ADDA > chpointをPythonで読込み、Jupyter/matplotlibを使って２D画像にする > v0.1

```txt:動作環境
GeForce GTX 1070 (8GB)
ASRock Z170M Pro4S [Intel Z170chipset]
Ubuntu 14.04 LTS desktop amd64
TensorFlow v0.11
cuDNN v5.1 for Linux
CUDA v8.0
Python 2.7.6
IPython 5.1.0 -- An enhanced Interactive Python.
gcc (Ubuntu 4.8.4-2ubuntu1~14.04.3) 4.8.4
```

数値光散乱シミュレーションで出力されるチェックポイントファイルの読込みをCで実装した。
http://qiita.com/7of9/items/0fbea38b48cdfe4588dd

これをmatplotlibで読むためにPython実装に変更しようとしている。


http://qiita.com/7of9/items/4aa9f546a4ff1bf2e1aa
の続き。

## v0.1

Jupyterに環境を移した。
あとで探せなくなるので`disp_checkpoint.ipynb`という記載を以下につけておく。

```py:disp_checkpoint.ipynb
'''
disp_checkpoint.ipynb
'''

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import array
import sys

'''
v0.1 Dec. 23, 2016
    - show plot of real part of xvec[]
       + add save_to_file()
    branched from [read_chpoint.py] to Jupyter environment
v0.6 Dec. 23, 2016
	- try to print fractional part of xvec[] in 4 digits
	   + xvec[idx] = np.array([drel, dimg]), also for rvec[], pvec[], vectors[][]
	   + add [np.set_printoptions(precision=4)]
v0.5 Dec. 23, 2016
	- read scalars[], xvec[], rvec[], pvec[], vectors[][]
    - refactor according to PEP8 (except for E501 line too long for long comment)
    - wrap_fromfile() takes [num] arg
v0.4 Dec. 21, 2016
    - read from "auxiliary" file [auxfp]
        + add read_auxiliary_info()
v0.3 Dec. 19, 2016
    - add wrap_fromfile()
    - read [inprodR],[prev_err],[resid_scale]
v0.2 Dec. 19, 2016
    - fix bug > read [local_nRows] for "size_t" type
v0.1 Dec. 18, 2016
    - read_chpoint_file() > read [ind_m],[local_nRows],[niter],[counter]
'''

np.set_printoptions(precision=6)

def wrap_fromfile(rfp, typecode, num):
    res = array.array(typecode)
    res.fromfile(rfp, num)
    return res


def read_auxiliary_info(auxFilename):
    with open(auxFilename, "rb") as auxfp:
        sc_N = wrap_fromfile(auxfp, 'i', num=1)  # the number of scalars' sizes

        sc_sizes = array.array('i')  #
        sc_sizes.fromfile(auxfp, *sc_N)  # '*':unpack list

        vec_N = wrap_fromfile(auxfp, 'i', num=1)  # the number of vercotrs' sizes

        vec_sizes = array.array('i')  #
        vec_sizes.fromfile(auxfp, *vec_N)  # '*':unpack list

        return sc_N, sc_sizes, vec_N, vec_sizes
    # TODO: 0m > error handling when file is NULL


def save_to_file(xvec, local_nRows):
    data_1d = []
    print(local_nRows[0])
    #for idx in range(local_nRows[0]):
    for idx in range(167*167):  # original size 27889=local_nRows[0]
        data_1d.append(*xvec[idx][0])  # real part of xvec[]
    data_2d = np.reshape(data_1d, (167,167))
    plt.imshow(data_2d, extent=(0,167,0,167),cmap=cm.gist_rainbow)
    plt.show()
    
    
def read_chpoint_file(chpFilename, auxFilename):
    print(chpFilename)
    print(auxFilename)

    # TODO: 0m > error handling when file is NULL
    with open(chpFilename, "rb") as chpfp:
        ind_m = wrap_fromfile(chpfp, 'i', num=1)  # index of iterative method
        print('ind_m:', ind_m)
        # number of local rows of decomposition (only real dipoles)
        #    where L: unsigned long = size_t
        local_nRows = wrap_fromfile(chpfp, 'L', num=1)
        print('local_nRows:', local_nRows)
        niter = wrap_fromfile(chpfp, 'i', num=1)  # iteration count
        print('niter:', niter)
        # number of successive iterations without residual decrease
        counter = wrap_fromfile(chpfp, 'i', num=1)
        print('counter:', counter)
        # used as |r_0|^2 and best squared norm of residual up to some iteration
        inprodR = wrap_fromfile(chpfp, 'd', num=1)
        print('inprodR:', inprodR)
        # previous relative error; used in ProgressReport, initialized in IterativeSolver
        prev_err = wrap_fromfile(chpfp, 'd', num=1)
        print('prev_err:', prev_err)
        # scale to get square of relative error
        resid_scale = wrap_fromfile(chpfp, 'd', num=1)
        print('resid_scale:', resid_scale)  # scale to get square of relative error

        # sc_N: the number of scalars' sizes
        # sc_sizes:
        # vec_N: the number of vercotrs' sizes
        # vec_sizes:
        sc_N, sc_sizes, vec_N, vec_sizes = read_auxiliary_info(auxFilename)
        print('sc_N:', sc_N)
        print('sc_sizes', sc_sizes)
        print('vec_N:', vec_N)
        print('vec_sizes:', vec_sizes)
        # scalars[]
        scalars = []
        for idx in range(sc_N[0]):
            if sc_sizes[idx] is 8:  # double
                scl = wrap_fromfile(chpfp, 'd', num=1)
                scalars.append(scl)
            elif sc_sizes[idx] is 16:  # double complex
                scl = wrap_fromfile(chpfp, 'd', num=2)
                scalars.append(scl)
        for idx in range(sc_N[0]):
       		print('scalars:', scalars[idx])
       	# xvec[] : total electric field on the dipoles
       	# TODO: 0m > replace 'local_nRows[0]' with function?
       	# TODO: 0m > replace 'vec_N[0]' with function?
       	# TODO: 0m > define function for read [drel,dimg]??
       	xvec = [[] for idx in range(local_nRows[0])]
       	for idx in range(local_nRows[0]):
       		drel = wrap_fromfile(chpfp, 'd', 1)
       		dimg = wrap_fromfile(chpfp, 'd', 1)
       		xvec[idx] = np.array([drel, dimg])
       	# rvec[] : current residual
       	rvec = [[] for idx in range(local_nRows[0])]
       	for idx in range(local_nRows[0]):
       		drel = wrap_fromfile(chpfp, 'd', 1)
       		dimg = wrap_fromfile(chpfp, 'd', 1)
       		rvec[idx] = np.array([drel, dimg] )      
       	# pvec[] : polarization of dipoles, also an auxiliary vector in iterative solvers
       	pvec = [[] for idx in range(local_nRows[0])]
       	for idx in range(local_nRows[0]):
       		drel = wrap_fromfile(chpfp, 'd', 1)
       		dimg = wrap_fromfile(chpfp, 'd', 1)
       		pvec[idx] = np.array([drel, dimg])
       	# vectors[] : ???
       	MAXNUM_VECTORS = 20
       	vectors = [[] for idx in range(MAXNUM_VECTORS)]
       	for idx_vecN in range(vec_N[0]):
       	    alist = []
            for idx_LnRows in range(local_nRows[0]):
                if vec_sizes[idx_vecN] is 16:
                    drel = wrap_fromfile(chpfp, 'd', 1)
                    dimg = wrap_fromfile(chpfp, 'd', 1)
                    alist.append(np.array([drel, dimg]))
            vectors[idx_vecN] = alist
       	#
       	for idx in range(3):
       		print('xvec:',np.array_repr(xvec[idx]).replace('\n',''))
       		print('rvec:',np.array_repr(rvec[idx]).replace('\n',''))
       		print('pvec:',np.array_repr(pvec[idx]).replace('\n',''))
       		print('vectors:',np.array_repr(vectors[idx][0]).replace('\n',''))
       		print('vectors:',np.array_repr(vectors[idx][1]).replace('\n',''))
       		print('vectors:',np.array_repr(vectors[idx][2]).replace('\n',''))
        #
        return xvec, local_nRows

argvs = sys.argv
argc = len(argvs)

print argvs
if (argc < 3):
    print("ERROR: chpoint file is not specified\r\n")
    print("   [cmd] [chpoint file] [auxiliary file]\r\n")
    sys.exit()

#read_chpoint_file(chpFilename=argvs[1], auxFilename=argvs[2])
xvec, local_nRows = read_chpoint_file("LN-CHP", "LN-AUX")
save_to_file(xvec, local_nRows)
```

```txt:結果
['/home/yasokada/tensorflow-GPU/lib/python2.7/site-packages/ipykernel/__main__.py', '-f', '/run/user/1000/jupyter/kernel-bbf67d4b-dda7-4d2b-b1ae-cd5205ce660d.json']
LN-CHP
LN-AUX
('ind_m:', array('i', [5]))
('local_nRows:', array('L', [27984L]))
('niter:', array('i', [209]))
('counter:', array('i', [139]))
('inprodR:', array('d', [4.317011265934948]))
('prev_err:', array('d', [0.3198525404317458]))
('resid_scale:', array('d', [0.020384616751203153]))
('sc_N:', array('i', [8]))
('sc_sizes', array('i', [8, 8, 8, 8, 16, 16, 16, 16]))
('vec_N:', array('i', [3]))
('vec_sizes:', array('i', [16, 16, 16]))
('scalars:', array('d', [26.040533833425627]))
('scalars:', array('d', [21.695179646708983]))
('scalars:', array('d', [0.01879089326833417]))
('scalars:', array('d', [0.01622964090344006]))
('scalars:', array('d', [2.393088070369695, 5.705068471314085]))
('scalars:', array('d', [0.2463390807093872, 0.2808495718014472]))
('scalars:', array('d', [0.8838701376516526, -0.4673547711298418]))
('scalars:', array('d', [0.34430517188699605, -0.9387175013645006]))
('xvec:', 'array([[ 0.000916],       [-0.001571]])')
('rvec:', 'array([[-0.0021  ],       [ 0.002827]])')
('pvec:', 'array([[-0.001287],       [-0.008525]])')
('vectors:', 'array([[-0.203491],       [ 0.033598]])')
('vectors:', 'array([[ 0.513358],       [-0.235685]])')
('vectors:', 'array([[ 0.548095],       [ 0.061967]])')
('xvec:', 'array([[ 0.032771],       [ 0.090957]])')
('rvec:', 'array([[ 0.004384],       [-0.023016]])')
('pvec:', 'array([[-0.00586 ],       [ 0.025872]])')
('vectors:', 'array([[ 0.100941],       [-0.212583]])')
('vectors:', 'array([[-0.220141],       [ 0.644602]])')
('vectors:', 'array([[-0.386403],       [ 0.429201]])')
('xvec:', 'array([[-0.004613],       [-0.000545]])')
('rvec:', 'array([[ 0.005385],       [-0.006732]])')
('pvec:', 'array([[-0.000544],       [ 0.022825]])')
('vectors:', 'array([[-0.000433],       [ 0.008886]])')
('vectors:', 'array([[ 0.007084],       [-0.024328]])')
('vectors:', 'array([[ 0.006453],       [-0.022867]])')
27984
```


![qiita.png](https://qiita-image-store.s3.amazonaws.com/0/32870/36fc4b27-ad7b-e330-cb5f-9ca7bfcda7a0.png)