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tritemio/nsALEX - Old LV format

Last active August 29, 2015 13:57
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An IPython Notebook that shows how to read the old LabView .sm format for nsALEX and save it in an HDF5 file.
Raw
nsALEX - Old LV format
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#nsALEX - Old LV format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and decode functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def load_nsalex_sm(fname, magic=184, magic_end=26, debug=False):\n",
" f = open(fname, 'rb')\n",
" \n",
" ## Read the header\n",
" \n",
" # Read all the lines until '*END is found', repeat 3 times\n",
" header = []\n",
" for i in range(3):\n",
" skip = True\n",
" while skip:\n",
" header.append(f.readline())\n",
" if header[-1] == '*END\\r\\n': skip = False\n",
" if debug: print f.tell()\n",
" \n",
" # Read the last part of header\n",
" header.append(f.readline())\n",
" assert header[-1] == '\\r\\n'\n",
" header.append(f.read(magic))\n",
" \n",
" ## Read the data\n",
" buffer = f.read()\n",
" \n",
" custom_dtype = np.dtype([('times', '>u8'), ('nanot', '>u4'), ('det', '>u4')])\n",
" assert np.mod(len(buffer) - magic_end, (8+4+4)) == 0\n",
" \n",
" data = np.ndarray(shape=((len(buffer) - magic_end)/(8+4+4),), \n",
" buffer=buffer, dtype=custom_dtype)\n",
" \n",
" assert (data['nanot'] < 4096).all()\n",
" assert (data['det'] < 8).all()\n",
" return data, header, buffer"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def decode_header_BH(header):\n",
" \n",
" ## Split the header chunks separated by '\\r\\n'\n",
" header_chunks = [[]]\n",
" for line in header:\n",
" if line == '\\r\\n':\n",
" header_chunks.append([])\n",
" continue\n",
" header_chunks[-1].append(line.strip())\n",
" \n",
" ## Make a dictionary of system parameters\n",
" start = False\n",
" sys_para = {}\n",
" for line in header_chunks[1]:\n",
" if line == 'SYS_PARA_BEGIN:':\n",
" start = True\n",
" continue\n",
" if line == 'SYS_PARA_END:':\n",
" break\n",
" if start and line.startswith('#'):\n",
" fields = line[5:-1].split(',')\n",
" \n",
" if fields[1] == 'B':\n",
" value = bool(fields[2])\n",
" elif fields[1] in ['I', 'U', 'L']:\n",
" value = int(fields[2])\n",
" elif fields[1] == 'F':\n",
" value = float(fields[2])\n",
" elif fields[1] == 'S':\n",
" value = fields[2]\n",
" else:\n",
" value = fields[1:]\n",
" \n",
" sys_para[fields[0]] = value\n",
" \n",
" return header_chunks, sys_para "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data load"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fn = \"0m gu 000.sm\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the full file name:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dir_ = r\"C:\\Data\\Antonio\\data\\Eitan/\"\n",
"fname = dir_+fn\n",
"fname"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"'C:\\\\Data\\\\Antonio\\\\data\\\\Eitan/0m gu 000.sm'"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the measurement:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data, header, buff = load_nsalex_sm(fname)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inside `data` there are 3 arrays of same length: macrotimes (`times`), nanotimes (`nanot`) and detector (`det`):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"times = data['times']\n",
"nanot = data['nanot']\n",
"det = data['det']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let check the array size (i.e. the number of photons):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.size"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"10028872"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Data save"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Save the 3 arrays in an HDF5 file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import h5py"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fname_new = fname[:-2] + 'hdf5'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with h5py.File(fname_new, \"w\") as f:\n",
" f.create_dataset(name='timestamps', data=times)\n",
" f.create_dataset(name='nanotime', data=nanot)\n",
" f.create_dataset(name='detector', data=det.astype('uint8'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Reload"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just to check, reload the arrays from the HDF5 file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with h5py.File(fname_new, \"r\") as f:\n",
" timestamps = f['timestamps'][:]\n",
" nanotime = f['nanotime'][:]\n",
" detector = f['detector'][:]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"timestamps"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"array([ 34991, 36291, 36296, ..., 35842824771,\n",
" 35842833192, 35842841249], dtype=uint64)"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nanotime"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"array([2137, 1413, 1511, ..., 894, 3055, 1691], dtype=uint32)"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"detector"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"array([2, 6, 2, ..., 0, 6, 2], dtype=uint8)"
]
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Metadata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Partial decoding of header infos:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"header_chunks, sys_params = decode_header_BH(header)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 206
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All the system parameters are in the dictionary `sys_params`:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sys_params"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 226,
"text": [
"{'DI_2DC1': True,\n",
" 'DI_2DC1C': 128,\n",
" 'DI_2DC1S': 0,\n",
" 'DI_2DC2': True,\n",
" 'DI_2DC2C': 8388736,\n",
" 'DI_2DC2S': 0,\n",
" 'DI_2DDIS': 0,\n",
" 'DI_2DTRNO': 4,\n",
" 'DI_3DC1C': 128,\n",
" 'DI_3DC2C': 8388736,\n",
" 'DI_3DCOL': 16777215,\n",
" 'DI_3DINCX': 0,\n",
" 'DI_3DMODE': 3,\n",
" 'DI_3DOFFX': 3,\n",
" 'DI_3DOFFY': 1,\n",
" 'DI_3DSTYL': 0,\n",
" 'DI_ASCALE': 0,\n",
" 'DI_BLINE': 0,\n",
" 'DI_BOD_C': 32896,\n",
" 'DI_COL2': 255,\n",
" 'DI_COL3': 65535,\n",
" 'DI_COL4': 65280,\n",
" 'DI_COL5': 16776960,\n",
" 'DI_COLMAX': 16711680,\n",
" 'DI_COLMIN': 0,\n",
" 'DI_COL_NO': 6,\n",
" 'DI_CUR': True,\n",
" 'DI_DETAIL': True,\n",
" 'DI_GCOL_B': 0,\n",
" 'DI_GCOL_F': 8421504,\n",
" 'DI_GRID': True,\n",
" 'DI_HICOL': 16777215,\n",
" 'DI_INTCOL': 1,\n",
" 'DI_INTPIX': 1,\n",
" 'DI_LBLINE': 1,\n",
" 'DI_MAXCNT': 65535,\n",
" 'DI_PFREQ': 1,\n",
" 'DI_PSTYLE': 9,\n",
" 'DI_RATE': True,\n",
" 'DI_SCALE': 1,\n",
" 'DI_SIZE': 0,\n",
" 'DI_SXWIN': 1,\n",
" 'DI_SYWIN': 1,\n",
" 'DI_TRACE': 0,\n",
" 'DI_TWIN': 1,\n",
" 'DI_XREV': True,\n",
" 'DI_XWIN': 1,\n",
" 'DI_YREV': True,\n",
" 'DI_YWIN': 1,\n",
" 'PR_PAUTO': True,\n",
" 'PR_PDEV': 2,\n",
" 'PR_PEJECT': True,\n",
" 'PR_PF': True,\n",
" 'PR_PFNAME': 'C:\\\\USER\\\\TED\\\\DATA\\\\10-11-02\\\\IMAGE.PRT',\n",
" 'PR_PFULL': True,\n",
" 'PR_PHEIGH': 120.0,\n",
" 'PR_PORIEN': 0,\n",
" 'PR_PPORT': 0,\n",
" 'PR_PWHAT': 0,\n",
" 'PR_PWIDTH': 180.0,\n",
" 'PR_SAVE_T': 0,\n",
" 'PR_SLEEP': 15,\n",
" 'PR_STP_FN': 'C:\\\\PROGRAM FILES\\\\BH\\\\SPCM\\\\STP.CFG',\n",
" 'SP_ACCUM': True,\n",
" 'SP_ADC_RE': 4096,\n",
" 'SP_ADC_ZOOM': 0,\n",
" 'SP_ADD_SEL': 0,\n",
" 'SP_ASAVE': 2,\n",
" 'SP_BORD_L': 0,\n",
" 'SP_BORD_U': 0,\n",
" 'SP_CFD_HF': 5.0,\n",
" 'SP_CFD_LH': 80.0,\n",
" 'SP_CFD_LL': -54.901962,\n",
" 'SP_CFD_ZC': 0.0,\n",
" 'SP_COL_T': 100.0,\n",
" 'SP_CYCLES': 1,\n",
" 'SP_DAEC': True,\n",
" 'SP_DAES': True,\n",
" 'SP_DIS_T': 1.0,\n",
" 'SP_DITH': 32,\n",
" 'SP_DLIM': 20480,\n",
" 'SP_DTCOMP': True,\n",
" 'SP_EAL_DE': 0,\n",
" 'SP_EN_DLIM': True,\n",
" 'SP_EN_INT': True,\n",
" 'SP_EPX_DIV': 1,\n",
" 'SP_EXTST': True,\n",
" 'SP_FCYCLES': 1,\n",
" 'SP_FIF_FNO': 1,\n",
" 'SP_FIF_TYP': 1,\n",
" 'SP_FLB_X': 0,\n",
" 'SP_FLB_Y': 0,\n",
" 'SP_INCR': 1,\n",
" 'SP_LDIV': 0,\n",
" 'SP_MEM_BANK': 0,\n",
" 'SP_MODE': 10,\n",
" 'SP_MTCLK': 0,\n",
" 'SP_NCX': 8,\n",
" 'SP_NCY': 1,\n",
" 'SP_OFFSET': 0.0,\n",
" 'SP_OVERFL': ['C', 'N'],\n",
" 'SP_PAGE': 1,\n",
" 'SP_PHOT_F': 50010000,\n",
" 'SP_PH_BUF': 20480,\n",
" 'SP_PIX': 0,\n",
" 'SP_PIX_CLK': 0,\n",
" 'SP_PIX_T': 2e-07,\n",
" 'SP_POL_F': 0,\n",
" 'SP_POL_L': 0,\n",
" 'SP_POL_P': 0,\n",
" 'SP_REPEAT': True,\n",
" 'SP_REP_T': 36000.0,\n",
" 'SP_ROUT': True,\n",
" 'SP_SCAN_RX': 1,\n",
" 'SP_SCAN_RY': 1,\n",
" 'SP_SCAN_X': 32,\n",
" 'SP_SCAN_Y': 32,\n",
" 'SP_SCF': 0,\n",
" 'SP_SPE_FN': 'c:\\\\user\\\\xiangxu\\\\data\\\\08_11_04.ci2.1_53+10.g120r60.4ns\\\\0m gu 000.spc',\n",
" 'SP_STEPS': 1,\n",
" 'SP_STOPT': True,\n",
" 'SP_SXWIN_N': 8,\n",
" 'SP_SX_EQU': True,\n",
" 'SP_SYN_FD': 4,\n",
" 'SP_SYN_FQ': -100.0,\n",
" 'SP_SYN_HF': 4.0,\n",
" 'SP_SYN_ZC': 0.0,\n",
" 'SP_SYWIN_N': 8,\n",
" 'SP_SY_EQU': True,\n",
" 'SP_TAC_EH': 10.0,\n",
" 'SP_TAC_G': 1,\n",
" 'SP_TAC_LH': 100.0,\n",
" 'SP_TAC_LL': 0.0,\n",
" 'SP_TAC_OF': 0.0,\n",
" 'SP_TAC_R': 7.0054369e-08,\n",
" 'SP_TAC_TC': 1.7103118e-11,\n",
" 'SP_TAC_TD': 7.0054371e-09,\n",
" 'SP_TRIGGER': 0,\n",
" 'SP_TWIN_N': 8,\n",
" 'SP_T_EQU': True,\n",
" 'SP_WL_STA': 1.0,\n",
" 'SP_WL_STE': 1.0,\n",
" 'SP_WL_STO': 128.0,\n",
" 'SP_XWIN_N': 8,\n",
" 'SP_X_EQU': True,\n",
" 'SP_YWIN_N': 8,\n",
" 'SP_Y_EQU': True}"
]
}
],
"prompt_number": 226
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dictionary can be filtered as usual:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for k, v in sys_params.iteritems():\n",
" if 'TAC' in k:\n",
" print k, v"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"SP_TAC_G 1\n",
"SP_TAC_R 7.0054369e-08\n",
"SP_TAC_LH 100.0\n",
"SP_TAC_LL 0.0\n",
"SP_TAC_EH 10.0\n",
"SP_TAC_TC 1.7103118e-11\n",
"SP_TAC_TD 7.0054371e-09\n",
"SP_TAC_OF 0.0\n"
]
}
],
"prompt_number": 220
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filter(lambda item: 'TAC' in item[0], sys_params.iteritems())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 221,
"text": [
"[('SP_TAC_G', 1),\n",
" ('SP_TAC_R', 7.0054369e-08),\n",
" ('SP_TAC_LH', 100.0),\n",
" ('SP_TAC_LL', 0.0),\n",
" ('SP_TAC_EH', 10.0),\n",
" ('SP_TAC_TC', 1.7103118e-11),\n",
" ('SP_TAC_TD', 7.0054371e-09),\n",
" ('SP_TAC_OF', 0.0)]"
]
}
],
"prompt_number": 221
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"HTML(open(\"./styles/custom2.css\", \"r\").read())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<script src=\"http://use.edgefonts.net/source-code-pro.js\"></script>\n",
"\n",
"<style>\n",
"\t/* Import the \"Computer Modern\" font */\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
" }\n",
"\t\n",
"\t/* Style for all the cells */\n",
"\t/*div.cell{\n",
" width:900px;\n",
" margin-left:8% !important;\n",
" margin-right:auto;\n",
" }*/\n",
"\n",
"\t/* Text in markdown cells */\n",
" div.text_cell_render{\n",
" font-family: Computer Modern, Liberation Sans, Arial, Helvetica, sans-serif;\n",
" line-height: 130%;\n",
" font-size: 120%;\n",
" /* width:800px;\n",
" margin-left:auto;\n",
" margin-right:auto;*/\n",
" }\n",
"\n",
"\t/* Quoted text in markdown cells */\n",
"\t/*blockquote p {\n",
"\tmargin: 1em 2em;\n",
"\tline-height: 140%;\n",
" }*/\n",
"\n",
"\t/* Input and output prompt (In[1], Out[1], ...) */\n",
"\tdiv.prompt{\n",
"\t\tfont-family: \"Source Code Pro\", source-code-pro, Computer Modern, Consolas, monospace;\n",
"\t}\n",
"\n",
"\t/* Output text (i.e. output of print or text display) */\n",
"\tdiv.output_area pre{\n",
"\t\tfont-family: \"Source Code Pro\", source-code-pro, Computer Modern, Consolas, monospace;\n",
"\t}\n",
"\n",
"\t/* Text in code markup, within markdown cells */\n",
" .rendered_html code {\n",
"\tfont-family: \"Source Code Pro\", source-code-pro, Computer Modern, Consolas, monospace;\n",
"\tbackground-color: #f7f7f7;\n",
"\tborder: 1px solid #dddddd;\n",
"\tcolor: black;\n",
"\tline-height: 1.1em;\n",
"\tpadding: 0.15em;\n",
"\tfont-size: 80%;\n",
" }\n",
"\n",
"\t/* Text in code cells */\n",
" .CodeMirror{\n",
" font-family: \"Source Code Pro\", source-code-pro, Consolas, monospace;\n",
" }\n",
"\t\n",
" /* Whether to displays In[] and Out[] prompt */\n",
"\t/*.prompt{\n",
" display: None;\n",
" }*/\n",
" \n",
"</style>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 224,
"text": [
"<IPython.core.display.HTML at 0xddc95c0>"
]
}
],
"prompt_number": 224
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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