From 083078faf0c4a5d35456ea3d5e94ea7d102d175c Mon Sep 17 00:00:00 2001 From: Tim Cera Date: Wed, 15 Aug 2018 13:38:37 -0400 Subject: [PATCH] REF: Refactored to have dependencies install from setup.py. --- .../PKG-INFO | 105 - .../SOURCES.txt | 18 - .../dependency_links.txt | 1 - .../entry_points.txt | 3 - .../installed-files.txt | 13 - .../not-zip-safe | 1 - .../requires.txt | 1 - .../top_level.txt | 1 - hspfbintoolbox/__init__.py | 1 - hspfbintoolbox/hspfbintoolbox.py | 415 --- mando-0.3.3.dist-info/DESCRIPTION.rst | 160 -- mando-0.3.3.dist-info/METADATA | 189 -- mando-0.3.3.dist-info/RECORD | 22 - mando-0.3.3.dist-info/WHEEL | 6 - mando-0.3.3.dist-info/metadata.json | 1 - mando-0.3.3.dist-info/top_level.txt | 1 - mando/__init__.py | 14 - mando/core.py | 146 - mando/tests/__init__.py | 0 mando/tests/run.py | 9 - mando/tests/test_core.py | 152 - mando/tests/test_unicode_docstring_on_py2.py | 20 - mando/tests/test_utils.py | 107 - mando/utils.py | 96 - setup.py | 7 +- tstoolbox-0.9.9.6-py2.7.egg-info/PKG-INFO | 182 -- tstoolbox-0.9.9.6-py2.7.egg-info/SOURCES.txt | 22 - 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FORTRAN binary files and prints to screen. -Home-page: http://timcera.bitbucket.org/hspfbintoolbox/docsrc/index.html -Author: Tim Cera, P.E. -Author-email: tim@cerazone.net -License: UNKNOWN -Description: .. image:: https://travis-ci.org/timcera/hspfbintoolbox.svg?branch=master - :target: https://travis-ci.org/timcera/hspfbintoolbox - :height: 20 - - .. image:: https://coveralls.io/repos/timcera/hspfbintoolbox/badge.png?branch=master - :target: https://coveralls.io/r/timcera/hspfbintoolbox?branch=master - :height: 20 - - .. image:: https://img.shields.io/pypi/v/hspfbintoolbox.svg - :alt: Latest release - :target: https://pypi.python.org/pypi/hspfbintoolbox - - .. image:: https://img.shields.io/pypi/dm/hspfbintoolbox.svg - :alt: PyPI downloads count - :target: https://pypi.python.org/pypi/hspfbintoolbox - - .. image:: http://img.shields.io/badge/license-BSD-lightgrey.svg - :alt: hspfbintoolbox license - :target: https://pypi.python.org/pypi/hspfbintoolbox/ - - Documentation for hspfbintoolbox - ================================ - The ``hspfbintoolbox`` is a Python script and library of functions to read - Hydrological Simulation Program Fortran (HSPF) binary files and print to - screen. The time series can then be redirected to file, or piped to other - command line programs like ``tstoolbox``. - - Requirements - ------------ - - * pandas - on Windows this is part of the Python(x,y), Enthought, or - Anaconda distributions - - * mando - command line parser - - * tstoolbox - utilities to process time-series - - Installation - ------------ - Should be as easy as running ``pip install hspfbintoolbox`` or ``easy_install - hspfbintoolbox`` at any command line. Not sure on Windows whether this will - bring in pandas but as mentioned above, if you start with Python(x,y) then - you won't have a problem. - - Usage - Command Line - -------------------- - Just run 'hspfbintoolbox' to get a list of subcommands: - - catalog - Prints out a catalog of data sets in the binary file. - - dump - Prints out ALL data from a HSPF binary output file. - - extract - Prints out data to the screen from a HSPF binary output file. - - time_series - DEPRECATED: Use 'extract' instead. - - The default for all of the subcommands is to accept data from stdin - (typically a pipe). If a subcommand accepts an input file for an argument, - you can use "--infile=filename", or to explicitly specify from stdin use - "--infile='-'" . - - For the subcommands that output data it is printed to the screen and you can - then redirect to a file. - - Usage - API - ----------- - You can use all of the command line subcommands as functions. The function - signature is identical to the command line subcommands. The return is always - a PANDAS DataFrame. Input can be a CSV or TAB separated file, or a PANDAS - DataFrame and is supplied to the function via the 'input_ts' keyword. - - Simply import hspfbintoolbox:: - - import hspfbintoolbox - - # Then you could call the functions - ntsd = hspfbintoolbox.dump('tests/test.hbn') - - # Once you have a PANDAS DataFrame you can use that as input. - ntsd = tstoolbox.aggregate(statistic='mean', agg_interval='daily', input_ts=ntsd) - -Keywords: hspf binary hydrologic -Platform: UNKNOWN -Classifier: Development Status :: 5 - Production/Stable -Classifier: Environment :: Console -Classifier: Intended Audience :: Developers -Classifier: Intended Audience :: End Users/Desktop -Classifier: License :: OSI Approved :: BSD License -Classifier: Operating System :: OS Independent -Classifier: Programming Language :: Python :: 2 -Classifier: Programming Language :: Python :: 3 -Classifier: Topic :: Scientific/Engineering -Classifier: Topic :: Software Development :: Libraries :: Python Modules diff --git a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/SOURCES.txt b/hspfbintoolbox-1.4.8.0-py2.7.egg-info/SOURCES.txt deleted file mode 100644 index 508040b3..00000000 --- a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/SOURCES.txt +++ /dev/null @@ -1,18 +0,0 @@ -AUTHORS.rst -CONTRIBUTING.rst -LICENSE.txt -MANIFEST.in -README.rst -VERSION -pip-delete-this-directory.txt -setup.cfg -setup.py -hspfbintoolbox/__init__.py -hspfbintoolbox/hspfbintoolbox.py -hspfbintoolbox.egg-info/PKG-INFO -hspfbintoolbox.egg-info/SOURCES.txt -hspfbintoolbox.egg-info/dependency_links.txt -hspfbintoolbox.egg-info/entry_points.txt -hspfbintoolbox.egg-info/not-zip-safe -hspfbintoolbox.egg-info/requires.txt -hspfbintoolbox.egg-info/top_level.txt \ No newline at end of file diff --git a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/dependency_links.txt b/hspfbintoolbox-1.4.8.0-py2.7.egg-info/dependency_links.txt deleted file mode 100644 index 8b137891..00000000 --- a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/entry_points.txt b/hspfbintoolbox-1.4.8.0-py2.7.egg-info/entry_points.txt deleted file mode 100644 index f3b5f624..00000000 --- a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/entry_points.txt +++ /dev/null @@ -1,3 +0,0 @@ -[console_scripts] -hspfbintoolbox = hspfbintoolbox.hspfbintoolbox:main - diff --git a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/installed-files.txt b/hspfbintoolbox-1.4.8.0-py2.7.egg-info/installed-files.txt deleted file mode 100644 index bc8fb847..00000000 --- a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/installed-files.txt +++ /dev/null @@ -1,13 +0,0 @@ -..\hspfbintoolbox\hspfbintoolbox.py -..\hspfbintoolbox\__init__.py -..\hspfbintoolbox\hspfbintoolbox.pyc -..\hspfbintoolbox\__init__.pyc -dependency_links.txt -entry_points.txt -not-zip-safe -PKG-INFO -requires.txt -SOURCES.txt -top_level.txt -..\..\..\Scripts\hspfbintoolbox-script.py -..\..\..\Scripts\hspfbintoolbox.exe diff --git a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/not-zip-safe b/hspfbintoolbox-1.4.8.0-py2.7.egg-info/not-zip-safe deleted file mode 100644 index 8b137891..00000000 --- a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/requires.txt b/hspfbintoolbox-1.4.8.0-py2.7.egg-info/requires.txt deleted file mode 100644 index e3858c9c..00000000 --- a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/requires.txt +++ /dev/null @@ -1 +0,0 @@ -tstoolbox >= 1.12.12.9 diff --git a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/top_level.txt b/hspfbintoolbox-1.4.8.0-py2.7.egg-info/top_level.txt deleted file mode 100644 index b6f2945b..00000000 --- a/hspfbintoolbox-1.4.8.0-py2.7.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -hspfbintoolbox diff --git a/hspfbintoolbox/__init__.py b/hspfbintoolbox/__init__.py deleted file mode 100644 index 72384df4..00000000 --- a/hspfbintoolbox/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from hspfbintoolbox import catalog, extract, about \ No newline at end of file diff --git a/hspfbintoolbox/hspfbintoolbox.py b/hspfbintoolbox/hspfbintoolbox.py deleted file mode 100644 index c350e00a..00000000 --- a/hspfbintoolbox/hspfbintoolbox.py +++ /dev/null @@ -1,415 +0,0 @@ -''' -hspfbintoolbox to read HSPF binary files. -''' - -from __future__ import print_function - -import datetime -import warnings -import os -import sys -import struct - -import mando -import pandas as pd - -from tstoolbox import tsutils - - -code2intervalmap = {5: 'yearly', - 4: 'monthly', - 3: 'daily', - 2: 'bivl'} - -interval2codemap = {'yearly': 5, - 'monthly': 4, - 'daily': 3, - 'bivl': 2} - -code2freqmap = {5: 'Y', - 4: 'M', - 3: 'D', - 2: None} - - -def tupleMatch(a, b): - '''Part of partial ordered matching. - See http://stackoverflow.com/a/4559604 - ''' - return len(a) == len(b) and all(i is None or j is None or i == j - for i, j in zip(a, b)) - - -def tupleCombine(a, b): - '''Part of partial ordered matching. - See http://stackoverflow.com/a/4559604 - ''' - return tuple([i is None and j or i for i, j in zip(a, b)]) - - -def tupleSearch(findme, haystack): - '''Partial ordered matching with 'None' as wildcard - See http://stackoverflow.com/a/4559604 - ''' - return [(i, tupleCombine(findme, h)) - for i, h in enumerate(haystack) if tupleMatch(findme, h)] - - -def _get_data(binfilename, - interval='daily', - labels=[',,,'], - catalog_only=True): - '''Underlying function to read from the binary file. Used by - 'extract', 'catalog', and 'dump'. - ''' - testem = {'PERLND': ['ATEMP', 'SNOW', 'PWATER', 'SEDMNT', - 'PSTEMP', 'PWTGAS', 'PQUAL', 'MSTLAY', - 'PEST', 'NITR', 'PHOS', 'TRACER', ''], - 'IMPLND': ['ATEMP', 'SNOW', 'IWATER', 'SOLIDS', - 'IWTGAS', 'IQUAL', ''], - 'RCHRES': ['HYDR', 'CONS', 'HTRCH', 'SEDTRN', - 'GQUAL', 'OXRX', 'NUTRX', 'PLANK', - 'PHCARB', 'INFLOW', 'OFLOW', 'ROFLOW', ''], - '': ['']} - - collect_dict = {} - lablist = [] - - # Normalize interval code - try: - intervalcode = interval2codemap[interval.lower()] - except AttributeError: - intervalcode = None - - # Fixup and test the labels - could be in it's own function - for lindex, label in enumerate(labels): - words = [lindex] + label.split(',') - if len(words) != 5: - raise ValueError(''' -* -* The label '{0}' has the wrong number of entries. -* -'''.format(label)) - - if words[1]: - words[1] = words[1].upper() - if words[1] not in testem.keys(): - raise ValueError(''' -* -* Operation type must be one of 'PERLND', 'IMPLND', or 'RCHRES', -* or missing (to get all) instead of {0}. -* -'''.format(words[1])) - - if words[2]: - try: - words[2] = int(words[2]) - if words[2] < 1 or words[2] > 999: - raise ValueError() - except (ValueError, TypeError): - raise ValueError(''' -* -* The land use element must be an integer from 1 to 999 inclusive, -* instead of {0}. -* -'''.format(words[2])) - - if words[3]: - words[3] = words[3].upper() - if words[3] not in testem[words[1]]: - raise ValueError(''' -* -* The {0} operation type only allows the sections: -* {1}, -* instead you gave {2}. -* -'''.format(words[1], testem[words[1]][:-1], words[3])) - - for index in list(range(len(words))): - if words[index] is '': - words[index] = None - - words.append(intervalcode) - lablist.append(words) - - with open(binfilename, 'rb') as fl: - - mindate = datetime.datetime.max - maxdate = datetime.datetime.min - - labeltest = {} - vnames = {} - ndates = {} - rectype = 0 - fl.read(1) - while 1: - try: - reclen1, reclen2, reclen3, reclen = struct.unpack('4B', - fl.read(4)) - except struct.error: - break - reclen1 = int(reclen1/4) - reclen2 = reclen2*64 + reclen1 - reclen3 = reclen3*16384 + reclen2 - reclen = reclen*4194304 + reclen3 - slen = 0 - rectype, optype, lue, section = struct.unpack('I8sI8s', - fl.read(24)) - - rectype = int(rectype) - lue = int(lue) - optype = optype.strip() - section = section.strip() - - if rectype == 0: - while slen < reclen - 28: - length = struct.unpack('I', fl.read(4))[0] - slen = slen + length + 4 - variable_name = struct.unpack('{0}s'.format(length), - fl.read(length))[0] - vnames.setdefault((lue, section), []).append(variable_name) - - if rectype == 1: - # Data record - numvals = len(vnames[(lue, section)]) - - (_, - level, - year, - month, - day, - hour, - minute) = struct.unpack('7I', fl.read(28)) - - vals = struct.unpack('{0}f'.format(numvals), - fl.read(4*numvals)) - if hour == 24: - ndate = datetime.datetime(year, month, day) + \ - datetime.timedelta(hours=24) + \ - datetime.timedelta(minutes=minute) - else: - ndate = datetime.datetime(year, month, day, hour, minute) - - for i, vname in enumerate(vnames[(lue, section)]): - tmpkey = (None, - optype.decode('ascii'), - int(lue), - section.decode('ascii'), - vname.decode('ascii'), - level) - if catalog_only is False: - res = tupleSearch(tmpkey, lablist) - if res: - nres = (res[0][0],) + res[0][1][1:] - labeltest[nres[0]] = 1 - collect_dict.setdefault(nres, []).append(vals[i]) - ndates.setdefault(level, {})[ndate] = 1 - else: - mindate = min(mindate, ndate) - maxdate = max(maxdate, ndate) - collect_dict[tmpkey[1:]] = (mindate, maxdate) - fl.read(2) - - if not collect_dict: - raise ValueError(''' -* -* The label specifications matched no records in the binary file. -* -''') - - if catalog_only is False: - not_in_file = [] - for loopcnt in list(range(len(lablist))): - if loopcnt not in labeltest.keys(): - not_in_file.append(labels[loopcnt]) - if not_in_file: - warnings.warn(''' -* -* The specification{0} {1} -* matched no records in the binary file. -* -'''.format("s"[len(not_in_file) == 1:], not_in_file)) - - return ndates, collect_dict - - -@mando.command -def extract(hbnfilename, interval, *labels, **kwds): - ''' - Prints out data to the screen from a HSPF binary output file. - - :param hbnfilename: The HSPF binary output file - :param interval: One of 'yearly', 'monthly', 'daily', or 'BIVL'. - The 'BIVL' option is a sub-daily interval defined in the UCI file. - Typically 'BIVL' is used for hourly output, but can be set to any - value that evenly divides into a day. - :param labels: The remaining arguments uniquely identify a time-series - in the binary file. The format is - 'OPERATIONTYPE,ID,SECTION,VARIABLE'. - - For example: 'PERLND,101,PWATER,UZS IMPLND,101,IWATER,RETS' - - Leaving a section without an entry will wildcard that - specification. To get all the PWATER variables for PERLND 101 the - label would read: - - 'PERLND,101,PWATER,' - - To get TAET for all PERLNDs: - - 'PERLND,,,TAET' - - Note that there are spaces ONLY between label specifications. - :param time_stamp: For the interval defines the location of the time - stamp. If set to 'begin', the time stamp is at the begining of the - interval. If set to any other string, the reported time stamp will - represent the end of the interval. Default is 'begin'. Place after - ALL labels. - :param sorted: Should ALL columns be sorted? - Default is False. Place after ALL labels. - ''' - try: - time_stamp = kwds.pop('time_stamp') - except KeyError: - time_stamp = 'begin' - if time_stamp not in ['begin', 'end']: - raise ValueError(''' -* -* The "time_stamp" optional keyword must be either -* "begin" or "end". You gave {0}. -* -'''.format(time_stamp)) - - try: - sortall = bool(kwds.pop('sorted')) - except KeyError: - sortall = False - if not(sortall is True or sortall is False): - raise ValueError(''' -* -* The "sorted" optional keyword must be either -* True or False. You gave {0}. -* -'''.format(sortall)) - - if len(kwds) > 0: - raise ValueError(''' -* -* The extract command only accepts optional keywords 'time_stamp' and -* 'sorted'. You gave {0}. -* -'''.format(list(kwds.keys()))) - - interval = interval.lower() - if interval not in ['bivl', 'daily', 'monthly', 'yearly']: - raise ValueError(''' -* -* The "interval" argument must be one of "bivl", -* "daily", "monthly", or "yearly". You supplied -* "{0}". -* -'''.format(interval)) - - index, data = _get_data(hbnfilename, interval, labels, - catalog_only=False) - index = index[interval2codemap[interval]] - index = list(index.keys()) - index.sort() - skeys = list(data.keys()) - if sortall is True: - skeys.sort(key=lambda tup: tup[1:]) - else: - skeys.sort() - - result = pd.concat([pd.Series(data[i], index=index) for i in skeys], - axis=1, join_axes=[pd.Index(index)]) - - columns = ['{0}_{1}_{2}_{3}'.format(i[1], i[2], i[4], i[5]) for i in skeys] - result.columns = columns - - if time_stamp == 'begin': - result = tsutils.asbestfreq(result) - result = result.tshift(-1) - - return tsutils.printiso(result) - - -@mando.command -def catalog(hbnfilename): - ''' - Prints out a catalog of data sets in the binary file. - - The first part of each line up to the first space is the label that can - be used with the 'extract' command. - - :param hbnfilename: The HSPF binary output file - ''' - catlog = _get_data(hbnfilename, None, [',,,'], catalog_only=True)[1] - if tsutils.test_cli() is False: - return catlog - catkeys = list(catlog.keys()) - catkeys.sort() - for cat in catkeys: - print('{0},{1},{2},{3} ,{5}, {6}, {7}'.format( - *(cat + catlog[cat] + - (code2intervalmap[cat[-1]],)))) - - -@mando.command -def dump(hbnfilename, time_stamp='begin'): - ''' - Prints out ALL data from a HSPF binary output file. - - :param hbnfilename: The HSPF binary output file - :param time_stamp: For the interval defines the location of the time - stamp. If set to 'begin', the time stamp is at the begining of the - interval. If set to any other string, the reported time stamp will - represent the end of the interval. Default is 'begin'. - ''' - if time_stamp not in ['begin', 'end']: - raise ValueError(''' -* -* The "time_stamp" optional keyword must be either -* "begin" or "end". You gave {0}. -* -'''.format(time_stamp)) - - index, data = _get_data(hbnfilename, None, [',,,'], catalog_only=False) - skeys = list(data.keys()) - skeys.sort() - - result = pd.concat([pd.Series(data[i], index=index) for i in skeys], - axis=1, join_axes=[pd.Index(index)]) - - columns = ['{0}_{1}_{2}_{3}'.format(i[1], i[2], i[4], i[5]) for i in skeys] - result.columns = columns - - if time_stamp == 'begin': - result = tsutils.asbestfreq(result) - result = result.tshift(-1) - - return tsutils.printiso(result) - - -@mando.command() -def about(): - """Display version number and system information. - """ - tsutils.about(__name__) - - -@mando.command -def time_series(hbnfilename, interval, *labels, **kwds): - ''' DEPRECATED: Use 'extract' instead. - ''' - return extract(hbnfilename, interval, *labels, **kwds) - - -def main(): - if not os.path.exists('debug_hspfbintoolbox'): - sys.tracebacklimit = 0 - mando.main() - - -if __name__ == '__main__': - main() diff --git a/mando-0.3.3.dist-info/DESCRIPTION.rst b/mando-0.3.3.dist-info/DESCRIPTION.rst deleted file mode 100644 index 16904644..00000000 --- a/mando-0.3.3.dist-info/DESCRIPTION.rst +++ /dev/null @@ -1,160 +0,0 @@ -mando: CLI interfaces for Humans! -================================= - -.. image:: https://img.shields.io/travis/rubik/mando/master.svg - :alt: Travis-CI badge - :target: https://travis-ci.org/rubik/mando - -.. image:: https://img.shields.io/coveralls/rubik/mando/master.svg - :alt: Coveralls badge - :target: https://coveralls.io/r/rubik/mando - -.. image:: https://img.shields.io/pypi/v/mando.svg - :alt: Latest release - :target: https://pypi.python.org/pypi/mando - -.. image:: https://img.shields.io/pypi/dm/mando.svg - :alt: PyPI downloads count - :target: https://pypi.python.org/pypi/mando - -.. image:: https://img.shields.io/pypi/format/mando.svg - :alt: Download format - :target: http://pythonwheels.com/ - -.. image:: https://img.shields.io/pypi/l/mando.svg - :alt: Mando license - :target: https://pypi.python.org/pypi/mando/ - -mando is a wrapper around ``argparse``, and allows you to write complete CLI -applications in seconds while maintaining all the flexibility. - -The problem ------------ - -While ``argparse`` is great for simple command line applications with only -one, default command, when you have to add multiple commands and manage them -things get really messy and long. But don't worry, mando comes to help! - -Quickstart ----------- - -.. code-block:: python - - from mando import command, main - - @command - def echo(text, capitalyze=False): - if capitalyze: - text = text.upper() - print(text) - - if __name__ == '__main__': - main() - -Generated help: - -.. code-block:: console - - $ python example.py -h - usage: example.py [-h] {echo} ... - - positional arguments: - {echo} - echo Echo the given text. - - optional arguments: - -h, --help show this help message and exit - - $ python example.py echo -h - usage: example.py echo [-h] [--capitalyze] text - - Echo the given text. - - positional arguments: - text - - optional arguments: - -h, --help show this help message and exit - --capitalyze - -Actual usage: - -.. code-block:: console - - $ python example.py echo spam - spam - $ python example.py echo --capitalyze spam - SPAM - - -A *real* example ----------------- - -Something more complex and real-world-*ish*. The code: - -.. code-block:: python - - from mando import command, main - - - @command - def push(repository, all=False, dry_run=False, force=False, thin=False): - '''Update remote refs along with associated objects. - - :param repository: Repository to push to. - :param --all: Push all refs. - :param -n, --dry-run: Dry run. - :param -f, --force: Force updates. - :param --thin: Use thin pack.''' - - print ('Pushing to {0}. All: {1}, dry run: {2}, force: {3}, thin: {4}' - .format(repository, all, dry_run, force, thin)) - - - if __name__ == '__main__': - main() - -mando understands Sphinx-style ``:param:``'s in the docstring, so it creates -short options and their help for you. - -.. code-block:: console - - $ python git.py push -h - usage: git.py push [-h] [--all] [-n] [-f] [--thin] repository - - Update remote refs along with associated objects. - - positional arguments: - repository Repository to push to. - - optional arguments: - -h, --help show this help message and exit - --all Push all refs. - -n, --dry-run Dry run. - -f, --force Force updates. - --thin Use thin pack. - -Let's try it! - -.. code-block:: console - - $ python git.py push --all myrepo - Pushing to myrepo. All: True, dry run: False, force: False, thin: False - $ python git.py push --all -f myrepo - Pushing to myrepo. All: True, dry run: False, force: True, thin: False - $ python git.py push --all -fn myrepo - Pushing to myrepo. All: True, dry run: True, force: True, thin: False - $ python git.py push --thin -fn myrepo - Pushing to myrepo. All: False, dry run: True, force: True, thin: True - $ python git.py push --thin - usage: git.py push [-h] [--all] [-n] [-f] [--thin] repository - git.py push: error: too few arguments - -Amazed uh? Yes, mando got the short options and the help from the docstring! -You can put much more in the docstring, and if that isn't enough, there's an -``@arg`` decorator to customize the arguments that get passed to argparse. - -For a complete documentation, visit https://mando.readthedocs.org/. -NOTE: Docs are still WIP! - - diff --git a/mando-0.3.3.dist-info/METADATA b/mando-0.3.3.dist-info/METADATA deleted file mode 100644 index c6f00353..00000000 --- a/mando-0.3.3.dist-info/METADATA +++ /dev/null @@ -1,189 +0,0 @@ -Metadata-Version: 2.0 -Name: mando -Version: 0.3.3 -Summary: Create Python CLI apps with little to no effort at all! -Home-page: https://mando.readthedocs.org/ -Author: Michele Lacchia -Author-email: michelelacchia@gmail.com -License: MIT -Download-URL: https://pypi.python.org/mando/ -Keywords: argparse,argument parser,arguments,cli,command line,commands,decorator,dispatch,flags,getopt,options,optparse,parser,subcommands -Platform: any -Classifier: Development Status :: 5 - Production/Stable -Classifier: Environment :: Console -Classifier: Intended Audience :: Developers -Classifier: License :: OSI Approved :: MIT License -Classifier: Natural Language :: English -Classifier: Operating System :: OS Independent -Classifier: Programming Language :: Python -Classifier: Programming Language :: Python :: 2 -Classifier: Programming Language :: Python :: 2.6 -Classifier: Programming Language :: Python :: 2.7 -Classifier: Programming Language :: Python :: 3 -Classifier: Programming Language :: Python :: 3.1 -Classifier: Programming Language :: Python :: 3.2 -Classifier: Programming Language :: Python :: 3.3 -Classifier: Topic :: Software Development -Classifier: Topic :: Software Development :: Libraries :: Python Modules -Classifier: Topic :: Utilities - -mando: CLI interfaces for Humans! -================================= - -.. image:: https://img.shields.io/travis/rubik/mando/master.svg - :alt: Travis-CI badge - :target: https://travis-ci.org/rubik/mando - -.. image:: https://img.shields.io/coveralls/rubik/mando/master.svg - :alt: Coveralls badge - :target: https://coveralls.io/r/rubik/mando - -.. image:: https://img.shields.io/pypi/v/mando.svg - :alt: Latest release - :target: https://pypi.python.org/pypi/mando - -.. image:: https://img.shields.io/pypi/dm/mando.svg - :alt: PyPI downloads count - :target: https://pypi.python.org/pypi/mando - -.. image:: https://img.shields.io/pypi/format/mando.svg - :alt: Download format - :target: http://pythonwheels.com/ - -.. image:: https://img.shields.io/pypi/l/mando.svg - :alt: Mando license - :target: https://pypi.python.org/pypi/mando/ - -mando is a wrapper around ``argparse``, and allows you to write complete CLI -applications in seconds while maintaining all the flexibility. - -The problem ------------ - -While ``argparse`` is great for simple command line applications with only -one, default command, when you have to add multiple commands and manage them -things get really messy and long. But don't worry, mando comes to help! - -Quickstart ----------- - -.. code-block:: python - - from mando import command, main - - @command - def echo(text, capitalyze=False): - if capitalyze: - text = text.upper() - print(text) - - if __name__ == '__main__': - main() - -Generated help: - -.. code-block:: console - - $ python example.py -h - usage: example.py [-h] {echo} ... - - positional arguments: - {echo} - echo Echo the given text. - - optional arguments: - -h, --help show this help message and exit - - $ python example.py echo -h - usage: example.py echo [-h] [--capitalyze] text - - Echo the given text. - - positional arguments: - text - - optional arguments: - -h, --help show this help message and exit - --capitalyze - -Actual usage: - -.. code-block:: console - - $ python example.py echo spam - spam - $ python example.py echo --capitalyze spam - SPAM - - -A *real* example ----------------- - -Something more complex and real-world-*ish*. The code: - -.. code-block:: python - - from mando import command, main - - - @command - def push(repository, all=False, dry_run=False, force=False, thin=False): - '''Update remote refs along with associated objects. - - :param repository: Repository to push to. - :param --all: Push all refs. - :param -n, --dry-run: Dry run. - :param -f, --force: Force updates. - :param --thin: Use thin pack.''' - - print ('Pushing to {0}. All: {1}, dry run: {2}, force: {3}, thin: {4}' - .format(repository, all, dry_run, force, thin)) - - - if __name__ == '__main__': - main() - -mando understands Sphinx-style ``:param:``'s in the docstring, so it creates -short options and their help for you. - -.. code-block:: console - - $ python git.py push -h - usage: git.py push [-h] [--all] [-n] [-f] [--thin] repository - - Update remote refs along with associated objects. - - positional arguments: - repository Repository to push to. - - optional arguments: - -h, --help show this help message and exit - --all Push all refs. - -n, --dry-run Dry run. - -f, --force Force updates. - --thin Use thin pack. - -Let's try it! - -.. code-block:: console - - $ python git.py push --all myrepo - Pushing to myrepo. All: True, dry run: False, force: False, thin: False - $ python git.py push --all -f myrepo - Pushing to myrepo. All: True, dry run: False, force: True, thin: False - $ python git.py push --all -fn myrepo - Pushing to myrepo. All: True, dry run: True, force: True, thin: False - $ python git.py push --thin -fn myrepo - Pushing to myrepo. All: False, dry run: True, force: True, thin: True - $ python git.py push --thin - usage: git.py push [-h] [--all] [-n] [-f] [--thin] repository - git.py push: error: too few arguments - -Amazed uh? Yes, mando got the short options and the help from the docstring! -You can put much more in the docstring, and if that isn't enough, there's an -``@arg`` decorator to customize the arguments that get passed to argparse. - -For a complete documentation, visit https://mando.readthedocs.org/. -NOTE: Docs are still WIP! - - diff --git a/mando-0.3.3.dist-info/RECORD b/mando-0.3.3.dist-info/RECORD deleted file mode 100644 index 4b3ea1d9..00000000 --- a/mando-0.3.3.dist-info/RECORD +++ /dev/null @@ -1,22 +0,0 @@ -mando-0.3.3.dist-info/top_level.txt,sha256=P_B0dprMbIWMNtZZHPMaMbIeJiQ5TLBEKyOp5YD-kd0,6 -mando-0.3.3.dist-info/RECORD,, -mando-0.3.3.dist-info/DESCRIPTION.rst,sha256=4-dqeST3lkoi8WVDEhti5PrQ-HDQebqlD9kVxM-OR2o,4404 -mando-0.3.3.dist-info/WHEEL,sha256=AvR0WeTpDaxT645bl5FQxUK6NPsTls2ttpcGJg3j1Xg,110 -mando-0.3.3.dist-info/metadata.json,sha256=cXw1uvzWIZTeI39S-1qrXVgSCeLYhiaHYIf2dHQZL2M,1369 -mando-0.3.3.dist-info/METADATA,sha256=wCMzPZfmncaJjbrrN-nqfE1HRsWKa37RnUcd_Gu0xA4,5643 -mando/core.py,sha256=8kG2vS6tWKfMC442oM8deZLGFBU0lPz31xfVwneXVwY,6201 -mando/__init__.py,sha256=chixi0ttFk8ylGSb5I_bJ3qwN7pdWoSdx0RW-eqU9RA,323 -mando/utils.py,sha256=xkImroNn1MytBAeqFwHRTzZCWoeHZbyf45UBNnGj6RQ,3019 -mando/tests/run.py,sha256=dwlmP3rAogRuueycxZZ4iZD9R4GPD_saXL2YDVpwxvE,228 -mando/tests/test_utils.py,sha256=_zJGz7LjKIseSUMVxFyrsZ9Oe4Rzb5yMosoYGL8oysA,3578 -mando/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 -mando/tests/test_unicode_docstring_on_py2.py,sha256=LHGWuYcEbGii8GB6eU-2t6XJ2tLzZXQeuQzOQttUr20,507 -mando/tests/test_core.py,sha256=vb6r4zYl3VIwrgUmxIPr-xu7b1VbQW0mIysb4VPn-yQ,4212 -mando/tests/test_utils.pyc,, -mando/tests/test_core.pyc,, -mando/tests/run.pyc,, -mando/utils.pyc,, -mando/tests/__init__.pyc,, -mando/tests/test_unicode_docstring_on_py2.pyc,, -mando/core.pyc,, -mando/__init__.pyc,, diff --git a/mando-0.3.3.dist-info/WHEEL b/mando-0.3.3.dist-info/WHEEL deleted file mode 100644 index 9dff69d8..00000000 --- a/mando-0.3.3.dist-info/WHEEL +++ /dev/null @@ -1,6 +0,0 @@ -Wheel-Version: 1.0 -Generator: bdist_wheel (0.24.0) -Root-Is-Purelib: true -Tag: py2-none-any -Tag: py3-none-any - diff --git a/mando-0.3.3.dist-info/metadata.json b/mando-0.3.3.dist-info/metadata.json deleted file mode 100644 index e544a216..00000000 --- a/mando-0.3.3.dist-info/metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"metadata_version": "2.0", "extensions": {"python.details": {"document_names": {"description": "DESCRIPTION.rst"}, "project_urls": {"Home": "https://mando.readthedocs.org/"}, "contacts": [{"email": "michelelacchia@gmail.com", "name": "Michele Lacchia", "role": "author"}]}}, "version": "0.3.3", "name": "mando", "keywords": ["argparse", "argument", "parser", "arguments", "cli", "command", "line", "commands", "decorator", "dispatch", "flags", "getopt", "options", "optparse", "parser", "subcommands"], "summary": "Create Python CLI apps with little to no effort at all!", "generator": "bdist_wheel (0.24.0)", "download_url": "https://pypi.python.org/mando/", "classifiers": ["Development Status :: 5 - Production/Stable", "Environment :: Console", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.1", "Programming Language :: Python :: 3.2", "Programming Language :: Python :: 3.3", "Topic :: Software Development", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Utilities"], "platform": "any", "license": "MIT"} \ No newline at end of file diff --git a/mando-0.3.3.dist-info/top_level.txt b/mando-0.3.3.dist-info/top_level.txt deleted file mode 100644 index c84014fa..00000000 --- a/mando-0.3.3.dist-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -mando diff --git a/mando/__init__.py b/mando/__init__.py deleted file mode 100644 index f3c3a215..00000000 --- a/mando/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -__version__ = '0.3.3' - -try: - from mando.core import Program -except ImportError as e: # pragma: no cover - # unfortunately the only workaround for Python2.6, argparse and setup.py - e.version = __version__ - raise e - -main = Program() -command = main.command -arg = main.arg -parse = main.parse -execute = main.execute diff --git a/mando/core.py b/mando/core.py deleted file mode 100644 index 4a297da8..00000000 --- a/mando/core.py +++ /dev/null @@ -1,146 +0,0 @@ -'''Main module containing the class Program(), which allows the conversion from -ordinary Python functions into commands for the command line. It uses -:py:module:``argparse`` behind the scenes.''' - -import sys -import inspect -import argparse -try: - from itertools import izip_longest -except ImportError: # pragma: no cover - from itertools import zip_longest as izip_longest -from mando.utils import (purify_doc, action_by_type, find_param_docs, - split_doc, ensure_dashes, purify_kwargs) - - -_POSITIONAL = type('_positional', (object,), {}) -_DISPATCH_TO = '_dispatch_to' - - -class Program(object): - - def __init__(self, prog=None, version=None, **kwargs): - self.parser = argparse.ArgumentParser(prog, **kwargs) - if version is not None: - self.parser.add_argument('-v', '--version', action='version', - version=version) - self.subparsers = self.parser.add_subparsers() - self.argspecs = {} - self.current_command = None - - def command(self, *args, **kwargs): - '''A decorator to convert a function into a command. It can be applied - as ``@command`` or as ``@command(new_name)``, specifying an alternative - name for the command (default one is ``func.__name__``).''' - if len(args) == 1 and hasattr(args[0], '__call__'): - return self._generate_command(args[0]) - else: - def _command(func): - return self._generate_command(func, *args, **kwargs) - return _command - - def arg(self, param, *args, **kwargs): - '''A decorator to override the parameters extracted from the docstring - or to add new ones. - - :param param: The parameter's name. It must be among the function's - arguments names.''' - def wrapper(func): - if not hasattr(func, '_argopts'): - func._argopts = {} - func._argopts[param] = (args, kwargs) - return func - return wrapper - - def _generate_command(self, func, name=None, *args, **kwargs): - '''Generate argparse's subparser. - - :param func: The function to analyze. - :param name: If given, a different name for the command. The default - one is ``func.__name__``.''' - func_name = func.__name__ - name = func_name if name is None else name - argspec = inspect.getargspec(func) - self.argspecs[func_name] = argspec - argz = izip_longest(reversed(argspec.args), reversed(argspec.defaults - or []), - fillvalue=_POSITIONAL()) - argz = reversed(list(argz)) - doc = (inspect.getdoc(func) or '').strip() + '\n' - cmd_help, cmd_desc = split_doc(purify_doc(doc)) - subparser = self.subparsers.add_parser(name, - help=cmd_help or None, - description=cmd_desc or None, - **kwargs) - for a, kw in self.analyze_func(func, doc, argz, argspec.varargs): - subparser.add_argument(*a, **purify_kwargs(kw)) - subparser.set_defaults(**{_DISPATCH_TO: func}) - return func - - def analyze_func(self, func, doc, argz, varargs_name): - '''Analyze the given function, merging default arguments, overridden - arguments (with @arg) and parameters extracted from the docstring. - - :param func: The function to analyze. - :param doc: The function's docstring. - :param argz: A list of the form (arg, default), containing arguments - and their default value. - :param varargs_name: The name of the variable arguments, if present, - otherwise ``None``.''' - params = find_param_docs(doc) - for arg, default in argz: - override = getattr(func, '_argopts', {}).get(arg, ((), {})) - yield merge(arg, default, override, *params.get(arg, ([], {}))) - if varargs_name is not None: - kwargs = {'nargs': '*'} - kwargs.update(params.get(varargs_name, (None, {}))[1]) - yield ([varargs_name], kwargs) - - def parse(self, args): - '''Parse the given arguments and return a tuple ``(command, args)``, - where ``args`` is a list consisting of all arguments. The command can - then be called as ``command(*args)``. - - :param args: The arguments to parse.''' - arg_map = self.parser.parse_args(args).__dict__ - command = arg_map.pop(_DISPATCH_TO) - argspec = self.argspecs[command.__name__] - real_args = [] - for arg in argspec.args: - real_args.append(arg_map.pop(arg)) - if arg_map and arg_map.get(argspec.varargs): - real_args.extend(arg_map.pop(argspec.varargs)) - return command, real_args - - def execute(self, args): - '''Parse the arguments and execute the resulting command. - - :param args: The arguments to parse.''' - command, a = self.parse(args) - self.current_command = command.__name__ - return command(*a) - - def __call__(self): # pragma: no cover - '''Parse ``sys.argv`` and execute the resulting command.''' - self.execute(sys.argv[1:]) - - -def merge(arg, default, override, args, kwargs): - '''Merge all the possible arguments into a tuple and a dictionary. - - :param arg: The argument's name. - :param default: The argument's default value or an instance of _POSITIONAL. - :param override: A tuple containing (args, kwargs) given to @arg. - :param args: The arguments extracted from the docstring. - :param kwargs: The keyword arguments extracted from the docstring.''' - opts = [arg] - if not isinstance(default, _POSITIONAL): - opts = list(ensure_dashes(args or opts)) - kwargs.update({'default': default, 'dest': arg}) - kwargs.update(action_by_type(default)) - else: - # positionals can't have a metavar, otherwise the help is screwed - # if one really wants the metavar, it can be added with @arg - kwargs['metavar'] = None - kwargs.update(override[1]) - return override[0] or opts, kwargs diff --git a/mando/tests/__init__.py b/mando/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/mando/tests/run.py b/mando/tests/run.py deleted file mode 100644 index 65c774c8..00000000 --- a/mando/tests/run.py +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/env python - -if __name__ == '__main__': - import unittest - from mando.tests.test_core import * - from mando.tests.test_utils import * - from mando.tests.test_unicode_docstring_on_py2 import * - - unittest.main() diff --git a/mando/tests/test_core.py b/mando/tests/test_core.py deleted file mode 100644 index 5805ada5..00000000 --- a/mando/tests/test_core.py +++ /dev/null @@ -1,152 +0,0 @@ -import unittest -from paramunittest import parametrized -from mando import Program - - -program = Program('example.py', '1.0.10') - -@program.command -def goo(pos, verbose=False, bar=None): - pass - - -@program.command -def vara(pos, foo, spam=24, *vars): - ''' - :param vars: Yeah, you got it right, the variable arguments. - ''' - pass - - -@program.command -def another(baw, owl=42, json=False, tomawk=None): - '''This yet another example showcasing the power of Mando! - - :param baw: That's the positional argument, obviously. - :param -o, --owl: Yeah, I know, this is too much. - :param -j, --json: In case you want to pipe it through something. - :param -t, --tomawk: Well, in this case -t isn't for time.''' - pass - - -@program.command('alias') -def analiased(a, b=4): - pass - - -@program.command -def power(x, y=2): - return int(x) ** y - - -@program.command('more-power') -def more_power(x, y=2): - '''This one really shows off complete power. - - :param x : Well, the base. - :param -y : You got it, the exponent.''' - - return x ** y - - -@program.command('more-powerful') -@program.arg('x', type=int) -@program.arg('y', '-y', '--epsilon', type=int) -def more_power_2(x, y=2): - return x ** y - - -@program.command -@program.arg('x', type=int) -@program.arg('y', type=int) -def overriding(x, y=4): - '''Yoo an override test. - - :param x : This is so wroong!!! Let's hope it gets overriden by @arg. - :param -y : This too!!''' - - return x - y - - -@program.command -def dashes(a, b=5): - '''Usual command help. - - :param a : A help obviously. - :param b : Yooo.''' - return a ** b - - -@program.command -def append(acc=[]): - return acc - - -@parametrized( - ('goo 2', ['2', False, None]), - ('goo 2 --verbose', ['2', True, None]), - ('goo 2 --bar 9', ['2', False, '9']), - ('goo 2 --verbose --bar 8', ['2', True, '8']), - ('vara 2 3', ['2', '3', 24]), - ('vara 2 3 --spam 8', ['2', '3', 8]), - # Unfortunately this is an argparse "bug". See: - # http://bugs.python.org/issue15112 - # You cannot intermix positional and optional arguments for now. - #('vara 1 2 --spam 8 9 8', ['1', '2', 8, '9', '8']), - ('vara 1 2 4 5 --spam 8', ['1', '2', 8, '4', '5']), - ('vara --spam 8 1 2 4 5', ['1', '2', 8, '4', '5']), - ('vara 9 8 1 2 3 4', ['9', '8', 24, '1', '2', '3', '4']), - ('another 2', ['2', 42, False, None]), - ('another 2 -j', ['2', 42, True, None]), - ('another 2 -t 1 -o 3', ['2', 3, False, '1']), - ('another 2 --owl 89 --tomawk 98', ['2', 89, False, '98']), - ('another 2 --json -o 1', ['2', 1, True, None]), - ('another 3 --owl 8 --json --tomawk 8', ['3', 8, True, '8']), - ('alias 5 -b 9', ['5', 9], 'analiased'), - ('more-power 9 -y 2', [9, 2], 'more_power'), - ('more-powerful 9 -y 3', [9, 3], 'more_power_2'), - ('more-powerful 9 --epsilon 3', [9, 3], 'more_power_2'), - ('overriding 2', [2, 4]), - ('overriding 2 -y 7', [2, 7]), - ('dashes 2', [2, 5]), - ('dashes 8 -b 7', [8, 7]), - ('append', [[]]), - ('append --acc 2', [['2']]), - ('append --acc 2 --acc 3', [['2', '3']]), -) -class TestGenericCommands(unittest.TestCase): - - def setParameters(self, args, to_args, real_name=None): - self.args = args.split() - self.to_args = to_args - self.real_name = real_name - - def testParsing(self): - name = self.args[0] if self.real_name is None else self.real_name - parsed = program.parse(self.args) - self.assertEqual(name, parsed[0].__name__) - self.assertEqual(self.to_args, parsed[1]) - - -@parametrized( - ('power 2', 4), - ('power 2 -y 4', 16), - ('more-power 3', 9), - ('more-power 3 -y 4', 81), - ('more-powerful 4 -y 2', 16), - ('more-powerful 4 --epsilon 2', 16), - ('overriding 2', -2), - ('overriding 2 -y 7', -5), - ('dashes 2', 32), - ('dashes 7 -b 3', 343), -) -class TestProgramExecute(unittest.TestCase): - - def setParameters(self, args, result): - self.args = args.split() - self.result = result - - def testExecute(self): - self.assertEqual(self.result, program.execute(self.args)) - self.assertEqual(program.parse(self.args)[0].__name__, - program.current_command) diff --git a/mando/tests/test_unicode_docstring_on_py2.py b/mando/tests/test_unicode_docstring_on_py2.py deleted file mode 100644 index 7ea9bec7..00000000 --- a/mando/tests/test_unicode_docstring_on_py2.py +++ /dev/null @@ -1,20 +0,0 @@ -# This is important: it will make all literals unicode under 2.x -from __future__ import unicode_literals - -import unittest - -from mando import Program - - -program = Program('example.py', '1.0.10') - - -class Test_unicode_docstring_on_py2(unittest.TestCase): - - def test_py2_unicode_literals(self): - @program.command - def some_command(): - 'this is a unicode doc-string!' - - assert not isinstance(some_command.__doc__, bytes) - # TODO: check that the generated help is correct diff --git a/mando/tests/test_utils.py b/mando/tests/test_utils.py deleted file mode 100644 index 78a89f36..00000000 --- a/mando/tests/test_utils.py +++ /dev/null @@ -1,107 +0,0 @@ -import unittest -from paramunittest import parametrized -from mando.utils import action_by_type, ensure_dashes, find_param_docs, split_doc - - -@parametrized( - dict(obj=True, result={'action': 'store_false'}), - dict(obj=False, result={'action': 'store_true'}), - dict(obj=[], result={'action': 'append'}), - dict(obj=[1, False], result={'action': 'append'}), - dict(obj=None, result={}), - dict(obj=1, result={'type': type(1)}), - dict(obj=1.1, result={'type': type(1.1)}), - dict(obj='1', result={'type': type('1')}), -) -class TestActionByType(unittest.TestCase): - - def setParameters(self, obj, result): - self.obj = obj - self.result = result - - def testFunc(self): - self.assertEqual(self.result, action_by_type(self.obj)) - - -@parametrized( - (['m'], ['-m']), - (['m', 'min'], ['-m', '--min']), - (['-m'], ['-m']), - (['-m', 'min'], ['-m', '--min']), - (['m', '--min'], ['-m', '--min']), - (['-m', '--min'], ['-m', '--min']), - (['-m', '--min', 'l', 'less'], ['-m', '--min', '-l', '--less']), -) -class TestEnsureDashes(unittest.TestCase): - - def setParameters(self, opts, result): - self.opts = opts - self.result = result - - def testFunc(self): - self.assertEqual(self.result, list(ensure_dashes(self.opts))) - - -@parametrized( - ('', ['', '']), - ('only help.', ['only help.', 'only help.']), - ('help.\nstill help.', ['help.\nstill help.', 'help.\nstill help.']), - ('help\n\ndesc', ['help', 'desc']), - ('help\n\n\ndesc\n', ['help', 'desc']), -) -class TestSplitDoc(unittest.TestCase): - - def setParameters(self, doc, parts): - self.doc = doc - self.parts = parts - - def testFunc(self): - self.assertEqual(self.parts, split_doc(self.doc)) - - -a_1 = {'a_param': (['a-param'], {'help': 'Short story.'})} -a_1_1 = {'a_param': (['a_param'], {'help': 'Short story.'})} -a_2 = {'j': (['-j'], {'help': 'Woow'})} -a_3 = {'noun': (['-n', '--noun'], {'help': 'cat'})} -a_all = {} -for a in (a_1, a_2, a_3): - a_all.update(a) - -@parametrized( - dict(doc='', params={}), - dict(doc='Brevity is the soul of wit.', params={}), - dict(doc=':param a-param: Short story.', params=a_1), - dict(doc=':param a_param: Short story.', params=a_1_1), - dict(doc=':param -j: Woow', params=a_2), - dict(doc=':param -n, --noun: cat', params=a_3), - dict(doc=''' - Some short text here and there. - - :param well: water''', params={'well': (['well'], {'help': 'water'})}), - dict(doc=''' - :param a-param: Short story. - :param -j: Woow - :param -n, --noun: cat''', params=a_all), - dict(doc=''' - Lemme see. - - :param long-story: A long storey belive me: when all started, Adam and Bob were just two little farmers. - ''', params={'long_story': (['long-story'], {'help': 'A long storey '\ - 'belive me: when all started, Adam and '\ - 'Bob were just two little farmers.'})}), -) -class TestFindParamDocs(unittest.TestCase): - - def setParameters(self, doc, params): - self.doc = doc - self.params = params - - def testFunc(self): - found_params = find_param_docs(self.doc) - self.assertTrue(self.params.keys() == found_params.keys()) - for key, value in self.params.items(): - self.assertTrue(key in found_params) - found_value = found_params[key] - self.assertTrue(value[0] == found_value[0]) - for kwarg, val in value[1].items(): - self.assertTrue(val == found_value[1][kwarg]) diff --git a/mando/utils.py b/mando/utils.py deleted file mode 100644 index 91916097..00000000 --- a/mando/utils.py +++ /dev/null @@ -1,96 +0,0 @@ -import re - -PARAM_RE = re.compile(r"^([\t ]*):param (.*?): (.*\n(\1[ \t]+.*\n*)*)", - re.MULTILINE) -ARG_RE = re.compile(r'-(?P-)?(?P(?(long)[^ =,]+|.))[ =]?' - '(?P[^ ,]+)?') -POS_RE = re.compile(r'(?P[^ ,]+)?') -ARG_TYPE_MAP = { - 'n': int, 'num': int, 'number': int, - 'i': int, 'int': int, 'integer': int, - 's': str, 'str': str, 'string': str, - 'f': float, 'float': float, - None: None, '': None, -} - - -def purify_doc(string): - '''Remove Sphinx's :param: lines from the docstring.''' - return PARAM_RE.sub('', string).rstrip() - - -def split_doc(string): - '''Split the documentation into help and description. - - A two-value list is returned, of the form ``[help, desc]``. If no - description is provided, the help is duplicated.''' - parts = [part.strip() for part in string.split('\n\n', 1)] - if len(parts) == 1: - return parts * 2 - return parts - - -def purify_kwargs(kwargs): - '''If type or metavar are set to None, they are removed from kwargs.''' - for key, value in kwargs.copy().items(): - if key in set(['type', 'metavar']) and value is None: - del kwargs[key] - return kwargs - - -def find_param_docs(docstring): - '''Find Sphinx's :param: lines and return a dictionary of the form: - ``param: (opts, {metavar: meta, type: type, help: help})``.''' - paramdocs = {} - for _, param, value, _ in PARAM_RE.findall(docstring + '\n'): - name, opts, meta = get_opts(param.strip()) - name = name.replace('-', '_') - paramdocs[name] = (opts, { - 'metavar': meta or None, - 'type': ARG_TYPE_MAP.get(meta.strip('<>')), - 'help': value.rstrip(), - }) - return paramdocs - - -def get_opts(param): - '''Extract options from a parameter name.''' - if param.startswith('-'): - opts = [] - names = [] - meta = None - for long, name, meta in ARG_RE.findall(param): - prefix = ['-', '--'][len(long)] - opts.append('{0}{1}'.format(prefix, name)) - names.append(name) - return max(names, key=len), opts, meta - opt, meta = (list(filter(None, POS_RE.findall(param))) + [''])[:2] - return opt, [opt], meta - - -def action_by_type(obj): - '''Determine an action and a type for the given object if possible.''' - kw = {} - if isinstance(obj, bool): - return {'action': ['store_true', 'store_false'][obj]} - elif isinstance(obj, list): - kw = {'action': 'append'} - kw.update(get_type(obj)) - return kw - - -def get_type(obj): - '''Determine the type of the object if among some of the built-in ones.''' - otype = type(obj) - if any(otype is t for t in set([int, float, str, bool])): - return {'type': otype} - return {} - - -def ensure_dashes(opts): - '''Ensure that the options have the right number of dashes.''' - for opt in opts: - if opt.startswith('-'): - yield opt - else: - yield '-' * (1 + 1 * (len(opt) > 1)) + opt diff --git a/setup.py b/setup.py index c6e632c3..6a6165a5 100644 --- a/setup.py +++ b/setup.py @@ -28,6 +28,8 @@ 'numba', 'h5py', 'networkx', + 'hspfbintoolbox', + 'wdmtoolbox', ] setup(name='HSPsquared', @@ -47,7 +49,9 @@ 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2.7', - 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', 'Topic :: Scientific/Engineering', 'Topic :: Software Development :: Libraries :: Python Modules', ], @@ -64,4 +68,5 @@ ['hsp2=HSP2.main:main'] }, test_suite='tests', + python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*', ) diff --git a/tstoolbox-0.9.9.6-py2.7.egg-info/PKG-INFO b/tstoolbox-0.9.9.6-py2.7.egg-info/PKG-INFO deleted file mode 100644 index 26dd321e..00000000 --- a/tstoolbox-0.9.9.6-py2.7.egg-info/PKG-INFO +++ /dev/null @@ -1,182 +0,0 @@ -Metadata-Version: 1.1 -Name: tstoolbox -Version: 0.9.9.6 -Summary: Command line script to manipulate time series files. -Home-page: http://pypi.python.org/pypi/tstoolbox -Author: Tim Cera, P.E. -Author-email: tim@cerazone.net -License: UNKNOWN -Description: .. image:: https://travis-ci.org/timcera/tstoolbox.svg?branch=master - :target: https://travis-ci.org/timcera/tstoolbox - :height: 20 - - .. image:: https://coveralls.io/repos/timcera/tstoolbox/badge.png?branch=master - :target: https://coveralls.io/r/timcera/tstoolbox?branch=master - :height: 20 - - .. image:: https://img.shields.io/pypi/v/tstoolbox.svg - :alt: Latest release - :target: https://pypi.python.org/pypi/tstoolbox - - .. image:: https://img.shields.io/pypi/dm/tstoolbox.svg - :alt: PyPI downloads count - :target: https://pypi.python.org/pypi/tstoolbox - - .. image:: http://img.shields.io/badge/license-BSD-lightgrey.svg - :alt: tstoolbox license - :target: https://pypi.python.org/pypi/tstoolbox/ - - TSToolbox - Quick Guide - ======================= - The tstoolbox is a Python script to manipulate time-series on the command line - or by function calls within Python. Uses pandas (http://pandas.pydata.org/) - or numpy (http://numpy.scipy.org) for any heavy lifting. - - Requirements - ------------ - * pandas - on Windows this is part scientific Python distributions like - Python(x,y), Anaconda, or Enthought. - - * mando - command line parser - - Installation - ------------ - Should be as easy as running ``pip install tstoolbox`` or ``easy_install - tstoolbox`` at any command line. Not sure on Windows whether this will bring - in pandas, but as mentioned above, if you start with scientific Python - distribution then you won't have a problem. - - Usage - Command Line - -------------------- - Just run 'tstoolbox --help' to get a list of subcommands - - accumulate - Calculates accumulating statistics. - - add_trend - Adds a trend. - - aggregate - Takes a time series and aggregates to specified - frequency, outputs to 'ISO-8601date,value' format. - - calculate_fdc - Returns the frequency distribution curve. DOES NOT - return a time-series. - - clip - Returns a time-series with values limited to [a_min, - a_max] - - convert - Converts values of a time series by applying a factor - and offset. See the 'equation' subcommand for a - generalized form of this command. - - date_slice - Prints out data to the screen between start_date and - end_date - - describe - Prints out statistics for the time-series. - - dtw - Dynamic Time Warping (beta) - - equation - Applies to the time series data. The - argument is a string contained in single - quotes with 'x' used as the variable representing the - input. For example, '(1 - x)*sin(x)'. - - fill - Fills missing values (NaN) with different methods. - Missing values can occur because of NaN, or because - the time series is sparse. The 'interval' option can - insert NaNs to create a dense time series. - - filter - Apply different filters to the time-series. - - normalization - Returns the normalization of the time series. - - pca - Returns the principal components analysis of the time - series. Does not return a time-series. (beta) - - peak_detection - Peak and valley detection. - - pick - Will pick a column or list of columns from input. - Start with 1. - - plot - Plots. - - read - Collect time series from a list of pickle or csv files - then print in the tstoolbox standard format. - - remove_trend - Removes a 'trend'. - - rolling_window - Calculates a rolling window statistic. - - stack - Returns the stack of the input table. - - stdtozrxp - Prints out data to the screen in a WISKI ZRXP format. - - tstopickle - Pickles the data into a Python pickled file. Can be - brought back into Python with 'pickle.load' or - 'numpy.load'. See also 'tstoolbox read'. - - unstack - Returns the unstack of the input table. - - The default for all of the subcommands is to accept data from stdin (typically - a pipe). If a subcommand accepts an input file for an argument, you can use - "--input_ts=input_file_name.csv", or to explicitly specify from stdin (the - default) "--input_ts='-'" . - - For the subcommands that output data it is printed to the screen and you can - then redirect to a file. - - Usage - API - ----------- - You can use all of the command line subcommands as functions. The function - signature is identical to the command line subcommands. The return is always - a PANDAS DataFrame. Input can be a CSV or TAB separated file, or a PANDAS - DataFrame and is supplied to the function via the 'input_ts' keyword. - - Simply import tstoolbox:: - - from tstoolbox import tstoolbox - - # Then you could call the functions - ntsd = tstoolbox.fill(method='linear', input_ts='tests/test_fill_01.csv') - - # Once you have a PANDAS DataFrame you can use that as input to other - # tstoolbox functions. - ntsd = tstoolbox.aggregate(statistic='mean', agg_interval='daily', input_ts=ntsd) - -Keywords: time_series -Platform: UNKNOWN -Classifier: Development Status :: 5 - Production/Stable -Classifier: Intended Audience :: Science/Research -Classifier: Intended Audience :: End Users/Desktop -Classifier: Intended Audience :: Developers -Classifier: Environment :: Console -Classifier: License :: OSI Approved :: BSD License -Classifier: Natural Language :: English -Classifier: Operating System :: OS Independent -Classifier: Programming Language :: Python :: 2 -Classifier: Programming Language :: Python :: 3 -Classifier: Topic :: Scientific/Engineering :: Information Analysis -Classifier: Topic :: Scientific/Engineering -Classifier: Topic :: Software Development :: Libraries :: Python Modules diff --git a/tstoolbox-0.9.9.6-py2.7.egg-info/SOURCES.txt b/tstoolbox-0.9.9.6-py2.7.egg-info/SOURCES.txt deleted file mode 100644 index d2ab7cec..00000000 --- a/tstoolbox-0.9.9.6-py2.7.egg-info/SOURCES.txt +++ /dev/null @@ -1,22 +0,0 @@ -AUTHORS.rst -CONTRIBUTING.rst -LICENSE.txt -MANIFEST.in -README.rst -VERSION -pip-delete-this-directory.txt -setup.cfg -setup.py -tstoolbox/__init__.py -tstoolbox/fill_functions.py -tstoolbox/filters.py -tstoolbox/peakdetect.py -tstoolbox/tstoolbox.py -tstoolbox/tsutils.py -tstoolbox.egg-info/PKG-INFO -tstoolbox.egg-info/SOURCES.txt -tstoolbox.egg-info/dependency_links.txt -tstoolbox.egg-info/entry_points.txt -tstoolbox.egg-info/not-zip-safe -tstoolbox.egg-info/requires.txt -tstoolbox.egg-info/top_level.txt \ No newline at end of file diff --git a/tstoolbox-0.9.9.6-py2.7.egg-info/dependency_links.txt b/tstoolbox-0.9.9.6-py2.7.egg-info/dependency_links.txt deleted file mode 100644 index 8b137891..00000000 --- a/tstoolbox-0.9.9.6-py2.7.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/tstoolbox-0.9.9.6-py2.7.egg-info/entry_points.txt b/tstoolbox-0.9.9.6-py2.7.egg-info/entry_points.txt deleted file mode 100644 index bbfca1dd..00000000 --- a/tstoolbox-0.9.9.6-py2.7.egg-info/entry_points.txt +++ /dev/null @@ -1,3 +0,0 @@ -[console_scripts] -tstoolbox = tstoolbox.tstoolbox:main - diff --git a/tstoolbox-0.9.9.6-py2.7.egg-info/installed-files.txt b/tstoolbox-0.9.9.6-py2.7.egg-info/installed-files.txt deleted file mode 100644 index e00304ec..00000000 --- a/tstoolbox-0.9.9.6-py2.7.egg-info/installed-files.txt +++ /dev/null @@ -1,21 +0,0 @@ -..\tstoolbox\fill_functions.py -..\tstoolbox\filters.py -..\tstoolbox\peakdetect.py -..\tstoolbox\tstoolbox.py -..\tstoolbox\tsutils.py -..\tstoolbox\__init__.py -..\tstoolbox\fill_functions.pyc -..\tstoolbox\filters.pyc -..\tstoolbox\peakdetect.pyc -..\tstoolbox\tstoolbox.pyc -..\tstoolbox\tsutils.pyc -..\tstoolbox\__init__.pyc -dependency_links.txt -entry_points.txt -not-zip-safe -PKG-INFO -requires.txt -SOURCES.txt -top_level.txt -..\..\..\Scripts\tstoolbox-script.py -..\..\..\Scripts\tstoolbox.exe diff --git a/tstoolbox-0.9.9.6-py2.7.egg-info/not-zip-safe b/tstoolbox-0.9.9.6-py2.7.egg-info/not-zip-safe deleted file mode 100644 index 8b137891..00000000 --- a/tstoolbox-0.9.9.6-py2.7.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/tstoolbox-0.9.9.6-py2.7.egg-info/requires.txt b/tstoolbox-0.9.9.6-py2.7.egg-info/requires.txt deleted file mode 100644 index bccd2b1e..00000000 --- a/tstoolbox-0.9.9.6-py2.7.egg-info/requires.txt +++ /dev/null @@ -1,4 +0,0 @@ -pandas >= 0.8.1 -python-dateutil >= 2.1 -mando >= 0.3.2 -matplotlib diff --git a/tstoolbox-0.9.9.6-py2.7.egg-info/top_level.txt b/tstoolbox-0.9.9.6-py2.7.egg-info/top_level.txt deleted file mode 100644 index 5d7f4103..00000000 --- a/tstoolbox-0.9.9.6-py2.7.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -tstoolbox diff --git a/tstoolbox/__init__.py b/tstoolbox/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tstoolbox/fill_functions.py b/tstoolbox/fill_functions.py deleted file mode 100644 index 2be4a202..00000000 --- a/tstoolbox/fill_functions.py +++ /dev/null @@ -1,154 +0,0 @@ -#!/sjr/beodata/local/python_linux/bin/python - -from __future__ import print_function -from __future__ import absolute_import - -import pandas as pd -from numpy import * -import mando - -from . import tsutils - - -@mando.command -def fill(method='ffill', interval='guess', print_input=False, input_ts='-'): - ''' - Fills missing values (NaN) with different methods. Missing values can - occur because of NaN, or because the time series is sparse. The - 'interval' option can insert NaNs to create a dense time series. - - :param method: String contained in single quotes or a number that - defines the method to use for filling. - 'ffill': assigns NaN values to the last good value - 'bfill': assigns NaN values to the next good value - number: fills all NaN to this number - 'linear': will linearly interpolate missing values - 'spline': spline interpolation - 'nearest': nearest good value - 'zero': - 'slinear': - 'quadratic': - 'cubic': - 'mean': fill with mean - 'median': fill with median - 'max': fill with maximum - 'min': fill with minimum - If a number will fill with that number. - :param interval: Will try to insert missing intervals. Can give any - of the pandas offset aliases, 'guess' (to try and figure the - interval), or None to not insert missing intervals. - :param -p, --print_input: If set to 'True' will include the input columns - in the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format - or '-' for stdin. - ''' - tsd = tsutils.read_iso_ts(input_ts) - if print_input is True: - ntsd = tsd.copy() - else: - ntsd = tsd - ntsd = tsutils.asbestfreq(ntsd) - offset = ntsd.index[1] - ntsd.index[0] - predf = pd.DataFrame(dict(zip(tsd.columns, tsd.mean().values)), - index=[tsd.index[0] - offset]) - postf = pd.DataFrame(dict(zip(tsd.columns, tsd.mean().values)), - index=[tsd.index[-1] + offset]) - ntsd = pd.concat([predf, ntsd, postf]) - if method in ['ffill', 'bfill']: - ntsd = ntsd.fillna(method=method) - elif method in ['linear']: - ntsd = ntsd.apply(pd.Series.interpolate, method='values') - elif method in ['nearest', 'zero', 'slinear', 'quadratic', 'cubic']: - from scipy.interpolate import interp1d - for c in ntsd.columns: - df2 = ntsd[c].dropna() - f = interp1d(df2.index.values.astype('d'), df2.values, kind=method) - slices = pd.isnull(ntsd[c]) - ntsd[c][slices] = f(ntsd[c][slices].index.values.astype('d')) - elif method in ['mean']: - ntsd = ntsd.fillna(ntsd.mean()) - elif method in ['median']: - ntsd = ntsd.fillna(ntsd.median()) - elif method in ['max']: - ntsd = ntsd.fillna(ntsd.max()) - elif method in ['min']: - ntsd = ntsd.fillna(ntsd.min()) - else: - try: - ntsd = ntsd.fillna(value=float(method)) - except ValueError: - pass - ntsd = ntsd.iloc[1:-1] - tsd.index.name = 'Datetime' - ntsd.index.name = 'Datetime' - return tsutils.print_input(print_input, tsd, ntsd, '_fill') - - -#@mando.command -def fill_by_correlation(method='move2', - maximum_lag=0, - interval='guess', - transform='log10', - choose_best='dtw', - print_input=False, - input_ts='-'): - ''' - Fills missing values (NaN) with different methods. Missing values can - occur because of NaN, or because the time series is sparse. The - 'interval' option can insert NaNs to create a dense time series. - :param method: String contained in single quotes or a number that - defines the method to use for filling. - 'move2': maintenance of variance extension - 2 - :param interval: Will try to insert missing intervals. Can give any - of the pandas offset aliases, 'guess' (to try and figure the - interval), or None to not insert missing intervals. - :param -p, --print_input: If set to 'True' will include the input columns - in the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format - or '-' for stdin. - ''' - tsd = tsutils.read_iso_ts(input_ts) - if print_input is True: - ntsd = tsd.copy() - else: - ntsd = tsd - ntsd = tsutils.asbestfreq(ntsd) - - if transform == 'log10': - ntsd = pd.np.log10(ntsd) - - firstcol = pd.DataFrame(ntsd.iloc[:, 0]) - basets = pd.DataFrame(ntsd.iloc[:, 1:]) - if choose_best is True: - firstcol = pd.DataFrame(ntsd.iloc[:, 0]) - allothers = pd.DataFrame(ntsd.iloc[:, 1:]) - collect = [] - for index in list(range(maximum_lag + 1)): - shifty = allothers.shift(index) - testdf = firstcol.join(shifty) - lagres = testdf.dropna().corr().iloc[1:, 0] - collect.append(pd.np.abs(lagres.values)) - collect = pd.np.array(collect) - bestlag, bestts = pd.np.unravel_index(collect.argmax(), collect.shape) - basets = pd.DataFrame(ntsd.iloc[:, bestts + 1].shift(bestlag)) - - single_source_ts = ['move1', 'move2', 'move3'] - if method.lower() in single_source_ts: - if len(basets.columns) != 1: - raise ValueError(''' -* -* For methods in {0} -* You can only have a single source column. You can pass in onlu 2 -* time-series or use the flag 'choose_best' along with 'maximum_lag'. -* Instead there are {1} source time series. -* -'''.format(single_source_ts, len(basets.columns))) - - if method == 'move1': - ntsd = firstcol.join(basets) - dna = ntsd.dropna() - means = pd.np.mean(dna) - stdevs = pd.np.std(dna) - print(means[1] + stdevs[1]/stdevs[0]*means[0]) - print(means, stdevs) - diff --git a/tstoolbox/filters.py b/tstoolbox/filters.py deleted file mode 100644 index c0a80e38..00000000 --- a/tstoolbox/filters.py +++ /dev/null @@ -1,117 +0,0 @@ -""" -The filter.py module contains a group of functions to filter -time-series values. -""" - -from __future__ import print_function - -#===imports====================== -import numpy as np - -#===globals====================== -modname = "filter" - -######## -# Exception classes - - -class MisMatchedKernel(Exception): - ''' - Error class for the wrong length kernel. - ''' - def __init__(self, rk, pw): - self.rk = rk - self.pw = pw - - def __str__(self): - return """ -* -* Length of kernel must be %i. -* Instead have %i -* -""" % (self.rk, self.pw) - - -class BadKernelValues(Exception): - ''' - Error class for the negative pad width. - ''' - def __init__(self): - pass - - def __str__(self): - return """ -* -* Should only have positive values. -* -""" - - -def _transform(vector, cutoff_period, window_len, lopass=None): - """ - - Parameters - ---------- - vector : array_like, evenly spaced samples in time - - Returns - ------- - vector of filtered values - - See Also - -------- - - Examples - -------- - - """ - if cutoff_period is None: - raise ValueError(''' -* -* The cutoff_period must be set. -* -''') - - if window_len is None: - raise ValueError(''' -* -* The window_len must be set. -* -''') - - import numpy.fft as F - result = F.rfft(vector, len(vector)) - - freq = F.fftfreq(len(vector))[:len(vector)/2 + 1] - factor = np.ones_like(freq) - - if lopass is True: - factor[freq > 1.0/float(cutoff_period)] = 0.0 - factor = np.pad(factor, window_len + 1, mode='constant', - constant_values=(1.0, 0.0)) - else: - factor[freq < 1.0/float(cutoff_period)] = 0.0 - factor = np.pad(factor, window_len + 1, mode='constant', - constant_values=(0.0, 1.0)) - - factor = np.convolve(factor, [1.0/window_len]*window_len, mode=1) - factor = factor[window_len + 1:-(window_len + 1)] - - result = result * factor - - rvector = F.irfft(result, len(vector)) - - return np.atleast_1d(rvector) - - -if __name__ == '__main__': - ''' This section is just used for testing. Really you should only import - this module. - ''' - arr = np.arange(100) - print(arr) - print(np.median(arr, (3, ))) - print(np.constant(arr, (-25, 20), (10, 20))) - arr = np.arange(30) - arr = np.reshape(arr, (6, 5)) - print(np.mean(arr, pad_width=((2, 3), (3, 2), (4, 5)), stat_len=(3, ))) diff --git a/tstoolbox/peakdetect.py b/tstoolbox/peakdetect.py deleted file mode 100644 index dc94b9ec..00000000 --- a/tstoolbox/peakdetect.py +++ /dev/null @@ -1,748 +0,0 @@ -''' Collection of peak detection algorithms ''' - -from __future__ import print_function - -import numpy as np - - -def _boolrelextrema(data, comparator, - axis=0, order=1, mode='clip'): - """ - Calculate the relative extrema of `data`. - - Relative extrema are calculated by finding locations where - comparator(data[n],data[n+1:n+order+1]) = True. - - Parameters - ---------- - data: ndarray - comparator: function - function to use to compare two data points. - Should take 2 numbers as arguments - axis: int, optional - axis over which to select from `data` - order: int, optional - How many points on each side to require - a `comparator`(n,n+x) = True. - mode: string, optional - How the edges of the vector are treated. - 'wrap' (wrap around) or 'clip' (treat overflow - as the same as the last (or first) element). - Default 'clip'. See numpy.take - - Returns - ------- - extrema: ndarray - Indices of the extrema, as boolean array - of same shape as data. True for an extrema, - False else. - - See also - -------- - argrelmax,argrelmin - - Examples - -------- - >>> testdata = np.array([1,2,3,2,1]) - >>> _boolrelextrema(testdata, np.greater, axis=0) - array([False, False, True, False, False], dtype=bool) - """ - - if (int(order) != order) or (order < 1): - raise ValueError('Order must be an int >= 1') - - datalen = data.shape[axis] - locs = np.arange(0, datalen) - - results = np.ones(data.shape, dtype=bool) - main = data.take(locs, axis=axis) - for shift in range(1, order + 1): - plus = np.take(data, locs + shift, axis=axis, mode=mode) - results &= comparator(main, plus) - minus = np.take(data, locs - shift, axis=axis, mode=mode) - results &= comparator(main, minus) - if ~results.any(): - return results - return results - - -def _argrel(data, axis=0, window=1): - """ - Private function to find relative min and max of data. - """ - tmpmin = _argrelmin(data, axis=axis, order=window) - tmpmax = _argrelmax(data, axis=axis, order=window) - return (zip(tmpmax[0], data[tmpmax[0]]), zip(tmpmin[0], data[tmpmin[0]])) - - -def _argrelmin(data, axis=0, order=1, mode='clip'): - """ - Calculate the relative minima of `data`. - - See also - -------- - argrelextrema,argrelmax - """ - return _argrelextrema(data, np.less, axis, order, mode) - - -def _argrelmax(data, axis=0, order=1, mode='clip'): - """ - Calculate the relative maxima of `data`. - - See also - -------- - argrelextrema,argrelmin - """ - return _argrelextrema(data, np.greater, axis, order, mode) - - -def _argrelextrema(data, comparator, - axis=0, order=1, mode='clip'): - """ - Calculate the relative extrema of `data` - - Returns - ------- - extrema: ndarray - Indices of the extrema, as an array - of integers (same format as argmin, argmax - - See also - -------- - argrelmin, argrelmax - - """ - results = _boolrelextrema(data, comparator, - axis, order, mode) - if ~results.any(): - return (np.array([]),) * 2 - else: - return np.where(results) - - -def _datacheck_peakdetect(x_axis, y_axis): - ''' - Checks x and y axis, creating an x data_set if necessary. - ''' - if x_axis is None: - x_axis = range(len(y_axis)) - - if len(y_axis) != len(x_axis): - raise ValueError(''' -* -* The length of y values must equal the length of x values. Instead the -* length of y values is {0} and the length of x values is {0}. -* -'''.format(len(y_axis), len(x_axis))) - - # needs to be a numpy array - y_axis = np.array(y_axis) - x_axis = np.array(x_axis) - return x_axis, y_axis - - -def _peakdetect_parabola_fitter(raw_peaks, x_axis, y_axis, points): - """ - Performs the actual parabola fitting for the _peakdetect_parabola function. - - keyword arguments: - raw_peaks -- A list of either the maximium or the minimum peaks, as given - by the _peakdetect_zero_crossing function, with index used as x-axis - x_axis -- A numpy list of all the x values - y_axis -- A numpy list of all the y values - points -- How many points around the peak should be used during curve - fitting, must be odd. - - return -- A list giving all the peaks and the fitted waveform, format: - [[x, y, [fitted_x, fitted_y]]] - - """ - from scipy.optimize import curve_fit - func = lambda x, k, tau, m: k * ((x - tau) ** 2) + m - fitted_peaks = [] - for peak in raw_peaks: - index = peak[0] - x_data = x_axis[index - points // 2: index + points // 2 + 1] - y_data = y_axis[index - points // 2: index + points // 2 + 1] - # get a first approximation of tau (peak position in time) - tau = x_axis[index] - # get a first approximation of peak amplitude - m = peak[1] - - # build list of approximations - # k = -m as first approximation? - p0 = (-m, tau, m) - popt, pcov = curve_fit(func, x_data, y_data, p0) - # retrieve tau and m i.e x and y value of peak - x, y = popt[1:3] - - # create a high resolution data set for the fitted waveform - x2 = np.linspace(x_data[0], x_data[-1], points * 10) - y2 = func(x2, *popt) - - fitted_peaks.append([x, y, [x2, y2]]) - - return fitted_peaks - - -def _peakdetect(y_axis, x_axis=None, window=24, delta=0): - """ - Converted from/based on a MATLAB script at: - http://billauer.co.il/peakdet.html - - function for detecting local maximas and minmias in a signal. - Discovers peaks by searching for values which are surrounded by lower - or larger values for maximas and minimas respectively - - keyword arguments: - y_axis -- A list containg the signal over which to find peaks - x_axis -- (optional) A x-axis whose values correspond to the y_axis list - and is used in the return to specify the postion of the peaks. If - omitted an index of the y_axis is used. (default: None) - window -- (optional) distance to look ahead from a peak candidate to - determine if it is the actual peak (default: 200) - '(sample / period) / f' where '4 >= f >= 1.25' might be a good value - delta -- (optional) this specifies a minimum difference between a peak and - the following points, before a peak may be considered a peak. Useful - to hinder the function from picking up false peaks towards to end of - the signal. To work well delta should be set to delta >= RMSnoise * 5. - (default: 0) - delta function causes a 20% decrease in speed, when omitted - Correctly used it can double the speed of the function - - return -- two lists [max_peaks, min_peaks] containing the positive and - negative peaks respectively. Each cell of the lists contains a tupple - of: (position, peak_value) - to get the average peak value do: np.mean(max_peaks, 0)[1] on the - results to unpack one of the lists into x, y coordinates do: - x, y = zip(*tab) - """ - max_peaks = [] - min_peaks = [] - dump = [] # Used to pop the first hit which almost always is false - - # check input data - x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis) - # store data length for later use - length = len(y_axis) - - # perform some checks - if window < 1: - raise ValueError("window must be '1' or above in value") - if not (np.isscalar(delta) and delta >= 0): - raise ValueError("delta must be a positive number") - - # maxima and minima candidates are temporarily stored in - # mx and mn respectively - mn, mx = np.Inf, -np.Inf - - #Only detect peak if there is 'window' amount of points after it - for index, (x, y) in enumerate(zip(x_axis[:-window], - y_axis[:-window])): - if y > mx: - mx = y - mxpos = x - if y < mn: - mn = y - mnpos = x - - ####look for max#### - if y < mx-delta and mx != np.Inf: - # Maxima peak candidate found - # look ahead in signal to ensure that this is a peak and not jitter - if y_axis[index:index+window].max() < mx: - max_peaks.append([mxpos, mx]) - dump.append(True) - # set algorithm to only find minima now - mx = np.Inf - mn = np.Inf - if index+window >= length: - #end is within window no more peaks can be found - break - continue - - ####look for min#### - if y > mn+delta and mn != -np.Inf: - # Minima peak candidate found - # look ahead in signal to ensure that this is a peak and not jitter - if y_axis[index:index+window].min() > mn: - min_peaks.append([mnpos, mn]) - dump.append(False) - # set algorithm to only find maxima now - mn = -np.Inf - mx = -np.Inf - if index+window >= length: - #end is within window no more peaks can be found - break - - # Remove the false hit on the first value of the y_axis - try: - if dump[0]: - max_peaks.pop(0) - else: - min_peaks.pop(0) - del dump - except IndexError: - # no peaks were found, should the function return empty lists? - pass - - return [max_peaks, min_peaks] - - -def _peakdetect_fft(y_axis, x_axis, pad_len=5): - """ - Performs a FFT calculation on the data and zero-pads the results to - increase the time domain resolution after performing the inverse fft and - send the data to the '_peakdetect' function for peak - detection. - - Omitting the x_axis is forbidden as it would make the resulting x_axis - value silly if it was returned as the index 50.234 or similar. - - Will find at least 1 less peak then the '_peakdetect_zero_crossing' - function, but should result in a more precise value of the peak as - resolution has been increased. Some peaks are lost in an attempt to - minimize spectral leakage by calculating the fft between two zero - crossings for n amount of signal periods. - - The biggest time eater in this function is the ifft and thereafter it's - the '_peakdetect' function which takes only half the time of the ifft. - Speed improvementd could include to check if 2**n points could be used for - fft and ifft or change the '_peakdetect' to the - '_peakdetect_zero_crossing', - which is maybe 10 times faster than 'peakdetct'. The pro of '_peakdetect' - is that it resutls in one less lost peak. It should also be noted that the - time used by the ifft function can change greatly depending on the input. - - keyword arguments: - y_axis -- A list containg the signal over which to find peaks - x_axis -- A x-axis whose values correspond to the y_axis list and is used - in the return to specify the postion of the peaks. - pad_len -- (optional) By how many times the time resolution should be - increased by, e.g. 1 doubles the resolution. The amount is rounded up - to the nearest 2 ** n amount (default: 5) - - return -- two lists [max_peaks, min_peaks] containing the positive and - negative peaks respectively. Each cell of the lists contains a tupple - of: (position, peak_value) - to get the average peak value do: np.mean(max_peaks, 0)[1] on the - results to unpack one of the lists into x, y coordinates do: - x, y = zip(*tab) - """ - from scipy import fft, ifft - # check input data - x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis) - zero_indices = zero_crossings(y_axis, window=11) - # select a n amount of periods - last_indice = - 1 - (1 - len(zero_indices) & 1) - # Calculate the fft between the first and last zero crossing - # this method could be ignored if the begining and the end of the signal - # are discardable as any errors induced from not using whole periods - # should mainly manifest in the beginning and the end of the signal, but - # not in the rest of the signal - if len(zero_indices) < 2: - fft_data = fft(y_axis) - else: - fft_data = fft(y_axis[zero_indices[0]:zero_indices[last_indice]]) - padd = lambda x, c: x[:len(x) // 2] + [0] * c + x[len(x) // 2:] - n = lambda x: (np.log(x) / np.log(2)).astype('i') + 1 - # padds to 2**n amount of samples - fft_padded = padd(list(fft_data), 2 ** - n(len(fft_data) * pad_len) - len(fft_data)) - - # There is amplitude decrease directly proportional to the sample increase - sf = len(fft_padded) / float(len(fft_data)) - # There might be a leakage giving the result an imaginary component - # Return only the real component - y_axis_ifft = ifft(fft_padded).real * sf # (pad_len + 1) - x_axis_ifft = np.linspace( - x_axis[zero_indices[0]], x_axis[zero_indices[last_indice]], - len(y_axis_ifft)) - # get the peaks to the interpolated waveform - max_peaks, min_peaks = _peakdetect(y_axis_ifft, x_axis_ifft, 500, - delta=abs(np.diff(y_axis).max() * 2)) - # max_peaks, min_peaks = _peakdetect_zero_crossing(y_axis_ifft, - # x_axis_ifft) - - return [max_peaks, min_peaks] - - -def _peakdetect_parabola(y_axis, x_axis, points=9): - """ - Function for detecting local maximas and minmias in a signal. - Discovers peaks by fitting the model function: y = k (x - tau) ** 2 + m - to the peaks. The amount of points used in the fitting is set by the - points argument. - - Omitting the x_axis is forbidden as it would make the resulting x_axis - value silly if it was returned as index 50.234 or similar. - - will find the same amount of peaks as the '_peakdetect_zero_crossing' - function, but might result in a more precise value of the peak. - - keyword arguments: - y_axis -- A list containg the signal over which to find peaks - x_axis -- A x-axis whose values correspond to the y_axis list and is used - in the return to specify the postion of the peaks. - points -- (optional) How many points around the peak should be used during - curve fitting, must be odd (default: 9) - - return -- two lists [max_peaks, min_peaks] containing the positive and - negative peaks respectively. Each cell of the lists contains a list - of: (position, peak_value) - to get the average peak value do: np.mean(max_peaks, 0)[1] on the - results to unpack one of the lists into x, y coordinates do: - x, y = zip(*max_peaks) - """ - # check input data - x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis) - # make the points argument odd - points += 1 - points % 2 - # points += 1 - int(points) & 1 slower when int conversion needed - - # get raw peaks - max_raw, min_raw = _peakdetect_zero_crossing(y_axis) - - # define output variable - max_peaks = [] - min_peaks = [] - - max_ = _peakdetect_parabola_fitter(max_raw, x_axis, y_axis, points) - min_ = _peakdetect_parabola_fitter(min_raw, x_axis, y_axis, points) - - max_peaks = map(lambda x: [x[0], x[1]], max_) - max_fitted = map(lambda x: x[-1], max_) - min_peaks = map(lambda x: [x[0], x[1]], min_) - min_fitted = map(lambda x: x[-1], min_) - - return [max_peaks, min_peaks] - - -def _peakdetect_sine(y_axis, x_axis, points=9, lock_frequency=False): - """ - Function for detecting local maximas and minmias in a signal. - Discovers peaks by fitting the model function: - y = A * sin(2 * pi * f * x - tau) to the peaks. The amount of points used - in the fitting is set by the points argument. - - Omitting the x_axis is forbidden as it would make the resulting x_axis - value silly if it was returned as index 50.234 or similar. - - will find the same amount of peaks as the '_peakdetect_zero_crossing' - function, but might result in a more precise value of the peak. - - The function might have some problems if the sine wave has a - non-negligible total angle i.e. a k*x component, as this messes with the - internal offset calculation of the peaks, might be fixed by fitting a - k * x + m function to the peaks for offset calculation. - - keyword arguments: - y_axis -- A list containg the signal over which to find peaks - x_axis -- A x-axis whose values correspond to the y_axis list and is used - in the return to specify the postion of the peaks. - points -- (optional) How many points around the peak should be used during - curve fitting, must be odd (default: 9) - lock_frequency -- (optional) Specifies if the frequency argument of the - model function should be locked to the value calculated from the raw - peaks or if optimization process may tinker with it. (default: False) - - return -- two lists [max_peaks, min_peaks] containing the positive and - negative peaks respectively. Each cell of the lists contains a tupple - of: (position, peak_value) - to get the average peak value do: np.mean(max_peaks, 0)[1] on the - results to unpack one of the lists into x, y coordinates do: - x, y = zip(*tab) - """ - from scipy.optimize import curve_fit - # check input data - x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis) - # make the points argument odd - points += 1 - points % 2 - # points += 1 - int(points) & 1 slower when int conversion needed - - # get raw peaks - max_raw, min_raw = _peakdetect_zero_crossing(y_axis) - - # define output variable - max_peaks = [] - min_peaks = [] - - # get global offset - offset = np.mean([np.mean(max_raw, 0)[1], np.mean(min_raw, 0)[1]]) - # fitting a k * x + m function to the peaks might be better - # offset_func = lambda x, k, m: k * x + m - - # calculate an approximate frequenzy of the signal - Hz = [] - for raw in [max_raw, min_raw]: - if len(raw) > 1: - peak_pos = [x_axis[index] for index in zip(*raw)[0]] - Hz.append(np.mean(np.diff(peak_pos))) - Hz = 1 / np.mean(Hz) - - # model function - # if cosine is used then tau could equal the x position of the peak - # if sine were to be used then tau would be the first zero crossing - if lock_frequency: - func = lambda x, A, tau: A * np.sin( - 2 * np.pi * Hz * (x - tau) + np.pi / 2) - else: - func = lambda x, A, Hz, tau: A * np.sin( - 2 * np.pi * Hz * (x - tau) + np.pi / 2) - # func = lambda x, A, Hz, tau: A * np.cos(2 * np.pi * Hz * (x - tau)) - - # get peaks - fitted_peaks = [] - for raw_peaks in [max_raw, min_raw]: - peak_data = [] - for peak in raw_peaks: - index = peak[0] - x_data = x_axis[index - points // 2: index + points // 2 + 1] - y_data = y_axis[index - points // 2: index + points // 2 + 1] - # get a first approximation of tau (peak position in time) - tau = x_axis[index] - # get a first approximation of peak amplitude - A = peak[1] - - # build list of approximations - if lock_frequency: - p0 = (A, tau) - else: - p0 = (A, Hz, tau) - - # subtract offset from waveshape - y_data -= offset - popt, pcov = curve_fit(func, x_data, y_data, p0) - # retrieve tau and A i.e x and y value of peak - x = popt[-1] - y = popt[0] - - # create a high resolution data set for the fitted waveform - x2 = np.linspace(x_data[0], x_data[-1], points * 10) - y2 = func(x2, *popt) - - # add the offset to the results - y += offset - y2 += offset - y_data += offset - - peak_data.append([x, y, [x2, y2]]) - - fitted_peaks.append(peak_data) - - # structure date for output - max_peaks = map(lambda x: [x[0], x[1]], fitted_peaks[0]) - max_fitted = map(lambda x: x[-1], fitted_peaks[0]) - min_peaks = map(lambda x: [x[0], x[1]], fitted_peaks[1]) - min_fitted = map(lambda x: x[-1], fitted_peaks[1]) - - return [max_peaks, min_peaks] - - -def _peakdetect_sine_locked(y_axis, x_axis, points=9): - """ - Convinience function for calling the '_peakdetect_sine' function with - the lock_frequency argument as True. - - keyword arguments: - y_axis -- A list containg the signal over which to find peaks - x_axis -- A x-axis whose values correspond to the y_axis list and is used - in the return to specify the postion of the peaks. - points -- (optional) How many points around the peak should be used during - curve fitting, must be odd (default: 9) - - return -- see '_peakdetect_sine' - """ - return _peakdetect_sine(y_axis, x_axis, points, True) - - -def _peakdetect_zero_crossing(y_axis, x_axis=None, window=5): - """ - Function for detecting local maximas and minmias in a signal. - Discovers peaks by dividing the signal into bins and retrieving the - maximum and minimum value of each the even and odd bins respectively. - Division into bins is performed by smoothing the curve and finding the - zero crossings. - - Suitable for repeatable signals, where some noise is tolerated. Excecutes - faster than '_peakdetect', although this function will break if the offset - of the signal is too large. It should also be noted that the first and - last peak will probably not be found, as this function only can find peaks - between the first and last zero crossing. - - keyword arguments: - y_axis -- A list containg the signal over which to find peaks - x_axis -- (optional) A x-axis whose values correspond to the y_axis list - and is used in the return to specify the postion of the peaks. If - omitted an index of the y_axis is used. (default: None) - window -- the dimension of the smoothing window; should be an odd integer - (default: 11) - - return -- two lists [max_peaks, min_peaks] containing the positive and - negative peaks respectively. Each cell of the lists contains a tupple - of: (position, peak_value) - to get the average peak value do: np.mean(max_peaks, 0)[1] on the - results to unpack one of the lists into x, y coordinates do: - x, y = zip(*tab) - """ - # check input data - x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis) - - zero_indices = zero_crossings(y_axis, window=window) - zero_indices = np.concatenate(([0], zero_indices, [len(y_axis) - 1])) - zero_indices = np.unique(zero_indices) - - period_lengths = np.diff(zero_indices) - - bins_y = [y_axis[index:index + diff] for index, diff in - zip(zero_indices, period_lengths)] - bins_x = [x_axis[index:index + diff] for index, diff in - zip(zero_indices, period_lengths)] - - even_bins_y = bins_y[::2] - odd_bins_y = bins_y[1::2] - even_bins_x = bins_x[::2] - odd_bins_x = bins_x[1::2] - hi_peaks_x = [] - lo_peaks_x = [] - - # check if even bin contains maxima - if abs(even_bins_y[0].max()) > abs(even_bins_y[0].min()): - hi_peaks = [bin.max() for bin in even_bins_y] - lo_peaks = [bin.min() for bin in odd_bins_y] - # get x values for peak - for bin_x, bin_y, peak in zip(even_bins_x, even_bins_y, hi_peaks): - hi_peaks_x.append(bin_x[np.where(bin_y == peak)[0][0]]) - for bin_x, bin_y, peak in zip(odd_bins_x, odd_bins_y, lo_peaks): - lo_peaks_x.append(bin_x[np.where(bin_y == peak)[0][0]]) - else: - hi_peaks = [bin.max() for bin in odd_bins_y] - lo_peaks = [bin.min() for bin in even_bins_y] - # get x values for peak - for bin_x, bin_y, peak in zip(odd_bins_x, odd_bins_y, hi_peaks): - hi_peaks_x.append(bin_x[np.where(bin_y == peak)[0][0]]) - for bin_x, bin_y, peak in zip(even_bins_x, even_bins_y, lo_peaks): - lo_peaks_x.append(bin_x[np.where(bin_y == peak)[0][0]]) - - # peaks or valley cannot be at 0 - max_peaks = [[x, y] for x, y in zip(hi_peaks_x, hi_peaks) if x != 0] - min_peaks = [[x, y] for x, y in zip(lo_peaks_x, lo_peaks) if x != 0] - - return [max_peaks, min_peaks] - - -def _smooth(x, window_len=11, window='hanning'): - """ - smooth the data using a window of the requested size. - - This method is based on the convolution of a scaled window on the signal. - The signal is prepared by introducing reflected copies of the signal - (with the window size) in both ends so that transient parts are minimized - in the begining and end part of the output signal. - - input: - x: the input signal - window_len: the dimension of the smoothing window; should be an odd - integer - window: the type of window from 'flat', 'hanning', 'hamming', - 'bartlett', 'blackman' - flat window will produce a moving average smoothing. - - output: - the smoothed signal - - example: - - t = linspace(-2,2,0.1) - x = sin(t)+randn(len(t))*0.1 - y = _smooth(x) - - see also: - - numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, - numpy.convolve, scipy.signal.lfilter - - TODO: the window parameter could be the window itself if a list instead of - a string - """ - if x.ndim != 1: - raise ValueError("smooth only accepts 1 dimension arrays.") - - if x.size < window_len: - raise ValueError("Input vector needs to be bigger than window size.") - - if window_len < 3: - return x - - if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']: - raise ValueError - - s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]] - # print(len(s)) - if window == 'flat': # moving average - w = np.ones(window_len, 'd') - else: - w = eval('np.' + window + '(window_len)') - - y = np.convolve(w / w.sum(), s, mode='valid') - return y - - -def zero_crossings(y_axis, window=11): - """ - Algorithm to find zero crossings. Smoothens the curve and finds the - zero-crossings by looking for a sign change. - - - keyword arguments: - y_axis -- A list containg the signal over which to find zero-crossings - window -- the dimension of the smoothing window; should be an odd integer - (default: 11) - - return -- the index for each zero-crossing - """ - # smooth the curve - length = len(y_axis) - x_axis = np.asarray(range(length), int) - - ymean = y_axis.mean() - y_axis = y_axis - ymean - - # discard tail of smoothed signal - y_axis = _smooth(y_axis, window)[:length] - zero_crossings = np.where(np.diff(np.sign(y_axis)))[0] - indices = [x_axis[index] for index in zero_crossings] - - # check if zero-crossings are valid - diff = np.diff(indices) -# if diff.std() / diff.mean() > 0.2: -# print diff.std() / diff.mean() -# print np.diff(indices) -# raise(ValueError, -# "False zero-crossings found, indicates problem {0} or {1}".format( -# "with smoothing window", "problem with offset")) - # check if any zero crossings were found - if len(zero_crossings) < 1: - raise ValueError - - try: - indices.remove(0) - except ValueError: - pass - - return indices - # used this to test the fft function's sensitivity to spectral leakage - # return indices + np.asarray(30 * np.random.randn(len(indices)), int) - -# Frequency calculation############################# -# diff = np.diff(indices) -# time_p_period = diff.mean() -# -# if diff.std() / time_p_period > 0.1: -# raise ValueError, -# "smoothing window too small, false zero-crossing found" -# -# #return frequency -# return 1.0 / time_p_period -############################################################################## diff --git a/tstoolbox/tstoolbox.py b/tstoolbox/tstoolbox.py deleted file mode 100644 index 0264019d..00000000 --- a/tstoolbox/tstoolbox.py +++ /dev/null @@ -1,2498 +0,0 @@ -#!/sjr/beodata/local/python_linux/bin/python -''' -tstoolbox is a collection of command line tools for the manipulation of time -series. -''' - -from __future__ import print_function -from __future__ import division -from __future__ import absolute_import - -import sys -import os.path -import warnings -from argparse import RawTextHelpFormatter -warnings.filterwarnings('ignore') - -import pandas as pd -# The numpy import is needed like this to be able to include numpy functions in -# the 'equation' subcommand. -from numpy import * -import mando - -from . import tsutils -from . import fill_functions -fill = fill_functions.fill - -_offset_aliases = { - 86400000000000: 'D', - 604800000000000: 'W', - 2419200000000000: 'M', - 2505600000000000: 'M', - 2592000000000000: 'M', - 2678400000000000: 'M', - 31536000000000000: 'A', - 31622400000000000: 'A', - 3600000000000: 'H', - 60000000000: 'M', - 1000000000: 'T', - 1000000: 'L', - 1000: 'U', - } - - -@mando.command(formatter_class=RawTextHelpFormatter) -def createts( - input_ts=None, - start_date=None, - end_date=None, - freq=None - ): - ''' - Create empty time series. Just produce the time-stamps. - - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. Default is stdin. If supplied, the - result will just be the input index. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param freq: To use this form --start_date and --end_date must be supplied also. - The pandas date offset code used to create the index. - ''' - if input_ts is not None: - columns = None - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - tsd = pd.DataFrame(index=tsd.index) - elif start_date is None or end_date is None or freq is None: - raise ValueError(''' -* -* If input_ts is None, then start_date, end_date, and freq must be supplied. -* Instead you have: -* start_date = {0}, -* end_date = {0}, -* freq = {0} -* -'''.format(start_date, end_date, freq)) - else: - tsd = pd.DataFrame(index=pd.date_range(start=start_date, - end=end_date, - freq=freq)) - return tsutils.printiso(tsd) - - -@mando.command(formatter_class=RawTextHelpFormatter) -def filter(filter_type, - print_input=False, - cutoff_period=None, - window_len=5, - float_format='%g', - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Apply different filters to the time-series. - - :param filter_type : 'flat', 'hanning', 'hamming', 'bartlett', 'blackman', - 'fft_highpass' and 'fft_lowpass' for Fast Fourier - Transform filter in the frequency domain. - :param window_len : For the windowed types, 'flat', 'hanning', 'hamming', - 'bartlett', 'blackman' specifies the length of the - window. Defaults to 5. - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. Default is stdin. - :param cutoff_period: The period in input time units that will form the - cutoff between low frequencies (longer periods) and - high frequencies (shorter periods). Filter will be - smoothed by `window_len` running average. For - 'fft_highpass' and 'fft_lowpass'. Default is None and - must be supplied if using 'fft_highpass' or - 'fft_lowpass'. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - from tstoolbox import filters - - if len(tsd.values) < window_len: - raise ValueError(''' -* -* Input vector (length={0}) needs to be bigger than window size ({1}). -* -'''.format(len(tsd.values), window_len)) - - # Trying to save some memory - if print_input: - otsd = tsd.copy() - else: - otsd = pd.DataFrame() - - for col in tsd.columns: - # fft_lowpass, fft_highpass - if filter_type == 'fft_lowpass': - tsd[col].values[:] = filters._transform( - tsd[col].values, cutoff_period, window_len, lopass=True) - elif filter_type == 'fft_highpass': - tsd[col].values[:] = filters._transform( - tsd[col].values, cutoff_period, window_len) - elif filter_type in ['flat', - 'hanning', - 'hamming', - 'bartlett', - 'blackman']: - if window_len < 3: - continue - s = pd.np.pad(tsd[col].values, window_len//2, 'reflect') - - if filter_type == 'flat': # moving average - w = pd.np.ones(window_len, 'd') - else: - w = eval('pd.np.' + filter_type + '(window_len)') - tsd[col].values[:] = pd.np.convolve(w / w.sum(), s, mode='valid') - else: - raise ValueError(''' -* -* Filter type {0} not implemented. -* -'''.format(filter_type)) - return tsutils.print_input(print_input, otsd, tsd, '_filter', - float_format=float_format) - - -def zero_crossings(y_axis, window=11): - """ - Algorithm to find zero crossings. Smooths the curve and finds the - zero-crossings by looking for a sign change. - - - keyword arguments: - y_axis -- A list containing the signal over which to find zero-crossings - window -- the dimension of the smoothing window; should be an odd integer - (default: 11) - - return -- the index for each zero-crossing - """ - # smooth the curve - length = len(y_axis) - x_axis = pd.np.asarray(range(length), int) - - ymean = y_axis.mean() - y_axis = y_axis - ymean - - # discard tail of smoothed signal - y_axis = _smooth(y_axis, window)[:length] - zero_crossings = pd.np.where(pd.np.diff(pd.np.sign(y_axis)))[0] - indices = [x_axis[index] for index in zero_crossings] - - # check if zero-crossings are valid -# diff = np.diff(indices) -# if diff.std() / diff.mean() > 0.2: -# print diff.std() / diff.mean() -# print np.diff(indices) -# raise(ValueError, -# "False zero-crossings found, indicates problem {0} or {1}".format( -# "with smoothing window", "problem with offset")) - # check if any zero crossings were found - if len(zero_crossings) < 1: - raise ValueError - - return indices - # used this to test the fft function's sensitivity to spectral leakage - # return indices + np.asarray(30 * np.random.randn(len(indices)), int) - -# Frequency calculation############################# -# diff = np.diff(indices) -# time_p_period = diff.mean() -# -# if diff.std() / time_p_period > 0.1: -# raise ValueError, -# "smoothing window too small, false zero-crossing found" -# -# #return frequency -# return 1.0 / time_p_period -############################################################################## - - # return tsutils.print_input(print_input, tsd, tmptsd, '_filter') - - -@mando.command -def read(filenames, start_date=None, end_date=None, dense=False, - float_format='%g', how='outer', columns=None): - ''' - Collect time series from a list of pickle or csv files then print - in the tstoolbox standard format. - - :param filenames : List of comma delimited filenames to read time series - from. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param dense: Set `dense` to True to have missing values inserted - such that there is a single interval. - :param how : Use PANDAS concept on how to join the separate - DataFrames read from each file. Default how='outer' - which is the union, 'inner' is the intersection, - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - filenames = filenames.split(',') - result = pd.concat([tsutils.common_kwds( - tsutils.read_iso_ts(i, - dense=dense, - extended_columns=True), - start_date=start_date, - end_date=end_date, - pick=columns) for i in filenames], - join=how, - axis=1) - - colnames = ['.'.join(i.split('.')[1:]) for i in result.columns] - if len(colnames) == len(set(colnames)): - result.columns = colnames - else: - result.columns = [i if result.columns.tolist().count(i) == 1 - else i + str(index) - for index, i in enumerate(result.columns)] - - return tsutils.printiso(result, float_format=float_format) - - -@mando.command -def date_slice(float_format='%g', - start_date=None, - end_date=None, - input_ts='-', - columns=None): - ''' - Prints out data to the screen between start_date and end_date - - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - return tsutils.printiso( - tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns), float_format=float_format) - - -@mando.command -def describe(input_ts='-', transpose=False, start_date=None, end_date=None, columns=None): - ''' - Prints out statistics for the time-series. - - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param transpose: If 'transpose' option is used, will transpose describe output. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - if transpose is True: - ntsd = tsd.describe().transpose() - else: - ntsd = tsd.describe() - - ntsd.index.name = 'Statistic' - return tsutils.printiso(ntsd, - force_print_index=True) - - -@mando.command -def peak_detection(method='rel', - type='peak', - window=24, - pad_len=5, - points=9, - lock_frequency=False, - float_format='%g', - print_input='', - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Peak and valley detection. - - :param type : 'peak', 'valley', or 'both' to determine what should - be returned. Default is 'peak'. - :param method : 'rel', 'minmax', 'zero_crossing', 'parabola', 'sine' - methods are available. The different algorithms have - different strengths and weaknesses. The 'rel' - algorithm is the default. - :param window : There will not usually be multiple peaks within the - window number of values. The different `method`s use - this variable in different ways. For 'rel' the - window keyword specifies how many points on each side - to require a `comparator`(n,n+x) = True. For - 'minmax' the window keyword is the distance to look - ahead from a peak candidate to determine if it is the - actual peak. - - '(sample / period) / f' where '4 >= f >= 1.25' - might be a good value - - For 'zero_crossing' the window keyword is the - dimension of the smoothing window; should be an odd - integer - :param points : For 'parabola' and 'sine' methods. How many points - around the peak should be used during curve fitting, - must be odd (default: 9) - :param lock_frequency: For 'sine method only. Specifies if the frequency - argument of the model function should be locked to - the value calculated from the raw peaks or if - optimization process may tinker with it. (default: - False) - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. Default is stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - # Couldn't get fft method working correctly. Left pieces in - # in case want to figure it out in the future. - - if type not in ['peak', 'valley', 'both']: - raise ValueError(''' -* -* The `type` argument must be one of 'peak', -* 'valley', or 'both'. You supplied {0}. -* -'''.format(type)) - - if method not in ['rel', 'minmax', 'zero_crossing', 'parabola', 'sine']: - raise ValueError(''' -* -* The `method` argument must be one of 'rel', 'minmax', -* 'zero_crossing', 'parabola', or 'sine'. You supplied {0}. -* -'''.format(method)) - - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - - window = int(window) - kwds = {} - if method == 'rel': - from tstoolbox.peakdetect import _argrel as func - window = window / 2 - if window == 0: - window = 1 - kwds['window'] = int(window) - elif method == 'minmax': - from tstoolbox.peakdetect import _peakdetect as func - window = int(window / 2) - if window == 0: - window = 1 - kwds['window'] = int(window) - elif method == 'zero_crossing': - from tstoolbox.peakdetect import _peakdetect_zero_crossing as func - if not window % 2: - window = window + 1 - kwds['window'] = int(window) - elif method == 'parabola': - from tstoolbox.peakdetect import _peakdetect_parabola as func - kwds['points'] = int(points) - elif method == 'sine': - from tstoolbox.peakdetect import _peakdetect_sine as func - kwds['points'] = int(points) - kwds['lock_frequency'] = lock_frequency - elif method == 'fft': # currently would never be used. - from tstoolbox.peakdetect import _peakdetect_fft as func - kwds['pad_len'] = int(pad_len) - - if type == 'peak': - tmptsd = tsd.rename(columns=lambda x: str(x) + '_peak', copy=True) - if type == 'valley': - tmptsd = tsd.rename(columns=lambda x: str(x) + '_valley', copy=True) - if type == 'both': - tmptsd = tsd.rename(columns=lambda x: str(x) + '_peak', copy=True) - tmptsd = tmptsd.join( - tsd.rename(columns=lambda x: str(x) + '_valley', copy=True), - how='outer') - - for c in tmptsd.columns: - if method in ['fft', 'parabola', 'sine']: - maxpeak, minpeak = func( - tmptsd[c].values, range(len(tmptsd[c])), **kwds) - else: - maxpeak, minpeak = func(tmptsd[c].values, **kwds) - if c[-5:] == '_peak': - datavals = maxpeak - if c[-7:] == '_valley': - datavals = minpeak - maxx, maxy = list(zip(*datavals)) - hold = tmptsd[c][array(maxx).astype('i')] - tmptsd[c][:] = pd.np.nan - tmptsd[c][array(maxx).astype('i')] = hold - - tmptsd.index.name = 'Datetime' - tsd.index.name = 'Datetime' - return tsutils.print_input(print_input, tsd, tmptsd, None, - float_format=float_format) - - -@mando.command -def convert( - factor=1.0, - offset=0.0, - print_input=False, - float_format='%g', - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Converts values of a time series by applying a factor and offset. - See the 'equation' subcommand for a generalized form of this - command. - - :param factor : Factor to multiply the time series values. - :param offset : Offset to add to the time series values. - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - tmptsd = tsd * factor + offset - return tsutils.print_input(print_input, tsd, tmptsd, '_convert', - float_format='%g') - - -def _parse_equation(equation): - ''' - Private function to parse the equation used in the calculations. - ''' - import re - # Get rid of spaces - nequation = equation.replace(' ', '') - - # Does the equation contain any x[t]? - tsearch = re.search(r'\[.*?t.*?\]', nequation) - - # Does the equation contain any x1, x2, ...etc.? - nsearch = re.search(r'x[1-9][0-9]*', nequation) - - # This beasty is so users can use 't' in their equations - # Indices of 'x' are a function of 't' and can possibly be negative or - # greater than the length of the DataFrame. - # Correctly (I think) handles negative indices and indices greater - # than the length by setting to nan - # AssertionError happens when index negative. - # IndexError happens when index is greater than the length of the - # DataFrame. - # UGLY! - - # testeval is just a list of the 't' expressions in the equation. - # for example 'x[t]+0.6*max(x[t+1],x[t-1]' would have - # testeval = ['t', 't+1', 't-1'] - testeval = set() - # If there is both function of t and column terms x1, x2, ...etc. - if tsearch and nsearch: - testeval.update(re.findall(r'x[1-9][0-9]*\[(.*?t.*?)\]', - nequation)) - # replace 'x1[t+1]' with 'x.ix[t+1,1-1]' - # replace 'x2[t+7]' with 'x.ix[t+7,2-1]' - # ...etc - nequation = re.sub(r'x([1-9][0-9]*)\[(.*?t.*?)\]', - r'x.ix[\2,\1-1]', - nequation) - # replace 'x1' with 'x.ix[t,1-1]' - # replace 'x4' with 'x.ix[t,4-1]' - nequation = re.sub(r'x([1-9][0-9]*)', - r'x.ix[t,\1-1]', - nequation) - # If there is only a function of t, i.e. x[t] - elif tsearch: - testeval.update(re.findall(r'x\[(.*?t.*?)\]', - nequation)) - nequation = re.sub(r'x\[(.*?t.*?)\]', - r'xxix[\1,:]', - nequation) - # Replace 'x' with underlying equation, but not the 'x' in a word, - # like 'maximum'. - nequation = re.sub(r'(? to the time series data. The argument is a - string contained in single quotes with 'x' used as the variable - representing the input. For example, '(1 - x)*sin(x)'. - - :param equation : String contained in single quotes that defines the - equation. The input variable place holder is 'x'. - Mathematical functions in the 'np' (numpy) name space - can be used. For example, - 'x*4 + 2', - 'x**2 + cos(x)', and - 'tan(x*pi/180)' are all valid - strings. The variable 't' is special - representing the time at which 'x' occurs. This - means you can do things like - 'x[t] + max(x[t-1], x[t+1])*0.6' - to add to the current value 0.6 times the maximum - adjacent value. - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - x = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns).astype('f') - - tsearch, nsearch, testeval, nequation = _parse_equation(equation) - if tsearch and nsearch: - y = pd.DataFrame(x.ix[:, 0].copy(), index=x.index, columns=['_']) - for t in range(len(x)): - try: - for tst in testeval: - if eval(tst) < 0: - raise IndexError() - y.ix[t, 0] = eval(nequation) - except (AssertionError, IndexError): - y.ix[t, 0] = pd.np.nan - y = pd.DataFrame(y, columns=['_'], dtype='float32') - elif tsearch: - y = x.copy() - for t in range(len(x)): - try: - for tst in testeval: - if eval(tst) < 0: - raise IndexError() - y.ix[t, :] = eval(nequation) - except (AssertionError, IndexError): - y.ix[t, :] = pd.np.nan - elif nsearch: - y = pd.DataFrame(x.ix[:, 0].copy(), index=x.index, columns=['_']) - y.ix[:, 0] = eval(nequation) - else: - y = eval(equation) - return tsutils.print_input(print_input, x, y, '_equation', - float_format=float_format) - - -@mando.command -def pick(columns, input_ts='-', start_date=None, end_date=None): - ''' - Will pick a column or list of columns from input. Can use column names or - column numbers. If using numbers, column number 1 is the first column. - - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - ''' - return tsutils.printiso( - tsutils.common_kwds( - tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns)) - - -@mando.command -def stdtozrxp( - rexchange=None, - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Prints out data to the screen in a WISKI ZRXP format. - - :param rexchange: The REXCHANGE ID to be written into the zrxp header. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - if len(tsd.columns) > 1: - raise ValueError(''' -* -* The "stdtozrxp" command can only accept a single -* 'time-series, instead it is seeing {0}. -* -'''.format(len(tsd.columns))) - if rexchange: - print('#REXCHANGE{0}|*|'.format(rexchange)) - for i in range(len(tsd)): - print(('{0.year:04d}{0.month:02d}{0.day:02d}{0.hour:02d}' - '{0.minute:02d}{0.second:02d}, {1}').format( - tsd.index[i], tsd[tsd.columns[0]][i])) - - -@mando.command -def tstopickle( - filename, - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Pickles the data into a Python pickled file. Can be brought back into - Python with 'pickle.load' or 'numpy.load'. See also 'tstoolbox read'. - - :param filename : The filename to store the pickled data. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - pd.core.common.save(tsd, filename) - - -@mando.command -def accumulate( - statistic='sum', - print_input=False, - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Calculates accumulating statistics. - - :param statistic : 'sum', 'max', 'min', 'prod', defaults to 'sum'. - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - if statistic == 'sum': - ntsd = tsd.cumsum() - elif statistic == 'max': - ntsd = tsd.cummax() - elif statistic == 'min': - ntsd = tsd.cummin() - elif statistic == 'prod': - ntsd = tsd.cumprod() - else: - raise ValueError(''' -* -* Statistic {0} is not implemented. -* -'''.format(statistic)) - return tsutils.print_input(print_input, tsd, ntsd, '_' + statistic) - - -@mando.command -def rolling_window( - span=None, - statistic='mean', - wintype=None, - center=False, - print_input=False, - input_ts='-', - start_date=None, - end_date=None, - columns=None, - freq=None, - groupby=None): - ''' - Calculates a rolling window statistic. - - :param span: The number of previous intervals to include in the - calculation of the statistic. If `span` is equal to - 0 will give an expanding rolling window. Defaults to - 2. - :param statistic : One of 'mean', 'corr', 'count', 'cov', 'kurtosis', - 'median', 'skew', 'stdev', 'sum', 'variance', - 'expw_mean', 'expw_stdev', 'expw_variance' - 'expw_corr', 'expw_cov' used to calculate the - statistic, defaults to 'mean'. - :param wintype : The 'mean' and 'sum' `statistic` calculation can also - be weighted according to the `wintype` windows. Some - of the following windows require additional keywords - identified in parenthesis: 'boxcar', 'triang', - 'blackman', 'hamming', 'bartlett', 'parzen', - 'bohman', 'blackmanharris', 'nuttall', 'barthann', - 'kaiser' (needs beta), 'gaussian' (needs std), - 'general_gaussian' (needs power, width) 'slepian' - (needs width). - :param center: If set to 'True' the calculation will be made for the - value at the center of the window. Default is - 'False'. - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - :param groupby: Time offset to groupby. Any PANDAS time offset. - This option supports what is probably an unusual - situation where the rolling_window is performed - separately within each groupby period. - ----- ----------- - Alias Description - ----- ----------- - B business day frequency - C custom business day frequency (experimental) - D calendar day frequency - W weekly frequency - M month end frequency - BM business month end frequency - CBM custom business month end frequency - MS month start frequency - BMS business month start frequency - CBMS custom business month start frequency - Q quarter end frequency - BQ business quarter endfrequency - QS quarter start frequency - BQS business quarter start frequency - A year end frequency - BA business year end frequency - AS year start frequency - BAS business year start frequency - H hourly frequency - T minutely frequency - S secondly frequency - L milliseonds - U microseconds - N nanoseconds - - Weekly has the following anchored frequencies: - W-SUN weekly frequency (sundays). Same as 'W'. - W-MON weekly frequency (mondays) - W-TUE weekly frequency (tuesdays) - W-WED weekly frequency (wednesdays) - W-THU weekly frequency (thursdays) - W-FRI weekly frequency (fridays) - W-SAT weekly frequency (saturdays) - - Quarterly frequencies (Q, BQ, QS, BQS) and - annual frequencies (A, BA, AS, BAS) have the following anchoring - suffixes: - -DEC year ends in December (same as 'Q' and 'A') - -JAN year ends in January - -FEB year ends in February - -MAR year ends in March - -APR year ends in April - -MAY year ends in May - -JUN year ends in June - -JUL year ends in July - -AUG year ends in August - -SEP year ends in September - -OCT year ends in October - -NOV year ends in November - - Defaults to None. - ''' - if span is None: - span = 2 - - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns, - groupby=groupby) - - def _process_tsd(tsd, - statistic='mean', - span=None, - center=False, - wintype=None, - freq=None): - if span is None: - span = len(tsd) - else: - span = int(span) - window_list = [ - 'boxcar', - 'triang', - 'blackman', - 'hamming', - 'bartlett', - 'parzen', - 'bohman', - 'blackmanharris', - 'nuttall', - 'barthann', - 'kaiser', - 'gaussian', - 'general_gaussian', - 'slepian', - ] - if wintype in window_list and statistic in ['mean', 'sum']: - meantest = statistic == 'mean' - newts = pd.stats.moments.rolling_window( - tsd, span, wintype, center=center, mean=meantest, freq=freq) - elif statistic == 'mean': - if span == 0: - newts = pd.stats.moments.expanding_mean(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_mean(tsd, span, center=center, freq=freq) - elif statistic == 'max': - if span == 0: - newts = pd.stats.moments.expanding_max(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_max(tsd, span, center=center, freq=freq) - elif statistic == 'min': - if span == 0: - newts = pd.stats.moments.expanding_min(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_min(tsd, span, center=center, freq=freq) - elif statistic == 'corr': - if span == 0: - newts = pd.stats.moments.expanding_corr(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_corr(tsd, span, center=center, freq=freq) - elif statistic == 'cov': - if span == 0: - newts = pd.stats.moments.expanding_cov(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_cov(tsd, span, center=center, freq=freq) - elif statistic == 'count': - if span == 0: - newts = pd.stats.moments.expanding_count(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_count(tsd, span, center=center, freq=freq) - elif statistic == 'kurtosis': - if span == 0: - newts = pd.stats.moments.expanding_kurt(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_kurt(tsd, span, center=center, freq=freq) - elif statistic == 'median': - if span == 0: - newts = pd.stats.moments.expanding_median(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_median(tsd, span, center=center, freq=freq) - elif statistic == 'skew': - if span == 0: - newts = pd.stats.moments.expanding_skew(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_skew(tsd, span, center=center, freq=freq) - elif statistic == 'stdev': - if span == 0: - newts = pd.stats.moments.expanding_std(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_std(tsd, span, center=center, freq=freq) - elif statistic == 'sum': - if span == 0: - newts = pd.stats.moments.expanding_sum(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_sum(tsd, span, center=center, freq=freq) - elif statistic == 'variance': - if span == 0: - newts = pd.stats.moments.expanding_var(tsd, center=center, freq=freq) - else: - newts = pd.stats.moments.rolling_var(tsd, span, center=center, freq=freq) - elif statistic == 'expw_mean': - newts = pd.stats.moments.ewma(tsd, span=span, center=center, freq=freq) - elif statistic == 'expw_stdev': - newts = pd.stats.moments.ewmstd(tsd, span=span, center=center, freq=freq) - elif statistic == 'expw_variance': - newts = pd.stats.moments.ewmvar(tsd, span=span, center=center, freq=freq) - elif statistic == 'expw_corr': - newts = pd.stats.moments.ewmcorr(tsd, span=span, center=center, freq=freq) - elif statistic == 'expw_cov': - newts = pd.stats.moments.ewmcov(tsd, span=span, center=center, freq=freq) - else: - raise ValueError(''' -* -* Statistic '{0}' is not implemented. -* -'''.format(statistic)) - return newts - - tmptsd = [] - if isinstance(tsd, pd.DataFrame): - for nspan in str(span).split(','): - tmptsd.append(_process_tsd(tsd, - statistic=statistic, - span=nspan, - center=center, - wintype=wintype, - freq=freq)) - else: - for nspan in str(span).split(','): - jtsd = pd.DataFrame() - for name, gb in tsd: - # Assume span should be yearly if 365 or 366 and you have - # groupby yearly. - xspan = nspan - if len(gb) in [365, 366] and int(nspan) in [365, 366]: - xspan = None - jtsd = jtsd.append(_process_tsd(gb, - statistic=statistic, - span=xspan, - center=center, - wintype=wintype, - freq=freq)) - tmptsd.append(jtsd) - ntsd = pd.concat(tmptsd, join='outer', axis=1) - - ntsd.columns = [i[0] + '_' + i[1] for i in zip(ntsd.columns, - str(span).split(','))] - return tsutils.print_input(print_input, - tsd, - ntsd, - '_' + statistic) - - -@mando.command(formatter_class=RawTextHelpFormatter) -def aggregate( - statistic='mean', - agg_interval='daily', - ninterval=1, - start_interval=1, - print_input=False, - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Takes a time series and aggregates to specified frequency, outputs to - 'ISO-8601date,value' format. - - :param statistic : 'mean', 'sum', 'std', 'max', 'min', 'median', - 'first', or 'last' to calculate the aggregation, - defaults to 'mean'. Can also be a comma separated - list of statistic methods. - :param agg_interval : The interval to aggragate the time series. Any of - the PANDAS offset codes. - ----- ----------- - Alias Description - ----- ----------- - B business day frequency - C custom business day frequency (experimental) - D calendar day frequency - W weekly frequency - M month end frequency - BM business month end frequency - CBM custom business month end frequency - MS month start frequency - BMS business month start frequency - CBMS custom business month start frequency - Q quarter end frequency - BQ business quarter endfrequency - QS quarter start frequency - BQS business quarter start frequency - A year end frequency - BA business year end frequency - AS year start frequency - BAS business year start frequency - H hourly frequency - T minutely frequency - S secondly frequency - L milliseonds - U microseconds - N nanoseconds - - Weekly has the following anchored frequencies: - W-SUN weekly frequency (sundays). Same as 'W'. - W-MON weekly frequency (mondays) - W-TUE weekly frequency (tuesdays) - W-WED weekly frequency (wednesdays) - W-THU weekly frequency (thursdays) - W-FRI weekly frequency (fridays) - W-SAT weekly frequency (saturdays) - - Quarterly frequencies (Q, BQ, QS, BQS) and - annual frequencies (A, BA, AS, BAS) have the following anchoring - suffixes: - -DEC year ends in December (same as 'Q' and 'A') - -JAN year ends in January - -FEB year ends in February - -MAR year ends in March - -APR year ends in April - -MAY year ends in May - -JUN year ends in June - -JUL year ends in July - -AUG year ends in August - -SEP year ends in September - -OCT year ends in October - -NOV year ends in November - - There are some deprecated aggregation interval names in tstoolbox: - hourly H - daily D - monthly M - yearly A - - Defaults to D (daily). - :param ninterval : The number of agg_interval to use for the - aggregation. Defaults to 1. - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - aggd = {'hourly': 'H', - 'daily': 'D', - 'monthly': 'M', - 'yearly': 'A' - } - try: - agg_interval = aggd[agg_interval] - except KeyError: - pass - - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - methods = statistic.split(',') - newts = pd.DataFrame() - for method in methods: - tmptsd = tsd.resample('{0:d}{1}'.format(ninterval, - agg_interval), - how=method) - tmptsd.rename(columns=lambda x: x + '_' + method, inplace=True) - newts = newts.join(tmptsd, how='outer') - return tsutils.print_input(print_input, tsd, newts, '') - - -@mando.command -def clip( - a_min=None, - a_max=None, - start_date=None, - end_date=None, - print_input=False, - input_ts='-', - columns=None): - ''' - Returns a time-series with values limited to [a_min, a_max] - - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - for col in tsd.columns: - if a_min is None: - try: - n_min = pd.np.finfo(tsd[col].dtype).min - except ValueError: - n_min = pd.np.iinfo(tsd[col].dtype).min - else: - n_min = float(a_min) - if a_max is None: - try: - n_max = pd.np.finfo(tsd[col].dtype).max - except ValueError: - n_max = pd.np.iinfo(tsd[col].dtype).max - else: - n_max = float(a_max) - return tsutils.print_input( - print_input, tsd, tsd.clip(n_min, n_max), '_clip') - - -@mando.command -def add_trend( - start_offset, - end_offset, - start_date=None, - end_date=None, - print_input=False, - input_ts='-', - columns=None): - ''' - Adds a trend. - - :param start_offset : The starting value for the applied trend. - :param end_offset : The ending value for the applied trend. - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - ntsd = tsd.copy().astype('f') - ntsd.ix[:, :] = pd.np.nan - ntsd.ix[0, :] = float(start_offset) - ntsd.ix[-1, :] = float(end_offset) - ntsd = ntsd.interpolate(method='values') - ntsd = ntsd + tsd - return tsutils.print_input( - print_input, tsd, ntsd, '_trend') - - -@mando.command -def remove_trend( - start_date=None, - end_date=None, - print_input=False, - input_ts='-', - columns=None): - ''' - Removes a 'trend'. - - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - ntsd = tsd.copy() - for col in tsd.columns: - index = tsd.index.astype('l') - index = index - index[0] - lin = pd.np.polyfit(index, tsd[col], 1) - ntsd[col] = lin[0]*index + lin[1] - ntsd[col] = tsd[col] - ntsd[col] - return tsutils.print_input( - print_input, tsd, ntsd, '_rem_trend') - - -@mando.command -def calculate_fdc( - percent_point_function=None, - plotting_position='weibull', - input_ts='-', - start_date=None, - end_date=None, - columns=None, - sort_order='ascending'): - ''' - Returns the frequency distribution curve. DOES NOT return a time-series. - - :param percent_point_function : The distribution used to shift the plotting position - values. Choose from 'norm', 'lognorm', 'weibull', - and None. Default is None. - :param plotting_position : 'california', 'hazen', or 'weibull'. The default is - 'weibull'. - - 'california': m/n - 'hazen': (2m - 1)/(2n) - 'weibull': m/(n + 1) - - Where 'm' is the sorted rank of the y value, and 'n' - is the total number of values to be plotted. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - :param sort_order : Either 'ascending' or 'descending'. Defaults to - 'ascending'. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - - cnt = tsd.count(axis='rows') - if pd.np.any(cnt != cnt[0]): - raise ValueError(''' -* -* All columns of data must have the same counts. You have -* {0}. -* -'''.format(cnt)) - - reverse = False - if sort_order == 'descending': - reverse = True - cnt = cnt[0] - ppf = _set_ppf(percent_point_function) - xdat = ppf(_set_plotting_position(cnt, plotting_position))*100 - tsd = tsd.apply(sorted, axis=0, reverse=reverse) - tsd.index = xdat - tsd.index.name = 'Plotting_position' - return tsutils.printiso(tsd, force_print_index=True) - - -@mando.command(formatter_class=RawTextHelpFormatter) -def stack( - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Returns the stack of the input table. - - The stack command takes the standard table and - converts to a three column table. - - From: - - Datetime,TS1,TS2,TS3 - 2000-01-01 00:00:00,1.2,1018.2,0.0032 - 2000-01-02 00:00:00,1.8,1453.1,0.0002 - 2000-01-03 00:00:00,1.9,1683.1,-0.0004 - - To: - - Datetime,Columns,Values - 2000-01-01,TS1,1.2 - 2000-01-02,TS1,1.8 - 2000-01-03,TS1,1.9 - 2000-01-01,TS2,1018.2 - 2000-01-02,TS2,1453.1 - 2000-01-03,TS2,1683.1 - 2000-01-01,TS3,0.0032 - 2000-01-02,TS3,0.0002 - 2000-01-03,TS3,-0.0004 - - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - - newtsd = pd.DataFrame(tsd.stack()).reset_index(1) - newtsd.sort(['level_1'], inplace=True) - newtsd.columns = ['Columns', 'Values'] - return tsutils.printiso(newtsd) - - -@mando.command(formatter_class=RawTextHelpFormatter) -def unstack( - column_names, - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Returns the unstack of the input table. - - The unstack command takes the stacked table and converts to a - standard tstoolbox table. - - From: - - Datetime,Columns,Values - 2000-01-01,TS1,1.2 - 2000-01-02,TS1,1.8 - 2000-01-03,TS1,1.9 - 2000-01-01,TS2,1018.2 - 2000-01-02,TS2,1453.1 - 2000-01-03,TS2,1683.1 - 2000-01-01,TS3,0.0032 - 2000-01-02,TS3,0.0002 - 2000-01-03,TS3,-0.0004 - - - To: - - Datetime,TS1,TS2,TS3 - 2000-01-01,1.2,1018.2,0.0032 - 2000-01-02,1.8,1453.1,0.0002 - 2000-01-03,1.9,1683.1,-0.0004 - - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param column_names: The column in the table that holds the column names - of the unstacked data. - :param -s, --start_date : - The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - - cols = list(tsd.columns) - cols.remove(column_names) - newtsd = pd.DataFrame(tsd[cols].values, - index=[tsd.index.values, - tsd[column_names].values]) - newtsd = newtsd.unstack() - newtsd.index.name = 'Datetime' - levels = newtsd.columns.levels - labels = newtsd.columns.labels - newtsd.columns = levels[1][labels[1]] - - # Remove weird characters from column names - newtsd.rename(columns=lambda x: ''.join([i for i in str(x) if i not in '\'" '])) - return tsutils.printiso(newtsd) - - -mark_dict = { - ".":"point", - ",":"pixel", - "o":"circle", - "v":"triangle_down", - "^":"triangle_up", - "<":"triangle_left", - ">":"triangle_right", - "1":"tri_down", - "2":"tri_up", - "3":"tri_left", - "4":"tri_right", - "8":"octagon", - "s":"square", - "p":"pentagon", - "*":"star", - "h":"hexagon1", - "H":"hexagon2", - "+":"plus", - "D":"diamond", - "d":"thin_diamond", - "|":"vline", - "_":"hline" - } - -colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', - 'aliceblue', - 'antiquewhite', - 'aqua', - 'aquamarine', - 'azure', - 'beige', - 'bisque', - 'blanchedalmond', - 'blueviolet', - 'burlywood', - 'cadetblue', - 'chartreuse', - 'chocolate'] - -def _set_ppf(ptype): - if ptype == 'norm': - from scipy.stats.distributions import norm - return norm.ppf - elif ptype == 'lognorm': - from scipy.stats.distributions import lognorm - return lognorm.freeze(0.5, loc=0).ppf - elif ptype == 'weibull': - def ppf(y): - return pd.np.log(-pd.np.log((1-pd.np.array(y)))) - return ppf - elif ptype is None: - def ppf(y): - return y - return ppf - -def _set_plotting_position(n, plotting_position='weibull'): - if plotting_position == 'weibull': - return pd.np.linspace(1./(n+1), 1-1./(n+1), n) - elif plotting_position == 'california': - return pd.np.linspace(1./n, 1., n) - elif plotting_position == 'hazen': - return pd.np.linspace(1./(2*n), 1-1./(2*n), n) - else: - raise ValueError(''' -* -* The plotting_position option accepts 'weibull', 'california', and 'hazen' -* plotting position options, you gave {0}. -* -'''.format(plotting_position)) - -@mando.command(formatter_class=RawTextHelpFormatter) -def plot( - ofilename='plot.png', - type='time', - xtitle='', - ytitle='', - title='', - figsize=(10, 6.0), - legend=None, - legend_names=None, - subplots=False, - sharex=True, - sharey=False, - style=None, - logx=False, - logy=False, - xaxis='arithmetic', - yaxis='arithmetic', - xlim=None, - ylim=None, - secondary_y=False, - mark_right=True, - scatter_matrix_diagonal='probability_density', - bootstrap_size=50, - bootstrap_samples=500, - norm_xaxis=False, - norm_yaxis=False, - lognorm_xaxis=False, - lognorm_yaxis=False, - xy_match_line='', - grid=None, - input_ts='-', - start_date=None, - end_date=None, - label_rotation=None, - label_skip=1, - drawstyle='default', - por=False, - columns=None, - invert_xaxis=False, - invert_yaxis=False, - plotting_position='weibull'): - ''' - Plots. - - :param ofilename : Output filename for the plot. Extension defines the - type, ('.png'). Defaults to 'plot.png'. - :param type : The plot type. Defaults to 'time'. - Can be one of the following: - - 'time' - standard time series plot, - 'xy' - (x,y) plot, also know as a scatter plot, - 'double_mass' - (x,y) plot of the cumulative sum of - x and y, - 'boxplot' - box extends from lower to upper quartile, - with line at the median. Depending on the - statistics, the wiskers represent the range - of the data or 1.5 times the inter-quartile - range (Q3 - Q1), - 'scatter_matrix' - plots all columns against each other, - 'lag_plot' - indicates structure in the data, - 'autocorrelation' - plot autocorrelation, - 'bootstrap' - visually asses aspects of a data set by - plotting random selections of values, - 'probability_density' - sometime called kernel density estimation - (KDE), - 'bar' - sometimes called a column plot, - 'barh' - a horizontal bar plot, - 'bar_stacked' - sometimes called a stacked column, - 'barh_stacked' - a horizontal stacked bar plot, - 'histogram' - calculate and create a histogram plot, - 'norm_xaxis' - sort, calculate probabilities, and plot data - against an x axis normal distribution, - 'norm_yaxis' - sort, calculate probabilities, and plot data - against an y axis normal distribution, - 'lognorm_xaxis' - sort, calculate probabilities, and plot data - against an x axis lognormal distribution, - 'lognorm_yaxis' - sort, calculate probabilities, and plot data - against an y axis lognormal distribution, - 'weibull_xaxis' - sort, calculate and plot data against an - x axis weibull distribution, - 'weibull_yaxis' - sort, calculate and plot data against an - y axis weibull distribution, - :param xtitle : Title of x-axis, default depend on ``type``. - :param ytitle : Title of y-axis, default depend on ``type``. - :param title : Title of chart, defaults to ''. - :param figsize: The (width, height) of plot as inches. Defaults to - (10,6.5). - :param legend: Whether to display the legend. Defaults to True. - :param legend_names : Legend would normally use the time-series names - associated with the input data. The 'legend_names' - option allows you to override the names in the data - set. You must supply a comma separated list of - strings for each time-series in the data set. - Defaults to None. - :param subplots: boolean, default False. Make separate subplots for - each time series - :param sharex: boolean, default True In case subplots=True, share - x axis - :param sharey: boolean, default False In case subplots=True, share y axis - :param style : Comma separated matplotlib style strings matplotlib - line style per time-series. Just combine codes in - 'ColorLineMarker' order, for example 'r--*' is a red - dashed line with star marker. - - Colors - Single Character Codes: - 'b' blue - 'g' green - 'r' red - 'c' cyan - 'm' magenta - 'y' yellow - 'k' black - 'w' white - --------------------- - Grays - Float: - '0.75' 0.75 gray - --------------------- - Colors - HTML Color Names - 'red' - 'burlywood' - 'chartreuse' - ...etc. - Color reference: - http://matplotlib.org/api/colors_api.html - - Lines - '-' solid - '--' dashed - '-.' dash_dot - ':' dotted - 'None' draw nothing - ' ' draw nothing - '' draw nothing - Line reference: - http://matplotlib.org/api/artist_api.html - - Markers - '.' point - 'o' circle - 'v' triangle down - '^' triangle up - '<' triangle left - '>' triangle right - '1' tri_down - '2' tri_up - '3' tri_left - '4' tri_right - '8' octagon - 's' square - 'p' pentagon - '*' star - 'h' hexagon1 - 'H' hexagon2 - '+' plus - 'x' x - 'D' diamond - 'd' thin diamond - '|' vline - '_' hline - 'None' nothing - ' ' nothing - '' nothing - Marker reference: - http://matplotlib.org/api/markers_api.html - - :param logx: DEPRECATED: use '--xaxis="log"' instead. - :param logy: DEPRECATED: use '--yaxis="log"' instead. - :param xlim: Comma separated lower and upper limits - (--xlim 1,1000) Limits for the x-axis. Default is based on range of - x values. - :param ylim: Comma separated lower and upper limits - (--ylim 1,1000) Limits for the y-axis. Default is based on range of - y values. - :param xaxis : Defines the type of the xaxis. One of - 'arithmetic', 'log'. Default is 'arithmetic'. - :param yaxis : Defines the type of the yaxis. One of - 'arithmetic', 'log'. Default is 'arithmetic'. - :param secondary_y: Boolean or sequence, default False Whether to plot on - the secondary y-axis If a list/tuple, which - time-series to plot on secondary y-axis - :param mark_right: Boolean, default True : When using a secondary_y - axis, should the legend label the axis of the various - time-series automatically - :param scatter_matrix_diagonal : If plot type is 'scatter_matrix', this specifies the - plot along the diagonal. Defaults to - 'probability_density'. - :param bootstrap_size: The size of the random subset for 'bootstrap' plot. - Defaults to 50. - :param bootstrap_samples: The number of random subsets of 'bootstrap_size'. - Defaults to 500. - :param norm_xaxis: DEPRECATED: use '--type="norm_xaxis"' instead. - :param norm_yaxis: DEPRECATED: use '--type="norm_yaxis"' instead. - :param lognorm_xaxis: DEPRECATED: use '--type="lognorm_xaxis"' instead. - :param lognorm_yaxis: DEPRECATED: use '--type="lognorm_yaxis"' instead. - :param xy_match_line : Will add a match line where x == y. Default is ''. - Set to a line style code. - :param grid: Boolean, default True Whether to plot grid lines on - the major ticks. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param label_rotation : Rotation for major labels for bar plots. - :param label_skip : Skip for major labels for bar plots. - :param drawstyle : 'default' connects the points with lines. The steps - variants produce step-plots. 'steps' is equivalent to - 'steps-pre' and is maintained for - backward-compatibility. - ACCEPTS: ['default' | 'steps' | 'steps-pre' - | 'steps-mid' | 'steps-post'] - :param por: Plot from first good value to last good value. Strip - NANs from beginning and end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - :param force_freq: Force this frequency for the plot. WARNING: you may - lose data if not careful with this option. In - general, letting the algorithm determine the - frequency should always work, but this option will - override. Use PANDAS offset codes, - :param invert_xaxis: Invert the x-axis. - :param invert_yaxis: Invert the y-axis. - :param plotting_position : 'california', 'hazen', or 'weibull'. The default is - 'weibull'. - - 'california': m/n - 'hazen': (2m - 1)/(2n) - 'weibull': m/(n + 1) - - Where 'm' is the sorted rank of the y value, and 'n' - is the total number of values to be plotted. - - Only used for norm_xaxis, norm_yaxis, lognorm_xaxis, - lognorm_yaxis, weibull_xaxis, and weibull_yaxis. - ''' - - # Need to work around some old option defaults with the implemntation of - # mando - if legend == '' or legend == 'True' or legend is None: - legend = True - else: - legend = False - - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - from matplotlib.ticker import FixedLocator - - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts, dense=False), - start_date=start_date, - end_date=end_date, - pick=columns) - - if por is True: - tsd.dropna(inplace=True, how='all') - tsd = tsutils.common_kwds(tsutils.read_iso_ts(tsd, dense=True), - start_date=start_date, - end_date=end_date) - - def _know_your_limits(xylimits, axis='arithmetic'): - ''' - This defines the xlim and ylim as lists rather than strings. - Might prove useful in the future in a more generic spot. - It normalizes the different representiations. - ''' - if isinstance(xylimits, str): - nlim = [] - for lim in xylimits.split(','): - if lim == '': - nlim.append(None) - elif '.' in lim: - nlim.append(float(lim)) - else: - nlim.append(int(lim)) - else: # tuples or lists... - nlim = xylimits - - - if axis in ['norm', 'lognormal', 'weibull']: - if nlim is None: - nlim = [None, None] - if nlim[0] is None: - nlim[0] = 0.01 - if nlim[1] is None: - nlim[1] = 0.99 - if (nlim[0] <= 0 or nlim[0] >= 1 or - nlim[1] <= 0 or nlim[1] >= 1): - raise ValueError(''' -* -* Both limits must be between 0 and 1 for the -* 'normal' or weibull axis. Instead you have {0} -* -'''.format(nlim)) - - if nlim is None: - return nlim - - if nlim[0] is not None and nlim[1] is not None: - if nlim[0] >= nlim[1]: - raise ValueError(''' -* -* The second limit must be greater than the first. -* You gave {0}. -* -'''.format(nlim)) - - if axis == 'log': - if ((nlim[0] is not None and nlim[0] <= 0) or - (nlim[1] is not None and nlim[1] <= 0)): - raise ValueError(''' -* -* If log plot cannot have limits less than or equal to 0. -* You have {0}. -* -'''.format(nlim)) - - return nlim - - - # This is to help pretty print the frequency - try: - try: - pltfreq = str(tsd.index.freq, 'utf-8').lower() - except TypeError: - pltfreq = str(tsd.index.freq).lower() - if pltfreq.split(' ')[0][1:] == '1': - beginstr = 3 - else: - beginstr = 1 - # short freq string (day) OR (2 day) - short_freq = '({0})'.format(pltfreq[beginstr:-1]) - except AttributeError: - short_freq = '' - - if legend_names: - lnames = legend_names.split(',') - if len(lnames) != len(set(lnames)): - raise ValueError(''' -* -* Each name in legend_names must be unique. -* -''') - if len(tsd.columns) == len(lnames): - renamedict = dict(zip(tsd.columns, lnames)) - elif type == 'xy' and len(tsd.columns)//2 == len(lnames): - renamedict = dict(zip(tsd.columns[2::2], lnames[1:])) - renamedict[tsd.columns[1]] = lnames[0] - else: - raise ValueError(''' -* -* For 'legend_names' you must have the same number of comma -* separated names as columns in the input data. The input -* data has {0} where the number of 'legend_names' is {1}. -* -* If 'xy' type you need to have legend names as x,y1,y2,y3,... -* -'''.format(len(tsd.columns), len(lnames))) - tsd.rename(columns=renamedict, inplace=True) - else: - lnames = tsd.columns - - if style: - style = style.split(',') - - if logx is True or logy is True or norm_xaxis is True: - import warnings - warnings.warn(''' -* -* The --logx, --logy, and --norm_xaxis options are deprecated. -* Use '--xaxis="log" or '--yaxis="log"', -* or '--type="norm_xaxis"' or '--type="norm_yaxis"'. -* -''') - - if xaxis == 'log': - logx = True - if yaxis == 'log': - logy = True - - if type in ['norm_xaxis', 'lognorm_xaxis', 'weibull_xaxis']: - xaxis = 'normal' - if logx is True: - logx = False - import warnings - warnings.warn(''' -* -* The --type={1} cannot also have the xaxis set to {0}. -* The {0} setting for xaxis is ignored. -* -'''.format(xaxis, type)) - - if type in ['norm_yaxis', 'lognorm_yaxis', 'weibull_yaxis']: - yaxis = 'normal' - if logy is True: - logy = False - import warnings - warnings.warn(''' -* -* The --type={1} cannot also have the yaxis set to {0}. -* The {0} setting for yaxis is ignored. -* -'''.format(yaxis, type)) - - xlim = _know_your_limits(xlim, axis=xaxis) - ylim = _know_your_limits(ylim, axis=yaxis) - - plt.figure(figsize=figsize) - if type == 'time': - tsd.plot(legend=legend, subplots=subplots, sharex=sharex, - sharey=sharey, style=style, logx=logx, logy=logy, xlim=xlim, - ylim=ylim, secondary_y=secondary_y, mark_right=mark_right, - figsize=figsize, drawstyle=drawstyle) - plt.xlabel(xtitle or 'Time') - plt.ylabel(ytitle) - if legend is True: - plt.legend(loc='best') - elif type in ['xy', - 'double_mass', - 'norm_xaxis', - 'norm_yaxis', - 'lognorm_xaxis', - 'lognorm_yaxis', - 'weibull_xaxis', - 'weibull_yaxis']: - # PANDAS was not doing the right thing with xy plots - # if you wanted lines between markers. - # Fell back to using raw matplotlib. - # Boy I do not like matplotlib. - fig, ax = plt.subplots(figsize=figsize) - if style is None: - if type in ['xy']: - style = zip(colors*(len(tsd.columns)//len(colors) + 1), - ['*'] * len(tsd.columns)) - else: - style = zip(colors*(len(tsd.columns)//len(colors) + 1), - ['.-'] * len(tsd.columns)) - style = [i + j for i, j in style] - - if type == 'double_mass': - tsd = tsd.cumsum() - - if type in ['norm_xaxis', - 'norm_yaxis', - 'lognorm_xaxis', - 'lognorm_yaxis', - 'weibull_xaxis', - 'weibull_yaxis']: - ppf = _set_ppf(type.split('_')[0]) - ys = tsd.iloc[:, :] - colcnt = tsd.shape[1] - else: - xs = pd.np.array(tsd.iloc[:, 0::2]) - ys = pd.np.array(tsd.iloc[:, 1::2]) - colcnt = tsd.shape[1]//2 - - for colindex in range(colcnt): - lstyle = style[colindex] - lcolor = 'b' - marker = '' - linest = '-' - if lstyle[0] in colors: - lcolor = lstyle[0] - lstyle = lstyle[1:] - linest = lstyle - if lstyle[0] in mark_dict: - marker = lstyle[0] - linest = lstyle[1:] - - if type in ['norm_xaxis', 'norm_yaxis', - 'lognorm_xaxis', 'lognorm_yaxis', - 'weibull_xaxis', 'weibull_yaxis']: - oydata = pd.np.array(ys.iloc[:, colindex].dropna()) - oydata = pd.np.sort(oydata)[::-1] - n = len(oydata) - norm_axis = ax.xaxis - oxdata = ppf(_set_plotting_position(n, plotting_position)) - else: - oxdata = xs[:, colindex] - oydata = ys[:, colindex] - - if type in ['norm_yaxis', 'lognorm_yaxis', 'weibull_yaxis']: - oxdata, oydata = oydata, oxdata - norm_axis = ax.yaxis - - # Make the plot for each column - if logy is True and logx is False: - ax.semilogy(oxdata, oydata, - linestyle=linest, - color=lcolor, - marker=marker, - label=lnames[colindex] - ) - elif logx is True and logy is False: - ax.semilogx(oxdata, oydata, - linestyle=linest, - color=lcolor, - marker=marker, - label=lnames[colindex] - ) - elif logx is True and logy is True: - ax.loglog(oxdata, oydata, - linestyle=linest, - color=lcolor, - marker=marker, - label=lnames[colindex] - ) - else: - ax.plot(oxdata, oydata, - linestyle=linest, - color=lcolor, - marker=marker, - label=lnames[colindex], - drawstyle=drawstyle - ) - - # Make it pretty - if type in ['norm_xaxis', 'norm_yaxis', - 'lognorm_xaxis', 'lognorm_yaxis', - 'weibull_xaxis', 'weibull_yaxis']: - xtmaj = pd.np.array([0.01, 0.1, 0.5, 0.9, 0.99]) - xtmaj_str = ['1', '10', '50', '90', '99'] - xtmin = pd.np.concatenate([pd.np.linspace(0.001, 0.01, 10), - pd.np.linspace(0.01, 0.1, 10), - pd.np.linspace(0.1, 0.9, 9), - pd.np.linspace(0.9, 0.99, 10), - pd.np.linspace(0.99, 0.999, 10), - ]) - xtmaj = ppf(xtmaj) - xtmin = ppf(xtmin) - - norm_axis.set_major_locator(FixedLocator(xtmaj)) - norm_axis.set_minor_locator(FixedLocator(xtmin)) - - if type in ['norm_xaxis', 'lognorm_xaxis', 'weibull_xaxis']: - ax.set_xticklabels(xtmaj_str) - ax.set_ylim(ylim) - ax.set_xlim(ppf([0.01, 0.99])) - - elif type in ['norm_yaxis', 'lognorm_yaxis', 'weibull_yaxis']: - ax.set_yticklabels(xtmaj_str) - ax.set_xlim(xlim) - ax.set_ylim(ppf(ylim)) - else: - ax.set_xlim(xlim) - ax.set_ylim(ylim) - - if xy_match_line: - if isinstance(xy_match_line, str): - xymsty = xy_match_line - else: - xymsty = 'g--' - nxlim = ax.get_xlim() - nylim = ax.get_ylim() - maxt = max(nxlim[1], nylim[1]) - mint = min(nxlim[0], nylim[0]) - ax.plot([mint, maxt], [mint, maxt], xymsty, zorder=1) - ax.set_ylim(nylim) - ax.set_xlim(nxlim) - - if type in ['xy', 'double_mass']: - xtitle = xtitle or tsd.columns[0] - ytitle = ytitle or tsd.columns[1] - elif type in ['norm_xaxis']: - xtitle = xtitle or 'Normal Distribution' - ytitle = ytitle or tsd.columns[0] - elif type in ['lognorm_xaxis']: - xtitle = xtitle or 'Log Normal Distribution' - ytitle = ytitle or tsd.columns[0] - elif type in ['weibull_xaxis']: - xtitle = xtitle or 'Weibull Distribution' - ytitle = ytitle or tsd.columns[0] - if type in ['norm_yaxis', 'weibull_yaxis']: - xtitle, ytitle = ytitle, xtitle - - ax.set_xlabel(xtitle or tsd.columns[0]) - ax.set_ylabel(ytitle or tsd.columns[1]) - if legend is True: - ax.legend(loc='best') - elif type == 'probability_density': - tsd.plot(kind='kde', legend=legend, subplots=subplots, sharex=sharex, - sharey=sharey, style=style, logx=logx, logy=logy, xlim=xlim, - ylim=ylim, secondary_y=secondary_y, - figsize=figsize) - plt.xlabel(xtitle) - plt.ylabel(ytitle or 'Density') - if legend is True: - plt.legend(loc='best') - elif type == 'boxplot': - tsd.boxplot() - elif type == 'scatter_matrix': - from pandas.tools.plotting import scatter_matrix - if scatter_matrix_diagonal == 'probablity_density': - scatter_matrix_diagonal = 'kde' - scatter_matrix(tsd, diagonal=scatter_matrix_diagonal, - figsize=figsize) - elif type == 'lag_plot': - from pandas.tools.plotting import lag_plot - lag_plot(tsd, - figsize=figsize) - plt.xlabel(xtitle or 'y(t)') - plt.ylabel(ytitle or 'y(t+{0})'.format(short_freq or 1)) - elif type == 'autocorrelation': - from pandas.tools.plotting import autocorrelation_plot - autocorrelation_plot(tsd, - figsize=figsize) - plt.xlabel(xtitle or 'Time Lag {0}'.format(short_freq)) - plt.ylabel(ytitle) - elif type == 'bootstrap': - if len(tsd.columns) > 1: - raise ValueError(''' -* -* The 'bootstrap' plot can only work with 1 time-series in the DataFrame. -* The DataFrame that you supplied has {0} time-series. -* -'''.format(len(tsd.columns))) - from pandas.tools.plotting import bootstrap_plot - bootstrap_plot(tsd, size=bootstrap_size, samples=bootstrap_samples, - color='gray', - figsize=figsize) - elif (type == 'bar' or - type == 'bar_stacked' or - type == 'barh' or - type == 'barh_stacked' - ): - stacked = False - if type[-7:] == 'stacked': - stacked = True - kind = 'bar' - if type[:4] == 'barh': - kind = 'barh' - ax = tsd.plot(kind=kind, legend=legend, stacked=stacked, - style=style, logx=logx, logy=logy, xlim=xlim, - ylim=ylim, - figsize=figsize) - freq = tsutils.asbestfreq(tsd, force_freq=force_freq)[1] - if freq is not None: - if 'A' in freq: - endchar = 4 - elif 'M' in freq: - endchar = 7 - elif 'D' in freq: - endchar = 10 - elif 'H' in freq: - endchar = 13 - else: - endchar = None - nticklabels = [] - if kind == 'bar': - taxis = ax.xaxis - else: - taxis = ax.yaxis - for index, i in enumerate(taxis.get_majorticklabels()): - if index % label_skip: - nticklabels.append(' ') - else: - nticklabels.append(i.get_text()[:endchar]) - taxis.set_ticklabels(nticklabels) - plt.setp(taxis.get_majorticklabels(), rotation=label_rotation) - plt.xlabel(xtitle) - plt.ylabel(ytitle) - if legend is True: - plt.legend(loc='best') - elif type == 'histogram': - tsd.hist(figsize=figsize) - plt.xlabel(xtitle) - plt.ylabel(ytitle) - if legend is True: - plt.legend(loc='best') - else: - raise ValueError(''' -* -* Plot 'type' {0} is not supported. -* -'''.format(type)) - - if grid is None: - grid = True - else: - grid = False - if invert_xaxis is True: - plt.gca().invert_xaxis() - if invert_yaxis is True: - plt.gca().invert_yaxis() - plt.grid(grid) - plt.title(title) - plt.savefig(ofilename) - - -def _dtw(ts_a, ts_b, d=lambda x, y: abs(x-y), window=10000): - """Returns the DTW similarity distance between two 2-D - timeseries numpy arrays. - - Arguments - --------- - ts_a, ts_b : array of shape [n_samples, n_timepoints] - Two arrays containing n_samples of timeseries data - whose DTW distance between each sample of A and B - will be compared - - d : DistanceMetric object (default = abs(x-y)) - the distance measure used for A_i - B_j in the - DTW dynamic programming function - - Returns - ------- - DTW distance between A and B - """ - - # Create cost matrix via broadcasting with large int - ts_a, ts_b = pd.np.array(ts_a), pd.np.array(ts_b) - M, N = len(ts_a), len(ts_b) - cost = sys.maxsize * pd.np.ones((M, N)) - - # Initialize the first row and column - cost[0, 0] = d(ts_a[0], ts_b[0]) - for i in range(1, M): - cost[i, 0] = cost[i-1, 0] + d(ts_a[i], ts_b[0]) - - for j in range(1, N): - cost[0, j] = cost[0, j-1] + d(ts_a[0], ts_b[j]) - - # Populate rest of cost matrix within window - for i in range(1, M): - for j in range(max(1, i - window), - min(N, i + window)): - choices = cost[i - 1, j - 1], cost[i, j-1], cost[i-1, j] - cost[i, j] = min(choices) + d(ts_a[i], ts_b[j]) - - # Return DTW distance given window - return cost[-1, -1] - -@mando.command -def dtw(window=10000, - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Dynamic Time Warping - - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts, dense=True), - start_date=start_date, - end_date=end_date, - pick=columns) - - process = {} - for i in tsd.columns: - for j in tsd.columns: - if (i, j) not in process and (j, i) not in process and i != j: - process[(i, j)] = _dtw(tsd[i], tsd[j], window=window) - - print(process.keys()) - - ntsd = pd.DataFrame(process.values(), process.keys()) - return tsutils.printiso(ntsd) - -@mando.command -def pca(n_components=None, - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Returns the principal components analysis of the time series. Does not - return a time-series. - - :param n_components : The number of groups to separate the time series - into. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format - or '-' for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : - The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - from sklearn.decomposition import PCA - - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - - pca = PCA(n_components) - pca.fit(tsd.dropna(how='any')) - print(pca.components_) - - -@mando.command -def normalization(mode='minmax', - min_limit=0, - max_limit=1, - pct_rank_method='average', - print_input=False, - float_format='%g', - input_ts='-', - start_date=None, - end_date=None, - columns=None): - ''' - Returns the normalization of the time series. - - :param mode : 'minmax', 'zscore', or 'pct_rank'. Default is - 'minmax' - - 'minmax' is - - min_limit + - (X-Xmin)/(Xmax-Xmin)*(max_limit-min_limit) - - 'zscore' is - - X-mean(X)/stddev(X) - - 'pct_rank' is - - rank(X)*100/N - :param min_limit : Defaults to 0. Defines the minimum limit of the - minmax normalization. - :param max_limit : Defaults to 1. Defines the maximum limit of the - minmax normalization. - :param pct_rank_method : Defaults to 'average'. Defines how tied ranks are - broken. Can be 'average', 'min', 'max', 'first', - 'dense'. - :param -p, --print_input: If set to 'True' will include the input columns in - the output table. Default is 'False'. - :param -i, --input_ts : Filename with data in 'ISOdate,value' format or '-' - for stdin. - :param -s, --start_date : The start_date of the series in ISOdatetime format, - or 'None' for beginning. - :param -e, --end_date : The end_date of the series in ISOdatetime format, or - 'None' for end. - :param columns: Columns to pick out of input. Can use column names - or column numbers. If using numbers, column number - 1 is the first column. To pick multiple columns; - separate by commas with no spaces. As used in 'pick' - command. - ''' - tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts), - start_date=start_date, - end_date=end_date, - pick=columns) - - # Trying to save some memory - if print_input: - otsd = tsd.copy() - else: - otsd = pd.DataFrame() - - if mode == 'minmax': - tsd = (min_limit + - (tsd - tsd.min())/ - (tsd.max() - tsd.min())* - (max_limit - min_limit)) - elif mode == 'zscore': - tsd = (tsd - tsd.mean())/tsd.std() - elif mode == 'pct_rank': - tsd = tsd.rank(method=pct_rank_method, pct=True) - else: - raise ValueError(''' -* -* The 'mode' options are 'minmax', 'zscore', or 'pct_rank', you gave me -* {0}. -* -'''.format(mode)) - - return tsutils.print_input(print_input, otsd, tsd, '_{0}'.format(mode), - float_format=float_format) - - -def main(): - ''' Main ''' - if not os.path.exists('debug_tstoolbox'): - sys.tracebacklimit = 0 - mando.main() diff --git a/tstoolbox/tsutils.py b/tstoolbox/tsutils.py deleted file mode 100644 index 4666b94a..00000000 --- a/tstoolbox/tsutils.py +++ /dev/null @@ -1,512 +0,0 @@ -''' -A collection of functions used by tstoolbox, wdmtoolbox, ...etc. -''' - -from __future__ import print_function -from __future__ import division - -import os -import sys -import gzip -import bz2 - -import pandas as pd -import numpy as np - - -def common_kwds(input_tsd, - start_date=None, - end_date=None, - pick=None, - force_freq=None, - groupby=None): - ntsd = input_tsd - if pick is not None: - ntsd = _pick(ntsd, pick) - if start_date is not None or end_date is not None: - ntsd = _date_slice(ntsd, start_date=start_date, end_date=end_date) - if force_freq is not None: - ntsd = asbestfreq(ntsd, force_freq=force_freq) - if ntsd.index.is_all_dates: - ntsd.index.name = 'Datetime' - if groupby is not None: - if groupby == 'months_across_years': - return ntsd.groupby(lambda x: x.month) - else: - return ntsd.groupby(pd.TimeGrouper(groupby)) - return ntsd - - -def _pick(tsd, columns): - columns = columns.split(',') - ncolumns = [] - - for i in columns: - if i in tsd.columns: - ncolumns.append(tsd.columns.tolist().index(i) + 1) - continue - elif i == tsd.index.name: - ncolumns.append(0) - continue - else: - try: - target_col = int(i) - except: - raise ValueError(''' -* -* The name {0} isn't in the list of column names -* {1}. -* -'''.format(i, tsd.columns)) - if target_col < 0: - raise ValueError(''' -* -* The request column index {0} must be greater than or equal to 0. -* First column is index 1, index is column 0. -* -'''.format(i)) - if target_col > len(tsd.columns): - raise ValueError(''' -* -* The request column index {0} must be less than the -* number of columns {1}. -* -'''.format(i, len(tsd.columns))) - - ncolumns.append(target_col) - - if len(ncolumns) == 1 and ncolumns[0] != 0: - return pd.DataFrame(tsd[tsd.columns[ncolumns]]) - - newtsd = pd.DataFrame() - for index, col in enumerate(ncolumns): - if col == 0: - jtsd = pd.DataFrame(tsd.index) - else: - jtsd = pd.DataFrame(tsd[tsd.columns[col - 1]]) - - newtsd = newtsd.join(jtsd, - lsuffix='_{0}'.format(index), how='outer') - return newtsd - - -def date_slice(input_tsd, start_date=None, end_date=None): - ''' - This is here for a while until I fix my other toolboxes to - use common_kwds instead. - ''' - return _date_slice(input_tsd, start_date=start_date, end_date=end_date) - - -def _date_slice(input_tsd, start_date=None, end_date=None): - ''' - Private function to slice time series. - ''' - - if input_tsd.index.is_all_dates: - accdate = [] - for testdate in [start_date, end_date]: - if testdate is None: - tdate = None - else: - tdate = pd.Timestamp(testdate) - # Is this comparison cheaper than the .join? - if not pd.np.any(input_tsd.index == tdate): - # Create a dummy column at the date I want, then delete - # Not the best, but... - row = pd.DataFrame([pd.np.nan], index=[tdate]) - row.columns = ['deleteme'] - input_tsd = input_tsd.join(row, how='outer') - input_tsd.drop('deleteme', inplace=True, axis=1) - accdate.append(tdate) - - return input_tsd[slice(*accdate)] - else: - return input_tsd - -_annuals = { - 0: 'DEC', - 1: 'JAN', - 2: 'FEB', - 3: 'MAR', - 4: 'APR', - 5: 'MAY', - 6: 'JUN', - 7: 'JUL', - 8: 'AUG', - 9: 'SEP', - 10: 'OCT', - 11: 'NOV', - 12: 'DEC', - } - -_weeklies = { - 0: 'MON', - 1: 'TUE', - 2: 'WED', - 3: 'THU', - 4: 'FRI', - 5: 'SAT', - 6: 'SUN', - } - - -def asbestfreq(data, force_freq=None): - ''' - This uses several techniques. - 1. If data.index.freqstr is None, just return. - 2. If force_freq or data.index.inferred_freq is set use .asfreq. - 3. Use pd.infer_freq - fails if any missing - 4. Use .is_* functions to establish A, AS, A-*, AS-*, Q, QS, M, MS - 5. Use minimum interval to establish the fixed time periods up to weekly - 6. Gives up returning None for PANDAS offset string - ''' - - if force_freq is not None: - return data.asfreq(force_freq) - - if data.index.freq is not None: - return data - - # Since pandas doesn't set data.index.freq and data.index.freqstr when - # using .asfreq, this function returns that PANDAS time offset alias code - # also. Not ideal at all. - - # This gets most of the frequencies... - try: - return data.asfreq(data.index.inferred_freq) - except ValueError: - pass - - # pd.infer_freq would fail if given a large dataset - if len(data.index) > 100: - slic = slice(None, 99) - else: - slic = slice(None, None) - infer_freq = pd.infer_freq(data.index[slic]) - if infer_freq is not None: - return data.asfreq(infer_freq) - - # At this point pd.infer_freq failed probably because of missing values. - # The following algorithm would not capture things like BQ, BQS - # ...etc. - if np.alltrue(data.index.is_year_end): - infer_freq = 'A' - elif np.alltrue(data.index.is_year_start): - infer_freq = 'AS' - elif np.alltrue(data.index.is_quarter_end): - infer_freq = 'Q' - elif np.alltrue(data.index.is_quarter_start): - infer_freq = 'QS' - elif np.alltrue(data.index.is_month_end): - if np.all(data.index.month == data.index[0].month): - # Actually yearly with different ends - infer_freq = 'A-{0}'.format(_annuals[data.index[0].month]) - else: - infer_freq = 'M' - elif np.alltrue(data.index.is_month_start): - if np.all(data.index.month == data.index[0].month): - # Actually yearly with different start - infer_freq = 'A-{0}'.format(_annuals[data.index[0].month] - 1) - else: - infer_freq = 'MS' - - if infer_freq is not None: - return data.asfreq(infer_freq) - - # Use the minimum of the intervals to test a new interval. - # Should work for fixed intervals. - mininterval = int(np.min(np.diff(data.index.values))) - if mininterval < 0: - raise ValueError - if mininterval < 1000: - infer_freq = '{0}N'.format(mininterval) - elif mininterval < 1000000: - infer_freq = '{0}U'.format(mininterval//1000) - elif mininterval < 1000000000: - infer_freq = '{0}L'.format(mininterval//1000000) - elif mininterval < 60000000000: - infer_freq = '{0}S'.format(mininterval//1000000000) - elif mininterval < 3600000000000: - infer_freq = '{0}T'.format(mininterval//60000000000) - elif mininterval < 86400000000000: - infer_freq = '{0}H'.format(mininterval//3600000000000) - elif mininterval < 604800000000000: - infer_freq = '{0}D'.format(mininterval//86400000000000) - elif mininterval < 2419200000000000: - infer_freq = '{0}W'.format(mininterval//604800000000000) - if np.all(data.index.dayofweek == data.index[0].dayofweek): - infer_freq = infer_freq + '-{0}'.format( - _weeklies[data.index[0].dayofweek]) - else: - infer_freq = 'D' - - if infer_freq is not None: - return data.asfreq(infer_freq) - - # Give up - return data - - -# Utility -def print_input(iftrue, intds, output, suffix, - date_format=None, sep=',', - float_format='%g', - force_print_index=False): - ''' Used when wanting to print the input time series also. - ''' - if suffix: - output.rename(columns=lambda xloc: xloc + suffix, inplace=True) - if iftrue: - return printiso(intds.join(output, - lsuffix='_1', - rsuffix='_2', - how='outer'), - date_format=date_format, - sep=sep, - float_format=float_format, - force_print_index=force_print_index) - else: - return printiso(output, date_format=date_format, sep=sep, - float_format=float_format, - force_print_index=force_print_index) - - -def _apply_across_columns(func, xtsd, **kwds): - ''' Apply a function to each column in turn. - ''' - for col in xtsd.columns: - xtsd[col] = func(xtsd[col], **kwds) - return xtsd - - -def _printiso(tsd, date_format=None, sep=',', - float_format='%g', force_print_index=False): - ''' Separate so can use in tests. - ''' - sys.tracebacklimit = 1000 - - print_index = True - if tsd.index.is_all_dates: - if tsd.index.name is None: - tsd.index.name = 'Datetime' - # Someone made the decision about the name - # This is how I include time zone info by tacking on to the - # index.name. - elif 'datetime' not in tsd.index.name.lower(): - tsd.index.name = 'Datetime' - else: - # This might be overkill, but tstoolbox is for time-series. - # Revisit if necessary. - print_index = False - - if tsd.index.name == 'UniqueID': - print_index = False - - if force_print_index is True: - print_index = True - - try: - tsd.to_csv(sys.stdout, float_format=float_format, - date_format=date_format, sep=sep, index=print_index) - except IOError: - return - - -def test_cli(): - ''' The structure to test the cli. - ''' - import traceback - try: - oldtracebacklimit = sys.tracebacklimit - except AttributeError: - oldtracebacklimit = 1000 - sys.tracebacklimit = 1000 - cli = False - for i in traceback.extract_stack(): - if os.path.sep + 'mando' + os.path.sep in i[0] or 'baker' in i[0]: - cli = True - break - sys.tracebacklimit = oldtracebacklimit - return cli - - -def printiso(tsd, date_format=None, - sep=',', float_format='%g', force_print_index=False): - ''' - Default output format for tstoolbox, wdmtoolbox, swmmtoolbox, - and hspfbintoolbox. - ''' - - # Not perfectly true, but likely will use force_print_index for indices - # that are not time stamps. - if force_print_index is True: - if not tsd.index.name: - tsd.index.name = 'UniqueID' - else: - tsd.index.name = 'Datetime' - - if test_cli(): - _printiso(tsd, float_format=float_format, - date_format=date_format, sep=sep, - force_print_index=force_print_index) - else: - return tsd - - -def openinput(filein): - """ - Opens the given input file. It can decode various formats too, such as - gzip and bz2. - """ - if filein == '-': - return sys.stdin - ext = os.path.splitext(filein)[1] - if ext in ['.gz', '.GZ']: - return gzip.open(filein, 'rb') - if ext in ['.bz', '.bz2']: - return bz2.BZ2File(filein, 'rb') - return open(filein, 'rb') - - -def read_iso_ts(indat, - dense=True, - parse_dates=True, - extended_columns=False, - force_freq=None): - ''' - Reads the format printed by 'print_iso' and maybe other formats. - ''' - import csv - from pandas.compat import StringIO - - if force_freq is not None: - # force_freq implies a dense series - dense = True - - index_col = 0 - if parse_dates is False: - index_col = False - - # Would want this to be more generic... - na_values = [] - for spc in range(20)[1:]: - spcs = ' '*spc - na_values.append(spcs) - na_values.append(spcs + 'nan') - - fpi = None - - # Handle Series by converting to DataFrame - if isinstance(indat, pd.Series): - indat = pd.DataFrame(indat) - - if isinstance(indat, pd.DataFrame): - if indat.index.is_all_dates: - indat.index.name = 'Datetime' - if dense: - return asbestfreq(indat, force_freq=force_freq) - else: - return indat - else: - indat.index.name = 'UniqueID' - return indat - - has_header = False - dialect = csv.excel - if isinstance(indat, str) or isinstance(indat, bytes): - try: - indat = str(indat, encoding='utf-8') - except: - pass - if indat == '-': - # if from stdin format must be the tstoolbox standard - has_header = True - fpi = openinput(indat) - elif '\n' in indat or '\r' in indat: - # a string - fpi = StringIO(indat) - elif os.path.exists(indat): - # Is it a pickled file? - try: - result = pd.io.pickle.read_pickle(indat) - fpi = False - except: - # Maybe a CSV file? - fpi = openinput(indat) - else: - raise ValueError(''' -* -* File {0} doesn't exist. -* -'''.format(indat)) - else: - raise ValueError(''' -* -* Can't figure out what was passed to read_iso_ts. -* -''') - - if fpi: - try: - fpi.seek(0) - readsome = fpi.read(2048) - fpi.seek(0) - dialect = csv.Sniffer().sniff(readsome, - delimiters=', \t:|') - has_header = csv.Sniffer().has_header(readsome) - except: - # This is an assumption. - has_header = True - - if extended_columns is True: - fname = os.path.splitext(os.path.basename(fpi.name))[0] - fstr = '{0}.{1}' - else: - fname = '' - fstr = '{1}' - if fname == '': - fname = '_' - if has_header: - result = pd.io.parsers.read_table(fpi, header=0, - dialect=dialect, - index_col=index_col, - parse_dates=True, - skipinitialspace=True) - result.columns = [fstr.format(fname, i.strip()) - for i in result.columns] - else: - result = pd.io.parsers.read_table(fpi, header=None, - dialect=dialect, - index_col=0, - parse_dates=True, - skipinitialspace=True) - if len(result.columns) == 1: - result.columns = [fname] - else: - result.columns = [fstr.format(fname, i.strip()) - for i in result.columns] - - if result.index.is_all_dates is True: - result.index.name = 'Datetime' - - if dense: - try: - return 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The class uses f2py to wrap the minimally necessary WDM -routines. -''' - -import datetime -import os -import os.path -import re -import sys - -import pandas as pd - -from . import wdm - -# Load in WDM subroutines - -# Mapping between WDM TCODE and pandas interval code -MAPTCODE = { - 1: 'S', - 2: 'T', - 3: 'H', - 4: 'D', - 5: 'M', - 6: 'A', - } - -MAPFREQ = { - 'S': 1, - 'T': 2, - 'H': 3, - 'D': 4, - 'M': 5, - 'A': 6, - } - - -class WDMError(Exception): - pass - - -class DSNDoesNotExist(Exception): - def __init__(self, dsn): - self.dsn = dsn - - def __str__(self): - if self.dsn < 1 or self.dsn > 32000: - return ''' -* -* The DSN number must be >= 1 and <= 32000. -* You supplied {0}. -* -'''.format(self.dsn) - - return ''' -* -* The DSN {0} does not exist in the dataset. -* -'''.format(self.dsn) - - -class LibraryNotFoundError(Exception): - pass - - -class WDMFileExists(Exception): - def __init__(self, filename): - self.filename = filename - - def __str__(self): - return ''' -* -* File {0} exists. -* -'''.format(self.filename) - - -class DSNExistsError(Exception): - def __init__(self, dsn): - self.dsn = dsn - - def __str__(self): - return ''' -* -* DSN {0} exists. -* -'''.format(self.dsn) - - -class WDM(): - ''' Class to open and read from WDM files. - ''' - def __init__(self): - - # timcvt: Convert times to account for 24 hour - # timdif: Time difference - # wdmopn: Open WDM file - # wdbsac: Set string attribute - # wdbsai: Set integer attribute - # wdbsar: Set real attribute - # wdbckt: Check if DSN exists - # wdflcl: Close WDM file - # wdlbax: Create label for new DSN - # wdtget: Get time-series data - # wdtput: Write time-series data - # wddsrn: Renumber a DSN - # wddsdl: Delete a DSN - # wddscl: Copy a label - - self.timcvt = wdm.timcvt - self.timdif = wdm.timdif - self.wdbopn = wdm.wdbopn - self.wdbsac = wdm.wdbsac - self.wdbsai = wdm.wdbsai - self.wdbsar = wdm.wdbsar - self.wdbsgc = wdm.wdbsgc - self.wdbsgi = wdm.wdbsgi - self.wdbsgr = wdm.wdbsgr - self.wdckdt = wdm.wdckdt - self.wdflcl = wdm.wdflcl - self.wdlbax = wdm.wdlbax - self.wdtget = wdm.wdtget - self.wdtput = wdm.wdtput - self.wtfndt = wdm.wtfndt - self.wddsrn = wdm.wddsrn - self.wddsdl = wdm.wddsdl - self.wddscl = wdm.wddscl - - self.openfiles = {} - - def wmsgop(self): - # WMSGOP is a simple open of the message file - afilename = os.path.join(sys.prefix, - 'share', - 'wdmtoolbox', - 'message.wdm') - return self._open(afilename, 100, ronwfg=1) - - def dateconverter(self, datestr): - words = re.findall(r'\d+', str(datestr)) - words = [int(i) for i in words] - dtime = [1900, 1, 1, 0, 0, 0] - dtime[:len(words)] = words - return dtime - - def _open(self, wdname, wdmsfl, ronwfg=0): - ''' Private method to open WDM file. - ''' - if wdname not in self.openfiles: - wdname = wdname.strip() - if ronwfg == 1: - if not os.path.exists(wdname): - raise ValueError(''' -* -* Trying to open -* {0} -* in read-only mode and it cannot be found. -* - '''.format(wdname)) - retcode = self.wdbopn(wdmsfl, - wdname, - ronwfg) - self._retcode_check(retcode, additional_info='wdbopn') - self.openfiles[wdname] = wdmsfl - return wdmsfl - - def _retcode_check(self, retcode, additional_info=' '): - retcode_dict = { - -1: 'non specific error on WDM file open', - -4: 'copy/update failed due to data overlap problem - part of source needed', - -5: 'copy/update failed due to data overlap problem', - -6: 'no data present', - -8: 'bad dates', - -9: 'data present in current group', - -10: 'no date in this group', - -11: 'no non-missing data, data has not started yet', - -14: 'data specified not within valid range for data set', - -15: 'time units and time step must match label exactly with VBTIME = 1', - -20: 'problem with on or more of GPGLG, DXX, NVAL, QUALVL, LTSTEP, LTUNIT', - -21: 'data from WDM does not match expected date', - -23: 'not a valid table', - -24: 'not a valid associated table', - -25: 'template already exists', - -26: 'can not add another table', - -27: 'no tables to return info about', - -28: 'table does not exist yet', - -30: 'more than whole table', - -31: 'more than whole extension', - -32: 'data header does not match', - -33: 'problems with row/space specs', - -36: 'missing needed following data for a get', - -37: 'no data present', - -38: 'missing part of time required', - -39: 'missing data group', - -40: 'no data available', - -41: 'no data to read', - -42: 'overlap in existing group', - -43: 'can not add another space time group', - -44: 'trying to get/put more data that in block', - -45: 'types do not match', - -46: 'bad space time group specification parameter', - -47: 'bad direction flag', - -48: 'conflicting spec of space time dim and number of timeseries data sets', - -49: 'group does not exist', - -50: 'requested attributes missing from this data set', - -51: 'no space for another DLG', - -61: 'old data set does not exist', - -62: 'new data set already exists', - -71: 'data set already exists', - -72: 'old data set does not exist', - -73: 'new data set already exists', - -81: 'data set does not exists', - -82: 'data set exists, but is wrong DSTYP', - -83: 'WDM file already open, can not create it', - -84: 'data set number out of valid range', - -85: 'trying to write to a read-only data set', - -87: 'can not remove message WDM file from buffer', - -88: 'can not open another WDM file', - -89: 'check digit on 1st record of WDM file is bad', - -101: 'incorrect character value for attribute', - -102: 'attribute already on label', - -103: 'no room on label for attribute', - -104: 'data present, can not update attribute', - -105: 'attribute not allowed for this type data set', - -106: 'can not delete attribute, it is required', - -107: 'attribute not present on this data set', - -108: 'incorrect integer value for attribute', - -109: 'incorrect real value for attribute', - -110: 'attributes not found on message file', - -111: 'attribute name not found (no match)', - -112: 'more attributes exists which match SAFNAM', - -121: 'no space for another attribute', - 1: 'varies - generally more data/groups/table', - 2: 'no more data available for this DLG group' - } - - if retcode in retcode_dict: - raise WDMError(''' -* -* WDM library function returned error code {0}. {1} -* WDM error: {2} -* -'''.format(retcode, additional_info, retcode_dict[retcode])) - if retcode != 0: - raise WDMError(''' -* -* WDM library function returned error code {0}. {1} -* -'''.format(retcode, additional_info)) - - def renumber_dsn(self, wdmpath, odsn, ndsn): - odsn = int(odsn) - ndsn = int(ndsn) - - wdmfp = self._open(wdmpath, 101) - retcode = self.wddsrn( - wdmfp, - odsn, - ndsn) - self._close(wdmpath) - self._retcode_check(retcode, additional_info='wddsrn') - - def delete_dsn(self, wdmpath, dsn): - dsn = int(dsn) - - wdmfp = self._open(wdmpath, 101) - if self.wdckdt(wdmfp, dsn) != 0: - retcode = self.wddsdl( - wdmfp, - dsn) - self._retcode_check(retcode, additional_info='wddsdl') - self._close(wdmpath) - - def copydsnlabel(self, inwdmpath, indsn, outwdmpath, outdsn): - assert inwdmpath != outwdmpath - indsn = int(indsn) - outdsn = int(outdsn) - dsntype = 0 - inwdmfp = self._open(inwdmpath, 101) - outwdmfp = self._open(outwdmpath, 102) - retcode = self.wddscl( - inwdmfp, - indsn, - outwdmfp, - outdsn, - dsntype) - self._close(inwdmpath) - self._close(outwdmpath) - self._retcode_check(retcode, additional_info='wddscl') - - #Added by RTH on 4/1/2017 - def exists_dsn(self, wdmpath, dsn): - wdmfp = self._open(wdmpath, 101, ronwfg=1) - value = self.wdckdt(wdmfp, dsn) - self._close(wdmpath) - return value - - - def describe_dsn(self, wdmpath, dsn): - wdmfp = self._open(wdmpath, 101, ronwfg=1) - if self.wdckdt(wdmfp, dsn) == 0: - raise DSNDoesNotExist(dsn) - - tdsfrc, llsdat, lledat, retcode = self.wtfndt( - wdmfp, - dsn, - 1) # GPFLG - get(1)/put(2) flag - # Ignore retcode == -6 which means that the dsn doesn't have any data. - # It it is a new dsn, of course it doesn't have any data. - if retcode == -6: - retcode = 0 - self._retcode_check(retcode, additional_info='wtfndt') - - tstep, retcode = self.wdbsgi( - wdmfp, - dsn, - 33, # saind = 33 for time step - 1) # salen - self._retcode_check(retcode, additional_info='wdbsgi') - - tcode, retcode = self.wdbsgi( - wdmfp, - dsn, - 17, # saind = 17 for time code - 1) # salen - self._retcode_check(retcode, additional_info='wdbsgi') - - tsfill, retcode = self.wdbsgr( - wdmfp, - dsn, - 32, # saind = 32 for tsfill - 1) # salen - # retcode = -107 if attribute not present - if retcode == -107: - # Since I use tsfill if not found will set to default. - tsfill = -999.0 - retcode = 0 - else: - tsfill = tsfill[0] - self._retcode_check(retcode, additional_info='wdbsgr') - - ostr, retcode = self.wdbsgc( - wdmfp, - dsn, - 290, # saind = 290 for location - 8) # salen - if retcode == -107: - ostr = 'NA' - retcode = 0 - self._retcode_check(retcode, additional_info='wdbsgr') - - scen_ostr, retcode = self.wdbsgc( - wdmfp, - dsn, - 288, # saind = 288 for scenario - 8) # salen - if retcode == -107: - scen_ostr = 'NA' - retcode = 0 - self._retcode_check(retcode, additional_info='wdbsgr') - - con_ostr, retcode = self.wdbsgc( - wdmfp, - dsn, - 289, # saind = 289 for constitiuent - 8) # salen - if retcode == -107: - con_ostr = 'NA' - retcode = 0 - self._retcode_check(retcode, additional_info='wdbsgr') - - base_year, retcode = self.wdbsgi( - wdmfp, - dsn, - 27, # saind = 27 for base_year - 1) # salen - self._retcode_check(retcode, additional_info='wdbsgi') - - self._close(wdmpath) - - self.timcvt(llsdat) - self.timcvt(lledat) - try: - sdate = datetime.datetime(*llsdat).isoformat() - except ValueError: - sdate = None - try: - edate = datetime.datetime(*lledat).isoformat() - except ValueError: - edate = None - - tstep = tstep[0] - tcode = tcode[0] - base_year = base_year[0] - - try: - ostr = str(ostr, "utf-8") - scen_ostr = str(scen_ostr, "utf-8") - con_ostr = str(con_ostr, "utf-8") - except TypeError: - ostr = ''.join(ostr) - scen_ostr = ''.join(scen_ostr) - con_ostr = ''.join(con_ostr) - - return {'dsn': dsn, - 'start_date': sdate, - 'end_date': edate, - 'llsdat': llsdat, - 'lledat': lledat, - 'tstep': tstep, - 'tcode': tcode, - 'tcode_name': MAPTCODE[tcode], - 'location': ostr.strip(), - 'scenario': scen_ostr.strip(), - 'constituent': con_ostr.strip(), - 'tsfill': tsfill, - 'freq': MAPTCODE[tcode], - 'base_year': base_year} - - def create_new_wdm(self, wdmpath, overwrite=False): - ''' Create a new WDM fileronwfg - ''' - if overwrite and os.path.exists(wdmpath): - os.remove(wdmpath) - elif os.path.exists(wdmpath): - raise WDMFileExists(wdmpath) - ronwfg = 2 - wdmfp = self._open(wdmpath, 101, ronwfg=ronwfg) - self._close(wdmpath) - - def create_new_dsn(self, wdmpath, dsn, tstype='', base_year=1900, tcode=4, - tsstep=1, statid=' ', scenario='', location='', - description='', constituent='', tsfill=-999.0): - ''' Create self.wdmfp/dsn. ''' - wdmfp = self._open(wdmpath, 101) - messfp = self.wmsgop() - - if self.wdckdt(wdmfp, dsn) == 1: - raise DSNExistsError(dsn) - - # Parameters for wdlbax taken from ATCTSfile/clsTSerWDM.cls - psa = self.wdlbax( - wdmfp, - dsn, - 1, # DSTYPE - always 1 for time series - 10, # NDN - number of down pointers - 10, # NUP - number of up pointers - 30, # NSA - number of search attributes - 100, # NSASP - amount of search attribute space - 300) # NDP - number of data pointers - # PSA - pointer to search attribute space - - for saind, salen, saval in [(34, 1, 6), # tgroup - (83, 1, 1), # compfg - (84, 1, 1), # tsform - (85, 1, 1), # vbtime - (17, 1, int(tcode)), # tcode - (33, 1, int(tsstep)), # tsstep - (27, 1, int(base_year)), # tsbyr - ]: - retcode = self.wdbsai( - wdmfp, - dsn, - messfp, - saind, - salen, - saval) - self._retcode_check(retcode, additional_info='wdbsai') - - for saind, salen, saval in [(32, 1, tsfill)]: # tsfill - retcode = self.wdbsar( - wdmfp, - dsn, - messfp, - saind, - salen, - saval) - self._retcode_check(retcode, additional_info='wdbsar') - - for saind, salen, saval, error_name in [ - (2, 16, statid, 'Station ID'), - (1, 4, tstype.upper(), 'Time series type - tstype'), - (45, 48, description.upper(), 'Description'), - (288, 8, scenario.upper(), 'Scenario'), - (289, 8, constituent.upper(), 'Constituent'), - (290, 8, location.upper(), 'Location'), - ]: - saval = saval.strip() - if len(saval) > salen: - raise ValueError(''' -* -* String "{0}" is too long for {1}. Must -* have a length equal or less than {2}. -* -'''.format(saval, error_name, salen)) - - saval = '{0: <{1}}'.format(saval, salen) - - retcode = self.wdbsac( - wdmfp, - dsn, - messfp, - saind, - salen, - saval) - self._retcode_check(retcode, additional_info='wdbsac') - self._close(wdmpath) - - def _tcode_date(self, tcode, date): - ''' Uses tcode to set the significant parts of the date tuple. ''' - rdate = [1, 1, 1, 0, 0, 0] - if tcode <= 6: - rdate[0] = date[0] - if tcode <= 5: - rdate[1] = date[1] - if tcode <= 4: - rdate[2] = date[2] - if tcode <= 3: - rdate[3] = date[3] - if tcode <= 2: - rdate[4] = date[4] - if tcode <= 1: - rdate[5] = date[5] - return rdate - - def write_dsn(self, wdmpath, dsn, data, start_date): - ''' Write to self.wdmfp/dsn the time-series data. ''' - dsn_desc = self.describe_dsn(wdmpath, dsn) - tcode = dsn_desc['tcode'] - tstep = dsn_desc['tstep'] - - dstart_date = start_date.timetuple()[:6] - llsdat = self._tcode_date(tcode, dstart_date) - if dsn_desc['base_year'] > llsdat[0]: - raise ValueError(''' -* -* The base year for this DSN is {0}. All data to insert must be after the -* base year. Instead the first year of the series is {1}. -* -'''.format(dsn_desc['base_year'], llsdat[0])) - - nval = len(data) - - wdmfp = self._open(wdmpath, 101) - retcode = self.wdtput( - wdmfp, - dsn, - tstep, - llsdat, - nval, - 1, - 0, - tcode, - data) - self._close(wdmpath) - self._retcode_check(retcode, additional_info='wdtput') - - def read_dsn(self, wdmpath, dsn, start_date=None, end_date=None): - ''' Read from a DSN. - ''' - - # Call wdatim_ to get LLSDAT, LLEDAT, TSTEP, TCODE - desc_dsn = self.describe_dsn(wdmpath, dsn) - - llsdat = desc_dsn['llsdat'] - lledat = desc_dsn['lledat'] - tcode = desc_dsn['tcode'] - tstep = desc_dsn['tstep'] - - # These calls convert 24 to midnight of the next day - self.timcvt(llsdat) - self.timcvt(lledat) - - if start_date is not None: - start_date = self.dateconverter(start_date) - llsdat = start_date - if end_date is not None: - end_date = self.dateconverter(end_date) - lledat = end_date - - # Determine the number of values (ITERM) from LLSDAT to LLEDAT - iterm = self.timdif( - llsdat, - lledat, - tcode, - tstep) - - dtran = 0 - qualfg = 30 - # Get the data and put it into dictionary - wdmfp = self._open(wdmpath, 101, ronwfg=1) - dataout, retcode = self.wdtget( - wdmfp, - dsn, - tstep, - llsdat, - iterm, - dtran, - qualfg, - tcode) - self._close(wdmpath) - self._retcode_check(retcode, additional_info='wdtget') - - # Find the begining in datetime.datetime format - tstart = datetime.datetime(llsdat[0], - llsdat[1], - llsdat[2], - llsdat[3], - llsdat[4], - llsdat[5]) - - # Convert time series to pandas DataFrame - index = pd.date_range( - tstart, - periods=len(dataout), - freq='{0:d}{1}'.format(tstep, MAPTCODE[tcode])) - - tmpval = pd.DataFrame( - pd.Series( - dataout, - index=index, - name='{0}_DSN_{1}'.format( - os.path.basename(wdmpath), dsn))) - return tmpval - - def read_dsn_por(self, wdmpath, dsn): - ''' Read the period of record for a DSN. - ''' - return self.read_dsn(wdmpath, dsn, start_date=None, end_date=None) - - def _close(self, wdmpath): - ''' Close the WDM file. - ''' - if wdmpath in self.openfiles: - retcode = self.wdflcl(self.openfiles[wdmpath]) - self._retcode_check(retcode, additional_info='wdflcl') - toss = self.openfiles.pop(wdmpath) - - -if __name__ == '__main__': - wdm_obj = WDM() - fname = 'test.wdm' - if os.name == 'nt': - fname = r'c:\test.wdm' - wdm_obj.create_new_wdm(fname, overwrite=True) - listonumbers = [34.2, 35.0, 36.9, 38.2, 40.2, 20.1, 18.4, 23.6] - wdm_obj.create_new_dsn(fname, 1003, tstype='EXAM', scenario='OBSERVED', tcode=4, location='EXAMPLE') - wdm_obj.write_dsn(fname, 1003, listonumbers, datetime.datetime(2000, 1, 1)) - print(wdm_obj.read_dsn_por(fname, 1003))