spike.util package

Submodules

spike.util.ConfigForm module

spike.util.ValErr module

untitled.py

Created by Marc-André on 2011-03-20. Copyright (c) 2011 IGBMC. All rights reserved.

spike.util.ValErr.ValErr(v, e=0.0)

nice print of a value with error bars

values are rounded to significative digits >>> print ValErr(1234.567,123.4) 1230 +/- 123

can be called with a pair of values or a tuple : >>> V=(1234.567,123.4); print ValErr(V) 1230 +/- 123

values are rounded to significative digits >>> print ValErr(7654.321,1234) 7700 +/- 1230 >>> print ValErr(7654.321,123.4) 7650 +/- 123 >>> print ValErr(7654.321,12.34) 7654 +/- 12

format depends on error >>> print ValErr(1234.567,12.34) 1235 +/- 12 >>> print ValErr(1234.567,1.234) 1234.6 +/- 1.2 >>> print ValErr(1234.567,0.1234) 1234.57 +/- 0.12 >>> print ValErr(1234.567,0.01234) 1234.567 +/- 0.012

spike.util.ValErr.round(x)

takes the nearest integer of a float >>> round(0.6) 1.0 >>> round(0.4) 0.0 >>> round(-0.6) -1.0 >>> round(-0.4) 0.0

spike.util.compat module

Compatibility methods and classes for the progressbar module.

spike.util.counter module

counter.py

This utility declare two useful decorators

counting and timeit

usage : @counting def func()

…

counting

The number of time the decorated function is called is counted, and the count is printed at the end of the prgm execution

timeit :

As counting, but additionally, the total (elapsed) time spent in the function is totalized and printed at the end of the prgm

Created by Marc-André on 2012-11-05.

Copyright (c) 2012 IGBMC. All rights reserved.

class spike.util.counter.Counter(ini=0)

Bases: object

defines an incremental counter c = Counter() # actually defines a function c() v = c() # each time c() is called, the internal value is incremented value is found at c.count

spike.util.counter.counting(func_to_decorate)

define a decorator that count the nmber of function/method call, and display at the end of the prgm

spike.util.counter.main()

should print : function test2 time : 3.000320 sec called 3 time/call : 1.000107 sec function test called 13 times

spike.util.counter.timeit(func_to_decorate)

define a decorator that count the total time passed in the function/method call, and display at the end of the prgm NOT PRECISE for very short times

spike.util.debug_tools module

Created by Lionel Chiron 07/10/2013 Copyright (c) 2013 __NMRTEC__. All rights reserved. decorate the class with decclassdebugging and in each method to be debugged make : if debug(self) : instructions..

class spike.util.debug_tools.Test_debug_tools(methodName='runTest')

Bases: unittest.case.TestCase

addClassCleanup_debug = False

addCleanup_debug = False

addTypeEqualityFunc_debug = False

assertAlmostEqual_debug = False

assertAlmostEquals_debug = False

assertCountEqual_debug = False

assertDictContainsSubset_debug = False

assertDictEqual_debug = False

assertEqual_debug = False

assertEquals_debug = False

assertFalse_debug = False

assertGreaterEqual_debug = False

assertGreater_debug = False

assertIn_debug = False

assertIsInstance_debug = False

assertIsNone_debug = False

assertIsNotNone_debug = False

assertIsNot_debug = False

assertIs_debug = False

assertLessEqual_debug = False

assertLess_debug = False

assertListEqual_debug = False

assertLogs_debug = False

assertMultiLineEqual_debug = False

assertNotAlmostEqual_debug = False

assertNotAlmostEquals_debug = False

assertNotEqual_debug = False

assertNotEquals_debug = False

assertNotIn_debug = False

assertNotIsInstance_debug = False

assertNotRegex_debug = False

assertNotRegexpMatches_debug = False

assertRaisesRegex_debug = False

assertRaisesRegexp_debug = False

assertRaises_debug = False

assertRegex_debug = False

assertRegexpMatches_debug = False

assertSequenceEqual_debug = False

assertSetEqual_debug = False

assertTrue_debug = False

assertTupleEqual_debug = False

assertWarnsRegex_debug = False

assertWarns_debug = False

assert__debug = False

countTestCases_debug = False

debug_debug = False

defaultTestResult_debug = False

doClassCleanups_debug = False

doCleanups_debug = False

failIfAlmostEqual_debug = False

failIfEqual_debug = False

failIf_debug = False

failUnlessAlmostEqual_debug = False

failUnlessEqual_debug = False

failUnlessRaises_debug = False

failUnless_debug = False

fail_debug = False

failureException_debug = False

hip()

hip_debug = False

hop()

hop_debug = False

id_debug = False

longMessage_debug = False

maxDiff_debug = False

pr(var, message='')

print

run_debug = False

setUpClass_debug = False

setUp_debug = False

shortDescription_debug = False

skipTest_debug = False

subTest_debug = False

tearDownClass_debug = False

tearDown_debug = False

test_debug()

On this example as just hip is activated, debug(self) is True only in hip.

test_debug_debug = False

spike.util.debug_tools.debug(cl)

returns the name of the class followed by “_debug”

spike.util.debug_tools.dec_class_pr(cl)

Decorates the class for printing the local variables

spike.util.debug_tools.decclassdebugging(cl)

decorator for debugging classes. It allows to trigger specifically which method in a class will be observed. if debug(self) : ” where debug is needed After instantiation of the class, add class.method_name_debug = True to activate the debugging.

spike.util.debug_tools.name_up()

spike.util.debug_tools.pr(self, var, message='')

print

spike.util.dynsubplot module

Code permitting to make easily subplot without having to anticipate the number of plot. Just the number of columns is needed. typical syntax is :

sub = subpl(nbsub_h = 2) # suplot organized in two columns sub.next() # adding new subplot sub.plot(np.arange(6), np.arange(6)**2, ‘g’, label = ‘one’) # first plot in first subplot sub.title(‘One’)

class spike.util.dynsubplot.Test_dynsubplot(methodName='runTest')

Bases: unittest.case.TestCase

Unittests for dynsubplot with four subplots. First plot contains 2 plots.

test_dynsub()

spike.util.dynsubplot.add_class(l)

Each method in the list l is associated to a method defined with add_meth. This method as the same name as the pyplot method, but it has a different function. It will append the arguments passed in the plotting code to a list “lmeth” which will contains the arguments. Those

spike.util.dynsubplot.add_meth(attr)

Evaluates methods created in dec_class. Replaces pyplot method by methods which keep the arguments. for example, subpl.title() will stock its arguments in the list “subpl.ltitle”

class spike.util.dynsubplot.subpl(nbsub_h=1, which_plot=None)

Bases: object

Iterator for returning automatically the number of the subplot iteratively. nbsub_h is the number of subplot horizontally which_plot is the kind of plot, fake plot or matplotlib pyplot plot..

classmethod ax(*args, **kwargs)

Appends the useful arguments to the list “subpl.lmeth” instead of executing pyplot method “meth”

l_num = []

lax = []

lplot = []

ltitle = []

lxlabel = []

lxticklabels = []

lylabel = []

make_str_kargs(func, i)

Extracts the list containing the arguments from sublist [1] of each method, in the list “self.lmeth”.

next()

Increments the number of plots “subpl.numplot” And calculates the number of vertical lines.

numplot = 0

classmethod plot(*args, **kwargs)

Appends the useful arguments to the list “subpl.lmeth” instead of executing pyplot method “meth”

read_kargs(l)

select_pos(func)

show()

Takes all the list of arguments from the fake methods sub.meth And evaluates them one after the other with the correct pyplot corresponding method.

classmethod title(*args, **kwargs)

Appends the useful arguments to the list “subpl.lmeth” instead of executing pyplot method “meth”

classmethod xlabel(*args, **kwargs)

Appends the useful arguments to the list “subpl.lmeth” instead of executing pyplot method “meth”

classmethod xticklabels(*args, **kwargs)

Appends the useful arguments to the list “subpl.lmeth” instead of executing pyplot method “meth”

classmethod ylabel(*args, **kwargs)

Appends the useful arguments to the list “subpl.lmeth” instead of executing pyplot method “meth”

spike.util.hashcheck module

Program for controlling if files are not deteriorated during transmission. Usage : write in command line “python hashcheck.py directory-pathname” Lionel Chiron, 11 oct 2012 copyright NMRTEC S.A.S.

class spike.util.hashcheck.Test_hashcheck(methodName='runTest')

Bases: unittest.case.TestCase

test_hash()

spike.util.hashcheck.hash_make_or_control(rootpath='.', verbose=False)

Makes the file hashfile in the directory hash if it doesn’t exists. If the hash directory exists, check if files are ok or not.

spike.util.hashcheck.hashdir(rootpath, pattern='*')

from directory rootpath make a dictionary whose entries are the hash numbers associated to address of the files

spike.util.hashcheck.md5sum(filename)

from filename make a hash name. able to hash big files

spike.util.log_all module

Created by Lionel Chiron 18/10/2013 Copyright (c) 2013 __NMRTEC__. All rights reserved.

Utility for reporting error from standard error output via gmail. When sys.stderr performs a “write”, a mail is sent with a report. Typical syntax is:

import sys sys.stderr = Logger() f = open(“fff.jpg”)

If the picture “fff.jpg” doesn’t exist, an arror message is sent by mail.

class spike.util.log_all.Logger(erase=False)

Bases: object

Standard error logger.

datetime()

write(message)

If error, sys.stderr will call this method

spike.util.log_stderr module

Created by Lionel Chiron 18/10/2013 Copyright (c) 2013 __NMRTEC__. All rights reserved.

Utility for reporting error from standard error output via gmail. When sys.stderr performs a “write”, a mail is sent with a report. Typical syntax is:

import sys sys.stderr = Logger() f = open(“fff.jpg”)

If the picture “fff.jpg” doesn’t exist, an arror message is sent by mail.

class spike.util.log_stderr.Logger

Bases: object

Standard error logger.

datetime()

mac()

write(message)

If error, sys.stderr will call this method

spike.util.log_stdout module

class spike.util.log_stdout.Logger

Bases: object

write(message)

spike.util.mail module

spike.util.mail_error_logger module

Created by Lionel Chiron 18/10/2013 Copyright (c) 2013 __NMRTEC__. All rights reserved.

Utility for reporting error from standard error output via gmail. When sys.stderr performs a “write”, a mail is sent with a report. Typical syntax is:

import sys sys.stderr = Logger() f = open(“fff.jpg”)

If the picture “fff.jpg” doesn’t exist, an arror message is sent by mail.

class spike.util.mail_error_logger.Logger

Bases: object

Standard error logger.

datetime()

mac()

mail_if_error()

send_mail()

write(message)

If error, sys.stderr will call this method

spike.util.mail_error_logger.mail(to, subject, text='', attach=None)

Mailing part

spike.util.mpiutil module

mpiutil.py

Utilities for using MPI

defines MPI_size : number of process in the pool MPI_rank : rank of the current process

typical use is presented in test_server_worker()

in main do :

if MPI_rank == 0:

compute() # your code, uses mpiutil.enum_imap(myfunction, xarg)

else:

mpiutil.slave() # slave is a generic function used to compute myfunction( arg )

create a function to do the processing def myfunction(arg)

return whatever

and in compute() :

# create an iterator over arguments M = 1000 # size of the pb result = M*[0.0] # create room for storing results (if needed) xarg = xrange(M) # create arguments res = mpiutil.enum_imap(myfunction, xarg) # enum_imap applies myfunction to the iterator xarg for i,r in res: # collect results

result[i] = r # and store them (order is not garantied)

Warning, (i,r) will not arrive in order

Will import even in MPI is not installed, however slave() will not work The situation is reported as MPI_size = 1

Created by Marc-Andre’ on 2012-04-19. extended 2014-03-25

Copyright (c) 2012-2014 IGBMC. All rights reserved.

spike.util.mpiutil.enum_imap(function, iterator)

applies function() to each element of iterator creates an iterator imap(function, iterator) that returns (i, function(iterator_i)) elements will be returned in an synchronous manner with no garanty on the order.

similar to enumerate( pool.imap_async(function, iterator) ) except for the order

spike.util.mpiutil.funct_array_simple1(a)

used for testing take an array and compute 2*sqr(a) +1 plus a random nap to simulate a random processing time

spike.util.mpiutil.funct_array_simple2(a)

used for testing take an array and compute 3*sqr(a+1) plus a random nap to simulate a random processing time

spike.util.mpiutil.initialize(func=<function _nill>)

bring all processes to a starting barrier to be called by master process (rank == 0)

spike.util.mpiutil.mprint(st)

print with prefixing with the process rank

spike.util.mpiutil.mprint_debug(st)

as mprint() but only if DEBUG is True

spike.util.mpiutil.shutdown()

closes all MPI processes all processes to be called by master process (rank == 0)

spike.util.mpiutil.slave()

to be called in conjunction with enum_imap

spike.util.mpiutil.test_compute_simple(N, M)

run funct_array_simple over enum_imap

spike.util.mpiutil.test_server_worker()

spike.util.near_prime module

spike.util.near_prime.nearfactor(num, thresh)

find the product of prime factors of num the nearest from thresh

spike.util.progrbarUi module

spike.util.progressbar module

Main ProgressBar class.

class spike.util.progressbar.ProgressBar(maxval=None, widgets=None, term_width=None, poll=1, left_justify=True, fd=<_io.TextIOWrapper name='<stderr>' mode='w' encoding='utf-8'>)

Bases: object

The ProgressBar class which updates and prints the bar.

A common way of using it is like: >>> pbar = ProgressBar().start() >>> for i in range(100): … # do something … pbar.update(i+1) … >>> pbar.finish()

You can also use a ProgressBar as an iterator: >>> progress = ProgressBar() >>> for i in progress(some_iterable): … # do something …

Since the progress bar is incredibly customizable you can specify different widgets of any type in any order. You can even write your own widgets! However, since there are already a good number of widgets you should probably play around with them before moving on to create your own widgets.

The term_width parameter represents the current terminal width. If the parameter is set to an integer then the progress bar will use that, otherwise it will attempt to determine the terminal width falling back to 80 columns if the width cannot be determined.

When implementing a widget’s update method you are passed a reference to the current progress bar. As a result, you have access to the ProgressBar’s methods and attributes. Although there is nothing preventing you from changing the ProgressBar you should treat it as read only.

Useful methods and attributes include (Public API):

• currval: current progress (0 <= currval <= maxval)

• maxval: maximum (and final) value

• finished: True if the bar has finished (reached 100%)

• start_time: the time when start() method of ProgressBar was called

• seconds_elapsed: seconds elapsed since start_time and last call to

  update

• percentage(): progress in percent [0..100]

currval

fd

finish()

Puts the ProgressBar bar in the finished state.

finished

last_update_time

left_justify

maxval

next()

next_update

num_intervals

property percent

Returns the progress as a percentage.

percentage()

Returns the progress as a percentage.

poll

seconds_elapsed

signal_set

start()

Starts measuring time, and prints the bar at 0%.

It returns self so you can use it like this: >>> pbar = ProgressBar().start() >>> for i in range(100): … # do something … pbar.update(i+1) … >>> pbar.finish()

start_time

term_width

update(value=None)

Updates the ProgressBar to a new value.

update_interval

widgets

class spike.util.progressbar.UnknownLength

Bases: object

spike.util.readwrite_tools module

spike.util.readwrite_tools.read(f)

reads Orbitrap .dat FID files returns a numpy array with the FID

spike.util.readwrite_tools.read_json(file_name)

class spike.util.readwrite_tools.readwrite_test(methodName='runTest')

Bases: unittest.case.TestCase

test_json_numpy1D()

test_json_numpy2D()

spike.util.readwrite_tools.write_json(data, file_name)

Save numpy data

spike.util.sendgmail module

Sending informations about result etc with Gmail recipient (to) and attached document (attach) can be a list

spike.util.sendgmail.add_to_msg(msg, f)

spike.util.sendgmail.mail(to, subject, text='', attach=None)

spike.util.shift_comments module

Usage: python shift_comment.py file.py nbcolumn nb column is the column nb at which we want the comment to be placed. If no nb is given will shift the comments from the end of the line at a regular intervall.

class spike.util.shift_comments.SHIFT_COMMENT(namefile, poscomment=None)

Bases: object

make_linecorr_fixedpos(posin, comment, line)

Correction of the line for fixed position

make_linecorr_shiftedpos(lenline, comment, line)

Correction of the line for shifted comment position

read_correc()

Read the file and moves the comments to the right place.

spike.util.signal_tools module

Created by Lionel Chiron 18/10/2013 Copyright (c) 2013 __NMRTEC__. All rights reserved. Various tools for performing signal processing (SNR calculation,

synthetic Fids with noise, noise level etc… )

class spike.util.signal_tools.SIGNAL_NOISE(lenfid, nbpeaks, amplitude, noise, LB=1.11, shape='triangular', noisetype='additive', shift=0, realfid=False, trunc=None, seed=None, baseline=None)

Bases: object

Tool for generating SIGNAL with/without NOISE.

input:

lenfid : length of the Fid nbpeaks : nb of frequencies amplitude : reference amplitude for signal LB : exponential apodisation factor noise : noise amplitude shape : shape of the spectrum. Available : triangular, list. noisetype : kind of noise, ie : additive, multiplicative etc… shift : shift of the frequencies, the shift is expressed in points. trunc : truncaton ratio for the Fid. seed : for using a precise seed for random processes. baseline : boolean for specifying if a baseline is added to the spectrum

output:

Noisy signal : self.fid Signal without noise : self.fid0 Noisy spectrum : self.spec Spectrum without noise : self.spec0

eg1: # simplest usage

sig = SIGNAL_NOISE( lenfid= 1000, nbpeaks = 20, amplitude = 27, noise = 5)

eg2: # with a list of amplitudes

lamplit = list(np.random.randn(20)*10) sig = st.SIGNAL_NOISE( lenfid= 10000, nbpeaks = 20, amplitude = lamplit, noise = 4, shape = “list”)

SNR_dB(processed_fid)

SNR of the denoised signal

cut()

Uses Fid truncated

full()

Uses full Fid.

property iSNR

Reference SNR

make_noisy_signal()

make_pure_signal()

Signal without noise

prepare_amplitudes()

Makes the list of amplitudes according to the case. triangular: makes a triangular shape beginning at amplitude “self.amplitude” with slope “self.slope” list: retrieves amplitudes from given list of amplitudes.

prepare_frequencies()

Frequencies for the signal

property spec

Noisy spectrum

property spec0

Spectrum wihtout noise

zerof(fid, fid_ref)

Zerofilling

spike.util.signal_tools.SNR(dataset, nbseg=20, peak='max', unit=None)

determines the signal to noise of NPKData, by dividing le largest peak witht the noiselevel handles complex spectra, taking the noiselevel and the peak on the real part noise is evaluated by segmenting the data-set in nbseg peices, check in findnoiselevel, peak is either max or an index on which to evaluate the signal intensity if unit is None, value is in ration, if uint is dB, value is in dB

spike.util.signal_tools.SNR_dB(noisy, target)

computes and return SNR value, in dB

class spike.util.signal_tools.Test_Units(methodName='runTest')

Bases: unittest.case.TestCase

Unittests test_signoise test_signoise_truncated test_noiselevel test_multitests_urQRdtrick test_multitests_urQRd_npk

ttest_findnoiselevel_2D_slice()

ttest_multitests_urQRd_npk()

Multitests on urQRd_trick

ttest_multitests_urQRdtrick()

Multitests on urQRd_trick

ttest_noiselevel()

Testing noiselevel on experimental data.

ttest_signoise()

Testing the class for generating noisy signal.

ttest_signoise_truncated()

Testing the class for generating noisy signal.

spike.util.signal_tools.apod(lb=1)

apodisation for improving sparsity

spike.util.signal_tools.dB(val)

converts a real value to dB 20*math.log(val)/math.log(10)

spike.util.signal_tools.em_wiener(x, lb)

multiply x(t) by exp(-lb*t)

spike.util.signal_tools.fid_signoise(nbpeaks, ampl, lengthfid, noise, shift=0, shape='triangular', seed=True)

Obsolete

Build fid with triangular spectrum from number of peaks : nbpeaks, amplitude : ampl, the length of the fid : lengthfid, and the noise level : noise. if shape is “triangular”, uses ampl as minimum amplitude. if shape is “list”, uses the given list to make the amplitudes. The seed of random generator can be activated or deactivated with boolean ‘seed’

spike.util.signal_tools.fid_signoise_type(nbpeaks, lendata, noise, noisetype)

Obsolete

spike.util.signal_tools.filtering(sig, window=None)

Filter for real signal window : list [f1, f2], eg: [0.1, 0.3], 0 < f < 1

spike.util.signal_tools.findnoiselevel(fid, nbseg=20)

Routine for determining the noise level from a numpy buffer “fid” cut the data in segments then make the standard deviation return makes the average on the 25% lowest segments

nbseg=10 nb of segment to cut the spectrum

spike.util.signal_tools.findnoiselevel2(fid, nbseg=20)

(findnoiselevel seems more reliable) Routine for determining the noise level from a numpy buffer “fid” cut the data in segments then make the standard deviation and returns the smallest deviation

nbseg=10 nb of segment to cut the spectrum

spike.util.signal_tools.findnoiselevel_2D(data_array, nbseg=20, nbrow=200)

measure noise level on a 2D spectrum

spike.util.signal_tools.findnoiselevel_2D_slice(data_array, ratio=1)

Routine for finding noise level by dividing the 2D spectrum in vertical slices.

spike.util.signal_tools.findnoiselevel_offset(fid, nbseg=10)

Routine for determining the noise level and mean value (considering a flat signal with gaussian noise and some outliers in the signal) cut the data in segments then make the standard deviation for each segment compares the std dev with the average one and eliminates segments giving a std dev above the mean finally makes the average on the sorted segments. nbseg=10 nb of segment to cut the spectrum

spike.util.signal_tools.mfft(v)

utility that returns the modulus of the fft of v

spike.util.signal_tools.mrfft(v)

utility that returns the modulus of the rfft of v

spike.util.signal_tools.signal_to_noise(fid, nbseg=20, peak='max')

determines the signal to noise of a numpy buffer, by dividing le largest peak witht the noiselevel see findnoiselevel

spike.util.simple_logger module

spike.util.simple_logger2 module

Created by Lionel Chiron 18/10/2013 Copyright (c) 2013 __NMRTEC__. All rights reserved.

Utility for copying Standard Error (stderr) and Standard Output (stdout) to a file

class spike.util.simple_logger2.TeeLogger(log_name='log_file.log', erase=False, date_in=True, err_prefix='___')

Bases: object

Simple logger.

TeeLogger(log_name = “log_file.log”, erase = False, date_in = True, err_prefix = “___”) or TeeLogger()

copies standard Error (stderr) and Standard Output (stdout) to a file

log_name : name of the log file, if no name given, it is called “log_file.log” erase : if True erase the existing log file with the same name. date_in: in True (defualt) date is indicated in log file err_prefix stderr is prefixed with this string

spike.util.simple_logger2.strdate()

spike.util.widgets module

Default ProgressBar widgets.

class spike.util.widgets.AdaptiveETA(format='Elapsed Time: %s')

Bases: spike.util.widgets.Timer

Widget which attempts to estimate the time of arrival.

Uses a weighted average of two estimates:

1. ETA based on the total progress and time elapsed so far

2. ETA based on the progress as per the last 10 update reports

The weight depends on the current progress so that to begin with the total progress is used and at the end only the most recent progress is used.

NUM_SAMPLES = 10

TIME_SENSITIVE = True

format_string

update(pbar)

Updates the widget to show the ETA or total time when finished.

class spike.util.widgets.AnimatedMarker(markers='|/-\\')

Bases: spike.util.widgets.Widget

An animated marker for the progress bar which defaults to appear as if it were rotating.

curmark

markers

update(pbar)

Updates the widget to show the next marker or the first marker when finished

class spike.util.widgets.Bar(marker='#', left='|', right='|', fill=' ', fill_left=True)

Bases: spike.util.widgets.WidgetHFill

A progress bar which stretches to fill the line.

fill

fill_left

left

marker

right

update(pbar, width)

Updates the progress bar and its subcomponents.

class spike.util.widgets.BouncingBar(marker='#', left='|', right='|', fill=' ', fill_left=True)

Bases: spike.util.widgets.Bar

fill

fill_left

left

marker

right

update(pbar, width)

Updates the progress bar and its subcomponents.

class spike.util.widgets.Counter(format='%d')

Bases: spike.util.widgets.Widget

Displays the current count.

format_string

update(pbar)

Updates the widget.

pbar - a reference to the calling ProgressBar

class spike.util.widgets.ETA(format='Elapsed Time: %s')

Bases: spike.util.widgets.Timer

Widget which attempts to estimate the time of arrival.

TIME_SENSITIVE = True

format_string

update(pbar)

Updates the widget to show the ETA or total time when finished.

class spike.util.widgets.FileTransferSpeed(unit='B')

Bases: spike.util.widgets.Widget

Widget for showing the transfer speed (useful for file transfers).

FORMAT = '%6.2f %s%s/s'

PREFIXES = ' kMGTPEZY'

unit

update(pbar)

Updates the widget with the current SI prefixed speed.

class spike.util.widgets.FormatLabel(format)

Bases: spike.util.widgets.Timer

Displays a formatted label.

format_string

mapping = {'elapsed': ('seconds_elapsed', <function Timer.format_time>), 'finished': ('finished', None), 'last_update': ('last_update_time', None), 'max': ('maxval', None), 'seconds': ('seconds_elapsed', None), 'start': ('start_time', None), 'value': ('currval', None)}

update(pbar)

Updates the widget to show the elapsed time.

class spike.util.widgets.Percentage

Bases: spike.util.widgets.Widget

Displays the current percentage as a number with a percent sign.

update(pbar)

Updates the widget.

pbar - a reference to the calling ProgressBar

class spike.util.widgets.ReverseBar(marker='#', left='|', right='|', fill=' ', fill_left=False)

Bases: spike.util.widgets.Bar

A bar which has a marker which bounces from side to side.

fill

fill_left

left

marker

right

spike.util.widgets.RotatingMarker

alias of spike.util.widgets.AnimatedMarker

class spike.util.widgets.SimpleProgress(sep=' of ')

Bases: spike.util.widgets.Widget

Returns progress as a count of the total (e.g.: “5 of 47”).

sep

update(pbar)

Updates the widget.

pbar - a reference to the calling ProgressBar

class spike.util.widgets.Timer(format='Elapsed Time: %s')

Bases: spike.util.widgets.Widget

Widget which displays the elapsed seconds.

TIME_SENSITIVE = True

format_string

static format_time(seconds)

Formats time as the string “HH:MM:SS”.

update(pbar)

Updates the widget to show the elapsed time.

class spike.util.widgets.Widget

Bases: abc.AbstractWidget

The base class for all widgets.

The ProgressBar will call the widget’s update value when the widget should be updated. The widget’s size may change between calls, but the widget may display incorrectly if the size changes drastically and repeatedly.

The boolean TIME_SENSITIVE informs the ProgressBar that it should be updated more often because it is time sensitive.

TIME_SENSITIVE = False

abstract update(pbar)

Updates the widget.

pbar - a reference to the calling ProgressBar

class spike.util.widgets.WidgetHFill

Bases: spike.util.widgets.Widget

The base class for all variable width widgets.

This widget is much like the hfill command in TeX, it will expand to fill the line. You can use more than one in the same line, and they will all have the same width, and together will fill the line.

abstract update(pbar, width)

Updates the widget providing the total width the widget must fill.

pbar - a reference to the calling ProgressBar width - The total width the widget must fill

spike.util.widgets.format_updatable(updatable, pbar)

Module contents

Utilities

Created by Marc-André on 2011-03-20. Copyright (c) 2011 IGBMC. All rights reserved.

spike.util.main()