkM7tg/vfot/kLoMAxe5UrCDoAuC4Ts1ZlchaM4QQc1v3JDOq22z9gui1RAOC032mKXCXYlwwvFvDLJOt7lYADANxF0AHAdX4rpRpOW1IdNO40ONu6z5ptSKRYicdy2D2/VeQxrIsFRn7Is163k2qHfLjZjkwEM3algAQeAOAegg4ArrKO6ci24Qwe9yLgsN6WaAC33WMlWmPDqT1SuANvDTisnXrr4yfb6U9UZuX0+In2Z4KX41YA5LjIB6df5GhamaADgKv8lOVwY7aqZI+PF3CYMxvmsirzPrvHizcuI1GbzMGMeVxGv2Lbmsz3qHnq3XjtiByb6PswlcHjmfpuzfaAdgDIdwQdAFxlHoSb7XYMh12WYDgBh/V2uyDEabxHokHo8dpqDjh6JfWZricqn0q3vMguSEnEL519gg8A8AZBBwBX2E29mq3gI9nndKNtyQ4yNwcY1oDDHEgUmx43sr/YdJvdfezaZB6b0Te4mYOBSPBRpNjgwJqpMAce8W6zk8ysWNbL2cJsVQAc+XRxwFxD0AHAFdZxHH7IdsQznMHYTsfYBV7m48zBhl22w/y3WFLJ4PViSSNN152Oizxnn8Ln/8Tg5RODWyRoiGQ5IgPH7cqtrGNz4gUe5vs5cQo4/MLP71UAyAcEHQBcYy7L8boTZ+0sJyvddqUy0Nt6rDXQkGKDBrsgIhJojJRUOrhvpKTRkk6XVCGpbHAbY3ocSTom6ejg9qmkzsEtZGpTv2mT7LMdqfzy73R+4t0/2fEkmfxRj2wHAHiDoAOAa/yy+rjT8yfTpuEGMnZjLazjNiJbsU6VU0WCikiwMVqngo7TJdUNbvWDW0mdpHGDV84cfMCgwj30TklHJP1Z0rvSH3qkdyX9QdIHg88XyXJExnokGlxuzXZExAsI7AIav3foze0jAAEgicUBXULQAWBYrAPHc/SzUFLqU+Q6HWc3CNyazYhsRToVXBQrnLUYPfi3QtLfSpog6fOnSZom6WJJUwZvqB48cKROnfg+hVMdJxROc+yTPv+W9PnnpPb3pTck/U5DAwHrdbtsh10nPN5ChlZ+G8dhx/wa/dpGAMhFBB0AhiVeB83rjEeqJVZ27Ul0/2TXv3C6n3XweGSMRqSMKlI+ZQ02JkiaLOnsaklXSZovaYakvymRVCPpc4PPcHJwOxG79Q5If2N6oK9IX/zfUt2G8HO/oXCGI5JZiTAHGk6Bhx1rVsPpuvWyH1iDKjIcAOA+gg4Aw2LtoMUb2+C2eJ1/NwKeZO5vnUXKmuUwDxaPBBpFih2jERmXEZR0rqSLJJ1XKelaSd+U9BVJI6YonNoYqfBH90nTFkltdA9uJ6SSbqnkhDRyIHyXcklnSlVjpav/h/TZ4D0ig8qtM1xJQwMPWW5z6pxb1wXxO7vXFtmfC+0H4DFmr3IFQQeAYfFzp2w4gUcya3I4HWMdSG+dpSqSXSgzbTWKJiQUuE7StyXNPU3SpZLOUjizYQ04BgMMjTA944jB/YNKIqGFwiVZN0g1ndJF/yr9VeHgI5LxsGYjzBkPM7sgJLI/UcCR7rof2fiO9fN7GwByDUEHAFdlexB5Ional0ywYT3OLssRuW4XbIzRqWDjdIVjgZmSzhsv6XZJ/6+kMy5ROO9RqXAOJPJxfVJSj6RPFA44/qrwqHGzkbFXiz87dble0tekL78o7eyWPhp8lB6dCjLszpE1CLEbE2I3cNwqnexBpmevkgg4AMBtBB0Ahi2Xg4xE40JSXY8jXpYjMlDcHHA0KJzLqGqS9D1Jl1cN7jlH0liFMxyR7MYRSX+S9IbU9W50dip1KZwqqZF0vqQzKnQqMyIpcFIq7g2fjNGDx1wpjXtGekfhqXWLFc54RNpuzVTYBQvmtT7sBqPb8XPAYUUAAkASs1e5hKADgKv8mOlId+HCZAMOu+tOWY7ROrXWxmRJV0gqXyxphaQLvjC49xyFA45IOdUnkn4rdf9/0mZJL0lqV3j+248VHoxRqvDUubMkfeNT6epPpVF1g/cfORh4DIQHk5RJOj88zvz0wYeIjDdx6uQPWC6nE3CkIxulVTn6fQ4AvkbQAWBYrDP++C3gcGJuazKzYCUKMMz7rVPjRgaNj9GpTMdEDQYct0i6U9LZX1I4BXGOTgUcJyT9Vur6N+lJSc9J2iEd/iQcb3yqcJKjT9LIPqmiW/p8hzR2h8Lpi0UfSYG6wcc6KRV9Fm7cSEn14RhltGKn8LUbw2Geocwu4PBSJgIOpymfCT4AwD0EHQDSlo9rGqQaOJnLqqz7rWVVkSxHNOD4e4VLqs7+gsLjN2oGj5TCYcVD0qOfSv8i6Q3pjyHpfYXjich8VZFOebHCawIekDTrPanmXyRNl3SBFP6oHyEFTpOKBqKNOk3hgCgyo5Z5MLk1oIgXcOTS4n927ALKXHwdADxCeZUrCDoApM36uef3LIc1oHDKdkT222VAnFYcj/y1K6uKBBtjFB40frGksdcpHHCc+7cKLy1eqWhGQm9Ie/9FulfSc9LeD6WdCo/oMK/9Zw4CSgaf46jCoz5q3pX0oQaDjsjjnpCKBsd2fBw+NpLZiAQcfabHjZwD8/MkGjSeqMOeaoc+0+VVBBwA4A2CDgDDkuuDbc3jPVJZbDBewBFZaTxSVhXZqiU1SjpnmqQ7JF1YofB0uKfr1GDxt6Rt/0f6sWT8H+l1Sfs0ZOm/aHAQeQ2RthcrPA2uTkRaOiJ26+8N3/mjcBBzQkMHv0v2gYYbcuF9kuvvaQDwI4IOAMOSaKXqXGHNbjhdN7MLOKzjOSJjOU5XuKyqMSjpNoVXF4+WU41QuPu/XXr5VelH0qFXpdcUzkZEFvKLrO0RuV6i2MAg0t6RGjxwtHRqMLrp4/6IpHfDA8hPaGhgYc5iWMussjGbVKayHQQZAGyxOKArCDoAuCKXBpEn4hR4SLGv0/qazYGHdbaqOklflKRFkq6UVFKlU9PhnpD0obT9VekuqfN1aYvCsUH/4N/pCgctRYPX/0PhoMEc4BQpOkY8fPAEKRzuRBYSHHyqPZK2nAo6Iq/ZHMD0yr4Tbg0AMhEQZOr7lQwHAHgn2UoCAHCUjc5aOh9eiYIiu1/0E81mZB3HEZmtKjKWI5KdmCDpnPGSmhWus4pZXfyI9Emr9M9S12DAcUjhLMfHCudCpkiq+pk0NiSde1Y4COkbbEMkwDlN4YDjnKCkeZKqztapTIoknQhHLK9Iu3rCg877Brde0+VEA8Mjrzcd6dyPLyoAyH18lgMYtmyUWGXi12/rmAk5XJeGBh7msRw1CicedO3ghRElOhVwnJD0O+lxSc9Jbyg8C9VnCg8Y75Hpg/qbksrvkiaeemxzCVeNwsGI5klaKEkXKmYl896B8Ij058PJjkimI1Ku1af4YzjMYz9Os1z2UqYrCfIlYwfAJdZUcLa3HC2vIugAMGy5UpYynPYlWpfCXFYVCQQif+sknVMp6SuSKqToSuE6KemYtP8j6f9If/wsPGi8z7T1K7wex6eS9AVJV/8XHfu19JHC6wFWKFxAVTH48GMukbRSUvmVih2gfkL6q6TnpD98FF7M/KhiB6b3yjnwcPqysAs83O60Z+KLivU5AMBbjOkAMGzmTEemxnZk6heTZBcONG/FpsuRDISmKzxfbknkXicVLa3aIak9vDJHpMN/TIOL/g1eflrSrANSxf8Jr9XxB4UX94uUb82SVPUVSf8o6YuXSPp/FB7HMTieo/fT8ErmT0rvKBy0WLMcdquLO73eyFS7AzbXc5X1fQwAcE/K39uvvvqqrrnmGtXV1SkQCOjZZ5+Nud0wDK1evVp1dXUaNWqUZs+erV27dsUc09PTo6VLl6qyslJjxozR/Pnz9eGHH8Ycc/jwYbW0tCgYDCoYDKqlpUVHjhxJ+QUCyKxMlaZkq3Nrft5IdiOyRa6bjdbgEI4vKpx4kHQq4DghqTs8uOLTcHARmSQlch77FI5TjkraLOl5hbMUpysc0PyNpKskVV0h6b9KuvxKhSOcSIbjmKTO8ECRtdJve6TdOrXAYCS4iWQ6zIGH0xgW81/rfqfFEv0sEijnSsYOQIZlu5zKbstBKQcdx44d0+TJk7V27Vrb2++9917dd999Wrt2rdra2lRTU6Mrr7xSR48ejR6zbNkyPfPMM9q4caNef/11dXd3a968eervP3UWFy5cqPb2dm3atEmbNm1Se3u7Wlpa0niJADIl05+F2Zq+NfLXnN2wipyHMQpnJHSWwimQqJOntsFeb2QAemQbrVMz30Yep1pSUOFyqmmSLquURt0s6X9IuuR6SZfq1GKARyR1Sru6pLXSH9+W3lbsWI5IsGEONMwBR+Sv07mO9yWSK4FHkcNlAIB7Ui6vam5uVnNzs+1thmHoZz/7me68805de+21kqTHHntM1dXV+uUvf6nvfOc7CoVCWr9+vZ544gldccUVkqQnn3xS9fX12rx5s6666iq999572rRpk7Zt26bp06dLkh5++GHNnDlTu3fv1vnnn5/u6wXggWyVo2SixMr8y7010DA//4DpbyQ70a9w8HBGscIRQ7RHawo4pPCUU2dJdR2nVh0v1qnZqSKzYBUrHIScK2nSaQonNL4paelpkv5B4ZXNT0gKKfxIf5U6/iz9d+ngv0tbJf1F4eyGXVmV3YB5OeyzniOnMis33hteB5fm9kX+bXP0h0QA8C1Xv7M7OjrU2dmppqam6L7S0lJdeuml2rJliyRp+/bt6uvrizmmrq5ODQ0N0WO2bt2qYDAYDTgkacaMGQoGg9FjrHp6etTV1RWzAfCWXTlKrv9S7FRSZA44rFPGRjrs5oHYkVKlSOAQ07M3enWqvGpkOGXxNemLo6XJCscgYxWOU2oGr0+QdLGkr5dKk76k8NiNDZKWXinpHkmRH2OOSfpE0p+kv/5e+qnU9Wh4OMcfdGqxQXNZlV2wkWzA4STewHK/vUfMgWQOVy4A8MqAD7cc5OpA8s7OTklSdXV1zP7q6mp98MEH0WNKSkp0xhlnDDkmcv/Ozk5VVVUNefyqqqroMVZr1qzR3XffPezXACB51s5ZpjuTyQzytko00N1psb94AYfVaQpnOnoV7twf7JOqOhU7NdSIE4quFF5ZId3+qVQmXfgf0oW7pd7PTg0kLwoqHHlMVLh6qlnS+EskXabBYeoKBzCHJH0o6XfSn3ZI/1069vPwWJB3FTuO44ROlVWZAw/z63EKOOJ1yp2yBLmUPciltgJArvBk9qpAIBBz3TCMIfusrMfYHR/vcVatWqXly5dHr3d1dam+vj6VZgNIkd9+sR6uZAIOM6cfm4oU7tD3KFzktE9S1QcK9/bLFI5IjF4pEAk8PiedO1L6x4+kv5P0rlTycXhTjcJVUxMknVeucNTxRYVHd0QGi3frVMDxW+m3e6V7pcPPhzMc7wy24zPTlkxZld31VLMd8c6R3zr21nEskj/bCQC5yNWgo6Ym/ItbZ2enamtro/sPHjwYzX7U1NSot7dXhw8fjsl2HDx4ULNmzYoec+DAgSGP//HHHw/JokSUlpaqtLTUtdcCIDnmaUZzQaJ2Oo3hsN7f2hEd0Klx4ubZoD5VeLapqa8pnKE4ffDA0xSb7dBIacR50hdGSF8YHOuhEwpHHX8jqXJwGxM+VicUMzuV3pd6X5Iek/Q/pL/sDE9YtWewDUd1al0Op7U47Mqq4gUcyWT4443vSLZDn82peAk4AEQHq/lFjn4wuXoKx48fr5qaGrW2tkb39fb26pVXXokGFFOnTlVxcXHMMfv379fOnTujx8ycOVOhUEhvvvlm9Jg33nhDoVAoegyA7LNONZrLzJkMuzIqKf6itFJsiVIk8DiicNDR+aakVxSOACKphpMDik6bqxODjzJC4eBinMIjPM4dvB6ZkeqYwlmNvyo8+e1bkv5d2vaS9B1JS6VtO6UXFS6pigQc5rIqczutgYeUOOBwKilOZjxEOuM7MhVwJJoOGACQvpQzHd3d3Xr//fej1zs6OtTe3q6KigqdffbZWrZsme655x5NmDBBEyZM0D333KPRo0dr4cKFkqRgMKjFixdrxYoVGjt2rCoqKrRy5UpNmjQpOpvVBRdcoLlz52rJkiV66KGHJEm33HKL5s2bx8xVgM/kwyByu4DDibUDfJqGdrRP06mg44TCQcdvJC1cp3Di4tLBA0dLGtmbxCfxScUeFCmnape2fyb9b0lPSXs7wiFIZMB4ZNC4OcMRCTYiA90jnAKQyG0RTgHAcH54y3YJUyR4tiuvsrsOAEhdykHHW2+9pTlz5kSvR8ZRLFq0SI8++qi+973v6fjx4/rud7+rw4cPa/r06fr1r3+tsrKy6H3uv/9+jRgxQjfccIOOHz+uyy+/XI8++qiKik51WTZs2KA77rgjOsvV/PnzHdcGAZA91nKjRAO1/STegnaJBotHjokMZrd2zPsV7uifpnC24XeSyj6VrvmxpNslzVF4cHiZwoHHyMgYj25Jnxv8O1Knyq80+IifSIcPhlcx/4/wduj98OO/q/AaHMd0KrvRo1ODxvtMfyOvy2kRQKcOuJ1UO+V2QUY2Aw9rhoMgA0AMv80Y5ae2pCBgGIaR7UZ4oaurS8FgUKMkxR/CDmA4zB21bAQbqdSI2gUV1oAjmcdzehzzgPNihceLR9bYKJP0t5IaFU50jL1C4TEekxXOfpwuqVSnVgiMPFCfwtHDxwqnTHZKek3SVmnvp+ECqz2DN0emwo2swRHJbkQCDbuVxyPjOyKsQUiiLEe8Dnq878V4gU4qj+M2Ag4gswxJxyWFQiGVl5dnuzkxIn3J0NVSeXHi4zOlq08KvujPcxaPJ7NXASgs1l/FMxl8JDttbrKrTtt1cK2PbxdgWe8XySaYg5oPFA4EOiVN3CxN3iydcb7CgcffKjxGPDLIPPLT/0cKT3/1vqQ90sHPwo+zb3D7VKcCjMiYjT7FDhhPJuBINaCId3uO/ghHwAEAHiLoADAs2V6rYzgBh11ZlR1zJzpyH/M4ALtSrMiMS5HgI5K4OKBwJuIjhcuh6nZLZ+4OJzrGKHYAe//gsUcUDi4ODf49othMhvly5LrdoHanjEa6CwCmK9XnyOTsVZRYARiC2atcQdABwDXZKK9KZ4FAWe4Tb8xGMiKBh7ktdpmPE6bj+3RqOt0xCldWWWcCi4wLMWctIoGMXcmUNXNhnhq333L/RAFHoulx08ly+P17MtsD2gEgnxF0AHBdJsd3JAo4ki2rsuNURmXNdtjdz/qcvYod93FscN8JhTMXTuxmmIrMQmUulbIGG+bjB0y3JTNDk13HO5lsg5cBh9fZjlyaAAEAchFBB4Bhs0436pfOW7yAw27qWzuJXo+1zMou82K+LRIQ9Gno4oOJBleb18+wTm8bOa7Pct0uKLFmOVLJcDi1M5czHFLsDFa50F4AGUR5lSsIOgAMmx8//6yBQqLgIdJptvtesd7XGljEe2zzd1VvnOOT6exH2mdX6uQ0INwcoCQKapJtg9v88v7xU8AMAPmGoAOAK/ya7TBLpl1OY0QSBR7xHj9SCuW00nWiaWnNnIIOJ+aAw7oKeby1ONxaBNAvAUWyzJkrAIB7CDoAuMLaWcvG9LkRicqhEh2X7uD0eNkO6+UiDS2FSlW87Ey6WQovVhyPJ5ude6cAg4ADQAxD/poLPEdX2CPoAOCqZFbydstwS2xTzcg4HW8el2Gd8jbCGiCYp9Id7nlK5v7JlFelYzhjOVJ5bi++71mJHAAyh6ADgGus2Y1slVi5Ud6VzqKD5ue3u2y+T+R+qWYW4gV1yWR44o3vcHPFcTdl4nkIOADAWwQdANJi9wu9n8Z1eB14OO1PtNCe+Vf1ROVYRRoayJkHjdvdL9FjOrXP7YAjVzrxudJOAFnULymQ7UaY5OgHF0EHgLQ4BRx+GkCezLiS4bTZKesQr6wqFYke0+o0JRcoWAOXVAOORJK5r5++M62BoJ/aBgD5gqADQE5yc8r0ZAMP87iNyP3s2K1/Yb1fohXM02EeJ2J+TrsFBtN9zkyvx+F1aRXjOQAgMwg6ALgi0TgGt6Uyw5QbGZhk1viIPJd1vzUDYV1M0MruF/dUBuibMzzW8RvJBByJgqlU7jPcYzPB/P7wW9sA+ADlVa4g6ADgOj+VWCXLLjBJddyGU6fcaTHBeJ3ddKZxtb6GRMGGW7NHefX9l6mB6pRUAYD3CDoAuMpuHIUfxnoMtw1O9f7xFupzWigwkvmI19lNNDNVsvezPneiY7zm1849gQcAeIugA4AnzJ18rwKOdBfxS4bTquSR57Xbb3d8KsFXvE6v3YxWqbALOOwyCamOMfGqrMpP63ABKHAD8teHkp/akgKCDgCe8UOGwyyV9rg1JazToHFztiPVx0u2jdYAItlSsGTlY8DB2A4A8AZBBwBP5XLgYXdft58/1UHb1tmo4kkUWCQzpbAbcqUDX2RzebgZJgBAGEEHAFdler0OL0us3Opk2p0Pu3YnE0ykmwWIN2uV0+tMZcasRM+bLL9UDaQzkB9AnmL2KlcQdADIeW4FHukuapdKydZpca5nUiYW8MvVgENyJ+ACAJxC0AHAVfnUQUt13EYqj2uX+XCD3WNn498kV8ZwJINxHgAwfAQdAFyTzbEb5oX4EokXJKTTsRxOSVmyM2GZn8t8rF2HOJ3F/dxApxxAXmL2KlcQdABwhR8Gi6dSquRmB7nf9DdeMJPoHKUzg1U6ryOV0qpEAdVwz2MufXeS8QCA9GWrnBhAnvBDsBHhhw5sog6p0wJ96QQcqUhl3Q27oKbfYRsOP/x7pcNulisAQHxkOgCkzevF/9KRSpmVVxKtSu5Hmfz1PpsD6N1C1gMoIMxe5QqCDgBp8Xtn2i8dWy9Lk5Jlzij4ZSrYXM1yAADSQ9ABIGV+DzgizB1bPwQgbrKucG4V77Zs8VNb3MLUugCQHIIOACnJlYDDKtEK35kISiJZj3jPl6hjbl6RPN6xqSwCmAn5GHAAKBAD8tevCjn6gUrQASChXA00kpGtMqxsrUieaJ/bcvS7EQDgMoIOAAXP68Hn6a7h4SU//WiXL/ywKCMA+BVBBwBbfuskey2b2Y58W7Sv0LMbBBtAnhmQv2avytEP2XwbWwkAacnUgrPWDqmXz+nGWhpIXaEF7ACQDDIdAKLoLHk3xiNTJVaprDbuhRz9AS5l5n9P8+rtAAB7BB0AMsKP4xqc5OIYD69XNE+kUIKNCOu/nzkAKRrcyDIBecJv/5n91p4kEXQAkDT8TnCijnSuBBxmmV7dPN73iB/XgyiUQMOa1bC7HLludxkAQNABFDy3goFcDCqyyVySk6mSKDrC6bEGGXYBtvk2zjMADEXQARQwAoXkeZX1yFQH1c3nKZQMh5Xd/xeyG0AB8Nt/br+1J0kEHQBsJfpV18ypxCRfgpoBm8u5NPWfG99PhRpoWPVb/gIAkkPQARQYa4BgnYHH6XinQMPcCSuy3JbPzJ3wZAOQbAymp3PsLnMgzrkFgOQRdAAFyNpZitd5SnSbOdDIpRmq3JTKNLt+mjo3GWQ4TqGUCihQLA7oilyqEADgkmRnQkrmtnwsp0pHKt8BXndY3SqnytHvtWGzCy7MU+GaFfJ7HgBSQaYDKDCpdkiTKSPxYnG0XMyaDDfjYQ3gUjmvZDa8kWg8ExkPAEgOQQeAuBJ1qrxc5K6QAo9kskqJOsDpItBIHWM6gALit//sfmtPkgg6AKTNjYDAblBuPnTokg0+nF6n3X63zwnBhj2n9zVjOgAgfQQdALLCrgNn/dU/17IcEQOWv34cPEfAEV+uvwcBwG8IOgCkbDgdsWRLpujseSMfgg2vA4JcLOsD4CFmr3KFH3+AA+BTww027C4nuk8+lLH44fsh12ejspbfeTW+JfL45sfNh/cgAGQbQQeApAxn4b9kV3G2dvDy6dfmXO7w+4HTgHsvggK7hTCLLNcBAKmhvAqAJ4bbEXQaSJ3LHb5UZrZy6/nySaLZvuyChXTYBRgMIgcKmN8+TP3WniQRdABIKJXOm1cdMmuHM1dXQDd/V5wm9web5+h3UdKS+TePvDcSBSHJjg0hyACA4aO8CoCjZBelS7Z8Kt02OP3KnGsBh5U14Eg3YBhQ7o/ZsBNvOuF45X7x7mddYdzpGPOxuf4+AwA/INMBIG2ZKjmxy3DkU4dwwHL5NOVfAJGseAGF+X0wnEDXujZMvECWLAcA9Usyst0Ikxz9giDTASAuu05YJmf0ifd8+RBw2MnR7xNXOAUDMu1za1HKePsL6f0GAJlA0AFgiMhsPdbOvl9+9TXPJuRlaRcyzxpU2L3/vP63dnoe3mMAkD7KqwAMEa/jl23W0piIXB1Yjlh2/7YAkFV+Sz/7rT1JItMBwJafO35OazVQj59bzBkFp38rp5moAAC5haADgG+lm7XIp0Hm+S5R+R7BBgDkB8qrAPhWKh1Ou7Icyq38yzpjFGNzAPgWs1e5gkwHgLyQqLNKZ9YfrNMdxxvDQcAIAPmDoANA3rAOfjd3aBmg7A/Wmcfi/Zvw7wUA+YPyKgB5w2mKU2vgYV1sEN4wB35Os6ERDALwPcqrXEGmA0DeKnK47Nf1R/KFXQbDadVvzj0AFAaCDgB5y24WK7vLZDuGxymAy/Zq9gAA/3A96DjnnHMUCASGbLfddpsk6eabbx5y24wZM2Ieo6enR0uXLlVlZaXGjBmj+fPn68MPP3S7qQAKRL/sO7yJfpFHcuzK1VIpYSPoA+BrAz7ccpDrQUdbW5v2798f3VpbWyVJ119/ffSYuXPnxhzz4osvxjzGsmXL9Mwzz2jjxo16/fXX1d3drXnz5qm/n+4AAHfEK7eCvWTPkXUAv1uPCwDIXa4PJD/zzDNjrv/0pz/Vueeeq0svvTS6r7S0VDU1Nbb3D4VCWr9+vZ544gldccUVkqQnn3xS9fX12rx5s6666iq3mwyggDgNXKbUKjG7c2M3WNw6cxgAAJ6O6ejt7dWTTz6pb33rWwoEAtH9L7/8sqqqqnTeeedpyZIlOnjwYPS27du3q6+vT01NTdF9dXV1amho0JYtW7xsLoAC4LQInVMHmY5zmFM2yCm4iBfYAUBOGdDQOt1sbjlaXuXplLnPPvusjhw5optvvjm6r7m5Wddff73GjRunjo4O3XXXXbrsssu0fft2lZaWqrOzUyUlJTrjjDNiHqu6ulqdnZ2Oz9XT06Oenp7o9a6uLtdfD4D8lmjgeSFzynLYXU8lEAEAFAZPg47169erublZdXV10X0LFiyIXm5oaNC0adM0btw4vfDCC7r22msdH8swjJhsidWaNWt09913u9NwAHkvXplVKuMRMBTBBQDAyrPyqg8++ECbN2/Wt7/97bjH1dbWaty4cdqzZ48kqaamRr29vTp8+HDMcQcPHlR1dbXj46xatUqhUCi67du3b/gvAkDeSracqtDW80j0Gp2mvSVAA5C3sj1TFbNXxffII4+oqqpKX/3qV+Med+jQIe3bt0+1tbWSpKlTp6q4uDg665Uk7d+/Xzt37tSsWbMcH6e0tFTl5eUxGwAMh9P4j3wWL3iIDBA3b+bbAABw4kl51cDAgB555BEtWrRII0aceoru7m6tXr1a1113nWpra/WXv/xFP/zhD1VZWalvfOMbkqRgMKjFixdrxYoVGjt2rCoqKrRy5UpNmjQpOpsVAGSCeTYm8/VEklmbwq+cXqs5AGNmKgBAqjwJOjZv3qy9e/fqW9/6Vsz+oqIivfPOO3r88cd15MgR1dbWas6cOXrqqadUVlYWPe7+++/XiBEjdMMNN+j48eO6/PLL9eijj6qoKFe/xgHkonilVfE+jXL9k8oug2EONAg4ABSUfknOw4ozz8h2A9ITMAwjR5seX1dXl4LBoEbJX+8TALnNaaXtXM5u2Ek0E5UZmQ8A6TIkHVd4nTa/lcZH+pKhz0nlPupMdhlSsNuf5yweT2evAoB849S5zoeAI91Zuwg4AACJEHQAQJqsq3HbrcqdC6zBRqKyMoIMAAWF8ipXeLoiOQDkE2sQkS8dcPNMVMlMmQsAQKrIdABAkuKt4WG3zy4D4icEEACATCHTAQAuSLS6uZ/ZBUR+DJIAICuyvRCgTxYH7Ojo0Jw5czRx4kRNmjRJx44dS+n+ZDoAwCPpDszOhEQrredCsAQAyJybb75ZP/nJT3TxxRfr008/VWlpaUr3J+gAAI/5sbwqV7IwAIDs27Vrl4qLi3XxxRdLkioqKlJ+DMqrAMAjRZa/fuLHQAgAfKnfh1uKXn31VV1zzTWqq6tTIBDQs88+O+SYBx54QOPHj9fIkSM1depUvfbaa9Hb9uzZo8997nOaP3++pkyZonvuuSflNhB0AIAL7Drwfl3B22/tAQB469ixY5o8ebLWrl1re/tTTz2lZcuW6c4779Tbb7+tiy++WM3Nzdq7d68kqa+vT6+99prWrVunrVu3qrW1Va2trSm1gaADAFyQS1PNJjs9LgDAv7q6umK2np4ex2Obm5v1k5/8RNdee63t7ffdd58WL16sb3/727rgggv0s5/9TPX19XrwwQclSWeddZYaGxtVX1+v0tJSXX311Wpvb0+pvQQdAJABlDIBQI7KdimVQ3lVfX29gsFgdFuzZk1aL6+3t1fbt29XU1NTzP6mpiZt2bJFktTY2KgDBw7o8OHDGhgY0KuvvqoLLrggpedhIDkAZABjKAAAbtq3b5/Ky8uj11OdTSrik08+UX9/v6qrq2P2V1dXq7OzU5I0YsQI3XPPPbrkkktkGIaampo0b968lJ6HoAMAAADIMeXl5TFBx3AFAoGY64ZhxOxrbm5Wc3Nz2o9P0AEAGeCnLId5/RDGdQBAAsbglqcqKytVVFQUzWpEHDx4cEj2YzgY0wEAGeDHzr0f2wQAyKySkhJNnTp1yGxUra2tmjVrlmvPQ6YDAAAAyGPd3d16//33o9c7OjrU3t6uiooKnX322Vq+fLlaWlo0bdo0zZw5Uz//+c+1d+9e3Xrrra61gaADAAAAcJDmenyeSactb731lubMmRO9vnz5cknSokWL9Oijj2rBggU6dOiQfvzjH2v//v1qaGjQiy++qHHjxrnUailgGEZeVql1dXUpGAxqlKRAwqMBwHt+GNfhpy9OADAkHZcUCoVcHRTthkhf8lNJfmpZl6QK+fOcxcOYDgDIAL8N2vZDAAQAKByUVwFABvhtnQ4/BUAA4Gf5UF7lB2Q6ACBD/PBF4afABwBQOAg6ACBD6PADAAoV5VUAkCHZLrHK9vMDQC4aGNz8wk9tSQWZDgAoEAQcAIBsIegAgAzyw7gOAAAyjfIqAMigbJdXAQBSw+xV7iDTAQA+k+wXSq5+8QAACg9BBwBkUCqBQr/pr90vbalmTRjTAQDIFsqrAMBnzMFBZMYp88xT6cxCRVYEANLD7FXuINMBABk0nOxEv2lfMkGE3+qQAQCFi6ADADIomUDAfEzkcpFigw1r8NJv85dyKgDIX42NjZo4caLWrVuX7aYkhfIqAPAJczBil82wBhLWAMQu8LA+LgAgNX7LGkfa0tbWpvLy8qy2JRVkOgDAR6wBhPU2py8+u6wGmQ4AgF+Q6QAAH3EKKvrjHJPMfQAAyCaCDgDIguHORpXsYHIAwPAMyF+ft7k6exVBBwBkSbzsRSr3BQDA7xjTAQAAAMBTZDoAAAAABywO6A4yHQAAAAA8RdABAAAAwFOUVwEAAAAO/Lo4YK4h0wEAAADAUwQdAAAAADxFeRUAAADggPIqd5DpAAAAAOApgg4AAAAAnqK8CgAAAHDA4oDuINMBAAAAwFMEHQAAAAA8RXkVAAAA4IDZq9xBpgMAAACApwg6AAAAgBzT2NioiRMnat26ddluSlIorwIAAAAc+HX2qra2NpWXl2e1Lakg0wEAAADAUwQdAAAAADxFeRUAAADgYED+mjHKT6VeqSDTAQAAAMBTBB0AAAAAPEV5FQAAAOCAxQHdQaYDAAAAgKcIOgAAAAB4ivIqAAAAwIFfFwfMNWQ6AAAAAHiKoAMAAACApyivAgAAABwwe5U7XM90rF69WoFAIGarqamJ3m4YhlavXq26ujqNGjVKs2fP1q5du2Ieo6enR0uXLlVlZaXGjBmj+fPn68MPP3S7qQAAAAAywJPyqgsvvFD79++Pbu+88070tnvvvVf33Xef1q5dq7a2NtXU1OjKK6/U0aNHo8csW7ZMzzzzjDZu3KjXX39d3d3dmjdvnvr7czW2AwAAAAqXJ+VVI0aMiMluRBiGoZ/97Ge68847de2110qSHnvsMVVXV+uXv/ylvvOd7ygUCmn9+vV64okndMUVV0iSnnzySdXX12vz5s266qqrvGgyAAAAMATlVe7wJNOxZ88e1dXVafz48brxxhv15z//WZLU0dGhzs5ONTU1RY8tLS3VpZdeqi1btkiStm/frr6+vphj6urq1NDQED3GTk9Pj7q6umI2AAAAANnnetAxffp0Pf744/rVr36lhx9+WJ2dnZo1a5YOHTqkzs5OSVJ1dXXMfaqrq6O3dXZ2qqSkRGeccYbjMXbWrFmjYDAY3err611+ZQAAAADS4Xp5VXNzc/TypEmTNHPmTJ177rl67LHHNGPGDElSIBCIuY9hGEP2WSU6ZtWqVVq+fHn0eldXF4EHAAAAhoXFAd3h+TodY8aM0aRJk7Rnz57oOA9rxuLgwYPR7EdNTY16e3t1+PBhx2PslJaWqry8PGYDAAAAkH2eBx09PT167733VFtbq/Hjx6umpkatra3R23t7e/XKK69o1qxZkqSpU6equLg45pj9+/dr586d0WMAAAAA5A7Xy6tWrlypa665RmeffbYOHjyon/zkJ+rq6tKiRYsUCAS0bNky3XPPPZowYYImTJige+65R6NHj9bChQslScFgUIsXL9aKFSs0duxYVVRUaOXKlZo0aVJ0NisAAAAgE5i9yh2uBx0ffvihvvnNb+qTTz7RmWeeqRkzZmjbtm0aN26cJOl73/uejh8/ru9+97s6fPiwpk+frl//+tcqKyuLPsb999+vESNG6IYbbtDx48d1+eWX69FHH1VRUZHbzQUAAADgsYBhGEa2G+GFrq4uBYNBjZIUf4g6AAAAssGQdFxSKBTy3XjcSF/yN5I+l+3GmHRLukz+PGfxeD6mAwAAAMhVhk7NYOWHLZItaGxs1MSJE7Vu3TrPXrubPFmRHAAAAIB32trayHQAAAAAQASZDgAAAMABs1e5g0wHAAAAAE8RdAAAAADwFOVVAAAAgAPKq9xBpgMAAACApwg6AAAAAHiK8ioAAADAQWRRPr/wU1tSQaYDAAAAgKcIOgAAAAB4ivIqAAAAwAGzV7mDTAcAAAAATxF0AAAAAPAU5VUAAACAA8qr3EGmAwAAAICnCDoAAAAAeIryKgAAAMABiwO6g0wHAAAAAE8RdAAAAADwFOVVAAAAgIMB+WvGKMqrAAAAAMAGQQcAAAAAT1FeBQAAADhg9ip3kOkAAAAA4CmCDgAAAACeIugAAAAA4CmCDgAAAMBBvw83SWpsbNTEiRO1bt06z167mxhIDgAAAOSYtrY2lZeXZ7sZSSPTAQAAAMBTZDoAAAAAB+aSJj/wU1tSQaYDAAAAgKcIOgAAAAB4ivIqAAAAwAErkruDTAcAAAAATxF0AAAAAPAU5VUAAACAA2avcgeZDgAAAACeIugAAAAA4CnKqwAAAAAHlFe5g0wHAAAAAE8RdAAAAADwFOVVAAAAgAND/lqQz8h2A9JEpgMAAACApwg6AAAAAHiK8ioAAADAAbNXuYNMBwAAAABPEXQAAAAA8BTlVQAAAICDAflr9io/tSUVZDoAAAAAeIqgAwAAAICnKK8CAAAAHDB7lTvIdAAAAADwFEEHAAAAAE9RXgUAAAA4oLzKHWQ6AAAAAHiKoAMAAACApyivAgAAABywOKA7yHQAAAAA8BRBBwAAAABPUV4FAAAAOGD2KneQ6QAAAAByTGNjoyZOnKh169ZluylJIdMBAAAA5Ji2tjaVl5dnuxlJI+gAAAAAHAzIXyVNzF41aM2aNWpsbFRZWZmqqqr09a9/Xbt374455uabb1YgEIjZZsyYEXNMT0+Pli5dqsrKSo0ZM0bz58/Xhx9+6HZzAQAAAHjM9aDjlVde0W233aZt27aptbVVJ0+eVFNTk44dOxZz3Ny5c7V///7o9uKLL8bcvmzZMj3zzDPauHGjXn/9dXV3d2vevHnq7/dTrAkAAAAgEdfLqzZt2hRz/ZFHHlFVVZW2b9+uSy65JLq/tLRUNTU1to8RCoW0fv16PfHEE7riiiskSU8++aTq6+u1efNmXXXVVW43GwAAABiCxQHd4fnsVaFQSJJUUVERs//ll19WVVWVzjvvPC1ZskQHDx6M3rZ9+3b19fWpqakpuq+urk4NDQ3asmWL7fP09PSoq6srZgMAAACQfZ4GHYZhaPny5frKV76ihoaG6P7m5mZt2LBBv/nNb/TP//zPamtr02WXXaaenh5JUmdnp0pKSnTGGWfEPF51dbU6Ozttn2vNmjUKBoPRrb6+3rsXBgAAACBpns5edfvtt+v3v/+9Xn/99Zj9CxYsiF5uaGjQtGnTNG7cOL3wwgu69tprHR/PMAwFAgHb21atWqXly5dHr3d1dRF4AAAAYFhYHNAdnmU6li5dqueff14vvfSSzjrrrLjH1tbWaty4cdqzZ48kqaamRr29vTp8+HDMcQcPHlR1dbXtY5SWlqq8vDxmAwAAAJB9rgcdhmHo9ttv19NPP63f/OY3Gj9+fML7HDp0SPv27VNtba0kaerUqSouLlZra2v0mP3792vnzp2aNWuW200GAAAA4CHXy6tuu+02/fKXv9Rzzz2nsrKy6BiMYDCoUaNGqbu7W6tXr9Z1112n2tpa/eUvf9EPf/hDVVZW6hvf+Eb02MWLF2vFihUaO3asKioqtHLlSk2aNCk6mxUAAADgNWavcofrQceDDz4oSZo9e3bM/kceeUQ333yzioqK9M477+jxxx/XkSNHVFtbqzlz5uipp55SWVlZ9Pj7779fI0aM0A033KDjx4/r8ssv16OPPqqioiK3mwwAAADAQwHDMIxsN8ILXV1d4eyKJPuh5wAAAMgmQ9JxhZdY8Nt43Ehf8m5JI7PdGJMTkn4kf56zeDydvQoAAADIZcxe5Q7PFwcEAAAAUNgIOgAAAAB4ivIqAAAAwAHlVe4g0wEAAADAUwQdAAAAADxFeRUAAADggMUB3UGmAwAAAICnCDoAAAAAeIryKgAAAMDBgPw1YxTlVQAAAABgg6ADAAAAgKcorwIAAAAcsDigO8h0AAAAAPAUQQcAAAAAT1FeBQAAADhgcUB3kOkAAAAA4CmCDgAAAACeorwKAAAAcMDsVe4g0wEAAADAUwQdAAAAADxFeRUAAADggNmr3EGmAwAAAICnCDoAAAAAeIryKgAAAMABs1e5g0wHAAAAAE8RdAAAAADwFEEHAAAA4KDfh5skNTY2auLEiVq3bp1nr91NjOkAAAAAckxbW5vKy8uz3YykkekAAAAA4CkyHQAAAIADQ/5akM/IdgPSRKYDAAAAgKcIOgAAAAB4ivIqAAAAwAGLA7qDTAcAAAAATxF0AAAAAPAU5VUAAACAA8qr3EGmAwAAAICnCDoAAAAAeIryKgAAAMDBgPy1OKCf2pIKMh0AAAAAPEXQAQAAAMBTlFcBAAAADpi9yh1kOgAAAAB4iqADAAAAgKcorwIAAAAcMHuVO8h0AAAAAPAUQQcAAAAAT1FeBQAAADhg9ip3kOkAAAAA4CmCDgAAAACeorwKAAAAcDAgf5U0MXsVAAAAANgg6AAAAADgKcqrAAAAAAcsDugOMh0AAAAAPEXQAQAAAMBTlFcBAAAADvrlr1/p/TSTVir8dA4BAAAA5CGCDgAAAACeorwKAAAAcEB5lTv8dA4BAAAA5CGCDgAAAACeorwKAAAAcMDigO7wfabjgQce0Pjx4zVy5EhNnTpVr732WrabBAAAACAFvg46nnrqKS1btkx33nmn3n77bV188cVqbm7W3r17s900AAAAAEkKGIZhZLsRTqZPn64pU6bowQcfjO674IIL9PWvf11r1qyJe9+uri4Fg0GNkhTwuJ0AAABInSHpuKRQKKTy8vJsNydGpC95laTibDfGpE/Sr+TPcxaPbzMdvb292r59u5qammL2NzU1acuWLVlqFQAAAIBU+XYg+SeffKL+/n5VV1fH7K+urlZnZ+eQ43t6etTT0xO9HgqFJIUjaAAAAPhPpJ/m48IbuMS3QUdEIBBbHGUYxpB9krRmzRrdfffdQ/af8KxlAAAAcMPRo0cVDAaz3QxbzF7lDt8GHZWVlSoqKhqS1Th48OCQ7IckrVq1SsuXL49eP3LkiMaNG6e9e/f69k3sJ11dXaqvr9e+fftyqj4wWzhfyeNcpYbzlRrOV/I4V6nhfKUm3fNlGIaOHj2quro6D1sHP/Bt0FFSUqKpU6eqtbVV3/jGN6L7W1tb9bWvfW3I8aWlpSotLR2yPxgM8mGRgvLycs5XCjhfyeNcpYbzlRrOV/I4V6nhfKUmnfPFj8OFwbdBhyQtX75cLS0tmjZtmmbOnKmf//zn2rt3r2699dZsNw0AAAAFYEBSf7YbYUJ5lQcWLFigQ4cO6cc//rH279+vhoYGvfjiixo3bly2mwYAAAAgSb4OOiTpu9/9rr773e+mfL/S0lL96Ec/si25wlCcr9RwvpLHuUoN5ys1nK/kca5Sw/lKDecLifh6cUAAAAAgGyKLA86Wv36lPynpZbE4IAAAAADEIOgAAAAA4Ck/ZYsAAAAAX2FxQHeQ6QAAAAByTGNjoyZOnKh169ZluylJydug44EHHtD48eM1cuRITZ06Va+99lq2m5Rxa9asUWNjo8rKylRVVaWvf/3r2r17d8wxN998swKBQMw2Y8aMmGN6enq0dOlSVVZWasyYMZo/f74+/PDDTL4Uz61evXrIeaipqYnebhiGVq9erbq6Oo0aNUqzZ8/Wrl27Yh6jEM5TxDnnnDPkfAUCAd12222SeF+9+uqruuaaa1RXV6dAIKBnn3025na33k+HDx9WS0uLgsGggsGgWlpadOTIEY9fnfvina++vj59//vf16RJkzRmzBjV1dXp7//+7/XRRx/FPMbs2bOHvOduvPHGmGPy4Xwlem+59X8vH86VlPh82X2OBQIB/dM//VP0mEJ5byXTZ+Czy1/a2tr07rvvRr97/S4vg46nnnpKy5Yt05133qm3335bF198sZqbm7V3795sNy2jXnnlFd12223atm2bWltbdfLkSTU1NenYsWMxx82dO1f79++Pbi+++GLM7cuWLdMzzzyjjRs36vXXX1d3d7fmzZun/n4/LZUzfBdeeGHMeXjnnXeit91777267777tHbtWrW1tammpkZXXnmljh49Gj2mUM6TFP6gM5+r1tZWSdL1118fPaaQ31fHjh3T5MmTtXbtWtvb3Xo/LVy4UO3t7dq0aZM2bdqk9vZ2tbS0eP763BbvfH322WfasWOH7rrrLu3YsUNPP/20/vjHP2r+/PlDjl2yZEnMe+6hhx6KuT0fzlei95bkzv+9fDhXUuLzZT5P+/fv1y9+8QsFAgFdd911MccVwnsrmT5DoX529ftwy0lGHvrSl75k3HrrrTH7Pv/5zxs/+MEPstQifzh48KAhyXjllVei+xYtWmR87Wtfc7zPkSNHjOLiYmPjxo3RfX/961+N0047zdi0aZOXzc2oH/3oR8bkyZNtbxsYGDBqamqMn/70p9F9J06cMILBoPG//tf/MgyjcM6Tk3/4h38wzj33XGNgYMAwDN5XZpKMZ555JnrdrffTu+++a0gytm3bFj1m69athiTjD3/4g8evyjvW82XnzTffNCQZH3zwQXTfpZdeavzDP/yD433y8XzZnSs3/u/l47kyjOTeW1/72teMyy67LGZfIb63DGNon6EQP7tCoZAhyfiyZFzqo+3LkiHJCIVC2T5FKcm7TEdvb6+2b9+upqammP1NTU3asmVLllrlD6FQSJJUUVERs//ll19WVVWVzjvvPC1ZskQHDx6M3rZ9+3b19fXFnM+6ujo1NDTk3fncs2eP6urqNH78eN14443685//LEnq6OhQZ2dnzDkoLS3VpZdeGj0HhXSerHp7e/Xkk0/qW9/6lgKBQHQ/7yt7br2ftm7dqmAwqOnTp0ePmTFjhoLBYN6fw1AopEAgoNNPPz1m/4YNG1RZWakLL7xQK1eujPn1tZDO13D/7xXSuTI7cOCAXnjhBS1evHjIbYX43rL2GfjswnDl3exVn3zyifr7+1VdXR2zv7q6Wp2dnVlqVfYZhqHly5frK1/5ihoaGqL7m5ubdf3112vcuHHq6OjQXXfdpcsuu0zbt29XaWmpOjs7VVJSojPOOCPm8fLtfE6fPl2PP/64zjvvPB04cEA/+clPNGvWLO3atSv6Ou3eUx988IEkFcx5svPss8/qyJEjuvnmm6P7eF85c+v91NnZqaqqqiGPX1VVldfn8MSJE/rBD36ghQsXxiyKddNNN2n8+PGqqanRzp07tWrVKv3ud7+Llv4Vyvly4/9eoZwrq8cee0xlZWW69tprY/YX4nvLrs9QyJ9d/ZICCY/KnFwtr8q7oCPC/IurFP4PZN1XSG6//Xb9/ve/1+uvvx6zf8GCBdHLDQ0NmjZtmsaNG6cXXnhhyAevWb6dz+bm5ujlSZMmaebMmTr33HP12GOPRQdhpvOeyrfzZGf9+vVqbm5WXV1ddB/vq8TceD/ZHZ/P57Cvr0833nijBgYG9MADD8TctmTJkujlhoYGTZgwQdOmTdOOHTs0ZcoUSYVxvtz6v1cI58rqF7/4hW666SaNHDkyZn8hvrec+gwSn11IX96VV1VWVqqoqGhItHzw4MEh0XmhWLp0qZ5//nm99NJLOuuss+IeW1tbq3HjxmnPnj2SpJqaGvX29urw4cMxx+X7+RwzZowmTZqkPXv2RGexiveeKtTz9MEHH2jz5s369re/Hfc43lenuPV+qqmp0YEDB4Y8/scff5yX57Cvr0833HCDOjo61NraGpPlsDNlyhQVFxfHvOcK6XxFpPN/rxDP1Wuvvabdu3cn/CyT8v+95dRn4LMLw5V3QUdJSYmmTp0aTXtGtLa2atasWVlqVXYYhqHbb79dTz/9tH7zm99o/PjxCe9z6NAh7du3T7W1tZKkqVOnqri4OOZ87t+/Xzt37szr89nT06P33ntPtbW10bS6+Rz09vbqlVdeiZ6DQj1PjzzyiKqqqvTVr3417nG8r05x6/00c+ZMhUIhvfnmm9Fj3njjDYVCobw7h5GAY8+ePdq8ebPGjh2b8D67du1SX19f9D1XSOfLLJ3/e4V4rtavX6+pU6dq8uTJCY/N1/dWoj5DIX92Dfhwy0kZHrieERs3bjSKi4uN9evXG++++66xbNkyY8yYMcZf/vKXbDcto/7zf/7PRjAYNF5++WVj//790e2zzz4zDMMwjh49aqxYscLYsmWL0dHRYbz00kvGzJkzjb/5m78xurq6oo9z6623GmeddZaxefNmY8eOHcZll11mTJ482Th58mS2XprrVqxYYbz88svGn//8Z2P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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(hdus[0][1].data, norm=LogNorm(), origin='lower', cmap='hot')\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "446bb3e8", + "metadata": {}, + "outputs": [], + "source": [ + "cmd_n = sim.UserCommands(use_instrument='METIS', set_modes=['img_n'], \n", + " properties={\"!OBS.exptime\": 3600})\n", + "metis_n = sim.OpticalTrain(cmd_n)\n", + "metis_n['skycalc_atmosphere'].include=False\n", + "metis_n['detector_linearity'].include=False\n", + "metis_n['chop_nod'].include=False\n", + "metis_n['detector_readout_parameters'].include=False" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5065793a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=21\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
elementnameclassincluded
str23str27str28bool
armazonesskycalc_atmosphereSkycalcTERCurveFalse
ELTtelescope_reflectionSurfaceListTrue
METIScommon_fore_opticsSurfaceListTrue
METISadc_wheel : [False]ADCWheelFalse
METISslit_wheel : [False]SlitWheelFalse
METIScold_stopPupilTransmissionTrue
METIScommon_fits_keywordsExtraFitsKeywordsTrue
METIS_IMG_Nimg_n_opticsSurfaceListTrue
METIS_IMG_Nfilter_wheel : [N2]FilterWheelTrue
METIS_IMG_Nnd_filter_wheel : [open]FilterWheelTrue
METIS_IMG_NpsfFieldConstantPSFTrue
METIS_DET_IMG_N_GeoSnapdetector_arrayDetectorListTrue
METIS_DET_IMG_N_GeoSnapdetector_readout_parametersDetectorModePropertiesSetterFalse
METIS_DET_IMG_N_GeoSnapquantum_efficiencyQuantumEfficiencyCurveTrue
METIS_DET_IMG_N_GeoSnapauto_exposureAutoExposureTrue
METIS_DET_IMG_N_GeoSnapsummed_exposureSummedExposureTrue
METIS_DET_IMG_N_GeoSnapdark_currentDarkCurrentTrue
METIS_DET_IMG_N_GeoSnapshot_noiseShotNoiseTrue
METIS_DET_IMG_N_GeoSnapdetector_linearityLinearityCurveFalse
METIS_DET_IMG_N_GeoSnapreadout_noiseBasicReadoutNoiseTrue
METIS_DET_IMG_N_GeoSnapchop_nodChopNodCombinerFalse
" + ], + "text/plain": [ + "\n", + " element name ... included\n", + " str23 str27 ... bool \n", + "----------------------- --------------------------- ... --------\n", + " armazones skycalc_atmosphere ... False\n", + " ELT telescope_reflection ... True\n", + " METIS common_fore_optics ... True\n", + " METIS adc_wheel : [False] ... False\n", + " METIS slit_wheel : [False] ... False\n", + " METIS cold_stop ... True\n", + " METIS common_fits_keywords ... True\n", + " METIS_IMG_N img_n_optics ... True\n", + " METIS_IMG_N filter_wheel : [N2] ... True\n", + " METIS_IMG_N nd_filter_wheel : [open] ... True\n", + " METIS_IMG_N psf ... True\n", + "METIS_DET_IMG_N_GeoSnap detector_array ... True\n", + "METIS_DET_IMG_N_GeoSnap detector_readout_parameters ... False\n", + "METIS_DET_IMG_N_GeoSnap quantum_efficiency ... True\n", + "METIS_DET_IMG_N_GeoSnap auto_exposure ... True\n", + "METIS_DET_IMG_N_GeoSnap summed_exposure ... True\n", + "METIS_DET_IMG_N_GeoSnap dark_current ... True\n", + "METIS_DET_IMG_N_GeoSnap shot_noise ... True\n", + "METIS_DET_IMG_N_GeoSnap detector_linearity ... False\n", + "METIS_DET_IMG_N_GeoSnap readout_noise ... True\n", + "METIS_DET_IMG_N_GeoSnap chop_nod ... False" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metis_n.effects" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "88ec413f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " FOVs: 0%| | 0/3 [00:00" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(hdus_n[0][1].data, norm=LogNorm(), origin='lower', cmap='hot')\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "03adf529", + "metadata": {}, + "outputs": [], + "source": [ + "cmd_n_chop = sim.UserCommands(use_instrument='METIS', set_modes=['img_n'], \n", + " properties={\"!OBS.exptime\": 3600})\n", + "metis_n_chop = sim.OpticalTrain(cmd_n_chop)\n", + "metis_n_chop['skycalc_atmosphere'].include=False\n", + "metis_n_chop['detector_linearity'].include=False\n", + "metis_n_chop['chop_nod'].include=True\n", + "metis_n_chop['detector_readout_parameters'].include=False" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e3dae69f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " FOVs: 0%| | 0/3 [00:00" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "plt.imshow(hdus_n_chop[0][1].data, origin='lower', cmap='hot')\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6247d466", + "metadata": {}, + "outputs": [], + "source": [ + "cmd_lms = sim.UserCommands(\n", + " use_instrument=\"METIS\",\n", + " set_modes=['lms'],\n", + " properties={\n", + " \"!OBS.wavelen\": 3.555,\n", + "\n", + " # These !SIM.spectral_* properties make the simulation faster, but less precise.\n", + " # Comment them out for your final simulations.\n", + " #\"!SIM.spectral_bin_width\": 1e-3,\n", + " #\"!SIM.spectral_resolution\": 1000,\n", + " \n", + " })\n", + "\n", + "metis_lms = sim.OpticalTrain(cmd_lms)\n", + "metis_lms['skycalc_atmosphere'].include=False" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "5012f61b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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elementnameclassincluded
str24str20str25bool
armazonesskycalc_atmosphereSkycalcTERCurveFalse
ELTtelescope_reflectionSurfaceListTrue
METIScommon_fore_opticsSurfaceListTrue
METISadc_wheel : [False]ADCWheelFalse
METISslit_wheel : [False]SlitWheelFalse
METIScold_stopPupilTransmissionTrue
METIScommon_fits_keywordsExtraFitsKeywordsTrue
METIS_LMSmetis_lms_surfacesSurfaceListTrue
METIS_LMSlms_efficiencyMetisLMSEfficiencyTrue
METIS_LMSlms_image_slicerMetisLMSImageSlicerTrue
METIS_LMSpsfFieldConstantPSFTrue
METIS_LMSlms_spectral_tracesMetisLMSSpectralTraceListTrue
metis_lms_detector_arraydetector_array_listDetectorListTrue
metis_lms_detector_arrayquantum_efficiencyQuantumEfficiencyCurveTrue
metis_lms_detector_arrayauto_exposureAutoExposureTrue
metis_lms_detector_arrayexposure_actionSummedExposureTrue
metis_lms_detector_arraydark_currentDarkCurrentTrue
metis_lms_detector_arrayshot_noiseShotNoiseTrue
metis_lms_detector_arraydetector_linearityLinearityCurveTrue
metis_lms_detector_arrayreadout_noiseBasicReadoutNoiseTrue
" + ], + "text/plain": [ + "\n", + " element name class included\n", + " str24 str20 str25 bool \n", + "------------------------ -------------------- ------------------------- --------\n", + " armazones skycalc_atmosphere SkycalcTERCurve False\n", + " ELT telescope_reflection SurfaceList True\n", + " METIS common_fore_optics SurfaceList True\n", + " METIS adc_wheel : [False] ADCWheel False\n", + " METIS slit_wheel : [False] SlitWheel False\n", + " METIS cold_stop PupilTransmission True\n", + " METIS common_fits_keywords ExtraFitsKeywords True\n", + " METIS_LMS metis_lms_surfaces SurfaceList True\n", + " METIS_LMS lms_efficiency MetisLMSEfficiency True\n", + " METIS_LMS lms_image_slicer MetisLMSImageSlicer True\n", + " METIS_LMS psf FieldConstantPSF True\n", + " METIS_LMS lms_spectral_traces MetisLMSSpectralTraceList True\n", + "metis_lms_detector_array detector_array_list DetectorList True\n", + "metis_lms_detector_array quantum_efficiency QuantumEfficiencyCurve True\n", + "metis_lms_detector_array auto_exposure AutoExposure True\n", + "metis_lms_detector_array exposure_action SummedExposure True\n", + "metis_lms_detector_array dark_current DarkCurrent True\n", + "metis_lms_detector_array shot_noise ShotNoise True\n", + "metis_lms_detector_array detector_linearity LinearityCurve True\n", + "metis_lms_detector_array readout_noise BasicReadoutNoise True" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metis_lms.effects" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "959d132d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " FOVs: 0%| | 0/1 [00:00" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ip = metis_lms.image_planes[0]\n", + "\n", + "plt.imshow(ip.data, norm=LogNorm())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f7531ef0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[35mastar.scopesim.effects.electronic - WARNING: DIT = 1.000 s < MINDIT = 1.300 s\u001b[0m\n", + "\u001b[32mastar.scopesim.detector.detector_array - Extracting from 4 detectors...\u001b[0m\n", + "\u001b[35mastar.scopesim.effects.electronic - WARNING: DIT = 1.000 s < MINDIT = 1.300 s\u001b[0m\n" + ] + } + ], + "source": [ + "hdul_lms = metis_lms.readout(exptime=3600.)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f5c8cd18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(hdul_lms)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "18853d11", + "metadata": {}, + "outputs": [], + "source": [ + "data_raw = hdul_lms[1].data" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "f7750e3a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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"\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 20\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 21\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 22\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 23\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 24\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 25\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 26\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 27\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Rectifying Slice 28\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - 3.53 .. 3.58 um\u001b[0m\n", + "\u001b[32mastar.scopesim.effects.spectral_trace_list_utils - Bin width 1e-05 um\u001b[0m\n" + ] + } + ], + "source": [ + "rectified = metis_lms[\"lms_spectral_traces\"].rectify_cube(hdul_lms)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "f04fdd52", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5517, 28, 110)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rectified.data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "6bdb08d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(rectified.data.sum(axis=0), aspect=2.6, origin='lower')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fd8bbd8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/METIS/docs/example_notebooks/IMG_L_N-examples.ipynb b/METIS/docs/example_notebooks/IMG_L_N-examples.ipynb index ded6cb23..c50b92ff 100644 --- a/METIS/docs/example_notebooks/IMG_L_N-examples.ipynb +++ b/METIS/docs/example_notebooks/IMG_L_N-examples.ipynb @@ -64,7 +64,7 @@ "metadata": {}, "outputs": [], "source": [ - "# sim.download_package(['instruments/METIS', 'telescopes/ELT', 'locations/Armazones'])" + "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])" ] }, { @@ -80,7 +80,7 @@ "metadata": {}, "outputs": [], "source": [ - "sim.download_example_data([\"HL_Tau_prep_for_Scopesim.fits\", \"AGN_uc0890_image_l12_i090_p000.fits\"])" + "paths = sim.download_example_data(\"HL_Tau_prep_for_Scopesim.fits\", \"AGN_uc0890_image_l12_i090_p000.fits\")" ] }, { @@ -103,7 +103,7 @@ "metadata": {}, "outputs": [], "source": [ - "input_hdul = fits.open(\"HL_Tau_prep_for_Scopesim.fits\")" + "input_hdul = fits.open(paths[0])" ] }, { @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -170,20 +170,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Lp'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cmd_l[\"!OBS.filter_name\"]" ] @@ -209,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -311,7 +300,7 @@ "metadata": {}, "outputs": [], "source": [ - "input_hdul = fits.open(\"AGN_uc0890_image_l12_i090_p000.fits\")" + "input_hdul = fits.open(paths[1])" ] }, { @@ -579,7 +568,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -593,7 +582,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/METIS/docs/example_notebooks/LSS-YSO_model_simulation.ipynb b/METIS/docs/example_notebooks/LSS-YSO_model_simulation.ipynb index ef799b76..8d183326 100644 --- a/METIS/docs/example_notebooks/LSS-YSO_model_simulation.ipynb +++ b/METIS/docs/example_notebooks/LSS-YSO_model_simulation.ipynb @@ -49,7 +49,7 @@ "metadata": {}, "outputs": [], "source": [ - "# sim.download_package([\"instruments/METIS\", \"telescopes/ELT\", \"locations/Armazones\"])" + "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])" ] }, { @@ -74,7 +74,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The input data are cubes of three different models of the same YSO, HD100546. We keep the names of FITS files, the `Source` objects and the results of the Scopesim simulations in dictionaries, indexed by short names for the models." + "The input data are cubes of three different models of the same YSO, HD100546. We keep the names of FITS files, the `Source` objects and the results of the ScopeSim simulations in dictionaries, indexed by short names for the models." ] }, { @@ -83,19 +83,28 @@ "metadata": {}, "outputs": [], "source": [ - "fitsfiles = {}\n", - "fitsfiles['cav'] = \"models_Lband_HD100546_cav_f100PAH.cube_3.0mas.fits\"\n", - "fitsfiles['emptycav'] = \"models_Lband_HD100546_empytcav.cube_3.0mas.fits\"\n", - "fitsfiles['gap'] = \"models_Lband_HD100546_gap100.cube_3.0mas.fits\"\n", - "models = list(fitsfiles.keys())\n", - "print(\"Model names:\", models)" + "fitsfiles = {\n", + " \"cav\": \"models_Lband_HD100546_cav_f100PAH.cube_3.0mas.fits\",\n", + " \"emptycav\": \"models_Lband_HD100546_empytcav.cube_3.0mas.fits\",\n", + " \"gap\": \"models_Lband_HD100546_gap100.cube_3.0mas.fits\",\n", + "}\n", + "print(\"Model names:\", list(fitsfiles))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The FITS files can be downloaded from the Scopesim server. If you already have them, make sure that the files are in the current working directory." + "The FITS files can be downloaded from the ScopeSim server. If you already have them, make sure that the files are in the correct location (e.g. current working directory, see also the note below). The next code cell will replace the file names with absolute paths to the download cache location. If you already have the files in the current working directory, simply skip that line and ScopeSim will look for them there." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Note: ScopeSim v0.9.0 or later will now download the example data to a hidden cache directory by default. To change this, pass the optional save_dir argument to sim.download_example_data() with the desired download location, e.g. save_dir=\"./\" for the current working directory (the default download location in previous versions).\n", + "
" ] }, { @@ -104,17 +113,17 @@ "metadata": {}, "outputs": [], "source": [ - "sim.download_example_data(list(fitsfiles.values()))" + "fitsfiles = dict(zip(fitsfiles, sim.download_example_data(*fitsfiles.values())))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The file headers are not yet in the form that Scopesim understands and we make two minor modifications: \n", + "The file headers are not yet in the form that ScopeSim understands and we make two minor modifications: \n", "- Set CRVAL to 0, because Scopesim cannot look elsewhere\n", - "- Set BUNIT keyword (files have UNITS, which is non-standard)\n", - "- The cubes contain the occasional negative value. We replace these with 0.\n", + "- Set BUNIT keyword (the files have the keyword UNITS, which is non-standard)\n", + "- The cubes contain some negative values. We replace these with 0.\n", "- We introduce a factor `scale_delt` to increase the pixel size, which makes features more visible. If you want to simulate the original source pixel scale, set `scale_delt` to 1." ] }, @@ -125,7 +134,7 @@ "outputs": [], "source": [ "sources = {}\n", - "scale_cdelt = 1\n", + "scale_cdelt = 5.\n", "for model, fitsfile in fitsfiles.items():\n", " with fits.open(fitsfile) as hdul:\n", " hdul[0].header['CRVAL1'] = 0.\n", @@ -133,7 +142,7 @@ " hdul[0].header['CDELT1'] *= scale_cdelt\n", " hdul[0].header['CDELT2'] *= scale_cdelt\n", " hdul[0].header['BUNIT'] = hdul[0].header['UNITS']\n", - " hdul[0].data[hdul[0].data < 0] = 0\n", + " hdul[0].data.clip(min=0, out=hdul[0].data)\n", " sources[model] = sim.Source(cube=hdul)" ] }, @@ -150,7 +159,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Determine plot limits in arcsec from header keywords\n", + "# Determine plot limits in arcsec from header keywords (these are in degrees)\n", "hdr = sources['cav'].cube_fields[0].header\n", "i_lim = np.array([0, hdr['NAXIS1']])\n", "x_lim = hdr['CRVAL1'] + hdr['CDELT1'] * (i_lim + 1 - hdr['CRPIX1']) * 3600\n", @@ -160,7 +169,7 @@ "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 4))\n", "for i, (model, src) in enumerate(sources.items()):\n", " im = axes[i].imshow(src.cube_fields[0].data.sum(axis=0) + 1e-14, # add small positive value to avoid 0 in LogNorm\n", - " origin='lower', norm=LogNorm(vmin=1e-4, vmax=10),\n", + " origin=\"lower\", norm=\"log\", vmin=1e-4, vmax=10,\n", " extent=(x_lim[0], x_lim[-1], y_lim[0], y_lim[-1]))\n", " axes[i].set_xlabel(\"arcsec\")\n", " axes[i].set_ylabel(\"arcsec\")\n", @@ -186,6 +195,13 @@ "sources['cav'].cube_fields[0].wave" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the source object does not cover the entire spatial and wavelength range permitted by METIS: The spatial extent is about 2.5 arcsec, compared to the METIS slit length of 8 arcsec. The wavelength range is 3.1 to 4 $\\mu$m, whereas METIS permits 2.9 to 4.2 $\\mu$m. This will be visible in the simulated raw and rectified spectra below." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -197,7 +213,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The cubes are observed in the long-slit spectroscopic mode in the L band. As usual, there are four steps: `UserCommands` -> `OpticalTrain` -> `observe` -> `readout` to arrive at a detector image. The optical train can be reused for observation of different source when `update=True` is set in `observe()`. " + "The cubes are observed in the long-slit spectroscopic mode in the L band. As usual, there are four steps: `UserCommands` -> `OpticalTrain` -> `observe` -> `readout` to arrive at a detector image. The optical train can be reused for observation of different sources when `update=True` is set in `observe()`. \n", + "\n", + "The simulation uses the `AutoExposure` effect to break down the requested exposure time into `NDIT` subexposures of integration time `DIT` so as to prevent saturation. For this to work, the parameters `dit` and `ndit` currently have to be set to `None` explicitly (here and in every `readout` command below). This will hopefully change in future versions of Scopesim." ] }, { @@ -206,11 +224,9 @@ "metadata": {}, "outputs": [], "source": [ - "exptime = 3600. # seconds\n", + "exptime = 3600 * u.s\n", "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"],\n", - " properties={\"!OBS.exptime\": exptime,\n", - " \"!SIM.spectral.spectral_resolution\": 20000,\n", - " \"!SIM.spectral.spectral_bin_width\": 2e-4})" + " properties={\"!OBS.exptime\": exptime.value, \"!OBS.dit\": None, \"!OBS.ndit\": None})" ] }, { @@ -232,7 +248,8 @@ "for model, src in sources.items():\n", " print(f'Observing model \"{model}...\"')\n", " metis.observe(src, update=True)\n", - " results[model] = metis.readout(detector_readout_mode='auto')[0]\n", + " results[model] = metis.readout(detector_readout_mode='auto', dit=None, ndit=None)[0]\n", + " results[model][1].data <<= u.electron # Apply unit\n", " print(\"-----\")" ] }, @@ -242,13 +259,13 @@ "metadata": {}, "outputs": [], "source": [ - "plt.figure(figsize=(15, 5))\n", - "for i, (model, result) in enumerate(results.items()):\n", - " plt.subplot(1, 3, i+1)\n", - " plt.imshow(result[1].data, origin='lower', norm=LogNorm())\n", - " plt.xlabel(\"pixel\")\n", - " plt.ylabel(\"pixel\")\n", - " plt.title(model);" + "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5.2))\n", + "for axis, (model, result) in zip(axes, results.items()):\n", + " axis.imshow(result[1].data.value, origin=\"lower\", norm=\"log\")\n", + " axis.set_xlabel(\"pixel\")\n", + " axis.set_ylabel(\"pixel\")\n", + " axis.set_title(model)\n", + "fig.suptitle(\"Raw spectra\", fontsize=20);" ] }, { @@ -266,14 +283,15 @@ "source": [ "sky = sim.source.source_templates.empty_sky()\n", "metis.observe(sky, update=True)\n", - "bgresult = metis.readout(detector_readout_mode=\"auto\")[0]" + "bgresult = metis.readout(detector_readout_mode=\"auto\", dit=None, ndit=None)[0]\n", + "bgresult[1].data <<= u.electron # Apply unit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Before showing the background subtracted spectra, we convert the pixel numbers to wavelength and spatial position along the slit. This is possible because the current version of Scopesim/METIS by default uses perfectly linear mapping of the spectra onto the detector. The WCS keywords to use are in `metis['spectral_traces'].meta`. The plots will be restricted to the area covered by the spectra." + "We subtract the background from the data before rectifying the spectra. Here's where the quantization effect would have caused problems: background subtraction from noisy data leads to negative values, which cannot be represented by unsigned integers and would wrap around to large positive values (in `uint16`: $1 - 2 = 2^{16} - 1 = 65535$). Therefore the data would have to be converted to `float` before proceeding further." ] }, { @@ -282,25 +300,15 @@ "metadata": {}, "outputs": [], "source": [ - "meta = metis['spectral_traces'].meta\n", - "\n", - "det_xi = meta['CRVAL1'] + meta['CDELT1'] * (np.arange(2048) + 1 - meta['CRPIX1']) * u.Unit(meta['CUNIT1'])\n", - "det_xi = det_xi.to(u.arcsec)\n", - "\n", - "det_wave = (meta['CRVAL2'] + meta['CDELT2'] * (np.arange(2048) + 1 - meta['CRPIX2'])) * u.Unit(meta['CUNIT2'])\n", - "det_wave = det_wave.to(u.um) " + "for result in results.values():\n", + " result[1].data -= bgresult[1].data" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "ilim = np.array([750, 1300]) # pixels along spatial direction\n", - "jlim = np.array([300, 1750]) # pixels along wavelength direction\n", - "xlim = det_xi[ilim].value\n", - "ylim = det_wave[jlim].value" + "We now use the `rectify_traces` method to resample the spectra to a linear grid in wavelength and spatial position. The method is attached to the `SpectraTraceList` effect, which holds the information on the geometrical mapping onto the detector. Note that this is an optimistic approach as it uses the same transformations used before to simulate the detector data. When reducing real data, the transformation parameters would have to be estimated from dedicated calibration measurements and would therefore have a degree of uncertainty." ] }, { @@ -309,21 +317,18 @@ "metadata": {}, "outputs": [], "source": [ - "plt.figure(figsize=(15, 8))\n", - "for i, (model, result) in enumerate(results.items()):\n", - " plt.subplot(1, 3, i+1)\n", - " plt.imshow((result[1].data - bgresult[1].data)[jlim[0]:jlim[1], ilim[0]:ilim[1]], origin='lower', norm=LogNorm(),\n", - " extent=(xlim[0], xlim[1], ylim[0], ylim[1]), aspect='auto')\n", - " plt.xlabel(r\"position along slit [arcsec]\")\n", - " plt.ylabel(r\"Wavelength [$\\mu$m]\")\n", - " plt.title(model);" + "rectified = {}\n", + "tracelist = metis['spectral_traces']\n", + "for model, result in results.items():\n", + " rectified[model] = tracelist.rectify_traces(result, -4.0, 4.0)\n", + " rectified[model][1].data <<= u.electron # Apply unit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The results have to be saved to disk explicitely so they can be analysed with external tools. " + "Before showing the rectified spectra, we convert the pixel numbers to wavelength and spatial position along the slit. For this purpose we use the WCS keywords in the headers of the rectified HDUs. " ] }, { @@ -332,26 +337,63 @@ "metadata": {}, "outputs": [], "source": [ - "from pathlib import Path\n", - "for i, (model, result) in enumerate(results.items()):\n", - " outfile = Path(fitsfiles[model]).stem + \"-simulated_LSS_L\" + Path(fitsfiles[model]).suffix\n", - " result.writeto(outfile, overwrite=True)\n", - " print(fitsfiles[model], \"--->\", outfile)\n", - "bgresult.writeto(\"models_Lband_HD100546-background_simulated_LSS_L.fits\", overwrite=True)" + "from astropy.wcs import WCS\n", + "hdr = rectified['cav'][1].header\n", + "wcs = WCS(hdr)\n", + "naxis1, naxis2 = hdr['NAXIS1'], hdr['NAXIS2']\n", + "det_xi = wcs.sub((2,)).pixel_to_world(np.arange(naxis2)).to(u.arcsec)\n", + "det_wave = wcs.spectral.pixel_to_world(np.arange(naxis1)).to(u.um) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(15, 15))\n", + "for axis, (model, result) in zip(axes, rectified.items()):\n", + " img = axis.imshow(\n", + " result[1].data.value, origin=\"lower\", norm=\"log\", aspect=\"auto\",\n", + " extent=(det_wave[0].value, det_wave[-1].value, det_xi[0].value, det_xi[-1].value))\n", + " axis.set_ylabel(f\"position along slit [{det_xi.unit.to_string('latex')}]\")\n", + " axis.set_xlabel(f\"Wavelength [{det_wave.unit.to_string('latex')}]\")\n", + " axis.set_title(model);\n", + " fig.colorbar(img, ax=axis)\n", + "fig.suptitle(\"Rectified spectra\", y=.99, fontsize=24)\n", + "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Note on the spectral mapping\n", - "To use the true spectral mapping of METIS, the trace file has to be set before building the optical train, for example by setting `\"!OBS.trace_file\"` in the `UserCommands`: \n", - "```python\n", - "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"],\n", - " properties={\"!OBS.exptime\": 3600,\n", - " \"!OBS.trace_file\": \"TRACE_LSS_L.fits\"})\n", - "```\n", - "For the METIS long-slit mode, the non-linear parts of the actual mapping are quite small. " + "As stated above the input source object does not fill the entire spatial and wavelength ranges covered by METIS. In a real observation the entire frame shown here would be filled." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results have to be saved to disk explicitely so they can be analysed with external tools. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "# Change to True if you want to save the rectified data\n", + "save_rectified = False\n", + "if save_rectified:\n", + " for model, result in rectified.items():\n", + " outfile = Path(fitsfiles[model]).stem + \"-simulated_LSS_L\" + Path(fitsfiles[model]).suffix\n", + " result.writeto(outfile, overwrite=True)\n", + " print(fitsfiles[model], \"--->\", outfile)\n", + " bgresult.writeto(\"models_Lband_HD100546-background_simulated_LSS_L.fits\", overwrite=True)" ] }, { @@ -414,7 +456,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The function is applied to the data cubes before creating the `Source` object. We choose an angle of 45 degrees. The simulation the proceeds as above." + "The function is applied to the data cubes before creating the `Source` object. We choose an angle of 45 degrees. The simulation then proceeds as above." ] }, { @@ -432,7 +474,7 @@ " hdul[0].header['CDELT1'] *= scale_cdelt\n", " hdul[0].header['CDELT2'] *= scale_cdelt\n", " hdul[0].header['BUNIT'] = hdul[0].header['UNITS']\n", - " hdul[0].data[hdul[0].data < 0] = 0\n", + " hdul[0].data.clip(min=0, out=hdul[0].data)\n", " hdul[0] = rotate_cube(hdul[0], angle)\n", " rotsources[model] = sim.Source(cube=hdul)" ] @@ -447,7 +489,8 @@ "for model, src in rotsources.items():\n", " print(f'Observing model \"{model}\"...')\n", " metis.observe(src, update=True)\n", - " rotresults[model] = metis.readout(detector_readout_mode='auto')[0]\n", + " rotresults[model] = metis.readout(detector_readout_mode='auto', dit=None, ndit=None)[0]\n", + " rotresults[model][1].data <<= u.electron # Apply unit\n", " print(\"-----\")" ] }, @@ -457,15 +500,30 @@ "metadata": {}, "outputs": [], "source": [ - "plt.figure(figsize=(15, 8))\n", - "for i, (model, result) in enumerate(rotresults.items()):\n", - " plt.subplot(1, 3, i+1)\n", - " plt.imshow((result[1].data - bgresult[1].data)[jlim[0]:jlim[1], ilim[0]:ilim[1]], \n", - " origin='lower', norm=LogNorm(),\n", - " extent=(xlim[0], xlim[1], ylim[0], ylim[1]), aspect='auto')\n", - " plt.xlabel(r\"position along slit [arcsec]\")\n", - " plt.ylabel(r\"Wavelength [$\\mu$m]\")\n", - " plt.title(model + \", rotated by \" + str(angle) + \" degrees\");" + "rotrectified = {}\n", + "for model, result in rotresults.items():\n", + " result[1].data -= bgresult[1].data\n", + " rotrectified[model] = tracelist.rectify_traces(result, -4.0, 4.0)\n", + " rotrectified[model][1].data <<= u.electron # Apply unit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(15, 15))\n", + "for axis, (model, result) in zip(axes, rotrectified.items()):\n", + " img = axis.imshow(\n", + " result[1].data.value, origin=\"lower\", norm=\"log\", aspect=\"auto\",\n", + " extent=(det_wave[0].value, det_wave[-1].value, det_xi[0].value, det_xi[-1].value))\n", + " axis.set_ylabel(f\"position along slit [{det_xi.unit.to_string('latex')}]\")\n", + " axis.set_xlabel(f\"Wavelength [{det_wave.unit.to_string('latex')}]\")\n", + " axis.set_title(model);\n", + " fig.colorbar(img, ax=axis)\n", + "fig.suptitle(\"Rectified rotated spectra\", y=.99, fontsize=24)\n", + "fig.tight_layout()" ] }, { @@ -474,10 +532,13 @@ "metadata": {}, "outputs": [], "source": [ - "for i, (model, result) in enumerate(rotresults.items()):\n", - " outfile = Path(fitsfiles[model]).stem + \"-rot_\" + str(angle) + \"-simulated_LSS_L\" + Path(fitsfiles[model]).suffix\n", - " result.writeto(outfile, overwrite=True)\n", - " print(fitsfiles[model], \"--->\", outfile)" + "# Change to True if you want to save the rectified data\n", + "save_rotrectified = False\n", + "if save_rotrectified:\n", + " for model, result in rotresults.items():\n", + " outfile = Path(fitsfiles[model]).stem + f\"-rot_{angle}-simulated_LSS_L\" + Path(fitsfiles[model]).suffix\n", + " result.writeto(outfile, overwrite=True)\n", + " print(fitsfiles[model], \"--->\", outfile)" ] }, { @@ -495,19 +556,20 @@ "source": [ "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(6, 12))\n", "\n", - "ax1.plot(det_xi, (results['emptycav'][1].data - bgresult[1].data)[500, :], label=\"no rotation\")\n", - "ax1.plot(det_xi, (rotresults['emptycav'][1].data - bgresult[1].data)[500, :], label=f\"rotation {angle} deg\")\n", - "ax1.set_xlim(xlim[0], xlim[-1])\n", + "ax1.plot(det_xi, rectified['emptycav'][1].data[:, 1500], label=\"no rotation\")\n", + "ax1.plot(det_xi, rotrectified['emptycav'][1].data[:, 1500], label=f\"rotation {angle} deg\")\n", + "ax1.set_xlim(x_lim[0], x_lim[-1])\n", "ax1.set_ylim(2e4, 1e9)\n", "ax1.set_xlabel(\"arcsec\")\n", "ax1.semilogy()\n", - "ax1.legend();\n", + "ax1.legend()\n", "\n", "fig.subplots_adjust(left=0.1)\n", - "ax2.imshow(rotsources['emptycav'].cube_fields[0].data.sum(axis=0) + 1e-14, norm=LogNorm(vmin=1e-4, vmax=10),\n", - " extent=(x_lim[0], x_lim[-1], y_lim[0], y_lim[-1]))\n", + "ax2.imshow(rotsources['emptycav'].cube_fields[0].data.sum(axis=0) + 1e-14,\n", + " norm=\"log\", vmin=1e-4, vmax=10,\n", + " extent=(x_lim[0], x_lim[-1], y_lim[0], y_lim[-1]))\n", "ax2.set_xlabel(\"arcsec\")\n", - "ax2.set_ylabel(\"arcsec\");" + "ax2.set_ylabel(\"arcsec\")" ] }, { @@ -521,7 +583,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The pixel values in the detector images give electrons accumulated over the exposure time. To get back to physical units, e.g. Jy, one has to perform a flux calibration. As in real observations, we do this here with the observational of a standard star. For simplicity, we use a star with a spectrum that is constant at $f_{\\nu} = 1\\,\\mathrm{Jy}$. We observe it with the identical setup as the science targets above, except that the exposure time is reduced to 1 second." + "The pixel values in the detector images give electrons accumulated over the exposure time. To get back to physical units, e.g. Jy, one has to perform a flux calibration. As in real observations, we do this here with the observation of a standard star. For simplicity, we use a star with a spectrum that is constant at $f_{\\nu} = 1\\,\\mathrm{Jy}$. We observe it with the identical setup as the science targets above, except that the exposure time is reduced to 1 second." ] }, { @@ -548,8 +610,16 @@ "metadata": {}, "outputs": [], "source": [ - "std_exptime = 1 # second\n", - "std_result = metis.readout(exptime=std_exptime, detector_readout_mode='auto')[0]" + "std_exptime = 1 * u.s\n", + "std_result = metis.readout(exptime=std_exptime.value, detector_readout_mode='auto', dit=None, ndit=None)[0]\n", + "std_result[1].data <<= u.electron" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We subtract the background (scaled to the exposure time of the standard exposure) and rectify." ] }, { @@ -558,13 +628,9 @@ "metadata": {}, "outputs": [], "source": [ - "std_bgsub = std_result[1].data - bgresult[1].data / exptime\n", - "\n", - "plt.figure(figsize=(10, 10))\n", - "plt.imshow(std_bgsub[250:1750, 800:1250], origin='lower', extent=(800, 1250, 250, 1750))\n", - "plt.xlabel('pixel')\n", - "plt.ylabel('pixel')\n", - "plt.title(r\"background-subtracted standard exposure ($T_\\mathrm{exp} = 1\\,\\mathrm{s}$)\");" + "std_result[1].data -= bgresult[1].data / exptime * std_exptime\n", + "std_rectified = tracelist.rectify_traces(std_result, -4, 4)\n", + "std_rectified[1].data <<= u.electron" ] }, { @@ -573,21 +639,37 @@ "metadata": {}, "outputs": [], "source": [ - "xmin, xmax = 975, 1075\n", + "plt.figure(figsize=(15, 7))\n", + "plt.imshow(std_rectified[1].data.value, origin=\"lower\", norm=\"log\", aspect=\"auto\",\n", + " extent=(det_wave[0].value, det_wave[-1].value, det_xi[0].value, det_xi[-1].value))\n", + "plt.ylabel(f\"position along slit [{det_xi.unit.to_string('latex')}]\")\n", + "plt.xlabel(f\"Wavelength [{det_wave.unit.to_string('latex')}]\")\n", + "plt.title(\"Standard star\")\n", + "plt.colorbar();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xmin, xmax = 675, 825\n", "xaxis = np.arange(xmin, xmax)\n", - "y_1, y_2 = 200, 1800\n", + "y_1, y_2 = 200, 1750\n", "lam_1, lam_2 = det_wave[y_1], det_wave[y_2]\n", "\n", "plt.figure(figsize=(10, 5))\n", - "plt.plot(xaxis, std_bgsub[200, xmin:xmax], label=fr\"$\\lambda = {lam_1:.2f}\\,\\mu\\mathrm{{m}}$\")\n", - "plt.plot(xaxis, std_bgsub[1800, xmin:xmax], label=fr\"$\\lambda = {lam_2:.2f}\\,\\mu\\mathrm{{m}}$\")\n", - "plt.plot(xaxis, std_bgsub[:, xmin:xmax].mean(axis=0), label='average')\n", - "plt.vlines((1024 - 10, 1024 + 10), 1, 1e5, colors='black', linestyles='dashed', label='extraction aperture')\n", + "plt.plot(xaxis, std_rectified[1].data[xmin:xmax, y_1], label=fr\"$\\lambda = ${lam_1.to_string(precision=3, format='latex')}\")\n", + "plt.plot(xaxis, std_rectified[1].data[xmin:xmax, y_2], label=fr\"$\\lambda = ${lam_2.to_string(precision=3, format='latex')}\")\n", + "plt.plot(xaxis, std_rectified[1].data[xmin:xmax, :].mean(axis=1), label='average')\n", + "plt.vlines((732 - 10, 732 + 10), 1, 1e5, colors='black', linestyles='dashed', label='extraction aperture')\n", "plt.xlabel(\"pixel\")\n", - "plt.ylabel(\"e/s\")\n", + "plt.ylabel(std_rectified[1].data.unit)\n", "plt.semilogy()\n", + "plt.ylim(10, 4e6)\n", "plt.legend()\n", - "plt.title(\"spatial cuts through standard spectrum\");" + "plt.title(\"Spatial cuts through standard spectrum\");" ] }, { @@ -604,10 +686,11 @@ "metadata": {}, "outputs": [], "source": [ + "print(metis['slit_wheel'].current_slit)\n", "# TODO: This used to return \"C-38_1\". The headers are made ESO\n", "# Compliant, and not all headers have been replaced.\n", "# See https://github.com/AstarVienna/irdb/pull/146\n", - "# std_result[1].header['SLIT']" + "#std_result[0].header" ] }, { @@ -624,7 +707,8 @@ "outputs": [], "source": [ "hwidth = 10 # pixels, half width of extraction aperture\n", - "std_1d = std_bgsub[:, 1024-hwidth:1024+hwidth].sum(axis=1)" + "# exptime = std_rectified[0].header[\"EXPTIME\"] * u.s\n", + "std_1d = std_rectified[1].data[731-hwidth:731+hwidth].sum(axis=0) / std_exptime" ] }, { @@ -634,10 +718,9 @@ "outputs": [], "source": [ "plt.plot(det_wave, std_1d)\n", - "plt.xlabel('Wavelength (um)')\n", - "exptime = std_result[1].header['EXPTIME']\n", - "plt.ylabel('electrons/s')\n", - "plt.title(r\"Extracted standard spectrum ($T_{\\mathrm{exp}} = 1\\,\\mathrm{s}$)\");" + "plt.xlabel(f\"Wavelength [{det_wave.unit.to_string('latex')}]\")\n", + "plt.ylabel(std_1d.unit)\n", + "plt.title(fr\"Extracted standard spectrum ($T_\\mathrm{{exp}} = ${std_exptime.to_string(format='latex')})\");" ] }, { @@ -653,11 +736,19 @@ "metadata": {}, "outputs": [], "source": [ - "flux_conv = 1 * u.Jy / (std_1d * u.electron)\n", + "flux_conv = 1 * u.Jy / std_1d" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "plt.plot(det_wave, flux_conv)\n", "plt.ylim(0, 1e-5)\n", - "plt.xlabel(\"Wavelength (um)\")\n", - "plt.ylabel(\"Jy / (e/s)\")\n", + "plt.xlabel(f\"Wavelength [{det_wave.unit.to_string('latex')}]\")\n", + "plt.ylabel(flux_conv.unit.to_string(\"latex_inline\"))\n", "plt.title(\"Flux conversion\");" ] }, @@ -674,8 +765,8 @@ "metadata": {}, "outputs": [], "source": [ - "emptycav_Jy = flux_conv[:, None] * (results['emptycav'][1].data - bgresult[1].data) / exptime\n", - "emptycav_Jy_rot = flux_conv[:, None] * (rotresults['emptycav'][1].data - bgresult[1].data) / exptime" + "emptycav_Jy = flux_conv * rectified['emptycav'][1].data * u.electron / exptime\n", + "emptycav_Jy_rot = flux_conv * rotrectified['emptycav'][1].data * u.electron / exptime" ] }, { @@ -684,22 +775,22 @@ "metadata": {}, "outputs": [], "source": [ - "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(13, 12))\n", - "ax1.imshow(emptycav_Jy[jlim[0]:jlim[1], ilim[0]:ilim[1]].value, origin='lower', \n", - " norm=LogNorm(vmin=1e-5, vmax=1), extent=(xlim[0], xlim[1], ylim[0], ylim[1]), aspect='auto')\n", - "ax1.set_xlabel(r\"position along slit [arcsec]\")\n", - "ax1.set_ylabel(r\"Wavelength [$\\mu$m]\") \n", - "ax1.set_title(model + \", no rotation\")\n", + "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(13, 12))\n", + "ax1.imshow(emptycav_Jy.value, origin=\"lower\", norm=\"log\", vmin=1e-5, vmax=1, aspect=\"auto\",\n", + " extent=(det_wave[0].value, det_wave[-1].value, det_xi[0].value, det_xi[-1].value))\n", + "ax1.set_ylabel(f\"position along slit [{det_xi.unit.to_string('latex')}]\")\n", + "ax1.set_xlabel(f\"Wavelength [{det_wave.unit.to_string('latex')}]\")\n", + "ax1.set_title(f\"{model}, no rotation\")\n", "\n", - "img = ax2.imshow(emptycav_Jy_rot[jlim[0]:jlim[1], ilim[0]:ilim[1]].value, origin='lower',\n", - " norm=LogNorm(vmin=1e-5, vmax=1), extent=(xlim[0], xlim[1], ylim[0], ylim[1]), aspect='auto')\n", - "ax2.set_xlabel(r\"position along slit [arcsec]\")\n", - "ax2.set_ylabel(r\"Wavelength [$\\mu$m]\") \n", - "ax2.set_title(model + \", rotated by \" + str(angle) + \" degrees\");\n", + "img = ax2.imshow(emptycav_Jy_rot.value, origin=\"lower\", norm=\"log\", vmin=1e-5, vmax=1, aspect=\"auto\", \n", + " extent=(det_wave[0].value, det_wave[-1].value, det_xi[0].value, det_xi[-1].value))\n", + "ax2.set_ylabel(f\"position along slit [{det_xi.unit.to_string('latex')}]\")\n", + "ax2.set_xlabel(f\"Wavelength [{det_wave.unit.to_string('latex')}]\") \n", + "ax2.set_title(f\"{model}, rotated by {angle} degrees\")\n", "\n", "fig.subplots_adjust(right=0.8)\n", "cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n", - "fig.colorbar(img, cax=cbar_ax, label='Jy')" + "fig.colorbar(img, cax=cbar_ax, label=emptycav_Jy_rot.unit);" ] }, { @@ -715,21 +806,25 @@ "metadata": {}, "outputs": [], "source": [ - "for i, (model, result) in enumerate(results.items()):\n", - " result[1].data = flux_conv[:, None].value * (result[1].data - bgresult[1].data) / exptime\n", + "# Change to True if you want to save the calibrated data\n", + "save_calibrated = False\n", + "for model, result in rectified.items():\n", + " result[1].data = flux_conv.value * result[1].data / exptime\n", " result[1].header['BUNIT'] = \"e/s\"\n", - " outfile = (Path(fitsfiles[model]).stem + \"-simulated_LSS_L-bgsub_fluxcal\" \n", - " + Path(fitsfiles[model]).suffix)\n", - " result.writeto(outfile, overwrite=True)\n", - " print(\"--->\", outfile)\n", + " if save_calibrated:\n", + " outfile = (Path(fitsfiles[model]).stem + \"-simulated_LSS_L-bgsub_fluxcal\"\n", + " + Path(fitsfiles[model]).suffix)\n", + " result.writeto(outfile, overwrite=True)\n", + " print(\"--->\", outfile)\n", "\n", - "for i, (model, result) in enumerate(rotresults.items()):\n", - " result[1].data = flux_conv[:, None].value * (result[1].data - bgresult[1].data) / exptime\n", + "for model, result in rotrectified.items():\n", + " result[1].data = flux_conv.value * result[1].data / exptime\n", " result[1].header['BUNIT'] = \"e/s\"\n", - " outfile = (Path(fitsfiles[model]).stem + \"-rot_\" + str(angle) \n", - " + \"-simulated_LSS_L-bgsub_fluxcal\" + Path(fitsfiles[model]).suffix)\n", - " result.writeto(outfile, overwrite=True)\n", - " print(\"--->\", outfile)" + " if save_calibrated:\n", + " outfile = (Path(fitsfiles[model]).stem + f\"-rot_{angle}-simulated_LSS_L-bgsub_fluxcal\"\n", + " + Path(fitsfiles[model]).suffix)\n", + " result.writeto(outfile, overwrite=True)\n", + " print(\"--->\", outfile)" ] }, { @@ -745,7 +840,7 @@ "metadata": {}, "outputs": [], "source": [ - "result_extract = results['emptycav'][1].data[:, 1018:1030].sum(axis=1)" + "result_extract = rectified['emptycav'][1].data[731-hwidth:731+hwidth].sum(axis=0)" ] }, { @@ -766,10 +861,21 @@ "source": [ "plt.figure(figsize=(12, 6))\n", "plt.plot(det_wave, result_extract, label=\"flux-calibrated simulated spectrum\")\n", - "plt.plot(src_wave, src_extract, label=\"input spectrum\")\n", + "# The scaling for the source spectrum has been estimated by eye\n", + "# plt.plot(src_wave, src_extract * 1500, label=\"input spectrum\")\n", + "plt.plot(src_wave, src_extract * .42, label=\"input spectrum\")\n", + "plt.xlabel(f\"Wavelength [{src_wave.unit.to_string('latex')}]\")\n", + "plt.ylabel(\"Relative flux\")\n", "plt.legend()\n", "plt.xlim(3.1, 4.0);" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/METIS/docs/example_notebooks/LSS_AGN-01_preparation.ipynb b/METIS/docs/example_notebooks/LSS_AGN-01_preparation.ipynb index 158eb7f5..77624279 100644 --- a/METIS/docs/example_notebooks/LSS_AGN-01_preparation.ipynb +++ b/METIS/docs/example_notebooks/LSS_AGN-01_preparation.ipynb @@ -60,7 +60,7 @@ "fitsfile = \"tor_oa20_t9-5sg_Rout1.5_Rin0.13_p0__hypSh_oa40-50_tV-0.01g_D40_a0.2-0.4_tlt0_i90_total.fits\"\n", "sedfile = \"tor_oa20_t9-5sg_Rout1.5_Rin0.13_p0__hypSh_oa40-50_tV-0.01g_D40_a0.2-0.4_tlt0_i90_sed.dat\"\n", "\n", - "sim.download_example_data([fitsfile, sedfile])" + "fitsfile, sedfile = sim.download_example_data(fitsfile, sedfile)" ] }, { @@ -236,7 +236,8 @@ "metadata": {}, "outputs": [], "source": [ - "src = sim.Source(cube=\"AGN_sim_prepared.fits\")" + "with fits.open(\"AGN_sim_prepared.fits\") as cube_hdul:\n", + " src = sim.Source(cube=cube_hdul)" ] }, { @@ -439,7 +440,8 @@ "metadata": {}, "outputs": [], "source": [ - "src50 = sim.Source(cube=\"AGN_sim_rotated_50.fits\")" + "with fits.open(\"AGN_sim_rotated_50.fits\") as cube_hdul:\n", + " src50 = sim.Source(cube=cube_hdul)" ] }, { diff --git a/METIS/docs/example_notebooks/LSS_AGN-02_simulation.ipynb b/METIS/docs/example_notebooks/LSS_AGN-02_simulation.ipynb index 6aab00e6..b6563d5b 100644 --- a/METIS/docs/example_notebooks/LSS_AGN-02_simulation.ipynb +++ b/METIS/docs/example_notebooks/LSS_AGN-02_simulation.ipynb @@ -15,6 +15,7 @@ "source": [ "from matplotlib import pyplot as plt\n", "from matplotlib.colors import LogNorm\n", + "from astropy.io import fits\n", "%matplotlib inline" ] }, @@ -44,7 +45,7 @@ "metadata": {}, "outputs": [], "source": [ - "# sim.download_package([\"instruments/METIS\", \"telescopes/ELT\", \"locations/Armazones\"])" + "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])" ] }, { @@ -54,7 +55,7 @@ "outputs": [], "source": [ "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=['lss_n'],\n", - " properties={\"!OBS.exptime\": 3600,\n", + " properties={\"!OBS.exptime\": 3600, \"!OBS.dit\": None, \"!OBS.ndit\": None,\n", " \"!SIM.spectral.spectral_bin_width\": 3.0e-3})\n", "metis = sim.OpticalTrain(cmd)" ] @@ -75,8 +76,10 @@ "cube_01 = \"AGN_sim_prepared.fits\"\n", "cube_02 = \"AGN_sim_rotated_50.fits\"\n", "\n", - "src_01 = sim.Source(cube=cube_01)\n", - "src_02 = sim.Source(cube=cube_02)\n", + "with fits.open(cube_01) as cube_hdul:\n", + " src_01 = sim.Source(cube=cube_hdul)\n", + "with fits.open(cube_02) as cube_hdul:\n", + " src_02 = sim.Source(cube=cube_hdul)\n", "\n", "sky = sim.source.source_templates.empty_sky()" ] @@ -133,9 +136,9 @@ "source": [ "plt.figure(figsize=(16, 8))\n", "plt.subplot(121)\n", - "plt.imshow(hdul_01[1].data - hdul_sky[1].data + 1e5, origin='lower', norm=LogNorm(vmin=1e5, vmax=5e8))\n", + "plt.imshow(hdul_01[1].data - hdul_sky[1].data + 1e5, origin='lower', norm=\"log\")\n", "plt.subplot(122)\n", - "plt.imshow(hdul_02[1].data - hdul_sky[1].data + 1e5, origin='lower', norm=LogNorm(vmin=1e5, vmax=5e8))" + "plt.imshow(hdul_02[1].data - hdul_sky[1].data + 1e5, origin='lower', norm=\"log\")" ] }, { @@ -173,7 +176,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/METIS/docs/example_notebooks/demos/demo_adc_wheel.ipynb b/METIS/docs/example_notebooks/demos/demo_adc_wheel.ipynb index 57a3515e..065c1bd5 100644 --- a/METIS/docs/example_notebooks/demos/demo_adc_wheel.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_adc_wheel.ipynb @@ -28,9 +28,7 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "%matplotlib inline" + "import numpy as np" ] }, { @@ -107,7 +105,7 @@ }, "outputs": [], "source": [ - "metis['adc_wheel'].adcs" + "metis['adc_wheel'].get_table()" ] }, { diff --git a/METIS/docs/example_notebooks/demos/demo_auto_exposure.ipynb b/METIS/docs/example_notebooks/demos/demo_auto_exposure.ipynb index 963eb197..bcc6885d 100644 --- a/METIS/docs/example_notebooks/demos/demo_auto_exposure.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_auto_exposure.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is a setup/test/demonstration notebook for the `AutoExposure` effect in Scopesim. The notebook uses the `irdb/METIS` configuration. The observed source is blank sky, except for the last example where a star of 0 mag is used (Vega)." + "This is a setup/test/demonstration notebook for the `AutoExposure` effect in Scopesim. This effect splits the requested total exposure time into NDIT subexposures of integration time DIT such that the maximum counts in a single subexposure does not exceed a certain fill fraction of the detector full well. The final readout is the sum over the NDIT subexposures, i.e. corresponds to the total requested exposure time.\n", + "\n", + "The notebook uses the `irdb/METIS` configuration. The observed source is blank sky, except for the last example where a star of 0 mag is used (Vega)." ] }, { @@ -79,14 +81,21 @@ "metis.observe(src)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For `AutoExposure` to work the exposure time has to be given explicitely as a parameter to the `readout` method. If this is not done, the default values for `DIT` and `NDIT` will be used. The following is for an exposure time of 1 second. The resulting readout is divided by `NDIT` to produce the average over the `NDIT` subexposures; this allows direct comparison to the detector full well. " + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "outimg = metis.readout()[0][1].data \n", - "outimg /= metis.cmds[metis['summed_exposure'].meta['ndit']]\n", + "outimg = metis.readout(exptime=1)[0][1].data \n", + "outimg = outimg / metis.cmds[metis['summed_exposure'].meta['ndit']]\n", "\n", "full_well = metis.cmds[\"!DET.full_well\"]\n", "print(\"\\nResult\\n======\")\n", @@ -99,7 +108,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Exposure time can be changed with an argument to `metis.readout()`:" + "The same with a much larger exposure time of 1000 seconds:" ] }, { @@ -109,7 +118,30 @@ "outputs": [], "source": [ "outimg = metis.readout(exptime = 1000)[0][1].data \n", - "outimg /= metis.cmds[metis['summed_exposure'].meta['ndit']]\n", + "outimg = outimg / metis.cmds[metis['summed_exposure'].meta['ndit']]\n", + "\n", + "full_well = metis.cmds[\"!DET.full_well\"]\n", + "print(\"\\nResult\\n======\")\n", + "print(\"Maximum value in readout (per DIT): {:8.1f}\".format(outimg.max()))\n", + "print(\"Detector full well: {:8.1f}\".format(full_well))\n", + "print(\"Fill fraction: {:8.1f} per cent\".format(100 * outimg.max()/ full_well))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The desired fill fraction can be changed with the argument `fill_frac`. The default value of 75 per cent is a typical good value that keeps the detector counts within the linear regime." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "outimg = metis.readout(exptime = 1000, fill_frac=0.9)[0][1].data \n", + "outimg = outimg / metis.cmds[metis['summed_exposure'].meta['ndit']]\n", "\n", "full_well = metis.cmds[\"!DET.full_well\"]\n", "print(\"\\nResult\\n======\")\n", @@ -158,8 +190,8 @@ "metadata": {}, "outputs": [], "source": [ - "outimg = metis.readout()[0][1].data \n", - "outimg /= metis.cmds[metis['summed_exposure'].meta['ndit']]\n", + "outimg = metis.readout(exptime=1)[0][1].data \n", + "outimg = outimg / metis.cmds[metis['summed_exposure'].meta['ndit']]\n", "\n", "full_well = metis.cmds[\"!DET.full_well\"]\n", "print(\"\\nResult\\n======\")\n", @@ -209,7 +241,7 @@ "outputs": [], "source": [ "outimg = metis.readout(exptime=3600.)[0][1].data \n", - "outimg /= metis.cmds[metis['summed_exposure'].meta['ndit']]\n", + "outimg = outimg / metis.cmds[metis['summed_exposure'].meta['ndit']]\n", "\n", "full_well = metis.cmds[\"!DET.full_well\"]\n", "\n", @@ -224,7 +256,7 @@ "metadata": {}, "source": [ "# What happens when the source saturates the detector?\n", - "Use N-band imaging of Vega. DIT is automatically set to the minimum possible value, but the centre of the star still saturates the detector. In the final image, the star's profile is capped at the full well of the detector." + "We take an N-band image of Vega. `DIT` is automatically set to the minimum value supported by the detector, but the centre of the star still saturates the detector. In the final image, the star's profile is capped at the full well of the detector." ] }, { @@ -269,8 +301,8 @@ "metadata": {}, "outputs": [], "source": [ - "outimg = metis.readout()[0][1].data\n", - "outimg /= metis.cmds[metis['summed_exposure'].meta['ndit']]\n", + "outimg = metis.readout(exptime=1)[0][1].data\n", + "outimg = outimg / metis.cmds[metis['summed_exposure'].meta['ndit']]\n", "\n", "full_well = metis.cmds[\"!DET.full_well\"]\n", "\n", @@ -316,12 +348,28 @@ "print(\"Number of saturated pixels:\", npix)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The default values for the detector full well in the various modes reflects our current best knowledge of the properties of the actual METIS detectors. These values can be changed as in the following example, but be aware that this makes the simulations unrealistic." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "full_well = 1000 * metis.cmds[\"!DET.full_well\"]\n", + "outimg = metis.readout(exptime=1, full_well=full_well)[0][1].data\n", + "outimg = outimg / metis.cmds[metis['summed_exposure'].meta['ndit']]\n", + "\n", + "print(\"\\nResult\\n======\")\n", + "print(\"Maximum value in readout (per DIT): {:9.1f}\".format(outimg.max()))\n", + "print(\"Detector full well: {:9.1f}\".format(full_well))\n", + "print(\"Fill fraction: {:9.1f} per cent\".format(100 * outimg.max()/ full_well))" + ] } ], "metadata": { @@ -340,7 +388,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.ipynb b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.ipynb index 32abb477..3b6f2a7c 100644 --- a/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_chopping_and_nodding.ipynb @@ -6,7 +6,7 @@ "source": [ "# Chopping and nodding in Scopesim\n", "\n", - "This notebook demonstrates how to use the `ChopNod` effect in Scopesim. Both chopping and nodding are currently defined as two-point patterns, where the throw direction is given as a 2D vector (dx, dy) in `metis['chop_nod'].meta['chop_offsets']` and `metis['chop_nod'].meta['nod_offsets']`. For parallel nodding, the two vectors are parallel (typically nod_offset = - chop_offset, giving a three-point pattern), for perpendicular nodding, the vectors are orthogonal. " + "This notebook demonstrates how to use the `ChopNodCombiner` effect in Scopesim. Both chopping and nodding are currently defined as two-point patterns, where the throw direction is given as a 2D vector (dx, dy) in `metis['chop_nod'].meta['chop_offsets']` and `metis['chop_nod'].meta['nod_offsets']`. For parallel nodding, the two vectors are parallel (typically nod_offset = - chop_offset, giving a three-point pattern), for perpendicular nodding, the vectors are orthogonal. " ] }, { @@ -100,7 +100,7 @@ "outputs": [], "source": [ "metis.observe(src, update=True)\n", - "imghdu = metis.readout()[0][1]" + "imghdu = metis.readout(exptime=1)[0][1]" ] }, { @@ -110,7 +110,7 @@ "outputs": [], "source": [ "plt.imshow(imghdu.data, origin='lower', vmin=-5e7, vmax=5e7)\n", - "plt.colorbar()" + "plt.colorbar();" ] }, { @@ -135,7 +135,7 @@ "metadata": {}, "outputs": [], "source": [ - "imghdu_par = metis.readout()[0][1]" + "imghdu_par = metis.readout(exptime=1)[0][1]" ] }, { @@ -144,7 +144,8 @@ "metadata": {}, "outputs": [], "source": [ - "plt.imshow(imghdu_par.data, origin='lower', vmin=-5e7, vmax=5e7)" + "plt.imshow(imghdu_par.data, origin='lower', vmin=-5e7, vmax=5e7)\n", + "plt.colorbar();" ] }, { @@ -161,8 +162,9 @@ "outputs": [], "source": [ "metis['chop_nod'].meta['nod_offsets'] = [-3, 3]\n", - "imghdu_3 = metis.readout()[0][1]\n", - "plt.imshow(imghdu_3.data, origin='lower', vmin=-5e7, vmax=5e7)" + "imghdu_3 = metis.readout(exptime=1)[0][1]\n", + "plt.imshow(imghdu_3.data, origin='lower', vmin=-5e7, vmax=5e7)\n", + "plt.colorbar();" ] }, { @@ -173,8 +175,9 @@ "source": [ "metis['chop_nod'].meta['chop_offsets'] = [-3, 2]\n", "metis['chop_nod'].meta['nod_offsets'] = [2, 3]\n", - "imghdu_4 = metis.readout()[0][1]\n", - "plt.imshow(imghdu_4.data, origin='lower', vmin=-5e7, vmax=5e7)" + "imghdu_4 = metis.readout(exptime=1)[0][1]\n", + "plt.imshow(imghdu_4.data, origin='lower', vmin=-5e7, vmax=5e7)\n", + "plt.colorbar();" ] } ], @@ -194,7 +197,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/METIS/docs/example_notebooks/demos/demo_detector_modes.ipynb b/METIS/docs/example_notebooks/demos/demo_detector_modes.ipynb index 13b0dfaa..2effccdd 100644 --- a/METIS/docs/example_notebooks/demos/demo_detector_modes.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_detector_modes.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Selecting detector modes in METIS" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -13,7 +20,6 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "import numpy as np\n", "from astropy import units as u\n", "from matplotlib import pyplot as plt\n", @@ -100,6 +106,22 @@ "metis.cmds[\"!DET\"]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metis['readout_noise'].meta['noise_std']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The \"bang string\" here means that the value is taken from the `cmds` structure in the previous cell, i.e." + ] + }, { "cell_type": "code", "execution_count": null, @@ -189,8 +211,7 @@ "metadata": {}, "outputs": [], "source": [ - "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"],\n", - " properties={\"!OBS.exptime\": 1000})\n", + "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"])\n", "metis = sim.OpticalTrain(cmd)" ] }, @@ -202,7 +223,7 @@ "source": [ "print(\"Detector mode:\", metis.cmds[\"!OBS.detector_readout_mode\"])\n", "metis.observe(sky, update=True)\n", - "hdul_slow = metis.readout()[0]" + "hdul_slow = metis.readout(exptime=1000)[0]" ] }, { @@ -241,7 +262,7 @@ "metadata": {}, "outputs": [], "source": [ - "hdul_fast = metis.readout(detector_readout_mode=\"fast\")[0]" + "hdul_fast = metis.readout(detector_readout_mode=\"fast\", exptime=1000)[0]" ] }, { @@ -267,13 +288,13 @@ "outputs": [], "source": [ "print(f\"\"\"\n", - "Fast: ndit = {ndit_fast} \n", - " bg = {bg_fast:5.1f} expected: {bg_fast_expected:5.1f}\n", + "Fast: ndit = {ndit_fast} dit = {dit_fast:.2f}\n", + " bg = {bg_fast:5.1f} expected: {bg_fast_expected:5.1f}\n", " noise = {noise_fast:5.1f} expected: {noise_fast_expected:5.1f}\"\"\")\n", "print(f\"\"\"\n", - "Slow: ndit = {ndit_slow} \n", - " bg = {bg_slow:5.1f} expected: {bg_slow_expected:5.1f} \n", - " noise = {noise_slow:5.1f} expected: {noise_slow_expected:.1f}\"\"\")" + "Slow: ndit = {ndit_slow} dit = {dit_slow:.2f}\n", + " bg = {bg_slow:5.1f} expected: {bg_slow_expected:5.1f} \n", + " noise = {noise_slow:5.1f} expected: {noise_slow_expected:.1f}\"\"\")" ] }, { @@ -289,7 +310,14 @@ "metadata": {}, "outputs": [], "source": [ - "hdul_auto = metis.readout(detector_readout_mode=\"auto\")[0]" + "hdul_auto = metis.readout(detector_readout_mode=\"auto\", exptime=1000)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The advantage of the \"slow\" mode of the H2RG detector is the low readout noise. The \"fast\" mode permit smaller DITs and would be selected for bright sources that would saturate the detector at the minimum DIT of the \"slow\" mode. " ] }, { @@ -328,13 +356,20 @@ "source": [ "metis_n.observe(star, update=True)\n", "print(\"--- high-capacity mode ---\")\n", - "hdul_high = metis_n.readout(detector_readout_mode=\"high_capacity\")[0]\n", - "fullwell_high = metis.cmds[\"!DET.full_well\"]\n", - "ndit_high = metis.cmds[\"!OBS.ndit\"]\n", + "hdul_high = metis_n.readout(detector_readout_mode=\"high_capacity\", exptime=1)[0]\n", + "fullwell_high = metis_n.cmds[\"!DET.full_well\"]\n", + "ndit_high = metis_n.cmds[\"!OBS.ndit\"]\n", "print(\"--- low-capacity mode ---\")\n", - "hdul_low = metis_n.readout(detector_readout_mode=\"low_capacity\")[0]\n", - "ndit_low = metis.cmds[\"!OBS.ndit\"]\n", - "fullwell_low = metis.cmds[\"!DET.full_well\"]" + "hdul_low = metis_n.readout(detector_readout_mode=\"low_capacity\", exptime=1)[0]\n", + "ndit_low = metis_n.cmds[\"!OBS.ndit\"]\n", + "fullwell_low = metis_n.cmds[\"!DET.full_well\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We take the average over NDIT exposures (units: electrons per DIT) so that we can immediately compare the counts to the full wells for the two modes." ] }, { @@ -384,7 +419,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/METIS/docs/example_notebooks/demos/demo_filter_wheel.ipynb b/METIS/docs/example_notebooks/demos/demo_filter_wheel.ipynb index 041666b4..33f30bc2 100644 --- a/METIS/docs/example_notebooks/demos/demo_filter_wheel.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_filter_wheel.ipynb @@ -79,7 +79,8 @@ "metadata": {}, "outputs": [], "source": [ - "metis = sim.OpticalTrain(cmd)" + "metis = sim.OpticalTrain(cmd)\n", + "metis['quantization'].include = False" ] }, { @@ -95,7 +96,23 @@ "metadata": {}, "outputs": [], "source": [ - "metis['filter_wheel'].filters" + "metis['filter_wheel'].filters.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More information about the filters can be obtained with the `get_table()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metis['filter_wheel'].get_table()" ] }, { @@ -111,7 +128,7 @@ "metadata": {}, "outputs": [], "source": [ - "metis['filter_wheel'].current_filter" + "print(metis['filter_wheel'].current_filter)" ] }, { @@ -136,7 +153,7 @@ "metadata": {}, "outputs": [], "source": [ - "metis['filter_wheel'].current_filter" + "print(metis['filter_wheel'].current_filter)" ] }, { @@ -204,7 +221,7 @@ "metadata": {}, "outputs": [], "source": [ - "metis['nd_filter_wheel'].filters" + "metis['nd_filter_wheel'].filters.keys()" ] }, { @@ -213,7 +230,7 @@ "metadata": {}, "outputs": [], "source": [ - "metis['nd_filter_wheel'].current_filter" + "print(metis['nd_filter_wheel'].current_filter)" ] }, { @@ -280,16 +297,15 @@ "metadata": {}, "outputs": [], "source": [ - "plt.figure(figsize=(15, 4))\n", - "plt.subplot(131)\n", - "plt.imshow(hdu_open.data[700:1350, 700:1350], origin='lower', norm=LogNorm(vmin=1e-3, vmax=2e6))\n", - "plt.colorbar()\n", - "plt.subplot(132)\n", - "plt.imshow(hdu_OD3.data[700:1350, 700:1350], origin='lower', norm=LogNorm(vmin=1e-3, vmax=2e6))\n", - "plt.colorbar()\n", - "plt.subplot(133)\n", - "plt.imshow(hdu_OD4.data[700:1350, 700:1350], origin='lower', norm=LogNorm(vmin=1e-3, vmax=2e6))\n", - "plt.colorbar();" + "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(15, 4))\n", + "\n", + "im1 = ax1.imshow(hdu_open.data[700:1350, 700:1350], origin='lower', norm=LogNorm(vmin=1e-3, vmax=2e6))\n", + "ax1.set_title(\"ND filter: open\")\n", + "ax2.imshow(hdu_OD3.data[700:1350, 700:1350], origin='lower', norm=LogNorm(vmin=1e-3, vmax=2e6))\n", + "ax2.set_title(\"ND filter: 1e-3\")\n", + "ax3.imshow(hdu_OD4.data[700:1350, 700:1350], origin='lower', norm=LogNorm(vmin=1e-3, vmax=2e6))\n", + "ax3.set_title(\"ND filter: 1e-4\")\n", + "plt.colorbar(im1, ax=(ax1, ax2, ax3));" ] }, { @@ -325,7 +341,7 @@ "newfilter = sim.effects.ter_curves.TopHatFilterCurve(transmission=0.9, blue_cutoff=3.8, red_cutoff=3.9, \n", " name=\"custom_tophat\")\n", "metis['filter_wheel'].add_filter(newfilter)\n", - "metis['filter_wheel'].filters" + "metis['filter_wheel'].filters.keys()" ] }, { diff --git a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.ipynb b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.ipynb index 82cee47b..785fc2a0 100644 --- a/METIS/docs/example_notebooks/demos/demo_grating_efficiency.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_grating_efficiency.ipynb @@ -385,7 +385,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/METIS/docs/example_notebooks/demos/demo_lss_simple.ipynb b/METIS/docs/example_notebooks/demos/demo_lss_simple.ipynb index 63182ece..b6ca3eb7 100644 --- a/METIS/docs/example_notebooks/demos/demo_lss_simple.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_lss_simple.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulating long-slit spectroscopy in METIS" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -96,7 +103,7 @@ "outputs": [], "source": [ "metis.observe(src, update=True)\n", - "result = metis.readout(detector_readout_mode=\"auto\")[0][1]" + "result = metis.readout(detector_readout_mode=\"auto\", exptime=1)[0]" ] }, { @@ -106,7 +113,7 @@ "outputs": [], "source": [ "plt.figure(figsize=(12,10))\n", - "plt.imshow(result.data, origin='lower', norm=LogNorm(vmin=100))\n", + "plt.imshow(result[1].data, origin='lower', norm=LogNorm(vmin=100))\n", "plt.colorbar();" ] }, @@ -114,8 +121,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Realistic spectral mapping\n", - "The default configuration for METIS applies a mapping of the two-dimensional spectrum onto the detector that is perfectly linear in both the wavelength and spatial directions. The trace definitions for the actual expected mappings are also available. To use them, the parameter `!OBS.trace_file` needs to be set. The difference is fairly small." + "# Rectifying the spectrum\n", + "The default configuration for METIS applies a non-linear mapping of the two-dimensional spectrum onto the detector as determined from ray-tracing simulations of the optical system. The mapping can be reversed to obtain a rectified version of the 2D spectrum that is linear in both wavelength and spatial position and can easily be analysed. Note that this is optimistic compared to an actual data reduction process, where the mapping parameters would have to be estimated from data with some uncertainty. " ] }, { @@ -124,8 +131,7 @@ "metadata": {}, "outputs": [], "source": [ - "cmds = sim.UserCommands(use_instrument=\"METIS\", set_modes=['lss_l'])\n", - "cmds['!OBS.trace_file'] = \"TRACE_LSS_L.fits\"" + "tracelist = metis['spectral_traces']" ] }, { @@ -134,7 +140,14 @@ "metadata": {}, "outputs": [], "source": [ - "metis = sim.OpticalTrain(cmds)" + "rectified = tracelist.rectify_traces(result, -4, 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`rectified` is again an `HDUList` with the data in the first extension. The header of this extension contains the WCS keywords needed to translate from pixels to wavelength and spatial position. " ] }, { @@ -143,8 +156,14 @@ "metadata": {}, "outputs": [], "source": [ - "metis.observe(src, update=True)\n", - "result_2 = metis.readout()[0][1]" + "from astropy.wcs import WCS\n", + "from astropy import units as u\n", + "wcs = WCS(rectified[1].header)\n", + "naxis1, naxis2 = wcs._naxis\n", + "det_wave = wcs.all_pix2world(np.arange(naxis1), 1, 0)[0] * u.Unit(wcs.wcs.cunit[0])\n", + "det_xi = wcs.all_pix2world(1, np.arange(naxis2), 0)[1] * u.Unit(wcs.wcs.cunit[1])\n", + "det_wave = det_wave.to(u.um).value # ensure desired units and dismiss for plotting\n", + "det_xi = det_xi.to(u.arcsec).value" ] }, { @@ -153,8 +172,12 @@ "metadata": {}, "outputs": [], "source": [ - "plt.figure(figsize=(12,10))\n", - "plt.imshow(result_2.data, origin='lower', norm=LogNorm(vmin=100))\n", + "plt.figure(figsize=(12,7))\n", + "plt.imshow(rectified[1].data, norm=LogNorm(vmin=100),\n", + " extent=(det_wave[0], det_wave[-1], det_xi[0], det_xi[-1]),\n", + " origin='lower', aspect='auto')\n", + "plt.xlabel(r\"Wavelength [$\\mu$m]\")\n", + "plt.ylabel(\"Position along slit [arcsec]\")\n", "plt.colorbar();" ] } diff --git a/METIS/docs/example_notebooks/demos/demo_rectify_traces.ipynb b/METIS/docs/example_notebooks/demos/demo_rectify_traces.ipynb index 6877c3f9..204d2088 100644 --- a/METIS/docs/example_notebooks/demos/demo_rectify_traces.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_rectify_traces.ipynb @@ -139,7 +139,7 @@ "source": [ "cmds = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"lss_l\"],\n", " properties={\"!OBS.trace_file\": \"TRACE_LSS_L.fits\",\n", - " \"!OBS.slit\": \"B-28_6\"})\n", + " \"!OBS.slit\": \"B-28_6\"})\n", "\n", "metis = sim.OpticalTrain(cmds)" ] @@ -180,7 +180,7 @@ "metadata": {}, "outputs": [], "source": [ - "readout = metis.readout()[0]" + "readout = metis.readout(exptime=1)[0]" ] }, { diff --git a/METIS/docs/example_notebooks/demos/demo_slit_wheel.ipynb b/METIS/docs/example_notebooks/demos/demo_slit_wheel.ipynb index 29de9a6a..bd849a6e 100644 --- a/METIS/docs/example_notebooks/demos/demo_slit_wheel.ipynb +++ b/METIS/docs/example_notebooks/demos/demo_slit_wheel.ipynb @@ -17,7 +17,7 @@ "sim.bug_report()\n", "\n", "# Edit this path if you have a custom install directory, otherwise comment it out.\n", - "# sim.rc.__config__[\"!SIM.file.local_packages_path\"] = \"../../../../\"" + "sim.rc.__config__[\"!SIM.file.local_packages_path\"] = \"../../../../\"" ] }, { @@ -102,7 +102,7 @@ "metadata": {}, "outputs": [], "source": [ - "metis['slit_wheel'].slits" + "metis['slit_wheel'].get_table()" ] }, { @@ -149,7 +149,7 @@ "newslit = sim.effects.ApertureMask(name=\"Square\", array_dict={\"x\": [-1, 1, 1, -1], \"y\": [-1, -1, 1, 1]}, \n", " x_unit=\"arcsec\", y_unit=\"arcsec\")\n", "metis['slit_wheel'].add_slit(newslit)\n", - "metis['slit_wheel'].slits" + "metis['slit_wheel'].get_table()" ] }, { diff --git a/METIS/tests/test_readout_exptime.py b/METIS/tests/test_readout_exptime.py new file mode 100644 index 00000000..5855cba3 --- /dev/null +++ b/METIS/tests/test_readout_exptime.py @@ -0,0 +1,83 @@ +import copy +import os +from matplotlib import pyplot as plt +import scopesim as sim +import numpy +import scipy + +PKGS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) +sim.rc.__config__["!SIM.file.local_packages_path"] = PKGS_DIR + +PLOTS = False + + +class TestReadoutExptime: + """Test whether giving exptime in readout() is respected. + + The implementation of Quantization and Autoexposure has lead + to some problems in the past: + - The Quantization was not undone when the detector was reset + when doing a second readout. + - The exptime specified in readout is ignored in favor of the + default DIT/NDIT. + + This end-to-end regression test should prevent such problems + from reoccurring in the future. + + See also https://github.com/AstarVienna/ScopeSim/issues/438 + """ + star = sim.source.source_templates.star() + # Shift the source, so it can be detected through center_of_mass. + star.shift(0.5, 1.0) + cmd_l = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"]) + + # The first readout might ignore the exptime. + metis_l = sim.OpticalTrain(cmd_l) + metis_l.observe(star, update=True) + result_first = metis_l.readout(exptime=100.)[0] + # We need to copy the first readeout, because + # the second readout might overwrite the first... + result_first_copy = copy.deepcopy(result_first) + + # The second readout might not properly have the detector reset. + metis_l = sim.OpticalTrain(cmd_l) + metis_l.observe(star, update=True) + result_temp = metis_l.readout(exptime=100.)[0] + result_temp_copy = copy.deepcopy(result_temp) + assert (result_temp[1].data == result_temp_copy[1].data).all() + result_second = metis_l.readout(exptime=100.)[0] + + if PLOTS: + plt.figure(figsize=(10, 5)) + plt.subplot(121) + # Plot the copy, because the original might be altered. + plt.imshow(result_first_copy[1].data, origin='lower') + plt.title(r"First readout") + plt.subplot(122) + plt.imshow(result_second[1].data, origin='lower') + plt.title(r"Second readout") + plt.savefig("extrareadoutornot.png") + plt.show() + + mean_first = result_first[1].data.mean() + data1 = numpy.abs(result_first[1].data - mean_first) + x1, y1 = scipy.ndimage.center_of_mass(data1) + flux1 = data1.sum() + + mean_second = result_second[1].data.mean() + data2 = numpy.abs(result_second[1].data - mean_second) + x2, y2 = scipy.ndimage.center_of_mass(data2) + flux2 = data2.sum() + + print(flux1, flux2) + + + assert 1090 < x2 < 1110 + assert 1050 < y2 < 1070 + assert 10**12 < flux2 + assert 1090 < x1 < 1110 + assert 1050 < y1 < 1070 + assert 10**12 < flux1 + + # TOOD: fix this bug, https://github.com/AstarVienna/ScopeSim/issues/439 + # assert (result_temp[1].data == result_temp_copy[1].data).all() diff --git a/METIS/version.yaml b/METIS/version.yaml index d878faf6..1a4d8bfd 100644 --- a/METIS/version.yaml +++ b/METIS/version.yaml @@ -1,3 +1,3 @@ release: stable -timestamp: '2023-07-11 10:03:42' -version: '2023-07-11' +timestamp: '2024-05-14 21:03:42' +version: '2024-05-14' diff --git a/MICADO/default.yaml b/MICADO/default.yaml index 0ba17d75..115356d6 100644 --- a/MICADO/default.yaml +++ b/MICADO/default.yaml @@ -5,6 +5,7 @@ object : configuration alias : OBS name : MICADO_default_configuration description : default parameters needed for a MICADO simulation +status: development date_modified: 2023-07-13 changes: - 2023-07-13 (OC) add modes for FDR slits, deprecate pre-FDR slits @@ -61,6 +62,7 @@ mode_yamls : alias: OBS name : IMG_4mas description : "wide-field imager : 4mas/pix" + status: development yamls : - MICADO_IMG_wide.yaml properties : @@ -72,6 +74,7 @@ mode_yamls : alias: OBS name : IMG_1.5mas description : "high resolution imager : 1.5mas/pix" + status: development yamls : - MICADO_IMG_zoom.yaml properties : @@ -83,6 +86,7 @@ mode_yamls : alias: OBS name : IMG_HCI description : "High contrast imaging" + status: experimental yamls : - MICADO_IMG_HCI.yaml @@ -90,6 +94,7 @@ mode_yamls : alias: OBS name : SPEC_15000x20 description : "spectrograph : slit size 15000x20mas" + status: development yamls : - MICADO_SPEC.yaml properties : @@ -103,6 +108,7 @@ mode_yamls : alias: OBS name : SPEC_3000x48 description : "spectrograph : slit size 3000x48mas" + status: development yamls : - MICADO_SPEC.yaml properties : @@ -116,6 +122,7 @@ mode_yamls : alias : OBS name : SPEC_3000x16 description : "spectrograph : slit size 3000x16mas" + status: development yamls : - MICADO_SPEC.yaml properties : @@ -129,6 +136,7 @@ mode_yamls : alias: OBS name : SPEC_15000x50 description : "spectrograph : slit size 15000x50mas" + status: deprecated deprecate : "Deprecated instrument mode. For spectroscopy use - SPEC_3000x16 - SPEC_3000x48 @@ -146,6 +154,7 @@ mode_yamls : alias: OBS name : SPEC_3000x50 description : "spectrograph : slit size 3000x50mas" + status: deprecated deprecate : "Deprecated instrument mode. For spectroscopy use - SPEC_3000x16 - SPEC_3000x48 @@ -163,6 +172,7 @@ mode_yamls : alias: OBS name : SPEC_3000x20 description : "spectrograph : slit size 3000x20mas" + status: deprecated deprecate : "Deprecated instrument mode. For spectroscopy use - SPEC_3000x16 - SPEC_3000x48 diff --git a/MICADO/docs/example_notebooks/3_scopesim_SCAO_4mas_fv-psf.ipynb b/MICADO/docs/example_notebooks/3_scopesim_SCAO_4mas_fv-psf.ipynb index a8035f34..f8311a02 100644 --- a/MICADO/docs/example_notebooks/3_scopesim_SCAO_4mas_fv-psf.ipynb +++ b/MICADO/docs/example_notebooks/3_scopesim_SCAO_4mas_fv-psf.ipynb @@ -21,20 +21,10 @@ "execution_count": 1, "id": "unlimited-cliff", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ghost\\AppData\\Local\\Temp\\ipykernel_17984\\2683991280.py:10: DeprecationWarning: In a future version top level function calls will be removed. Always use this syntax: from module.submodule import function\n", - " import scopesim_templates as sim_tp\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import datetime\n", - "import shutil\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", @@ -87,7 +77,16 @@ "execution_count": 2, "id": "altered-paris", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32mimf - sample_imf: Setting maximum allowed mass to 10000\u001b[0m\n", + "\u001b[32mimf - sample_imf: Loop 0 added 1.01e+04 Msun to previous total of 0.00e+00 Msun\u001b[0m\n" + ] + } + ], "source": [ "cluster = sim_tp.cluster(mass=10000, core_radius=0.5, distance=8000)" ] @@ -268,12 +267,17 @@ { "cell_type": "code", "execution_count": 7, - "id": "5d21be74", + "id": "c9b0961b", "metadata": {}, "outputs": [], "source": [ - "from astropy.utils.data import download_file\n", - "fname2 = download_file(\"https://scopesim.univie.ac.at/InstPkgSvr/psfs/AnisoCADO_SCAO_FVPSF_4mas_EsoMedian_20190328.fits\", cache=True)" + "from pooch import retrieve\n", + "fname = retrieve(\n", + " \"https://scopesim.univie.ac.at/InstPkgSvr/psfs/AnisoCADO_SCAO_FVPSF_4mas_EsoMedian_20190328.fits\",\n", + " known_hash=\"a9bd309e5ac025f2aa5f8ded85323465578906e47025ce86501985c9b258474c\",\n", + " fname=\"AnisoCADO_SCAO_FVPSF_4mas_EsoMedian_20190328.fits\",\n", + " progressbar=True,\n", + ")" ] }, { @@ -283,8 +287,6 @@ "metadata": {}, "outputs": [], "source": [ - "fname = \"AnisoCADO_SCAO_FVPSF_4mas_EsoMedian_20190328.fits\"\n", - "shutil.copy(fname2, fname)\n", "fv_psf = sim.effects.psfs.FieldVaryingPSF(filename=fname, name=\"SCAO_FV_PSF\")" ] }, diff --git a/MICADO/test_micado/test_micado_spec.py b/MICADO/test_micado/test_micado_spec.py index 9b820594..1d7ae879 100644 --- a/MICADO/test_micado/test_micado_spec.py +++ b/MICADO/test_micado/test_micado_spec.py @@ -67,12 +67,23 @@ def test_runs_spec_hk_15000x50(self): cmds.cmds["!DET.dit"] = 3600 cmds.cmds["!OBS.filter_name_fw1"] = "open" cmds.cmds["!OBS.filter_name_fw2"] = "Ks" + # cmds.cmds["!SIM.spectral."] micado = sim.OpticalTrain(cmds) FULL_DETECTOR = True micado["detector_window"].include = not FULL_DETECTOR micado["full_detector_array"].include = FULL_DETECTOR micado.observe(src) + hdul = micado.readout() + + if PLOTS: + plt.subplot(121) + plt.imshow(micado.image_planes[0].data, norm=LogNorm(), + origin="lower") + plt.subplot(122) + plt.imshow(hdul[1].data, norm=LogNorm(), origin="lower") + + plt.show() spec_int_flux = np.sum(micado.image_planes[0].data, axis=0) spec_av_flux = np.median(spec_int_flux[40:2600]) @@ -82,12 +93,3 @@ def test_runs_spec_hk_15000x50(self): scale_factor = slit_width * grating_efficiency assert spec_av_flux / scale_factor == approx(img_av_flux, rel=0.1) - - # plt.subplot(121) - # plt.imshow(micado.image_planes[0].data, norm=LogNorm(), - # origin="lower") - # - # plt.subplot(122) - # plt.imshow(hdul[1].data, norm=LogNorm(), origin="lower") - # - # plt.show() diff --git a/MICADO_Sci/README.md b/MICADO_Sci/README.md new file mode 100644 index 00000000..4d908172 --- /dev/null +++ b/MICADO_Sci/README.md @@ -0,0 +1,4 @@ +# MICADO Science package + +> [!CAUTION] +> The MICADO_Sci package is **NO LONGER SUPPORTED**!! Use the MICADO package instead. MICADO_Sci will be removed soon! diff --git a/MICADO_Sci/default.yaml b/MICADO_Sci/default.yaml index 5082693d..2250f54e 100644 --- a/MICADO_Sci/default.yaml +++ b/MICADO_Sci/default.yaml @@ -9,6 +9,7 @@ object : configuration alias : OBS name : MICADO_sci_default_configuration description : default parameters needed for a MICADO-Sci simulation +status : deprecated packages : - Armazones diff --git a/MICADO_Sci/sci_readme.md b/MICADO_Sci/sci_readme.md deleted file mode 100644 index b02370d6..00000000 --- a/MICADO_Sci/sci_readme.md +++ /dev/null @@ -1,43 +0,0 @@ -# MICADO Science package - -## What effects are needed - -### General -* SkycalcTERCurve [MICADO_Sci.yaml:ATMO] -* Telescope system TER [MICADO_Sci.yaml:TEL] -* FilterCurve [MICADO_Sci.yaml:INST] -* MICADO common optics [MICADO_Sci.yaml:INST] -* QE curve [MICADO_Sci_detector.yaml:DET] -* RON [MICADO_Sci_detector.yaml:DET] -* Dark [MICADO_Sci_detector.yaml:DET] -* average stack ** -* Shot_noise [MICADO_Sci_detector.yaml:DET] - -#### SCAO -* RO TER [MICADO_Sci_SCAO.yaml:INST] -* Detector Window [MICADO_sci_detector.yaml:DET, (w,h) in MICADO_Sci_SCAO.yaml:DET] -* PSF - * SCAO FVPSF ** - * SCAO AnisoCADO ConstPSF [MICADO_Sci_SCAO.yaml:INST] - -#### MCAO -* MORFEO TER [MICADO_Sci_MCAO.yaml:INST] -* Detector Window [MICADO_sci_detector.yaml:DET, (w,h) in MICADO_Sci_MCAO.yaml:DET] -* PSF - * MCAO StrehlPSF (max SR JHK-13/30/50) [MICADO_Sci_SCAO.yaml:INST] - - -### SPEC -* RO TER -* SPEC TER -* SkyCalcTERCurve -* Detector Window - -* ApertureMask - * filename_format - * mask_name - -* XiLamConverter -* XiLamStrehlPSF (max SR JHK-40/60/80) -* BasicTraceMap -* AtmopshericDiffraction diff --git a/MICADO_Sci/test_micado_sci/test_micado_sci.py b/MICADO_Sci/test_micado_sci/test_micado_sci.py index 7a2de785..a82bfa8b 100644 --- a/MICADO_Sci/test_micado_sci/test_micado_sci.py +++ b/MICADO_Sci/test_micado_sci/test_micado_sci.py @@ -9,6 +9,8 @@ import scopesim as sim from scopesim import rc +pytest.skip(allow_module_level=True) + TOP_PATH = pth.abspath(pth.join(pth.dirname(__file__), "../../")) rc.__config__["!SIM.file.local_packages_path"] = TOP_PATH diff --git a/MICADO_Sci/test_micado_sci/test_radiometry.py b/MICADO_Sci/test_micado_sci/test_radiometry.py index c16d9ead..ec6485e0 100644 --- a/MICADO_Sci/test_micado_sci/test_radiometry.py +++ b/MICADO_Sci/test_micado_sci/test_radiometry.py @@ -37,6 +37,8 @@ import scopesim as sim from scopesim import rc +pytest.skip(allow_module_level=True) + TOP_PATH = pth.abspath(pth.join(pth.dirname(__file__), "../../")) rc.__config__["!SIM.file.local_packages_path"] = TOP_PATH diff --git a/MICADO_Sci/test_micado_sci/test_spectral_trace_files.py b/MICADO_Sci/test_micado_sci/test_spectral_trace_files.py index bcb223e7..efc7629f 100644 --- a/MICADO_Sci/test_micado_sci/test_spectral_trace_files.py +++ b/MICADO_Sci/test_micado_sci/test_spectral_trace_files.py @@ -9,6 +9,8 @@ from scopesim.tests.mocks.py_objects import header_objects as ho from scopesim import rc +pytest.skip(allow_module_level=True) + PLOTS = False DATA_DIR = pth.abspath(pth.join(pth.dirname(__file__), "../")) rc.__search_path__.insert(0, DATA_DIR) diff --git a/OSIRIS/default.yaml b/OSIRIS/default.yaml index af99ccc1..288298ae 100644 --- a/OSIRIS/default.yaml +++ b/OSIRIS/default.yaml @@ -4,6 +4,7 @@ object : configuration alias : OBS name : OSIRIS_default_configuration description : default parameters needed for a OSIRIS simulation +status: experimental date_created: 2022-03-02 date_modified: 2022-03-02 changes: diff --git a/OSIRIS/tests/test_compiles.py b/OSIRIS/tests/test_compiles.py index b572c991..9e4c0f93 100644 --- a/OSIRIS/tests/test_compiles.py +++ b/OSIRIS/tests/test_compiles.py @@ -72,15 +72,21 @@ def test_run_lss_simulation(self): src_comb = src1 + src3 + src4 + src5 - cmds = sim.UserCommands(use_instrument="OSIRIS", set_modes=["LSS"], - properties={"!OBS.dit": 60}) - # cmds["!OBS.dit"] = 60 + cmds = sim.UserCommands( + use_instrument="OSIRIS", + set_modes=["LSS"], + properties={ + # dit is used in the optical train, + # while exptime ends up in the headers + "!OBS.dit": 60, + "!OBS.exptime": 60, + }) cmds["!ATMO.seeing"] = 0.8 cmds["!OBS.grating_name"] = "R2500V" osiris = sim.OpticalTrain(cmds) osiris.observe(src_comb) - hdulist = osiris.readout(exptime=60)[0] + hdulist = osiris.readout()[0] if PLOTS: plt.imshow(hdulist[1].data, norm=LogNorm()) @@ -102,15 +108,19 @@ def test_run_maat_simulation(self): # n stars, mag_min, mag_max, width=[arcsec] src = star_field(5**2, 10, 10, 8, use_grid=True) - cmds = sim.UserCommands(use_instrument="OSIRIS", set_modes=["MAAT"], - properties={"!OBS.dit": 60}) - # cmds["!OBS.dit"] = 60 + cmds = sim.UserCommands( + use_instrument="OSIRIS", + set_modes=["MAAT"], + properties={ + "!OBS.dit": 60, + "!OBS.exptime": 60, + }) cmds["!ATMO.seeing"] = 0.8 # cmds["!OBS.grating_name"] = "R2500V" osiris = sim.OpticalTrain(cmds) osiris.observe(src) - hdulist = osiris.readout(exptime=60)[0] + hdulist = osiris.readout()[0] if PLOTS: plt.imshow(hdulist[1].data, norm=LogNorm()) diff --git a/Paranal/tests/test_paranal.py b/Paranal/tests/test_paranal.py index a4970f8b..48a0189f 100644 --- a/Paranal/tests/test_paranal.py +++ b/Paranal/tests/test_paranal.py @@ -116,8 +116,8 @@ class : SkycalcTERCurve "gain": [1.0]} x_cen_unit : mm y_cen_unit : mm - x_len_unit : mm - y_len_unit : mm + x_size_unit : mm + x_size_unit : mm pixsize_unit : mm angle_unit : deg gain_unit : electron/adu @@ -152,7 +152,7 @@ def test_flux_scales_with_pixel_scale(self, pixel_scale): @pytest.mark.parametrize("filter_name, bg_level", [("J", 674), ("H", 4693), ("Ks", 1026)]) - def test_flux_scales_with_pixel_scale(self, filter_name, bg_level): + def test_flux_scales_with_pixel_scale_JHK(self, filter_name, bg_level): yaml_text = YAML_TEXT % (WAVE_MIN, WAVE_MAX, PIXEL_SCALE, PIXEL_SCALE, filter_name) diff --git a/ViennaLT/default.yaml b/ViennaLT/default.yaml index 52639b5e..ea41c315 100644 --- a/ViennaLT/default.yaml +++ b/ViennaLT/default.yaml @@ -4,6 +4,7 @@ object : configuration alias : OBS name : ViLT_default_configuration description : Vienna Little Telescope configuration +status: experimental packages : - LFOA diff --git a/WFC3/default.yaml b/WFC3/default.yaml index 7a0d7f92..e8ab9209 100644 --- a/WFC3/default.yaml +++ b/WFC3/default.yaml @@ -7,6 +7,7 @@ object : configuration alias : OBS name : HST_WFC3_default_config description : defatult configuration for HST WFC3 UVIS and NIR imaging +status: experimental packages: - HST diff --git a/WFC3/test_wfc3/test_full_package_wfc3_ir.py b/WFC3/test_wfc3/test_full_package_wfc3_ir.py index 9a263416..8ad95f34 100644 --- a/WFC3/test_wfc3/test_full_package_wfc3_ir.py +++ b/WFC3/test_wfc3/test_full_package_wfc3_ir.py @@ -129,8 +129,8 @@ def test_works_seamlessly_for_wfc3_package(self, capsys): # test assert there are 1 detector hdu = opt.readout()[0] - assert len(opt.detector_arrays[0].detectors) == 1 - for detector in opt.detector_arrays[0].detectors: + assert len(opt.detector_managers[0]) == 1 + for detector in opt.detector_managers[0]: assert detector.hdu.header["NAXIS1"] == 1024 assert detector.hdu.header["NAXIS2"] == 1024 @@ -157,12 +157,12 @@ def test_background_is_similar_to_online_etc(self): def test_actually_produces_stars(self): cmd = scopesim.UserCommands(use_instrument="WFC3", - properties={"!OBS.dit": 100, - "!OBS.ndit": 10}) + properties={"!OBS.dit": 1, + "!OBS.ndit": 1}) cmd.ignore_effects += ["detector_linearity"] opt = scopesim.OpticalTrain(cmd) - src = scopesim.source.source_templates.star_field(10000, 5, 15, 440) + src = scopesim.source.source_templates.star_field(10000, 10, 10, 440, use_grid=True) opt.observe(src) hdu = opt.readout()[0] @@ -171,8 +171,6 @@ def test_actually_produces_stars(self): hdu_av = np.average(hdu[1].data) exptime = cmd["!OBS.ndit"] * cmd["!OBS.dit"] - assert hdu_av == approx(implane_av * exptime, rel=0.01) - if PLOTS: plt.subplot(1, 2, 1) plt.imshow(opt.image_planes[0].image[128:2048, 128:2048].T, @@ -180,8 +178,10 @@ def test_actually_produces_stars(self): plt.colorbar() plt.subplot(1, 2, 2) - plt.imshow(hdu[1].data[128:2048, 128:2048].T, norm=LogNorm(), - vmax=3e7) + plt.imshow(hdu[1].data[128:2048, 128:2048].T, + norm=LogNorm(vmax=3e7, vmin=1e4)) plt.colorbar() plt.show() + + assert hdu_av == approx(implane_av * exptime, rel=0.01) diff --git a/docs/source/pkg_status.md b/docs/source/pkg_status.md new file mode 100644 index 00000000..dcd372e0 --- /dev/null +++ b/docs/source/pkg_status.md @@ -0,0 +1,19 @@ +# Definition of status keyword + +This can be added optionally to both the top-level of an instrument's `default.yaml` or to a mode section therein. By convention, it usually goes after the "description" key. + +## Permitted values and their meaning: + +- concept: The package or mode is not yet implemented and exists merely as a placeholder or to collect resource needed to actually implement it. Selecting this mode will result in a `NotImplementedError`. +- experimental: Initial implementation of the package or mode is functional, but may still contain placeholders. Simulation results might not be representative of physical instrument. +- development: Mostly stable working prototype, using best-guess values for effect definitions and data. Simulation results should be relatively close to physical instrument. +- production: The package or mode is fully functional and stable and not expected to change substantially in the future. Simulation results are validated with reference documents. +- deprecated: No longer supported, selecting this mode or package will result in a `DeprecationWarning`. + +The following values are currently under consideration to be added, but their role is not yet defined: + +- engineering + +## Combining package and mode statuses + +Basically, a package should only be marked as being in "production" status if all modes also have this status (except deprecated modes). A package in "development" status can contain modes of all statuses. In a "deprecated" package, all modes are implicitly considered deprecated. A package still in "concept" status should only contain modes of the same status. Once one mode is at least "experimental" (and thus functional), the whole package should be marked with the same status (otherwise it cannot be used), but other modes may very well still be in "concept" stage. diff --git a/setup.py b/setup.py index bbc0869e..af6a444e 100644 --- a/setup.py +++ b/setup.py @@ -36,7 +36,7 @@ def setup_package(): "docutils", "requests>=2.20", "beautifulsoup4>=4.4", - "lxml", + "lxml[html_clean]", "pyyaml>5.1", "pysftp", diff --git a/test_package/default.yaml b/test_package/default.yaml index 5be61d83..812210c3 100644 --- a/test_package/default.yaml +++ b/test_package/default.yaml @@ -2,6 +2,7 @@ object : observation alias : OBS name : test_instrument +status: production packages : - test_package