How to Read in Pickle Data in D3.js
Serialization with pickle and json
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Serialization
Serialization is the process of converting a data structure or object land into a format that tin can be stored (for example, in a file or retention buffer, or transmitted beyond a network connectedness link) and resurrected later on in the same or another computer environment.
When the resulting series of bits is reread according to the serialization format, information technology can be used to create a semantically identical clone of the original object.
This procedure of serializing an object is also chosen deflating or marshalling an object. The contrary operation, extracting a information structure from a series of bytes, is deserialization (which is also called inflating or unmarshalling). wiki.
In Python, we have the pickle module. The majority of the pickle module is written in C, like the Python interpreter itself. It can store arbitrarily complex Python data structures. It is a cantankerous-version customisable only dangerous (not secure against erroneous or malicious data) serialization format.
The standard library as well includes modules serializing to standard information formats:
- json with built-in support for basic scalar and collection types and able to support capricious types via encoding and decoding hooks.
- XML-encoded property lists. (plistlib), limited to plist-supported types (numbers, strings, booleans, tuples, lists, dictionaries, datetime and binary blobs)
Finally, it is recommended that an object'due south __repr__ exist evaluable in the right environment, making it a rough match for Common Lisp's print-object. wiki
Pickle
What information blazon can pickle store?
Hither are the things that the pickle module shop:
- All the native datatypes that Python supports: booleans, integers, floating point numbers, complex numbers, strings, bytes objects, byte arrays, and None.
- Lists, tuples, dictionaries, and sets containing any combination of native datatypes.
- Lists, tuples, dictionaries, and sets containing any combination of lists, tuples, dictionaries, and sets containing whatsoever combination of native datatypes (and and so on, to the maximum nesting level that Python supports).
- Functions, classes, and instances of classes (with caveats).
Constructing Pickle data
We will use two Python Shells, 'A' & 'B':
>>> shell = 'A'
Open another Shell:
>>> shell = 'B'
Hither is the dictionary type data for Vanquish 'A':
>>> shell 'A' >>> book = {} >>> volume['title'] = 'Light Science and Magic: An Introduction to Photographic Lighting, Kindle Edition' >>> book['page_link'] = 'http://www.amazon.com/Fil-Hunter/e/B001ITTV7A' >>> volume['comment_link'] = None >>> volume['id'] = b'\xAC\xE2\xC1\xD7' >>> book['tags'] = ('Photography', 'Kindle', 'Light') >>> book['published'] = True >>> import time >>> book['published_time'] = fourth dimension.strptime('Mon Sep x 23:xviii:32 2012') >>> book['published_time'] time.struct_time(tm_year=2012, tm_mon=9, tm_mday=10, tm_hour=23, tm_min=18, tm_sec=32, tm_wday=0, tm_yday=254, tm_isdst=-1) >>> Here, we're trying to utilise as many data types as possible.
The time module contains a information structure, struct_time to represent a betoken in time and functions to manipulate fourth dimension structs. The strptime() function takes a formatted string an converts information technology to a struct_time. This string is in the default format, only we tin can control that with format codes. For more details, visit the time module.
Saving data as a pickle file
At present, we have a dictionay that has all the data nigh the book. Let'south save it as a pickle file:
>>> import pickle >>> with open('volume.pickle', 'w b ') every bit f: pickle.dump(book, f) We prepare the file mode to wb to open the file for writing in binary way. Wrap it in a with argument to ensure the file is closed automatically when nosotros're washed with it. The dump() role in the pickle module takes a serializable Python data construction, serializes information technology into a binary, Python-specific format using the latest version of the pickle protocol, and saves it to an open file.
- The pickle module takes a Python data construction and saves it to a file.
- Serializes the data structure using a data format called the pickle protocol.
- The pickle protocol is Python-specific; there is no guarantee of cantankerous-language compatibility.
- Not every Python data construction can be serialized by the pickle module. The pickle protocol has changed several times every bit new data types take been added to the Python linguistic communication, but there are however limitations.
- So, in that location is no guarantee of compatibility between dissimilar versions of Python itself.
- Unless we specify otherwise, the functions in the pickle module will utilize the latest version of the pickle protocol.
- The latest version of the pickle protocol is a binary format. Be sure to open our pickle files in binary way, or the information will become corrupted during writing.
Loading data from a pickle file
Let's load the saved information from a pickle file on some other Python Shell B.
>>> shell 'B' >>> import pickle >>> with open('volume.pickle', 'rb') every bit f: b = pickle.load(f) >>> b {'published_time': fourth dimension.struct_time(tm_year=2012, tm_mon=9, tm_mday=10, tm_hour=23, tm_min=eighteen, tm_sec=32, tm_wday=0, tm_yday=254, tm_isdst=-one), 'title': 'Light Science and Magic: An Introduction to Photographic Lighting, Kindle Edition', 'tags': ('Photography', 'Kindle', 'Light'), 'page_link': 'http://www.amazon.com/Fil-Hunter/due east/B001ITTV7A', 'published': True, 'id': b'\xac\xe2\xc1\xd7', 'comment_link': None} - There is no volume variable defined here since nosotros divers a book variable in Python Shell A.
- We opened the volume.pickle file we created in Python Shell A. The pickle module uses a binary data format, and so we should always open pickle files in binary way.
- The pickle.load() office takes a stream object, reads the serialized data from the stream, creates a new Python object, recreates the serialized information in the new Python object, and returns the new Python object.
- The pickle.dump()/pickle.load() cycle creates a new information structure that is equal to the original data structure.
Allow'southward switch dorsum to Python Shell A.
>>> shell 'A' >>> with open('book.pickle', 'rb') as f: book2 = pickle.load(f) >>> book2 == volume True >>> book2 is volume False - Nosotros opened the book.pickle file, and loaded the serialized information into a new variable, book2.
- The 2 dictionaries, book and book2, are equal.
- Afterwards we serialized this dictionary and stored information technology in the book.pickle file, and then read it back the serialized information from that file and created a perfect replica of the original information structure.
- Equality is not the same as identity. We've created a perfect replica of the original data structure, which is true. But it'south still a copy.
Serializing data in retentiveness with pickle
if we don't want use a file, we can still serialize an object in memory.
>>> crush 'A' >>> thousand = pickle.dumps(volume) >>> blazon(g) <class 'bytes'> >>> book3 = pickle.loads(g) >>> book3 == book True
- The pickle.dumps() function (note that nosotros're using the s at the end of the function proper name, non the dump()) performs the same serialization as the pickle.dump() function. Instead of taking a stream object and writing the serialized information to a file on disk, it simply returns the serialized data.
- Since the pickle protocol uses a binary data format, the pickle.dumps() function returns a bytes object.
- The pickle.loads() function (once again, note the s at the finish of the function name) performs the same deserialization as the pickle.load() function. Instead of taking a stream object and reading the serialized information from a file, it takes a bytes object containing serialized data, such as the one returned by the pickle.dumps() function.
- The cease issue is the same: a perfect replica of the original dictionary.
Python serialized object and JSON
The information format used by the pickle module is Python-specific. It makes no attempt to be compatible with other programming languages. If cross-language compatibility is ane of our requirements, we need to look at other serialization formats. One such format is json.
JSON(JavaScript Object Annotation) is a text-based open standard designed for human-readable data interchange. It is derived from the JavaScript scripting language for representing uncomplicated data structures and associative arrays, chosen objects. Despite its relationship with JavaScript, information technology is linguistic communication-independent, with parsers available for many languages. json is explicitly designed to be usable across multiple programming languages. The JSON format is often used for serializing and transmitting structured data over a network connexion. It is used primarily to transmit information between a server and web application, serving as an alternative to XML - from wiki
Python three includes a json module in the standard library. Similar the pickle module, the json module has functions for serializing information structures, storing the serialized information on disk, loading serialized data from deejay, and unserializing the data back into a new Python object. Merely there are some important differences, too.
- The json data format is text-based, non binary. All json values are case-sensitive.
- As with any text-based format, in that location is the issue of whitespace. json allows capricious amounts of whitespace (spaces, tabs, railroad vehicle returns, and line feeds) betwixt values. This whitespace is insignificant, which means that json encoders can add together equally much or equally piffling whitespace equally they like, and json decoders are required to ignore the whitespace between values. This allows us to pretty-impress our json data, nicely nesting values within values at dissimilar indentation levels so we tin can read it in a standard browser or text editor. Python'due south json module has options for pretty-printing during encoding.
- At that place's the perennial problem of character encoding. json encodes values equally plain text, but every bit we know, there are no such matter as manifestly text. json must be stored in a Unicode encoding (UTF-32, UTF-16, or the default, utf-8). Regarding an encoding with json, please visit RFC 4627
Saving data to JSON
Nosotros're going to create a new data construction instead of re-using the existing entry data structure. json is a text-based format, which ways we need to open this file in text mode and specify a character encoding. We can never go wrong with utf-8.
attempt: import simplejson equally json except: import json book = {} book['title'] = 'Light Scientific discipline and Magic: An Introduction to Photographic Lighting, Kindle Edition' book['tags'] = ('Photography', 'Kindle', 'Low-cal') book['published'] = True book['comment_link'] = None book['id'] = 1024 with open('ebook.json', 'westward') as f: json.dump(book, f) Like the pickle module, the json module defines a dump() function which takes a Python data structure and a writable stream object. The dump() part serializes the Python data structure and writes it to the stream object. Doing this inside a with statement will ensure that the file is closed properly when nosotros're done.
Permit's see what's in ebook.json file:
$ cat ebook.json {"published": truthful, "tags": ["Photography", "Kindle", "Light"], "id": 1024, "com ment_link": null, "championship": "Light Science and Magic: An Introduction to Photographic Lighting, Kindle Edition"} It's conspicuously more readable than a pickle file. Merely json can contain capricious whitespace between values, and the json module provides an easy manner to accept advantage of this to create even more readable json files:
>>> with codecs.open('book_more_friendly.json', way='westward', encoding='utf-8') equally f: json.dump(book, f, indent=iii) We passed an indent parameter to the json.dump() function, and it made the resulting json file more readable, at the expense of larger file size. The indent parameter is an integer.
$ true cat book_more_friendly.json { "published": true, "tags": [ "Photography", "Kindle", "Light" ], "id": 1024, "comment_link": null, "title": "Lite Science and Magic: An Introduction to Photographic Lighting, Kindle Edition" } Here is some other instance for json:
#!/isr/bin/python import psutil import os import subprocess import string import bitstring import json import codecs try: import xml.etree.cElementTree equally ET except ImportError: import xml.etree.ElementTree equally ET procs_id = 0 procs = {} procs_data = [] ismvInfo = { 'baseName':' ', 'video': { 'src':[], 'TrackIDvalue':[], 'Duration': 0, 'QualityLevels': ane, 'Chunks': 0, 'Url': '', 'alphabetize':[], 'bitrate':[], 'fourCC':[], 'width': [], 'height':[], 'codecPrivateData': [], 'fragDurations':[] }, 'audio': { 'src':[], 'TrackIDvalue':[], 'QualityLevels': one, 'index':[], 'bitrate':[], 'fourCC':[], 'samplingRate':[], 'channels':[], 'bitsPerSample':[], 'packetSize':[], 'audioTag': [], 'codecPrivateData': [], 'fragDurations': [], } } def runCommand(cmd, use_shell = False, return_stdout = Imitation, busy_wait = Truthful, poll_duration = 0.5): # Sanitize cmd to cord cmd = map(lambda x: '%s' % x, cmd) if use_shell: command = ' '.join(cmd) else: command = cmd if return_stdout: proc = psutil.Popen(cmd, crush = use_shell, stdout = subprocess.Pipage, stderr = subprocess.PIPE) else: proc = psutil.Popen(cmd, trounce = use_shell, stdout = open('/dev/null', 'w'), stderr = open('/dev/nix', 'due west')) global procs_id global procs global procs_data proc_id = procs_id procs[proc_id] = proc procs_id += one data = { } while busy_wait: returncode = proc.poll() if returncode == None: endeavour: data = proc.as_dict(attrs = ['get_io_counters', 'get_cpu_times']) except Exception, east: pass time.slumber(poll_duration) else: pause (stdout, stderr) = proc.communicate() returncode = proc.returncode del procs[proc_id] if returncode != 0: heighten Exception(stderr) else: if data: procs_data.append(data) render stdout # server manifest def ismParse(data): # need to remove the string beneath to make xml parse work data = data.supercede(' xmlns="http://www.w3.org/2001/SMIL20/Linguistic communication"','') root = ET.fromstring(data) # caput for m in root.iter('head'): for p in m.iter('meta'): ismvInfo['baseName'] = (p.attrib['content']).divide('.')[0] # videoAttributes for five in root.iter('video'): ismvInfo['video']['src'].append(v.attrib['src']) for p in v.iter('param'): ismvInfo['video']['TrackIDvalue'].append(p.attrib['value']) # audioAttributes for a in root.iter('audio'): ismvInfo['sound']['src'].suspend(a.attrib['src']) for p in a.iter('param'): ismvInfo['sound']['TrackIDvalue'].suspend(p.attrib['value']) # customer manifest def ismcParse(information): root = ET.fromstring(information) # duration # streamDuration = root.attrib['Duration'] ismvInfo['video']['Duration'] = root.attrib['Duration'] for due south in root.iter('StreamIndex'): if(due south.attrib['Type'] == 'video'): ismvInfo['video']['QualityLevels'] = s.attrib['QualityLevels'] ismvInfo['video']['Chunks'] = s.attrib['Chunks'] ismvInfo['video']['Url'] = s.attrib['Url'] for q in s.iter('QualityLevel'): ismvInfo['video']['index'].suspend(q.attrib['Index']) ismvInfo['video']['bitrate'].append(q.attrib['Bitrate']) ismvInfo['video']['fourCC'].append(q.attrib['FourCC']) ismvInfo['video']['width'].append(q.attrib['MaxWidth']) ismvInfo['video']['acme'].append(q.attrib['MaxHeight']) ismvInfo['video']['codecPrivateData'].append(q.attrib['CodecPrivateData']) # video frag duration for c in s.iter('c'): ismvInfo['video']['fragDurations'].append(c.attrib['d']) elif(s.attrib['Type'] == 'sound'): ismvInfo['audio']['QualityLevels'] = s.attrib['QualityLevels'] ismvInfo['sound']['Url'] = s.attrib['Url'] for q in s.iter('QualityLevel'): #ismvInfo['audio']['index'] = q.attrib['Index'] ismvInfo['audio']['index'].suspend(q.attrib['Alphabetize']) ismvInfo['audio']['bitrate'].append(q.attrib['Bitrate']) ismvInfo['sound']['fourCC'].suspend(q.attrib['FourCC']) ismvInfo['audio']['samplingRate'].append(q.attrib['SamplingRate']) ismvInfo['sound']['channels'].append(q.attrib['Channels']) ismvInfo['audio']['bitsPerSample'].append(q.attrib['BitsPerSample']) ismvInfo['sound']['packetSize'].append(q.attrib['PacketSize']) ismvInfo['audio']['audioTag'].append(q.attrib['AudioTag']) ismvInfo['audio']['codecPrivateData'].append(q.attrib['CodecPrivateData']) # audio frag duration for c in southward.iter('c'): #audioFragDuration.append(c.attrib['d']) ismvInfo['sound']['fragDurations'].append(c.attrib['d']) def populateManifestMetadata(base of operations): try: # parse server manifest and populate ismv info information with open(base+'.ism', 'rb') as manifest: ismData = manifest.read() ismParse(ismData) # parse client manifest and populate ismv info data with open(base+'.ismc', 'rb') every bit manifest: ismcData = manifest.read() ismcParse(ismcData) except Exception, e: raise RuntimeError("issue opening ismv manifest file") # input # ismvFIles - list of ismv files # base - basename of ismv files def setManifestMetadata(ismvFiles, base of operations): #cmd = ['ismindex','-n', ismTmpName,'bunny_400.ismv','bunny_894.ismv','bunny_2000.ismv' ] cmd = ['ismindex','-n', base] for ism in ismvFiles: cmd.append(ism) stdout = runCommand(cmd, return_stdout = True, busy_wait = Simulated) populateManifestMetadata(base) if __name__ == '__main__': ismvFiles = ['bunny_400.ismv','bunny_894.ismv','bunny_2000.ismv'] base = 'bunny' setManifestMetadata(ismvFiles, base) # salvage to json file with codecs.open up('ismvInfo.json', 'w', encoding='utf-eight') as f: json.dump(ismvInfo, f) The output is ismvInfo.json.
Data blazon mapping
There are some mismatches in JSON'south coverage of Python datatypes. Some of them are simply naming differences, but there are two important Python datatypes that are completely missing: tuples and bytes.
| Python3 | Json |
|---|---|
| dictionary | object |
| list | array |
| tuple | Due north/A |
| bytes | N/A |
| float | real number |
| True | truthful |
| False | faux |
| None | null |
Loading data from a JSON file
>>> import json >>> import codecs >>> with codecs.open('j.json', 'r', encoding='utf-eight') as f: data_from_jason = json.load(f) >>> data_from_jason {'championship': 'Light Scientific discipline and Magic: An Introduction to Photographic Lighting, Kindle Edition', 'tags': ['Photography', 'Kindle', 'Light'], 'id': 1024, 'comment_link': None, 'published': True} >>> Listing to JSON file
The post-obit lawmaking makes a list of dictionary items and the salvage it to json. The input used in the code is semicolon separated with three columns like this:
protocol;service;plugin
Earlier making it as a list of dictionary items, we add together additional info field, 'value':
try: import simplejson equally json except ImportError: import json def get_data(dat): with open('input.txt', 'rb') as f: for fifty in f: d = {} line = ((l.rstrip()).dissever(';')) line.append(0) d['protocol'] = line[0] d['service'] = line[i] d['plugin'] = line[two] d['value'] = line[iii] dat.append(d) return dat def convert_to_json(data): with open('data.json', 'due west') as f: json.dump(data, f) if __name__ == '__main__': data = [] data = get_data(information) convert_to_json(data) The output json file looks like this:
[{"protocol": "pro1", "value": 0, "service": "service1", "plugin": "check_wmi_plus.pl -H 10.half dozen.88.72 -m checkfolderfilecount -u administrator -p c0c1c -w 1000 -c 2000 -a 's:' -o 'error/' --nodatamode"}, {"protocol": "proto2", "value": 1, "service": "service2", "plugin": "check_wmi_plus.pl -H 10.6.88.72 -1000 checkdirage -u administrator -p a23aa8 --nodatamode -c :1 -a s -o input/ -3 `date --utc --date '-30 mins' +\"%Y%one thousand%d%H%M%S.000000+000\" `"},...] Some of the sections (pickle) of this affiliate is largely based on http://getpython3.com/diveintopython3/serializing.html
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