<commit_before># data8; foundation
datascience==0.17.0
matplotlib==3.4.3
ipympl==0.7.0
pandas==1.3.2
scipy==1.7.1
statsmodels==0.12.2
okpy==1.18.1
folium==0.12.1
#
# r
jupyter-server-proxy==3.1.0
jupyter-rsession-proxy==1.4
jupyter-shiny-proxy==1.1
#
# cogsci88;
ggplot==0.11.5
#
# geog88; spring 2018
mplleaflet==0.0.5
geopandas==0.9.0
geopy==2.2.0
pysal==2.5.0
rtree==0.9.7
netCDF4==1.5.7
#
# cogsci131; spring 2018
nose==1.3.7
#
# modules
scikit-learn==0.24.2
seaborn==0.11.2
bokeh==2.3.3
decorator==5.0.9
networkx==2.6.2
# pygame==1.9.6
nltk==3.6.2
beautifulsoup4==4.9.3
spacy==3.1.1
qgrid==1.3.1
nb2pdf==0.6.2
#
# ls 88-3; neuro
lxml==4.6.3
tqdm==4.62.1
mne==0.23.0
nibabel==3.2.1
h5py==3.3.0
pillow==8.3.1
numexpr==2.7.3
openpyxl==3.0.7
nilearn==0.8.0

# phys 151;
emcee==3.1.0
daft==0.1.2
corner==2.2.1
#
# ce88;
pymdptoolbox==4.0-b3
#
# data-x; DL
tensorflow==2.6.0
scikit-image==0.18.2
tables==3.6.1
opencv-python==4.5.3.56

# astr 128/256; spring 2021
astroquery==0.4.3
astropy==4.3.1
dustmaps==1.0.9
george==0.4.0
exoplanet==0.5.1
torch==1.9.0
torchvision==0.10.0
pyvo==1.1
joblib==1.0.1
theano-pymc==1.1.2
pymc3==3.11.2

# eep 153; spring 2019
requests==2.26.0
Pint==0.17
# Google spreadsheets, <NAME> / <NAME> / Modules
gspread-pandas==2.3.0
gspread==4.0.1

# eps 109; fall 2019
ffmpeg-python==0.2.0
# see https://github.com/berkeley-dsep-infra/datahub/issues/1796
Cartopy==0.19.0.post1 --no-binary Cartopy

# data 88e; spring 2021
plotly==5.2.1
mpmath==1.2.1
# sympy==1.6.2
chart-studio==1.1.0
csaps==1.0.4
nbforms==0.5.1

# issue #875, global 150Q/pacs 190 - fall 2019
wordcloud==1.8.1

# issue #929, SW 282 - fall 2019
pyreadstat==1.1.2

# issue 954, EPS24 - fall 2019
xarray==0.19.0

# issue 1001, Physics 188/288 - fall 2019
umap-learn==0.5.1
hdbscan==0.8.27

# espm 125/bio 105; fall 2019
# see https://github.com/berkeley-dsep-infra/datahub/issues/1796
shapely==1.7.1 --no-binary shapely
pyvcf==0.6.8
bitarray==2.3.0
nlmpy==1.0.1

# physics 188/288 fall, 2019
getdist==1.3.1
tensorflow-hub==0.12.0
tensorflow-probability==0.13.0

# mcb 163l, fall 2019
pydot==1.4.2
allensdk==2.12.2

# cs16A/B, spring 2020
lcapy==0.96

# EPS 256, https://github.com/berkeley-dsep-infra/datahub/issues/1775
obspy==1.2.2

# ds198 mch infodemiology, fall 2020/spring 2021
# google apis
google-api-python-client==2.15.0
google-auth-httplib2==0.1.0
google-auth-oauthlib==0.4.5
google==3.0.0

# issue 1847; LS 22 spring 2021
graphviz==0.17

# issue #1903, data h195A fall 2020
habanero==0.7.4

# https://github.com/berkeley-dsep-infra/datahub/issues/1981
ipycanvas==0.9.0

# Issue #1222, for PH 142 - Spring 2020
PyPDF2==1.26.0

# data100 scientific packages
ray==1.5.2
xlrd==2.0.1

# data100 visualization
colorlover==0.3.0
cufflinks==0.17.3
altair==4.1.0

# data100 tools to access things
tweepy==3.10.0
pytz==2021.1
psycopg2==2.9.1

# data100 teaching
jassign==0.0.7
dsassign==0.0.8

# data102
dask==2021.8.0
distributed==2021.8.0
keras-applications==1.0.8
keras-preprocessing==1.1.2
keras==2.6.0
keras-vis==0.4.1
plotly-express==0.4.1
cytoolz==0.11.0
pyro-ppl==1.7.0
lime==0.2.0.1
shap==0.39.0

# prob140 2021 Spring
prob140==0.4.1.5
sympy==1.8

# ESPM 167 - https://github.com/berkeley-dsep-infra/datahub/issues/2278
contextily==1.1.0

# INDENG 142 Spring 2021 (future semesters as well) - https://github.com/berkeley-dsep-infra/datahub/issues/2314
fancyimpute==0.6.0

# JupyterLab pypi extensions
jupyterlab-geojson==3.1.2

# LBL summer research internship in materials informatics. Jupyter Book curriculum at https://enze-chen.github.io/mi-book/
pymatgen==2022.0.11
matminer==0.7.4<commit_msg>Adding geonomics for issue #2696<commit_after># data8; foundation
datascience==0.17.0
matplotlib==3.4.3
ipympl==0.7.0
pandas==1.3.2
scipy==1.7.1
statsmodels==0.12.2
okpy==1.18.1
folium==0.12.1
#
# r
jupyter-server-proxy==3.1.0
jupyter-rsession-proxy==1.4
jupyter-shiny-proxy==1.1
#
# cogsci88;
ggplot==0.11.5
#
# geog88; spring 2018
mplleaflet==0.0.5
geopandas==0.9.0
geopy==2.2.0
pysal==2.5.0
rtree==0.9.7
netCDF4==1.5.7
#
# cogsci131; spring 2018
nose==1.3.7
#
# modules
scikit-learn==0.24.2
seaborn==0.11.2
bokeh==2.3.3
decorator==5.0.9
networkx==2.6.2
# pygame==1.9.6
nltk==3.6.2
beautifulsoup4==4.9.3
spacy==3.1.1
qgrid==1.3.1
nb2pdf==0.6.2
#
# ls 88-3; neuro
lxml==4.6.3
tqdm==4.62.1
mne==0.23.0
nibabel==3.2.1
h5py==3.3.0
pillow==8.3.1
numexpr==2.7.3
openpyxl==3.0.7
nilearn==0.8.0

# phys 151;
emcee==3.1.0
daft==0.1.2
corner==2.2.1
#
# ce88;
pymdptoolbox==4.0-b3
#
# data-x; DL
tensorflow==2.6.0
scikit-image==0.18.2
tables==3.6.1
opencv-python==4.5.3.56

# astr 128/256; spring 2021
astroquery==0.4.3
astropy==4.3.1
dustmaps==1.0.9
george==0.4.0
exoplanet==0.5.1
torch==1.9.0
torchvision==0.10.0
pyvo==1.1
joblib==1.0.1
theano-pymc==1.1.2
pymc3==3.11.2

# eep 153; spring 2019
requests==2.26.0
Pint==0.17
# Google spreadsheets, <NAME> / <NAME> / Modules
gspread-pandas==2.3.0
gspread==4.0.1

# eps 109; fall 2019
ffmpeg-python==0.2.0
# see https://github.com/berkeley-dsep-infra/datahub/issues/1796
Cartopy==0.19.0.post1 --no-binary Cartopy

# data 88e; spring 2021
plotly==5.2.1
mpmath==1.2.1
# sympy==1.6.2
chart-studio==1.1.0
csaps==1.0.4
nbforms==0.5.1

# issue #875, global 150Q/pacs 190 - fall 2019
wordcloud==1.8.1

# issue #929, SW 282 - fall 2019
pyreadstat==1.1.2

# issue 954, EPS24 - fall 2019
xarray==0.19.0

# issue 1001, Physics 188/288 - fall 2019
umap-learn==0.5.1
hdbscan==0.8.27

# espm 125/bio 105; fall 2019
# see https://github.com/berkeley-dsep-infra/datahub/issues/1796
shapely==1.7.1 --no-binary shapely
pyvcf==0.6.8
bitarray==2.3.0
nlmpy==1.0.1

# physics 188/288 fall, 2019
getdist==1.3.1
tensorflow-hub==0.12.0
tensorflow-probability==0.13.0

# mcb 163l, fall 2019
pydot==1.4.2
allensdk==2.12.2

# cs16A/B, spring 2020
lcapy==0.96

# EPS 256, https://github.com/berkeley-dsep-infra/datahub/issues/1775
obspy==1.2.2

# ds198 mch infodemiology, fall 2020/spring 2021
# google apis
google-api-python-client==2.15.0
google-auth-httplib2==0.1.0
google-auth-oauthlib==0.4.5
google==3.0.0

# issue 1847; LS 22 spring 2021
graphviz==0.17

# issue #1903, data h195A fall 2020
habanero==0.7.4

# https://github.com/berkeley-dsep-infra/datahub/issues/1981
ipycanvas==0.9.0

# Issue #1222, for PH 142 - Spring 2020
PyPDF2==1.26.0

# data100 scientific packages
ray==1.5.2
xlrd==2.0.1

# data100 visualization
colorlover==0.3.0
cufflinks==0.17.3
altair==4.1.0

# data100 tools to access things
tweepy==3.10.0
pytz==2021.1
psycopg2==2.9.1

# data100 teaching
jassign==0.0.7
dsassign==0.0.8

# data102
dask==2021.8.0
distributed==2021.8.0
keras-applications==1.0.8
keras-preprocessing==1.1.2
keras==2.6.0
keras-vis==0.4.1
plotly-express==0.4.1
cytoolz==0.11.0
pyro-ppl==1.7.0
lime==0.2.0.1
shap==0.39.0

# prob140 2021 Spring
prob140==0.4.1.5
sympy==1.8

# ESPM 167 - https://github.com/berkeley-dsep-infra/datahub/issues/2278
contextily==1.1.0

# INDENG 142 Spring 2021 (future semesters as well) - https://github.com/berkeley-dsep-infra/datahub/issues/2314
fancyimpute==0.6.0

# JupyterLab pypi extensions
jupyterlab-geojson==3.1.2

# LBL summer research internship in materials informatics. Jupyter Book curriculum at https://enze-chen.github.io/mi-book/
pymatgen==2022.0.11
matminer==0.7.4

# IB 105 / ESPM 125, Fall 2021 - https://github.com/berkeley-dsep-infra/datahub/issues/2696
geonomics==1.3.0
{'fraction_non_alphanumeric': 0.19322681960944968, 'fraction_numerical': 0.18039055032090673, 'mean_word_length': 3.922043010752688, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 15, 'lorem ipsum': 0, 'www.': 0, 'xml': 2}, 'pii_count': 6, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2553984', 'n_tokens_mistral': 4180, 'n_tokens_neox': 3640, 'n_words': 581}