repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
jpilgram/phys202-2015-work | assignments/assignment05/InteractEx04.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 4
Imports
End of explanation
"""
def random_line(m, b, sigma, size=10):
"""Create a line y = m*x + b + N(0,si... |
sreedom/fpExperiments | WhyFP.ipynb | mit | def count_w(filename):
count = 0
offset = 0
file = open(filenames)
for line in file:
for w in line.split():
count += 1
return count
# But This Doesnt Scale!
"""
Explanation: Functional Programming
What, Why and How
We will try to explain the first principles of functional progra... |
cloudmesh/book | notebooks/machinelearning/seabornexercies.ipynb | apache-2.0 | # please watch out how we import seaborn package and how we rename it as sns
import seaborn as sns
import pandas as pd
# read CSV file directly from a URL and save the results
iris = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')
iris.head()
iris["species"].value_counts()
... |
dinrker/Algorithms_DataStructures | 01_Sort.ipynb | mit | class Solution(object):
def wiggleSort(self, nums):
"""
:type nums: List[int]
:rtype: void Do not return anything, modify nums in-place instead.
"""
for i in range(len(nums)-1):
if (i%2 == 0 and nums[i] > nums[i+1]) or (i%2 ==1 and nums[i] < nums[i+1]):
... |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | gan_mnist/Intro_to_GANs_Solution.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... |
marcinofulus/ProgramowanieRownolegle | MPI/PR_MPI_p2p.ipynb | gpl-3.0 | import numpy as np
import ipyparallel as ipp
c = ipp.Client(profile='mpi')
print(c.ids)
view = c[:]
view.activate()
"""
Explanation: MPI - point to point operations
We will use mpi4py
End of explanation
"""
%time np.max(np.random.randn(5000,5000))
%%px --block
from mpi4py import MPI
import time
import numpy as np ... |
anhaidgroup/py_entitymatching | notebooks/guides/step_wise_em_guides/.ipynb_checkpoints/Generating Features Manually-checkpoint.ipynb | bsd-3-clause | # Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
"""
Explanation: Introduction
This IPython notebook illustrates how to generate features for blocking/matching manually.
First, we need to import py_entitymatching package and other libraries as follows:
End of explanation
... |
root-mirror/training | SummerStudentCourse/2019/Exercises/ROOTBooks/graphDraw_Solution.ipynb | gpl-2.0 | import ROOT
c = ROOT.TCanvas()
"""
Explanation: Interactively Draw a Graph
End of explanation
"""
g = ROOT.TGraph()
for i in range(5): g.SetPoint(i,i,i*i)
g.Draw("APL")
c.Draw()
"""
Explanation: The simple graph
End of explanation
"""
%jsroot on
g.SetMarkerStyle(ROOT.kFullTriangleUp)
g.SetMarkerSize(3)
g.SetMark... |
anilcs13m/MachineLearning_Mastering | predicting Housing price/.ipynb_checkpoints/Predicting house prices-checkpoint.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Predicting the house prices data set for king county
Loading graphlab
End of explanation
"""
sales = graphlab.SFrame('home_data.gl/')
sales.head(5)
"""
Explanation: Load some house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is lo... |
rasbt/bugreport | pytorch-lightning/csvlogger-stepsbug/01.ipynb | mit | BATCH_SIZE = 64
NUM_EPOCHS = 200
LEARNING_RATE = 0.01
NUM_WORKERS = 0
"""
Explanation: MLP Classifier -- Cement Dataset
General settings and hyperparameters
End of explanation
"""
import pytorch_lightning as pl
import torch
import torchmetrics
"""
Explanation: Setting up the PyTorch Lightning model
End of explanat... |
allafort/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',\
... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/mediation_survival.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import statsmodels.api as sm
from statsmodels.stats.mediation import Mediation
"""
Explanation: Mediation analysis with duration data
This notebook demonstrates mediation analysis when the
mediator and outcome are duration variables, modeled
using proportional hazards regression.... |
evanmiltenburg/python-for-text-analysis | Chapters-colab/Chapter_16_Data_formats_I_(CSV_and_TSV).ipynb | apache-2.0 | %%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d ./
!unzip Ext... |
ernestyalumni/CompPhys | crack/TreesGraphs.ipynb | apache-2.0 | # binary tree
class Node:
def __init__(self,val):
self.l=None
self.r=None
self.v=val
class Tree:
def __init__(self):
self.root = None
def getRoot(self):
return self.root
def add(self, val):
if (self.root == None):
self.root = Nod... |
ramseylab/networkscompbio | class03_igraph_python3_template.ipynb | apache-2.0 | import pandas
df = pandas.read_csv("shared/pathway_commons.sif",
sep="\t",
names=["species1","interaction_type","species2"])
"""
Explanation: Load the Pathway Commons SIF file into a pandas data frame, naming the three columns
End of explanation
"""
interaction_types_ppi =... |
BillyLjm/CS100.1x.__CS190.1x | ML_lab3_linear_reg_student.ipynb | mit | labVersion = 'cs190_week3_v_1_3'
"""
Explanation: Linear Regression Lab
This lab covers a common supervised learning pipeline, using a subset of the Million Song Dataset from the UCI Machine Learning Repository. Our goal is to train a linear regression model to predict the release year of a song given a set of audio f... |
andsor/pyfssa | docs/tutorial.ipynb | isc | from __future__ import division
# configure plotting
%config InlineBackend.rc = {'figure.dpi': 300, 'savefig.dpi': 300, \
'figure.figsize': (6, 6 / 1.6), 'font.size': 12, \
'figure.facecolor': (1, 1, 1, 0)}
%matplotlib inline
import itertools
from cycler import... |
robertoalotufo/ia898 | master/iadftdecompose.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft2
from numpy.fft import ifft2
import sys,os
ia898path = os.path.abspath('/etc/jupyterhub/ia898_1s2017/')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
f = 50 * np.ones((128,128))
f[:, ... |
jokedurnez/RequiredEffectSize | Figure1_Power/.ipynb_checkpoints/fig_power-checkpoint.ipynb | mit | % matplotlib inline
from __future__ import division
import os
import nibabel as nib
import numpy as np
from neuropower import peakdistribution
import scipy.integrate as integrate
import pandas as pd
import matplotlib.pyplot as plt
import palettable.colorbrewer as cb
if not 'FSLDIR' in os.environ.keys():
raise Exce... |
Javier-AG/SMC_thesis | preliminary_user_evaluation/evaluation_run.ipynb | gpl-3.0 | instrument, category, accordion = load_interface1()
check1, slider1, check2, slider2, check3, slider3, check4, slider4 = load_interface2()
display(accordion)
display(check1,slider1)
display(check2,slider2)
display(check3,slider3)
display(check4,slider4)
"""
Explanation: INSTRUCTIONS
Go on by clicking on "Run cell" but... |
planetlabs/notebooks | jupyter-notebooks/crop-classification/segment-knn-tuning.ipynb | apache-2.0 | from __future__ import print_function
import os
import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.neighbors import KNeighborsClassifier as KNN
"""
Explanation: KNN Parameter Tuning
In Segmentation: KNN, we perform KNN classification of ... |
ecervera/mindstorms-nb | task/sound.ipynb | mit | from functions import connect, sound, forward, stop
connect()
"""
Explanation: <img src="img/nao.jpg" align="right" width=200>
Sensor de so (micròfon)
El micròfon del robot detecta el soroll ambiental. No sap reconèixer paraules, però si pot reaccionar a una palmada, o un crit. Altres robots més sofisticats com el de... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/ml_fairness_explainability/explainable_ai/labs/xai_structured_caip.ipynb | apache-2.0 | import os
PROJECT_ID = "" # TODO: your PROJECT_ID here.
os.environ["PROJECT_ID"] = PROJECT_ID
BUCKET_NAME = "" # TODO: your BUCKET_NAME here.
REGION = "us-central1"
os.environ[
"BUCKET_NAME"
] = PROJECT_ID # Replace your BUCKET_NAME, if needed. You can leave it as is!
os.environ["REGION"] = REGION
"""
Explan... |
rebeccabilbro/viz | seaborn/energy_viz.ipynb | mit | %matplotlib inline
import os
import requests
import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pandas.tools.plotting import scatter_matrix
"""
Explanation: Visualization basics with Matplotlib, Pandas and Seaborn
Demo: Visualizing Energy Efficiency
Imp... |
joshnsolomon/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 -1*a*(x**2) + b*(x**4)
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
... |
Danghor/Formal-Languages | Python/FixedPoint.ipynb | gpl-2.0 | def fixpoint(S0, f):
Result = S0.copy() # don't change S0
while True:
NewElements = { x for o in Result
for x in f(o)
}
if NewElements.issubset(Result):
return Result
Result |= NewElements
"""
Explanation: Fixed-Point Iterati... |
MTG/sms-tools | notebooks/E3-Fourier-properties.ipynb | agpl-3.0 | from scipy.fftpack import fft, fftshift
import numpy as np
from math import gcd, ceil, floor
import sys
sys.path.append('../software/models/')
from dftModel import dftAnal, dftSynth
from scipy.signal import get_window
import matplotlib.pyplot as plt
# E3 - 1.1: Complete the function minimize_energy_spread_dft()
d... |
chungjjang80/FRETBursts | notebooks/Example - Selecting FRET populations.ipynb | gpl-2.0 | from fretbursts import *
sns = init_notebook(apionly=True)
print('seaborn version: ', sns.__version__)
# Tweak here matplotlib style
import matplotlib as mpl
mpl.rcParams['font.sans-serif'].insert(0, 'Arial')
mpl.rcParams['font.size'] = 12
%config InlineBackend.figure_format = 'retina'
"""
Explanation: Example - Sel... |
lwahedi/CurrentPresentation | talks/MDI3/.ipynb_checkpoints/networkslides-checkpoint.ipynb | mit | import pandas as pd
import networkx as nx
import numpy as np
import scipy as sp
import itertools
import matplotlib.pyplot as plt
import statsmodels.api as sm
%matplotlib inline
"""
Explanation: Collecting and Using Data in Python
Laila A. Wahedi
Massive Data Institute Postdoctoral Fellow <br>McCourt School of Public P... |
blua/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb.LOCAL.17033.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... |
malogrisard/NTDScourse | algorithms/01_sol_graph_science.ipynb | mit | # Load libraries
# Math
import numpy as np
# Visualization
%matplotlib notebook
import matplotlib.pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import ndimage
# High-res visualization (but no rotation possible)
%matplotlib inlin... |
dcavar/python-tutorial-for-ipython | notebooks/spaCy Tutorial.ipynb | apache-2.0 | import spacy
"""
Explanation: spaCy Tutorial
(C) 2019-2020 by Damir Cavar
Version: 1.4, February 2020
Download: This and various other Jupyter notebooks are available from my GitHub repo.
This is a tutorial related to the L665 course on Machine Learning for NLP focusing on Deep Learning, Spring 2018 at Indiana Univers... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/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', 'messy-consortium', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical... |
barronh/GCandPython | PNC_02Figures.ipynb | gpl-3.0 | # Prepare my slides
%pylab inline
%cd working
"""
Explanation: Python Analysis Figures
Author: Barron H. Henderson
End of explanation
"""
%mkdir icartt
!curl -Lo icartt/dc3-mrg60-dc8_merge_20120518_R7_thru20120622.ict http://www-air.larc.nasa.gov/cgi-bin/enzFile?e38EE03EFAE02C04F06E9647DAF98F48D6A2f7075622d6169722f5... |
chetan51/nupic.research | projects/whydense/cifar-100/results-06-05-19.ipynb | gpl-3.0 | metrics = ['epochs', 'test_accuracy_max', 'test_accuracy', 'noise_accuracy_max', 'noise_accuracy']
df[df['name'].str.startswith('C10_')][['name'] + metrics]
# (['dataset', 'name'])['test_accuracy_max', 'test_accuracy', 'noise_accuracy_max', 'noise_accuracy']
metrics = ['epochs', 'test_accuracy', 'test_accuracy_max',... |
DanilBaibak/crash_planes | summaries_investigation.ipynb | mit | df = pd.read_csv('data/data.csv')
"""
Explanation: Raw data
End of explanation
"""
df = pci.clean_database(df)
df.head()
print('Total number of the data: {}'.format(df.shape[0]))
print('Number of the not empty summaries: {}'.format(df[df.Summary.isnull()].shape[0]))
"""
Explanation: Clean(er) Data
End of explanati... |
MihaiLai/digit_recognition | digit_recognition.ipynb | mit | from keras.datasets import mnist
(X_raw, y_raw), (X_raw_test, y_raw_test) = mnist.load_data()
n_train, n_test = X_raw.shape[0], X_raw_test.shape[0]
"""
Explanation: 机器学习工程师纳米学位
深度学习
项目：搭建一个数字识别项目
在此文件中，我们提供给你了一个模板，以便于你根据项目的要求一步步实现要求的功能，进而完成整个项目。如果你认为需要导入另外的一些代码，请确保你正确导入了他们，并且包含在你的提交文件中。以'练习'开始的标题表示接下来你将开始实现你的项目。注意有一... |
tzk/EDeN_examples | graph_format.ipynb | gpl-2.0 | %matplotlib inline
import pylab as plt
import networkx as nx
G=nx.Graph()
G.add_node(0, label='A')
G.add_node(1, label='B')
G.add_node(2, label='C')
G.add_edge(0,1, label='x')
G.add_edge(1,2, label='y')
G.add_edge(2,0, label='z')
from eden.util import display
print display.serialize_graph(G)
from eden.util import d... |
projectmesa/Presentations | scipy_2015/Demographic Prisoner's Dilemma Activation Schedule.ipynb | apache-2.0 | from pd_grid import PD_Model
import random
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec
%matplotlib inline
"""
Explanation: Demographic Prisoner's Dilemma
The Demographic Prisoner's Dilemma is a family of variants on the classic two-player Prisoner's Dilemma, first developed by Joshu... |
google/lifetime_value | notebooks/kaggle_acquire_valued_shoppers_challenge/preprocess_data.ipynb | apache-2.0 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import pandas as pd
import tqdm
import multiprocessing
pd.options.mode.chained_assignment = None # default='warn'
"""
Explanation: <table align="left">
<td>
<a target="_bl... |
tensorflow/recommenders | docs/examples/multitask.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... |
materialsvirtuallab/matgenb | notebooks/2017-04-03-Slab generation and Wulff shape.ipynb | bsd-3-clause | # Import the neccesary tools to generate surfaces
from pymatgen.core.surface import SlabGenerator, generate_all_slabs, Structure, Lattice
# Import the neccesary tools for making a Wulff shape
from pymatgen.analysis.wulff import WulffShape
import os
# Let's start with fcc Ni
lattice = Lattice.cubic(3.508)
Ni = Structu... |
NeuroDataDesign/seelviz | Jupyter/.ipynb_checkpoints/Ilastik and Membrane Detection-checkpoint.ipynb | apache-2.0 | print cwd
"""
Explanation: October 19, 2016
Ilastik Membrane Detection
Decision Tree and Random Forest
Decision trees are a type of regression technique that aims to discern some set of discrete features from a data set. Decision trees function are built from a subset of branches (specific features) and nodes (where ... |
google/BIG-bench | bigbench/bbseqio/docs/seqio_tasks_from_json.ipynb | apache-2.0 | !pip install git+https://github.com/google/BIG-bench.git
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
import os
from typing import Any, Dict, List
import seqio
import t5.data
import t5.evaluation.metrics
import tensorflow_datasets as tfds
from bigbench.bbseqio import task_api as bb_task_api
from bigbe... |
jakevdp/sklearn_tutorial | notebooks/03.2-Regression-Forests.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('seaborn')
"""
Explanation: <small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Supervised Learning In-Depth: Random Forests
Previously we saw a powerf... |
p-chambers/Python_OOP_Workshop | index.ipynb | mit | # Run this cell before trying examples
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Python Object Orientation Workshop
Paul Chambers
P.R.Chambers@soton.ac.uk
<img style="float: left;" src="images/ngcm.png">
<img style="float: right;" src="images/epsrc_logo.jpg">
Prerequisites
... |
UWSEDS/LectureNotes | Fall2018/04_ProjectOverview_AnalysisWorkflow/analysis_workflow.ipynb | bsd-2-clause | # Packages
from urllib import request
import os
import pandas as pd
# Constants used in analysis
TRIP_DATA = "https://data.seattle.gov/api/views/tw7j-dfaw/rows.csv?accessType=DOWNLOAD"
TRIP_FILE = "pronto_trips.csv"
WEATHER_DATA = "http://uwseds.github.io/data/pronto_weather.csv"
WEATHER_FILE = "pronto_weather.csv"
... |
gregorjerse/rt2 | 2015_2016/lab3/triangulation.ipynb | gpl-3.0 | class Triangle:
"""
A triangle is represented as a list of its
vertices (labeled with natural numbers).
"""
def __init__(self, vertices, neighbours=None):
assert len(vertices) == 3, 'A triangle should have 3 vertices'
self.vertices = sorted(vertices)
self.neighbours = neighbo... |
ellisztamas/faps | docs/tutorials/07_dealing_with_multiple_half-sib_families.ipynb | mit | import numpy as np
import faps as fp
import matplotlib.pyplot as plt
print("Created using FAPS version {}.".format(fp.__version__))
"""
Explanation: Dealing with multiple half-sib families
Tom Ellis, March 2018, updated June 2020
End of explanation
"""
%pylab inline
adults = fp.read_genotypes('../../data/parents_... |
MatthewDaws/OSMDigest | notebooks/Geopandas.ipynb | mit | point_features = [{"geometry": {
"type": "Point",
"coordinates": [102.0, 0.5]
},
"properties": {
"prop0": "value0", "prop1": "value1"
}
}]
point_data = gpd.GeoDataFrame.from_features(point_features)
point_data
point_data.ix[0].geome... |
graphistry/pygraphistry | demos/upload_csv_miniapp.ipynb | bsd-3-clause | #!pip install graphistry -q
import pandas as pd
import graphistry
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more options, see https://github.com/graphistry/pygraphistry#configure
"""
Explanation: Vi... |
phockett/ePSproc | epsproc/vol/set_plot_options_json.ipynb | gpl-3.0 | import json
import pprint
pp = pprint.PrettyPrinter(indent=4)
import sys
from pathlib import Path
# ePSproc test codebase (local)
# For package version this shouldn't be necessary
if sys.platform == "win32":
modPath = r'D:\code\github\ePSproc' # Win test machine
else:
modPath = r'/home/femtolab/github/ePSpro... |
msampathkumar/kaggle-quora-tensorflow | references/starters/keras_starter.ipynb | apache-2.0 | import os
import csv
import codecs
import numpy as np
import pandas as pd
np.random.seed(1337)
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical
from keras.layers import Dense, Input, Flatten, merge, LSTM, Lambda, Dropo... |
tpin3694/tpin3694.github.io | sql/select_values_between_two_values.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Select Values Between Two Values
Slug: select_values_between_two_values
Summary: Select values between two values in SQL.
Date: 2016-05-01 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial was ... |
mitdbg/modeldb | client/workflows/demos/registry/data-tranformation-modelless-deployment.ipynb | mit | # restart your notebook if prompted on Colab
try:
import verta
except ImportError:
!pip install verta
import os
# Ensure credentials are set up, if not, use below
# os.environ['VERTA_EMAIL'] =
# os.environ['VERTA_DEV_KEY'] =
# os.environ['VERTA_HOST'] =
from verta import Client
client = Client(os.environ... |
Ensembl/cttv024 | tests/__reports__/postgap.20180108.asthma.tsv.gz.REPORT.20180206170054.ipynb | apache-2.0 | from reports import helpers
helpers.calc_run_str()
# pg = pd.read_csv(filename, sep='\t', na_values=['None'])
pg = helpers.load_file(filename)
"""
Explanation: POSTGAP Report
This notebook was automatically generated as a summary of POSTGAP output.
Setup
End of explanation
"""
print(pg.shape)
"""
Explanation: Hea... |
juditacs/labor | notebooks/bi_ea_demo/cat_dog.ipynb | lgpl-3.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.io import wavfile
import os
from sklearn.metrics import precision_recall_fscore_support
from sklearn.preprocessing import StandardScaler
%matplotlib inline
from keras.layers import Input, Dense, Bidirectional, Dropout, Conv1D, MaxPoolin... |
Upward-Spiral-Science/spect-team | Code/Assignment-10/SubjectSelectionExperiments (rCBF data with baseline).ipynb | apache-2.0 | # Standard
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# Dimensionality reduction and Clustering
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn import manifold, datasets
from i... |
ES-DOC/esdoc-jupyterhub | notebooks/nuist/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nuist', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NUIST
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Ra... |
danijel3/ASRDemos | notebooks/MLP_TIMIT.ipynb | apache-2.0 | import os
os.environ['CUDA_VISIBLE_DEVICES']='0'
"""
Explanation: Simple MLP demo for TIMIT using Keras
This notebook describes how to reproduce the results for the simple MLP architecture described in this paper:
ftp://ftp.idsia.ch/pub/juergen/nn_2005.pdf
And in Chapter 5 of this thesis:
http://www.cs.toronto.edu/~g... |
wgong/open_source_learning | fun_with_jupyter.ipynb | apache-2.0 | HTML("<img src=images/office-suite.jpg>")
"""
Explanation: Fun with Jupyter
Table of Contents
Motivation
Introduction
Problem Statement
Import packages
Estimate x range
Use IPython as a calculator
Use Python programming to find solution
Graph the solution with matplotlib
Solve equation precisely using SymPy
Pandas fo... |
DominikDitoIvosevic/Uni | STRUCE/2018/.ipynb_checkpoints/SU-2018-LAB01-Regresija-checkpoint.ipynb | mit | # Učitaj osnovne biblioteke...
import numpy as np
import sklearn
import matplotlib.pyplot as plt
import scipy as sp
%pylab inline
"""
Explanation: Sveučilište u Zagrebu
Fakultet elektrotehnike i računarstva
Strojno učenje 2018/2019
http://www.fer.unizg.hr/predmet/su
Laboratorijska vježba 1: Regresija
Verzija: 1.1
Z... |
psumank/DATA643 | Final/DATA643_Final_Project.ipynb | mit | import os
import sys
import urllib2
import collections
import matplotlib.pyplot as plt
import math
from time import time, sleep
%pylab inline
"""
Explanation: DATA 643 - Final Project
Sreejaya Nair and Suman K Polavarapu
Description:
Explore the Apache Spark Cluster Computing Framework by analysing the movielens datas... |
facaiy/book_notes | Mining_of_Massive_Datasets/Link_Analysis/note.ipynb | cc0-1.0 | plt.imshow(plt.imread('./res/fig_5_1.png'))
plt.imshow(plt.imread('./res/eg_5_1.png'))
"""
Explanation: 5 Link Analysis
5.1 PageRank
5.1.1 Eearly Search Engines and Term Spam
inverted index:
a data structure that makes it easy to find all the palces where that a term given occurs.
term spam:
techniques for fooli... |
antoniomezzacapo/qiskit-tutorial | community/terra/qis_intro/superposition.ipynb | apache-2.0 | # useful additional packages
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
# importing Qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute
from qiskit import Aer, IBMQ
# import basic plot tools
from qiskit.tools.visualization import matplotlib_circuit_drawer ... |
Ironlors/SmartIntersection-Ger | Journal/Seminararbeit.ipynb | apache-2.0 | v1 = int(input('v1: '))
v2 = int(input('v2: '))
h1 = int(input('h1: '))
h2 = int(input('h2: '))
v = v1+v2
h = h1+h2
print ('V', v)
print ('H', h)
"""
Explanation: Seminararbeit - autonome Verkehrsleitsysteme
von Kay Kleinvogel und Lisa-Marie Nehring
Übersicht:
Die Hauptaufgabe dieser Arbeit ist das Forschen an effizi... |
Diyago/Machine-Learning-scripts | clustering/Базовая кластеризация.ipynb | apache-2.0 | #импортируем библиотеки
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.cluster import DBSCAN
plt.figure(figsize=(12, 12))
n_samples = 2300
random_state = 220
X, y = make_blobs(n_samples=n_samples, random_state=random_sta... |
tensorflow/docs-l10n | site/ja/tensorboard/hyperparameter_tuning_with_hparams.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... |
esa-as/2016-ml-contest | MSS_Xmas_Trees/ml_seg_sub5_CRAW.ipynb | apache-2.0 | from numpy.fft import rfft
from scipy import signal
import numpy as np
import matplotlib.pyplot as plt
import plotly.plotly as py
import pandas as pd
import timeit
from sqlalchemy.sql import text
from sklearn import tree
#from sklearn.model_selection import LeavePGroupsOut
from sklearn import metrics
from sklearn.tree ... |
cranmer/look-elsewhere-2d | create_gaussian_process_examples-fill_holes.ipynb | mit | %pylab inline --no-import-all
"""
Explanation: Testing look-elsewhere effect by creating 2d chi-square random fields with a Gaussian Process
by Kyle Cranmer, Dec 7, 2015
The correction for 2d look-elsewhere effect presented in
Estimating the significance of a signal in a multi-dimensional search by Ofer Vitells and ... |
balarsen/pymc_learning | Deconvolution/convolution2.ipynb | bsd-3-clause | np.random.seed(8675309)
sim_pa = np.arange(20,175)
sim_c = 890*np.sin(np.deg2rad(sim_pa))**0.8
# at each point draw a poisson variable with that mean
sim_c_n = np.asarray([np.random.poisson(v) for v in sim_c ])
prob=0.1
sim_c_n2 = np.asarray([np.random.negative_binomial((v*prob)/(1-prob), prob) for v in sim_c ])
... |
david-hagar/NLP-Analytics | rnn-lstm-text-classification/LSTM Text Classification.ipynb | mit | import numpy as np
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, LSTM, GRU, Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.callbacks import TensorBoard
from keras import backend
# fix random seed for reprod... |
quantumlib/Cirq | docs/tutorials/educators/ion_device.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... |
IST256/learn-python | content/lessons/05-Functions/Slides.ipynb | mit | try:
n = int(input("Enter n: "))
if n > 0:
q = 1
elif n == 0:
q = 2
else:
q = 3
except:
q = 4
"""
Explanation: IST256 Lesson 05
Functions
Zybook Ch 5
P4E Ch 4
Links
Participation: https://poll.ist256.com
In-Class Questions: Ask over Zoom Chat!
Agenda
Exam 1 - Frequently... |
aboucaud/python-euclid2016 | notebooks/02-Numpy.ipynb | bsd-3-clause | # uncomment that line if you are using python 2
# from __future__ import print_function, division
import numpy as np
"""
Explanation: Numpy
NumPy is the fundamental package for scientific computing with Python. You can find more tutorials at http://wiki.scipy.org/Tentative_NumPy_Tutorial . Also check http://www.nump... |
AllenDowney/ThinkStats2 | solutions/chap09soln.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/th... |
chapagain/kaggle-competitions-solution | Sentiment Analysis on Movie Reviews/Sentiment-Analysis-on-Movie-Reviews-RNN-LSTM-Kaggle.ipynb | mit | import numpy as np
import pandas as pd
from gensim import corpora
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import SnowballStemmer
from keras.preprocessing import sequence
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dens... |
huajianmao/learning | coursera/deep-learning/1.neural-networks-deep-learning/week2/pa.1.Python_Basics_With_Numpy_v2.ipynb | mit | ### START CODE HERE ### (≈ 1 line of code)
test = "Hello World"
### END CODE HERE ###
print ("test: " + test)
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Python-Basics-with-Numpy-(optional-assignment)" data-toc-modified-id="Python-Basics-with-Numpy-(optional-assignment)-1"><span class="... |
materialsproject/mapidoc | example_notebooks/mpcomplete_submit_structures_example.ipynb | bsd-3-clause | zipfilename = '/Users/dwinston/Dropbox/best/structures/ever.zip'
"""
Explanation: Submit Structures to MPComplete
This notebook documents the process of
1. Taking and validating a collection of CIFs (e.g. in a ZIP file), creating pymatgen Structure objects
3. Filtering for structures that are submittable to MP (e.g. t... |
google/applied-machine-learning-intensive | content/02_data/01_introduction_to_pandas/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... |
PrincetonACM/princetonacm.github.io | events/code-at-night/archive/python_talk/intro_to_python_soln.ipynb | mit | # When a line begins with a '#' character, it designates a comment. This means that it's not actually a line of code
# This is how you say hello world
print('hello world')
# Can you make Python print the staircase below:
#
# ========
# | |
# =============== ... |
cathalmccabe/PYNQ | boards/Pynq-Z1/base/notebooks/pmod/pmod_grove_usranger.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
"""
Explanation: Grove Ultrasonic Ranger Example
This example shows how to use the
Grove ultrasonic_ranger
on the board. The Ultrasonic sensor has a maximal range of 400 cm,
a minimal range of 3 cm and resolution of 1 cm.
If no obstacle is se... |
anthonyng2/FX-Trading-with-Python-and-Oanda | Oanda v1 REST-oandapy/06.00 Position Management.ipynb | mit | from datetime import datetime, timedelta
import pandas as pd
import oandapy
import configparser
config = configparser.ConfigParser()
config.read('../config/config_v1.ini')
account_id = config['oanda']['account_id']
api_key = config['oanda']['api_key']
oanda = oandapy.API(environment="practice",
a... |
EvenStrangest/tensorflow | tensorflow/examples/udacity/2_fullyconnected.ipynb | apache-2.0 | # 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
from six.moves import range
"""
Explanation: Deep Learning
Assignment 2
Previously in 1_n... |
magwenelab/mini-term-2016 | ode-modeling1-instructor.ipynb | cc0-1.0 | # import statements to make numeric and plotting functions available
%matplotlib inline
from numpy import *
from matplotlib.pyplot import *
## define your function in this cell
def hill_activating(X, B, K, n):
Xn = X**n
return (B * Xn)/(K**n + Xn)
## generate a plot using your hill_activating function define... |
jegibbs/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
localmax=[]
for x in range(len(a)):
if x==0:
... |
flaxandteal/python-course-lecturer-notebooks | Basic control structures.ipynb | mit | x = # Insert something before the hash
"""
Explanation: Basic control structures and variables
Nails for the hammer
As one of the prereqs for this course was some knowledge of MATLAB or a decent understanding of programming, we won't spend a huge amount of time on concepts, assuming you have a fair idea, and focus on ... |
turbomanage/training-data-analyst | CPB100/lab4c/mlapis.ipynb | apache-2.0 | APIKEY="CHANGE-THIS-KEY" # Replace with your API key
"""
Explanation: <h1> Using Machine Learning APIs </h1>
First, visit <a href="http://console.cloud.google.com/apis">API console</a>, choose "Credentials" on the left-hand menu. Choose "Create Credentials" and generate an API key for your application. You should p... |
mne-tools/mne-tools.github.io | 0.17/_downloads/d4848b046d4a566cb8cc0dae39f6b211/plot_eeg_erp.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
"""
Explanation: EEG processing and Event Related Potentials (ERPs)
For a generic introduction to the computation of ERP and ERF
see tut_epoching_and_averaging. Here we cover the specifics
of EEG, namely:
- setting the reference
- using standard montages :func:`mne.channels.M... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/feateng/asl_2.0_feat_eng.ipynb | apache-2.0 | %%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-west1-b" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["P... |
qqwjq/lightFM | examples/crossvalidated/example.ipynb | apache-2.0 | import data
(interactions, question_features,
user_features, question_vectorizer,
user_vectorizer) = data.read_data() # This will download the data if not present
"""
Explanation: Recommending questions on CrossValidated
In this example, we'll try to recommend questions to be answered to users of stats.stackexchang... |
fluxcapacitor/source.ml | jupyterhub.ml/notebooks/train_deploy/zz_under_construction/zz_old/Spark/ML/SparkML_To_Production_Airbnb_Hybrid_Cloud.ipynb | apache-2.0 | df = spark.read.format("csv") \
.option("inferSchema", "true").option("header", "true") \
.load("s3a://datapalooza/airbnb/airbnb.csv.bz2")
df.registerTempTable("df")
print(df.head())
print(df.count())
"""
Explanation: Step 0: Load Libraries and Data
End of explanation
"""
df_filtered = df.filter("price >= 50 ... |
tritemio/FRETBursts | notebooks/Example - Exporting Burst Data Including Timestamps.ipynb | gpl-2.0 | from fretbursts import *
sns = init_notebook()
"""
Explanation: Exporting Burst Data
This notebook is part of a tutorial series for the FRETBursts burst analysis software.
In this notebook, show a few example of how to export FRETBursts
burst data to a file.
<div class="alert alert-info">
Please <b>cite</b> FRETBu... |
scottlittle/solar-sensors | IPnotebooks/important-IPNBs/prune-X.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
from data_helper_functions import *
from IPython.display import display
pd.options.display.max_columns = 999
%matplotlib inline
with np.load('data/X.npz') as data: #old X, don't use, start at "Now with all channels..."
X = data['X']
with np.load('data/Y.npz') as... |
mne-tools/mne-tools.github.io | 0.17/_downloads/8b68ef11c9dcc68ed3cd0ccec9a41a34/plot_decoding_unsupervised_spatial_filter.ipynb | bsd-3-clause | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Asish Panda <asishrocks95@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.decoding import UnsupervisedSpatialFilter
from sklearn.decomposition import PCA, FastI... |
phoebe-project/phoebe2-docs | 2.3/tutorials/gravb_bol.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Gravity Brightening/Darkening (gravb_bol)
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units... |
usantamaria/iwi131 | ipynb/17-CicloFor/For.ipynb | cc0-1.0 | j = 0
while j<10:
print j,
j += 1
# Toda la información de los valores utilizados está en range(10)
for j in range(10):
print j,
"""
Explanation: <header class="w3-container w3-teal">
<img src="images/utfsm.png" alt="" align="left"/>
<img src="images/inf.png" alt="" align="right"/>
</header>
<br/><br/>... |
robertoalotufo/ia898 | master/tutorial_contraste_iterativo_2.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import sys,os
ia898path = os.path.abspath('/etc/jupyterhub/ia898_1s2017/')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
def TWL(L,W):
Pmin = max(0,L-W//2)
Pmax = min... |
rishuatgithub/MLPy | torch/1.Tensor Basics.ipynb | apache-2.0 | import torch
import numpy as np
print(torch.__version__)
arr = np.array([1,2,4,12,34])
arr
arr.dtype
x = torch.from_numpy(arr)
x
type(x)
torch.as_tensor(arr)
### creating 2D array
arr2d = np.arange(0.0,12.0)
arr2d
arr2d = arr2d.reshape(4,3)
arr2d
## create a 2d torch
x2 = torch.from_numpy(arr2d)
x2
### pro... |
yevheniyc/C | 1t_DataAnalysisMLPython/1j_ML/DS_ML_Py_SBO/DataScience/3_Distributions/Distributions.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
values = np.random.uniform(-10.0, 10.0, 100000)
plt.hist(values, 50)
plt.show()
"""
Explanation: Examples of Data Distributions
Uniform Distribution
End of explanation
"""
from scipy.stats import norm
import matplotlib.pyplot as plt
x = np.aran... |
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