repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
esumitra/minecraft-programming | notebooks/Adventure2.ipynb | mit | from mcpi.minecraft import *
import time
mc = Minecraft.create()
# Type Task 2 program here
"""
Explanation: Welcome Home
Usually when Steve comes home, there is no one at home. Steve can get lonely at times especially after long hard battle with creepers and zombies.
In this programming adventure we'll make Minecra... |
sjchoi86/Tensorflow-101 | notebooks/rnn_mnist_simple.ipynb | mit | import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
print ("Packages imported")
mnist = input_data.read_data_sets("data/", one_hot=True)
trainimgs, trainlabels, testimgs, testlabels \
= mnist.train.images, m... |
rcurrie/tumornormal | pathways.ipynb | apache-2.0 | import os
import json
import numpy as np
import pandas as pd
import tensorflow as tf
import keras
import matplotlib.pyplot as plt
# fix random seed for reproducibility
np.random.seed(42)
# See https://github.com/h5py/h5py/issues/712
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
"""
Explanation: Pathway Classificati... |
merryjman/astronomy | Elements.ipynb | gpl-3.0 | # Import modules that contain functions we need
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Elements and the periodic table
This data came from Penn State CS professor Doug Hogan.
Thanks to UCF undergraduates Sam Borges, for finding the data set, and Lissa... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/launching_into_ml/labs/supplemental/intro_logistic_regression.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
"""
Explanation: Introduction to Logistic Regression
Learning Objectives
Create Seaborn plots for Exploratory Data Analysis
T... |
lukemans/Hello-world | t81_558_class3_training.ipynb | apache-2.0 | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Encode text values to dummy variables(i.e. [1,0,0],[0,1,0],[0,0,1] for red,green,blue)
def encode_text_dummy(df,name):
dummies = pd.get_dummies(df[name])
for x in dummies.columns:
dummy_name = "{}... |
feststelltaste/software-analytics | notebooks/Java Type Dependency Analysis.ipynb | gpl-3.0 | import py2neo
query="""
MATCH
(:Project)-[:CONTAINS]->(artifact:Artifact)-[:CONTAINS]->(type:Type)
WHERE
// we don't want to analyze test artifacts
NOT artifact.type = "test-jar"
WITH DISTINCT type, artifact
MATCH
(type)-[:DEPENDS_ON*0..1]->(directDependency:Type)<-[:CONTAINS]-(artifact)
RETURN type.f... |
vzg100/Post-Translational-Modification-Prediction | old/Phosphorylation Sequence Tests -Bagging -dbptm+ELM-VectorAvr..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... |
DillmannFrench/Intro-PYTHON | Cours13-DILLMANN-ISEP2016.ipynb | gpl-3.0 | import pandas
from pandas.io.data import DataReader
import matplotlib.pyplot as plt
%matplotlib inline
# Initialisation d'un type dict
d = {}
"""
Explanation: Programation Fonctionnelle
Nous allons exploiter la puissance des fonctions offerte par les différentes librairies de Python. L'objectif est de ne pas refaire c... |
theandygross/MethylTools | .ipynb_checkpoints/Probe_Annotations-checkpoint.ipynb | apache-2.0 | import pandas as pd
DATA_STORE = '/data_ssd/methylation_annotation_2.h5'
store = pd.HDFStore(DATA_STORE)
islands = pd.read_hdf(DATA_STORE, 'islands')
locations = pd.read_hdf(DATA_STORE, 'locations')
other = pd.read_hdf(DATA_STORE, 'other')
snps = pd.read_hdf(DATA_STORE, 'snps')
probe_annotations = pd.read_hdf(DATA_ST... |
mne-tools/mne-tools.github.io | stable/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | bsd-3-clause | # Authors: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Nicolas P. Rougier (graph code borrowed from his matplotlib gallery)
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as ... |
huajianmao/learning | coursera/deep-learning/4.convolutional-neural-networks/week2/pa.1.Keras - Tutorial - Happy House v1.ipynb | mit | import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import im... |
lee212/simpleazure | ipynb/Tutorial - Account Setup for Azure Resource Manager (ARM).ipynb | gpl-3.0 | !yes|azure login
"""
Explanation: Azure CLI Account Setup for Azure Resource Manager (ARM)
Azure CLI provides an easy way to setup an account for Azure Resource Manager (ARM) and furthermore creates an new service principal for the Simple Azure access. In this tutorial, we use IPython helper (!) to run Azure CLI.
Int... |
chicagopython/CodingWorkshops | problems/data_science/trackcoder.ipynb | gpl-3.0 | import pandas
"""
Explanation: Data Analysis and visualization for tracking developer productivity
Chipy's mentorship program is an extra-ordinary jounery for becoming a better developer. As a mentee, you are expected to do a lot - you read new articles/books, write code, debug and troubleshoot, pair program with othe... |
erdewit/ib_insync | notebooks/contract_details.ipynb | bsd-2-clause | from ib_insync import *
util.startLoop()
import logging
# util.logToConsole(logging.DEBUG)
ib = IB()
ib.connect('127.0.0.1', 7497, clientId=11)
"""
Explanation: Contract details
End of explanation
"""
amd = Stock('AMD')
cds = ib.reqContractDetails(amd)
len(cds)
"""
Explanation: Suppose we want to find the contr... |
t-vi/pytorch-tvmisc | misc/2D-Wavelet-Transform.ipynb | mit | import pywt
from matplotlib import pyplot
%matplotlib inline
import numpy
from PIL import Image
import urllib.request
import io
import torch
URL = 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/bc/Zuse-Z4-Totale_deutsches-museum.jpg/315px-Zuse-Z4-Totale_deutsches-museum.jpg'
"""
Explanation: 2D Wavelet Tran... |
msschwartz21/craniumPy | experiments/glial_bridge/landmarks.ipynb | gpl-3.0 | import deltascope as ds
import deltascope.alignment as ut
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
from scipy.optimize import minimize
import os
import tqdm
import json
import datetime
"""
Explanation: Introduction: Landmarks
End of explanati... |
jtwhite79/pyemu | verification/Freyberg/verify_unc_results.ipynb | bsd-3-clause | %matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pyemu
"""
Explanation: verify pyEMU results with the henry problem
End of explanation
"""
la = pyemu.Schur("freyberg.jcb",verbose=False,forecasts=[])
la.drop_prior_information()
jco_ord = la.jco.get(la.pst.obs_... |
EricKightley/sparsekmeans | plots/heuristic/heuristic.ipynb | mit | import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from scipy.linalg import hadamard
from scipy.fftpack import dct
%matplotlib inline
n = 6 #number of data points (columns in plot)
K = 3 #number of centroids
m = 4 #subsampling dimension
p = 10 #latent (data) dime... |
lwahedi/CurrentPresentation | talks/MDI1/Slides.ipynb | mit | my_string = 'Hello World'
print(my_string)
"""
Explanation: Manipulating Data in Python
Laila A. Wahedi
Massive Data Institute Postdoctoral Fellow <br>McCourt School of Public Policy
Follow along: Wahedi.us, Current Presentation
Installing packages:
On a Mac:
Open terminal
On Windows:
Type cmd into the start menu
... |
IESD/cegads-domestic-model | cegads/examples/Basic usage.ipynb | gpl-2.0 | %pylab inline
import pandas as pd
"""
Explanation: cegads-domestic-model
This ipython notebook describes the basic usage of the cegads-domestic-model python library. The library implements a simple domestic appliance model based on data from chapter three of the DECC ECUK publication (https://www.gov.uk/government/col... |
hanhanwu/Hanhan_Data_Science_Practice | sequencial_analysis/after_2020_practice/ts_RNN_basics.ipynb | mit | import pandas as pd
import numpy as np
import datetime
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
df = pd.read_csv('data/pm25.csv')
print(df.shape)
df.head()
df.isnull().sum()*100/df.shape[0]
df.dropna(subset=['pm2.5'], axis=0, inplace=True)
df.reset_in... |
osamoylenko/YSDA_deeplearning17 | Seminar1/Homework 1 (Face Recognition).ipynb | mit | import scipy.io
image_h, image_w = 32, 32
data = scipy.io.loadmat('faces_data.mat')
X_train = data['train_faces'].reshape((image_w, image_h, -1)).transpose((2, 1, 0)).reshape((-1, image_h * image_w))
y_train = (data['train_labels'] - 1).ravel()
X_test = data['test_faces'].reshape((image_w, image_h, -1)).transpose((2... |
joonasfo/python | Assignment_03_notebook.ipynb | mit | %matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import *
from numpy import *
"""
Explanation: Introduction to Numerical Problem Solving TX00BY09-3007
Assignment: 03 Graphical analysis
Description: Solve the problem 8 from previous exercises 02. Solve the problems 1c, 2a, ... |
dato-code/tutorials | notebooks/AnyGivenSunday.ipynb | apache-2.0 | #Fire up the GraphLab engine
import graphlab as gl
import graphlab.aggregate as agg
"""
Explanation: Any Given Sunday: Football and a Machine Learning Rookie
I love football more than engineers love coffee, all my Turi friends know that. Throughout the course of an NFL season I have fantasy teams, point-spread pools, ... |
xpharry/Udacity-DLFoudation | tutorials/sentiment-rnn/Sentiment RNN Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('reviews.txt', 'r') as f:
reviews = f.read()
with open('labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analysis.... |
MaxRobinson/CS449 | project1/mrobi100.ipynb | apache-2.0 | from __future__ import division # so that 1/2 = 0.5 and not 0
from IPython.core.display import *
import csv, math, copy, random
"""
Explanation: Module 12 - Programming Assignment
Directions
There are general instructions on Blackboard and in the Syllabus for Programming Assignments. This Notebook also has instruction... |
unpingco/Python-for-Probability-Statistics-and-Machine-Learning | chapters/probability/notebooks/ProbabilityInequalities.ipynb | mit | from pprint import pprint
import textwrap
import sys, re
"""
Explanation: Python for Probability, Statistics, and Machine Learning
End of explanation
"""
import sympy
import sympy.stats as ss
t=sympy.symbols('t',real=True)
x=ss.ChiSquared('x',1)
"""
Explanation: Useful Inequalities
In practice, few quantities can b... |
LucaCanali/Miscellaneous | Spark_Physics/HEP_benchmark/ADL_HEP_Query_Benchmark_Q1_Q5.ipynb | apache-2.0 | # Download the data if not yet available locally
# Download the reduced data set (2 GB)
! wget -r -np -R "index.html*" -e robots=off https://sparkdltrigger.web.cern.ch/sparkdltrigger/Run2012B_SingleMu_sample.orc/
# This downloads the full dataset (16 GB)
# ! wget -r -np -R "index.html*" -e robots=off https://sparkdlt... |
kingsgeocomp/code-camp | notebook-10-recap.ipynb | mit | cities = ["Bristol", "London", "Manchester", "Edinburgh", "Belfast", "York"]
"""
Explanation: Recap 2
Second Checkpoint
Since the first recap, you've learned about lists, dictionaries and loops. Let's revise those concepts and how to use them in this notebook before continuing on to some new material. Answer the quest... |
n-witt/MachineLearningWithText_SS2017 | exercises/solutions/1 Numpy.ipynb | gpl-3.0 | import numpy as np
try:
np
except NameError:
print('Numpy not correctly imported')
"""
Explanation: Import the numpy package under the name np
End of explanation
"""
Z = np.zeros(10)
print(Z)
assert type(Z).__module__ == np.__name__
assert len(Z) == 10
assert sum(Z) == 0
"""
Explanation: 2. Create a null ... |
mayuanucas/notes | python/python下划线命名规则.ipynb | apache-2.0 | 8 * 9
_ + 8
"""
Explanation: 在 python 中，下划线命名规则往往令人相当疑惑：单下划线、双下划线、双下划线还分前后……那它们的作用与使用场景到底有何区别呢？
1、单下划线（_）
通常情况下，单下划线（_）会在以下3种场景中使用：
1.1 在解释器中：
在这种情况下，“_”代表交互式解释器会话中上一条执行的语句的结果。这种用法首先被标准CPython解释器采用，然后其他类型的解释器也先后采用。
End of explanation
"""
for _ in range(1, 11):
print(_, end='、 ')
"""
Explanation: 1.2 作为一个名称：
这与... |
AndreySheka/dl_ekb | hw3/HW3_Modules.ipynb | mit | class Module(object):
def __init__ (self):
self.output = None
self.gradInput = None
self.training = True
"""
Basically, you can think of a module as of a something (black box)
which can process `input` data and produce `ouput` data.
This is like applying a function which is ... |
GoogleCloudPlatform/training-data-analyst | quests/serverlessml/03_tfdata/solution/input_pipeline.ipynb | apache-2.0 | %%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
!pip install tensorflow==2.1.0 --user
"""
Explanation: Input pipeline into Keras
In this notebook, we will look at how to read large datasets, datasets that may not fit into memory, usi... |
YaniLozanov/Software-University | Python/Jupyter notebook/04.Complex Conditional Statements/Jupyter notebook/04.Complex Conditional Statements.ipynb | mit | age = float(input())
sex = input()
if sex == "m":
if age >= 16:
print("Mr.")
else:
print("Master")
else:
if age >= 16:
print("Ms.")
else:
print("Miss")
"""
Explanation: <h1 align="center">Complex Conditional Statements<h1>
<h2>01.Personal Titles</h2>
The first task... |
stijnvanhoey/course_gis_scripting | _solved/01-python-introduction.ipynb | bsd-3-clause | print("Hello INBO_course!") # python 3(!)
"""
Explanation: <p><font size="6"><b>Python the essentials: A minimal introduction</b></font></p>
Introduction to GIS scripting
May, 2017
© 2017, Stijn Van Hoey (stijnvanhoey@gmail.&#... |
xray/xray | doc/examples/ROMS_ocean_model.ipynb | apache-2.0 | import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
%matplotlib inline
import xarray as xr
"""
Explanation: ROMS Ocean Model Example
The Regional Ocean Modeling System (ROMS) is an open source hydrodynamic model that is used for simulating currents and wate... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Framework, Advecti... |
csaladenes/csaladenes.github.io | present/mcc2/PythonDataScienceHandbook/05.06-Linear-Regression.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; t... |
shernshiou/CarND | Term1/02-CarND-Traffic-Sign-Classifier-Project/Traffic_Sign_Classifier1.ipynb | mit | # Load pickled data
import pickle
import csv
import cv2
import numpy as np
import math
import matplotlib.pyplot as plt
signnames = []
with open("signnames.csv", 'r') as f:
next(f)
reader = csv.reader(f)
signnames = list(reader)
n_classes = len(signnames)
training_file = "./train.p"
testing_file = "./test.... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/tfx_pipelines/pipeline/labs/tfx_pipeline_vertex.ipynb | apache-2.0 | from google.cloud import aiplatform as vertex_ai
"""
Explanation: Continuous training with TFX and Vertex
Learning Objectives
Containerize your TFX code into a pipeline package using Cloud Build.
Use the TFX CLI to compile a TFX pipeline.
Deploy a TFX pipeline version to run on Vertex Pipelines using the Vertex Pytho... |
gatakaba/kalmanfilter | examples/kalmanfilter/free_fall.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import os
os.chdir('..')
from kalmanfilter.kalmanfilter import KalmanFilter
dt = 10 ** -3
"""
Explanation: 自由落下の状態方程式
質量M,ダンパ係数C,弾性係数Kの運動方程式は以下のようになる
$$ M \frac{d^{2}x}{dt^{2}} + C \frac{dx}{dt} + K x = f(t) $$
ここで空気抵抗を無視して方程式を整理すると
$$ M \frac{d^{2}x}{dt^{2}} = Mg... |
yfur/basic-mechanics-python | 1_modeling/1_modeling.ipynb | apache-2.0 | import numpy as np
from scipy.integrate import odeint
from math import sin
''' constants '''
m = 1 # mass of the pendulum [kg]
l = 1 # length of the pendulum [m]
g = 10 # Gravitational acceleration [m/s^2]
c = 0.3 # Damping constant [kg.m/(rad.s)]
''' time setting '''
t_end = 10 # simulation time [s]
t_fps = 50 # fra... |
scotthuang1989/Python-3-Module-of-the-Week | internet/urllib.parse — Split URLs into Components.ipynb | apache-2.0 | from urllib.parse import urlparse
url = 'http://netloc/path;param?query=arg#frag'
parsed = urlparse(url)
print(parsed)
"""
Explanation: Parsing
End of explanation
"""
from urllib.parse import urlparse
url = 'http://user:pwd@NetLoc:80/path;param?query=arg#frag'
parsed = urlparse(url)
print('scheme :', parsed.schem... |
pchmieli/h2o-3 | h2o-py/demos/H2O_tutorial_medium.ipynb | apache-2.0 | import pandas as pd
import numpy
from numpy.random import choice
from sklearn.datasets import load_boston
from h2o.estimators.random_forest import H2ORandomForestEstimator
import h2o
h2o.init()
# transfer the boston data from pandas to H2O
boston_data = load_boston()
X = pd.DataFrame(data=boston_data.data, columns=b... |
jokedurnez/RequiredEffectSize | Figure2_CorrSimulation/Correlation_simulation.ipynb | mit | import numpy
import nibabel
import os
import nilearn.plotting
import matplotlib.pyplot as plt
from statsmodels.regression.linear_model import OLS
import nipype.interfaces.fsl as fsl
import scipy.stats
if not 'FSLDIR' in os.environ.keys():
raise Exception('This notebook requires that FSL is installed and the FSLDIR... |
KushajveerSingh/Data-Science-Libraries | .ipynb_checkpoints/Advancd Python-checkpoint.ipynb | mit | if __name__ == "__main__":
# Do anything you want
pass
"""
Explanation: Python is a programming language. Python is often refereed as a scripting language as scripting languages are often interpreted and not compiled.<br>
End of explanation
"""
# If you want to give arguments to a function as a list
def f(x,... |
mdiaz236/DeepLearningFoundations | gan_mnist/Intro_to_GANs_Solution.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
mwegrzyn/mindReading2017 | content/_006_neurosynthDecoding_pt1.ipynb | gpl-3.0 | import os
imgList = ['../training/%s'%x for x in os.listdir('../training/')]; imgList.sort()
"""
Explanation: Wir haben bisher immer nur Daten unserer Person verwendet um Vorhersagen zu treffen. Das macht Sinn, da die Daten der Person gut die Besonderheiten ihrer Art zu denken widerspiegeln. Zum Beispiel könnte man b... |
kmclaugh/fastai_courses | kevin_files/lesson1.ipynb | apache-2.0 | %matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.
Introduction to this week's task: 'Do... |
thehackerwithin/berkeley | code_examples/intropy_sp17/thw-python.ipynb | bsd-3-clause | 2 + 3 # Press <Ctrl-Enter to evaluate a cell>
2 + int(3.5 * 4) * float("8")
9 // 2 # Press <Ctrl-Enter to evaluate>
"""
Explanation: Introduction to Python
1. Installing Python
2. The Language
Expressions
List, Tuple and Dictionary
Strings
Functions
3. Example: Word Frequency Analysis with Python
Reading... |
cavaunpeu/willwolf.io-source | content/downloads/notebooks/intercausal_reasoning.ipynb | mit | from collections import namedtuple
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
from scipy.optimize import fmin_powell
from scipy.stats import beta as beta_distribution
import seaborn as sns
from sklearn.linear_model import LogisticRegression
%matplotlib inline
plt.style.us... |
amuniversity/am-mooc | module 2/Ex 2b - Housing ,Linear Regression - Open.ipynb | gpl-2.0 | # import libraries
import matplotlib
import IPython
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import pylab
import seaborn as sns
import sklearn as sk
%matplotlib inline
"""
Explanation: Linear Regression , Housing
Prerequisites: Hopefully you have a good unders... |
dlab-berkeley/python-taq | tests/Basic HDF5 Operations.ipynb | bsd-2-clause | # But what symbol is that?
max_sym = None
max_rows = 0
for sym, rows in rec_counts.items():
if rows > max_rows:
max_rows = rows
max_sym = sym
max_sym, max_rows
"""
Explanation: Anyway, under a gigabyte. So, nothing to worry about even if we have 24 cores.
End of explanation
"""
# Most symbols al... |
viper-framework/har2tree | tutorial/tutorial.ipynb | bsd-3-clause | from pathlib import Path
Path.home()
"""
Explanation: Har2Tree Tutorial
Crawling a web page can sound like a bit of an abstract concept at first. How exactly can we extract data from a web page? What data is really interesting to look at? Where can it be found?
→ Every web browser generates a HAR file (short fo... |
rogerallen/kaggle | dogscats/roger.ipynb | apache-2.0 | #Verify we are in the lesson1 directory
%pwd
%matplotlib inline
import os, sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
from utils import *
from vgg16 import Vgg16
from PIL import Image
from keras.preprocessing import image
from sklearn.metrics import confusion_matrix
"""
Explanation: Creating my ow... |
ericmjl/hiv-resistance-prediction | old_notebooks/Prototype Neural Network for predicting HA host tropism.ipynb | mit | ! echo $PATH
! echo $CUDA_ROOT
import pandas as pd
import numpy as np
from Bio import SeqIO
from Bio import AlignIO
from Bio.Align import MultipleSeqAlignment
from collections import Counter
from sklearn.preprocessing import LabelBinarizer
from sklearn.cross_validation import train_test_split
from sklearn.ensemble imp... |
scikit-optimize/scikit-optimize.github.io | 0.7/notebooks/auto_examples/optimizer-with-different-base-estimator.ipynb | bsd-3-clause | print(__doc__)
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
"""
Explanation: Use different base estimators for optimization
Sigurd Carlen, September 2019.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
To use different base_estimator or create a regressor with different para... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/SphericalMeshing/CartesianTriangulations/Ex6-Scattered-Data.ipynb | mit | import numpy as np
HFdata = np.loadtxt("../Data/HeatFlowSEAustralia.csv", delimiter=',', usecols=(3,4,5), skiprows=1)
eastings = HFdata[:,0]
northings = HFdata[:,1]
heat_flow = HFdata[:,2]
%matplotlib inline
import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
# local coordinate reference syst... |
joekasp/ionic_liquids | ionic_liquids/Interface.ipynb | mit | from ipywidgets import interact, interact_manual, HBox, VBox
import ipywidgets as widgets
from IPython.display import display, clear_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from visualization import core, plots
import utils
"""
Explanation: ILest: Ionic Liquids Estimation and Stat... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_evoked_whitening.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.datasets import sample
from mne.cov import compute_covariance
print(__doc__)
"""
Explanation: Whitening evoked data with ... |
nwfpug/python-primer | notebooks/06-lists.ipynb | gpl-3.0 | # import thr random numbers module. More on modules in a future notebook
import random
"""
Explanation: Lists
<img src="../images/python-logo.png">
Lists are sequences that hold heterogenous data types that are separated by commas between two square brackets. Lists have zero-based indexing, which means that the first ... |
Mynti207/cs207project | docs/stock_example_returns.ipynb | mit | # load data
with open('data/returns_include.json') as f:
stock_data_include = json.load(f)
with open('data/returns_exclude.json') as f:
stock_data_exclude = json.load(f)
# keep track of which stocks are included/excluded from the database
stocks_include = list(stock_data_include.keys())
stocks_exclude ... |
mdeff/ntds_2017 | projects/reports/movie_success/Treat_Metacritic_ROI.ipynb | mit | %matplotlib inline
import configparser
import os
import requests
from tqdm import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from scipy import sparse, stats, spatial
import scipy.sparse.linalg
from sklearn import preprocessing, decomposition
import libro... |
rflamary/POT | notebooks/plot_barycenter_1D.ipynb | mit | # Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
import ot
# necessary for 3d plot even if not used
from mpl_toolkits.mplot3d import Axes3D # noqa
from matplotlib.collections import PolyCollection
"""
Explanation: 1D Wasserstein barycenter demo
... |
YoungKwonJo/mlxtend | docs/examples/classifier_nn_mlp.ipynb | bsd-3-clause | from mlxtend.data import iris_data
X, y = iris_data()
X = X[:, 2:]
"""
Explanation: mlxtend - Multilayer Perceptron Examples
Sections
Classify Iris
Classify handwritten digits from MNIST
<br>
<br>
Classify Iris
Load 2 features from Iris (petal length and petal width) for visualization purposes.
End of explanation
""... |
NeuroDataDesign/fngs | docs/ebridge2/fngs_merge/week_0613/friston.ipynb | apache-2.0 | import numpy as np
def friston_model(mc_params):
(t, m) = mc_params.shape
friston = np.zeros((t, 4*m))
# the motion parameters themselves
friston[:, 0:m] = mc_params
# square the motion parameters
friston[:, m:2*m] = np.square(mc_params)
# use the motion estimated at the preceding timepoin... |
mdda/fossasia-2016_deep-learning | notebooks/2-CNN/6-StyleTransfer/4-Art-Style-Transfer-inception_tf.ipynb | mit | import tensorflow as tf
import numpy as np
import scipy
import scipy.misc # for imresize
import matplotlib.pyplot as plt
%matplotlib inline
import time
from urllib.request import urlopen # Python 3+ version (instead of urllib2)
import os # for directory listings
import pickle
AS_PATH='./images/art-style'
"""
E... |
mdeff/ntds_2016 | toolkit/04_ex_visualization.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Random time series.
n = 1000
rs = np.random.RandomState(42)
data = rs.randn(n, 4).cumsum(axis=0)
# plt.figure(figsize=(15,5))
# plt.plot(data[:, 0])
# df = pd.DataFrame(...)
# df.plot(...)
"""
Explanation: A Python Tour of D... |
keylime1/courses_12-752 | assignments/2/12-752_Assignment_2_Starter.ipynb | mit | temperatureDateConverter = lambda d : dt.datetime.strptime(d,'%Y-%m-%d %H:%M:%S')
temperature = np.genfromtxt('../../data/temperature.csv',delimiter=",",dtype=[('timestamp', type(dt.datetime.now)),('tempF', 'f8')],converters={0: temperatureDateConverter}, skiprows=1)
"""
Explanation: Section 1.1 - Importing the Data
L... |
jegibbs/phys202-2015-work | assignments/assignment12/FittingModelsEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 1
Imports
End of explanation
"""
a_true = 0.5
b_true = 2.0
c_true = -4.0
N = 30
dy = 2.0
"""
Explanation: Fitting a quadratic curve
For this problem we are going to work with t... |
ehthiede/PyEDGAR | examples/Delay_Embedding/Delay_Embedding.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pyedgar
from pyedgar.data_manipulation import tlist_to_flat, flat_to_tlist, delay_embed, lift_function
%matplotlib inline
"""
Explanation: Delay Embedding and the MFPT
Here, we give an example script, showing the effect of Delay Embedding on a Brownian motion ... |
Flaviolib/dx | 08_dx_fourier_pricing.ipynb | agpl-3.0 | import dx
import datetime as dt
"""
Explanation: <img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="45%" align="right" border="4">
Fourier-based Option Pricing
For several reasons, it is beneficial to have available alternative valuation and pricing approaches to the Monte Carlo simulation appr... |
mohanprasath/Course-Work | machine_learning/learning_python_3.ipynb | gpl-3.0 | print("Hello World!")
print("Hello Again")
print("I like typing this.")
print("This is fun.")
print('Yay! Printing.')
print("I'd much rather you 'not'.")
print('I "said" do not touch this.')
'''
Notes:
octothorpe, mesh, or pund #
'''
"""
Explanation: Learning Python3 from URL
https://learnpythonthehardway.org/pyt... |
frederickayala/session-based-recsys | Qvik Session-Based Recommender Systems/GRU4RecLab.ipynb | mit | # -*- coding: utf-8 -*-
import theano
import pickle
import sys
import os
sys.path.append('../..')
import numpy as np
import pandas as pd
import gru4rec #If this shows an error probably the notebook is not in GRU4Rec/examples/rsc15/
import evaluation
# Validate that the following assert makes sense in your platform
# T... |
fabriziocosta/pyMotif | meme_example.ipynb | mit | # Meme().display_meme_help()
from eden.util import configure_logging
import logging
configure_logging(logging.getLogger(),verbosity=2)
from utilities import Weblogo
wl = Weblogo(color_scheme='classic')
meme1 = Meme(alphabet="dna", # {ACGT}
gap_in_alphabet=False,
mod="anr", # Any number... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/structured/labs/3c_bqml_dnn_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
%%bash
pip freeze | grep google-cloud-bigquery==1.6.1 || \
pip install google-cloud-bigquery==1.6.1
"""
Explanation: LAB 3c: BigQuery ML Model Deep Neural Network.
Learning Objectives
Create and evaluate DNN model with BigQuery ML.
Create and evalua... |
CORE-GATECH-GROUP/serpent-tools | examples/Detector.ipynb | mit | import os
pinFile = os.path.join(
os.environ["SERPENT_TOOLS_DATA"],
"fuelPin_det0.m",
)
bwrFile = os.path.join(
os.environ["SERPENT_TOOLS_DATA"],
"bwr_det0.m",
)
"""
Explanation: Copyright (c) 2017-2020 Serpent-Tools developer team, GTRC
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,... |
ernestyalumni/MLgrabbag | supervised-theano.ipynb | mit | import theano
import theano.tensor as T
# cf. https://github.com/lisa-lab/DeepLearningTutorials/blob/c4db2098e6620a0ac393f291ec4dc524375e96fd/code/logistic_sgd.py
"""
Explanation: I started here: Deep Learning tutorial
End of explanation
"""
import cPickle, gzip, numpy
import os
os.getcwd()
os.listdir( os.getcw... |
saudijack/unfpyboot | Day_02/00_Scipy/04_Breakout_trapezoid_rule_solution.ipynb | mit | %pylab inline
def trapz(x, y):
return 0.5*np.sum((x[1:]-x[:-1])*(y[1:]+y[:-1]))
"""
Explanation: Basic numerical integration: the trapezoid rule
Illustrates: basic array slicing, functions as first class objects.
In this exercise, you are tasked with implementing the simple trapezoid rule
formula for numerical in... |
borja876/Thinkful-DataScience-Borja | Amazon+Reviews 180108.ipynb | mit | #Import data from json file and create a list
data = []
with open('/home/borjaregueral/Digital_Music_5.json') as f:
for line in f:
data.append(json.loads(line))
#Create a dataframe with the columns that are interesting for this exercise
#Columns left out: 'helpful', 'reviewTime', 'reviewerID','reviewerNam... |
NervanaSystems/neon_course | 08 Overfitting Tutorial.ipynb | apache-2.0 | from neon.initializers import Gaussian
from neon.optimizers import GradientDescentMomentum, Schedule
from neon.layers import Conv, Dropout, Activation, Pooling, GeneralizedCost
from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, Misclassification
from neon.models import Model
from neon.data import CIFAR10
... |
fotis007/python_intermediate | Python_2_1.ipynb | gpl-3.0 | #List
a = [1, 5, 2, 84, 23]
b = list("hallo")
c = range(10)
list(c)
#dictionary
z = dict(a=2,b=5,c=1)
z
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Python-für-Fortgeschrittene" data-toc-modified-id="Python-für-Fortgeschrittene-1"><span class="toc-item-num">1 </span>Python für... |
bocklund/notebooks | thermodynamics/.ipynb_checkpoints/miscibility-gaps-checkpoint.ipynb | mit | import warnings
warnings.simplefilter('ignore') # ignore warnings for nicer output
import numpy as np
from sympy import symbols, log, lambdify, solve
import scipy.constants
from ipywidgets import interact
from bokeh.io import push_notebook, show, output_notebook
from bokeh.plotting import figure
from bokeh.models imp... |
cstrelioff/ARM-ipynb | Chapter3/chptr3.3-R.ipynb | mit | %%R
# I had to import foreign to get access to read.dta
library("foreign")
kidiq <- read.dta("../../ARM_Data/child.iq/kidiq.dta")
# I won't attach kidiq-- i generally don't attach to avoid confusion(s)
#attach(kidiq)
"""
Explanation: 3.3 Interactions
Read the data
Data are in the child.iq directory of the ARM_Data do... |
Uberi/zen-and-the-art-of-telemetry | Moon Phase Correlation Analysis.ipynb | mit | import ujson as json
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import plotly.plotly as py
from moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client
from moztelemetry.histogram import Histogram
import datetime as dt
%pylab inline
"""
Explanation: Moon Phase Correl... |
rishuatgithub/MLPy | nlp/3. Word Vectors + PCA + Cosine Similarity.ipynb | apache-2.0 | ## load the word embeddings from the google news vectors. Load it once.
#embeddings = KeyedVectors.load_word2vec_format('../../Data/GoogleNews-vectors-negative300.bin', binary=True)
## Building a word embeddings for the small subset that is required in here
f = open('../../Data/word_vectors/capitals.txt', 'r').read... |
ganprad/rentorbuy | rentorbuy.ipynb | mit | import quandl
quandl.ApiConfig.api_key = '############'
"""
Explanation: Import Data:
Explore the data.
Pick a starting point and create visualizations that might help understand the data better.
Come back and explore other parts of the data and create more visualizations and models.
Quandl is a great place to star... |
griffinfoster/fundamentals_of_interferometry | 1_Radio_Science/1_9_a_brief_introduction_to_interferometry.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
"""
Explanation: Outline
Glossary
1. Radio Science using Interferometric Arrays
Previous: 1.8 Astronomical radio sources
Next: 1.10 The Limits of Single Dish Astronomy
... |
Housebeer/Natural-Gas-Model | .ipynb_checkpoints/Fitting curve-checkpoint.ipynb | mit | import numpy as np
from scipy.optimize import leastsq
import pylab as plt
import pandas as pd
N = 1000 # number of data points
t = np.linspace(0, 4*np.pi, N)
data = 3.0*np.sin(t+0.001) + 0.5 + np.random.randn(N) # create artificial data with noise
guess_mean = np.mean(data)
guess_std = 3*np.std(data)/(2**0.5)
guess_p... |
google/neural-tangents | notebooks/function_space_linearization.ipynb | apache-2.0 | !pip install --upgrade pip
!pip install -q tensorflow-datasets
!pip install --upgrade jax[cuda11_cudnn805] -f https://storage.googleapis.com/jax-releases/jax_releases.html
!pip install -q git+https://www.github.com/google/neural-tangents
"""
Explanation: <a href="https://colab.research.google.com/github/google/neural-... |
krismolendyke/den | notebooks/Authorization.ipynb | mit | import os
DEN_CLIENT_ID = os.environ["DEN_CLIENT_ID"]
DEN_CLIENT_SECRET = os.environ["DEN_CLIENT_SECRET"]
"""
Explanation: Authorization
Following the Nest authorization documentation.
Setup
Get the values of Client ID and Client secret from the clients page and set them in the environment before running this IPython... |
Aniruddha-Tapas/Applied-Machine-Learning | Clustering/Seeds Clustering.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import cross_validation, metrics
from sklearn import preprocessing
import matplotlib.pyplot as plt
cols = ['Area', 'Perimeter','Compactness','Kernel_Length','Kernel_Width','Assymetry_Coefficien... |
AllenDowney/ThinkStats2 | code/chap07ex.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/thi... |
RaoUmer/lightning-example-notebooks | plots/circle.ipynb | mit | from lightning import Lightning
from numpy import random, asarray
"""
Explanation: <img style='float: left' src="http://lightning-viz.github.io/images/logo.png"> <br> <br> Circle plots in <a href='http://lightning-viz.github.io/'><font color='#9175f0'>Lightning</font></a>
<hr> Setup
End ... |
mdeff/ntds_2016 | project/reports/airbnb_booking/Main Machine Learning.ipynb | mit | import pandas as pd
import numpy as np
import time
import machine_learning_helper as machine_learning_helper
import metrics_helper as metrics_helper
import sklearn.neighbors, sklearn.linear_model, sklearn.ensemble, sklearn.naive_bayes
from sklearn.model_selection import KFold, train_test_split, ShuffleSplit
from sklear... |
khrapovs/metrix | notebooks/doppler_nonparametrics.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as ss
import sympy as sp
sns.set_context('notebook')
%matplotlib inline
"""
Explanation: Nonparametric estimatio of Doppler function
End of explanation
"""
x = np.linspace(.01, .99, num=1e3)
doppler = lambda x : np.sqrt(x * ... |
albahnsen/ML_RiskManagement | exercises/03-IncomePrediction.ipynb | mit | import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# read the data and set the datetime as the index
import zipfile
with zipfile.ZipFile('../datasets/income.csv.zip', 'r') as z:
f = z.open('income.csv')
income = pd.read_csv(f, index_col=0)
income.head()
income.shape
"... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/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', 'mohc', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MOHC
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbul... |
EstevesDouglas/UNICAMP-FEEC-IA369Z | dev/checkpoint/2017-05-09-estevesdouglas-notebook.ipynb | gpl-3.0 | -- Campainha IoT - LHC - v1.1
-- ESP Inicializa pinos, Configura e Conecta no Wifi, Cria conexão TCP
-- e na resposta de um "Tocou" coloca o ESP em modo DeepSleep para economizar bateria.
-- Se nenhuma resposta for recebida em 15 segundos coloca o ESP em DeepSleep.
led_pin = 3
status_led = gpio.LOW
ip_servidor = "192.1... |
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