repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
paris-saclay-cds/python-workshop | Day_2_Software_engineering_best_practices/solutions/03_code_style.ipynb | bsd-3-clause | def read_spectra(path_csv):
"""Read and parse data in pandas DataFrames.
Parameters
----------
path_csv : str
Path to the CSV file to read.
Returns
-------
spectra : pandas DataFrame, shape (n_spectra, n_freq_point)
DataFrame containing all Raman spectra.
... |
vsingla2/Self-Driving-Car-NanoDegree-Udacity | Term1-Computer-Vision-and-Deep-Learning/Project1-Finding-Lane-Lines/P1.ipynb | mit | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
"""
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to ide... |
drivendata/data-science-is-software | notebooks/lectures/3.0-refactoring.ipynb | mit | %matplotlib inline
from __future__ import print_function
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
PROJ_ROOT = os.path.join(os.pardir, os.pardir)
"""
Explanation: <table style="width:100%; border: 0px solid black;">
<tr style="width: 100%; border: 0px solid black;">
... |
ituethoslab/navcom-2017 | exercises/Week 11-Tooltrack 3/Social media scraping.ipynb | gpl-3.0 | biposts = pd.read_csv('page_20446254070_2017_11_14_15_20_00.tab',
sep='\t',
parse_dates=['post_published'])
"""
Explanation: Social media scraping 3/3
What have we achieved in the past 2 week?
1. Sanity checks
Do them
Srsly
E.g. a student email 💬
Message from Netvizz
Getti... |
samoturk/HUB-ipython | notebooks/Intro to Python and Jupyter.ipynb | mit | print('This is cell with code')
"""
Explanation: Python
Python is widely used general-purpose high-level programming language. Its design philosophy emphasizes code readability. It is very popular in science.
Jupyter
The Jupyter Notebook is a web application that allows you to create and share documents that contain l... |
ucsdlib/python-novice-inflammation | 5-functions.ipynb | cc0-1.0 | import numpy
import matplotlib.pyplot
def fahr_to_kelvin(temp):
return ((temp - 32) * (5/9)) + 273.15
"""
Explanation: up to now:
* we've written code to draw out some interesting featurs on the inflammation,
* looped over our data files to draw plots,
* and have python to make decisions based on conditions re... |
camillescott/boink | notebooks/decision-nodes-Ast_gla.ipynb | mit | k27_df.hash.nunique(), k35_df.hash.nunique()
"""
Explanation: We can find the number of decision nodes in the dBG by counting unique hashes...
End of explanation
"""
k35_df['degree'] = k35_df['l_degree'] + k35_df['r_degree']
k27_df['degree'] = k27_df['l_degree'] + k27_df['r_degree']
"""
Explanation: We'll make a ne... |
afronski/playground-notes | scalable-machine-learning/solutions/ML_lab1_review_student.ipynb | mit | labVersion = 'cs190_week1_v_1_1'
"""
Explanation: Math and Python review and CTR data download
This notebook reviews vector and matrix math, the NumPy Python package, and Python lambda expressions. It also covers downloading the data required for Lab 4, where you will analyze website click-through rates. Part 1 cove... |
pybel/pybel-notebooks | summary/Summarizing Multiple Graphs Together.ipynb | apache-2.0 | import os
import time
import sys
import pybel
import pybel_tools
from pybel_tools.summary import info_str
"""
Explanation: Summarizing Multiple Graphs Together
Author: Charles Tapley Hoyt
Estimated Run Time: 45 seconds
This notebook shows how to combine multiple graphs from different sources and summarize them togeth... |
navierula/Subreddit-Analysis-on-Eating-Disorders | Data Preprocessing.ipynb | mit | import pandas as pd
json_file = 'sample_data'
list(pd.read_json(json_file, lines=True))
"""
Explanation: Load a sample of the raw JSON data into pandas.
End of explanation
"""
import csv
import json
from nltk.tokenize import TweetTokenizer
from tqdm import tqdm
MIN_NUM_WORD_TOKENS = 10
TOTAL_NUM_LINES = 53851542 #... |
zzsza/Datascience_School | 30. 딥러닝/01. 신경망 기초 이론.ipynb | mit | %%tikz
\tikzstyle{neuron}=[circle, draw, minimum size=23pt,inner sep=0pt]
\tikzstyle{bias}=[text centered]
\node[neuron] (node) at (2,0) {$z$};
\node[neuron] (x1) at (0, 1) {$x_1$};
\node[neuron] (x2) at (0, 0) {$x_2$};
\node[neuron] (x3) at (0,-1) {$x_3$};
\node[neuron] (b) at (0,-2) {$1$};
\node[neuron] (output) at... |
dwhswenson/openpathsampling | examples/alanine_dipeptide_tps/AD_tps_3a_analysis_flex.ipynb | mit | from __future__ import print_function
%matplotlib inline
import openpathsampling as paths
import numpy as np
import matplotlib.pyplot as plt
import os
import openpathsampling.visualize as ops_vis
from IPython.display import SVG
"""
Explanation: Analyzing the flexible path length simulation
End of explanation
"""
# n... |
UPML/complexityTheory | toGit/TSP/tsp/results.ipynb | apache-2.0 | class Node:
def __init__(self, number, cost, time, answer):
self.number = int(number)
self.cost = float(cost)
self.time = float(time) / 10**9
self.size = self.number / 100
self.answer = answer
def write(self):
print("n = ", self.number," \n")
print("cost ... |
manolomartinez/skyrms | Signal Tutorial.ipynb | gpl-3.0 | sender = np.identity(3)
receiver = np.identity(3)
state_chances = np.array([1/3, 1/3, 1/3])
"""
Explanation: 1. Setting things up
Let's take a look at game.py, which we use to create games. Right now, Signal only does cheap-talk games with a chance player. That is, games in which the state the sender observes is exoge... |
rmdort/clipper | examples/tutorial/tutorial_part_one.ipynb | apache-2.0 | cifar_loc = ""
%run ./download_cifar.py $cifar_loc
"""
Explanation: Clipper Tutorial: Part 1
This tutorial will walk you through the process of starting Clipper, creating and querying a Clipper application, and deploying models to Clipper. In the first part of the demo, you will set up Clipper and create an applicatio... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/building_production_ml_systems/solutions/0_export_data_from_bq_to_gcs.ipynb | apache-2.0 | #%load_ext google.cloud.bigquery
import os
from google.cloud import bigquery
"""
Explanation: Exporting data from BigQuery to Google Cloud Storage
In this notebook, we export BigQuery data to GCS so that we can reuse our Keras model that was developed on CSV data.
Uncomment the following line if you are running the ... |
Neuroglycerin/neukrill-net-work | notebooks/model_run_and_result_analyses/Analyse Extra MLP Layers with Dropout.ipynb | mit | import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.dense_dataset
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import holoviews as hl
%load_ext holoviews.ipython
import sklearn.metrics
"""
Explanation: Started some more runs with extra MLP l... |
n-witt/MachineLearningWithText_SS2017 | exercises/solutions/0 Python basics exercises.ipynb | gpl-3.0 | def maximum(x, y):
if x > y:
return x
else:
return y
assert maximum(3, 3) == 3
assert maximum(1, 2) == 2
assert maximum(3, 2) == 3
"""
Explanation: 1. Define a function maximum that takes two numbers as arguments and returns the largest of them. Use the if-then-else construct available in Pyth... |
erdewit/ib_insync | notebooks/tick_data.ipynb | bsd-2-clause | from ib_insync import *
util.startLoop()
ib = IB()
ib.connect('127.0.0.1', 7497, clientId=15)
"""
Explanation: Tick data
For optimum results this notebook should be run during the Forex trading session.
End of explanation
"""
contracts = [Forex(pair) for pair in ('EURUSD', 'USDJPY', 'GBPUSD', 'USDCHF', 'USDCAD', 'A... |
letsgoexploring/teaching | winter2017/econ129/python/Econ129_Class_05.ipynb | mit | # Use the requests module to download money growth and inflation data
url = 'http://www.briancjenkins.com/data/quantitytheory/csv/qtyTheoryData.csv'
r = requests.get(url,verify=True)
with open('qtyTheoryData.csv','wb') as newFile:
newFile.write(r.content)
"""
Explanation: Class 5: Pandas
Pandas is a Python p... |
mayankjohri/LetsExplorePython | Section 3 - Machine Learning/Supervised Learning Algorithm/Regression Analysis/3. Ridge Regression.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 10
#Define input array with angles from 60deg to 300deg converted to radians
x = np.array([i*np.pi/180 for i in range(60,300,4)])
np.random.seed... |
fweik/espresso | doc/tutorials/ferrofluid/ferrofluid_part3.ipynb | gpl-3.0 | import espressomd
espressomd.assert_features('DIPOLES', 'LENNARD_JONES')
from espressomd.magnetostatics import DipolarP3M
import numpy as np
"""
Explanation: Ferrofluid - Part 3
Table of Contents
Susceptibility with fluctuation formulas
Derivation of the fluctuation formula
Simulation
Magnetization curve of a 3D s... |
mromanello/SunoikisisDC_NER | participants_notebooks/Sunoikisis - Named Entity Extraction 1b-GB.ipynb | gpl-3.0 | ########
# NLTK #
########
import nltk
from nltk.tag import StanfordNERTagger
########
# CLTK #
########
import cltk
from cltk.tag.ner import tag_ner
##############
# MyCapytain #
##############
import MyCapytain
from MyCapytain.resolvers.cts.api import HttpCTSResolver
from MyCapytain.retrievers.cts5 import CTS
from M... |
informatics-isi-edu/deriva-py | docs/derivapy-datapath-example-3.ipynb | apache-2.0 | # Import deriva modules
from deriva.core import ErmrestCatalog, get_credential
# Connect with the deriva catalog
protocol = 'https'
hostname = 'www.facebase.org'
catalog_number = 1
# If you need to authenticate, use Deriva Auth agent and get the credential
credential = get_credential(hostname)
catalog = ErmrestCatalog... |
muratcemkose/cy-rest-python | advanced/integratingDrugbank.ipynb | mit | import requests
import json
import pandas as pd
PORT_NUMBER = 1234
BASE = 'http://localhost:' + str(PORT_NUMBER) + '/v1/'
HEADERS = {'Content-Type': 'application/json'}
requests.post(BASE + 'networks?source=url&collection=KEGG', data=json.dumps(['http://rest.kegg.jp/get/eco00250/kgml']), headers=HEADERS)
"""
Explana... |
karlstroetmann/Formal-Languages | Python/Regexp-Tutorial.ipynb | gpl-2.0 | import re
"""
Explanation: Regular Expressions in Python (A Short Tutorial)
This is a tutorial showing how regular expressions are supported in Python.
The assumption is that the reader already has a grasp of the concept of
regular expressions as it is taught in lectures
on formal languages, for example in
Formal L... |
simpeg/simpegmt | notebooks/Derivative test MT1D.ipynb | mit | import SimPEG as simpeg
import simpegEM as simpegem, simpegMT as simpegmt
from SimPEG.Utils import meshTensor
import numpy as np
simpegmt.FieldsMT.FieldsMT_1D
# Setup the problem
sigmaHalf = 1e-2
# Frequency
nFreq = 33
# freqs = np.logspace(3,-3,nFreq)
freqs = np.array([100])
# Make the mesh
ct = 5
air = meshTensor([... |
aschaffn/phys202-2015-work | assignments/assignment03/NumpyEx01.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 1
Imports
End of explanation
"""
# there's got to be a more efficient way using some sort
# of list comprehension
def checkerbo... |
spencer2211/deep-learning | sentiment-rnn/Sentiment_RNN_Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/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... |
phoebe-project/phoebe2-docs | 2.2/tutorials/ETV.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: ETV Datasets and Options
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotlib inl... |
teuben/astr288p | notebooks/05-images.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
# import pyfits as fits # deprecated
from astropy.io import fits
"""
Explanation: Images: rows, columns and all that jazzy mess....
Two dimensional data arrays are normally stored in column-major or row-major order. In row-ma... |
tuanavu/coursera-university-of-washington | machine_learning/4_clustering_and_retrieval/assigment/week6/.ipynb_checkpoints/6_hierarchical_clustering_graphlab-checkpoint.ipynb | mit | import graphlab
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
import time
from scipy.sparse import csr_matrix
from sklearn.cluster import KMeans
from sklearn.metrics import pairwise_distances
%matplotlib inline
'''Check GraphLab Create version'''
from distutils.version import StrictVersion
as... |
ryan-leung/PHYS4650_Python_Tutorial | notebooks/02-Python-Data-Structures.ipynb | bsd-3-clause | a = 1 # integer
b = 1.1 #floating point numbers
c = True; d = False # Boolean (logical expression)
e = "Hello" # Strings
"""
Explanation: Python Data Structures
Data structure in computing
Data structures are how computer programs store information. Theses information can be processed, analyzed
and visualized easily ... |
mathemage/h2o-3 | examples/deeplearning/notebooks/deeplearning_image_reconstruction_and_clustering.ipynb | apache-2.0 | %matplotlib inline
import matplotlib
import numpy as np
import pandas as pd
import scipy.io
import matplotlib.pyplot as plt
from IPython.display import Image, display
import h2o
from h2o.estimators.deeplearning import H2OAutoEncoderEstimator
h2o.init()
"""
Explanation: Image Space Projection using Autoencoders
In t... |
minesh1291/Practicing-Kaggle | MNIST_2017/dump_/men_2018_0ld_logistic_script.ipynb | gpl-3.0 | #the seed information
#df_seeds = pd.read_csv('../input/NCAATourneySeeds.csv')
#print(df_seeds.shape)
#print(df_seeds.head())
#print(df_seeds.Season.value_counts())
#the seed information
df_seeds = pd.read_csv('../input/NCAATourneySeeds_SampleTourney2018.csv')
print(df_seeds.shape)
print(df_seeds.head())
#print(df_see... |
guyhoffman/hri-statistics | notebooks/Binary_HMM_Filtering.ipynb | mit | import numpy as np
from matplotlib import pyplot as plt
class BinaryHMM:
"""
startprob: np.array(shape(2,))
transmat: np.array(shape(2,2)) - First column is P(X|x), second is P(X|~x)
emissionprob: np.array(shape(2,2)) - First column is P(E|x), second is P(E|~x)
"""
def __init__(self, startprob,... |
phobson/statsmodels | examples/notebooks/tsa_dates.ipynb | bsd-3-clause | from __future__ import print_function
import statsmodels.api as sm
import numpy as np
import pandas as pd
"""
Explanation: Dates in timeseries models
End of explanation
"""
data = sm.datasets.sunspots.load()
"""
Explanation: Getting started
End of explanation
"""
from datetime import datetime
dates = sm.tsa.datet... |
BjornFJohansson/pydna-examples | notebooks/strawberry_aat/strawberry.ipynb | bsd-3-clause | # Import the pydna package functions
from pydna.all import *
# Give your email address to Genbank, so they can contact you.
# This is a requirement for using their services
gb=Genbank("bjornjobb@gmail.com")
# download the SAAT CDS from Genbank
# We know from inspecting the
saat = gb.nucleotide("AF193791 REGION: 78..1... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/27_seq2seq/notebooks/seq2seq/sequence_to_sequence_implementation.ipynb | mit | import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that ta... |
telecombcn-dl/2017-cfis | sessions/convnets.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib inline
from utils import plot_samples, plot_curves
import time
import numpy as np
# force random seed for results to be reproducible
SEED = 4242
np.random.seed(SEED)
"""
Explanation: Convolutional Neural Networks
So far we have been treating images as flattened arrays of ... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_channel_epochs_image.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Visualize channel over epochs as an ima... |
whitead/numerical_stats | unit_12/hw_2017/problem_set_1.ipynb | gpl-3.0 | import scipy.stats as ss
ss.shapiro([-26.3,-24.2, -20.9, -25.8, -24.3, -22.6, -23.0, -26.8, -26.5, -23.1, -20.0, -23.1, -22.4, -22.8])
"""
Explanation: Problem 1 Instructions
Answer the following short-answer questions using Markdown cells
Problem 1.1
A $t$-test and $zM$ test rely on the assumption of normality. How ... |
GoogleCloudPlatform/training-data-analyst | courses/ai-for-finance/solution/aapl_regression_scikit_learn.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: Building a Regression Model for a Financial Dataset
In this notebook, you will build a simple linear regression model to predict the closing AAPL stock price. The lab objectives are:
*... |
mne-tools/mne-tools.github.io | 0.19/_downloads/162648d33d7b9ea4f5ce1e8bb494a02d/plot_mne_inverse_label_connectivity.ipynb | bsd-3-clause | # Authors: 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 plt
import mne
from mne.datasets import sample
f... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-lm/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-lm', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-LM
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbul... |
phuongxuanpham/SelfDrivingCar | CarND-Behavioral-Cloning-Project3/model.ipynb | gpl-3.0 | import os
import csv
import cv2
import numpy as np
import sklearn
"""
Explanation: Behavioral Cloning
This is the Project 3 in Self Driving Car Nano degree from Udacity
The purpose of this project is using deep learning to train a deep neural network to drive a car automously in a simulator.
Behavioral Cloning Projec... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/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', 'mpi-m', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MPI-M
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turb... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_compute_mne_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from mne.datasets import sample
from mne import read_evokeds
from mne.minimum_norm import apply_inverse, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname_inv = d... |
ddtm/dl-course | Seminar9/Seminar9_ru.ipynb | mit | low_RAM_mode = True
very_low_RAM = False #если у вас меньше 3GB оперативки, включите оба флага
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Использование глубокого обучения в NLP
Смотрите в этой серии:
* Простые способы работать с текстом, bag of words
... |
dipanjank/ml | data_analysis/computer_hardware_uci.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
import seaborn as sns
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/cpu-performance/machine.data'
data = pd.read_csv(url, header=None)
data.head()
"""
Explanation: Computer Hardware Dataset Analysis - UCI
This is a regr... |
ercanezin/ce888labs | lab3/facebook_regression.ipynb | gpl-3.0 | df = pd.read_csv("./dataset_Facebook.csv", delimiter = ";")
features = ["Category",
"Page total likes",
"Type",
"Post Month",
"Post Hour",
"Post Weekday",
"Paid"]
df[features].head()
outcomes= ["Lifetime Post Total Reach",
"Lifeti... |
rajul/tvb-library | tvb/simulator/demos/region_deterministic_larterbreakspear.ipynb | gpl-2.0 | # Third party python libraries
import numpy
# Try and import from "The Virtual Brain"
from tvb.simulator.lab import *
from tvb.datatypes.time_series import TimeSeriesRegion
import tvb.analyzers.fmri_balloon as bold
from tvb.simulator.plot import timeseries_interactive as timeseries_interactive
"""
Explanation: Explor... |
facaiy/book_notes | machine_learning/tree/decision_tree/demo.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
data = load_iris()
# 准备特征数据
X = pd.DataFrame(data.data,
columns=["sepal_length", "sepal_width", "petal_length", "petal_width"])
X.head(2)
# 准备标签数据
y = pd.DataFrame(data.target, columns=['target'])
y.replace(to_replace=range(3), value=data.target_names, inplace=... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/exponential_smoothing.ipynb | bsd-3-clause | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
%matplotlib inline
data = [
446.6565,
454.4733,
455.663,
423.6322,
456.2713,
440.5881,
425.3325,
485.1494,
506.0482,
5... |
wd15/chimad-phase-field | hackathons/hackathon1/fipy/1c.ipynb | mit | %matplotlib inline
import sympy
import fipy as fp
import numpy as np
A, c, c_m, B, c_alpha, c_beta = sympy.symbols("A c_var c_m B c_alpha c_beta")
f_0 = - A / 2 * (c - c_m)**2 + B / 4 * (c - c_m)**4 + c_alpha / 4 * (c - c_alpha)**4 + c_beta / 4 * (c - c_beta)**4
print f_0
sympy.diff(f_0, c, 2)
"""
Explanation: Ta... |
sourabhrohilla/ds-masterclass-hands-on | session-2/python/TopicModel.ipynb | mit | PATH_NEWS_ARTICLES = ""
from nltk.corpus import stopwords
from nltk.tokenize import TweetTokenizer
from nltk.stem.snowball import SnowballStemmer
import re
import pickle
import pandas as pd
import gensim
from gensim import corpora, models
"""
Explanation: Topic Modeling using LDA
Topic Modeling Using LDA
Text Proces... |
dtamayo/rebound | ipython_examples/RemovingParticlesFromSimulation.ipynb | gpl-3.0 | import rebound
import numpy as np
sim = rebound.Simulation()
sim.add(m=1., hash=0)
for i in range(1,10):
sim.add(a=i, hash=i)
sim.move_to_com()
print("Particle hashes:{0}".format([sim.particles[i].hash for i in range(sim.N)]))
"""
Explanation: Removing particles from the simulation
This tutorial shows the differ... |
mne-tools/mne-tools.github.io | 0.18/_downloads/8e91c4d84fe688d78859cf6274554a8b/plot_compute_csd.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# License: BSD (3-clause)
from matplotlib import pyplot as plt
import mne
from mne.datasets import sample
from mne.time_frequency import csd_fourier, csd_multitaper, csd_morlet
print(__doc__)
"""
Explanation: ==================================================
Compu... |
zzsza/Datascience_School | 06. 기초 선형대수/04. NumPy를 활용한 선형대수 입문.ipynb | mit | x = np.array([1, 2, 3, 4])
x
x = np.array([[1], [2], [3], [4]])
x
"""
Explanation: NumPy를 활용한 선형대수 입문
선형대수(linear algebra)는 데이터 분석에 필요한 각종 계산을 위한 기본적인 학문이다.
데이터 분석을 하기 위해서는 실제로 수많은 숫자의 계산이 필요하다. 하나의 데이터 레코드(record)가 수십개에서 수천개의 숫자로 이루어져 있을 수도 있고 수십개에서 수백만개의 이러한 데이터 레코드를 조합하여 계산하는 과정이 필요할 수 있다.
선형대수를 사용하는 첫번째 장점은 이러... |
tensorflow/docs-l10n | site/ko/guide/tf_numpy.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... |
AshleySetter/optoanalysis | Damping_radius_relation.ipynb | mit | # constants
k_B = Boltzmann
eta_air = 18.27e-6 # Pa # (J.T.R.Watson (1995)).
d_gas = 0.372e-9 #m #(Sone (2007)), ρSiO2
rho_SiO2 = 1800 # #kg/m^3 - Number told to us by
T0 = 300
R = 50e-9 # m
def mfp(P_gas):
mfp_val = k_B*T0/(2**0.5*pi*d_gas**2*P_gas)
return mfp_val
"""
Explanation: Relation 1
The form of t... |
beangoben/HistoriaDatos_Higgs | Dia1/.ipynb_checkpoints/3_Intro a Matplotlib-checkpoint.ipynb | gpl-2.0 | import numpy as np # modulo de computo numerico
import matplotlib.pyplot as plt # modulo de graficas
import pandas as pd # modulo de datos
# esta linea hace que las graficas salgan en el notebook
%matplotlib inline
"""
Explanation: Intro a Matplotlib
Matplotlib = Libreria para graficas cosas matematicas
Que es Matplot... |
jinzishuai/learn2deeplearn | deeplearning.ai/C5.SequenceModel/Week1_RNN/assignment/Dinosaur Island -- Character-level language model/Dinosaurus Island -- Character level language model final - v2-Copy1.ipynb | gpl-3.0 | import numpy as np
from utils import *
import random
from random import shuffle
"""
Explanation: Character level language model - Dinosaurus land
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers... |
planetlabs/notebooks | jupyter-notebooks/in-class-exercises/band-math-generate-ndvi/generate-ndvi-exercise-key.ipynb | apache-2.0 | # To use Planet's CLI from this Notebook, begin your line as follows:
!planet data
# Here is an example of using Planet's CLI to search for a known item id:
# !planet data download --item-type PSScene --asset-type ortho_analytic_4b_sr --dest data --string-in id 20160831_180302_0e26
"""
Explanation: Deriving a vegetat... |
awsteiner/o2sclpy | doc/static/examples/table.ipynb | gpl-3.0 | import o2sclpy
import matplotlib.pyplot as plot
import sys
plots=True
if 'pytest' in sys.modules:
plots=False
"""
Explanation: O$_2$scl table example for O$_2$sclpy
See the O$_2$sclpy documentation at
https://neutronstars.utk.edu/code/o2sclpy for more information.
End of explanation
"""
link=o2sclpy.linker()
li... |
aaossa/Dear-Notebooks | More/FacebookGraphAPI_ES.ipynb | gpl-3.0 | import json
import requests
BASE = "https://graph.facebook.com"
VERSION = "v2.5"
# Si queremos imprimir los json de respuesta
# de una forma mas agradable a la vista podemos usar
def print_pretty(jsonstring, indent=4, sort_keys=False):
print(json.dumps(jsonstring, indent=indent, sort_keys=sort_keys))
"""
Explan... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/kubeflow_pipelines/pipelines/labs/kfp_pipeline.ipynb | apache-2.0 | # Set `PATH` to include the directory containing TFX CLI and skaffold.
PATH = %env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
"""
Explanation: Continuous training pipeline with KFP and Cloud AI Platform
Learning Objectives:
1. Learn how to use KF pre-build components (BiqQuery, CAIP training and predictions)
1. Le... |
mayank-johri/LearnSeleniumUsingPython | Section 1 - Core Python/Chapter 05 - Data Types/String.ipynb | gpl-3.0 | #### Standard String Examples:
friend = 'Chandu\tNalluri'
print(friend)
manager_details = "# Roshan Musheer:\nExcellent Manager and human being."
print(manager_details)
"""
Explanation: String
Strings are Python builtins datatype for handling text. They are immutable thus you can not add, remove or updated any char... |
pbeens/ICS-Computer-Studies | Python/Class Demos/Demo Notebook (work in progress).ipynb | mit | import numpy as np
nums1 = np.random.randint(1,11, 15)
nums1
"""
Explanation: Class Python Demos
I will be using this Notebook for class demos. To use at home, load Anaconda (https://www.continuum.io/downloads) or WinPython (https://winpython.github.io/)
Set() demo
First let's create a random list using the numpy libr... |
Danghor/Algorithms | Python/Chapter-06/Ordered-Binary-Tree.ipynb | gpl-2.0 | class OrderedBinaryTree:
def __init__(self):
self.mKey = None
self.mValue = None
self.mLeft = None
self.mRight = None
"""
Explanation: Ordered Binary Trees
This notebook implements ordered binary trees. In order to define this notion, we first have to define
the concept of orde... |
NYUDataBootcamp/Projects | UG_S16/Acosta-NHL-GRIT.ipynb | mit | import pandas as pd #PandasPandas
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
print('PandaPandaPanda ', pd.__version__)
df=pd.read_csv('NHLQUANT.csv')
"""
Explanation: Data BootCamp Project
End of explanation
"""
plt.plot(df.index,df['Grit'])
"""
Explanation: Who has Grit?
Hockey has alway... |
scheema/Machine-Learning | Datascience_Lab0.ipynb | mit | import numpy as np
from io import BytesIO
import matplotlib
import matplotlib.pyplot as plt
import random
from mpl_toolkits.mplot3d import Axes3D
from bs4 import BeautifulSoup
import urllib.request
%matplotlib inline
"""
Explanation: Solution Implementation by Srinivas Cheemalapati
CS 109A/AC 209A/STAT 121A Data Sc... |
zizouvb/deeplearning | gan_mnist/Intro_to_GANs_Exercises.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... |
Autodesk/molecular-design-toolkit | moldesign/_notebooks/Tutorial 3. Quantum Chemistry.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
from matplotlib.pylab import *
try: import seaborn #optional, makes plots look nicer
except ImportError: pass
import moldesign as mdt
from moldesign import units as u
"""
Explanation: <span style="float:right"><a href="http://moldesign.bionano.autodesk.com/" target="_blank" titl... |
pysal/pysal | notebooks/explore/pointpats/distance_statistics.ipynb | bsd-3-clause | import scipy.spatial
import pysal.lib as ps
import numpy as np
from pysal.explore.pointpats import PointPattern, PoissonPointProcess, as_window, G, F, J, K, L, Genv, Fenv, Jenv, Kenv, Lenv
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Distance Based Statistical Method for Planar Point Patterns
Au... |
tclaudioe/Scientific-Computing | SC1/Bonus_Newton_Rn.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from ipywidgets import interact
"""
Explanation: <center>
<h1> ILI285 - Computación Científica I / INF285 - Computación Científica </h1>
<h2> Newton's Method in $\mathbb{R}^n$ </h2>
<h2> <a href="#acknowledgements"> [S]cientific [C]ompu... |
tensorflow/docs-l10n | site/en-snapshot/probability/examples/Eight_Schools.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
zzsza/Datascience_School | 09. 기초 확률론2 - 확률 변수/05. 누적 분포 함수와 확률 밀도 함수.ipynb | mit | %%tikz
\filldraw [fill=white] (0,0) circle [radius=1cm];
\foreach \angle in {60,30,...,-270} {
\draw[line width=1pt] (\angle:0.9cm) -- (\angle:1cm);
}
\draw (0,0) -- (90:0.8cm);
"""
Explanation: 누적 분포 함수와 확률 밀도 함수
누적 분포 함수(cumulative distribution function)와 확률 밀도 함수(probability density function)는 확률 변수의 분포 즉, 확률 분포를... |
Pybonacci/notebooks | tutormagic.ipynb | bsd-2-clause | %load_ext tutormagic
"""
Explanation: Esta será una microentrada para presentar una extensión para el notebook que estoy usando en un curso interno que estoy dando en mi empresa.
Si a alguno más os puede valer para mostrar cosas básicas de Python (2 y 3, además de Java y Javascript) para muy principiantes me alegro.
N... |
phoebe-project/phoebe2-docs | 2.0/tutorials/backend.ipynb | gpl-3.0 | %matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
b['q'] = 0.8
b['ecc'] = 0.05
"""
Explanation: IPython Notebook | Python Script
Advanced: Digging into the Backend
Setup
As always, let's do imports a... |
borja876/Thinkful-DataScience-Borja | Housing Prices.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import scipy
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import math
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error
from sklearn.model_selection import tr... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_source_alignment.ipynb | bsd-3-clause | import os.path as op
import numpy as np
from mayavi import mlab
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
trans_fname = op.join(data_path, 'MEG', 'sam... |
tuwien-musicir/rp_extract | RP_extract_Tutorial.ipynb | gpl-3.0 | # to install iPython notebook on your computer, use this in Terminal
sudo pip install "ipython[notebook]"
"""
Explanation: <center><h1>Rhythm and Timbre Analysis from Music</h1></center>
<center><h2>Rhythm Pattern Music Features</h2></center>
<center><h2>Extraction and Application Tutorial</h2></center>
<br>
<center><... |
AllenDowney/ProbablyOverthinkingIt | generations.ipynb | mit | from __future__ import print_function, division
from thinkstats2 import Pmf, Cdf
import thinkstats2
import thinkplot
import pandas as pd
import numpy as np
from scipy.stats import entropy
%matplotlib inline
"""
Explanation: Do generations exist?
This notebook contains a "one-day paper", my attempt to pose a resear... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/wls.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm
from scipy import stats
from statsmodels.iolib.table import SimpleTable, default_txt_fmt
np.random.seed(1024)
"""
Explanation: Weighted Least Squares
End of explanation
"""
nsample = 50
x = np.linspace(0, 20, nsample... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/tfx_pipelines/walkthrough/solutions/tfx_walkthrough.ipynb | apache-2.0 | import os
import tempfile
import time
from pprint import pprint
import absl
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
import tfx
from tensorflow_metadata.proto.v0 import (
anomalies_pb2,
schema_pb2,
statisti... |
Neuroglycerin/neukrill-net-work | notebooks/troubleshooting_and_sysadmin/Brute force venv comparison.ipynb | mit | cd ../..
"""
Explanation: In the last notebook compared pip freeze output and installed packages so that it matched. Did not find error. Must be some other difference in the virtualenv. So, going to use rsync to compare everything in both venvs.
End of explanation
"""
!rsync -nrvl --ignore-times --size-only --exclud... |
JamesSample/enviro_mod_notes | notebooks/odes.ipynb | mit | alpha = 0.75
# Download Tarland data
data_url = r'https://drive.google.com/uc?export=&id=0BximeC_RweaecHNIZF9GMHkwaWc'
met_df = pd.read_csv(data_url, parse_dates=True, dayfirst=True, index_col=0)
del met_df['Q_Cumecs']
# Linear interpolation of any missing values
met_df.interpolate(method='linear', inplace=True)
# ... |
quoniammm/mine-tensorflow-examples | dpAI/Logistic+Regression+with+a+Neural+Network+mindset+v3.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
"""
Explanation: Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a ... |
liyigerry/msm_test | mdtraj_clustering.ipynb | apache-2.0 | from __future__ import print_function
%matplotlib inline
import mdtraj as md
import numpy as np
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy
"""
Explanation: In this example, we cluster our alanine dipeptide trajectory using the RMSD distance metric and Ward's method.
End of explanation
"""
traj = ... |
AndreySheka/dl_ekb | hw10/Bonus-handcrafted-rnn.ipynb | mit | start_token = " "
with open("names") as f:
names = f.read()[:-1].split('\n')
names = [start_token+name for name in names]
print 'n samples = ',len(names)
for x in names[::1000]:
print x
"""
Explanation: Generate names
Struggle to find a name for the variable? Let's see how you'll come up with a nam... |
samuelshaner/openmc | docs/source/pythonapi/examples/mdgxs-part-ii.ipynb | mit | import math
import pickle
from IPython.display import Image
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.mgxs
import openmoc
import openmoc.process
from openmoc.opencg_compatible import get_openmoc_geometry
from openmoc.materialize import load_openmc_mgxs_lib
%matplotlib inline
"""... |
ronnydw/data-science-projects | class-central-survey-2016-17/Class Central Survey - Latin America vs Rest of the World.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white")
sns.set_context("talk")
"""
Explanation: Class Central Survey: compare target group 'Latin America' with the rest of the sample
End of explanation
"""
df = pd.read_csv('raw/2016-17-ClassCentral-Survey-data-noUserText.csv... |
ES-DOC/esdoc-jupyterhub | notebooks/mri/cmip6/models/mri-agcm3-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'mri-agcm3-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MRI
Source ID: MRI-AGCM3-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Con... |
NlGG/Projects | 不動産/research02.ipynb | mit | print(data['CITY_NAME'].value_counts())
"""
Explanation: 変数名とデータの内容メモ
CENSUS: 市区町村コード(9桁)
P: 成約価格
S: 専有面積
L: 土地面積
R: 部屋数
RW: 前面道路幅員
CY: 建築年
A: 建築後年数(成約時)
TS: 最寄駅までの距離
TT: 東京駅までの時間
ACC: ターミナル駅までの時間
WOOD: 木造ダミー
SOUTH: 南向きダミー
RSD: 住居系地域ダミー
CMD: 商... |
Almaz-KG/MachineLearning | ml-for-finance/python-for-financial-analysis-and-algorithmic-trading/02-NumPy/Numpy Exercise - Solutions.ipynb | apache-2.0 | import numpy as np
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
<center>Copyright Pierian Data 2017</center>
<center>For more information, visit us at www.pieriandata.com</center>
NumPy Exercises - Solutions
Now that we've learned about NumPy let's test your knowle... |
YAtOff/python0-reloaded | week2/Expressions, variables and errors.ipynb | mit | 2 * 3 + 2
2 * (3 + 2)
"""
Explanation: Изрази
Изразите в Python са като изразите в математиката.
Всеки изразе е изграден от сотйности (като напр. числата 1, 2, 3, ...) и оператори (+, -, ...).
Типове
Всяка стойност се характеризира с определн тип.
А типът е:
- Множеството от стойности
- Множество от операции, които м... |
qaisermazhar/qaisermazhar.github.io | markdown_generator/publications.ipynb | mit | !cat publications.tsv
"""
Explanation: Publications markdown generator for academicpages
Takes a TSV of publications 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 publications.py. Run either from the m... |
NagyAttila/Udacity_DLND_Assigments | 3_tv-script-generation/dlnd_tv_script_generation.ipynb | gpl-3.0 | """
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... |
jovanbrakus/data-analysis-tools | week-2-assignment.ipynb | gpl-3.0 | import pandas
import numpy
import scipy.stats
import seaborn
import matplotlib.pyplot as plt
data = pandas.read_csv('nesarc_pds.csv', low_memory=False)
# MAJORDEPLIFE - Diagnosed major depressions in lifetime
# S2AQ5A - Drink beer in last 12 months
# S2AQ5B - How often drank a beer in last year
data['MAJORDEPLIFE'] ... |
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