repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
dipanjank/ml | data_analysis/concrete_slump.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
import seaborn as sns
data = pd.read_csv('concrete_slump.csv')
data = data.drop('No', axis=1)
data.head()
"""
Explanation: Concrete Slump Test - UCI
Analysis of the <a href="https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test">co... |
arasdar/DL | impl-dl/cnn-tensorflow-from-scratch.ipynb | unlicense | # GPUs or CPU
import tensorflow as tf
# Check TensorFlow Version
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
"""
Explanation: CNN from scratch
In this notebook, we're going to build a convolutional neural network for recog... |
dataventures/workshops | 6/Applications in Finance.ipynb | mit | import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import numpy.linalg as la
import pandas as pd
import scipy.cluster.hierarchy as sch
import seaborn as sns
from pandas.io.data import DataReader
from scipy.stats import mode
from scipy.stats.stats import pearsonr
from IPython.display import Ima... |
philferriere/tfoptflow | tfoptflow/pwcnet_train_lg-6-2-cyclic-chairsthingsmix.ipynb | mit | """
pwcnet_train.ipynb
PWC-Net model training.
Written by Phil Ferriere
Licensed under the MIT License (see LICENSE for details)
Tensorboard:
[win] tensorboard --logdir=E:\\repos\\tf-optflow\\tfoptflow\\pwcnet-lg-6-2-cyclic-chairsthingsmix
[ubu] tensorboard --logdir=/media/EDrive/repos/tf-optflow/tfoptflow/... |
pschragger/big-data-python-class | Lectures/Week 5 - MapReduce - MRJob/MapReduce Intro.ipynb | mit | def cube(x): return x*x*x
map(cube,range(1,11))
"""
Explanation: Challenge of processing large amounts of data
<ul>
<li>How to process is quickly?
<li>So how do we go about making the problem map so that it can be distributed computation?
<li> Distributed/Parrallel Programming is hard
</ul>
Mapreduce addresses all t... |
phoebe-project/phoebe2-docs | development/tutorials/fti.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Finite Time of Integration (fti)
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import n... |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | Project4/language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/01_bigquery/a_sample_explore_clean.ipynb | apache-2.0 | PROJECT = "cloud-training-demos" # Replace with your PROJECT
REGION = "us-central1" # Choose an available region for Cloud MLE
import os
os.environ["PROJECT"] = PROJECT
os.environ["REGION"] = REGION
"""
Explanation: Sample, Explore, and Clean Taxifare Dataset
Learning Objectives
- Practice querying BigQue... |
jldinh/multicell | examples/07 - Gierer-Meinhardt with growth and divisions.ipynb | mit | %matplotlib notebook
"""
Explanation: In this example, we will use Multicell to simulate the self-reorganization of a geometrical Turing pattern, based on equations developed by Gierer and Meinhardt, as the cells of the underlying tissue grow and divide.
Preparation
End of explanation
"""
import multicell
import num... |
apozas/BIST-Python-Bootcamp | 7_BestPractices_Testing_Performance.ipynb | gpl-3.0 | import this
"""
Explanation: 7 Best Practices, Testing and Performance
Advanced bits marked with ⚠ might be more for reference later.
PEP8 -- Style Guide for Python Code
Intendation is significant in Python, preventing the worst mess. The remaining stylistic questions, like naming conventions, used in the standard lib... |
bjornstenqvist/faunus | examples/multipole/multipole.ipynb | mit | %matplotlib inline
import matplotlib
import matplotlib.cm as cm
import unittest
import numpy as np
import matplotlib.pyplot as plt
import sys
plt.rcParams.update({'font.size': 18, 'figure.figsize': [8.0, 6.0]})
"""
Explanation: Decomposition of electrostatic interaction energy between two multipolar molecules
This is ... |
DanielAndreasen/Programmers-Club | previous_versions/notebook/Adding locality to regressors - CAUP.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from astroML.linear_model import NadarayaWatson
import statsmodels.api as sm
import scipy.stats as stats
%matplotlib inline
sns.set_context('talk')
sns.set_style('whitegrid', {"grid.color": ".95", 'grid.linestyle': u'-'})
def make_fake_data():
... |
tolaoniyangi/dmc | notebooks/week-4/01-tensorflow ANN for regression.ipynb | apache-2.0 | %matplotlib inline
import math
import random
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
import numpy as np
import tensorflow as tf
sns.set(style="ticks", color_codes=True)
"""
Explanation: Lab 4 - Tensorflow ANN for regression
In this lab we wi... |
ugo-nama-kun/pydata_okinawa2017 | introduction_deep_rl.ipynb | mit | # 数値計算に必須のもろもろ
import numpy as np
# 可視化パッケージ
import matplotlib.pyplot as plt
from IPython import display
%matplotlib inline
# seaborn を入れてない人は以下をコメントアウト
import seaborn as sns
sns.set_style('darkgrid')
"""
Explanation: keras-rl で深層強化学習入門
深層強化学習とは？
keras-rl とは？
Karlsruhe Institute of Technology (カールスルーエ工科大学， KIT) の人た... |
InsightLab/data-science-cookbook | 2019/06-decision-tree/Exercise_Tree_Decision.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import KFold, cross_val_score
import numpy as np
dataset = pd.read_csv(... |
csdms/pymt | notebooks/gipl.ipynb | mit | import pymt.models
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import matplotlib.colors as mcolors
from matplotlib.colors import LinearSegmentedColormap
sns.set(style='whitegrid', font_scale= 1.2)
"""
Explanation: Basic use of the GIPL model
Before you begin, install:
conda install -c cond... |
csaladenes/csaladenes.github.io | present/sapientia1/projects/meteorit/v2/Meteorite.ipynb | mit | df
"""
Explanation: Jo lenne tudni pontosabban, hogy mit jelent itt a neha..
End of explanation
"""
df=pd.read_csv(csv_path)
df[(df[u'year'] <= 2016)]
"""
Explanation: lassuk lepesekben:
End of explanation
"""
print pd.Timestamp.min
print pd.Timestamp.max
"""
Explanation: ugy nez ki ez a kifejezes a hibas a 2016... |
dereneaton/RADmissing | emp_nb_Phrynosomatidae.ipynb | mit | ### Notebook 2
### Data set 2 (Phrynosomatidae)
### Authors: Leache et al. (2015)
### Data Location: NCBI SRA SRP063316
"""
Explanation: Notebook 2:
This is an Jupyter/IPython notebook. Most of the code is composed of bash scripts, indicated by %%bash at the top of the cell, otherwise it is IPython code. This notebook... |
bjornaa/ladim | examples/jupyter/line.ipynb | mit | # Basic
from io import StringIO
import numpy as np
from netCDF4 import Dataset
# Plotting
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
%matplotlib inline
# Ladim
import ladim
from postladim import ParticleFile
"""
Explanation: Jupyter notebook version of t... |
antoniomezzacapo/qiskit-tutorial | community/teach_me_qiskit_2018/state_distribution_in_qubit_chains/qubit_chain_mod_many.ipynb | apache-2.0 | from pprint import pprint
import math
import numpy as np
# importing the Qiskit
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import Aer, execute
# import state tomography functions
from qiskit.tools.visualization import plot_histogram, plot_state
# Definition of matchgate
def gat... |
QuantStack/quantstack-talks | 2019-06-04-deRSE19-widgets/notebooks/1 - ipywidgets.ipynb | bsd-3-clause | from ipywidgets import IntSlider
slider = IntSlider()
slider
slider.value
slider.value = 20
slider
"""
Explanation: <center><img src="src/ipywidgets.svg" width="50%"></center>
Repository: https://github.com/jupyter-widgets/ipywidgets
Installation:
conda install -c conda-forge ipywidgets
Simple slider for driving ... |
mitdbg/modeldb | client/workflows/demos/tf-text-classification.ipynb | mit | # Python 3.6
!pip install verta
!pip install wget
!pip install pandas
!pip install tensorflow==1.14.0
!pip install scikit-learn
!pip install lxml
!pip install beautifulsoup4
"""
Explanation: StackOverflow Question Multiclassification (Keras)
This example uses a dataset from stackoverflow, with the input being posts an... |
daniestevez/jupyter_notebooks | Lucy/Lucy frames EB3FRN 2021-10-19.ipynb | gpl-3.0 | def timestamps(packets):
epoch = np.datetime64('2000-01-01T12:00:00')
t = np.array([struct.unpack('>I', p[ccsds.SpacePacketPrimaryHeader.sizeof():][:4])[0]
for p in packets], 'uint32')
return epoch + t * np.timedelta64(1, 's')
def load_frames(path):
frame_size = 223 * 5 - 2
frames... |
IsacLira/data-science-cookbook | 2017/05-naive-bayes/naiveBayes_otacilio_bezerra.ipynb | mit | # Libraries
import numpy as np
import pandas as pd
from sklearn.naive_bayes import MultinomialNB, GaussianNB
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, classification_report
# Creating a class for the Naive Bayes
clas... |
yorkerlin/shogun | doc/ipython-notebooks/neuralnets/autoencoders.ipynb | gpl-3.0 | %pylab inline
%matplotlib inline
from scipy.io import loadmat
from modshogun import RealFeatures, MulticlassLabels, Math
# load the dataset
dataset = loadmat('../../../data/multiclass/usps.mat')
Xall = dataset['data']
# the usps dataset has the digits labeled from 1 to 10
# we'll subtract 1 to make them in the 0-9 r... |
thehackerwithin/berkeley | code_examples/nltk/THW_NLTK.ipynb | bsd-3-clause | text = '''Hello, my name is Chris.
I'll be talking about the python library NLTK today.
NLTK is a popular tool to conduct text processing tasks in NLP.'''
from nltk.tokenize import word_tokenize
print("Notice the difference!")
print()
print(word_tokenize(text))
print()
print("vs.")
print()
print(text.split())
""... |
sebp/scikit-survival | doc/user_guide/00-introduction.ipynb | gpl-3.0 | from sksurv.datasets import load_veterans_lung_cancer
data_x, data_y = load_veterans_lung_cancer()
data_y
"""
Explanation: Introduction to Survival Analysis with scikit-survival
scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the... |
jrg365/gpytorch | examples/08_Advanced_Usage/Simple_GP_Regression_Derivative_Information_2d.ipynb | mit | import torch
import gpytorch
import math
from matplotlib import cm
from matplotlib import pyplot as plt
import numpy as np
%matplotlib inline
%load_ext autoreload
%autoreload 2
"""
Explanation: GPyTorch regression with derivative information in 2d
Introduction
In this notebook, we show how to train a GP regression mo... |
timzhangau/ml_nano | practice_projects/imdb/Student_Admissions.ipynb | mit | import pandas as pd
data = pd.read_csv('student_data.csv')
data
"""
Explanation: Predicting Student Admissions
In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:
- GRE Scores (Test)
- GPA Scores (Grades)
- Class rank (1-4)
The dataset originally came from here: ht... |
daniel-koehn/Theory-of-seismic-waves-II | 02_Mesh_generation/1_Cartesian_Finite_Difference_grid.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 D. Koehn, notebook style s... |
ksooklall/deep_learning_foundation | transfer-learning/Transfer_Learning.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/04_01/Final/.ipynb_checkpoints/Create-checkpoint.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
"""
Explanation: Creating Data Frames
documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it
like a spreadsheet or SQL table, o... |
ioos/notebooks_demos | notebooks/2016-12-19-exploring_csw.ipynb | mit | from datetime import datetime
# Region: Northwest coast.
bbox = [-127, 43, -123.75, 48]
min_lon, max_lon = -127, -123.75
min_lat, max_lat = 43, 48
bbox = [min_lon, min_lat, max_lon, max_lat]
crs = "urn:ogc:def:crs:OGC:1.3:CRS84"
# Temporal range of 1 week.
start = datetime(2017, 4, 14, 0, 0, 0)
stop = datetime(2017,... |
brettavedisian/phys202-2015-work | days/day20/Cython.ipynb | mit | import numpy as np
%load_ext Cython
"""
Explanation: Short Tour of Cython
In general, Python is slower than other programming languages such as C, C++ and Java. Much of this comes from the fact that Python is dynamically typed. This means that when Python is compiling code to be run by the interpreter, it doesn't ma... |
jcmgray/quijy | docs/tensor-basics.ipynb | mit | bra = qtn.Tensor(qu.rand_ket(4).reshape(2, 2), inds=('b0', 'b1'), tags={'BRA'})
TN = ket.H & X & Y & bra
print(TN)
TN.graph(color=['KET', 'PAULI', 'BRA'], figsize=(4, 4))
"""
Explanation: And finally, a random 'bra' to complete the inner product:
End of explanation
"""
TN.graph(color=['KET', 'X', 'BRA', 'Y'], figs... |
idf/NumPy-Quicksheet | Vectorization.ipynb | bsd-3-clause | v = np.array([1, 2, 3])
np.repeat(v[:, np.newaxis], 3, 1)
np.repeat(v[np.newaxis, :], 3, 0)
"""
Explanation: Vectorization - use a single matrix multiplication to avoid for-loop vector multiplicaions.
Axis
Repeat
Repeat a vector to matrix
End of explanation
"""
def my_func(a):
"""Average first and last eleme... |
SciTools/cube_browser | doc/write_your_own/components/OptionsCascade.ipynb | bsd-3-clause | import ipywidgets
import IPython.display
import iris
import numpy as np
import iris.quickplot as iplt
"""
Explanation: Options List
This example shows how to construct a set of widgets that can be used to select a series of plot customization options.
End of explanation
"""
cube = iris.load_cube(iris.sample_data_pa... |
zhuanxuhit/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
DFEC-R2D2/r2d2 | docs/trades/motor_controller/motors.ipynb | mit | from __future__ import division
from __future__ import print_function
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from math import sin, pi, tan
"""
Explanation: Motor Controller Sizing
There are a lot of things that go into sizing a motor controller. This will look at the torque needed to cli... |
mrphyja/bioinfo-intro-python | bioinformatics_intro_python.ipynb | mit | YouTubeVideo('fCd6B5HRaZ8', start=135)
"""
Explanation: The Source Code of Life: Using Python to Explore Our DNA
Researchers just found the gene responsible for mistakenly thinking we've found the gene for specific things. It's the region between the start and the end of every chromosome, plus a few segments in our m... |
csaladenes/aviation | code/airport_freq_parser.ipynb | mit | L=json.loads(file('../json/L.json','r').read())
M=json.loads(file('../json/M.json','r').read())
N=json.loads(file('../json/N.json','r').read())
import requests
AP={}
for c in M:
if c not in AP:AP[c]={}
for i in range(len(L[c])):
AP[c][N[c][i]]=L[c][i]
"""
Explanation: Load airports of each country
En... |
cgpotts/cs224u | tutorial_pytorch_models.ipynb | apache-2.0 | __author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2022"
"""
Explanation: Tutorial: Using and extending the course PyTorch models
End of explanation
"""
%ls torch*
"""
Explanation: Contents
Overview
Set-up
General optimization choices
Classifiers
Softmax classifier
A deeper neural classifier
... |
zchq88/DLminiProject | handwritten-digit-recognition/handwritten-digit-recognition-with-tflearn-exercise.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
mne-tools/mne-tools.github.io | 0.23/_downloads/4d3b714a9291625bb4b01d7f9c7c3a16/compute_source_psd_epochs.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd_epochs
print(__doc__)
data_path = sample.data_path()
fname_inv = data_path + '/MEG/sampl... |
supergis/git_notebook | other/aiyagari_continuous_time.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import sparse
from scipy.sparse.linalg import spsolve
"""
Explanation: Aiyagari (1994) in Continuous Time
By SeHyoun Ahn and Benjamin Moll
The material in this notebook is based on Achdou et al. (2015) "Heterogeneous Agent Models in Conti... |
astro4dev/OAD-Data-Science-Toolkit | Teaching Materials/Programming/Python/PythonISYA2018/04.Astropy/04_query.ipynb | gpl-3.0 | # Required to see plots when running on mybinder
import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Python standard-libraries to download data from the web
from urllib.parse import urlencode
from urllib.request import urlretrieve
#Some astropy submodule... |
phobson/statsmodels | examples/notebooks/statespace_dfm_coincident.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
np.set_printoptions(precision=4, suppress=True, linewidth=120)
from pandas.io.data import DataReader
# Get the datasets from FRED
start = '1979-01-01'
end = '2014-12-01'
indprod = DataReader('IPMAN... |
AWS-Spot-Analysis/spot-analysis | TSC_1.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import matplotlib.pylab as plt
x=np.linspace(0,50,100)
ts1=pd.Series(3.1*np.sin(x/1.5)+3.5)
ts2=pd.Series(2.2*np.sin(x/3.5+2.4)+3.2)
ts3=pd.Series(0.04*x+3.0)
#ts1.plot()
#ts2.plot()
#ts3.plot()
#plt.ylim(-2,10)
#plt.legend(['ts1','ts2','ts3'])
#plt.show()
"""
Explanation: Tim... |
datactive/bigbang | examples/activity/Plot Activity.ipynb | mit | import bigbang.ingress.mailman as mailman
import bigbang.analysis.graph as graph
import bigbang.analysis.process as process
from bigbang.parse import get_date
#from bigbang.functions import *
from bigbang.archive import Archive
"""
Explanation: This notebook shows how BigBang can help you explore a mailing list archiv... |
OMS-NetZero/FAIR | notebooks/Example-Usage.ipynb | apache-2.0 | %matplotlib inline
import fair
import numpy as np
from matplotlib import pyplot as plt
plt.style.use('seaborn-darkgrid')
plt.rcParams['figure.figsize'] = (16, 9)
"""
Explanation: FaIR
This notebook gives some simple examples of how to run and use the Finite Amplitude Impulse Response (FaIR) model.
The Finite Amp... |
YufeiZhang/Principles-of-Programming-Python-3 | Lectures/Lecture_2/Jupyter_notebook_sheets/functions.ipynb | gpl-3.0 | a = 0
m = 0
n = 0
x = 0
print('PRINT 0: a =', a,
' m =', m, ' n =', n,
' x =', x)
def f_1():
m = 1
global n
n = 1
x = 1
y = 1
z = 1
print('PRINT 1: a =', a,
' m =', m, ' n =', n,
' x =', x, ' y =', y, ' z =', z)
def f_2():
gl... |
Kaggle/learntools | notebooks/sql/raw/tut6.ipynb | apache-2.0 | #$HIDE_INPUT$
from google.cloud import bigquery
# Create a "Client" object
client = bigquery.Client()
# Construct a reference to the "github_repos" dataset
dataset_ref = client.dataset("github_repos", project="bigquery-public-data")
# API request - fetch the dataset
dataset = client.get_dataset(dataset_ref)
# Const... |
Djabbz/PythonTuto | python_prebootcamp_tuto.ipynb | bsd-2-clause | # Here is a comment
"""
Explanation: Introduction
This course is meant to provide a very gentle introduction to the python scientific ecosystem. We will try to cover as much as humanly possible within the timespan of 2 hours. It is essentially a warm-up before the upcoming machine learning bootcamps for those who woul... |
gaufung/PythonStandardLibrary | DataStructures/collection/ChainMap.ipynb | mit | import collections
a = {'a':'A', 'c':'C'}
b = {'b':'B', 'd':'D'}
m = collections.ChainMap(a, b)
for k, v in m.items():
print('{}={}'.format(k, v))
"""
Explanation: The ChainMap class manages a sequence of dictionaries, and searches through them in the order they are given to find values associated with keys. A Cha... |
grokkaine/biopycourse | day3/DL1_FFN.ipynb | cc0-1.0 | import tensorflow as tf
from tensorflow.keras.datasets import mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
print(train_images.shape)
print(train_labels.shape)
# reshape (flatten) and scale images
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.a... |
EliseJ/astroABC | examples/supernovae.ipynb | mit | %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
import numpy as np
from scipy.stats import skewnorm
import math
import astroabc
from distance_calc import DistanceCalc
from bin_data import *
"""
Explanation: Supernova Example
Introduction
In this t... |
luofan18/deep-learning | transfer-learning/Transfer_Learning_Solution.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
jskksj/cv2stuff | cv2stuff/.ipynb_checkpoints/pathlib-checkpoint.ipynb | isc | p = Path('.')
[x for x in p.iterdir() if x.is_dir()]
list(p.glob('**/*.py'))
p = Path('/etc')
p
q = p / 'init.d' / 'reboot'
q
q.resolve()
q.exists()
q.is_dir()
with q.open() as f:
print(f.readline())
"""
Explanation: Path is the basic operator for portability.
End of explanation
"""
PurePosixPath('foo') ... |
gilgeorges/antsearch | AntSearch for CHPswarm.ipynb | mit | a, b, P_el = sympy.symbols("a b P_el ")
eta_el = a * sympy.log(P_el) + b
t_op, E_th, eta_th = sympy.symbols("t_op E_th eta_th")
P_th = P_el / eta_el * (1 - eta_el) * eta_th
sols = sympy.solve(sympy.Eq(P_th, E_th/t_op), sympy.log(P_el))
assert len(sols) == 1
sols[0]
"""
Explanation: Ant Colony Optimization for Energy... |
AnasFullStack/Awesome-Full-Stack-Web-Developer | algorithms/algorithms_in_python.ipynb | mit | class Stack():
def __init__(self):
self.items = []
def push(self, item):
self.items.append(item)
def pop(self):
return self.items.pop()
def peek(self):
return self.items[len(self.items) - 1]
def isEmpty(self):
return self.items == []
... |
mne-tools/mne-tools.github.io | 0.23/_downloads/d194fa1c3e67c7ada95e4807f9e5f3e5/50_artifact_correction_ssp.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import (create_eog_epochs, create_ecg_epochs,
compute_proj_ecg, compute_proj_eog)
"""
Explanation: Repairing artifacts with SSP
This tutorial covers the basics of signal-space projection (SSP) ... |
fw1121/Roary | contrib/roary_plots/roary_plots.ipynb | gpl-3.0 | # Plotting imports
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
# Other imports
import os
import pandas as pd
import numpy as np
from Bio import Phylo
"""
Explanation: Roary pangenome plots
<h6><a href="javascript:toggle()" target="_self">Toggle source code</a></h6... |
DEIB-GECO/PyGMQL | examples/notebooks/01_A_simple_example.ipynb | apache-2.0 | import gmql as gl
"""
Explanation: Simple example of local processing
In this first tutorial we will show a complete example of usage of the library using some example datasets provided with it.
Importing the library
Importing the library
End of explanation
"""
dataset1 = gl.get_example_dataset("Example_Dataset_1")
... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/feature_engineering/solutions/3_keras_basic_feat_eng.ipynb | apache-2.0 | # Install Sklearn
!python3 -m pip install --user sklearn
# Ensure the right version of Tensorflow is installed.
!pip3 freeze | grep 'tensorflow==2\|tensorflow-gpu==2' || \
!python3 -m pip install --user tensorflow==2
import os
import tensorflow.keras
import matplotlib.pyplot as plt
import pandas as pd
import tensorf... |
jseabold/statsmodels | examples/notebooks/robust_models_0.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
"""
Explanation: Robust Linear Models
End of explanation
"""
data = sm.datasets.stackloss.load(as_pandas=False)
data.exog = sm.add_constant(data.ex... |
arnoldlu/lisa | ipynb/tutorial/07_PerfAnalysis.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
# Execute this cell to report devlib debugging information
logging.getLogger('ssh').setLevel(logging.DEBUG)
# Generate plots inline
%pylab inline
import json
import os
"""
Explanation: Tutorial Goal
This tutorial aims to show how RTApp performance metr... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session07/Day1/MachLearnAstroData.ipynb | mit | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: An Astronomical Application of Machine Learning:
Separating Stars and Galaxies from SDSS
Version 0.1
By AA Miller 2018 Nov 06
The problems in the following notebook develop an end-to-end ma... |
rahulremanan/python_tutorial | Beginners/notebook/Objects_Classes_Constructors_and_Functions.ipynb | mit | a = int(input())
b = int(input())
print (a+b)
"""
Explanation: Object oriented programming in python:
Author: Dr. Rahul Remanan
CEO and Chief Imagination Officer
Moad Computer
Objects are an encapsulation of variables and functions into a single entity.
Objects get their variables and functions from classes.
Class... |
tensorflow/docs-l10n | site/ko/guide/keras/masking_and_padding.ipynb | apache-2.0 | #@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... |
Dima806/udacity-mlnd-capstone | capstone-step1-sensitivity-check-run2.ipynb | apache-2.0 | # Select test_size and random_state for splitting a subset
test_size=0.1
random_state=1
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import time
import gzip
import shutil
import seaborn as sns
from collections import Counter
from sklearn.mixture ... |
tensorflow/docs-l10n | site/ko/tutorials/text/text_generation.ipynb | apache-2.0 | #@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... |
LucaCanali/Miscellaneous | Impala_SQL_Jupyter/Impala_SQL_Magic.ipynb | apache-2.0 | %load_ext sql
"""
Explanation: Apache Impala and SQL magic for IPython/Jupyter notebooks
1. Load SQL magic extension (uses ipython-sql by Catherine Devlin)
End of explanation
"""
%%sql
impala://impalasrv-test:21050/test2
"""
Explanation: 2. Connect to the target database (requires Cloudera impyla)
End of explanatio... |
Flaviolib/dx | 02_dx_models.ipynb | agpl-3.0 | from dx import *
import seaborn as sns; sns.set()
np.set_printoptions(precision=3)
"""
Explanation: <img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="45%" align="right" border="4">
Model Classes
The model classes represent the fundamental building blocks to model a financial market. They are ... |
Danghor/Algorithms | Python/Chapter-04/Merge-Sort.ipynb | gpl-2.0 | def sort(L):
n = len(L)
if n < 2:
return L
L1, L2 = L[:n//2], L[n//2:]
return merge(sort(L1), sort(L2))
"""
Explanation: Merge Sort
In order to sort a list $L$ using <em style="color:blue;">merge sort</em> we proceed as follows:
If $L$ has less than two elements, then $L$ is already sorted. T... |
ericmjl/scikit-learn-tutorial | 04 Regression & Comparing Models.ipynb | mit | # Load the sequence data as a Pandas dataframe.
seqids = [s.id for s in SeqIO.parse('data/hiv-protease-sequences-expanded.fasta', 'fasta')]
sequences = [s for s in SeqIO.parse('data/hiv-protease-sequences-expanded.fasta', 'fasta')]
sequences = MultipleSeqAlignment(sequences)
sequences = pd.DataFrame(np.array(sequence... |
shareactorIO/pipeline | oreilly.ml/high-performance-tensorflow/notebooks/05_Debug_Model.ipynb | apache-2.0 | %%bash
cat /root/src/main/python/debug/debug_model_cpu.py
"""
Explanation: Debug Models Using tfdbg
Open a Terminal through Jupyter Notebook
(Menu Bar -> Terminal -> New Terminal)
Run the Next Cell to Display the Code
Find the DebugWrapper around the tf.Session
sess = tf.Session(config=config)
sess = tf_debug.LocalC... |
swirlingsand/deep-learning-foundations | gans/batch-norm/.ipynb_checkpoints/Batch_Normalization_Exercises-checkpoint.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... |
Hades1996/nummeth | finding roots/primerPunto.ipynb | mit | import math
def funcion(x):
return (math.pow(math.e,6*x))+(1.44*math.pow(math.e,2*x))-(2.079*math.pow(math.e,4*x))-(0.333)
def biseccion(intA, intB, errorA, noMaxIter):
if(funcion(intA)*funcion(intB)<0):
noIter = 0
errorTmp = 1
intTmp = 0
oldInt = intA
... |
banneker-aztlan/python-week-2 | Part 1/Introductory Exercises (Part 1).ipynb | mit | # Ensures compatibility between Python 2 and 3.
from __future__ import print_function, division
from __builtin__ import range
"""
Explanation: Introductory Exercises
These exercises are meant to supplement the lecture notes based on Jack VanderPlas's excellent book as well as material from http://introtopython.org.
Yo... |
afedynitch/MCEq | examples/Simple_zenith_averaged_neutrinos.ipynb | bsd-3-clause | #basic imports and ipython setup
import matplotlib.pyplot as plt
import numpy as np
#import solver related modules
from MCEq.core import MCEqRun
#import primary model choices
import crflux.models as pm
mceq_run = MCEqRun(
#provide the string of the interaction model
interaction_model='SIBYLL2.3c',
#primary cosmic ray... |
voyageth/udacity-Deep_Learning_Foundations_Nanodegree | first-neural-network/dlnd-your-first-neural-network.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... |
LDSSA/learning-units | units/16-tuning-hyper-parameters/examples/hyper_parameter_tuning_example.ipynb | mit | path_to_data = r'E:\Universidade\Projects\DSSA\learning-units\units\16-tuning-hyper-parameters\data\affairs.csv'
data = pd.read_csv(path_to_data)
data.head(10)
"""
Explanation: Import the data:
During this example we will be using a dataset of politicians affairs. This dataset has a number of politicians, their attri... |
evanmiltenburg/python-for-text-analysis | Chapters/Chapter 08 - Comparison of lists and sets.ipynb | apache-2.0 | list1 = [1, 2, 1, 3, 4, 1]
set1 = {1, 2, 3, 4}
set2 = {1, 2, 1, 3, 4, 1}
print('list1', list1)
print('set1', set1)
print('set2', set2)
print('set1 is the same as set2:', set1 == set2)
"""
Explanation: Chapter 8 - Comparison of lists and sets
You've been introduced to two containers in this topic: lists and sets. How... |
arcyfelix/Courses | 18-11-22-Deep-Learning-with-PyTorch/02-Introduction to PyTorch/Part 7 - Loading Image Data.ipynb | apache-2.0 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import torch
from torchvision import datasets, transforms
import helper
"""
Explanation: Loading Image Data
So far we've been working with fairly artificial datasets that you wouldn't typically be using in real project... |
goodwordalchemy/thinkstats_notes_and_exercises | code/chap04_Cdfs_notes.ipynb | gpl-3.0 | def PercentileRank(scores, your_score):
count = 0
for score in scores:
if score <= youre_score:
count += 1
percentile_rank = 100.0 * count / len(scores)
return percentile_rank
def Percentile(scores, percentile_rank):
scores.sort()
for score in scores:
if PercentileRa... |
theonaun/notebooks | presentations/python_data_tools/03_python.ipynb | mit | # Remember, lines starting with # comments and are not run.
a = 5
'''Multiline
comments
done
like
this.'''
if a > 1:
print('Greater than 1.')
else:
print('Not greater than 1.')
"""
Explanation: <img style="margin-right: 0;" src="static/small.jpg">
<hr>
<div style="display: table; width: 100%">
... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/solutions/tfdv_advanced_taxi.ipynb | apache-2.0 | !pip install pyarrow==5.0.0
!pip install numpy==1.19.2
!pip install tensorflow-data-validation
"""
Explanation: Advanced Visualizations with TensorFlow Data Validaiton
Learning Objectives
Install TFDV
Compute and visualize statistics
Infer a schema
Check evaluation data for errors
Check for evaluation anomalies and f... |
JackDi/phys202-2015-work | assignments/assignment06/ProjectEuler17.ipynb | mit | number={}
number[0]="zero"
number[1]="one"
number[2]="two"
number[3]="three"
number[4]='four'
number[5]='five'
number[6]='six'
number[7]='seven'
number[8]='eight'
number[9]='nine'
number[10]='ten'
number[11]='eleven'
number[12]='twelve'
number[13]='thirteen'
number[14]='fourteen'
number[15]='fifteen'
number[16]='teen'
... |
tiegz/ThreatExchange | ipynb/Getting Started with Sharing.ipynb | bsd-3-clause | from pytx.access_token import access_token
# Specify the location of your token via one of several ways:
# https://pytx.readthedocs.org/en/latest/pytx.access_token.html
access_token()
"""
Explanation: Getting Started with ThreatExchange Sharing
Purpose
The ThreatExchange APIs are designed to make the sharing of indic... |
undercertainty/ou_nlp | 06_decision_trees.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
... |
oscarvarto/oscarvarto.github.io | content/immutability-principles.ipynb | apache-2.0 | x = [1, 2, 3]
x.reverse()
x
"""
Explanation: title: Immutability Principles
author: Oscar Vargas Torres
date: 2018-09-12
category: Functional Programming
tags: Immutability
Mutability is hard to reason about
A lot of programming languages support mutability. For example, some objects in Python are mutable:
End of ex... |
SKA-ScienceDataProcessor/algorithm-reference-library | workflows/notebooks/mpc_simulation.ipynb | apache-2.0 | %matplotlib inline
from data_models.parameters import arl_path
results_dir = arl_path('test_results')
import numpy
from astropy.coordinates import SkyCoord
from astropy import units as u
from astropy.wcs.utils import pixel_to_skycoord
from matplotlib import pyplot as plt
from data_models.memory_data_models import... |
mattweirick/mattweirick.github.io | markdown_generator/.ipynb_checkpoints/talks-checkpoint.ipynb | mit | import pandas as pd
import os
"""
Explanation: Talks markdown generator for academicpages
Takes a TSV of talks with metadata and converts them for use with academicpages.github.io. This is an interactive Jupyter notebook (see more info here). The core python code is also in talks.py. Run either from the markdown_gener... |
Raag079/self-driving-car | Term01-Computer-Vision-and-Deep-Learning/Labs/04-Keras/traffic-sign-classification-with-keras.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile
from tqdm import tqdm
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = b... |
pastas/pasta | examples/notebooks/17_changing_responses.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pastas as ps
ps.set_log_level("ERROR")
ps.show_versions(numba=True)
"""
Explanation: Changing response functions
R.A. Collenteur, University of Graz, 2021
In this notebook the new ChangeModel is tested, based on the work by Obergfjell et al... |
tpin3694/tpin3694.github.io | python/matplotlib_simple_scatterplot.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# Set ipython's max row display
pd.set_option('display.max_row', 1000)
# Set iPython's max column width to 50
pd.set_option('display.max_columns', 50)
"""
Explanation: Title: Scatterplot In MatPlotLib
Slug: matplotlib_simple_sc... |
tpin3694/tpin3694.github.io | sql/select_rows_based_on_conditionals.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Selecting Rows Based On Conditionals
Slug: select_rows_based_on_conditionals
Summary: Selecting Rows Based On Conditionals in SQL.
Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This ... |
jsub10/MLCourse | Notebooks/Regression-with-a-Single-Feature.ipynb | mit | # Share functions used in multiple notebooks
%run Shared-Functions.ipynb
"""
Explanation: Regression With a Single Feature
Using a single feature to make a numerical prediction
TO DO
- nothing for the moment
End of explanation
"""
# Load up the packages to investigate the data
import numpy as np
import pandas as pd
... |
monicathieu/cu-psych-r-tutorial | public/tutorials/python/4_plotting/challenge_key.ipynb | mit | # load packages we will be using for this challenge
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
sns.set(rc={'figure.figsize':(12,8)})
"""
Explanation: Python Plotting Challenge
Goals of this Challenge
Students will test their ability to:... |
xaibeing/cn-deep-learning | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: 你的第一个神经网络
在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。
End of explanation
"""
data_path = 'Bike-Sharing-Dataset/hour.c... |
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