spacepy.LANLstar.LANLmax¶
- spacepy.LANLstar.LANLmax(inputdict, extMag)[source]¶
Calculate last closed drift shell (Lmax)
Based on the L* artificial neural network (ANN) trained from different magnetospheric field models.
- Parameters:
- extMaglist of string(s)
containing one or more of the following external Magnetic field models: ‘OPDYN’, ‘OPQUIET’, ‘T89’, ‘T96’, ‘T01QUIET’, ‘T01STORM’, ‘T05’
- inputdictdictionary
containing the following keys, each entry is a list or array. Note the keys for the above models are different.
- – For OPDYN:
[‘Year’, ‘DOY’, ‘Hr’, ‘Dst’, ‘dens’, ‘velo’, ‘BzIMF’, ‘PA’]
- – For OPQUIET:
[‘Year’, ‘DOY’, ‘Hr’, ‘Dst’, ‘dens’, ‘velo’, ‘BzIMF’, ‘PA’]
- – For T89:
[‘Year’, ‘DOY’, ‘Hr’, ‘Kp’, ‘Pdyn’, ‘ByIMF’, ‘BzIMF’, ‘PA’]
- – For T96:
[‘Year’, ‘DOY’, ‘Hr’, ‘Dst’, ‘Pdyn’, ‘ByIMF’, ‘BzIMF’,’PA’]
- – For T01QUIET:
[‘Year’, ‘DOY’, ‘Hr’, ‘Dst’, ‘Pdyn’, ‘ByIMF’, ‘BzIMF’, ‘G1’, ‘G2’,’PA’]
- – For T01STORM:
[‘Year’, ‘DOY’, ‘Hr’, ‘Dst’, ‘Pdyn’, ‘ByIMF’, ‘BzIMF’, ‘G2’, ‘G3’, ‘PA’]
- – For T05:
[‘Year’, ‘DOY’, ‘Hr’, ‘Dst’, ‘Pdyn’, ‘ByIMF’, ‘BzIMF’, ‘W1’,’W2’,’W3’,’W4’,’W5’,’W6’, ‘PA’]
Dictionaries with numpy vectors are allowed.
- Returns:
- outdictionary
Lmax array for each key which corresponds to the specified magnetic field model.
Examples
>>> import spacepy.LANLstar as LS >>> inputdict = {} >>> inputdict['Kp'] = [2.7 ] # Kp index >>> inputdict['Dst'] = [7.7777 ] # Dst index (nT) >>> inputdict['dens'] = [4.1011 ] # solar wind density (/cc) >>> inputdict['velo'] = [400.1011 ] # solar wind velocity (km/s) >>> inputdict['Pdyn'] = [4.1011 ] # solar wind dynamic pressure (nPa) >>> inputdict['ByIMF'] = [3.7244 ] # GSM y component of IMF magnetic field (nT) >>> inputdict['BzIMF'] = [-0.1266 ] # GSM z component of IMF magnetic field (nT) >>> inputdict['G1'] = [1.029666 ] # as defined in Tsganenko 2003 >>> inputdict['G2'] = [0.549334 ] >>> inputdict['G3'] = [0.813999 ] >>> inputdict['W1'] = [0.122444 ] # as defined in Tsyganenko and Sitnov 2005 >>> inputdict['W2'] = [0.2514 ] >>> inputdict['W3'] = [0.0892 ] >>> inputdict['W4'] = [0.0478 ] >>> inputdict['W5'] = [0.2258 ] >>> inputdict['W6'] = [1.0461 ] >>> # now add date >>> inputdict['Year'] = [1996 ] >>> inputdict['DOY'] = [6 ] >>> inputdict['Hr'] = [1.2444 ] >>> # and pitch angle, which doesn't come if taking params from OMNI >>> inputdict['PA'] = [57.3874 ] # pitch angle [deg] >>> # and then call the neural network >>> LS.LANLmax(inputdict, ['OPDYN','OPQUIET','T01QUIET','T01STORM','T89','T96','T05']) {'OPDYN': array([10.6278]), 'OPQUIET': array([9.3352]), 'T01QUIET': array([10.0538]), 'T01STORM': array([9.9300]), 'T89': array([8.2888]), 'T96': array([9.2410]), 'T05': array([9.9295])}