Running .mean() on an empty Histogram object (Histogram(None, 1000, {0: 0.0})) fails with a divide by zero error:

  File "/src/traces/traces/histogram.py", line 30, in mean
    return weighted_sum / float(self.total())
ZeroDivisionError: float division by zero

Not a bug, but just curious about how you've plotted the charts in the documentation and what the recommended approach for plotting TimeSeries objects is? I couldn't find a trace of this information in the repo. Thanks in advance!

This covers an initial implementation of the EventSeries features described in #229

I ended up leaving out the histogram plotting feature as creating reasonable and responsive log binned histograms of time units felt a little outside the scope of this project, though something I may yet tackle.

Would love a review for readability, test coverage, or feature suggestions!

I have a use case where I need to change the value of a timeseries on an interval without changing the value outside of the interval, ie do something like ts[start:end] = value. Just setting

ts[end] = ts[end]   # freezing/anchoring the current value of ts as of [end, ...)
ts[start] = value      # changing the value as of [start, ...)

may fail as intermediate points in [start,end) may exist ==> we need to remove all intermediate points (which is easy as ts.iterperiods(start,end) provides them nicely).

I think the function below does it properly (but it would be better integrated in the item to use the slice notation)

def set_slice(ts, start, end, value):
    """
   ts[start:end] = value ==> call set_slice(ts, start, end, value)
    Set the value of the ts so that
    - on the interval [start, end) we have the new value
    - on [end, ...) we haven't change the value
    - on (..., start) we haven't change the value neither
    We replace the value of the ts on an interval.

    :param ts: 
    :param start: 
    :param end: 
    :param value: 
    :return: 
    """
    # for each interval to render
    for i, (s, e, v) in enumerate(list(ts.iterperiods(start, end))):
        # look at all intervals included in the current interval
        # (always at least 1)
        if i == 0:
            # if the first, set initial value to new value of range
            ts[s] = value
        else:
            # otherwise, remove intermediate key
            del ts[s]
    # finish by setting the end of the interval to the previous value
    ts[end] = v

If you are using strings as values in a TimeSeries:

ts = traces.TimeSeries()
ts[1] = JUNK
ts[3] = JANK
ts[5] = WHAT

If you call something like ts.distrubution(min, max), you would see something like this:

Histogram(None, 1000, {'Jank': 0.16008504570112725, 'Junk': 0.04229136076598496, 'What': 0.797577092766277})

It looks like somewhere along the line, the string-values are getting sentence-cased. Not sure exactly where yet, but this could be confusing or cause silly bugs if looking-up these objects with the wrong value.

With commit 05a14608d06b06dfc589ae9c247d300b89f956b5, using a timedelta as sampling_period in moving_average throws an exception when converting window_size to a float. Multiplying by 1. (as previously done) serves the same purpose and still allows timedelta to be used.

So I recently discovered this nice library and decided to try it since I got unevenly spaced data, however I found out today that the .mean() wasn't doing linear interpolation as I thought it would be:

>>> from traces import TimeSeries
>>> t = TimeSeries()
>>> t[0] = 0
>>> t[1] = 0
>>> t[3] = 20
>>> t.mean(0, 2)
0.0

With linear interpolation between 2 points we would find that t[2] = 10 and doing the average from 0 to 2 would give us 3.333 in this example. A simple optional argument in mean() to choose the interpolation method would be fantastic, and I really think that it would be useful to many users who are not using traces exclusively for binary data (where linear interpolation would make no sense). I know that we can re-sample the TimeSeries but I think a shortcut like this would be really neat since this library is designed with ease of use in mind.

Thanks for reading and have a nice day 👋

I wonder, how could one plot traces' Signature plot? ?

I was wondering if the library has anything to do with the charts (as per the docs that is not the case) but seeing a couple of charts like that in the docs made me think that maybe producing that kind of charts is within the scope of the projects.

[UPDATE] This only seems to happen on python 2.7

Trying to pickle a TimeSeries object:

import traces
ofile = open('test.pkl', 'wb')
import pickle
ts = traces.TimeSeries()
ts[23]="blah"
ts[2]="foo"
pickle.dump(ts, ofile)

I get the following error:

In [9]: pickle.dump(ts, ofile)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-9-f1eed5bd8d83> in <module>()
----> 1 pickle.dump(ts, ofile)

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in dump(obj, file, protocol)
   1374
   1375 def dump(obj, file, protocol=None):
-> 1376     Pickler(file, protocol).dump(obj)
   1377
   1378 def dumps(obj, protocol=None):

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in dump(self, obj)
    222         if self.proto >= 2:
    223             self.write(PROTO + chr(self.proto))
--> 224         self.save(obj)
    225         self.write(STOP)
    226

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in save(self, obj)
    329
    330         # Save the reduce() output and finally memoize the object
--> 331         self.save_reduce(obj=obj, *rv)
    332
    333     def persistent_id(self, obj):

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in save_reduce(self, func, args, state, listitems, dictitems, obj)
    423
    424         if state is not None:
--> 425             save(state)
    426             write(BUILD)
    427

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in save(self, obj)
    284         f = self.dispatch.get(t)
    285         if f:
--> 286             f(self, obj) # Call unbound method with explicit self
    287             return
    288

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in save_dict(self, obj)
    653
    654         self.memoize(obj)
--> 655         self._batch_setitems(obj.iteritems())
    656
    657     dispatch[DictionaryType] = save_dict

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in _batch_setitems(self, items)
    667             for k, v in items:
    668                 save(k)
--> 669                 save(v)
    670                 write(SETITEM)
    671             return

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in save(self, obj)
    284         f = self.dispatch.get(t)
    285         if f:
--> 286             f(self, obj) # Call unbound method with explicit self
    287             return
    288

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in save_dict(self, obj)
    653
    654         self.memoize(obj)
--> 655         self._batch_setitems(obj.iteritems())
    656
    657     dispatch[DictionaryType] = save_dict

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in _batch_setitems(self, items)
    667             for k, v in items:
    668                 save(k)
--> 669                 save(v)
    670                 write(SETITEM)
    671             return

/Users/vlad/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc in save(self, obj)
    304             reduce = getattr(obj, "__reduce_ex__", None)
    305             if reduce:
--> 306                 rv = reduce(self.proto)
    307             else:
    308                 reduce = getattr(obj, "__reduce__", None)

/Users/vlad/.pyenv/versions/2.7.13/envs/prelude_monitor/lib/python2.7/copy_reg.pyc in _reduce_ex(self, proto)
     68     else:
     69         if base is self.__class__:
---> 70             raise TypeError, "can't pickle %s objects" % base.__name__
     71         state = base(self)
     72     args = (self.__class__, base, state)

TypeError: can't pickle instancemethod objects```

I think this will be fixed with the next bump; looked for an issue related to this but didn't find one. Feel free to close this out if it was as simple as defining start() for TimeSeries.

Traceback (most recent call last):
  File "run_plots.py", line 25, in <module>
    make_plots()
  File "/Users/mjfm/projects/modustri/analysis/plots/see_cart_trips.py", line 55, in make_plots
    mask = front_ts,
  File "/Users/mjfm/Virtualenvs/modustri/lib/python2.7/site-packages/traces/timeseries.py", line 622, in distribution
    new_ts = self.slice(mask.start(), mask.end())
AttributeError: 'TimeSeries' object has no attribute 'start'

This would merge adjacent periods that have the same value and return them as only one period. Ideally this would be done efficiently, although I'm unclear what that means (store a compact version of the timeseries along with the non-compact one?)

Hello, there seems to be a bug with the Histogram initialization when a start and end are passed.

versions:

• python: 3.10.5
• traces: 0.6.0

Given the following TimeSeries:

from traces import TimeSeries
from pandas import Timestamp


ts = TimeSeries(
    {
        Timestamp('2022-10-09 08:48:47'): 5.5,
        Timestamp('2022-10-09 10:36:47'): 51.4,
        Timestamp('2022-10-09 10:38:47'): 15.2,
        Timestamp('2022-10-09 10:38:56'): 0.1,
        Timestamp('2022-10-09 10:41:25'): 4.5
    }
)

Computing the maximum value with

ts.distribution().max()

gives 51.4 (as expected)

However

ts.distribution(
    start=Timestamp('2022-10-09 07:55:10'),
    end=Timestamp('2022-10-09 10:56:32'),
).max()

gives 5.5

Thank you.

This repo looks like it's no longer maintained, with the last PR merged over two years ago. Are you looking for active maintainers? What's the plan for this repo?

In the following example, although ts1 and ts2 are equal, ts2.distribution() fails with a TypeError as if Numpy arrays weren't recognized properly.
Somewhat similar to issue #145

import numpy as np  # Numpy version 1.22.3
import traces  # traces version 0.6.0

ts1 = traces.TimeSeries(zip(range(4), range(4)), default=0)
ts2 = traces.TimeSeries(zip(np.arange(4), range(4)), default=0)

ts1 == ts2  # True
ts1.distribution()  # Histogram({0: 0.3333333333333333, 1: 0.3333333333333333, 2: 0.3333333333333333})
ts2.distribution()  # TypeError: duration is an unknown type (1)

Hi, Thanks for creating traces. I am trying to learn it. But while I run the following command,

[... list of forty traces.TimeSeries ...]

I get an error which is mentioned below,

 File "/tmp/ipykernel_51/3316415681.py", line 3
    trace_list = [... list of forty traces.TimeSeries ...]
                         ^
SyntaxError: invalid syntax

Could anybody please help? Thanks a lot.

The current way of checking int and float cannot handle numpy's data types, such as np.int64 and np.float64, which requires extra effort to convert a numpy element into int or float to pass the check.

Using numeric ABCs solves the problem and allows more flexible "implementations" of integers and real numbers. (Better than the approach in #224, no extra dependencies needed)

=== Updated === Tests can be passed locally. CI seems to complain something about repo_token and marked them as failed.