“Marvin”的版本间差异
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(→配置) |
(→安装) |
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(未显示同一用户的10个中间版本) | |||
第2行: | 第2行: | ||
*pip install sdss-marvin |
*pip install sdss-marvin |
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*pip install -U sdss-marvin |
*pip install -U sdss-marvin |
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*marvin的依赖包比较多, conda 环境更新后会出现不兼容现象,可以用pip install abc==versionfna方法降级相关包 |
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==配置== |
==配置== |
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第12行: | 第13行: | ||
marvin.config.mode = 'local' # or 'remote',‘auto’ |
marvin.config.mode = 'local' # or 'remote',‘auto’ |
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marvin.config.access = 'collab' # 'DR15' |
marvin.config.access = 'collab' # 'DR15' |
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marvin.config.setRelease("MPL-10") |
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* collab模式下要设置.netrc文件 |
* collab模式下要设置.netrc文件 |
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==Maps== |
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*读取dap中的map |
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*map的datamodel,可以参考 [https://sdss-marvin.readthedocs.io/en/latest/datamodel/mpl10.html?highlight=property%20name#dap-datamodel] |
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dapmap = Maps(DAPfile) # Read dap maps |
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dapmap.datamodel |
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ha = dapmap['gflux ha'] |
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gflux=dapmap.getMap('spx_mflux',channel=None) |
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* 速度弥散度的改正 inst_sigma_correction() #marvin.tools.quantities.map.Map.inst_sigma_correction |
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Ha_sig=dapmap.getMap('emline_gsigma',channel='ha') |
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#Ha_sigcorr=dapmap.getMap('emline_gsigmacorr',channel='ha') |
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Ha_vdis=Ha.inst_sigma_correction() |
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st_sig=dapmap.getMap('stellar_sigma') |
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#st_sigcorr=dapmap.getMap('stellar_sigmacorr',channel='fit') |
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st_vdis=st_sig.inst_sigma_correction() |
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==Mask== |
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===pixmask=== |
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*DAP中的map类都有pixmap的类属性,可以help查看 |
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*比如获得ha的map之后,可以用ha.pixmask.schema查看 |
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:pixmask的第0位是'NOCOV',就是是否天区覆盖,gflux 并没有设置这个bitmask,但是画图同样可以显示NOCOV天区,原因是因为用了ivar==0判据 |
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:np.where(gflux.ivar == 0)[0].shape |
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*ha.mask其实就是ha.pixmask.mask |
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===manga_target1.mask=== |
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*选源的时候的mask |
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from marvin.utils.general.maskbit import Maskbit |
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mngtarg1 = Maskbit('MANGA_TARGET1') |
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mngtarg1.schema |
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*map也可以查看这个mask |
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ha.manga_target1.mask |
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ha.manga_target1.bits |
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ha.manga_target1.labels |
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===Translating Amongst Mask Values, Bits, and labels=== |
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ha.pixmask.values_to_bits(1073741843) # [0, 1, 4, 30] |
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ha.pixmask.values_to_labels(1073741843) #['NOCOV', 'LOWCOV', 'NOVALUE', 'DONOTUSE'] |
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* Translate one label |
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ha.pixmask.labels_to_value('NOCOV') # 1 |
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ha.pixmask.labels_to_bits('NOCOV') # [0] |
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* Translate multiple labels |
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ha.pixmask.labels_to_value(['NOCOV', 'UNRELIABLE']) # 33 |
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ha.pixmask.labels_to_bits(['NOCOV', 'UNRELIABLE']) # [0, 5] |
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===Making a Custom Mask=== |
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* Mask of regions with no IFU coverage |
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nocov = ha.pixmask.get_mask('NOCOV') |
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* Mask of regions with low Halpha flux and marked as DONOTUSE |
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low_ha = (ha.value < 1e-17) * ha.pixmask.labels_to_value('DONOTUSE') |
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* Combine masks using bitwise OR (`|`) |
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my_mask = nocov | low_ha |
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fig, ax = ha.plot(mask=my_mask) |
2023年2月20日 (一) 15:07的最新版本
安装
- pip install sdss-marvin
- pip install -U sdss-marvin
- marvin的依赖包比较多, conda 环境更新后会出现不兼容现象,可以用pip install abc==versionfna方法降级相关包
配置
- 注意设置一些环境变量,关键是要找到dapall文件
setenv SAS_BASE_DIR $HOME/MaNGA setenv MANGA_SPECTRO_REDUX $SAS_BASE_DIR/mangawork/manga/spectro/redux setenv MANGA_SPECTRO_ANALYSIS $SAS_BASE_DIR/mangawork/manga/spectro/analysis
- mode
import marvin marvin.config.mode = 'local' # or 'remote',‘auto’ marvin.config.access = 'collab' # 'DR15' marvin.config.setRelease("MPL-10")
- collab模式下要设置.netrc文件
Maps
- 读取dap中的map
- map的datamodel,可以参考 [1]
dapmap = Maps(DAPfile) # Read dap maps dapmap.datamodel ha = dapmap['gflux ha'] gflux=dapmap.getMap('spx_mflux',channel=None)
- 速度弥散度的改正 inst_sigma_correction() #marvin.tools.quantities.map.Map.inst_sigma_correction
Ha_sig=dapmap.getMap('emline_gsigma',channel='ha') #Ha_sigcorr=dapmap.getMap('emline_gsigmacorr',channel='ha') Ha_vdis=Ha.inst_sigma_correction() st_sig=dapmap.getMap('stellar_sigma') #st_sigcorr=dapmap.getMap('stellar_sigmacorr',channel='fit') st_vdis=st_sig.inst_sigma_correction()
Mask
pixmask
- DAP中的map类都有pixmap的类属性,可以help查看
- 比如获得ha的map之后,可以用ha.pixmask.schema查看
- pixmask的第0位是'NOCOV',就是是否天区覆盖,gflux 并没有设置这个bitmask,但是画图同样可以显示NOCOV天区,原因是因为用了ivar==0判据
- np.where(gflux.ivar == 0)[0].shape
- ha.mask其实就是ha.pixmask.mask
manga_target1.mask
- 选源的时候的mask
from marvin.utils.general.maskbit import Maskbit mngtarg1 = Maskbit('MANGA_TARGET1') mngtarg1.schema
- map也可以查看这个mask
ha.manga_target1.mask ha.manga_target1.bits ha.manga_target1.labels
Translating Amongst Mask Values, Bits, and labels
ha.pixmask.values_to_bits(1073741843) # [0, 1, 4, 30] ha.pixmask.values_to_labels(1073741843) #['NOCOV', 'LOWCOV', 'NOVALUE', 'DONOTUSE']
- Translate one label
ha.pixmask.labels_to_value('NOCOV') # 1 ha.pixmask.labels_to_bits('NOCOV') # [0]
- Translate multiple labels
ha.pixmask.labels_to_value(['NOCOV', 'UNRELIABLE']) # 33 ha.pixmask.labels_to_bits(['NOCOV', 'UNRELIABLE']) # [0, 5]
Making a Custom Mask
- Mask of regions with no IFU coverage
nocov = ha.pixmask.get_mask('NOCOV')
- Mask of regions with low Halpha flux and marked as DONOTUSE
low_ha = (ha.value < 1e-17) * ha.pixmask.labels_to_value('DONOTUSE')
- Combine masks using bitwise OR (`|`)
my_mask = nocov | low_ha fig, ax = ha.plot(mask=my_mask)