repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
google/sentencepiece | python/sentencepiece_python_module_example.ipynb | apache-2.0 | !pip install sentencepiece
!wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt
"""
Explanation: <a href="https://colab.research.google.com/github/google/sentencepiece/blob/master/python/sentencepiece_python_module_example.ipynb" target="_parent"><img src="https://colab.research.google.... |
UWSEDS/LectureNotes | week_4/Exceptions.ipynb | bsd-2-clause | def divide(numerator, denominator):
result = numerator/denominator
print("result = %f" % result)
divide(1.0, 0)
def divide1(numerator, denominator):
try:
GARBAGE
result = numerator/denominator
print("result = %f" % result)
except (ZeroDivisionError, NameError) as err:
i... |
opencobra/cobrapy | documentation_builder/faq.ipynb | gpl-2.0 | from cobra.io import load_model
model = load_model("iYS1720")
for metabolite in model.metabolites:
metabolite.id = f"test_{metabolite.id}"
try:
model.metabolites.get_by_id(model.metabolites[0].id)
except KeyError as e:
print(repr(e))
"""
Explanation: FAQ
This document will address frequently asked questi... |
wuhuikai/GP-GAN | GP_GAN.ipynb | mit | !gdown https://drive.google.com/uc?id=0Bybnpq8dvwudXzZFLU1iOXVOZ3c
!gdown https://drive.google.com/uc?id=0Bybnpq8dvwudRzRLUm1xYmt6R1E
!gdown https://drive.google.com/uc?id=0Bybnpq8dvwudOEFELWkzcGJJVjA
"""
Explanation: Download the pretrained models
End of explanation
"""
%cd ..
!pip install -r requirements/test/req... |
chongxi/xike_hls_module | spkDect/How spkDect Works.ipynb | mit | din = np.fromfile('spkDect_test_spk.bin', dtype='float32')
data = np.zeros((40,8))
k = 0
for t in range(40):
for ch in range(8):
data[t,ch] = din[k]
k+=1
fig,ax = subplots(1,2,figsize=(15,5))
ax[0].plot(data[:,:4], '-o');
ax[1].plot(data[:,4:], '-o');
"""
Explanation: Load test file
End of expla... |
SebastianBocquet/pygtc | demo.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina' # For mac users with Retina display
from matplotlib import pyplot as plt
import numpy as np
import pygtc
"""
Explanation: Example 1: Making a GTC/triangle plot with pygtc
This example is built from a jupyter notebook hosted on the pyGTC GitHub repositor... |
piskvorky/gensim | docs/notebooks/topic_coherence_model_selection.ipynb | lgpl-2.1 | from __future__ import print_function
import os
import re
import logging
from collections import OrderedDict
from gensim.corpora import TextCorpus, MmCorpus
from gensim import utils, models
logging.basicConfig(level=logging.ERROR) # disable warning logging
"""
Explanation: Performing Model Selection Using Topic Co... |
quoniammm/happy-machine-learning | Udacity-ML/finding_donors-master/finding_donors.ipynb | mit | # 为这个项目导入需要的库
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # 允许为DataFrame使用display()
# 导入附加的可视化代码visuals.py
import visuals as vs
# 为notebook提供更加漂亮的可视化
%matplotlib inline
# 导入人口普查数据
data = pd.read_csv("census.csv")
# 成功 - 显示第一条记录
display(data.head(n=1))
"""
Explan... |
h-mayorquin/time_series_basic | presentations/2016-01-23(Scaling Analysis of Wall Street).ipynb | bsd-3-clause | import numpy as np
import h5py
from sklearn import svm, cross_validation
"""
Explanation: Scaling analysis of Nexa on Wall Street
Here I will present a scaling analysis of Nexa wall street with regards to the number of clusters in the sensors space and the number of clusters in the data space.
Load the libraries
End ... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-3/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Transport, ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/introduction_to_tensorflow/labs/load_diff_filedata.ipynb | apache-2.0 | import functools
import numpy as np
import tensorflow as tf
print("TensorFlow version: ", tf.version.VERSION)
TRAIN_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
TEST_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/eval.csv"
train_file_path = tf.keras.utils.get_file("train... |
magenta/magenta-demos | jupyter-notebooks/Sketch_RNN_TF_To_JS_Tutorial.ipynb | apache-2.0 | # import the required libraries
import numpy as np
import time
import random
import codecs
import collections
import os
import math
import json
import tensorflow as tf
from six.moves import xrange
# libraries required for visualisation:
from IPython.display import SVG, display
import svgwrite # conda install -c omnia... |
tensorflow/workshops | extras/amld/notebooks/solutions/1_data.ipynb | apache-2.0 | data_path = '/content/gdrive/My Drive/amld_data'
# Alternatively, you can also store the data in a local directory. This method
# will also work when running the notebook in Jupyter instead of Colab.
# data_path = './amld_data
if data_path.startswith('/content/gdrive/'):
from google.colab import drive
assert data_... |
google/learned_optimization | docs/notebooks/Part2_CustomTasks.ipynb | apache-2.0 | !pip install git+https://github.com/google/learned_optimization.git
import numpy as np
import jax.numpy as jnp
import jax
from matplotlib import pylab as plt
from learned_optimization.outer_trainers import full_es
from learned_optimization.outer_trainers import truncated_pes
from learned_optimization.outer_trainers i... |
TheOregonian/articles | health/vaccination/Oregon Herd Immunity Analysis 2016.ipynb | mit | import math
import numpy as np
import pandas as pd
"""
Explanation: “Low vaccination rates put some Oregon schools at high risk for measles”
Lynne Terry and Melissa Lewis, <em>The Oregonian</em>, Feb. 11, 2018
End of explanation
"""
school_types_df = pd.read_csv('data/schools_types.csv', dtype={'ID': s... |
gfeiden/Notebook | Projects/upper_sco_age/apsidal_motion_age.ipynb | mit | def c2(masses, radii, e, a, rotation=None):
f = (1.0 - e**2)**-2
g = (8.0 + 12.0*e**2 + e**4)*f**(5.0/2.0) / 8.0
if rotation == None:
omega_ratio_sq = 0.0
elif rotation == 'synchronized':
omega_ratio_sq = (1.0 + e)/(1.0 - e)**3
else:
omega_ratio_sq = 0.0
c2_0 = (ome... |
quoniammm/mine-tensorflow-examples | dpAI/Python+Basics+With+Numpy+v3.ipynb | mit | ### START CODE HERE ### (≈ 1 line of code)
test = None
### END CODE HERE ###
print ("test: " + test)
"""
Explanation: Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help familiarize y... |
CalPolyPat/phys202-2015-work | assignments/assignment05/InteractEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import math
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 2
Imports
End of explanation
"""
def plot_sine1(a,b):
f = plt.figure()
ax = plt.subplot... |
WNoxchi/Kaukasos | FAI_old/statefarm-sample-codealong.ipynb | mit | import theano
import os, sys
sys.path.insert(1, os.path.join(os.getcwd(), 'utils'))
%matplotlib inline
from __future__ import print_function, division
# path = "data/sample/"
path = "data/statefarm/sample/"
import utils; reload(utils)
from utils import *
from IPython.display import FileLink
# batch_size = 64
batch_s... |
robertoalotufo/ia898 | src/comb.ipynb | mit | import numpy as np
def comb(shape, delta, offset):
shape = np.array(shape)
assert shape.size <= 3
g = np.zeros(shape)
if shape.size == 1:
g[offset::delta] = 1
elif shape.size == 2:
g[offset[0]::delta[0], offset[1]::delta[1]] = 1
elif shape.size == 3:
g[offset[0]::delta[... |
rishuatgithub/MLPy | hugging-face/1. Introduction-hugging-face (Pipeline).ipynb | apache-2.0 | from transformers import pipeline
classifier = pipeline('sentiment-analysis')
classifier("I've been waiting for a HuggingFace course my whole life.")
## passing multiple sentences
classifier([
"I've been waiting for a HuggingFace course my whole life.",
"I hate this so much!"
])
"""
Explanation: What is N... |
saashimi/code_guild | interactive-coding-challenges/arrays_strings/compress/compress_challenge.ipynb | mit | def compress_string(string):
# TODO: Implement me
pass
"""
Explanation: <small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem: Compress a string such that 'AAABCCDDDD' becomes 'A3B1C2D4'. Only compress the string if it saves space.
... |
Akshay-lodha/MTP | notebooks/LungCancerDetection.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import glob
import SimpleITK as sitk
from PIL import Image
from scipy.misc import imread
%matplotlib inline
from IPython.display import clear_output
pd.options.mode.chained_assignment = None
"""
Explanation: I... |
mfouesneau/pyphot | examples/Sun_Vega.ipynb | mit | %matplotlib inline
import pylab as plt
import numpy as np
import sys
sys.path.append('../')
import pyphot
"""
Explanation: pyphot - A tool for computing photometry from spectra
Some examples are provided in this notebook
Full documentation available at http://mfouesneau.github.io/docs/pyphot/
End of explanation
"""
... |
tleonhardt/CodingPlayground | dataquest/JupyterNotebook/Basics.ipynb | mit | import pandas as pd
white_house = pd.read_csv("../data/2015_white_house.csv")
print(white_house.shape)
print(white_house.iloc[-1])
white_house
%matplotlib notebook
import matplotlib.pyplot as plt
plt.hist(white_house["Salary"])
plt.show()
"""
Explanation: White House Employee Data
The 2015_white_house.csv* file co... |
openai/openai-python | examples/finetuning/finetuning-classification.ipynb | mit | from sklearn.datasets import fetch_20newsgroups
import pandas as pd
import openai
categories = ['rec.sport.baseball', 'rec.sport.hockey']
sports_dataset = fetch_20newsgroups(subset='train', shuffle=True, random_state=42, categories=categories)
"""
Explanation: Fine tuning classification example
We will fine-tune an a... |
JannesKlaas/MLiFC | Week 2/Ch. 9 - Feature engineering & data preparation.ipynb | mit | # As always, first some libraries
# Numpy handles matrices
import numpy as np
# Pandas handles data
import pandas as pd
# Matplotlib is a plotting library
import matplotlib.pyplot as plt
# Set matplotlib to render imediately
%matplotlib inline
# Seaborn is a plotting library built on top of matplotlib that can handle ... |
michrawson/nyu_ml_lectures | notebooks/04.1 Cross Validation.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X, y = iris.data, iris.target
classifier = KNeighborsClassifier()
"""
Explanation: Cross-Validation and scoring methods
To evaluate how well our supervised models generalize, so far we split our data into a t... |
wikistat/Intro-Python | Cal5-PythonSklearnApprent.ipynb | mit | # Importations
import matplotlib.pyplot as plt
from sklearn import datasets
%matplotlib inline
# les données
digits = datasets.load_digits()
# Contenu et mode d'obtention
print(digits)
images_and_labels = list(zip(digits.images,
digits.target))
for index, (image, label) in enumerate(images_and_labels[:8]):
... |
mathLab/RBniCS | tutorials/07_nonlinear_elliptic/tutorial_nonlinear_elliptic_exact.ipynb | lgpl-3.0 | from dolfin import *
from rbnics import *
"""
Explanation: Tutorial 07 - Non linear Elliptic problem
Keywords: exact parametrized functions, POD-Galerkin
1. Introduction
In this tutorial, we consider a non linear elliptic problem in a two-dimensional spatial domain $\Omega=(0,1)^2$. We impose a homogeneous Dirichlet c... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/cnrm-esm2-1/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-esm2-1', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: CNRM-ESM2-1
Topic: Aerosol
Sub-Topics: Transpo... |
robertoalotufo/ia898 | dev/2017-01-14-RAL-Aprendendo a usar o nbconvert geração de HTML e Python.ipynb | mit | !ls
!jupyter nbconvert 2016-12-20-RMS-JupShow.ipynb
"""
Explanation: nbconvert - Convertendo o notebook para HTML e arquivos Python
O nbconvert permite a conversão de notebooks em outros formatos, permitindo:
- apresentação do notebook em PDF
- publicação de texto com LaTeX, permitindo a geração de artigos criados po... |
nikbearbrown/Deep_Learning | NEU/Singh_Palod_DL/Generative Adversarial Networks/GANS for Text.ipynb | mit | import tensorflow as tf
import numpy as np
import os
from collections import Counter
from nltk.tokenize import TweetTokenizer
import codecs
from random import randint
tf.__version__
with codecs.open(os.path.join('../data', 'sent.csv'), 'r', encoding='utf-8') as f:
corpus_line_by_line = f.read().lower().split("\n"... |
sdpython/cpyquickhelper | _doc/notebooks/cbenchmark_sum_type.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: Measures a vector sum with different accumulator type
This notebook compares how fast is the sum if the accumulator used to store the sum is of a different type than the summed elements.
End of explanation
"""
try:
... |
domino14/macondo | notebooks/superleaves/make_pseudo_superleaves.ipynb | gpl-3.0 | t0 = time.time()
maximum_superleave_length = 6
leaves = {i:sorted(list(set(list(combinations(tilebag,i))))) for i in
range(1,maximum_superleave_length+1)}
for i in range(1,maximum_superleave_length+1):
leaves[i] = [''.join(leave) for leave in leaves[i]]
t1 = time.time()
print('Calculated superleav... |
mbuchove/notebook-wurk-b | stats/astro283_hw6.ipynb | mit | import numpy as np
from scipy import optimize, special
from matplotlib import pyplot as plt
from astropy.io import fits
%matplotlib inline
"""
Explanation: <h1> Homework Set 6</h1>
Matt Buchovecky
Astro 283 / Fitz
End of explanation
"""
# open the data file and load data into a list of points
infile = open("./samp... |
deisi/SFG2D | docs/Tutorial_Pump_Probe.ipynb | mit | # Import and load some packages
# makes python a little like matlab.
%pylab
import sfg2d # This imports the sfg2d package
"""
Explanation: You can run a cell by hitting Shift + Enter on the keyboard
A more extensive tutorial can be found here Skip to: "Getting Started With Jupyter Notebooks" if you are hazy.
End ... |
feststelltaste/software-analytics | notebooks/Defect Analysis using pandas.ipynb | gpl-3.0 | URL = "https://youtrack.jetbrains.com/api/issues?" \
"$top=1000&"\
"fields=created,resolved&"\
"query=Type:%20Bug%20Project%3A%20%7BCode%20With%20Me%7D"
URL
"""
Explanation: Introduction
Defect Analysis is a classic analysis technique to get insights into how buggy your system might be. One variant of Defect Analysis... |
andreyf/machine-learning-examples | sklearn/sklearn.random_forest.ipynb | gpl-3.0 | %pylab inline
from sklearn import ensemble, cross_validation, learning_curve, metrics
import numpy as np
import pandas as pd
"""
Explanation: Sklearn
sklearn.ensemble.RandomForestClassifier
документация: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
End of explanatio... |
sympy/scipy-2017-codegen-tutorial | notebooks/60-chemical-kinetics-reaction-diffusion.ipynb | bsd-3-clause | reactions = [
('k', {'A': 1}, {'B': 1, 'A': -1}),
]
names, params = 'A B'.split(), ['k']
"""
Explanation: In this notebook we will add diffusion in addition to reactions. We will first study the simplest possible chemical reaction set:
$$
A \overset{k}{\rightarrow} B
$$
we will consider a flat geometry where we as... |
AllenDowney/ModSim | python/soln/chap13.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
dariox2/CADL | session-1/lecture-1.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
"""
Explanation: Session 1: Introduction to Tensorflow
<p class='lead'>
Creative Applications of Deep Learning with Tensorflow<br />
Parag K. Mital<br />
Kadenze, Inc.<br />
</p>
<a name="learning-goals"></a>
Learning Goals
... |
GoogleCloudPlatform/dfcx-scrapi | examples/intent_analysis/compare_intent_tp_data.ipynb | apache-2.0 | # If you haven't already, make sure you install the `dfcx-scrapi` library
!pip install dfcx-scrapi
"""
Explanation: Introduction
In this notebook, we will show you how to evaluate the levenshtein ration between the training phrases of two intents.
Prerequisites
Ensure you have a GCP Service Account key with the Dial... |
rashikaranpuria/Machine-Learning-Specialization | Machine Learning Foundations: A Case Study Approach/Assignment_one/Predicting house prices.ipynb | mit | import graphlab
"""
Explanation: Fire up graphlab create
End of explanation
"""
sales = graphlab.SFrame('home_data.gl/home_data.gl')
sales
"""
Explanation: Load some house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
End of explanation
"""
graphlab.... |
CartoDB/cartoframes | docs/examples/data_visualization/grid_layout/layout_viewport.ipynb | bsd-3-clause | from cartoframes.auth import set_default_credentials
set_default_credentials('cartoframes')
"""
Explanation: Layout viewport
Use the Layout class to create a variety of map views for comparison.
For more information, run help(Layout).
The first example sets a common viewport for all maps while the second sets two dif... |
kmyokoyama/mathings | taylor.ipynb | apache-2.0 | def taylor(f, x, var, max_terms=6, x0=0):
def taylor_terms():
for k in range(max_terms):
term = (sp.diff(f, var, k).subs(var, x0).evalf()/np.math.factorial(k)) * (x - x0)**k
yield term
serie = 0
for term in taylor_terms():
serie += term
return se... |
mne-tools/mne-tools.github.io | stable/_downloads/0ae9af7fe8e152942071a6b23acc0958/40_sensor_locations.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
# the following import is required for matplotlib < 3.2:
from mpl_toolkits.mplot3d import Axes3D # noqa
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
... |
watsonyanghx/CS231n | assignment3/ImageGradients.ipynb | mit | # As usual, a bit of setup
import time, os, json
import numpy as np
import skimage.io
import matplotlib.pyplot as plt
from cs231n.classifiers.pretrained_cnn import PretrainedCNN
from cs231n.data_utils import load_tiny_imagenet
from cs231n.image_utils import blur_image, deprocess_image
%matplotlib inline
plt.rcParams... |
SSQ/Coursera-UW-Machine-Learning-Classification | Programming Assignment 1/Predicting sentiment from product reviews with pandas.ipynb | mit | products = pd.read_csv('amazon_baby.csv')
"""
Explanation: Load Amazon dataset
1.
Load the dataset consisting of baby product reviews on Amazon.com. Store the data in a data frame products.
End of explanation
"""
products = products.fillna({'review':''}) # fill in N/A's in the review column
def remove_punctuation(... |
UWSEDS/LectureNotes | Spring2018/Debugging-and-Exceptions/Exceptions.ipynb | bsd-2-clause | def divide1(numerator, denominator):
try:
result = numerator/denominator
print("result = %f" % result)
except:
print("You can't divide by 0!!")
divide1(1.0, 2)
divide1(1.0, 0)
divide1("x", 2)
"""
Explanation: Exceptions
An exception is an event, which occurs during the execution of a progr... |
donaghhorgan/COMP9033 | labs/10 - Association rule mining.ipynb | gpl-3.0 | import itertools
import pandas as pd
import pyspark
from pyspark.ml.fpm import FPGrowth
from pyspark.sql.functions import split
"""
Explanation: Lab 10: Association rule mining
Introduction
In this lab, we're going to look at association rule mining for grocery data. At the end of the lab, you should be able to:
Fin... |
McDermott-Group/Simulation | Projects/chargeNoiseMaskDesignCorr.ipynb | gpl-2.0 | ri = (40,50,60,70,80,90,100,108)
ro = (40.7,52.9,69,90.5,123,182,305,500)
#Cq = (46.3,47.0, 46.9,47.0,47.0,47.0,46.9,46.9)
Cq = (46.3,49.5, 46.9,49.5,47.0,51.7,46.9,54)
Cg = (1.5, 1.44, 1.47, 1.45, 1.46, 1.49, 1.42, 1.48)
Cgnd = (39.0, 30.9, 32.8, 35.6, 41.9, 54.1, 74.0, 108)
r_xy = (np.nan, 110, np.nan, 175, np.nan, 2... |
Kaggle/learntools | notebooks/geospatial/raw/tut1.ipynb | apache-2.0 | import geopandas as gpd
"""
Explanation: Introduction
In this micro-course, you'll learn about different methods to wrangle and visualize geospatial data, or data with a geographic location.
<center>
<img src="https://i.imgur.com/v6ZUGgI.png"><br/>
</center>
Along the way, you'll offer solutions to several real-world ... |
statsmodels/statsmodels.github.io | v0.12.0/examples/notebooks/generated/stationarity_detrending_adf_kpss.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: Stationarity and detrending (ADF/KPSS)
Stationarity means that the statistical properties of a time series i.e. mean, variance and covariance do not change over time. Many statistical... |
jrrembert/cybernetic-organism | dato/deeplearning/Deep Features for Image Retrieval.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Building an image retrieval system with deep features
Fire up GraphLab Create
End of explanation
"""
image_train = graphlab.SFrame('image_train_data/')
image_test = graphlab.SFrame('image_test_data/')
"""
Explanation: Load the CIFAR-10 dataset
We will use a popular benchmark dataset... |
ES-DOC/esdoc-jupyterhub | notebooks/ncar/cmip6/models/sandbox-3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncar', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NCAR
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbul... |
pombredanne/gensim | docs/notebooks/dtm_example.ipynb | lgpl-2.1 | import logging
import os
from gensim import corpora, utils
from gensim.models.wrappers.dtmmodel import DtmModel
import numpy as np
"""
Explanation: DTM Example
In this example we will present a sample usage of the DTM wrapper. Prior to using this you need to compile the DTM code yourself or use one of the binaries.
Th... |
sdpython/ensae_teaching_cs | _doc/notebooks/expose/ml_table_mortalite.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.i - Table de mortalité dans plusieurs containers
Pas de calcul d'espérence de vie, seulement différentes façons de lire les données d'une table de mortalité.
End of explanation
"""
... |
ozorich/phys202-2015-work | assignments/assignment04/TheoryAndPracticeEx01.ipynb | mit | from IPython.display import Image
"""
Explanation: Theory and Practice of Visualization Exercise 1
Imports
End of explanation
"""
# Add your filename and uncomment the following line:
Image(filename='drugs_and_poverty_Chicago.0.png')
"""
Explanation: Graphical excellence and integrity
Find a data-focused visualizat... |
uranc/locmea | tutorials/.ipynb_checkpoints/Untitled-Copy1-checkpoint.ipynb | gpl-3.0 | # Data path/filename
t_ind = 38
data_path = '../data/'
file_name = data_path + 'data_sim_low.hdf5'
data_options = {'flag_cell': True, 'flag_electode': False}
data = data_in(file_name, **data_options)
"""
Explanation: We start by adding some data from an hdf file.
You will also need the python package h5py.
The data o... |
mattilyra/suckerpunch | examples/Introduction.ipynb | lgpl-3.0 | document = 'Lorem Ipsum dolor sit amet'
# shingle and discard the last 5 as these are just the last n<5 characters from the document
shingles = [document[i:i+5] for i in range(len(document))][:-5]
shingles
other_document = 'Lorem Ipsum dolor sit amet is how dummy text starts'
# shingle and discard the last 5 as these ... |
ilayn/harold | notebooks/harold_introduction.ipynb | mit | import numpy as np
from harold import *
import matplotlib.pyplot as plt
"""
Explanation: A Brief Introduction to harold
harold is a control systems toolbox that provides essential control engineering tools. In this notebook, we cover the basics of working with models and standard operations using them.
Let us first im... |
sraejones/Phys202-project | project/NeuralNetworks.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from IPython.html.widgets import interact
from sklearn.datasets import load_digits
digits = load_digits()
print(digits.data.shape)
def show_digit(i):
plt.matshow(digits.images[i]);
interact(show_digit, i=(0,100));
"""
Explanation: Neural Networks
This project w... |
solomonvimal/UCLA-Hydro | LakeArea_Altimetry/Altimetry_MODIS_SurfaceArea_lake610.ipynb | gpl-3.0 | % matplotlib inline
import pandas as pd
import glob
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy
import numpy as np
mpl.rcParams['ytick.labelsize'] = 14
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['font.size'] = 14
GRLM = "/home2/svimal/Github/Nina/data/lake_610/610_GRLM10.txt"; print GR... |
xgcm/xrft | doc/DFT-iDFT_example.ipynb | mit | import numpy as np
import numpy.testing as npt
import xarray as xr
import xrft
import numpy.fft as npft
import scipy.signal as signal
import dask.array as dsar
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Example of discrete and inverse discrete Fourier transform
End of explanation
"""
k0 = 1/... |
cbare/Etudes | notebooks/random_forest_regression_example.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt
"""
Explanation: Random forest regression example
As an experiment, we'll look at a dataset uniquely well suited to modeling with random forest regression.
En... |
ProfessorKazarinoff/staticsite | content/code/sympy/sympy-solving-2-equations-for-2-unknowns-and-statics-problem.ipynb | gpl-3.0 | import numpy as np
from sympy import symbols, Eq, solve
"""
Explanation: SymPy (http://www.sympy.org) is a Python library for symbolic math.
In symbolic math, symbols represent mathematical expressions. In a numerical calculation, the value of pi is stored as an estimate of pi, a floating point number close to 3.14.... |
dtamayo/MachineLearning | Day5/Transit/Findout_Blended_stars.ipynb | gpl-3.0 | import matplotlib
from matplotlib import pyplot as plt
import numpy as np
import scipy as sp
%matplotlib inline
"""
Explanation: Find out Blended stars
Today our job is to find out blending stars and distinguish them with the true signals.
When two stars are close to each other in the CCD, part of their lights may fal... |
loisaidasam/gensim | docs/notebooks/sklearn_wrapper.ipynb | lgpl-2.1 | from gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel import SklearnWrapperLdaModel
"""
Explanation: Using wrappers for Scikit learn API
This tutorial is about using gensim models as a part of your scikit learn workflow with the help of wrappers found at gensim.sklearn_integration
The wrapper available (as o... |
rasbt/algorithms_in_ipython_notebooks | ipython_nbs/data-structures/hashtable-1.ipynb | gpl-3.0 | class HashTable():
def __init__(self, size=17):
pass
def insert(self, key, value):
"""Insert a new key-value pair or overwrites an existing one."""
pass
def __setitem__(self, key, value):
return self.insert(key, value)
def get(self, key):
"""Retrie... |
MPBA/pyphysio | tutorials/2-algorithms.ipynb | gpl-3.0 | # import packages
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# import data from included examples
from pyphysio.tests import TestData
from pyphysio import EvenlySignal
ecg_data = TestData.ecg()
eda_data = TestData.eda()
# create two signals
fsamp = 2048
tst... |
mit-eicu/eicu-code | notebooks/careplan.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import getpass
import pdvega
# for configuring connection
from configobj import ConfigObj
import os
%matplotlib inline
# Create a database connection using settings from config file
config='../db/config.ini'
#... |
bmcfee/pescador | examples/Pescador demo.ipynb | isc | import pescador
import numpy as np
np.set_printoptions(precision=4)
import sklearn
import sklearn.datasets
import sklearn.linear_model
import sklearn.metrics
import sklearn.model_selection
def batch_sampler(X, Y, batch_size=20, scale = 1e-1):
'''A gaussian noise generator for data
Parameters
--------... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_whitened.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
"""
Explanation: Plotting whitened data
This tutorial demonstrates how to plot whitened evoked data.
Data are whitened for many processes, including dipole fitting, source
localization and some decoding algorithms. Viewing whitened data thus gives
a different perspective on t... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/statespace_varmax.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
dta = sm.datasets.webuse('lutkepohl2', 'https://www.stata-press.com/data/r12/')
dta.index = dta.qtr
dta.index.freq = dta.index.inferred_freq
endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dl... |
peterwittek/ipython-notebooks | Simulating_POVMs.ipynb | gpl-3.0 | from __future__ import print_function, division
from fractions import Fraction
import numpy as np
import numpy.linalg
import random
import time
from povm_tools import basis, check_ranks, complex_cross_product, dag, decomposePovmToProjective, \
enumerate_vertices, find_best_shrinking_factor, get_random_qutrit, \
... |
publicityreform/findbyimage | notebooks/word2vec/word2vec.ipynb | gpl-3.0 | import sklearn
import matplotlib.pyplot as plt
import scipy
import numpy as np
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers import Flatten, Activation, Merge
from keras.preprocessing.text import Tokenizer, base_filter
from keras.preprocessing.sequence import skipgr... |
brettavedisian/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The follo... |
snowicecat/umich-eecs445-f16 | handsOn_lecture00_python_tutorial/lecture00_python_tutorial.ipynb | mit | print ('Hello Python!')
"""
Explanation: EECS 445: Python Tutorial
Presented by: Zhao Fu
September 12, 2016
References:
1. https://docs.python.org/3/tutorial/
2. https://docs.python.org/3/library/
3. http://cs231n.github.io/python-numpy-tutorial/
4. https://github.com/donnemartin/data-science-ipython-notebooks
Why Pyt... |
robotcator/gensim | gensim Quick Start.ipynb | lgpl-2.1 | raw_corpus = ["Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"System and human system engineering testing of EPS",
"Relation of user pe... |
machinelearningnanodegree/stanford-cs231 | solutions/kvn219/assignment1/knn.ipynb | mit | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
import numpy.linalg as la
import seaborn as sns
import itertools
import pandas as pd
sns.set_style('whitegrid')
# create a palette generator
palette = itertools.cycle(s... |
ericmjl/protein-systematic-characterization | experiments/20160805-mutagenesis/primer design.ipynb | mit | %load_ext autoreload
%autoreload 2
from Bio import SeqIO
def report(seqrecord):
print(seqrecord)
print('Length: {0} n.t.'.format(len(seqrecord.seq)))
"""
Explanation: Mutagenesis primer design
The goal here is to design primers for mutagenesis on PB2.
The specific PB2 sequence that we are going to use is A/V... |
JorisBolsens/PYNQ | Pynq-Z1/notebooks/examples/tracebuffer_i2c.ipynb | bsd-3-clause | from pprint import pprint
from time import sleep
from pynq import PL
from pynq import Overlay
from pynq.drivers import Trace_Buffer
from pynq.iop import Pmod_TMP2
from pynq.iop import PMODA
from pynq.iop import PMODB
from pynq.iop import ARDUINO
ol = Overlay("base.bit")
ol.download()
"""
Explanation: Trace Buffer - T... |
IS-ENES-Data/submission_forms | test/forms/test/test_ki_12345.ipynb | apache-2.0 | # please edit the (red) information below: Name, email and project the data belongs to
from dkrz_forms import form_handler
my_project = "DKRZ_CDP"
my_first_name = "...." # example: sf.first_name = "Harold"
my_last_name = "...." # example: sf.last_name = "Mitty"
my_email = "...." # example: sf.email = "Mr.Mitt... |
ogaway/Econometrics | Dummy.ipynb | gpl-3.0 | %matplotlib inline
# -*- coding:utf-8 -*-
from __future__ import print_function
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
# データ読み込み
data = pd.read_csv('example/k0901.csv')
data
# 説明変数設定
X = data[['X', 'D']]
X ... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Sequence Tests -XGB -dbptm+ELM-VectorAvr.-phos_stripped-checkpoint.ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Trainin... |
rjurney/Agile_Data_Code_2 | ch06/Exploring_Data_with_Reports.ipynb | mit | import pyspark.sql.functions as F
import pyspark.sql.types as T
from pyspark.sql import SparkSession
# Initialize PySpark with MongoDB and Elastic support
spark = (
SparkSession.builder.appName("Exploring Data with Reports")
# Load support for MongoDB and Elasticsearch
.config("spark.jars.packages", "org.... |
numeristical/introspective | examples/SplineCalib_Tutorial.ipynb | mit | # "pip install ml_insights" in terminal if needed
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import ml_insights as mli
%matplotlib inline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss, brier_sco... |
neurodata/ndmg | tutorials/Overview.ipynb | apache-2.0 | import os
import os.path as op
import glob
import shutil
import warnings
import subprocess
from pathlib import Path
from ndmg.scripts import ndmg_dwi_pipeline
from ndmg.scripts.ndmg_bids import get_atlas
from ndmg.utils import cloud_utils
"""
Explanation: Ndmg Tutorial: Running Inside Python
This tutorial provides a ... |
kit-cel/wt | mloc/ch1_Preliminaries/KKT_waterfilling.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from ipywidgets import interactive
import ipywidgets as widgets
%matplotlib inline
"""
Explanation: Water-filling Visualized
This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communications (... |
srcole/qwm | misc/paper_data/Extract time series from a published figure.ipynb | mit | # Load image and libraries
%matplotlib inline
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from scipy import misc
input_image = misc.imread('figure_processed.png')
# Convert input image from RGBA to binary
input_image = input_image - 255
input_image = np.mean(input_image,2)
binary_image... |
seansweeney/NEARC-2017 | Updating AGOL item metadata.ipynb | unlicense | from arcgis.gis import GIS
from getpass import getpass
from IPython.display import display
"""
Explanation: Import the GIS module and other needed Python modules
The IPython.display module has some helper functions that the Python API takes advantage of for displaying objects like item details and maps in the notebook... |
jinzishuai/learn2deeplearn | google_dl_udacity/lesson4/4_convolutions.ipynb | gpl-3.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
pickle_file = '../notMNIST.pickle'
with open(pickle_file, 'r... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/giss-e2-1h/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: GISS-E2-1H
Sub-Topics: Radiative Forcings.
Propert... |
AlphaGit/deep-learning | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
joelagnel/lisa | ipynb/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
%matplotlib inline
import os
# Support to access the remote target
from env import TestEnv
# Support to access cpuidle information from the target
from devlib import *
# Support to configure and run RTApp based workloads
from wlgen import RTA, Ramp
#... |
jriehl/numba | examples/notebooks/numba.ipynb | bsd-2-clause | %pylab inline
import numpy as np
from numba import jit
"""
Explanation: This example uses trunk version of numba at Github.
End of explanation
"""
def sum(arr):
M, N = arr.shape
sum = 0.0
for i in range(M):
for j in range(N):
sum += arr[i,j]
return sum
fastsum = jit('f8(f8[:,:])'... |
ivastar/clear | notebooks/forward_modeling/Foward_model.ipynb | mit | from grizli import model
import fsps
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from scipy.interpolate import interp1d
from astropy.table import Table
from astropy import wcs
from astropy.io import fits
from glob import glob
import os
## Seaborn is used to make plots lo... |
dwhswenson/openpathsampling | docs/wip/sequential_ensembles.ipynb | mit | xval = paths.FunctionCV(name="x", f=lambda snap : snap.coordinates[0][0])
state = paths.CVDefinedVolume(xval, lambda_min=float("-inf"), lambda_max=0.0)
# building example trajectories
delta = 0.1
x_out = 0.9; x_in = -1.1
traj1 = trajectory_1D([x_in, x_out, x_in])
x_in += delta; x_out += delta
traj2 = trajectory_1D([x_... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n01_day7_model_choosing_close_feat_all_syms_equal.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (20.0, 10... |
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