Functions
def	dphi_highpass (r, x0, tabx, taby)

def	dphi_lowpass (r, x0, L0, tabx, taby)

def	Ij0t83 (x, tabx, taby)

def	unMoinsJ0 (x)

def	tabulateIj0 (L0)

def	asymp_macdo (x)

def	macdo (x)

def	rodconan (r, L0)

def	variance (f, contributors, method="Default")
	Return the error variance of specified contributors params: f (h5py.File) : roket hdf5 file opened with h5py contributors (list of string) : list of the contributors method (optional, default="Default") : if "Independence", the function returns ths sum of the contributors variances. More...

def	varianceMultiFiles (fs, frac_per_layer, contributors)
	Return the variance computed from the sum of contributors of roket files fs, ponderated by frac params: fs (list) : list of hdf5 files opened with h5py frac_per_layer (dict) : frac for each layer contributors (list of string) : list of the contributors return: v (np.array(dim=1)) : variance vector. More...

def	cumulativeSR (v, Lambda_tar)
	Returns the cumulative Strehl ratio over the modes from the variance on each mode params: v (np.array(dim=1)) : variance vector return: s (np.array(dim=1)) : cumulative SR. More...

def	update (attrs, old, new)

Variables
string	datapath = "/home/fferreira/Data/correlation/"

	filenames = glob.glob(datapath + "roket_8m_1layer_dir*_cpu.h5")

list	files = []

	ff = h5py.File(f, mode='r')

tuple	nmodes = (files[0])["P"][:].shape[0]

list	xpos = files[0].attrs["wfs.xpos"][0]

list	ypos = files[0].attrs["wfs.ypos"][0]

list	contributors = ["tomography", "bandwidth"]

list	Lambda_tar = files[0].attrs["target.Lambda"][0]

list	Lambda_wfs = files[0].attrs["wfs.Lambda"][0]

list	L0 = files[0].attrs["L0"][0]

list	dt = files[0].attrs["ittime"]

list	H = files[0].attrs["atm.alt"][0]

int	RASC = 180 / np.pi * 3600.

int	Htheta

list	r0 = files[0].attrs["r0"] * (Lambda_tar / Lambda_wfs)**(6. / 5.)

	nfiles = len(files)

	data = np.zeros((nmodes, 4, nfiles))

	theta = np.zeros(nfiles)

	speeds = np.zeros(nfiles)

	gain = np.zeros(nfiles)

	tabx

	taby

int	ind = 0

	f

	method

tuple	covar = (data[:, 0, :] - data[:, 3, :]) / 2.

	xaxis_select

	yaxis_select

	speed_select

	dir_select

	gain_select

	source = ColumnDataSource(data=dict(x=[], y=[], speed=[], theta=[], gain=[]))

	source_model = ColumnDataSource(data=dict(x=[], y=[], speed=[], theta=[], gain=[]))

	hover

string	TOOLS = "resize,save,pan,box_zoom,tap, box_select, wheel_zoom, lasso_select,reset"

	p = figure(plot_height=600, plot_width=700, title="", tools=[hover, TOOLS])

	x

	y

	size

	color

dictionary	xmap = {"Windspeed": speeds, "Winddir": theta, "Gain": gain}

dictionary	ymap

list	buttons = [xaxis_select, speed_select, dir_select, yaxis_select, gain_select]

Function Documentation

◆ asymp_macdo()

def correlation_bokeh.asymp_macdo ( x )

Definition at line 65 of file correlation_bokeh.py.

 def asymp_macdo(x):
     k2 = 1.00563491799858928388289314170833
     k3 = 1.25331413731550012081
     a1 = 0.22222222222222222222
     a2 = -0.08641975308641974829
     a3 = 0.08001828989483310284
  
     x_1 = 1. / x
     res = k2 - k3 * np.exp(-x) * x**(1. / 3.) * (1.0 + x_1 * (a1 + x_1 *
                                                               (a2 + x_1 * a3)))
     return res
  
  

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◆ cumulativeSR()

def correlation_bokeh.cumulativeSR	(	v,
		Lambda_tar
	)

Returns the cumulative Strehl ratio over the modes from the variance on each mode params: v (np.array(dim=1)) : variance vector return: s (np.array(dim=1)) : cumulative SR.

Definition at line 190 of file correlation_bokeh.py.

     """
     s = np.cumsum(v)
     s = np.exp(-s * (2 * np.pi / Lambda_tar)**2)
  
     return s

◆ dphi_highpass()

def correlation_bokeh.dphi_highpass	(	r,
		x0,
		tabx,
		taby
	)

Definition at line 23 of file correlation_bokeh.py.

 def dphi_highpass(r, x0, tabx, taby):
     return (r**
             (5. / 3.)) * (1.1183343328701949 - Ij0t83(r * (np.pi / x0), tabx, taby)) * (
                     2 * (2 * np.pi)**(8 / 3.) * 0.0228956)
  
  

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◆ dphi_lowpass()

def correlation_bokeh.dphi_lowpass	(	r,
		x0,
		L0,
		tabx,
		taby
	)

Definition at line 29 of file correlation_bokeh.py.

 def dphi_lowpass(r, x0, L0, tabx, taby):
     return rodconan(r, L0) - dphi_highpass(r, x0, tabx, taby)
     #return (r**(5./3.)) * Ij0t83(r*(np.pi/x0), tabx, taby) * (2*(2*np.pi)**(8/3.)*0.0228956)
  
  

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◆ Ij0t83()

def correlation_bokeh.Ij0t83	(	x,
		tabx,
		taby
	)

Definition at line 34 of file correlation_bokeh.py.

 def Ij0t83(x, tabx, taby):
     if (x < np.exp(-3.0)):
         return 0.75 * x**(1. / 3) * (1 - x**2 / 112.)
     else:
         return np.interp(x, tabx, taby)
  
  

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◆ macdo()

def correlation_bokeh.macdo ( x )

Definition at line 78 of file correlation_bokeh.py.

 def macdo(x):
     a = 5. / 6.
     x2a = x**(2. * a)
     x22 = x * x / 4.
     s = 0.0
  
     Ga = [
             0, 12.067619015983075, 5.17183672113560444, 0.795667187867016068,
             0.0628158306210802181, 0.00301515986981185091, 9.72632216068338833e-05,
             2.25320204494595251e-06, 3.93000356676612095e-08, 5.34694362825451923e-10,
             5.83302941264329804e-12
     ]
  
     Gma = [
             -3.74878707653729304, -2.04479295083852408, -0.360845814853857083,
             -0.0313778969438136685, -0.001622994669507603, -5.56455315259749673e-05,
             -1.35720808599938951e-06, -2.47515152461894642e-08, -3.50257291219662472e-10,
             -3.95770950530691961e-12, -3.65327031259100284e-14
     ]
  
     x2n = 0.5
  
     s = Gma[0] * x2a
     s *= x2n
  
     x2n *= x22
  
     for n in np.arange(10) + 1:
  
         s += (Gma[n] * x2a + Ga[n]) * x2n
         x2n *= x22
  
     return s
  
  

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◆ rodconan()

def correlation_bokeh.rodconan	(	r,
		L0
	)

Definition at line 113 of file correlation_bokeh.py.

 def rodconan(r, L0):
     res = 0
     k1 = 0.1716613621245709486
     dprf0 = (2 * np.pi / L0) * r
     if (dprf0 > 4.71239):
         res = asymp_macdo(dprf0)
     else:
         res = -macdo(dprf0)
  
     res *= k1 * L0**(5. / 3.)
  
     return res
  
  

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◆ tabulateIj0()

def correlation_bokeh.tabulateIj0 ( L0 )

Definition at line 49 of file correlation_bokeh.py.

 def tabulateIj0(L0):
     n = 10000
     t = np.linspace(-4, 10, n)
     dt = (t[-1] - t[0]) / (n - 1)
     smallx = np.exp(-4.0)
     A = 0.75 * smallx**(1. / 3) * (1 - smallx**2 / 112.)
     X = np.exp(t)
     #Y = np.exp(-t*(5./3.))*unMoinsJ0(X)
     Y = (np.exp(2 * t) + (1. / L0)**2)**(-8. / 6.) * unMoinsJ0(X) * np.exp(t)
     Y[1:] = np.cumsum(Y[:-1] + np.diff(Y) / 2.)
     Y[0] = 0.
     Y = Y * dt + A
  
     return X, Y
  
  

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◆ unMoinsJ0()

def correlation_bokeh.unMoinsJ0 ( x )

Definition at line 41 of file correlation_bokeh.py.

 def unMoinsJ0(x):
     # if(x<0.1):
     #     x22 = (x/2.)**2
     #     return (1-x22/4.)*x22
     # else:
     return 1 - jv(0, x)
  
  

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◆ update()

def correlation_bokeh.update	(	attrs,
		old,
		new
	)

Definition at line 197 of file correlation_bokeh.py.

 def update(attrs, old, new):
     speed = speed_select.value
     direction = dir_select.value
     g = gain_select.value
     xname = xaxis_select.value
     yname = yaxis_select.value
  
     ydata = ymap[yname]
     x = xmap[xname]
  
     ind = np.ones(ydata.shape[0])
     if (direction != "All"):
         ind *= (xmap["Winddir"] == float(direction))
     if (speed != "All"):
         ind *= (xmap["Windspeed"] == float(speed))
     if (g != "All"):
         ind *= (xmap["Gain"] == float(g))
  
     ind = np.where(ind)
     if (yname == b"Var(t)"):
         Hthetak = Htheta / xmap["Gain"]
         y_model = np.ones(ind[0].shape[0])
         #y_model = y_model * 6.88 * (Htheta/r0)**(5./3.) * 0.5
         for k in range(ind[0].shape[0]):
             y_model[k] = dphi_lowpass(Htheta, 0.2, L0, tabx, taby) * (1 / r0)**(
                     5. / 3.) * 0.5  #* xmap["Gain"][ind][k]**2
     if (yname == b"Var(bp)"):
         vdt = xmap["Windspeed"] * dt / xmap["Gain"]
         y_model = np.zeros(vdt[ind].shape[0])
         for k in range(vdt[ind].shape[0]):
             y_model[k] = dphi_lowpass(vdt[ind][k], 0.2, L0, tabx,
                                       taby) * (1. / r0)**(5. / 3.) * 0.5
     if (yname == b"Covar"):
         vdt = xmap["Windspeed"] * dt / xmap["Gain"]
         Hthetak = Htheta / xmap["Gain"]
         gamma = np.arctan2(ypos, xpos) - xmap["Winddir"] * np.pi / 180.
         rho = np.sqrt(Htheta**2 + (vdt)**2 - 2 * Htheta * vdt * np.cos(gamma))
         Drho = np.zeros(rho[ind].shape[0])
         Dt = Drho.copy()
         for k in range(rho[ind].shape[0]):
             Drho[k] = dphi_lowpass(rho[ind][k], 0.2, L0, tabx,
                                    taby) * (1 / r0)**(5. / 3.)
         #Drho = 6.88 * (rho[ind]/r0)**(5./3.)
         for k in range(Dt.shape[0]):
             Dt[k] = dphi_lowpass(Htheta, 0.2, L0, tabx, taby) * (1 / r0)**(
                     5. / 3.)  # * xmap["Gain"][ind][k]**2
         #Dt =  6.88 * (Htheta/r0)**(5./3.)
         Dbp = np.zeros(vdt[ind].shape[0])
         for k in range(vdt[ind].shape[0]):
             Dbp[k] = dphi_lowpass(vdt[ind][k], 0.2, L0, tabx, taby) * (1 / r0)**(5. / 3.)
         #Dbp = 6.88 * (vdt[ind]/r0) ** (5./3.)
         y_model = 0.5 * (Dt + Dbp - Drho)
  
     source.data = dict(x=x[ind], y=ydata[ind], speed=xmap["Windspeed"][ind],
                        theta=xmap["Winddir"][ind], gain=xmap["Gain"][ind])
     source_model.data = dict(x=x[ind], y=y_model, speed=xmap["Windspeed"][ind],
                              theta=xmap["Winddir"][ind], gain=xmap["Gain"][ind])
  
  

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◆ variance()

def correlation_bokeh.variance	(	f,
		contributors,
		method = `"Default"`
	)

Return the error variance of specified contributors params: f (h5py.File) : roket hdf5 file opened with h5py contributors (list of string) : list of the contributors method (optional, default="Default") : if "Independence", the function returns ths sum of the contributors variances.

If "Default", it returns the variance of the contributors sum

Definition at line 135 of file correlation_bokeh.py.

     """
     P = f["P"][:]
     nmodes = P.shape[0]
     swap = np.arange(nmodes) - 2
     swap[0:2] = [nmodes - 2, nmodes - 1]
     if (method == b"Default"):
         err = f[contributors[0]][:] * 0.
         for c in contributors:
             err += f[c][:]
         return np.var(P.dot(err), axis=1)[swap], np.var(
                 P.dot(f["tomography"][:]),
                 axis=1)[swap], np.var(P.dot(f["bandwidth"][:]), axis=1)[swap]
  
     elif (method == b"Independence"):
         nmodes = P.shape[0]
         v = np.zeros(nmodes)
         for c in contributors:
             v += np.var(P.dot(f[c][:]), axis=1)
         return v[swap]
  
     else:
         raise TypeError("Wrong method input")
  
  

◆ varianceMultiFiles()

def correlation_bokeh.varianceMultiFiles	(	fs,
		frac_per_layer,
		contributors
	)

Return the variance computed from the sum of contributors of roket files fs, ponderated by frac params: fs (list) : list of hdf5 files opened with h5py frac_per_layer (dict) : frac for each layer contributors (list of string) : list of the contributors return: v (np.array(dim=1)) : variance vector.

Definition at line 168 of file correlation_bokeh.py.

     """
     f = fs[0]
     P = f["P"][:]
     nmodes = P.shape[0]
     swap = np.arange(nmodes) - 2
     swap[0:2] = [nmodes - 2, nmodes - 1]
     err = f[contributors[0]][:] * 0.
     for f in fs:
         frac = frac_per_layer[f.attrs["atm.alt"][0]]
         for c in contributors:
             err += np.sqrt(frac) * f[c][:]
  
     return np.var(P.dot(err), axis=1)[swap]
  
  

Variable Documentation

◆ buttons

list correlation_bokeh.buttons = [xaxis_select, speed_select, dir_select, yaxis_select, gain_select]

Definition at line 333 of file correlation_bokeh.py.

◆ color

correlation_bokeh.color

Definition at line 321 of file correlation_bokeh.py.

◆ contributors

correlation_bokeh.contributors = ["tomography", "bandwidth"]

Definition at line 268 of file correlation_bokeh.py.

◆ covar

tuple correlation_bokeh.covar = (data[:, 0, :] - data[:, 3, :]) / 2.

Definition at line 300 of file correlation_bokeh.py.

◆ data

correlation_bokeh.data = np.zeros((nmodes, 4, nfiles))

Definition at line 280 of file correlation_bokeh.py.

◆ datapath

string correlation_bokeh.datapath = "/home/fferreira/Data/correlation/"

Definition at line 256 of file correlation_bokeh.py.

◆ dir_select

correlation_bokeh.dir_select

Initial value:

1 = Select(title="Winddirs", value="All",

2 options=["All"] + [str(s) for s in np.unique(theta)])

Definition at line 310 of file correlation_bokeh.py.

◆ dt

list correlation_bokeh.dt = files[0].attrs["ittime"]

Definition at line 272 of file correlation_bokeh.py.

◆ f

correlation_bokeh.f

Definition at line 294 of file correlation_bokeh.py.

◆ ff

correlation_bokeh.ff = h5py.File(f, mode='r')

Definition at line 261 of file correlation_bokeh.py.

◆ filenames

correlation_bokeh.filenames = glob.glob(datapath + "roket_8m_1layer_dir*_cpu.h5")

Definition at line 257 of file correlation_bokeh.py.

◆ files

list correlation_bokeh.files = []

Definition at line 259 of file correlation_bokeh.py.

◆ gain

correlation_bokeh.gain = np.zeros(nfiles)

Definition at line 283 of file correlation_bokeh.py.

◆ gain_select

correlation_bokeh.gain_select

Initial value:

1 = Select(title="Gain", value="All",

2 options=["All"] + [str(s)[:3] for s in np.unique(gain)])

Definition at line 312 of file correlation_bokeh.py.

◆ H

list correlation_bokeh.H = files[0].attrs["atm.alt"][0]

Definition at line 273 of file correlation_bokeh.py.

◆ hover

correlation_bokeh.hover

Initial value:

1 = HoverTool(tooltips=[("Speed", "@speed"), ("Winddir", "@theta"), ("Gain",

2 "@gain")])

Definition at line 316 of file correlation_bokeh.py.

◆ Htheta

int correlation_bokeh.Htheta

Initial value:

 =  np.linalg.norm(
         [xpos, ypos]
 ) / RASC * H

Definition at line 275 of file correlation_bokeh.py.

◆ ind

int correlation_bokeh.ind = 0

Definition at line 291 of file correlation_bokeh.py.

◆ L0

list correlation_bokeh.L0 = files[0].attrs["L0"][0]

Definition at line 271 of file correlation_bokeh.py.

◆ Lambda_tar

list correlation_bokeh.Lambda_tar = files[0].attrs["target.Lambda"][0]

Definition at line 269 of file correlation_bokeh.py.

◆ Lambda_wfs

list correlation_bokeh.Lambda_wfs = files[0].attrs["wfs.Lambda"][0]

Definition at line 270 of file correlation_bokeh.py.

◆ method

correlation_bokeh.method

Definition at line 294 of file correlation_bokeh.py.

◆ nfiles

correlation_bokeh.nfiles = len(files)

Definition at line 279 of file correlation_bokeh.py.

◆ nmodes

tuple correlation_bokeh.nmodes = (files[0])["P"][:].shape[0]

Definition at line 265 of file correlation_bokeh.py.

◆ p

correlation_bokeh.p = figure(plot_height=600, plot_width=700, title="", tools=[hover, TOOLS])

Definition at line 320 of file correlation_bokeh.py.

◆ r0

list correlation_bokeh.r0 = files[0].attrs["r0"] * (Lambda_tar / Lambda_wfs)**(6. / 5.)

Definition at line 278 of file correlation_bokeh.py.

◆ RASC

int correlation_bokeh.RASC = 180 / np.pi * 3600.

Definition at line 274 of file correlation_bokeh.py.

◆ size

correlation_bokeh.size

Definition at line 321 of file correlation_bokeh.py.

◆ source

correlation_bokeh.source = ColumnDataSource(data=dict(x=[], y=[], speed=[], theta=[], gain=[]))

Definition at line 314 of file correlation_bokeh.py.

◆ source_model

correlation_bokeh.source_model = ColumnDataSource(data=dict(x=[], y=[], speed=[], theta=[], gain=[]))

Definition at line 315 of file correlation_bokeh.py.

◆ speed_select

correlation_bokeh.speed_select

Initial value:

1 = Select(title="Windspeeds", value="All",

2 options=["All"] + [str(s) for s in np.unique(speeds)])

Definition at line 308 of file correlation_bokeh.py.

◆ speeds

correlation_bokeh.speeds = np.zeros(nfiles)

Definition at line 282 of file correlation_bokeh.py.

◆ tabx

correlation_bokeh.tabx

Definition at line 285 of file correlation_bokeh.py.

◆ taby

correlation_bokeh.taby

Definition at line 285 of file correlation_bokeh.py.

◆ theta

correlation_bokeh.theta = np.zeros(nfiles)

Definition at line 281 of file correlation_bokeh.py.

◆ TOOLS

string correlation_bokeh.TOOLS = "resize,save,pan,box_zoom,tap, box_select, wheel_zoom, lasso_select,reset"

Definition at line 318 of file correlation_bokeh.py.

◆ x

correlation_bokeh.x

Definition at line 321 of file correlation_bokeh.py.

◆ xaxis_select

correlation_bokeh.xaxis_select

Initial value:

1 = Select(title="X-axis", value="Windspeed",

2 options=["Windspeed", "Winddir", "Gain"])

Definition at line 302 of file correlation_bokeh.py.

◆ xmap

dictionary correlation_bokeh.xmap = {"Windspeed": speeds, "Winddir": theta, "Gain": gain}

Definition at line 324 of file correlation_bokeh.py.

◆ xpos

list correlation_bokeh.xpos = files[0].attrs["wfs.xpos"][0]

Definition at line 266 of file correlation_bokeh.py.

◆ y

correlation_bokeh.y

Definition at line 321 of file correlation_bokeh.py.

◆ yaxis_select

correlation_bokeh.yaxis_select

Initial value:

 =  Select(
         title="Y-axis", value="Covar",
         options=["Covar", "Var(t+bp)", "Var(t)", "Var(bp)", "Var(t)+Var(bp)"])

Definition at line 304 of file correlation_bokeh.py.

◆ ymap

dictionary correlation_bokeh.ymap

Initial value:

 =  {
         "Covar": np.sum(covar, axis=0),
         "Var(t+bp)": np.sum(data[:, 0, :], axis=0),
         "Var(t)": np.sum(data[:, 1, :], axis=0),
         "Var(bp)": np.sum(data[:, 2, :], axis=0),
         "Var(t)+Var(bp)": np.sum(data[:, 3, :], axis=0)
 }

Definition at line 325 of file correlation_bokeh.py.

◆ ypos

list correlation_bokeh.ypos = files[0].attrs["wfs.ypos"][0]

Definition at line 267 of file correlation_bokeh.py.

Functions

Variables

Function Documentation

◆ asymp_macdo()

◆ cumulativeSR()

◆ dphi_highpass()

◆ dphi_lowpass()

◆ Ij0t83()

◆ macdo()

◆ rodconan()

◆ tabulateIj0()

◆ unMoinsJ0()

◆ update()

◆ variance()

◆ varianceMultiFiles()

Variable Documentation

◆ buttons

◆ color

◆ contributors

◆ covar

◆ data

◆ datapath

◆ dir_select

◆ dt

◆ f

◆ ff

◆ filenames

◆ files

◆ gain

◆ gain_select

◆ H

◆ hover

◆ Htheta

◆ ind

◆ L0

◆ Lambda_tar

◆ Lambda_wfs

◆ method

◆ nfiles

◆ nmodes

◆ p

◆ r0

◆ RASC

◆ size

◆ source

◆ source_model

◆ speed_select

◆ speeds

◆ tabx

◆ taby

◆ theta

◆ TOOLS

◆ x

◆ xaxis_select

◆ xmap

◆ xpos

◆ y

◆ yaxis_select

◆ ymap

◆ ypos