irfpy.cena.gfactor

An implementation of CENA g-factor.

G-Factor implementation according to the section 3.5 of the calibration report.

The implementation is done in GFactorH class. But this is somehow a test purpose, so the class will be re-implemented some day.

There are two implementations.

GFactorH_Table starts with a tabulared G_i in Table 3.5 in the calibration report. GFactorH_Component starts with a calculation from component-based parameters.

One can start from

from irfpy.cena.gfactor import GFactorH_Table as GFactorH

or

from irfpy.cena.gfactor import GFactorH_Component as GFactorH

Warning

Apart from the updated definition in 3.19 in calibration report i1r2, the dead time is in the g-factor.

It concerns the class:GFactorH_Component. Also, to calculate the flux, you must use dt=0.5 sec.

class irfpy.cena.gfactor.GFactorH_Base[source]

Bases: object

An base class of GFactor classes.

This is an abstract class. Derived classes should implement energyDependentGfactor() (\(G_{n,i}\)).

Constructor.

G_n_i(sigma=0)[source]

Alias to geometricFactor().

G_i(sigma=0)[source]

Alias to energyDependentGfactor().

energyDependentGfactor(sigma=0)[source]

Return the energy dependent g-factor. (Abstract class)

Override this method in the subclass.

geometricFactor(sigma=0)[source]

Return the g-factor.

The g-factor is a kind of conversion factor between the differential flux, J, and the counts, C.

Here, we denote \(J_{n,i}^{H}\) [#/cm^2 sr eV s] is the differential number flux seen by each sector n and energy step i for hydrogen ENA. The corresponding count rate, \(C_{n,i}^{H}\) [#] is calculated by

\[C_{n,i}^{H} = J_{n,i}^{H} \cdot G_{n,i}^{H} \cdot E_i \cdot \Delta t\]

as in (3.15).

param sigma

You can give an error parameter \(\sigma\).

type sigma

float

returns

geometric factor as a numpy.ma.masked_array. The dimension is [i=16][n=7]

rtype

Instance of numpy.ma.masked_array with dimensions of [i=16][n=7].

Usage

>>> gfH = GFactorH_Table()
>>> gf = gfH.geometricFactor()
>>> gf.shape
(16, 7)

> print '%.1e' % gf[8][3]
5.8e-07
# While this method returns 6.6e-07 ...

Note Indeed, the calculated value is different by about 10% from the calibration report. Last column in Table 3.5 shows the gf[3][:], but the values are different….

Todo

The g-factor related classes is an objective of reimplementation. The inconsist g-factors in the calibration report.

cena_flux class also should be re-implemented.

class irfpy.cena.gfactor.GFactorH_Table[source]

Bases: irfpy.cena.gfactor.GFactorH_Base

An implementation of G-factor for hydrogen ENA.

This is based on section 3.5.2 in the calibration report.

For developer

The class is low-level class. Thus, in the near future, more high-level methods may be needed. However, so far I used this low-level class also for ebugging purpose. Thus, the class itself would also needed to be re-implemented.

Constructor.

energyDependentGfactor(sigma=0)[source]

Return the energy dependent g-factor.

The energy dependent g-factor for \(H^0\), \(G_i^H\), is listed in Table 3.5. There are uncertainty of factor 3, corresponding to \(1\sigma\).

param sigma

You can give a error parameter \(\sigma\). For default value (None), central value of g-factor will be returned. If you give +1, the g-factor becomes 3 times larger than the default according to the uncertainty. If you give -1, the g-factor becomes 3 times smaller than the default.

type sigma

float

returns

Energy depenent g-factor, as an array of 16 elements. Each index corresponds to the generic energy steps. (See also irfpy.cena.energy) However, the data is only available for 4 to 11. Thus, the unavailable element is a masked numpy array.

rtype

numpy.ma.masked_array

>>> gf = GFactorH_Table()
>>> g_i_H = gf.energyDependentGfactor()
>>> numpy.ma.is_masked(g_i_H[0])
True
>>> numpy.ma.is_masked(g_i_H[5])
False

>>> print((g_i_H[8]))
4.5e-05

>>> g_i_H_lower = gf.energyDependentGfactor(sigma=-1)
>>> print('%.2f' % (g_i_H_lower[5]/g_i_H[5]))
0.33
>>> g_i_H_upper = gf.energyDependentGfactor(sigma=1)
>>> print('%.2f' % (g_i_H_upper[5]/g_i_H[5]))
3.00
class irfpy.cena.gfactor.GFactorH_Component[source]

Bases: irfpy.cena.gfactor.GFactorH_Base

Another implementation of G-factor for hydrogen ENA.

This is based on section 3.5.2 in the calibration report. The energy dependent geometric factors are calculated from scratch.

For developer

The class is low-level class. Thus, in the near future, more high-level methods may be needed. However, so far I used this low-level class also for ebugging purpose. Thus, the class itself would also needed to be re-implemented.

Constructor.

energyDependentGfactor(sigma=0)[source]

Return the energy dependent g-factor.

The energy dependent g-factor for \(H^0\), \(G_i^H\), is listed in Table 3.5. However, the values are inconsistent with those calculate from the equations.

Apaart from the inconsistency, There are uncertainty of factor 3, corresponding to \(1\sigma\).

param sigma

You can give a error parameter \(\sigma\). For default value (None), central value of g-factor will be returned. If you give +1, the g-factor becomes 3 times larger than the default according to the uncertainty. If you give -1, the g-factor becomes 3 times smaller than the default.

type sigma

float

returns

Energy depenent g-factor, as an array of 16 elements. Each index corresponds to the generic energy steps. (See also irfpy.cena.energy) However, the data is only available for 4 to 11. Thus, the unavailable element is a masked numpy array.

rtype

numpy.ma.masked_array

>>> gf = GFactorH_Component()
>>> g_i_H = gf.energyDependentGfactor()
>>> numpy.ma.is_masked(g_i_H[0])
False
>>> numpy.ma.is_masked(g_i_H[5])
False

>>> print(('%.2e' % g_i_H[8]))
4.38e-05

>>> g_i_H_lower = gf.energyDependentGfactor(sigma=-1)
>>> print('%.2f' % (g_i_H_lower[5]/g_i_H[5]))
0.33
>>> g_i_H_upper = gf.energyDependentGfactor(sigma=1)
>>> print('%.2f' % (g_i_H_upper[5]/g_i_H[5]))
3.00

Definition of energy dependent g-factor

From equation 3.19 in calibration report, \(G_i^H\) is defined as

\[ \begin{align}\begin{aligned}G_i^H = \sigma{\cdot}A{\cdot}e_{i,H}{\cdot}\frac{\Delta E_i}{E_i}\\\sigma = T_1{\cdot}T_2{\cdot}r_{CS}{\cdot}\eta_{START}{\cdot} \eta_{MCP}{\cdot}\eta_{TOF}{\cdot}(1-\frac{t_D}{t_{SLOT}})\\A = 1.09 \textrm{cm}^2\end{aligned}\end{align} \]
class irfpy.cena.gfactor.GFactorParams[source]

Bases: object

getSectorSensitivity()[source]

Returns the sector sensitivities.

Sensitivity depends on the sector of interest. This method returns the sensitivities, \(s_n\). Returned is 7-element numpy.array, which is listed in Table 3.4 in the calibration report.

s_n()

Alias to getSectorSensitivity().

centralPixelsSensitivity()[source]

Return the sensitivity of the sector 2-3-4.

It is sometimes worth to consider the sectors 2-3-4 is a single sector. This method returns the sensitivity of this aggregated sector.

See also sectorSensitivity().

s_234()

Alias to centralPixelsSensitivity()

getTransparencyGrid1()[source]

Returns the first grid transparency, T1

This is geometrically defined. T1 = 0.88 (see calibration report)

T1()

Alias to getTransparencyGrid1()

getTransparencyGrid2()[source]

Returns the second grid transparency, T2

This is geometrically defined. T2 = 0.88 (see calibration report)

T2()

Alias to getTransparencyGrid2()

getCollectionEfficiencyCS()[source]

Returns the collection efficiency of wave system from CS.

Collection efficiency of wave system from conversion surface, r_CS is 0.7

rcs()

Alias to getCollectionEfficiencyCS().

getPositiveIonYieldCS(energy_in_eV)[source]

Returns the positive ion yield at CS.

Positive ion yield at the conversion surface is taken from Wieser et al. 2002. The value depends on the energy of the impinging particle.

param energy_in_ev

Energy of particle in eV

type energy_in_ev

float

returns

The yield

rtype

float

eta_i_H_plus(energy_in_eV)

Alias to getPositiveIonYieldCS().

eta_start()

Alias to getStartMcpEfficiency().

getStartMcpEfficiency()[source]

TOF efficiency.

Defining factors are found correted rate and start rate fro inflight data. Using 2.1 kV start MCP bias, the quantiy is defined to be 0.15

eta_mcp()

Alias to getStartMcpEfficiency()

getTofEfficiency()[source]

Sector sensitivity

The quantity is obtained from flight data.

eta_tof()

Alias to getTofEfficiency().

getOpenAperture()[source]

Return the aperture in cm^2, based on (3.9).

A()

Alias to getOpenAperture()