repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
zaqwes8811/micro-apps | self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/02-Discrete-Bayes.ipynb | mit | from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
"""
Explanation: Table of Contents
Discrete Bayes Filter
End of explanation
"""
import numpy as np
belief = np.array([1./10]*10)
print(belief)
"""
Explanation: The Kalman filter belongs to... |
cbpygit/pypmj | examples/Extensions explained - materials.ipynb | gpl-3.0 | import os
os.environ['PYPMJ_CONFIG_FILE'] = '/path/to/your/config.cfg'
"""
Explanation: Imports and configuration
We set the path to the config.cfg file using the environment variable 'PYPMJ_CONFIG_FILE'. If you do not have a configuration file yet, please look into the Setting up a configuration file example.
End of ... |
BrownDwarf/ApJdataFrames | notebooks/Douglas2017_extra_1.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
pd.options.display.max_columns = 150
%config InlineBackend.figure_format = 'retina'
import astropy
from astropy.table import Table
from astropy.io import ascii
import numpy as np
"""
Explanation: ApJdataFrames Douglas_2017 Ext... |
thiagoqd/queirozdias-deep-learning | 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... |
sueiras/training | sklearn/03 - Control overfit and hyperparameter optimization.ipynb | gpl-3.0 | from __future__ import print_function
from sklearn import __version__ as sklearn_version
print('Sklearn version:', sklearn_version)
"""
Explanation: Sklearn control overfit example
- Use the California house database to show how to control overfit tuning the model parameters
End of explanation
"""
from sklearn impo... |
Lattecom/HYStudy | scripts/[HYStudy 14th] SymPy, Matplotlib 1.ipynb | mit | import sympy
sympy.init_printing(use_latex='mathjax')
"""
Explanation: Symbolic operation with sympy
End of explanation
"""
# define symbol
x = sympy.symbols('x')
print(type(x))
x
# define fuction
f = x**2 + 4*x
f
# differentiation
sympy.diff(f)
# simplify function
sympy.simplify(f)
# solving equation
from symp... |
NYUDataBootcamp/Materials | Code/notebooks/bootcamp_practice_a_answerkey.ipynb | mit | # to make sure things are working, run this
import pandas as pd
print('Pandas version: ', pd.__version__)
"""
Explanation: Data Bootcamp: Code Practice A (answerkey)
Optional Code Practice A: Jupyter basics and Python's graphics tools (the Matplotlib package). The goals are to become familiar with Jupyter and Matp... |
walkon302/CDIPS_Recommender | notebooks/Plotting_Sequences_in_low_dimensions.ipynb | apache-2.0 | # our lib
from lib.resnet50 import ResNet50
from lib.imagenet_utils import preprocess_input, decode_predictions
#keras
from keras.preprocessing import image
from keras.models import Model
import glob
def preprocess_img(img_path):
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(... |
ericmjl/systems-microbiology-hiv | Problem Set.ipynb | mit | # This cell loads the data and cleans it for you, and log10 transforms the drug resistance values.
# Remember to run this cell if you want to have the data loaded into memory.
DATA_HANDLE = 'drug_data/hiv-protease-data.csv' # specify the relative path to the protease drug resistance data
N_DATA = 8 # specify the numb... |
whitead/numerical_stats | unit_9/hw_2017/problem_set_2.ipynb | gpl-3.0 | from scipy.integrate import quad
import numpy as np
quad(np.sin, 0, np.pi)[0]
"""
Explanation: Problem 1 Instructions
Evaluate the following definite integrals using the quad function and the lambda keyword. You may only report your answer in Python and you should only print the integral area and nothing else. Do not... |
Dataweekends/odsc_intro_to_data_science | Titanic Survival Workshop.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Predicting survival of Titanic Passengers
This notebook explores a dataset containing information of passengers of the Titanic.
The dataset can be downloaded from Kaggle
Tutorial goals
Explore the dataset
Build... |
xpharry/Udacity-DLFoudation | your-first-network/.ipynb_checkpoints/dlnd-your-first-neural-network-checkpoint.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... |
shapiromatron/bmds-server | scripts/tdist-approximation.ipynb | mit | %matplotlib inline
import json
import numpy as np
import pandas as pd
from scipy.stats import t
"""
Explanation: tdist estimation proof in javascript
This notebook should act as a proof to the reliability of the javascript estimation. This method is used for plotting confidence intervals on group summary data. Since ... |
mathinmse/mathinmse.github.io | Lecture-22-Phase-Field-Basics.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib notebook
def plot_p_and_g():
phi = np.linspace(-0.1, 1.1, 200)
g=phi**2*(1-phi)**2
p=phi**3*(6*phi**2-15*phi+10)
# Changed 3 to 1 in the figure call.
plt.figure(1, figsize=(12,6))
plt.subplot(121)
plt.plot(phi, g, linewidth=1.0... |
lhcb/opendata-project | Example-Analysis.ipynb | gpl-2.0 | from __future__ import print_function
from __future__ import division
%pylab inline
execfile('Data/setup_example.py')
"""
Explanation: Analysis of Nobel prize winners
Welcome to the programming example page. This page shows an example analysis of Nobel prize winners. The coding commands and techniques that are demons... |
UltronAI/Deep-Learning | CS231n/reference/CS231n-master/assignment1/features.ipynb | mit | import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading extenrnal modu... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/03_tensorflow/e_ai_platform.ipynb | apache-2.0 | import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# For Python Code
# Model Info
MODEL_NAME = 'taxifare'
# Model Version
MODEL_VERSION = 'v1'
# Training D... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day18-kinematics-terminal-velocity-of-a-skydiver/Day_18_pre_class_notebook.ipynb | agpl-3.0 | from IPython.display import YouTubeVideo
# WATCH THE VIDEO IN FULL-SCREEN MODE
YouTubeVideo("JXJQYpgFAyc",width=640,height=360) # Numerical integration
"""
Explanation: Day 18 Pre-class assignment
Goals for today's pre-class assignment
In this pre-class assignment, you are going to learn how to:
Numerically integ... |
ajrader/timeseries | notebooks/Prophet_QuickStart_Example.ipynb | apache-2.0 | peyton_dataset_url = 'https://github.com/facebookincubator/prophet/blob/master/examples/example_wp_peyton_manning.csv'
peyton_filename = '../datasets/example_wp_peyton_manning.csv'
import pandas as pd
import numpy as np
from fbprophet import Prophet
# NB: this didn't work as of 8/22/17
#import io
#import requests
#s=... |
ClimateTools/Correlation_EPSL | Proctor_NAO_bandwidth.ipynb | mit | %matplotlib inline
from scipy import interpolate
from scipy import special
from scipy.signal import butter, lfilter, filtfilt
import matplotlib.pyplot as plt
import numpy as np
from numpy import genfromtxt
from nitime import algorithms as alg
from nitime import utils
from scipy.stats import t
import pandas as pd
"""
E... |
moble/PostNewtonian | PNTerms/AngularMomentum.ipynb | mit | AngularMomentum_NoSpin = PNCollection()
AngularMomentum_Spin = PNCollection()
"""
Explanation: The following PNCollection objects will contain all the terms in the different parts of the binding energy.
End of explanation
"""
AngularMomentum_NoSpin.AddDerivedVariable('L_coeff', M**2*nu/v)
"""
Explanation: Individua... |
phanrahan/magmathon | notebooks/intermediate/PopCount.ipynb | mit | import magma as m
"""
Explanation: PopCount8
In this tutorial, we show how to construct a circuit to compute an 8-bit PopCount (population count).
End of explanation
"""
from mantle import FullAdder
"""
Explanation: In this example, we are going to use the built-in fulladder from Mantle.
End of explanation
"""
# ... |
EmissionsIndex/Emissions-Index | Code/EIA bulk download - non-facility (distributed PV & state-level).ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import io, time, json
import pandas as pd
import os
import numpy as np
import math
"""
Explanation: National generation and fuel consumption
The data in this notebook is generation and consumption by fuel type for the entire US. These values are ... |
weleen/mxnet | example/notebooks/tutorials/char_lstm.ipynb | apache-2.0 | import os
import urllib
import zipfile
if not os.path.exists("char_lstm.zip"):
urllib.urlretrieve("http://data.mxnet.io/data/char_lstm.zip", "char_lstm.zip")
with zipfile.ZipFile("char_lstm.zip","r") as f:
f.extractall("./")
with open('obama.txt', 'r') as f:
print f.read()[0:1000]
"""
Explanation: Cha... |
lsanomaly/lsanomaly | lsanomaly/notebooks/digits.ipynb | mit | import os
from IPython.display import Image
import numpy as np
from pathlib import Path
from sklearn import metrics
cwd = os.getcwd()
os.chdir(Path(cwd).parents[1])
from lsanomaly import LSAnomaly
import lsanomaly.notebooks.digits as demo
digits = os.path.join(os.getcwd(), "lsanomaly", "notebooks", "digits.png")
""... |
CPernet/LanguageDecision | notebooks/individuals/controls.ipynb | gpl-3.0 | # Environment setup
%matplotlib inline
%cd /lang_dec
# Imports
import warnings; warnings.filterwarnings('ignore')
import hddm
import numpy as np
import matplotlib.pyplot as plt
from utils import model_tools, signal_detection
# Import control models
controls_data = hddm.load_csv('/lang_dec/data/controls_clean.csv')
con... |
StefanoAllesina/ISC | regex/solutions/MapOfScience_solution.ipynb | gpl-2.0 | import re
import csv
"""
Explanation: Map of Science Solution
Read the file pubmed_results.txt, and extract all the US ZIP codes.
First, import the modules we'll need.
End of explanation
"""
with open('../data/MapOfScience/pubmed_results.txt') as f:
my_text = f.read()
len(my_text)
"""
Explanation: Now read the... |
pyconsk/meetup | Bratislava/201508/Ludolph.ipynb | cc0-1.0 | pip install ludolph
"""
Explanation: Ludolph
Ludolph je jednoduchý XMPP klient napísaný v Pythone, ktorý dokáže odpovedať na správy podľa toho ako si ho naprogramujeme ;)
XMPP
Extensible Messaging and Presence Protocol (XMPP) (predtým známy ako Jabber) je protokol používaný na sieťovú komunikáciu, podobne ako AIM, I... |
tensorflow/docs-l10n | site/ja/io/tutorials/prometheus.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... |
shakhova/BananaML | kaggle_flight/Desicion_trees_practise.ipynb | gpl-3.0 | from __future__ import division, print_function
# отключим всякие предупреждения Anaconda
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
%matplotlib inline
import seaborn as sns
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = (6,4)
xx = np.linspace(0,1,50... |
Diyago/Machine-Learning-scripts | classification/ods_session3_decision_trees.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier, export_graphviz
"""
Explanation: <center> Деревья решений для кла... |
jlema/Udacity-Self-Driving-Car-Engineer-Nanodegree | Term 1- Computer Vision and Deep Learning/Project 1 - Finding Lane Lines in a Video Stream/P1.ipynb | apache-2.0 | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dimensions:', i... |
ANTsX/ANTsPy | tutorials/motionCorrectionExample.ipynb | apache-2.0 | import ants
import numpy as np
"""
Explanation: Motion correction in ANTsPy
We rely on ants.registration to do motion correction which provides the user with full access to parameters and outputs. The key steps, then, are to:
* split the N dimensional (e.g. N=4) image to a list of N-1 dimensional images
* run registr... |
certik/chess | examples_manual/Convergence3.ipynb | mit | %pylab inline
! grep "multipv 1" log4.txt | grep -v lowerbound | grep -v upperbound > log4_g.txt
def parse_info(l):
D = {}
k = l.split()
i = 0
assert k[i] == "info"
i += 1
while i < len(k):
if k[i] == "depth":
D[k[i]] = int(k[i+1])
i += 2
elif k[i] == "... |
mne-tools/mne-tools.github.io | 0.23/_downloads/51cca4c9f4bd40623cb6bfa890e2eb4b/20_erp_stats.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
import mne
from mne.channels import find_ch_adjacency, make_1020_channel_selections
from mne.stats import spatio_temporal_cluster_test
np.random.seed(0)
# Load the data
path = mne.datasets.kiloword.data_path() + '/kword_metadata-epo... |
kaleoyster/ProjectNBI | nbi-utilities/data_gen/decisionFlowChart/Untitled.ipynb | gpl-2.0 | category = Counter(df['category']).keys()
values = Counter(df['category']).values()
plt.bar(category, values)
plt.xticks(rotation='vertical')
plt.show()
"""
Explanation: Number of bridges with respect to baseline difference score
End of explanation
"""
category = Counter(df['intervention']).keys()
values = Counter(d... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/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', 'inpe', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbul... |
FRESNA/atlite | examples/plotting_with_atlite.ipynb | gpl-3.0 | import os
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns
import geopandas as gpd
import pandas as pd
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import cartopy.crs as ccrs
from cartopy.crs import PlateCarree as plate
import... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n03_day14_model_choosing_close_feat_all_syms_equal.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (20.0, 10... |
esa-as/2016-ml-contest | MandMs/Facies_classification-M&Ms_SVM_rbf_kernel.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.metrics import f1_score, accuracy_score, make_scorer
from sklearn.model_selection import LeaveOneGroupOut, validation_curve
import pandas as pd
from pandas import set_option
se... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/regression_plots.ipynb | bsd-3-clause | %matplotlib inline
from statsmodels.compat import lzip
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import ols
plt.rc("figure", figsize=(16, 8))
plt.rc("font", size=14)
"""
Explanation: Regression Plots
End of explanation
"""
prestige = sm.datasets.ge... |
openai/openai-python | examples/embeddings/Zero-shot_classification.ipynb | mit | import pandas as pd
import numpy as np
from sklearn.metrics import classification_report
df = pd.read_csv('output/embedded_1k_reviews.csv')
df['babbage_similarity'] = df.babbage_similarity.apply(eval).apply(np.array)
df['babbage_search'] = df.babbage_search.apply(eval).apply(np.array)
df= df[df.Score!=3]
df['sentime... |
eric-haibin-lin/mxnet | example/multi-task/multi-task-learning.ipynb | apache-2.0 | import logging
import random
import time
import matplotlib.pyplot as plt
import mxnet as mx
from mxnet import gluon, nd, autograd
import numpy as np
"""
Explanation: Multi-Task Learning Example
This is a simple example to show how to use mxnet for multi-task learning.
The network is jointly going to learn whether a n... |
liganega/Gongsu-DataSci | ref_materials/excs/Lab-08.ipynb | gpl-3.0 | Celsius = [36.2, 36.7, 47.3, 17.8]
"""
Explanation: 연습문제
리스트 조건제시법
예제
섭씨 온도로 이루어진 리스트가 다음과 있다.
End of explanation
"""
Fahrenheit = [1.8 * C + 32 for C in Celsius]
Fahrenheit
"""
Explanation: 위 리스트를 이용하여 화씨 온도로 이루어진 리스트를 구현하는 방법은 아래와 같다.
End of explanation
"""
colors = ["red", "purple", "yellow", "blue", "green"]
... |
arturops/deep-learning | intro-to-tflearn/TFLearn_Sentiment_Analysis.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
robotcator/gensim | docs/notebooks/word2vec.ipynb | lgpl-2.1 | # import modules & set up logging
import gensim, logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = [['first', 'sentence'], ['second', 'sentence']]
# train word2vec on the two sentences
model = gensim.models.Word2Vec(sentences, min_count=1)
"""
Explanation:... |
gaufung/PythonStandardLibrary | mathematic/math.ipynb | mit | import math
print('pi', math.pi)
print('e', math.e)
print('nan', math.nan)
print('inf', math.inf)
"""
Explanation: The math module implements many of the IEEE functions that would normally be found in the native platform C libraries for complex mathematical operations using floating point values, including logarithms ... |
survey-methods/samplics | docs/source/tutorial/estimation.ipynb | mit | from IPython.core.display import Image, display
import numpy as np
import pandas as pd
import samplics
from samplics.datasets import Nhanes2, Nhanes2brr, Nhanes2jk, Nmihs
from samplics.estimation import TaylorEstimator, ReplicateEstimator
"""
Explanation: Estimation of population parameters
The objective of this tu... |
empet/Plotly-plots | Les-miserables-network.ipynb | gpl-3.0 | import igraph as ig
"""
Explanation: A 3D graph representing the network of coappearances of characters in Victor Hugo's novel Les Miserables ##
We define our graph as an igraph.Graph object. Python igraph
is a library for high-performance graph generation and analysis.
End of explanation
"""
import json
data = []... |
wbbeyourself/cn-deep-learning | dog-project/dog_app.ipynb | mit | from sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
# 定义函数来加载train，test和validation数据集
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categorical(np.array(data['target']), ... |
rabernat/xgcm | doc/example_mitgcm.ipynb | mit | import xarray as xr
import numpy as np
import xgcm
from matplotlib import pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10,6)
"""
Explanation: MITgcm Example
xgcm is developed in close coordination with the xmitgcm package.
The metadata in datasets constructed by xmitgcm should always be compatibl... |
trangel/Data-Science | bayesian_modeling/TRG_finding_suspect.ipynb | gpl-3.0 | try:
import google.colab
IN_COLAB = True
except:
IN_COLAB = False
if IN_COLAB:
print("Downloading Colab files")
! shred -u setup_google_colab.py
! wget https://raw.githubusercontent.com/hse-aml/bayesian-methods-for-ml/master/setup_google_colab.py -O setup_google_colab.py
import setup_google_... |
saashimi/code_guild | interactive-coding-challenges/graphs_trees/bst/bst_challenge.ipynb | mit | class Node(object):
def __init__(self, data):
# TODO: Implement me
pass
def insert(root, data):
# TODO: Implement me
pass
"""
Explanation: <small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem: Implement a binar... |
matousc89/Python-Adaptive-Signal-Processing-Handbook | notebooks/adaptive_filters_realtime.ipynb | mit | import numpy as np
import matplotlib.pylab as plt
import padasip as pa
%matplotlib inline
plt.style.use('ggplot') # nicer plots
np.random.seed(52102) # always use the same random seed to make results comparable
"""
Explanation: Adaptive Filters Real-time Use with Padasip Module
This tutorial shows how to use Padasip ... |
mkliegl/custom-sklearn | heavytail.ipynb | mit | from __future__ import print_function
import numpy as np
from sklearn.linear_model import Ridge
from flexible_linear import FlexibleLinearRegression
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
%matplotlib inline
np.random.seed(1)
"""
Explanation: Cost function for heavy-tailed n... |
AEW2015/PYNQ_PR_Overlay | docs/source/6a_base_overlay_iop.ipynb | bsd-3-clause | from pynq import Overlay
from pynq.iop import Pmod_OLED
from pynq.iop import PMODB
ol = Overlay("base.bit")
ol.download()
oled = Pmod_OLED(PMODB)
"""
Explanation: Using Peripherals with the Base overlay
Base overlay
The PYNQ-Z1 has 2 Pmod connectors. PMODA and PMODB as indicated below are connected to the FPGA fabric... |
atulsingh0/MachineLearning | python_DC/ST_Python_02b.ipynb | gpl-3.0 | # import
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set()
%run ST_Python_02a.py
#import ipynb.fs.full.ST_Python_02a
import io
from nbformat import current
def execute_notebook(nbfile):
with io.open(nbfile) as f:
nb = current.re... |
DawesLab/LabNotebooks | Jones Calculus for EIT Setup.ipynb | mit | qwp = np.matrix([[1, 0],[0, -1j]])
R(-np.pi/4)*qwp*R(np.pi/4)
qwp45 = wp(np.pi/2, np.pi/4)
qwp45
wp(np.pi/2, 0)
vpol = np.matrix([[0,0],[0,1]])
vpol
np.exp(1j*np.pi/4)
horiz = np.matrix([[1],[0]])
output = qwp*horiz
intensity(output)
before_cell = wp(np.pi/2,np.pi/4)*wp(np.pi,np.pi/10)*horiz
output = vpol*wp(... |
ellisonbg/leafletwidget | examples/DrawControl.ipynb | mit | dc = DrawControl(marker={'shapeOptions': {'color': '#0000FF'}},
rectangle={'shapeOptions': {'color': '#0000FF'}},
circle={'shapeOptions': {'color': '#0000FF'}},
circlemarker={},
)
def handle_draw(target, action, geo_json):
print(action)
print(... |
cdt15/lingam | examples/BottomUpParceLiNGAM.ipynb | mit | import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import print_causal_directions, print_dagc, make_dot
import warnings
warnings.filterwarnings('ignore')
print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])
np.set_printoptions(precision=3, suppress=Tru... |
metpy/MetPy | v0.11/_downloads/8532b75251585046a16f04a9afaef079/Advanced_Sounding.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
"""
Explanation: Advanced Sounding
Plot a sounding using MetPy with more advanced features.
Beyond just plotting data, this ... |
sevamoo/SOMPY | sompy/examples/.ipynb_checkpoints/AirFlights_hexagonal_grid-checkpoint.ipynb | apache-2.0 | %matplotlib inline
import math
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import urllib3
from sklearn.externals import joblib
import random
import matplotlib
from sompy.sompy import SOMFactory
from sompy.visualization.plot_tools import plot_hex_map
import logging
"""
Explanatio... |
LFPy/LFPy | examples/LFPy-example-08.ipynb | gpl-3.0 | # importing some modules, setting some matplotlib values for pl.plot.
import LFPy
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size' : 12,
'figure.facecolor' : '1',
'figure.subplot.wspace' : 0.5,
'figure.... |
PyLCARS/PythonUberHDL | myHDL_ComputerFundamentals/Memorys/.ipynb_checkpoints/FirstInFirstOutMemory-checkpoint.ipynb | bsd-3-clause | from myhdl import *
from myhdlpeek import Peeker
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sympy import *
init_printing()
import random
#https://github.com/jrjohansson/version_information
%load_ext version_information
%version_information myhdl, myhdlpeek, numpy, ... |
yl565/statsmodels | examples/notebooks/predict.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
"""
Explanation: Prediction (out of sample)
End of explanation
"""
nsample = 50
sig = 0.25
x1 = np.linspace(0, 20, nsample)
X = np.column_stack((x1, np.sin(x1), (x1-5)**2))
X = sm.add_constant(X)
beta = [5., 0.5... |
science-of-imagination/nengo-buffer | Project/trained_mental_rotation_ens_inhibition.ipynb | gpl-3.0 | import nengo
import numpy as np
import cPickle
from nengo_extras.data import load_mnist
from nengo_extras.vision import Gabor, Mask
from matplotlib import pylab
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import scipy.ndimage
from scipy.ndimage.interpolation import rotate
"""
Explanation: ... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/automl/showcase_automl_video_classification_batch.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = '--user'
else:
USER_FLAG = ''
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: AutoML video classification model for batch prediction
<table align=... |
jasonpcasey/ipeds-peers | .ipynb_checkpoints/peer_examples-checkpoint.ipynb | mit | nx.degree(g, 3)
nx.degree(g, 4)
"""
Explanation: A node's degree is the number of connections it has.
End of explanation
"""
nx.clustering(g, 0)
nx.clustering(g, 4)
nx.clustering(g, 1)
"""
Explanation: The local clustering coefficient is the fraction of a node's connections that are also connected.
End of explan... |
CUBoulder-ASTR2600/lectures | lecture_02_basics.ipynb | isc | 10 / 3 # We provide integers
# What will the output be?
"""
Explanation: Saving your iPython notebook
File -> Save and Checkpoint
Can change the name also in that menu. But also possible via clicking the name above.
Talk about command mode and edit mode of cells. And the help window.
Data Types: Integers vs. Floa... |
Microno95/DESolver | docs/examples/numpy/Example 3 - NumPy - N-Body Systems.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import desolver as de
import desolver.backend as D
D.set_float_fmt('float64')
"""
Explanation: N-Body Gravitationally Interacting System
Let's try doing something more complicated: N-body dynamics. As the name implies, we have $N$ interacting bodies where the i... |
SParadiso18/juliasets | juliaplots.ipynb | mit | from juliaset import JuliaSet
"""
Explanation: Julia Set Plotting Extension
Load module for a JuliaSet that conforms to the specified interface.
It is wise to run the test suite in test_juliaset.py with nosetests prior to attempting to plot here.
End of explanation
"""
# Math libraries
import numpy as np
from math i... |
phobson/statsmodels | examples/notebooks/tsa_arma_1.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
import pandas as pd
from statsmodels.tsa.arima_process import arma_generate_sample
np.random.seed(12345)
"""
Explanation: Autoregressive Moving Average (ARMA): Artificial data
End of explanation
"""
arparams = n... |
ttsuchi/ttsuchi.github.io | notebooks/PCA.ipynb | mit | from numpy.random import standard_normal # Gaussian variables
N = 1000; P = 5
X = standard_normal((N, P))
W = X - X.mean(axis=0,keepdims=True)
print(dot(W[:,0], W[:,1]))
"""
Explanation: Principal Component Analysis and EigenFaces
In this notebook, I will go through the basic concepts behind the principal component a... |
ucsd-ccbb/jupyter-genomics | notebooks/networkAnalysis/drug_gene_networks/drug_gene_networks.ipynb | mit | # import some useful packages
import numpy as np
import matplotlib.pyplot as plt
import seaborn
import networkx as nx
import pandas as pd
import random
import json
# latex rendering of text in graphs
import matplotlib as mpl
mpl.rc('text', usetex = False)
mpl.rc('font', family = 'serif')
% matplotlib inline
"""
Expl... |
stevetjoa/stanford-mir | audio_representation.ipynb | mit | x, sr = librosa.load('audio/c_strum.wav')
ipd.Audio(x, rate=sr)
"""
Explanation: ← Back to Index
Audio Representation
In performance, musicians convert sheet music representations into sound which is transmitted through the air as air pressure oscillations. In essence, sound is simply air vibrating (Wikipedia). ... |
kraemerd17/kraemerd17.github.io | courses/python/material/ipynbs/NumPy Basics.ipynb | mit | import numpy as np
"""
Explanation: NumPy: Vectorized Array Processing in Python
NumPy, short for Numerical Python, is the fundamental package required for high performance scientific computing and data analysis. It is the foundation on which nearly all of the higher-level tools we will use are built. Here are some of... |
angelmtenor/data-science-keras | property_maintenance_fines.ipynb | mit | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import helper
import keras
helper.info_gpu()
#sns.set_palette("GnBu_d")
#helper.reproducible(seed=0) # Setup reproducible results from run to run using Keras
%matplotlib inline
"""
Explanation: Property Maintenance... |
sinamoeini/mapp4py | examples/fracture-gcmc-tutorial/dislocation.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import mapp4py
from mapp4py import md
from lib.elasticity import rot, cubic, resize, displace, HirthEdge, HirthScrew
"""
Explanation: Introdcution
This trial describes how to create edge and screw dislocations in iron BCC strating with one unitcell containing two atom... |
programmingscience/code | 2014/20141230_2DPlotsonPythonP2.ipynb | gpl-3.0 | from pylab import *
t = arange(0.0, 2.0,0.01)
y = sin(2*pi*t)
plot(t, y)
xlabel('Time (s)')
ylabel('Voltage (mV)')
title('The simplest one, buddies')
grid(True)
show()
"""
Explanation: 20141230_2DPlotsonPythonP2.ipynb
Two-dimensional plots on Python [Part II]
Support material for the blog post "Two-dimensional p... |
nsrchemie/code_guild | wk1/notebooks/wk1.0.ipynb | mit | count = 1
for elem in range(1, 3 + 1):
count *= elem
print(count)
"""
Explanation: Wk1.0
Warm-up: I got 32767 problems and overflow is one of them.
1. Swap the values of two variables, a and b without using a temporary variable.
2. Suppose I had six different sodas. In how many different combinations could I... |
sharynr/notebooks | Import from Cloudant Python example.ipynb | apache-2.0 | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
"""
Explanation: Python example using Spark SQL over Cloudant as a source
This sample notebook is written in Python and expects the Python 3.5 or higher runtime. Make sure the kernel is started and you are connected to it when executing t... |
dolittle007/dolittle007.github.io | notebooks/getting_started.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
# Initialize random number generator
np.random.seed(123)
# True parameter values
alpha, sigma = 1, 1
beta = [1, 2.5]
# Size of dataset
size = 100
# Predictor variable
X1 = np.random.randn(size)
X2 = np.random.randn(size) * 0.2
# Simulate outcome variable
Y = alpha... |
mne-tools/mne-tools.github.io | 0.24/_downloads/64e3b6395952064c08d4ff33d6236ff3/evoked_whitening.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
import mne
from mne import io
from mne.datasets import sample
from mne.cov import compute_covariance
print(__doc__)
"""
Explanation: Whitening evoked data with a noise covari... |
kmunve/APS | aps/notebooks/new_snow_problem.ipynb | mit | # -*- coding: utf-8 -*-
%matplotlib inline
from __future__ import print_function
import pylab as plt
import datetime
import numpy as np
plt.rcParams['figure.figsize'] = (14, 6)
plt.rcParams.update({'font.size': 22})
plt.xkcd()
"""
Explanation: APS - new snow
Imports
End of explanation
"""
from matplotlib.patches imp... |
p0licat/university | Experiments/Crawling/Jupyter Notebooks/Maria-Iuliana Bocicor.ipynb | mit | class HelperMethods:
@staticmethod
def IsDate(text):
# print("text")
# print(text)
for c in text.lstrip():
if c not in "1234567890 ":
return False
return True
import pandas
import requests
page = requests.get('https://sites.google.com/view/iuliana-bo... |
NuSTAR/nustar_lunar_pointing | notebooks/Convert_Example.ipynb | mit | import sys
from os.path import *
import os
# For loading the NuSTAR data
from astropy.io import fits
# Load the NuSTAR python libraries
import nustar_pysolar as nustar
"""
Explanation: Code for converting an observation to solar coordinates
Step 1: Run the pipeline on the data to get mode06 files with the correct... |
ajrichards/phylogenetic-models | lda/herve-vertebrates-example.ipynb | bsd-3-clause | import os
import numpy as np
from vertebratesLib import *
split = "SPLIT1"
summaryTree,summarySpecies,splitPositions = get_split_data(split)
print summaryTree.shape
"""
Explanation: LDA on vertebrates
Notes on the data
In this example the tree is contstrained
In this example we have to extract position,transition,... |
cfjhallgren/shogun | doc/ipython-notebooks/neuralnets/autoencoders.ipynb | gpl-3.0 | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat
from shogun import RealFeatures, MulticlassLabels, Math
# load the dataset
dataset = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = dataset['data']
# the usps dat... |
CrowdTruth/CrowdTruth-core | tutorial/notebooks/Sparse Multiple Choice Task - Person Annotation in Video.ipynb | apache-2.0 | import pandas as pd
test_data = pd.read_csv("../data/person-video-sparse-multiple-choice.csv")
test_data.head()
"""
Explanation: CrowdTruth for Sparse Multiple Choice Tasks: Person Annotation in Video
In this tutorial, we will apply CrowdTruth metrics to a multiple choice crowdsourcing task for Person Annotation from... |
tpin3694/tpin3694.github.io | machine-learning/ridge_regression.ipynb | mit | # Load libraries
from sklearn.linear_model import Ridge
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
"""
Explanation: Title: Ridge Regression
Slug: ridge_regression
Summary: How to conduct ridge regression in scikit-learn for machine learning in Python.
Date: 2017-09-... |
urgedata/pythondata | fbprophet/.ipynb_checkpoints/fbprophet_part_one-checkpoint.ipynb | mit | import pandas as pd
import numpy as np
from fbprophet import Prophet
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize']=(20,10)
plt.style.use('ggplot')
"""
Explanation: Import necessary libraries
End of explanation
"""
sales_df = pd.read_csv('../examples/retail_sales.csv', index_co... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-PR-raw-out-all-ph-17d.ipynb | mit | ph_sel_name = "all-ph"
data_id = "17d"
# ph_sel_name = "all-ph"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:34:28 2017
Duration: 8 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
"""
fr... |
google/applied-machine-learning-intensive | content/03_regression/03_regression_quality/colab.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
drabastomek/learningPySpark | Chapter09/LearningPySpark_Chapter09.ipynb | gpl-3.0 | import blaze as bl
"""
Explanation: Hybrid data representation using Blaze
Import the Blaze.
End of explanation
"""
import numpy as np
simpleArray = np.array([
[1,2,3],
[4,5,6]
])
"""
Explanation: Abstract data
Working with NumPy array
Let's create a simple NumPy array: we first load NumPy and ... |
aanishn/aanishn.github.io | artifacts/Kaggle_Dogs_Vs_Cats_Using_LeNet_on_Google_Colab_TPU.ipynb | mit | !pip install kaggle
api_token = {"username":"xxxxx","key":"xxxxxxxxxxxxxxxxxxxxxxxx"}
import json
import zipfile
import os
os.mkdir('/root/.kaggle')
with open('/root/.kaggle/kaggle.json', 'w') as file:
json.dump(api_token, file)
!chmod 600 /root/.kaggle/kaggle.json
# !kaggle config path -p /root
!kaggle competi... |
herruzojm/udacity-deep-learning | batch-norm/Batch_Normalization_Lesson.ipynb | mit | # Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"... |
tensorflow/tfx | docs/tutorials/tfx/template.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... |
sthuggins/phys202-2015-work | assignments/assignment04/MatplotlibEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 1
Imports
End of explanation
"""
import os
assert os.path.isfile('yearssn.dat')
"""
Explanation: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from th... |
IST256/learn-python | content/lessons/02-Variables/LAB-Variables.ipynb | mit | a = "4"
type(a) # should be str
a = 4
type(a) # should be int
"""
Explanation: Class Coding Lab: Variables And Types
The goals of this lab are to help you to understand:
Python data types
Getting input as different types
Formatting output as different types
Basic arithmetic operators
How to create a program from an ... |
google/starthinker | colabs/barnacle_dv360.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: DV360 User Audit
Gives DV clients ability to see which users have access to which parts of an account. Loads DV user profile mappings using the API into BigQuery and connects to a DataStudio dashboard.
License
Copyright 2020 Google LLC,
Licensed ... |
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