The-Evolution-of-Linux
Tux - the Linux mascot
</a>
Version control repositories like CVS, Subversion or Git store rich evolution information about a software project. In this project, you’ll be challenged to read in, clean up and visualize a real world Git repository dataset of the Linux kernel. With almost 700k commits and thousands of contributors (find out the exact number in this project ;-) ) there are some little data cleaning and wrangling challenges that you’ll encounter. But you’ll also gain insights about the development activities over the last 13 years.
For this Project, you need to be familiar with Pandas DataFrames, especially the read_csv and groupby functions, as well as working with time series data.
Authors
I am Very Proud to Be a Part of this Project, Thanks Datacamp
1. Introduction
Version control repositories like CVS, Subversion or Git can be a real gold mine for software developers. They contain every change to the source code including the date (the "when"), the responsible developer (the "who"), as well as a little message that describes the intention (the "what") of a change.
In this notebook, we will analyze the evolution of a very famous open-source project – the Linux kernel. The Linux kernel is the heart of some Linux distributions like Debian, Ubuntu or CentOS. Our dataset at hand contains the history of kernel development of almost 13 years (early 2005 - late 2017). We get some insights into the work of the development efforts by
- identifying the TOP 10 contributors and
- visualizing the commits over the years.
# Printing the content of git_log_excerpt.csv
with open("./datasets/git_log_excerpt.csv") as f:
print(f.read())
1502382966#Linus Torvalds
1501368308#Max Gurtovoy
1501625560#James Smart
1501625559#James Smart
1500568442#Martin Wilck
1502273719#Xin Long
1502278684#Nikolay Borisov
1502238384#Girish Moodalbail
1502228709#Florian Fainelli
1502223836#Jon Paul Maloy
%%nose
def test_listing_of_file_contents():
# FIXME1: if student executes cell more than once, variable _i2 is then not defined. Solution?
#PATH = "datasets/git_log_excerpt.csv"
# hard coded cell number: maybe a little bit fragile
#cell_input_from_sample_code = _i2
#assert PATH in cell_input_from_sample_code, \
#"The file %s should be read in." % PATH
# FIXME2: can't access the sample code cell's output here because of the use of 'print'
# test currently deactivated: too hard to create a table test case
assert True
1/1 tests passed
2. Reading in the dataset
The dataset was created by using the command git log --encoding=latin-1 --pretty="%at#%aN" in late 2017. The latin-1 encoded text output was saved in a header-less CSV file. In this file, each row is a commit entry with the following information:
timestamp: the time of the commit as a UNIX timestamp in seconds since 1970-01-01 00:00:00 (Git log placeholder "%at")author: the name of the author that performed the commit (Git log placeholder "%aN")
The columns are separated by the number sign #. The complete dataset is in the datasets/ directory. It is a gz-compressed csv file named git_log.gz.
# Loading in the pandas module as 'pd'
import pandas as pd
# Reading in the log file
git_log = pd.read_csv(
'datasets/git_log.gz',
sep='#',
encoding='latin-1',
header=None,
names=['timestamp', 'author']
)
# Printing the first 5 rows
git_log.head()
DEBUG:matplotlib:(private) matplotlib data path: /usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data
DEBUG:matplotlib:loaded rc file /etc/matplotlibrc
DEBUG:matplotlib:matplotlib version 3.2.2
DEBUG:matplotlib:interactive is False
DEBUG:matplotlib:platform is linux
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'numpy.polynomial.chebyshev', 'numpy.polynomial.legendre', 'numpy.polynomial.hermite', 'numpy.polynomial.hermite_e', 'numpy.polynomial.laguerre', 'numpy.random', 'numpy.random._pickle', 'numpy.random.mtrand', 'numpy.random.bit_generator', '_cython_0_29_21', 'numpy.random._common', 'secrets', 'numpy.random._bounded_integers', 'numpy.random._mt19937', 'numpy.random._philox', 'numpy.random._pcg64', 'numpy.random._sfc64', 'numpy.random._generator', 'numpy.ctypeslib', 'numpy.ma', 'numpy.ma.core', 'numpy.ma.extras', 'numpy.testing', 'numpy.testing._private', 'numpy.testing._private.utils', 'numpy.testing._private.decorators', 'numpy.testing._private.nosetester', 'pytz', 'pytz.exceptions', 'pytz.lazy', 'pytz.tzinfo', 'pytz.tzfile', 'pandas.compat', 'distutils', 'distutils.version', 'pandas.compat.chainmap', 'pandas.compat.numpy', 'pandas._libs', 'pandas._libs.tslib', 'pandas._libs.tslibs', 'pandas._libs.tslibs.conversion', 'pandas._libs.tslibs.np_datetime', '_cython_0_28_2', 'pandas._libs.tslibs.nattype', 'pandas._libs.tslibs.timedeltas', 'pandas._libs.tslibs.timezones', 'pandas._libs.tslibs.parsing', 'pandas._libs.tslibs.ccalendar', 'pandas._libs.tslibs.strptime', 'pandas._libs.tslibs.timestamps', 'pandas._libs.tslibs.fields', 'pandas._libs.hashtable', 'pandas._libs.missing', 'pandas._libs.lib', 'pandas.core', 'pandas.core.config_init', 'pandas.core.config', 'pandas.io', 'pandas.io.formats', 'pandas.io.formats.printing', 'pandas.core.dtypes', 'pandas.core.dtypes.inference', 'pandas.io.formats.console', 'pandas.io.formats.terminal', 'pandas.core.api', 'pandas.core.algorithms', 'pandas.core.dtypes.cast', 'pandas.core.dtypes.common', 'pandas._libs.algos', 'pandas.core.dtypes.dtypes', 'pandas.core.dtypes.generic', 'pandas.core.dtypes.base', 'pandas.errors', 'pandas.core.dtypes.missing', 'pandas.core.common', 'pandas.util', 'pandas.util._decorators', 'pandas._libs.properties', 'pandas.core.util', 'pandas.core.util.hashing', 'pandas._libs.hashing', 'pandas.core.arrays', 'pandas.core.arrays.base', 'pandas.compat.numpy.function', 'pandas.util._validators', 'pandas.core.arrays.categorical', 'pandas.core.accessor', 'pandas.core.base', 'pandas.core.nanops', 'pandas.core.missing', 'pandas.core.groupby', 'pandas.core.groupby.groupby', 'pandas.core.index', 'pandas.core.indexes', 'pandas.core.indexes.api', 'pandas.core.indexes.base', 'pandas._libs.index', 'pandas._libs.tslibs.period', 'pandas._libs.tslibs.frequencies', 'pandas._libs.tslibs.resolution', 'pandas.tseries', 'pandas.tseries.offsets', 'pandas.core.tools', 'pandas.core.tools.datetimes', 'dateutil.easter', 'pandas._libs.tslibs.offsets', 'pandas.tseries.frequencies', 'pandas._libs.join', 'pandas.core.ops', 'pandas._libs.ops', 'pandas.core.indexes.frozen', 'pandas.core.dtypes.concat', 'pandas.core.sorting', 'pandas.core.strings', 'pandas.core.indexes.category', 'pandas.core.indexes.multi', 'pandas.core.indexes.interval', 'pandas._libs.interval', 'pandas.core.indexes.datetimes', 'pandas.core.indexes.numeric', 'pandas.core.indexes.datetimelike', 'pandas.core.tools.timedeltas', 'pandas.core.indexes.timedeltas', 'pandas.core.indexes.range', 'pandas.core.indexes.period', 'pandas.core.frame', 'pandas.core.generic', 'pandas.core.indexing', 'pandas._libs.indexing', 'pandas.core.internals', 'pandas._libs.internals', 'pandas.core.sparse', 'pandas.core.sparse.array', 'pandas._libs.sparse', 'pandas.io.formats.format', 'pandas.io.common', 'csv', '_csv', 'mmap', 'pandas.core.series', 'pandas.core.indexes.accessors', 'pandas.plotting', 'pandas.plotting._misc', 'pandas.plotting._style', 'pandas.plotting._compat', 'pandas.plotting._tools', 'pandas.plotting._core', 'pandas.plotting._converter', 'matplotlib', 'matplotlib.cbook', 'gzip', 'matplotlib.cbook.deprecation', 'matplotlib.rcsetup', 'matplotlib.fontconfig_pattern', 'pyparsing', 'matplotlib.colors', 'matplotlib.docstring', 'matplotlib._color_data', 'cycler', 'matplotlib._version', 'matplotlib.ft2font', 'kiwisolver']
| timestamp | author | |
|---|---|---|
| 0 | 1502826583 | Linus Torvalds |
| 1 | 1501749089 | Adrian Hunter |
| 2 | 1501749088 | Adrian Hunter |
| 3 | 1501882480 | Kees Cook |
| 4 | 1497271395 | Rob Clark |
%%nose
def test_is_pandas_loaded_as_pd():
try:
pd # throws NameError
pd.DataFrame # throws AttributeError
except NameError:
assert False, "Module pandas not loaded as pd."
except AttributeError:
assert False, "Variable pd is used as short name for another module."
def test_is_git_log_data_frame_existing():
try:
# checks implicitly if git_log by catching the NameError exception
assert isinstance(git_log, pd.DataFrame), "git_log isn't a DataFrame."
except NameError as e:
assert False, "Variable git_log doesn't exist."
def test_has_git_log_correct_columns():
expected = ['timestamp', 'author']
assert all(git_log.columns.get_values() == expected), \
"Expected columns are %s" % expected
def test_is_logfile_content_read_in_correctly():
correct_git_log = pd.read_csv(
'datasets/git_log.gz',
sep='#',
encoding='latin-1',
header=None,
names=['timestamp', 'author'])
assert correct_git_log.equals(git_log), \
"The content of datasets/git_log.gz wasn't correctly read into git_log. Check the parameters of read_csv."
4/4 tests passed
3. Getting an overview
The dataset contains the information about every single code contribution (a "commit") to the Linux kernel over the last 13 years. We'll first take a look at the number of authors and their commits to the repository.
# calculating number of commits
number_of_commits = len(git_log)
# calculating number of authors
number_of_authors = len(git_log['author'].dropna().unique())
# printing out the results
print("%s authors committed %s code changes." % (number_of_authors, number_of_commits))
17385 authors committed 699071 code changes.
%%nose
def test_basic_statistics():
assert number_of_commits == len(git_log), \
"The number of commits should be right."
assert number_of_authors == len(git_log['author'].dropna().unique()), \
"The number of authors should be right."
1/1 tests passed
4. Finding the TOP 10 contributors
There are some very important people that changed the Linux kernel very often. To see if there are any bottlenecks, we take a look at the TOP 10 authors with the most commits.
# Identifying the top 10 authors
top_10_authors = git_log['author'].value_counts().head(10)
# Listing contents of 'top_10_authors'
top_10_authors.head(10)
Linus Torvalds 23361
David S. Miller 9106
Mark Brown 6802
Takashi Iwai 6209
Al Viro 6006
H Hartley Sweeten 5938
Ingo Molnar 5344
Mauro Carvalho Chehab 5204
Arnd Bergmann 4890
Greg Kroah-Hartman 4580
Name: author, dtype: int64
%%nose
def test_is_series_or_data_frame():
assert isinstance(top_10_authors, pd.Series) or isinstance(top_10_authors, pd.DataFrame), \
"top_10_authors isn't a Series or DataFrame, but of type %s." % type(top_10_authors)
def test_is_result_structurally_alright():
top10 = top_10_authors.squeeze()
# after a squeeze(), the DataFrame with one Series should be converted to a Series
assert isinstance(top10, pd.Series), \
"top_10_authors should only contain the data for authors and the number of commits."
def test_is_right_number_of_entries():
expected_number_of_entries = 10
assert len(top_10_authors.squeeze()) is expected_number_of_entries, \
"The number of TOP 10 entries should be %r. Be sure to store the result into the 'top_10_authors' variable." % expected_number_of_entries
def test_is_expected_top_author():
expected_top_author = "Linus Torvalds"
assert top_10_authors.squeeze().index[0] == expected_top_author, \
"The number one contributor should be %s." % expected_top_author
def test_is_expected_top_commits():
expected_top_commits = 23361
assert top_10_authors.squeeze()[0] == expected_top_commits, \
"The number of the most commits should be %r." % expected_top_commits
5/5 tests passed
5. Wrangling the data
For our analysis, we want to visualize the contributions over time. For this, we use the information in the timestamp column to create a time series-based column.
# converting the timestamp column
git_log['timestamp'] = pd.to_datetime(git_log['timestamp'], unit="s")
# summarizing the converted timestamp column
git_log['timestamp'].describe()
count 699071
unique 668448
top 2008-09-04 05:30:19
freq 99
first 1970-01-01 00:00:01
last 2037-04-25 08:08:26
Name: timestamp, dtype: object
%%nose
def test_timestamps():
START_DATE = '1970-01-01 00:00:01'
assert START_DATE in str(git_log['timestamp'].min()), \
'The first timestamp should be %s.' % START_DATE
END_DATE = '2037-04-25 08:08:26'
assert END_DATE in str(git_log['timestamp'].max()), \
'The last timestamp should be %s.' % END_DATE
1/1 tests passed
6. Treating wrong timestamps
As we can see from the results above, some contributors had their operating system's time incorrectly set when they committed to the repository. We'll clean up the timestamp column by dropping the rows with the incorrect timestamps.
# determining the first real commit timestamp
first_commit_timestamp = git_log.iloc[-1]['timestamp']
# determining the last sensible commit timestamp
last_commit_timestamp = pd.to_datetime('2018')
# filtering out wrong timestamps
corrected_log = git_log[
(git_log['timestamp'] >= first_commit_timestamp) &
(git_log['timestamp'] <= last_commit_timestamp)]
# summarizing the corrected timestamp column
corrected_log['timestamp'].describe()
count 698569
unique 667977
top 2008-09-04 05:30:19
freq 99
first 2005-04-16 22:20:36
last 2017-10-03 12:57:00
Name: timestamp, dtype: object
%%nose
def test_corrected_timestamps():
FIRST_REAL_COMMIT = '2005-04-16 22:20:36'
assert FIRST_REAL_COMMIT in str(corrected_log['timestamp'].min()), \
'The first real commit timestamp should be %s.' % FIRST_REAL_COMMIT
LAST_REAL_COMMIT = '2017-10-03 12:57:00'
assert LAST_REAL_COMMIT in str(corrected_log['timestamp'].max()), \
'The last real commit timestamp should be %s.' % LAST_REAL_COMMIT
1/1 tests passed
7. Grouping commits per year
To find out how the development activity has increased over time, we'll group the commits by year and count them up.
# Counting the no. commits per year
commits_per_year = corrected_log.groupby(
pd.Grouper(key='timestamp', freq='AS')).count()
# Listing the first rows
commits_per_year.head()
| author | |
|---|---|
| timestamp | |
| 2005-01-01 | 16229 |
| 2006-01-01 | 29255 |
| 2007-01-01 | 33759 |
| 2008-01-01 | 48847 |
| 2009-01-01 | 52572 |
%%nose
def test_number_of_commits_per_year():
YEARS = 13
assert len(commits_per_year) == YEARS, \
'Number of years should be %s.' % YEARS
def test_new_beginning_of_git_log():
START = '2005-01-01 00:00:00'
assert START in str(commits_per_year.index[0]), \
'DataFrame should start at %s' % START
2/2 tests passed
8. Visualizing the history of Linux
Finally, we'll make a plot out of these counts to better see how the development effort on Linux has increased over the the last few years.
# Setting up plotting in Jupyter notebooks
%matplotlib inline
# plot the data
commits_per_year.plot(kind='bar', title="Commits per year (Linux kernel)", legend=False)
DEBUG:matplotlib:CACHEDIR=/home/repl/.cache/matplotlib
DEBUG:matplotlib:matplotlib data path: /usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data
DEBUG:matplotlib.font_manager:font search path [PosixPath('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf'), PosixPath('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/afm'), PosixPath('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/pdfcorefonts')]
INFO:matplotlib.font_manager:generated new fontManager
DEBUG:matplotlib:CONFIGDIR=/home/repl/.config/matplotlib
DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.
DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.
<matplotlib.axes._subplots.AxesSubplot at 0x7fee44b20f60>
DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold_Italic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbd.ttf) normal normal 700 normal>) = 6.698636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (Impact.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (Arial_Black.ttf) normal normal black normal>) = 10.525
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal 700 normal>) = 3.9713636363636367
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold_Italic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbi.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Italic.ttf) italic normal 400 normal>) = 7.413636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman.ttf) normal normal roman normal>) = 10.145
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Italic.ttf) italic normal roman normal>) = 11.145
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold_Italic.ttf) italic normal 700 normal>) = 4.971363636363637
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (Webdings.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbd.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (Andale_Mono.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana.ttf) normal normal 400 normal>) = 3.6863636363636365
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold_Italic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal 700 normal>) = 4.971363636363637
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold_Italic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Italic.ttf) italic normal 400 normal>) = 4.6863636363636365
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial.ttf) normal normal 400 normal>) = 6.413636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (ariblk.ttf) normal normal black normal>) = 10.525
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (andalemo.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold.ttf) normal normal 700 normal>) = 3.9713636363636367
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold.ttf) normal normal 700 normal>) = 6.698636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbi.ttf) italic normal 700 normal>) = 7.698636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold_Italic.ttf) italic normal 700 normal>) = 7.698636363636363
DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.
DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996
DEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold_Italic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbd.ttf) normal normal 700 normal>) = 6.698636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (Impact.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (Arial_Black.ttf) normal normal black normal>) = 10.525
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal 700 normal>) = 3.9713636363636367
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold_Italic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbi.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Italic.ttf) italic normal 400 normal>) = 7.413636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman.ttf) normal normal roman normal>) = 10.145
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Italic.ttf) italic normal roman normal>) = 11.145
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold_Italic.ttf) italic normal 700 normal>) = 4.971363636363637
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (Webdings.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbd.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (Andale_Mono.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana.ttf) normal normal 400 normal>) = 3.6863636363636365
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold_Italic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal 700 normal>) = 4.971363636363637
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold_Italic.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Italic.ttf) italic normal 400 normal>) = 4.6863636363636365
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial.ttf) normal normal 400 normal>) = 6.413636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (ariblk.ttf) normal normal black normal>) = 10.525
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Italic.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (andalemo.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold.ttf) normal normal 700 normal>) = 3.9713636363636367
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold.ttf) normal normal 700 normal>) = 10.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold.ttf) normal normal 700 normal>) = 6.698636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbi.ttf) italic normal 700 normal>) = 7.698636363636363
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal 700 normal>) = 11.335
DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold_Italic.ttf) italic normal 700 normal>) = 7.698636363636363
DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.
DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.
%%nose
def test_call_to_plot():
# FIXME: Different results local and on build server.
# - local (expected): AssertionError: Plot type should be a bar chart.
# - build server: NameError: name '_i20' is not defined
# deactivating tests
#assert "kind='bar'" in _i20, "Plot type should be a bar chart."
# test currently deactivated: too hard to create a table test case
assert True
1/1 tests passed
9. Conclusion
Thanks to the solid foundation and caretaking of Linux Torvalds, many other developers are now able to contribute to the Linux kernel as well. There is no decrease of development activity at sight!
# calculating or setting the year with the most commits to Linux
year_with_most_commits = 2016
%%nose
def test_year_with_most_commits():
assert str(year_with_most_commits).endswith("16") , \
"Write the year with the most commits as 20??, but with ?? replaced."
1/1 tests passed
🚀 About Me
I am Naem Azam. I’m Self-taught Python Programmer And an open-source enthusiast and maintainer.
Contributing
Contributions are always welcome!
make Pull Request for ways to get started.
