def calc_veldist_2d(
ulinspace,
vlinspace,
R=1.0,
t=-4.0,
pot="bar",
beta=0.0,
potparams=(0.9, 0.01, 25.0 * _degtorad, 0.8, None),
dfparams=(1.0 / 3.0, 1.0, 0.2),
dftype="dehnen",
correct=True,
):
"""
NAME:
calc_veldist_2d
PURPOSE:
calculate the velocity distribution at a given R on a grid in (u,v)
INPUT:
ulinspace, vlinspace - build the grid using scipy's linspace with
these arguments
R - Galactocentric Radius
t - time to integrate backwards for
(interpretation depends on potential)
pot - type of non-axisymmetric, time-dependent potential
beta - power-law index of rotation curve
potparams - parameters for this potential
dfparams - parameters of the DF (xD,xS,Sro)
dftype - type of DF ('dehnen' or 'shu')
OUTPUT:
f(u,v) on the grid
HISTORY:
2010-03-08 - Written - Bovy (NYU)
"""
us = sc.linspace(*ulinspace)
vs = sc.linspace(*vlinspace)
nus = len(us)
nvs = len(vs)
out = sc.zeros((nus, nvs))
if dftype == "dehnen":
thisDF = dehnenDF
df = thisDF(profileParams=dfparams, beta=beta, correct=correct, niter=_NCORRECT)
for ii in range(nus):
for jj in range(nvs):
E, L = uvToELz(UV=(-us[ii], vs[jj]), R=R, t=t, pot=pot, potparams=potparams, beta=beta)
out[ii, jj] = df.eval(E, L)
return out
def calc_meanR_2d(
ulinspace,
vlinspace,
R=1.0,
t=-4.0,
pot="bar",
beta=0.0,
potparams=(0.9, 0.01, 25.0 * _degtorad, 0.8, None),
correct=True,
xE=True,
):
"""
NAME:
calc_meanR_2d
PURPOSE:
calculate the mean-radii distribution at a given R on a grid in (u,v)
INPUT:
ulinspace, vlinspace - build the grid using scipy's linspace with
these arguments
R - Galactocentric Radius
t - time to integrate backwards for
(interpretation depends on potential)
pot - type of non-axisymmetric, time-dependent potential
beta - power-law index of rotation curve
potparams - parameters for this potential
xE - if True, calculate mean radius based on energy,
else calculate mean radius based on angular momentum
(default: True)
OUTPUT:
f(u,v) on the grid
HISTORY:
2010-05-03 - Written - Bovy (NYU)
"""
us = sc.linspace(*ulinspace)
vs = sc.linspace(*vlinspace)
nus = len(us)
nvs = len(vs)
out = sc.zeros((nus, nvs))
for ii in range(nus):
for jj in range(nvs):
E, L = uvToELz(UV=(-us[ii], vs[jj]), R=R, t=t, pot=pot, potparams=potparams, beta=beta)
out[ii, jj] = calc_meanR(E, L, beta, xE=xE)
return out
def integrandSinAlphaSmall(u,cosalpha,tanalpha,vlos,R,t,potparams,beta,df,
pot):
return df.eval(*uvToELz(UV=(tanalpha*u-vlos/cosalpha,u),R=R,t=t,pot=pot,potparams=potparams,beta=beta))
if __name__ == '__main__':
print interpret_as_df((0.54636764432720319, 0.99999999999999989),type='dehnen')
from integrate_orbits import uvToELz
"""
step=0.1
EL= uvToELz((0.,0.))
print "step= ", step
print "(0,0):",EL, interpret_as_df(EL)
EL= uvToELz((step,0.))
print "(1,0):",EL, interpret_as_df(EL)
EL= uvToELz((0.,step))
print "(0,1):",EL, interpret_as_df(EL)
EL= uvToELz((-step,0.))
print "(-1,0):",EL, interpret_as_df(EL)
EL= uvToELz((0.,-step))
print "(0,-1):",EL, interpret_as_df(EL)
"""
_degtorad= sc.pi/180.
print interpret_as_df(uvToELz((0,0),potparams=(1.,0.01,25.*_degtorad,.8,None)))
print interpret_as_df(uvToELz((-.1,-.1),potparams=(1.,0.01,25.*_degtorad,.8,None)))
def integrandSinAlphaLarge(u,sinalpha,cotalpha,vlos,R,t,potparams,beta,df,pot):
return df.eval(*uvToELz(UV=(-u,-cotalpha*u+vlos/sinalpha),R=R,t=t,pot=pot,potparams=potparams,beta=beta))
