I am trying to extract some information from a set of files sent to me by a collaborator. Each file contains some python code which names a sequence of lists. They look something like this:
#PHASE = 0
x = np.array(1,2,...)
y = np.array(3,4,...)
z = np.array(5,6,...)
#PHASE = 30
x = np.array(1,4,...)
y = np.array(2,5,...)
z = np.array(3,6,...)
#PHASE = 40
...
And so on. There are 12 files in total, each with 7 phase sets. My goal is to convert each phase into it's own file which can then be read by ascii.read() as a Table object for manipulation in a different section of code.
My current method is extremely inefficient, both in terms of resources and time/energy required to assemble. It goes something like this: Start with a function
def makeTable(a,b,c):
output = Table()
output['x'] = a
output['y'] = b
output['z'] = c
return output
Then for each phase, I have manually copy-pasted the relevant part of the text file into a cell and appended a line of code
fileName_phase = makeTable(a,b,c)
Repeat ad nauseam. It would take 84 iterations of this to process all the data, and naturally each would need some minor adjustments to match the specific fileName and phase.
Finally, at the end of my code, I have a few lines of code set up to ascii.write each of the tables into .dat files for later manipulation.
This entire method is extremely exhausting to set up. If it's the only way to handle the data, I'll do it. I'm hoping I can find a quicker way to set it up, however. Is there one you can suggest?
If efficiency and code reuse instead of copy is the goal, I think that Classes might provide a good way. I'm going to sleep now, but I'll edit later. Here's my thoughts: create a class called FileWithArrays and use a parser to read the lines and put them inside the object FileWithArrays you will create using the class. Once that's done, you can then create a method to transform the object in a table.
P.S. A good idea for the parser is to store all the lines in a list and parse them one by one, using list.pop() to auto shrink the list. Hope it helps, tomorrow I'll look more on it if this doesn't help a lot. Try to rewrite/reformat the question if I misunderstood anything, it's not very easy to read.
I will suggest a way which will be scorned by many but will get your work done.
So apologies to every one.
The prerequisites for this method is that you absolutely trust the correctness of the input files. Which I guess you do. (After all he is your collaborator).
So the key point here is that the text in the file is code which means it can be executed.
So you can do something like this
import re
import numpy as np # this is for the actual code in the files. You might have to install numpy library for this to work.
file = open("xyz.txt")
content = file.read()
Now that you have all the content, you have to separate it by phase.
For this we will use the re.split function.
phase_data = re.split("#PHASE = .*\n", content)
Now we have the content of each phase in an array.
Now comes for the part of executing it.
for phase in phase_data:
if len(phase.strip()) == 0:
continue
exec(phase)
table = makeTable(x, y, z) # the x, y and z are defined by the exec.
# do whatever you want with the table.
I will reiterate that you have to absolutely trust the contents of the file. Since you are executing it as code.
But your work seems like a scripting one and I believe this will get your work done.
PS : The other "safer" alternative to exec is to have a sandboxing library which takes the string and executes it without affecting the parent scope.
To avoid the safety issue of using exec as suggested by #Ajay Brahmakshatriya, but keeping his first processing step, you can create your own minimal 'phase parser', something like:
VARS = 'xyz'
def makeTable(phase):
assert len(phase) >= 3
output = Table()
for i in range(3):
line = [s.strip() for s in phase[i].split('=')]
assert len(line) == 2
var, arr = line
assert var == VARS[i]
assert arr[:10]=='np.array([' and arr[-2:]=='])'
output[var] = np.fromstring(arr[10:-2], sep=',')
return output
and then call
table = makeTable(phase)
instead of
exec(phase)
table = makeTable(x, y, z)
You could also skip all these assert statements without compromising safety, if the file is corrupted or not formatted as expected the error that will be thrown might just be harder to understand...
Related
I am trying to get all data from view(Lotus Notes) with lotusscript and Python(noteslib module) and export it to csv, but problem is that this takes too much time. I have tried two ways with loop through all documents:
import noteslib
db = noteslib.Database('database','file.nsf')
view = db.GetView('My View')
doc = view.GetFirstDocument()
data = list()
while doc:
data.append(doc.ColumnValues)
doc = view.GetNextDocument(doc)
To get about 1000 lines of data it took me 70 seconds, but view has about 85000 lines so get all data will be too much time, because manually when I use File->Export in Lotus Notes it is about 2 minutes to export all data to csv.
And I tried second way with AllEntries, but it was even slower:
database = []
ec = view.AllEntries
ent = ec.Getfirstentry()
while ent:
row = []
for v in ent.Columnvalues:
row.append(v)
database.append(row)
ent = ec.GetNextEntry(ent)
Everything that I found on the Internet is based on "NextDocument" or "AllEntries". Is there any way to do it faster?
It is (or at least used to be) very expensive from a time standpoint to open a Notes document, like you are doing in your code.
Since you are saying that you want to export the data that is being displayed in the view, you could use the NotesViewEntry class instead. It should be much faster.
Set col = view.AllEntries
Set entry = col.GetFirstEntry()
Do Until entry Is Nothing
values = entry.ColumnValues '*** Array of column values
'*** Do stuff here
Set entry = col.GetNextEntry(entry)
Loop
I wrote a blog about this back in 2013:
http://blog.texasswede.com/which-is-faster-columnvalues-or-getitemvalue/
Something is going on with your code "outside" the view navigation: You already chose the most performant way to navigate a view using "GetFirstDocument" and "GetNextDocument". Using the NotesViewNavigator as mentioned in the comments will be slightly better, but not significant.
You might get a little bit of performance out of your code by setting view.AutoUpdate = False to prohibit the view object to refresh when something in the backend changes. But as you only read data and not change view data that will not give you much of a performance boost.
My suggestion: Identify the REAL bottleneck of your code by commenting out single sections to find out when it starts to get slower:
First attempt:
while doc:
doc = view.GetNextDocument(doc)
Slow?
If not then next attempt:
while doc:
arr = doc.ColumnValues
doc = view.GetNextDocument(doc)
Slow?
If yes: ColumnValues is your enemy...
If not then next attempt:
while doc:
arr = doc.ColumnValues
data.append(arr)
doc = view.GetNextDocument(doc)
I would be very interested to get your results of where it starts to become slow.
I would suspect the performance issue is using COM/ActiveX in Python to access Notes databases. Transferring data via COM involves datatype 'marshalling', possibly at every step, and especially for 'out-of-process' method/property calls.
I don't think there is any way around this in COM. You should consider arranging a Notes 'agent' to do this for you instead (LotusScript or Java maybe). Even a basic LotusScript agent can export 000's of docs per minute. A further alternative may be to look at the Notes C-API (not an easy option and requires API calls from Python).
How to get/extract number of lines added and deleted?
(Just like we do using git diff --numstat).
repo_ = Repo('git-repo-path')
git_ = repo_.git
log_ = g.diff('--numstat','HEAD~1')
print(log_)
prints the entire output (lines added/deleted and file-names) as a single string. Can this output format be modified or changed so as to extract useful information?
Output format: num(added) num(deleted) file-name
For all files modified.
If I understand you correctly, you want to extract data from your log_ variable and then re-format it and print it? If that's the case, then I think the simplest way to fix it, is with a regular expression:
import re
for line in log_.split('\n'):
m = re.match(r"(\d+)\s+(\d+)\s+(.+)", line)
if m:
print("{}: rows added {}, rows deleted {}".format(m[3], m[1], m[2]))
The exact output, you can of course modify any way you want, once you have the data in a match m. Getting the hang of regular expressions may take a while but it can be very helpful for small scripts.
However, be adviced, reg exps tend to be write-only code and can be very hard to debug. However, for extracting small parts like this, it is very helpful.
I'm writing a python script that generates another python script based off an external file. A small section of my code can be seen below. I haven't been exposed to many examples of these kinds of scripts, so I was wondering what the best practices were.
As seen in the last two lines of the code example, the techniques that I'm using can be unwieldy at times.
SIG_DICT_NAME = "sig_dict"
SIG_LEN_KEYWORD = "len"
SIG_BUS_IND_KEYWORD = "ind"
SIG_EP_ADDR_KEYWORD = "ep_addr"
KEYWORD_DEC = "{} = \"{}\""
SIG_LEN_KEYWORD_DEC = KEYWORD_DEC.format(SIG_LEN_KEYWORD, SIG_LEN_KEYWORD)
SIG_BUS_IND_KEYWORD_DEC = KEYWORD_DEC.format(SIG_BUS_IND_KEYWORD,
SIG_BUS_IND_KEYWORD)
SIG_EP_ADDR_KEYWORD_DEC = KEYWORD_DEC.format(SIG_EP_ADDR_KEYWORD,
SIG_EP_ADDR_KEYWORD)
SIG_DICT_DEC = "{} = dict()"
SIG_DICT_BODY_LINE = "{}[{}.{}] = {{{}:{}, {}:{}, {}:{}}}"
#line1 = SIG_DICT_DEC.format(SIG_DICT_NAME)
#line2 = SIG_DICT_BODY.format(SIG_DICT_NAME, x, y, z...)
You don't really see examples of this kind of thing because your solution might be a wee bit over-engineered ;)
I'm guessing that you're trying to collect some "state of things", and then you want to run a script to process that "state of things". Rather than writing a meta-script, what is typically far more convenient is to write a script that will do the processing (say, process.py), and another script that will do the collecting of the "state of things" (say, collect.py).
Then you can take the results from collect.py and throw them at process.py and write out todays_results.txt or some such:
collect.py -> process.py -> 20150207_results.txt
If needed, you can write intermediate files to disk with something like:
with open('todays_progress.txt') as f_out:
for thing, state in states_of_things.iteritems():
f.write('{}<^_^>{}\n'.format(state, thing))
Then you can parse it back in later with something like:
with open('todays_progress.txt') as f_in:
lines = f_in.read().splitlines()
things, states = [x, y for x, y in lines.split('<^_^>')]
states_of_things = dict(zip(things, states))
More complicated data structures than a flat dict? Well, this is Python. There's probably more than one module for that! Off the top of my head I would suggest json if plaintext will do, or pickle if you need some more detailed structures. Two warnings with pickle: custom objects don't always get reinstantiated well, and it's vulnerable to code injection attacks, so only use it if your entire workflow is trusted.
Hope this helps!
You seem to be translating keyword-by-keyword.
It would almost certainly be better to read each "sentence" into a representative Python class; you could then run the simulation directly, or have each class write itself to an "output sentence".
Done correctly, this should be much easier to write and debug and produce more idiomatic output.
I have a number of files where I want to replace all instances of a specific string with another one.
I currently have this code:
mappings = {'original-1': 'replace-1', 'original-2': 'replace-2'}
# Open file for substitution
replaceFile = open('file', 'r+')
# read in all the lines
lines = replaceFile.readlines()
# seek to the start of the file and truncate
# (this is cause i want to do an "inline" replace
replaceFile.seek(0)
replaceFile.truncate()
# Loop through each line from file
for line in lines:
# Loop through each Key in the mappings dict
for i in mappings.keys():
# if the key appears in the line
if i in line:
# do replacement
line = line.replace(i, mappings[i])
# Write the line to the file and move to next line
replaceFile.write(line)
This works ok, but it is very slow for the size of the mappings and the size of the files I am dealing with.
For instance, in the "mappings" dict there are 60728 key value pairs.
I need to process up to 50 files and replace all instances of "key" with the corresponding value, and each of the 50 files is approximately 250000 lines.
There are also multiple instances where there are multiple keys that need to be replaced on the one line, hence I cant just find the first match and then move on.
So my question is:
Is there a faster way to do the above?
I have thought about using a regex, but I am not sure how to craft one that will do multiple in-line replaces using key/value pairs from a dict.
If you need more info, let me know.
If this performance is slow, you'll have to find something fancy. It's just about all running at C-level:
for filename in filenames:
with open(filename, 'r+') as f:
data = f.read()
f.seek(0)
f.truncate()
for k, v in mappings.items():
data = data.replace(k, v)
f.write(data)
Note that you can run multiple processes where each process tackles a portion of the total list of files. That should make the whole job a lot faster. Nothing fancy, just run multiple instances off the shell, each with a different file list.
Apparently str.replace is faster than regex.sub.
So I got to thinking about this a bit more: suppose you have a really huge mappings. So much so that the likelihood of any one key in mappings being detected in your files is very low. In this scenario, all the time will be spent doing the searching (as pointed out by #abarnert).
Before resorting to exotic algorithms, it seems plausible that multiprocessing could at least be used to do the searching in parallel, and thereafter do the replacements in one process (you can't do replacements in multiple processes for obvious reasons: how would you combine the result?).
So I decided to finally get a basic understanding of multiprocessing, and the code below looks like it could plausibly work:
import multiprocessing as mp
def split_seq(seq, num_pieces):
# Splits a list into pieces
start = 0
for i in xrange(num_pieces):
stop = start + len(seq[i::num_pieces])
yield seq[start:stop]
start = stop
def detect_active_keys(keys, data, queue):
# This function MUST be at the top-level, or
# it can't be pickled (multiprocessing using pickling)
queue.put([k for k in keys if k in data])
def mass_replace(data, mappings):
manager = mp.Manager()
queue = mp.Queue()
# Data will be SHARED (not duplicated for each process)
d = manager.list(data)
# Split the MAPPINGS KEYS up into multiple LISTS,
# same number as CPUs
key_batches = split_seq(mappings.keys(), mp.cpu_count())
# Start the key detections
processes = []
for i, keys in enumerate(key_batches):
p = mp.Process(target=detect_active_keys, args=(keys, d, queue))
# This is non-blocking
p.start()
processes.append(p)
# Consume the output from the queues
active_keys = []
for p in processes:
# We expect one result per process exactly
# (this is blocking)
active_keys.append(queue.get())
# Wait for the processes to finish
for p in processes:
# Note that you MUST only call join() after
# calling queue.get()
p.join()
# Same as original submission, now with MUCH fewer keys
for key in active_keys:
data = data.replace(k, mappings[key])
return data
if __name__ == '__main__':
# You MUST call the mass_replace function from
# here, due to how multiprocessing works
filenames = <...obtain filenames...>
mappings = <...obtain mappings...>
for filename in filenames:
with open(filename, 'r+') as f:
data = mass_replace(f.read(), mappings)
f.seek(0)
f.truncate()
f.write(data)
Some notes:
I have not executed this code yet! I hope to test it out sometime but it takes time to create the test files and so on. Please consider it as somewhere between pseudocode and valid python. It should not be difficult to get it to run.
Conceivably, it should be pretty easy to use multiple physical machines, i.e. a cluster with the same code. The docs for multiprocessing show how to work with machines on a network.
This code is still pretty simple. I would love to know whether it improves your speed at all.
There seem to be a lot of hackish caveats with using multiprocessing, which I tried to point out in the comments. Since I haven't been able to test the code yet, it may be the case that I haven't used multiprocessing correctly anyway.
According to http://pravin.paratey.com/posts/super-quick-find-replace, regex is the fastest way to go for Python. (Building a Trie data structure would be fastest for C++) :
import sys, re, time, hashlib
class Regex:
# Regex implementation of find/replace for a massive word list.
def __init__(self, mappings):
self._mappings = mappings
def replace_func(self, matchObj):
key = matchObj.group(0)
if self._mappings.has_key(key):
return self._mappings[key]
else:
return key
def replace_all(self, filename):
text = ''
with open(filename, 'r+') as fp
text = fp.read()
text = re.sub("[a-zA-Z]+", self.replace_func, text)
fp = with open(filename, "w") as fp:
fp.write(text)
# mapping dictionary of (find, replace) tuples defined
mappings = {'original-1': 'replace-1', 'original-2': 'replace-2'}
# initialize regex class with mapping tuple dictionary
r = Regex(mappings)
# replace file
r.replace_all( 'file' )
The slow part of this is the searching, not the replacing. (Even if I'm wrong, you can easily speed up the replacing part by first searching for all the indices, then splitting and replacing from the end; it's only the searching part that needs to be clever.)
Any naive mass string search algorithm is obviously going to be O(NM) for an N-length string and M substrings (and maybe even worse, if the substrings are long enough to matter). An algorithm that searched M times at each position, instead of M times over the whole string, might be offer some cache/paging benefits, but it'll be a lot more complicated for probably only a small benefit.
So, you're not going to do much better than cjrh's implementation if you stick with a naive algorithm. (You could try compiling it as Cython or running it in PyPy to see if it helps, but I doubt it'll help much—as he explains, all the inner loops are already in C.)
The way to speed it up is to somehow look for many substrings at a time. The standard way to do that is to build a prefix tree (or suffix tree), so, e.g, "original-1" and "original-2" are both branches off the same subtree "original-", so they don't need to be handled separately until the very last character.
The standard implementation of a prefix tree is a trie. However, as Efficient String Matching: An Aid to Bibliographic Search and the Wikipedia article Aho-Corasick string matching algorithm explain, you can optimize further for this use case by using a custom data structure with extra links for fallbacks. (IIRC, this improves the average case by logM.)
Aho and Corasick further optimize things by compiling a finite state machine out of the fallback trie, which isn't appropriate to every problem, but sounds like it would be for yours. (You're reusing the same mappings dict 50 times.)
There are a number of variant algorithms with additional benefits, so it might be worth a bit of further research. (Common use cases are things like virus scanners and package filters, which might help your search.) But I think Aho-Corasick, or even just a plain trie, is probably good enough.
Building any of these structures in pure Python might add so much overhead that, at M~60000, the extra cost will defeat the M/logM algorithmic improvement. But fortunately, you don't have to. There are many C-optimized trie implementations, and at least one Aho-Corasick implementation, on PyPI. It also might be worth looking at something like SuffixTree instead of using a generic trie library upside-down if you think suffix matching will work better with your data.
Unfortunately, without your data set, it's hard for anyone else to do a useful performance test. If you want, I can write test code that uses a few different modules, that you can then run against you data. But here's a simple example using ahocorasick for the search and a dumb replace-from-the-end implementation for the replace:
tree = ahocorasick.KeywordTree()
for key in mappings:
tree.add(key)
tree.make()
for start, end in reversed(list(tree.findall(target))):
target = target[:start] + mappings[target[start:end]] + target[end:]
This use a with block to prevent leaking file descriptors. The string replace function will ensure all instances of key get replaced within the text.
mappings = {'original-1': 'replace-1', 'original-2': 'replace-2'}
# Open file for substitution
with open('file', 'r+') as fd:
# read in all the data
text = fd.read()
# seek to the start of the file and truncate so file will be edited inline
fd.seek(0)
fd.truncate()
for key in mappings.keys():
text = text.replace(key, mappings[key])
fd.write(text)
This is my python file:-
TestCases-2
Input-5
Output-1,1,2,3,5
Input-7
Ouput-1,1,2,3,5,8,13
What I want is this:-
A variable test_no = 2 (No. of testcases)
A list testCaseInput = [5,7]
A list testCaseOutput = [[1,1,2,3,5],[1,1,2,3,5,8,13]]
I've tried doing it in this way:
testInput = testCase.readline(-10)
for i in range(0,int(testInput)):
testCaseInput = testCase.readline(-6)
testCaseOutput = testCase.readline(-7)
The next step would be to strip the numbers on the basis of (','), and then put them in a list.
Weirdly, the readline(-6) is not giving desired results.
Is there a better way to do this, which obviously I'm missing out on.
I don't mind using serialization here but I want to make it very simple for someone to write a text file as the one I have shown and then take the data out of it. How to do that?
A negative argument to the readline method specifies the number of bytes to read. I don't think this is what you want to be doing.
Instead, it is simpler to pull everything into a list all at once with readlines():
with open('data.txt') as f:
full_lines = f.readlines()
# parse full lines to get the text to right of "-"
lines = [line.partition('-')[2].rstrip() for line in full_lines]
numcases = int(lines[0])
for i in range(1, len(lines), 2):
caseinput = lines[i]
caseoutput = lines[i+1]
...
The idea here is to separate concerns (the source of the data, the parsing of '-', and the business logic of what to do with the cases). That is better than having a readline() and redundant parsing logic at every step.
I'm not sure if I follow exactly what you're trying to do, but I guess I'd try something like this:
testCaseIn = [];
testCaseOut = [];
for line in testInput:
if (line.startsWith("Input")):
testCaseIn.append(giveMeAList(line.split("-")[1]));
elif (line.startsWith("Output")):
testCaseOut.append(giveMeAList(line.split("-")[1]));
where giveMeAList() is a function that takes a comma seperated list of numbers, and generates a list datathing from it.
I didn't test this code, but I've written stuff that uses this kind of structure when I've wanted to make configuration files in the past.
You can use regex for this and it makes it much easier. See question: python: multiline regular expression
For your case, try this:
import re
s = open("input.txt","r").read()
(inputs,outputs) = zip(*re.findall(r"Input-(?P<input>.*)\nOutput-(?P<output>.*)\n",s))
and then split(",") each output element as required
If you do it this way you get the benefit that you don't need the first line in your input file so you don't need to specify how many entries you have in advance.
You can also take away the unzip (that's the zip(*...) ) from the code above, and then you can deal with each input and output a pair at a time. My guess is that is in fact exactly what you are trying to do.
EDIT Wanted to give you the full example of what I meant just then. I'm assuming this is for a testing script so I would say use the power of the pattern matching iterator to help keep your code shorter and simpler:
for (input,output) in re.findall(r"Input-(?P<input>.*)\nOutput-(?P<output>.*)\n",s):
expectedResults = output.split(",")
testResults = runTest(input)
// compare testResults and expectedResults ...
This line has an error:
Ouput-1,1,2,3,5,8,13 // it should be 'Output' not 'Ouput
This should work:
testCase = open('in.txt', 'r')
testInput = int(testCase.readline().replace("TestCases-",""))
for i in range(0,int(testInput)):
testCaseInput = testCase.readline().replace("Input-","")
testCaseOutput = testCase.readline().replace("Output-","").split(",")