repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
ES-DOC/esdoc-jupyterhub | notebooks/snu/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties: 6... |
antoniomezzacapo/qiskit-tutorial | community/aqua/general/vqe.ipynb | apache-2.0 | from qiskit_aqua import Operator, run_algorithm
from qiskit_aqua.input import get_input_instance
"""
Explanation: Using Qiskit Aqua algorithms, a how to guide
This notebook demonstrates how to use the Qiskit Aqua library to invoke a specific algorithm and process the result.
Further information is available for the al... |
jinzishuai/learn2deeplearn | google_dl_udacity/lesson3/3_regularization.ipynb | gpl-3.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
"""
Explanation: Deep Learning
Assignment 3
Previously in 2_fullyconnected.ipynb, you tra... |
sdpython/pymyinstall | _doc/notebooks/example_profiling.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: test about profiling
How to profile from a notebook with cProfile, memory_profiler
End of explanation
"""
def big_list1(n):
l = []
for i in range(n):
l.append(i)
return l
def big_list2(n):
return list(range(n))
... |
woobe/h2o_tutorials | introduction_to_machine_learning/py_03c_regression_ensembles.ipynb | mit | # Import all required modules
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.random_forest import H2ORandomForestEstimator
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
from h2o.grid.gri... |
ARM-software/lisa | ipynb/deprecated/releases/ReleaseNotes_v16.12.ipynb | apache-2.0 | !head -n12 $LISA_HOME/logging.conf
"""
Explanation: Target Connectivity
Configurable logging system
All LISA modules have been updated to use a more consistent logging which can be configured using a single configuraton file:
End of explanation
"""
!head -n30 $LISA_HOME/logging.conf | tail -n5
"""
Explanation: Each... |
AC209ConsumerConfidence/AC209ConsumerConfidence.github.io | DynamicVAR_Final.ipynb | gpl-3.0 | # Load Datasets
dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m')
cci = pd.read_csv('Economic_Sentiment_Forecast/CCI.csv', parse_dates=True, index_col='TIME',date_parser=dateparse)
cci = cci["Value"]
cci.columns = ["CCI"]
cci = cci['1990-01-01':]
dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%... |
AaronCWong/phys202-2015-work | assignments/assignment09/IntegrationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
"""
Explanation: Integration Exercise 1
Imports
End of explanation
"""
def trapz(f, a, b, N):
h = (b - a)/N
i = np.arange(1,N)
c = h*(0.5*f(a)+f(b)*0.5+f(a+i*h).sum())
return c
f = lambda x: x**2
g = la... |
matousc89/PPSI | podklady/notebooks/funkce_a_tridy.ipynb | mit | def my_function(a, b):
"""
This function sum together two variables (if they are summable).
"""
return a + b
"""
Explanation: Funkce a třídy
Funkce
Následuje příklad definice funkce, která sečte dva argumenty - a, b.
End of explanation
"""
my_function(2, 5)
my_function("Spam ", "eggs")
my_functi... |
sujitpal/polydlot | src/tensorflow/04-mnist-rnn.ipynb | apache-2.0 | from __future__ import division, print_function
from tensorflow.contrib import keras
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
%matplotlib inline
DATA_DIR = "../../dat... |
ivukotic/ML_platform_tests | tutorial/jupyter python numpy plotting/4_Plotting_Basics.ipynb | gpl-3.0 | fig, ax = pl.subplots(2,2, figsize=(8,6))
fig
ax
ax[0,0]
"""
Explanation: We start by importing NumPy which you should be familiar with from the previous tutorial. The next library introduced is called MatPlotLib which is the roughly the Python equivalent of Matlab's plotting functionality. Think of it as a Mathema... |
mgalardini/2017_python_course | notebooks/6-Useful_third_party_libraries_for_data_analysis.ipynb | gpl-2.0 | # setup.py example
# %%bash
# wget https://github.com/biopython/biopython/archive/biopython-168.tar.gz
# tar -xvf biopython-168.tar.gz
# cd biopython-168.tar.gz
# sudo python setup.py install
# using pip
# !pip install biopython
# using anaconda
# !conda install biopython
# using ap-get
# !sudo apt-get install pyth... |
hdesmond/StatisticalMethods | examples/SDSScatalog/CorrFunc.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
import numpy as np
import SDSS
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import copy
# We want to select galaxies, and then are only interested in their positions on the sky.
data = pd.read_csv("downloads/SDSSobjects.csv",usecols=['ra','dec','u','g',\
... |
sbenthall/bigbang | examples/experimental_notebooks/EME Diversity Analysis.ipynb | agpl-3.0 | import bigbang.mailman as mailman
import bigbang.process as process
from bigbang.archive import Archive
import pandas as pd
import datetime
from commonregex import CommonRegex
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: This work was done by Harsh Gupta as part of his internship at The Cent... |
mdiaz236/DeepLearningFoundations | sentiment_network/Sentiment Classification - Mini Project 2.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()... |
5agado/data-science-learning | graphics/physarum/Physarum.ipynb | apache-2.0 | import numpy as np
import cupy as cp
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import tqdm
import math
import os
import sys
from pathlib import Path
%matplotlib inline
%load_ext autoreload
%autoreload 2
import Physarum as physarum
from Physarum import Physarum
from ds_utils.sim_util... |
hpparvi/PyTransit | notebooks/contamination/example_1b.ipynb | gpl-2.0 | %pylab inline
import sys
from corner import corner
sys.path.append('.')
from src.mocklc import MockLC, SimulationSetup
from src.blendlpf import MockLPF
import src.plotting as pl
"""
Explanation: Contamination example 1b
No contamination and informative priors on orbit parameters
Hannu Parviainen<br>
Instituto de As... |
LucaCanali/Miscellaneous | Spark_Physics/HEP_benchmark/ADL_HEP_Query_Benchmark_Q1_Q5_Parquet_sparkhistogram.ipynb | apache-2.0 | # Install PySpark if needed
# !pip install pyspark
# Install sparkhistogram
! pip install sparkhistogram
# Note: if you cannot install the package sparkhistogram,
# create the computeHistogram function as detailed at the end of this notebook.
# See https://github.com/LucaCanali/Miscellaneous/blob/master/Spark_Notes/... |
JoseGuzman/myIPythonNotebooks | SignalProcessing/Complex numbers.ipynb | gpl-2.0 | # initiation and examples
z = complex(3,4)
print('The complex {}, where {} is the real and {} the imaginary part'.format(z, z.real, z.imag))
"""
Explanation: <H1>Complex numbers</H1>
A complex number has the property that multipied by itself get a negative answer. For example, if an imaginary number like z could be ... |
revspete/self-driving-car-nd | sem1/p1-lane-lines/.ipynb_checkpoints/P1-checkpoint.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... |
DOV-Vlaanderen/pydov | docs/notebooks/search_boringen.ipynb | mit | %matplotlib inline
import inspect, sys
import warnings; warnings.simplefilter('ignore')
# check pydov path
import pydov
"""
Explanation: Example of DOV search methods for boreholes (boringen)
Use cases explained below
Get boreholes in a bounding box
Get boreholes with specific properties
Get boreholes in a bounding... |
dismalpy/dismalpy | doc/notebooks/varmax.ipynb | bsd-2-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import dismalpy as dp
import matplotlib.pyplot as plt
dta = pd.read_stata('data/lutkepohl2.dta')
dta.index = dta.qtr
endog = dta.ix['1960-04-01':'1978-10-01', ['dln_inv', 'dln_inc', 'dln_consump']]
"""
Explanation: VARMAX models
T... |
UWSEDS/LectureNotes | Fall2018/07-Jupyter-Notebook-In-Depth/LorenzSystem.ipynb | bsd-2-clause | %matplotlib inline
from ipywidgets import interact, interactive
from IPython.display import clear_output, display, HTML
import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import cnames
from matplotlib import animation
""... |
unmrds/cc-python | .ipynb_checkpoints/Space Analysis -checkpoint.ipynb | apache-2.0 | # Import a very useful and powerful module for interacting with tabular data
import pandas as pd
# Install and import tabulate for generating tables for hardcopy reports
!TABULATE_INSTALL=lib-only; pip install tabulate
from tabulate import tabulate
# Set up the report generation variables
report_file_name = 'report.m... |
stanfordnqp/spins-b | examples/invdes/wdm2/monitor_processing_example.ipynb | gpl-3.0 | ## Import libraries necessary for monitor data processing. ##
from matplotlib import pyplot as plt
import numpy as np
import os
import pandas as pd
import pickle
from spins.invdes.problem_graph import log_tools
## Define filenames. ##
# `save_folder` is the full path to the directory containing the Pickle (.pkl) log... |
sarajcev/logreg-linreg | logreg-compare.ipynb | gpl-2.0 | from __future__ import print_function
import numpy as np
import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from scipy import stats
import seaborn as sns
sns.set(style='darkgrid', font_scale=1.2)
%matplot... |
changhoonhahn/centralMS | notebook/local_sfs_prior.ipynb | mit | import numpy as np
# -- centralms --
from centralMS import util as UT
from centralMS import sfh as SFH
from centralMS import catalog as Cat
import corner as DFM
import matplotlib as mpl
import matplotlib.pyplot as pl
mpl.rcParams['text.usetex'] = True
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['axes.linewi... |
QuantScientist/Deep-Learning-Boot-Camp | day02-PyTORCH-and-PyCUDA/PyTorch/18-PyTorch-NUMER.AI-Binary-Classification-BCELoss.ipynb | mit | import torch
import sys
import torch
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
from torchvision import transforms
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from sklearn import cross_validation
from skl... |
MTG/essentia | src/examples/python/tutorial_pitch_melody.ipynb | agpl-3.0 | # For embedding audio player
import IPython
# Plots
import matplotlib.pyplot as plt
from pylab import plot, show, figure, imshow
plt.rcParams['figure.figsize'] = (15, 6)
import numpy
import essentia.standard as es
audiofile = '../../../test/audio/recorded/flamenco.mp3'
# Load audio file.
# It is recommended to app... |
ga7g08/ga7g08.github.io | _notebooks/2015-05-12-GDP-by-country.ipynb | mit | # %load ../data_sets/GDP_by_Country_WorldBank/Makefile
DOWNLOAD = data.zip
OUT = ny.gdp.mktp.cd_Indicator_en_csv_v2.csv
.PHONY: download clean
download:
rm -f ${DOWNLOAD}
wget http://api.worldbank.org/v2/en/indicator/ny.gdp.mktp.cd?downloadformat=csv -O data.zip
unzip $(DOWNLOAD)
rm -f ${DOWNLOAD} Metadata*csv *xm... |
eds-uga/csci1360e-su17 | lectures/L5.ipynb | mit | for i in range(10):
print(i, end = " ")
"""
Explanation: Lecture 5: Advanced Data Structures
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
We've covered list, tuples, sets, and dictionaries. These are the foundational data structures in Python. In this lecture, we'll go over some mo... |
dtsmith2001/p-data-challenge | Report.ipynb | mit | def convert_list(query_string):
"""Parse the query string of the url into a dictionary.
Handle special cases:
- There is a single query "error=True" which is rewritten to 1 if True, else 0.
- Parsing the query returns a dictionary of key-value pairs. The value is a list.
We must get the list ... |
cliburn/sta-663-2017 | notebook/03_Classes.ipynb | mit | class A:
"""Base class."""
def __init__(self, x):
self.x = x
def __repr__(self):
return '%s(%a)' % (self.__class__.__name__, self.x)
def report(self):
"""Report type of contained value."""
return 'My value is of type %s' % type(self.x)
"""
Explanation: Classes
As you... |
Almaz-KG/MachineLearning | ml-for-finance/python-for-financial-analysis-and-algorithmic-trading/02-NumPy/1-NumPy-Arrays.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
NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is so im... |
BenLangmead/comp-genomics-class | projects/UnpairedAsmChallenge.ipynb | gpl-2.0 | # Download the file containing the reads to "reads.fa" in current directory
! wget http://www.cs.jhu.edu/~langmea/resources/f2020_hw4_reads.fa
# Following line is so we can see the first few lines of the reads file
# from within IPython -- don't paste this into your Python code
! head f2020_hw4_reads.fa
"""
Explanati... |
DS-100/sp17-materials | sp17/labs/lab06/lab06_master.ipynb | gpl-3.0 | !pip install ipython-sql
%load_ext sql
%sql sqlite:///./lab06.sqlite
import sqlalchemy
engine = sqlalchemy.create_engine("sqlite:///lab06.sqlite")
connection = engine.connect()
!pip install -U okpy
from client.api.notebook import Notebook
ok = Notebook('lab06.ok')
"""
Explanation: Lab 6: SQL
End of explanation
"... |
madHatter106/DataScienceCorner | posts/a-bayesian-tutorial-in-python-part-I.ipynb | mit | import pickle
import warnings
import sys
import pandas as pd
import numpy as np
from scipy.stats import norm as gaussian, uniform
import seaborn as sb
import matplotlib.pyplot as pl
from matplotlib import rcParams
from matplotlib import ticker as mtick
print('Versions:')
print('---------')
print(f'python: {sys.vers... |
hunterherrin/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
def hat(x,a,b):
return b*x**4-a*x**2
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0)==-9.0
"""
Expl... |
cranium/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... |
strandbygaard/deep-learning | weight-initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
Reddone/CarIncidentJupyter | RandomForest.ipynb | mit | # Load dataset
load_path = r"0_CarIncident_2014"
dataset = pd.read_pickle(load_path)
dataset.drop('IDProtocollo', inplace=True, axis=1)
dataset.drop('Progressivo', inplace=True, axis=1)
dataset.describe()
"""
Explanation: Partendo dal dataset salvato precedentemente, cerchiamo di mettere su un algoritmo predittivo, at... |
ayushmaskey/ayushmaskey.github.io | jupyter/pandas_resampling.ipynb | mit | rng = pd.date_range('1/1/2011', periods=72, freq='H')
rng[1:4]
ts = pd.Series(list(range(len(rng))), index=rng)
ts.head()
"""
Explanation: resampling
does not have frequency and we want it
does not have the frequency we want
End of explanation
"""
converted = ts.asfreq('45Min', method='ffill')
converted.head(10)
... |
postBG/DL_project | sentiment-network/Sentiment_Classification_Projects.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()... |
YuriyGuts/kaggle-quora-question-pairs | notebooks/preproc-embeddings-fasttext.ipynb | mit | from pygoose import *
import os
import subprocess
"""
Explanation: Preprocessing: Create a FastText Vector Database
Based on the vocabulary extracted from question texts, use a pretrained FastText model to query and save word vectors.
Imports
This utility package imports numpy, pandas, matplotlib and a helper kg modu... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Long-Short_Equity/notebook.ipynb | unlicense | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# We'll generate a random factor
current_factor_values = np.random.normal(0, 1, 10000)
equity_names = ['Equity ' + str(x) for x in range(10000)]
# Put it into a dataframe
factor_data = pd.Series(current_factor_values, index = equity_names)
factor_d... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-2/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-2', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
Kaggle/learntools | notebooks/deep_learning_intro/raw/ex5.ipynb | apache-2.0 | # Setup plotting
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
# Set Matplotlib defaults
plt.rc('figure', autolayout=True)
plt.rc('axes', labelweight='bold', labelsize='large',
titleweight='bold', titlesize=18, titlepad=10)
plt.rc('animation', html='html5')
# Setup feedback system
from lear... |
rasbt/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... |
borja876/Thinkful-DataScience-Borja | The%2BBrandy%2BBunch%2BShow.ipynb | mit | df2=pd.DataFrame()
df2['BB_age']=[14, 12, 11, 10, 8, 7, 8]
#Calculate Mean & Median
mean = np.mean(df2['BB_age'])
median = np.median(df2['BB_age'])
print(mean)
print(median)
#Calculate Mode
(values, counts) = np.unique(df2['BB_age'], return_counts=True)
ind = np.argmax(counts)
print(ind)
values[ind]
#Calculate varia... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_artifacts_correction_rejection.ipynb | bsd-3-clause | import numpy as np
import mne
from mne.datasets import sample
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname)
raw.set_eeg_reference()
"""
Explanation: Rejecting bad data (channels and segments)
End of explanation
"""
raw.info[... |
statkraft/shyft-doc | notebooks/nea-example/simulation-configured.ipynb | lgpl-3.0 | # Pure python modules and jupyter notebook functionality
# first you should import the third-party python modules which you'll use later on
# the first line enables that figures are shown inline, directly in the notebook
%pylab inline
import os
import datetime as dt
from os import path
import sys
from matplotlib import... |
sevo/closure_decorator | Other functional features.ipynb | mit | def add(a, b):
return a + b
def make_adder(a) :
def adder(b) :
return add(a, b)
return adder
add_two = make_adder(20)
add_two(4)
"""
Explanation: 1. Partial function application
2. Pattern matching
Ciastocna aplikacia - Partially applied functions
http://blog.dhananjaynene.com/tags/functional-pro... |
mikekestemont/ghent1516 | Chapter 8 - Parsing XML.ipynb | mit | from lxml import etree
"""
Explanation: Parsing XML in Python
XML in a nutshell
So far, we have primarily dealt with unstructured data in this course: we have learned to read, for example, the contents of plain text files in the previous chapters. Such raw textual data is often called 'unstructured', because it lacks ... |
SylvainCorlay/bqplot | examples/Tutorials/Updating Plots.ipynb | apache-2.0 | import numpy as np
import bqplot.pyplot as plt
x = np.linspace(-10, 10, 100)
y = np.sin(x)
fig = plt.figure()
line = plt.plot(x=x, y=y)
fig
"""
Explanation: Updating Plots
bqplot is an interactive plotting library. Attributes of plots can be updated in place without recreating the whole figure and marks. Let's look ... |
thempel/adaptivemd | examples/tutorial/2_example_run.ipynb | lgpl-2.1 | import sys, os
from adaptivemd import Project, Event, FunctionalEvent, Trajectory
"""
Explanation: Example 2 - The Tasks
Imports
End of explanation
"""
project = Project('tutorial')
"""
Explanation: Let's open our test project by its name. If you completed the previous example this should all work out of the box.
... |
NuGrid/NuPyCEE | DOC/Teaching/.ipynb_checkpoints/Section2.1-checkpoint.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import sygma
import omega
import stellab
#loading the observational data module STELLAB
stellab = stellab.stellab()
"""
Explanation: Section 2.1: Tracing the origin of C
Result: Identification of which star is responsible for the origin of C
End of explanation
"""
# OMEGA parameters ... |
geoneill12/phys202-2015-work | assignments/assignment03/NumpyEx04.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Numpy Exercise 4
Imports
End of explanation
"""
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
"""
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or n... |
openconnectome/ocpdocs | mrgraphs/dataset_variance/dataset_variance.ipynb | apache-2.0 | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import nibabel as nb
import os
from histogram_window import histogram_windowing
"""
Explanation: Analysis of Dataset Variance
Data which is collected differently, look differently. This principle extends to all data (that I can think of), and of c... |
mne-tools/mne-tools.github.io | 0.20/_downloads/a47d41a5d6e12802ada8e8ab7ecc9ffc/plot_50_configure_mne.ipynb | bsd-3-clause | import os
import mne
"""
Explanation: Configuring MNE-Python
This tutorial covers how to configure MNE-Python to suit your local system and
your analysis preferences.
:depth: 1
We begin by importing the necessary Python modules:
End of explanation
"""
print(mne.get_config('MNE_USE_CUDA'))
print(type(mne.get_confi... |
rsterbentz/phys202-2015-work | days/day08/Display.ipynb | mit | class Ball(object):
pass
b = Ball()
b.__repr__()
print(b)
"""
Explanation: Display of Rich Output
In Python, objects can declare their textual representation using the __repr__ method.
End of explanation
"""
class Ball(object):
def __repr__(self):
return 'TEST'
b = Ball()
print(b)
"""
Explanatio... |
bioinformatica-corso/lezioni | laboratorio/lezione10-29ott21/esercizio5-soluzione.ipynb | cc0-1.0 | import re
"""
Explanation: Esercizio 5
Prendere in input un file in formato GTF (Gene Transfer Format), che annota un set di geni su una genomica di riferimento, insieme al file FASTA della genomica di riferimento e produrre:
le sequenze dei trascritti oppure le sequenze delle coding sequences (CDS) per i geni annota... |
mdda/fossasia-2016_deep-learning | notebooks/work-in-progress/2018-08_DidTheModelUnderstandTheQuestion/VQA_playground.ipynb | mit | # Upgrade pillow to latest version (solves a colab Issue) :
! pip install -U 'Pillow>=5.2.0'
import os, sys
from matplotlib import pyplot as plt
import warnings
warnings.filterwarnings("ignore", category=UserWarning) # Cleaner demos : Don't do this normally...
"""
Explanation: VQA : Use and Abuse
To answer a questi... |
jrg365/gpytorch | examples/01_Exact_GPs/Spectral_Delta_GP_Regression.ipynb | mit | import gpytorch
import torch
"""
Explanation: Spectral GP Learning with Deltas
In this paper, we demonstrate another approach to spectral learning with GPs, learning a spectral density as a simple mixture of deltas. This has been explored, for example, as early as Lázaro-Gredilla et al., 2010.
Compared to learning Gau... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CMCC
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
ivannz/crossing_paper2017 | experiments/bellcore_traffic_data.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Bellcore LAN traffic
This notebook uses uncompressed data from here.
Namely the datasets: BC-pAug89 and BC-pOct89.
Description:
The files whose names end in TL are ASCII-format tracing data, consisting of
one 20-byte line per Ethe... |
tensorflow/docs-l10n | site/zh-cn/lattice/tutorials/shape_constraints.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... |
amitkaps/machine-learning | time_series/3-Refine.ipynb | mit | # Import the two library we need, which is Pandas and Numpy
import pandas as pd
import numpy as np
# Read the csv file of Month Wise Market Arrival data that has been scraped.
df = pd.read_csv('MonthWiseMarketArrivals.csv')
df.head()
df.tail()
"""
Explanation: 2. Refine the Data
"Data is messy"
We will be perform... |
probml/pyprobml | deprecated/arhmm_example.ipynb | mit | !pip install git+git://github.com/lindermanlab/ssm-jax-refactor.git
import ssm
import copy
import jax.numpy as np
import jax.random as jr
from tensorflow_probability.substrates import jax as tfp
from ssm.distributions.linreg import GaussianLinearRegression
from ssm.arhmm import GaussianARHMM
from ssm.utils import... |
tyarkoni/pliers | examples/Quickstart.ipynb | bsd-3-clause | from pliers.extractors import FaceRecognitionFaceLocationsExtractor
# A picture of Barack Obama
image = join(get_test_data_path(), 'image', 'obama.jpg')
# Initialize Extractor
ext = FaceRecognitionFaceLocationsExtractor()
# Apply Extractor to image
result = ext.transform(image)
result.to_df()
"""
Explanation: Plie... |
gwu-libraries/notebooks | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | mit | !head -n5 tweets.json | jq -c '[.id_str, .text]'
"""
Explanation: Recipes for processing Twitter data with jq
This notebook is a companion to Getting Started Working with Twitter Data Using jq. It focuses on recipes that the Social Feed Manager team has used when preparing datasets of tweets for researchers.
We will c... |
BrainIntensive/OnlineBrainIntensive | resources/matplotlib/Examples/3dplots.ipynb | mit | %load_ext watermark
%watermark -u -v -d -p matplotlib,numpy
"""
Explanation: Sebastian Raschka
back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery
End of explanation
"""
%matplotlib inline
"""
Explanation: <font size="1.5em">More info about the %watermark extension</font>
End of explanati... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_sensors_decoding.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from sklearn.cross_validation import StratifiedKFold
import mne
from mne.datasets import sample
from mne.decoding import TimeDecoding, GeneralizationAcrossTime
data_path = sample.data_path()
plt.close('all')
"""
Explanatio... |
kaushik94/sympy | examples/notebooks/Macaulay_resultant.ipynb | bsd-3-clause | x, y, z = sym.symbols('x, y, z')
a_1_1, a_1_2, a_1_3, a_2_2, a_2_3, a_3_3 = sym.symbols('a_1_1, a_1_2, a_1_3, a_2_2, a_2_3, a_3_3')
b_1_1, b_1_2, b_1_3, b_2_2, b_2_3, b_3_3 = sym.symbols('b_1_1, b_1_2, b_1_3, b_2_2, b_2_3, b_3_3')
c_1, c_2, c_3 = sym.symbols('c_1, c_2, c_3')
variables = [x, y, z]
f_1 = a_1_1 * x ** ... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session02/Day1/ReIntroToMachineLearning.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Re-Introduction to Machine Learning:
Classifying the Iris Dataset with K-Nearest Neighbors
Version 0.1
By AA Miller (Northwestern University, Adler Planetarium)
During the first session of the LSSTC DSFP we had an opportunity to wo... |
leriomaggio/deep-learning-keras-tensorflow | 6. AutoEncoders and Embeddings/6.1. AutoEncoders and Embeddings.ipynb | mit | from keras.layers import Input, Dense
from keras.models import Model
from keras.datasets import mnist
import numpy as np
# this is the size of our encoded representations
encoding_dim = 32 # 32 floats -> compression of factor 24.5, assuming the input is 784 floats
# this is our input placeholder
input_img = Input(... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch2-Problem_2-02.ipynb | unlicense | %pylab notebook
%precision 4
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 2
Problem 2-2
End of explanation
"""
Zline = 38.2 + 140.0j # [Ohm]
Zeq = 0.10 + 0.4j # [Ohm]
V_high = 14e3 # [V]
V_low = 2.4e3 # [V]
Pout = 90e3 # [W] load
PF = 0.8 # lagging
VS = 2.3e3 # [V] seco... |
rafburzy/Python_EE | Capacitors/Discharging_capacitors.ipynb | bsd-3-clause | # Definitions of parameters of the circuit
# Capacitance of generator [F]
C = 1e-6
# Parallel resistance (discharging the capacitor in the generator forming the tail of the impulse) [Ohm]
R1 = 4
# Series resistance (forming the head) [Ohm]
R2 = 150
# Inductance of the loop [H]
L = 1e-3
# Capacitance of the test o... |
cassiogreco/udacity-data-analyst-nanodegree | P1/P1_Cassio.ipynb | mit | import pandas as pd
import math
%pylab inline
import matplotlib.pyplot as plt
CONGRUENT = 'Congruent'
INCONGRUENT = 'Incongruent'
TCRITICAL = 2.807 # two-tailed difference with 99% Confidence and Degree of Freedom of 23
path = r'~/udacity-data-analyst-nanodegree/P1/stroopdata.csv'
initialData = pd.read_csv(path)
dat... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topic... |
GraysonRicketts/collegeScorecard | notebooks/.ipynb_checkpoints/Initial Exploration-checkpoint.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import sqlite3
import pandas as pd
import seaborn as sns
sns.set_style("white")
"""
Explanation: Initial Eploration
Goals
Load dataset into sqlite server
Make basic queries against database
Understand basic structure and fields of dataset
Start exploring different ... |
cuthbertLab/bali | documentation/Using bali module.ipynb | bsd-3-clause | import bali
"""
Explanation: Here is how you load the "bali" module
End of explanation
"""
fileReader = bali.FileReader()
fileReader.taught
fileReader.transcribed
"""
Explanation: Now we make a FileReader
End of explanation
"""
fp = bali.FileParser()
fp.taught
"""
Explanation: More useful Object
The FileParser... |
vravishankar/Jupyter-Books | Functions.ipynb | mit | # Simple Function
def greet():
'''Simple Greet Function'''
print('Hello World')
greet()
"""
Explanation: Functions
Function is a group of related statements that perform a specific task.
Function help break large programs into smaller and modular chunks
Function makes the code more organised and easy to ... |
Illedran/NIPSTimeMachine | topic_evolution/topic_evolution.ipynb | gpl-3.0 | import csv
import pandas as pd
import os, re
import codecs
import os
DATA_DIR = "../nips-data"
MODEL_DIR = "../models"
papers = pd.read_csv(os.path.join(DATA_DIR, 'papers.csv'))
with open(os.path.join(MODEL_DIR, 'stopwords.txt')) as f:
stopwords=[]
for line in f:
stopwords.append(line.strip())
"""
... |
unmrds/cc-python | .ipynb_checkpoints/Name_Data-checkpoint.ipynb | apache-2.0 | # http://api.census.gov/data/2010/surname
import requests
import json
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: An Introductory Python Workflow: US Census Surname Data
This notebook provides working examples of many of the concepts introduced earlier:
Importing modules or libraries to exten... |
SciTools/courses | course_content/iris_course/5.Cube_Plotting.ipynb | gpl-3.0 | import iris
"""
Explanation: Iris introduction course
5. Cube Plotting
Learning Outcome: by the end of this section, you will be able to visualise the data stored in Iris Cubes.
Duration: 30 mins
Overview:<br>
5.1 Plotting Data<br>
5.2 Maps with cartopy<br>
5.3 Exercise<br>
5.4 Summary of the Section
Setup
End of expl... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, Emission... |
ivukotic/ML_platform_tests | tutorial/jupyter python numpy plotting/3_NumPy_Basics.ipynb | gpl-3.0 | import numpy as np
from __future__ import print_function
"""
Explanation: NumPy Basics
Numerical Python, or "NumPy" for short, is a foundational package on which many of the most common data science packages are built. Numpy provides us with high performance multi-dimensional arrays which we can use as vectors or mat... |
david-hoffman/scripts | notebooks/mandelbrot_numbapro.ipynb | apache-2.0 | %pylab inline
import numpy as np
from timeit import default_timer as timer
"""
Explanation: A NumbaPro Mandelbrot Example
This notebook was written by Mark Harris based on code examples from Continuum Analytics that I modified somewhat. This is an example that demonstrates accelerating a Mandelbrot fractal computation... |
flsantos/startup_acquisition_forecast | exploratory_code/2_dataset_preparation.ipynb | mit | import pandas as pd
startups = pd.read_csv('data/startups_1_1.csv', index_col=0)
startups[:3]
"""
Explanation: Dataset Preparation
Here we'll be removing nan's, normalizing numerical features, converting date features to numerical normalized features, and so on...
Importing the dataset
End of explanation
"""
#drop f... |
rbharath/deepchem | examples/broken/protein_ligand_complex_notebook.ipynb | mit | %load_ext autoreload
%autoreload 2
%pdb off
# set DISPLAY = True when running tutorial
DISPLAY = False
# set PARALLELIZE to true if you want to use ipyparallel
PARALLELIZE = False
import warnings
warnings.filterwarnings('ignore')
dataset_file= "../datasets/pdbbind_core_df.pkl.gz"
from deepchem.utils.save import load_f... |
Unidata/unidata-python-workshop | notebooks/XArray/XArray and CF.ipynb | mit | # Convention for import to get shortened namespace
import numpy as np
import xarray as xr
# Create some sample "temperature" data
data = 283 + 5 * np.random.randn(5, 3, 4)
data
"""
Explanation: <div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.co... |
mne-tools/mne-tools.github.io | 0.22/_downloads/1af5a35cbb809b9480120842884536c5/plot_brainstorm_auditory.ipynb | bsd-3-clause | # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import os.path as op
import pandas as pd
import numpy as np
import mne
from mne import combine_evoked
from mne.minimum_norm impor... |
elenduuche/deep-learning | autoencoder/Simple_Autoencoder_Solution.ipynb | mit | %matplotlib inline
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', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
GuillaumeDec/machine-learning | deep-multivariate-lstm-tensorflow/tensorflow deep multivariate lstm .ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import tensorflow as tf
import utils as utl
from collections import Counter
"""
Explanation: Modeling Stock Market Sentiment with LSTMs and TensorFlow
In this tutorial, we will build a Long Short Term Memory (LSTM) Network to predict the stock market sentiment based on a comment ... |
karhohs/boardgame-bookie | boardgames/seafall/game_engine/Logic Test.ipynb | bsd-3-clause | %matplotlib inline
import numpy
import matplotlib
from matplotlib.patches import Circle, Wedge, Polygon
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
import matplotlib.path as mpath
import numpy as np
import sea... |
abatula/MachineLearningIntro | KNN_Tutorial.ipynb | gpl-2.0 | # Print figures in the notebook
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets # Import the nerest neighbor function and dataset from scikit-learn
from sklearn.model_selection import train_test_split, KFold
# ... |
gully/starfish-demo | demo6/lnprior.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
% config InlineBackend.figure_format = 'retina'
"""
Explanation: How to set priors on stellar parameters.
gully
https://github.com/iancze/Starfish/issues/32
The strategy here is to define a lnprior and add it to the lnprob.
We... |
VUInformationRetrieval/IR2015_2016 | 04_analysis.ipynb | gpl-2.0 | import pickle, bz2
from collections import *
import numpy as np
import matplotlib.pyplot as plt
# show plots inline within the notebook
%matplotlib inline
# set plots' resolution
plt.rcParams['savefig.dpi'] = 100
from IPython.display import display, HTML
Ids_file = 'data/air__Ids.pkl.bz2'
Summaries_file = 'data/air... |
lalonica/PhD | vehicles/VehiclesTimeCycles.ipynb | gpl-3.0 | %matplotlib inline
from pandas import Series, DataFrame
import pandas as pd
from itertools import *
import numpy as np
import csv
import math
import matplotlib.pyplot as plt
from matplotlib import pylab
from scipy.signal import hilbert, chirp
import scipy
import networkx as nx
"""
Explanation: Loading the necessary l... |
godfreyduke/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... |
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