動作環境

```
GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 16.04 LTS desktop amd64
TensorFlow v1.2.1
cuDNN v5.1 for Linux
CUDA v8.0
Python 3.5.2
IPython 6.0.0 -- An enhanced Interactive Python.
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609
GNU bash, version 4.3.48(1)-release (x86_64-pc-linux-gnu)
scipy v0.19.1
geopandas v0.3.0
MATLAB R2017b (Home Edition)
ADDA v.1.3b6
```

This article is related to ADDA (light scattering simulator based on the discrete dipole approximation).

### avg_params.dat

See [B.2 avg_params.dat] in the manual.pdf for the setting of the file.

### Evaluations for different values of [Jmin] and [Jmax] of the [beta] and [gamma].

I have checked [the number of evaluations] for the different values of [Jmin] and [Jmax] in the [avg_params.dat].

Executed commands were like followings:

```
$./adda -orient avg CODE_171216_Euler/avg_params_beta_4_6_gamma_4_6.dat -shape chebyshev 0.7 12 -size 10
```

excerpt_of_avg_params.dat

```
...
beta:
# default: min=0;max=180;Jmin=2;Jmax=4;eps=1e-3;equiv=false;periodic=false
# xy - symmetry plane: max=90;Jmax=3
# Do not use periodic=true since the function is multiplied by sin(beta) before integration.
min=0
max=180
Jmin=3
Jmax=5
eps=1e-3
equiv=false
periodic=false
gamma:
# default: min=0;max=360;Jmin=2;Jmax=4;eps=1e-3;equiv=true;periodic=true
# axysymmetrical: max=0
# more precisely: max=45;Jmax=2;equiv=false
min=0
max=360
Jmin=3
Jmax=5
eps=1e-3
equiv=true
periodic=true
...
```

Jmin Jmax | Jmin Jmax | evaluations |
---|---|---|

gamma 2 4 | beta 2 4 | 14 |

gamma 3 5 | beta 2 4 | 122 |

gamma 2 4 | beta 3 5 | 62 |

gamma 3 5 | beta 3 5 | 122 |

gamma 3 5 | beta 4 6 | 122 |

gamma 4 6 | beta 4 6 | 242 |

## Output example

### beta 4 6 gamma 4 6

log_orient_avg

```
GAMMA(rad) cos(BETA)
EPS 0.001 0.001
Refinement stages:
Minimum 4 4
Maximum 6 6
lower boundary 0 -1
upper boundary 6.28319 1
equivalent min&max true false
periodic true false
Outer-Loop Inner Loop
init 2 integrand-values were used.
1 16 integrand-values were used.
2 32 integrand-values were used.
3 64 integrand-values were used.
4 128 integrand-values were used.
All inner integrations converged
The outer integration converged
In total 242 evaluations were used
```

In total 242 evaluations were used

### beta 3 5 gamma 3 5

log_orient_avg

```
GAMMA(rad) cos(BETA)
EPS 0.001 0.001
Refinement stages:
Minimum 3 3
Maximum 5 5
lower boundary 0 -1
upper boundary 6.28319 1
equivalent min&max true false
periodic true false
Outer-Loop Inner Loop
init 2 integrand-values were used.
1 8 integrand-values were used.
2 16 integrand-values were used.
3 32 integrand-values were used.
4 64 integrand-values were used.
All inner integrations converged
The outer integration converged
In total 122 evaluations were used
```

### beta 4 6 gamma 3 5

log_orient_avg

```
GAMMA(rad) cos(BETA)
EPS 0.001 0.001
Refinement stages:
Minimum 3 4
Maximum 5 6
lower boundary 0 -1
upper boundary 6.28319 1
equivalent min&max true false
periodic true false
Outer-Loop Inner Loop
init 2 integrand-values were used.
1 8 integrand-values were used.
2 16 integrand-values were used.
3 32 integrand-values were used.
4 64 integrand-values were used.
All inner integrations converged
The outer integration converged
In total 122 evaluations were used
```

### Note

(Updated Dec. 24, 2017)

The above results were obtained using the adaptive approach.

Therefore the numbers of evaluations were same for different combinations of the (beta, gamma)'s (Jmin,Jmax) values.

See the following post for the detail:

avg_params.dat > different Jmin and Jmax with same [number of evaluations]