repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
bwgref/nustar_pysolar | notebooks/20170911/Planning_20170911.ipynb | mit | fname = io.download_occultation_times(outdir='../data/')
print(fname)
"""
Explanation: Download the list of occultation periods from the MOC at Berkeley.
Note that the occultation periods typically only are stored at Berkeley for the future and not for the past. So this is only really useful for observation planning.
... |
girving/tensorflow | tensorflow/contrib/eager/python/examples/workshop/2_models.ipynb | apache-2.0 | import tensorflow as tf
tf.enable_eager_execution()
tfe = tf.contrib.eager
"""
Explanation: View in Colaboratory
End of explanation
"""
# Creating variables
v = tf.Variable(1.0)
v
v.assign_add(1.0)
v
"""
Explanation: Variables
TensorFlow variables are useful to store the state in your program. They are integrated ... |
murali-munna/pattern_classification | data_collecting/reading_mnist.ipynb | gpl-3.0 | import os
import struct
import numpy as np
def load_mnist(path, which='train'):
if which == 'train':
labels_path = os.path.join(path, 'train-labels-idx1-ubyte')
images_path = os.path.join(path, 'train-images-idx3-ubyte')
elif which == 'test':
labels_path = os.path.join(path, 't10k-la... |
gouthambs/karuth-source | content/extra/notebooks/moment_matching.ipynb | artistic-2.0 | import QuantLib as ql
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.integrate import cumtrapz
ql.__version__
"""
Explanation: Variance Reduction in Hull-White Monte Carlo Simulation Using Moment Matching
Goutham Balaraman
In an earlier blog post on how the Hull-White Monte Carlo simu... |
hposborn/Namaste | Example.ipynb | mit | from namaste import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#%matplotlib inline
%reload_ext autoreload
%autoreload 2
"""
Explanation: Namaste 2 example
Here is a short readable (and copy-and-pastable) example as to how to use Namaste 2 to fit a single transit.
End of explanation
"""
... |
scoaste/showcase | machine-learning/regression/week-2-multiple-regression-assignment-1-complete.ipynb | mit | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (Interpretation)
The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.
In this notebook you will use data on house sales in King County to predict prices using multiple regressi... |
ysasaki6023/NeuralNetworkStudy | examples/exploitation vs exploration.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from bayes_opt import BayesianOptimization
# use sklearn's default parameters for theta and random_start
gp_params = {"corr": "cubic", "theta0": 0.1, "thetaL": None, "thetaU": None, "random_start": 1}
"""
Explanation: Exploitation vs Exploration
... |
mayankjohri/LetsExplorePython | Section 2 - Advance Python/Chapter S2.01 - Functional Programming/02_02_map_reduce_and_filter.ipynb | gpl-3.0 | names = [ "Manish", "Aalok", "Mayank","Durga"]
lst = []
for name in names:
lst.append(len(name))
print(lst)
names = ("Manish", "Aalok", "Mayank","Durga")
tmp = map(len, names)
print(tmp)
lst = tuple(tmp)
print(lst)
# This is a map that squares every number in the passed collection:
power = map(lambda x: ... |
eweill/DeepROAD | Samples/TensorFlow/MNISTForMLBeginners.ipynb | mit | !sudo unlink /usr/local/cuda
!sudo ln -s /usr/local/cuda-7.5 /usr/local/cuda
"""
Explanation: MNIST For ML Beginners
End of explanation
"""
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"""
Explanation: This tutorial is intended for readers... |
miaecle/deepchem | examples/tutorials/09_Creating_a_high_fidelity_model_from_experimental_data.ipynb | mit | %tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(version='2.3.0')
"""
Explanation: Tutorial Part 9: Creating a high fidelity dataset from experimental data
Suppose you w... |
sthuggins/phys202-2015-work | assignments/assignment05/InteractEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 01
Import
End of explanation
"""
def print_sum(a, b):
return a+b
"""
Explanation: Interact basics
Write ... |
stubz/deep-learning | intro-to-rnns/Anna KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
ghvn7777/ghvn7777.github.io | content/fluent_python/9_python_object.ipynb | apache-2.0 | v1 = Vector2d(3, 4)
print(v1.x, v1.y) # 可以直接通过属性访问
x, y = v1 # 可以拆包成元祖
x, y
v1
v1_clone = eval(repr(v1)) # repr 函数调用 Vector2d 实例，结果类似于构建实例的源码
v1 == v1_clone # 支持 == 比较
print(v1) # 会调用 str 函数，对 Vector2d 来说，输出的是一个有序对
octets = bytes(v1) # 调用 __bytes__ 方法，生成实例的二进制表示形式
octets
abs(v1) # 会调用 __abs__ 方法，返回 Vector2d 实例的模
... |
clauwag/WikipediaGenderInequality | notebooks/Lexical Analysis - 02 PMI of Common Vocabulary.ipynb | mit | from __future__ import print_function, unicode_literals, division
from cytoolz.dicttoolz import valmap
from collections import Counter
import pandas as pd
import json
import gzip
import numpy as np
import pandas as pd
import dbpedia_config
target_folder = dbpedia_config.TARGET_FOLDER
"""
Explanation: Words Associat... |
tdhopper/notes-on-dirichlet-processes | pages/2015-10-07-econtalk-topics.ipynb | mit | %matplotlib inline
import pyLDAvis
import json
import sys
import cPickle
from microscopes.common.rng import rng
from microscopes.lda.definition import model_definition
from microscopes.lda.model import initialize
from microscopes.lda import utils
from microscopes.lda import model, runner
from numpy import genfromtxt ... |
mne-tools/mne-tools.github.io | 0.20/_downloads/5f84ce88b4773e5ca1f9b3502aef334a/plot_eeg_erp.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
"""
Explanation: EEG processing and Event Related Potentials (ERPs)
:depth: 1
End of explanation
"""
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.f... |
pyreaclib/pyreaclib | examples/pynucastro-examples.ipynb | bsd-3-clause | import pynucastro as pyrl
"""
Explanation: pynucastro usage examples
This notebook illustrates some of the higher-level data structures in pynucastro.
Note to run properly, you install pynucastro via:
python setup.py install
(optionally with --user)
or make sure that you have pynucastro/ in your PYTHONPATH
End of expl... |
bhattacharjee/courses | CourseraDeepLearningSpecialization/2.HyperparameterTrainingRegularizationAndOptimization/Week2/Exercises/Optimization+methods.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import math
import sklearn
import sklearn.datasets
from opt_utils import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation
from opt_utils import compute_cost, predict, predict_dec, plot_decision_boundary, load_data... |
hail-is/hail | datasets/notebooks/GTEx_Tables.ipynb | mit | # Generate list of all eQTL all association files in gs://gtex-resources
list_eqtl_files_gz = subprocess.run(["gsutil",
"-u",
"broad-ctsa",
"ls",
"gs://gtex-resources/GTEx_... |
henchc/Rediscovering-Text-as-Data | 08-Classification/02-Underwood-Sellers.ipynb | mit | metadata_tb = Table.read_table('data/poemeta.csv', keep_default_na=False)
metadata_tb.show(5)
"""
Explanation: This notebook is designed to reproduce several findings from Ted Underwood and Jordan Sellers's article "How Quickly Do Literary Standards Change?" (draft (2015), forthcoming in <i>Modern Language Quarterly</... |
ucsd-ccbb/visJS2jupyter | notebooks/autism_prioritization/validate_heat_prop_autism_2.ipynb | mit | # import standard scientific computing tools
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import networkx as nx
# use mygene.info to translate between entrez and gene symbol
import mygene
mg = mygene.MyGeneInfo()
# latex rendering of text in graphs
import matplotlib as mpl
mpl.rc('text... |
mklokocka/seminator | notebooks/bSCC.ipynb | gpl-3.0 | def example(**opts):
in_a = spot.translate("(FGp2 R !p2) | GFp1")
in_a.highlight_states([3,4], 2).set_name("input")
# Note: the pure=True option disables all optimizations that are usually on by default.
out_a = seminator(in_a, pure=True, postprocess=False, highlight=True, **opts)
out_a.set_name("ou... |
tpin3694/tpin3694.github.io | machine-learning/dimensionality_reduction_with_pca.ipynb | mit | # Load libraries
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn import datasets
"""
Explanation: Title: Dimensionality Reduction With PCA
Slug: dimensionality_reduction_with_pca
Summary: How to reduce the dimensions of the feature matrix for machine learning in Pyth... |
AWS-Spot-Analysis/spot-analysis | plot_stock_market.ipynb | apache-2.0 | print(__doc__)
# Author: Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
import datetime
import numpy as np
import matplotlib.pyplot as plt
try:
from matplotlib.finance import quotes_historical_yahoo_ochl
except ImportError:
# quotes_historical_yahoo_ochl was named quotes_historical_yaho... |
wangyum/spark | python/docs/source/getting_started/quickstart_df.ipynb | apache-2.0 | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
"""
Explanation: Quickstart: DataFrame
This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of RDDs. When Spark transforms data, it does not immedi... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/solutions/basic_intro_logistic_regression.ipynb | apache-2.0 | import os
import matplotlib.pyplot as plt
import tensorflow as tf
print("TensorFlow version: {}".format(tf.__version__))
print("Eager execution: {}".format(tf.executing_eagerly()))
"""
Explanation: Introduction to Logistic Regression Using TF 2.0
Learning Objectives
Build a model,
Train this model on example data, ... |
ud3sh/coursework | deeplearning.ai/coursera-improving-neural-networks/week2/Optimization_methods_v1b.ipynb | unlicense | import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import math
import sklearn
import sklearn.datasets
from opt_utils_v1a import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation
from opt_utils_v1a import compute_cost, predict, predict_dec, plot_decision_boundary, l... |
jsub10/Machine-Learning-By-Example | Chapter-6-Non-Linear-Logistic-Regression.ipynb | gpl-3.0 | # Use the functions from another notebook in this notebook
%run SharedFunctions.ipynb
# Import our usual libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Think Like a Machine - Chapter 6
Non-Linear Logistic Regression (and Regularization)
ACKNOWLEDGE... |
anthonyng2/FX-Trading-with-Python-and-Oanda | Oanda v20 REST-oandapyV20/01.02 Understanding the Documentations.ipynb | mit | import oandapyV20
from oandapyV20 import API
import oandapyV20.endpoints.pricing as pricing
"""
Explanation: <!--NAVIGATION-->
< Setting Up | Contents | Rates Information >
Understanding the Documentations
In order to make use of the API effective, we need to understand the input parameters and the corresponding outpu... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/projects/01-first-neural-network/notebooks/Your_first_neural_network_v0.01.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
bloomberg/bqplot | examples/Marks/Object Model/Graph.ipynb | apache-2.0 | fig_layout = Layout(width="960px", height="500px")
"""
Explanation: Nodes and Links should be supplied to the Graph mark.
<p>Node attributes
| Attribute| Type | Description | Default |
|:----------:|:-------------:|:------:|
| label | str | node label | mandatory attribute |
| label_display | {center, out... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a/td1a_cenonce_session5.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.2 - Classes, méthodes, attributs, opérateurs et carré magique
Les classes proposent une façon différente de structurer un programme informatique. Pas indispensable mais souvent élégant.
End of explanation
"""
class MesParametres:
... |
slimn/Data-Analyst | P2-Investigate Titanic data/Investigate_titanic_data.ipynb | gpl-2.0 | # file location
file_path = "./titanic_data.csv"
# import pandas
import numpy as np
import pandas as pd
def get_dataframe(csv_file):
'''read .csv file.
parameters:
-----------
csv_file : a file path in csv format.
return:
-----------
return pandas dataframe
'''
return ... |
ceos-seo/data_cube_notebooks | notebooks/water/coastline/Coastal_Change_Classifier.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
%matplotlib inline
from datetime import datetime
import numpy as np
import utils.data_cube_utilities.dc_utilities as utils
from utils.data_cube_utilities.clean_mask import landsat_qa_clean_mask
from utils.data_cube_utilities.dc_mosaic import crea... |
hasadna/knesset-data-pipelines | jupyter-notebooks/Extract_meeting_topics/Calculate_topics-analysis_and_graphs.ipynb | mit | import pandas as pd
from matplotlib import pyplot as plt
import warnings
warnings.filterwarnings('ignore')
# Normalize the topics' scores
def normalize_scores(scores):
max_i = (0, -1)
second_i = (0, -1)
third_i = (0, -1)
for i in range(len(scores)):
if scores[i] != 0:
if scores[i] >... |
italoPontes/Machine-learning | Tarefas/Implementando-Regressao-Multipla-do-Zero/.ipynb_checkpoints/Regressão Linear Simples-checkpoint.ipynb | lgpl-3.0 | import numpy as np
import math
import time
"""
Explanation: Regressão Linear com NumPy
End of explanation
"""
# y = mx + b
# m is slope, b is y-intercept
def compute_mse(b, m, points):
totalError = 0
for i in range(0, len(points)):
x = points[i, 0]
y = points[i, 1]
totalError += (y - ... |
google-research/torchsde | examples/demo.ipynb | apache-2.0 | import torch
from torch import nn
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
%matplotlib inline
import matplotlib.pyplot as plt
import torchsde
def plot(ts, samples, xlabel, ylabel, title=''):
ts = ts.cpu()
samples ... |
opesci/notebooks | AcousticFWI/MultiOrder_2d-3d.ipynb | bsd-3-clause | # Choose dimension (2 or 3)
dim = 2
# Choose order
time_order = 6
space_order = 12
# half width for indexes, goes from -half to half
width_t = int(time_order/2)
width_h = int(space_order/2)
# Define functions and symbols
p=Function('p')
s,h = symbols('s h')
if dim==2:
m=M(x,z)
q=Q(x,z,t)
d=D(x,z,t)
so... |
tpin3694/tpin3694.github.io | machine-learning/create_interaction_features.ipynb | mit | # Load libraries
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
"""
Explanation: Title: Create Interaction Features
Slug: create_interaction_features
Summary: How to create interaction features for machine learning in Python.
Date: 2016-09-06 12:00
Category: Machine Learning
Tags: Preproce... |
wdbm/Psychedelic_Machine_Learning_in_the_Cenozoic_Era | Keras_CNN_newsgroups_text_classification.ipynb | gpl-3.0 | %autosave 120
import numpy as np
np.random.seed(1337)
from IPython.display import SVG
from keras.models import Model
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import (
Concatenate,
Conv1D,
Dense,
Dropout,
Embedding,
Fl... |
jinntrance/MOOC | coursera/ml-regression/assignments/week-4-ridge-regression-assignment-2-blank.ipynb | cc0-1.0 | import graphlab
"""
Explanation: Regression Week 4: Ridge Regression (gradient descent)
In this notebook, you will implement ridge regression via gradient descent. You will:
* Convert an SFrame into a Numpy array
* Write a Numpy function to compute the derivative of the regression weights with respect to a single feat... |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/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', 'csiro-bom', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiati... |
folivetti/BIGDATA | Spark/Lab04.ipynb | mit | import os
import numpy as np
def parseRDD(point):
""" Parser for the current dataset. It receives a data point and return
a sentence (third field).
Args:
point (str): input data point
Returns:
str: a string
"""
data = point.split('\t')
return (int(data[0]),data[2])
... |
napsternxg/gensim | docs/notebooks/Poincare Evaluation.ipynb | gpl-3.0 | % cd ../..
# Some libraries need to be installed that are not part of Gensim
! pip install click>=6.7 nltk>=3.2.5 prettytable>=0.7.2 pygtrie>=2.2
import csv
from collections import OrderedDict
from IPython.display import display, HTML
import logging
import os
import pickle
import random
import re
import click
from g... |
hannorein/rebound | ipython_examples/PoincareSurfaceOfSection.ipynb | gpl-3.0 | import rebound
import numpy as np
import matplotlib.pyplot as plt
def get_sim(m_pert,n_pert,a_tp,l_pert,l_tp,e_tp,pomega_tp):
sim = rebound.Simulation()
sim.add(m=1)
P_pert = 2 * np.pi / n_pert
sim.add(m=m_pert,P=P_pert,l=l_pert)
sim.add(m=0.,a = a_tp,l=l_tp,e=e_tp,pomega=pomega_tp)
sim.move_to... |
GHorace/ma2823_2016 | lab_notebooks/Lab 6 2016-11-04 Tree-based methods.ipynb | mit | import numpy as np
%pylab inline
# Load the data
# TODO
# Normalize the data
from sklearn import preprocessing
X = preprocessing.normalize(X)
# Set up a stratified 10-fold cross-validation
from sklearn import cross_validation
folds = cross_validation.StratifiedKFold(y, 10, shuffle=True)
def cross_validate(design_ma... |
AndreySheka/dl_ekb | hw4/Seminar4-ru-mnist.ipynb | mit | !pip install install Theano==0.8.2
!pip install https://github.com/Lasagne/Lasagne/archive/master.zip
import numpy as np
def sum_squares(N):
return сумма квадратов чисел от 0 до N
%%time
sum_squares(10**8)
"""
Explanation: Theano, Lasagne
и с чем их едят
разминка
напиши на numpy функцию, которая считает сумму к... |
mohanprasath/Course-Work | coursera/python_for_data_science/2.4_Sets.ipynb | gpl-3.0 | set1={"pop", "rock", "soul", "hard rock", "rock", "R&B", "rock", "disco"}
set1
"""
Explanation: <a href="http://cocl.us/topNotebooksPython101Coursera"><img src = "https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width = 750, align = "center"></a>
<a href="https://www.bigdatauniversity.com"><img ... |
GoogleCloudPlatform/gcp-getting-started-lab-jp | machine_learning/cloud_ai_building_blocks/sight_ja.ipynb | apache-2.0 | import getpass
APIKEY = getpass.getpass()
"""
Explanation: <a href="https://colab.research.google.com/github/GoogleCloudPlatform/gcp-getting-started-lab-jp/blob/master/machine_learning/cloud_ai_building_blocks/sight_ja.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Ope... |
amirziai/learning | deep-learning/Tensorflow-Tutorial.ipynb | mit | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
%matplotlib inline
np.random.seed(1)
"""
Explanation: TensorFlow Tutorial
Welcome to this w... |
brookisme/gitnb | GitNB Example Notebook.ipynb | mit | 1+1
"""
Explanation: This is an example notebook
The main purpose of this notebook is to have something to convert with gitnb. There is nothing interesting to see here. In order to make this point perfectly clear, I will start with some difficult math...
End of explanation
"""
import numpy as np
eps=1e-10
def pre... |
espressomd/espresso | doc/tutorials/raspberry_electrophoresis/raspberry_electrophoresis.ipynb | gpl-3.0 | import espressomd
import espressomd.interactions
import espressomd.electrostatics
import espressomd.lb
import espressomd.virtual_sites
import sys
import tqdm
import logging
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
espressomd.assert_features(["ELECTROSTATICS", "ROTATION", "ROTATIONAL_INERTIA", "EXTER... |
xpharry/Udacity-DLFoudation | tutorials/sentiment_network/.ipynb_checkpoints/Sentiment Classification - Project 3 Solution-checkpoint.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
inakic/matsoft | Numpy.ipynb | unlicense | from numpy import *
"""
Explanation: Numpy
numpy je paket (modul) za (efikasno) numeričko računanje u Pythonu. Naglasak je na efikasnom računanju s nizovima, vektorima i matricama, uključivo višedimenzionalne stukture. Napisan je u C-u i Fortanu te koristi BLAS biblioteku.
End of explanation
"""
v = array([1,2,3,4])... |
eweill/DeepROAD | Samples/TensorFlow/DeepMNISTForExperts.ipynb | mit | !sudo unlink /usr/local/cuda
!sudo ln -s /usr/local/cuda-7.5 /usr/local/cuda
"""
Explanation: Deep MNIST for Experts
End of explanation
"""
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)
"""
Explanation: TensorFlow is a powerful library for ... |
mauriciogtec/PropedeuticoDataScience2017 | Alumnos/MiguelCastañeda/Tarea2_MiguelCastañeda.ipynb | mit | %matplotlib inline
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
def readImage(pathFile):
im = Image.open(pathFile)
im = im.convert('LA')
plt.figure(figsize=(6, 3))
plt.imshow(im,cmap='gray')
data = np.array(list(im.getdata(band=0)),int)
data.shape = (im.size[1], im.... |
jgrizou/explauto | notebook/summary_available_models.ipynb | gpl-3.0 | from explauto.environment.environment import Environment
environment = Environment.from_configuration('simple_arm', 'mid_dimensional')
"""
Explanation: Summary of Available Sensorimotor and Interest Models
In this notebook, we summarize the different sensorimotor and interest models available in the Explauto library, ... |
erikdrysdale/erikdrysdale.github.io | _rmd/extra_cord19/incubation_pediatric.ipynb | mit | import numpy as np
import pandas as pd
import os
import re
import seaborn as sns
from datetime import datetime as dt
from support_funs_incubation import stopifnot, uwords, idx_find, find_beside, ljoin, sentence_find, record_vals
!pip install ansicolors
# Takes a tuple (list(idx), sentence) and will print in red anyt... |
nwfpug/meetings | 2017-01-23/pandas.ipynb | gpl-3.0 | # conventional way to import pandas
import pandas as pd
# get Pansda's vesrion #
print ('Pandas version', pd.__version__)
"""
Explanation: Python pandas Q&A video series by Data School
YouTube playlist and GitHub repository
Table of contents
<a href="#1.-What-is-pandas%3F-%28video%29">What is pandas?</a>
<a href="#2.... |
cfe-lab/MiCall | docs/compute_micall_results.ipynb | agpl-3.0 | from pathlib import Path
import os
import csv
import pandas as pd
import yaml
import numpy as np
import statistics
from operator import itemgetter
def get_mixtures(row):
total = row['A'] + row['T'] + row['C'] + row['G']
thresh = 0.05 * total
alleles = {
'qpos': int(row['query.nuc.pos']),
'm... |
mitchshack/data_analysis_with_python_and_pandas | 5 - pandas Advanced/5-1 Pandas IO Data, Different Ways of Indexing Data, Hierarchical Indexing and Panels.ipynb | apache-2.0 | import pandas.io.data
?pandas.io.data # <tab>
"""
Explanation: In this section we will be analyzing some financial data. Now pandas gives us access to some data through pandas.io.data
This is basically pandas remote data access:
http://pandas.pydata.org/pandas-docs/stable/remote_data.html
Functions from pandas.io.dat... |
michrawson/nyu_ml_lectures | notebooks/01.3 Data Representation for Machine Learning.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
iris = load_iris()
"""
Explanation: Representation and Visualization of Data
Machine learning is about creating models from data: for that reason, we'll start by
discussing how data can be represented in order to be understood by the computer. Along
with this, we'll build on our... |
sdpython/actuariat_python | _doc/notebooks/decouverte/pandas_start.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: DataFrames Pandas
Un Data Frame est un objet qui est présent dans la plupart des logiciels de traitements de données, c’est une matrice à 2 dimensions, chaque colonne a un type et toutes les cellules de cette colonne sont de ce type (nomb... |
qutip/qutip-notebooks | examples/piqs-spin-squeezing-noise.ipynb | lgpl-3.0 | from time import clock
from scipy.io import mmwrite
import matplotlib.pyplot as plt
from qutip import *
from qutip.piqs import *
from scipy.sparse import load_npz, save_npz
def isdicke(N, j, m):
"""
Check if an element in a matrix is a valid element in the Dicke space.
Dicke row: j value index. Dicke colum... |
spulido99/Programacion | Alex/.ipynb_checkpoints/Taller 2 - Archivos y Bases de Datos-checkpoint.ipynb | mit | import mysql.connector
"""
Explanation: Archivos y Bases de datos
End of explanation
"""
import pandas as pd
df= pd.read_csv('C:/Users/Alex/Documents/eafit/semestres/X semestre/programacion/taller2.tsv', sep = '\t')
df[:1]
"""
Explanation: La idea de este taller es manipular archivos (leerlos, parsearlos y escribi... |
enoordeh/StatisticalMethods | examples/StraightLine/ModelEvaluation.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (6.0, 6.0)
plt.rcParams['savefig.dpi'] = 100
from straightline_utils import *
"""
Explanation: Testing the Straight Line Model
End of expla... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/03_persistence/WIN-190810170510.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: WMI Eventing
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/08/10 |
| modification date | 2020/09/20 |
| playbook related | [] |
Hypothesis
A... |
Lattecom/HYStudy | scripts/[HYStudy 28th] Decorator Pattern 3.ipynb | mit | class DecoClass:
def __init__(self, function):
self.function = function
print("DecoClass '__init__' function has been called.")
def __call__(self, *args, **kwargs):
print("DecoClass has been called.")
return self.function(*args, **kwargs)
@DecoClass
def func_1():
pr... |
WormLabCaltech/mprsq | src/stats_tutorials/Orthogonal Distance Regression.ipynb | mit | import numpy as np
import scipy as scipy
import scipy.odr as odr
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import rc
# set to use tex, but make sure it is sans-serif fonts only
rc('text', usetex=True)
rc('text.latex', preamble=r'\usepackage{cmbright}')
rc('font', *... |
DJCordhose/ai | notebooks/workshops/tss/nn-intro.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
t... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 18-01-22-workcamp-ml/18-01-22-workcamp-ml-pandas-grundlagen-30.ipynb | gpl-3.0 | import pandas as pd
dateipfad = 'SN_d_tot_V2.0.csv'
sunsets = pd.read_csv(dateipfad, sep=';', header=None)
sunsets.info()
sunsets.head(10)
"""
Explanation: <h1>Workcamp Maschinelles Lernen</h1>
<h2>Grundlagen - Arbeiten mit Panda Dataframes</h2>
<h3>EInlesen von Dateien in Dataframes</h3>
Lassen Sie uns jetzt unsere ... |
ES-DOC/esdoc-jupyterhub | notebooks/cams/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CAMS
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
ShiroJean/Breast-cancer-risk-prediction | .ipynb_checkpoints/Breast-cancer-clean-checkpoint.ipynb | mit | #load dataset from ucsi website
import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)
df.head()
"""
Explanation: Dataset
Breast Cancer Wisconsin dataset, which contains 569 samples of malignant and benign tumor cells.
* The fir... |
nkarast/notebooks | .ipynb_checkpoints/sympy-checkpoint.ipynb | mit | from sympy import *
3 + math.sqrt(3)
expr = 3 * sqrt(3)
expr
init_printing(use_latex='mathjax')
expr
expr = sqrt(8)
expr
"""
Explanation: Tutorial Brief
SymPy is symbolic mathematics library written completely in Python and doesn't require any dependencies.
Finding Help:
http://docs.sympy.org/latest/index.html
h... |
gonzmg88/cnn_basic_course | FC_and_CNN.ipynb | gpl-3.0 | import numpy as np
import dogs_vs_cats as dvc
import matplotlib.pyplot as plt
%matplotlib inline
all_files = dvc.image_files()
n_images_train=5000
n_images_val=500
n_images_test=500
input_image_shape = (50,50,3)
train_val_features, train_val_labels,train_val_files, \
test_features, test_labels, test_files = dvc.trai... |
geoneill12/phys202-2015-work | assignments/assignment07/AlgorithmsEx01.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
"""
Explanation: Algorithms Exercise 1
Imports
End of explanation
"""
def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\:;"<,>.?/}\t'):
"""Split a string into a list of words, removing punctuation and stop words."""
... |
AkshanshChahal/BTP | Satellite/Learning from Data.ipynb | mit | select = colss[8:226]
X = rice[select]
y = rice["Value"]*1000
X.describe()
# Z-Score Normalization
colms = list(X.columns)
for col in colms:
col_zscore = col + '_zscore'
X[col_zscore] = (X[col] - X[col].mean())/X[col].std(ddof=0)
cols = list(X.columns.values)
len(cols)
# Contains all the features (Last 2... |
Kaggle/learntools | notebooks/pandas/raw/tut_0.ipynb | apache-2.0 | import pandas as pd
"""
Explanation: Introduction
In this micro-course, you'll learn all about pandas, the most popular Python library for data analysis.
Along the way, you'll complete several hands-on exercises with real-world data. We recommend that you work on the exercises while reading the corresponding tutorial... |
nwjs/chromium.src | third_party/tensorflow-text/src/docs/guide/decoding_api.ipynb | bsd-3-clause | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
fujii-team/Henbun | notebooks/GaussianProcess.ipynb | apache-2.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import Henbun as hb
# random state
rng = np.random.RandomState(0)
"""
Explanation: Gaussian Process Demo
This notebook briefly describes how to make an variational inference with Henbun.
Keisuke Fujii, 21st Nov. 2016
We sho... |
azjps/usau-py | notebooks/2018_D-I_College_Nationals_Fantasy_Stats_Day4.ipynb | mit | from usau.reports import USAUResults as Results
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
matplotlib.rcParams.update({'font.size': 16})
style_args = {"alpha": 0.5, "markeredgewidth": 0.5}
sns_blue, sns_orange, sns_green, sns_red, *sns_pallete... |
rpmuller/TightBinding | Harry Tight Binding.ipynb | bsd-2-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from numpy.linalg import eigvalsh
from collections import namedtuple
import TB
TB.band(TB.Si)
TB.band(TB.GaAs)
TB.band(TB.Ge)
"""
Explanation: Tight Binding program to compute the band structure of simple semiconductors.
Parameters taken from Vo... |
tesera/pygypsy | notebooks/#32-address-testing-findings/#32-isolated-profiling-4.ipynb | mit | %%timeit
pass
%%timeit
pass
"""
Explanation: Recap
In order of priority/time taken
basalareaincremementnonspatialaw
this is actually slow because of the number of times the BAFromZeroToDataAw function is called as shown above
relaxing the tolerance may help
indeed the tolerance is 0.01 * some value while the other f... |
google-coral/tutorials | retrain_ssdlite_mobiledet_qat_tf1.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
pdamodaran/yellowbrick | examples/zjpoh/stacked_feature_importance.ipynb | apache-2.0 | import os
import sys
sys.path.insert(0, "../..")
import importlib
import numpy as np
import pandas as pd
import yellowbrick
import yellowbrick as yb
from yellowbrick.features.importances import FeatureImportances
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn import manifold, datasets
from skle... |
mne-tools/mne-tools.github.io | 0.20/_downloads/2784a8d5822ed9797c0330f973573c10/plot_stats_cluster_erp.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
import mne
from mne.channels import find_ch_connectivity, make_1020_channel_selections
from mne.stats import spatio_temporal_cluster_test
np.random.seed(0)
# Load the data
path = mne.datasets.kiloword.data_path() + '/kword_metadata-... |
chungjjang80/FRETBursts | notebooks/FRETBursts - ns-ALEX example.ipynb | gpl-2.0 | from fretbursts import *
sns = init_notebook()
"""
Explanation: FRETBursts - ns-ALEX example
This notebook is part of a tutorial series for the FRETBursts burst analysis software.
For a step-by-step introduction to FRETBursts usage please refer to
us-ALEX smFRET burst analysis.
In this notebook we present a typical... |
ishakaur/sandbox | caltech_machine_learning/homework 4 (VC bounds, aggregate hypotheses and bias-variance analysis.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import display
# Generalization bound (VC dimension)
# δ = 4 * (mH(2N)) * e ^ (- epsilon ^ 2 * N / 8)
# epsilon = sqrt((8 * logn(4 * (mH(2N)) / δ)) / N) = Omega(N, H, δ)
# Replacing growth function with the s... |
plipp/informatica-pfr-2017 | nbs/4/1-Classification-Decision-Tree-Primer.ipynb | mit | from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from plotting_utilities import plot_decision_tree, plot_feature_importances
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
iris = load_iris()
iris.DESCR.s... |
t-vi/pytorch-tvmisc | misc/pytorch_automatic_optimization_jit.ipynb | mit | import torch
import torch.utils.cpp_extension
"""
Explanation: Automatic optimization with the PyTorch JIT
a worked example
by Thomas Viehmann tv@lernapparat.de
Today, I would like to discuss in detail some aspects of optimizing code in mo... |
AssembleSoftware/IoTPy | examples/ExamplesOfMulticorePartTwo.ipynb | bsd-3-clause | import threading
from IoTPy.agent_types.sink import stream_to_queue
def f(in_streams, out_streams):
map_element(lambda v: v+100, in_streams[0], out_streams[0])
def source_thread_target(procs):
for i in range(3):
extend_stream(procs, data=list(range(i*2, (i+1)*2)), stream_name='x')
time.sleep(0... |
mungobungo/deep | part3.ipynb | mit | import matplotlib.pyplot as plt
hist = history
train_loss=hist.history['loss']
val_loss=hist.history['val_loss']
train_acc=hist.history['acc']
val_acc=hist.history['val_acc']
xc=range(epochs)
plt.figure(1,figsize=(7,5))
plt.plot(xc,train_loss)
plt.plot(xc,val_loss)
plt.xlabel('num of Epochs')
plt.ylabel('loss')
plt.... |
andreyf/machine-learning-examples | visualization/telecom_churn_inclass_as_is.ipynb | gpl-3.0 | df['Total day minutes'].hist();
sns.boxplot(df['Total day minutes']);
df.hist();
"""
Explanation: 1. Признаки по одному
1.1. Количественные
Гистограмма и боксплот
End of explanation
"""
df['State'].value_counts().head()
df['Churn'].value_counts()
sns.countplot(df['Churn']);
sns.countplot(df['State']);
sns.coun... |
arturops/deep-learning | tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
BradHub/SL-SPH | BEM_problem.ipynb | mit | #Q = 2000/3 #strength of the source-sheet,stb/d
h=25.26 #thickness of local gridblock,ft
phi=0.2 #porosity
kx=200 #pemerability in x direction,md
ky=200 #pemerability in y direction,md
kr=kx/ky #pemerability ratio
miu=1 #viscosity,cp
Nw=1 #Number of well
Qwell... |
avincartemard/avincartemard.github.io | content/articles/2017/07/stochastic-optimization/stochastic-optimization.ipynb | apache-2.0 | import numpy as np
from scipy.io import loadmat
# load data from MATLAB file
datamat = loadmat('quantum.mat')
X = datamat['X']
y = datamat['y']
class LogisticRegressionSGD(object):
def __init__(self, X, y, progTol=1e-4, nEpochs=10):
self.X = X
self.y = y
self.n, self.d = X.shape
... |
flohorovicic/pynoddy | docs/notebooks/Marks Fault Uncertainty Study.ipynb | gpl-2.0 | # some basic inputs and settings
import sys, os
import matplotlib.pyplot as plt
# adjust some settings for matplotlib
from matplotlib import rcParams
# print rcParams
rcParams['font.size'] = 15
# determine path of repository to set paths corretly below
os.chdir(r'/Users/Florian/git/pynoddy/docs/notebooks/')# some basic... |
daniel-koehn/Theory-of-seismic-waves-II | 06_2D_SH_Love_wave_modelling/4_2D_SH_FD_modelling_Love_waves.ipynb | gpl-3.0 | # Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../style/custom.css'
HTML(open(css_file, "r").read())
"""
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, notebook styl... |
snucsne/CSNE-Course-Source-Code | CSNE2444-Intro-to-CS-I/jupyter-notebooks/ch11-dictionaries.ipynb | mit | birthdays = dict()
print( birthdays )
"""
Explanation: Chapter 11: Dictionaries
Contents
- A dictionary is a mapping
- Dictionary as a set of counters
- Looping and dictionaries
- Reverse lookup
- Dictionaries and lists
- Global variables
- Debugging
- Exercises
This notebook is based on "Think Python, 2Ed" by Allen... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/sandbox-3/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-3', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties: 3... |
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