repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
sys-bio/tellurium | examples/notebooks/core/tellurium_examples.ipynb | apache-2.0 | import warnings
warnings.filterwarnings("ignore")
import tellurium as te
te.setDefaultPlottingEngine('matplotlib')
%matplotlib inline
# model Definition
r = te.loada ('''
#J1: S1 -> S2; Activator*kcat1*S1/(Km1+S1);
J1: S1 -> S2; SE2*kcat1*S1/(Km1+S1);
J2: S2 -> S1; Vm2*S2/(Km2+S2);
... |
liyigerry/msm_test | examples/hmm-and-msm.ipynb | apache-2.0 | from __future__ import print_function
import os
%matplotlib inline
from matplotlib.pyplot import *
from msmbuilder.featurizer import SuperposeFeaturizer
from msmbuilder.example_datasets import AlanineDipeptide
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import Mark... |
csdms/bmi-live-2017 | nb/visualize.ipynb | mit | %matplotlib auto
import matplotlib.pyplot as plt
from ipywidgets import interact
from bmi_live.bmi_diffusion import BmiDiffusion
"""
Explanation: <img src="img/csdms_logo.jpg">
Visualization with ipywidgets
Let's visualize the evolution of the 2D temperature field as heat diffuses across the plate. We can do this inte... |
muatik/my-coding-challenges | python/challenge-reverse-words.ipynb | mit | def reverse_words(string):
if not string:
return string
newString = []
for word in string.split():
newWord = []
for char in word:
newWord.insert(0, char)
newString.insert(0, "".join(newWord))
return " ".join(newString)
"""
Explanation: Constraints
Can I assu... |
geektoni/shogun | doc/ipython-notebooks/ica/ecg_sep.ipynb | bsd-3-clause | # change to the shogun-data directory
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
os.chdir(os.path.join(SHOGUN_DATA_DIR, 'ica'))
import numpy as np
# load data
# Data originally from:
# http://perso.telecom-paristech.fr/~cardoso/icacentral/base_single.html
data = np.loadtxt('foetal_ecg.dat... |
computational-class/cjc2016 | code/08.01-statistics_thinking.ipynb | mit | from collections import Counter
#from linear_algebra import sum_of_squares, dot
import math
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Introduction to Statistical Thinking
<img align="left" style="padding-right:10px;" width ="200px" src="./img/stats/preface2.png">
Table of Contents
Introduc... |
zzsza/TIL | python/dask.ipynb | mit | import dask
import pandas as pd
df = pd.read_csv('./user_log_2018_01_01.csv')
df
import dask.dataframe as dd
dask_df = dd.read_csv('./user_log_2018_01_01.csv')
dask_df
dir(dask_df)
dask_df["0"]
dask_df.index
len(dask_df.index)
dask_df.info
"""
Explanation: Dask
Dask 공식 문서
numpy, pandas, sklearn이랑 통합 가능
Da... |
Ivanhehe/Sharings | affectiveComputing/ComparisonAnalysis.ipynb | mit | # all the function we need to parse the data
def extract_split_data(data):
content = re.findall("\[(.*?)\]", data)
timestamps = []
values = []
for c in content[0].split(","):
c = (c.strip()[1:-1])
if len(c)>21:
x, y = c.split("#")
values.append(int(x))
... |
paulmorio/grusData | basics/NaiveBayes.ipynb | mit | from sklearn.datasets import make_blobs
X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');
"""
Explanation: Gaussian Naive Bayes
We are going to start off with the simplest Naive Bayes model, using Gaussian fits to generate likelihoods, but befo... |
wllmtrng/wllmtrng.github.io-src | content/2017-10-22-supervised-learning-part-1.ipynb | gpl-3.0 | import pandas as pd
import matplotlib
matplotlib.style.use('ggplot')
%matplotlib inline
training_data = {
'x': [0, 1, 2, 3],
'y': [4, 7, 7, 8]
}
train_df = pd.DataFrame.from_dict(training_data)
train_df
"""
Explanation: Supervised Learning, Part 1: Regression
What is Supervised Learning?
Supervised learning... |
desihub/desispec | doc/nb/Cosmics.ipynb | bsd-3-clause | from astropy.io import fits
import numpy as np
import matplotlib.pyplot as plt
from skimage import measure
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
"""
Explanation: Cosmic Ray Track Finder
This is a primitive track finder in CCD images. It thresholds the image to find "blo... |
Diyago/Machine-Learning-scripts | time series regression/DL aproach for timeseries/Air_Pressure 1D_Conv.ipynb | apache-2.0 | from __future__ import print_function
import os
import sys
import pandas as pd
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import datetime
#set current working directory
os.chdir('D:/Practical Time Series')
#Read the dataset into a pandas.DataFrame
df = pd.read_csv... |
sequana/resources | coverage/03-fungus/fungus.ipynb | bsd-3-clause | %pylab inline
matplotlib.rcParams['figure.figsize'] = [10,7]
"""
Explanation: sequana_coverage test case example (fungus)
This notebook creates the BED file S_pombe.filtered.bed provided in
- https://github.com/sequana/resources/tree/master/coverage and
- https://www.synapse.org/#!Synapse:syn10638358/wiki/465309
geno... |
tensorflow/federated | docs/tutorials/private_heavy_hitters.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... |
rileyrustad/pdxapartmentfinder | analysis/Third_Analysis.ipynb | mit | # start with imports
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import json
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: This is my third attempt at creating a model using sklearn alogithms
In this iteration of analy... |
sdpython/code_beatrix | _doc/notebooks/algorithmes/postier_chinois.ipynb | mit | import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Postier chinois
Postier chinois, chemin eulérien, deux noms pour le même problème, illustrés sur les rues de Seattle.
End of explanation
"""
vertices = [(-122.3... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/copula.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import stats
sns.set_style("darkgrid")
sns.mpl.rc("figure", figsize=(8, 8))
%%javascript
IPython.OutputArea.prototype._should_scroll = function(lines) {
return false;
}
"""
Explanation: Copula - Multivariate joint distribution
En... |
deepmind/dm_control | dm_control/mujoco/tutorial.ipynb | apache-2.0 | #@title Run to install MuJoCo and `dm_control`
import distutils.util
import subprocess
if subprocess.run('nvidia-smi').returncode:
raise RuntimeError(
'Cannot communicate with GPU. '
'Make sure you are using a GPU Colab runtime. '
'Go to the Runtime menu and select Choose runtime type.')
print('Ins... |
widdowquinn/Teaching-SfAM-ECS | workshop/03a-building.ipynb | mit | # The line below allows the notebooks to show graphics inline
%pylab inline
import io # This lets us handle streaming data
import os # This lets us communicate with the operating system
import pandas as pd # This lets us use dataframes
import seab... |
sthuggins/phys202-2015-work | assignments/assignment06/InteractEx05.ipynb | mit | from IPython.display import display, SVG
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
from IPython.html.widgets import interact, interactive, fixed
"""
Explanation: Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
En... |
jonathanmorgan/msu_phd_work | methods/precision_recall/prelim_month_human-confusion_matrix.ipynb | lgpl-3.0 | # set the label we'll be looking at throughout
current_label = "prelim_month_human"
"""
Explanation: prelim_month_human - confusion matrix
old file name: 2017.10.21 - work log - prelim_month_human - confusion matrix
Confusion matrix for data where coder 1 is ground truth, coder 2 is uncorrected human coding.
<h1>Table... |
sympy/scipy-2017-codegen-tutorial | notebooks/_37-chemical-kinetics-numba.ipynb | bsd-3-clause | import json
import numpy as np
import sympy as sym
from scipy2017codegen.odesys import ODEsys
from scipy2017codegen.chem import mk_rsys
"""
Explanation: NOTE
This notebook doesn't work yet. I have previously written my own version of lambdify here.
Don't know if that's the path to go, or wait for next release of numba... |
dnc1994/MachineLearning-UW | ml-regression/polynomial-regression.ipynb | mit | import graphlab
"""
Explanation: Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means to examine this topic. In particular you will:
* Write a function to take a... |
albahnsen/ML_RiskManagement | notebooks/05-data_preparation_evaluation.ipynb | mit | import pandas as pd
import zipfile
with zipfile.ZipFile('../datasets/titanic.csv.zip', 'r') as z:
f = z.open('titanic.csv')
titanic = pd.read_csv(f, sep=',', index_col=0)
titanic.head()
# check for missing values
titanic.isnull().sum()
"""
Explanation: 05 - Data Preparation and Advanced Model Evaluation
by Al... |
vinitsamel/udacitydeeplearning | 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... |
ThyrixYang/LearningNotes | MOOC/stanford_cnn_cs231n/assignment2/Dropout.ipynb | gpl-3.0 | # As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solv... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Con... |
nikbearbrown/Deep_Learning | NEU/Singh_Palod_DL/Generative Adversarial Networks/GANS Mode Collapse.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
"""
Explanation: Generative Adversarial Networks for Natural Language Processing
End of explanation
"""
def xavier_init(n_inputs, n_ou... |
deeplycloudy/MetPy | talks/2015 Unidata Users Workshop.ipynb | bsd-3-clause | # Level 3 example with multiple products
import numpy as np
import matplotlib.pyplot as plt
from numpy import ma
from metpy.cbook import get_test_data
from metpy.io.nexrad import Level3File
from metpy.plots import ctables
# Helper code for making sense of these products. This is hidden from the slideshow
# and eventu... |
unpingco/Python-for-Probability-Statistics-and-Machine-Learning | chapters/statistics/notebooks/Convergence.ipynb | mit | from __future__ import division
import numpy as np
np.random.seed(123456)
"""
Explanation: Python for Probability, Statistics, and Machine Learning
End of explanation
"""
from scipy import stats
u=stats.uniform()
xn = lambda i: u.rvs(i).max()
xn(5)
"""
Explanation: The absence of the probability density for the raw... |
prasants/pyds | 07.Loop_it_up.ipynb | mit | collection = [1,2,3,4,5]
len(collection)
if len(collection) == 5:
print("Woohoo!")
collection[1]
if collection[0] % 2 == 0:
print("Divisible")
else:
print("Not Divisible")
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Control-Flow" data-toc-modified-id="Control-Flow-1"><spa... |
msampathkumar/data_science_sessions | Session-2-Hands-Experience-for-ML/DataScience_Presentation2-LR2.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
%matplotlib inline
"""
Explanation: Linear Regression - Part 2
In this tutorial we shall see, where linear regression limitations.
Imports
End of explanation
"""
n_samples = 30
true_fun = lambda X: np.cos(1.5 * np.pi * X)
X = np.so... |
omoju/Fundamentals | Data/data_time_Series_1.ipynb | gpl-3.0 | %pylab inline
# Import libraries
from __future__ import absolute_import, division, print_function
# Ignore warnings
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import math
# Graphing Libraries
import matplotlib.pyplot as pyplt
from matplotlib.pylab import rcParams
rcPar... |
bspalding/research_public | presentations/How To - Estimate Pi.ipynb | apache-2.0 | # Import libraries
import math
import numpy as np
import matplotlib.pyplot as plt
in_circle = 0
outside_circle = 0
n = 10 ** 4
# Draw many random points
X = np.random.rand(n)
Y = np.random.rand(n)
for i in range(n):
if X[i]**2 + Y[i]**2 > 1:
outside_circle += 1
else:
in_circle += 1
are... |
empet/Plotly-plots | Moebius-Normals.ipynb | gpl-3.0 | import numpy as np
import plotly.graph_objects as go
"""
Explanation: Normals along the central circle of the Moebius strip
The aim of this notebook is twofold:
- first, to show how we can define a standard 3d arrow and place it at different positions in space;
- second, to illustrate the non-orientability of this... |
maojrs/riemann_book | Euler.ipynb | bsd-3-clause | %matplotlib inline
%config InlineBackend.figure_format = 'svg'
from exact_solvers import euler
from exact_solvers import euler_demos
from ipywidgets import widgets
from ipywidgets import interact
State = euler.Primitive_State
gamma = 1.4
"""
Explanation: The Euler equations of gas dynamics
In this notebook, we discus... |
tensorflow/docs-l10n | site/ko/tutorials/keras/text_classification.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... |
ES-DOC/esdoc-jupyterhub | notebooks/snu/cmip6/models/sandbox-2/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-2', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection, ... |
tommyogden/maxwellbloch | docs/examples/mbs-two-sech-4pi.ipynb | mit | import numpy as np
SECH_FWHM_CONV = 1./2.6339157938
t_width = 1.0*SECH_FWHM_CONV # [τ]
print('t_width', t_width)
mb_solve_json = """
{
"atom": {
"fields": [
{
"coupled_levels": [[0, 1]],
"rabi_freq_t_args": {
"n_pi": 4.0,
"centre": 0.0,
"width": %f
},
... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/02_02/Begin/.ipynb_checkpoints/Selection-checkpoint.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
sample_numpy_data = np.array(np.arange(24)).reshape((6,4))
dates_index = pd.date_range('20160101', periods=6)
sample_df = pd.DataFrame(sample_numpy_data, index=dates_index, columns=list('ABCD'))
sample_df
"""
Explanation: Differences between interactive and production work
Note:... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n08_simple_q_learner_fast_learner_3_actions-checkpoint.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
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
mjbommar/cscs-530-w2015 | code/002-basic-space/003-basic_network.ipynb | bsd-2-clause | %matplotlib inline
# Imports
import networkx as nx
import numpy
import matplotlib.pyplot as plt
import pandas
import seaborn; seaborn.set()
# Import widget methods
from IPython.html.widgets import *
"""
Explanation: CSCS530 Winter 2015
Complex Systems 530 - Computer Modeling of Complex Systems (Winter 2015)
Course... |
eds-uga/csci1360e-su17 | lectures/L13.ipynb | mit | import numpy as np
np.random.seed(29384924)
data = np.random.randint(10, size = 100) # 100 random numbers, from 0 to 9
print(data)
"""
Explanation: Lecture 13: Statistics
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
Continuing this week's departure from Python, today we'll jump into ... |
trangel/Data-Science | reinforcement_learning/practice_vi.ipynb | gpl-3.0 | # If you Colab, uncomment this please
# !wget -q https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week02_value_based/mdp.py
transition_probs = {
's0': {
'a0': {'s0': 0.5, 's2': 0.5},
'a1': {'s2': 1}
},
's1': {
'a0': {'s0': 0.7, 's1': 0.1, 's2': 0.2},
'a... |
balavenkatesan/yellowbrick | examples/ndanielsen/Yellowbrick in the Flower Garden.ipynb | apache-2.0 | # read the iris data into a DataFrame
import pandas as pd
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
iris = pd.read_csv(url, header=None, names=col_names)
iris.head()
"""
Explanation: Using Yello... |
geilerloui/deep-learning | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: 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... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/hadgem3-gc31-hm/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'hadgem3-gc31-hm', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: NERC
Source ID: HADGEM3-GC31-HM
Topic: Ocean
Sub-Topics: Timestepping Framewor... |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/access-1-0/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'access-1-0', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: ACCESS-1-0
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, ... |
konstantinstadler/pymrio | doc/source/notebooks/buildflowmatrix.ipynb | gpl-3.0 | import pymrio
io = pymrio.load_test()
"""
Explanation: Analysing the source of stressors (flow matrix)
To calculate the source (in terms of regions and sectors) of a certain stressor or impact driven by consumption, one needs to diagonalize this stressor/impact. This section shows how to do this based on the
small te... |
kit-cel/lecture-examples | mloc/ch1_Preliminaries/gradient_descent.ipynb | gpl-2.0 | import importlib
autograd_available = True
# if automatic differentiation is available, use it
try:
import autograd
except ImportError:
autograd_available = False
pass
if autograd_available:
import autograd.numpy as np
from autograd import elementwise_grad as egrad
else:
import numpy as np
... |
carlosmartinezvillar/4001finalproject | descriptive_statistics.ipynb | mit | import MySQLdb as mdb
import sys
import time
import csv
import numpy as np
import pandas as pd
%matplotlib inline
con = mdb.connect('128.206.116.195', 'tg4_ro', '?3stEt7!3hUbRa-R', 'tw4_db')
if not(con):
con = mdb.connect('opendata.missouri.edu','datascience','datascience','datascience')
if not(con):
print('Co... |
tamasjozsa/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... |
AstroHackWeek/AstroHackWeek2017 | day3/start_here-code_repos.ipynb | mit | ! #complete
! #complete
"""
Explanation: Code Repositories
Astro Hack Week 2017
The notebook contains problems oriented around building a basic Python code repository and making it public via Github. Of course there are other places to put code repositories, with complexity ranging from services comparable to github ... |
paris-saclay-cds/python-workshop | Day_2_Software_engineering_best_practices/04_reusing_code_modules.ipynb | bsd-3-clause | %%file test.py
message = "Hello how are you?"
for word in message.split():
print(word)
"""
Explanation: Reusing code: modules and packages
This notebook is largely based on material of the Python Scientific Lecture Notes (https://scipy-lectures.github.io/), adapted with some exercises.
Introduction
For now, we ... |
ispmarin/text_norm | src/Search Engine.ipynb | mit | from retrieve.search import *
"""
Explanation: Search using Whoosh
We will use Whoosh, a search engine with Python, to retrieve a few candidates. The search engine is already doing some parsing, but with a more complex problem we can use it for a few fields.
End of explanation
"""
doc1 = {
'street': 'XV de novem... |
molgor/spystats | notebooks/Sandboxes/TensorFlow/Getting Started with Tensor Flow.ipynb | bsd-2-clause | ## importation
import tensorflow as tf
"""
Explanation: Getting Started with Tensor Flow
Here I´m taking the tutorials from: https://www.tensorflow.org/get_started/get_started
End of explanation
"""
node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)
... |
bearing/dosenet-analysis | calibration/Thorium Otherdetector.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
csv = np.genfromtxt('Thorium_102566_2019-03-28_D3S.csv', delimiter= ",").T
summed = np.sum(csv, axis=1)
plt.plot(summed)
plt.yscale('log')
plt.show()
"""
Explanation: First I import the thorium data from det 2
End of explanation
""... |
MarsUniversity/ece387 | website/block_3_vision/lsn19/lsn19.ipynb | mit | %matplotlib inline
from __future__ import print_function
from __future__ import division
import numpy as np
from matplotlib import pyplot as plt
import cv2
import time
# make sure you have installed the library with:
# pip install -U ar_markers
from ar_markers import detect_markers
"""
Explanation: Augmented Real... |
RogueAstro/RV_PS2017 | notebooks/the_basics.ipynb | mit | from radial import body
import astropy.units as u
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: The basics of radial velocities
We can almost completely characterize the orbits of massive bodies around a star using a set of five orbital parameters for each body. Different param... |
probml/pyprobml | notebooks/misc/dropout_MLP_torch.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
np.random.seed(seed=1)
import math
import torch
from torch import nn
from torch.nn import functional as F
!mkdir figures # for saving plots
!wget https://raw.githubusercontent.com/d2l-ai/d2l-en/master/d2l/torch.py -q -O d2l.py
import d2l
"""
Explanation: <a href="... |
kkkddder/dmc | notebooks/week-3/01-basic ann.ipynb | apache-2.0 | %matplotlib inline
import random
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set(style="ticks", color_codes=True)
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import shuffle
"""
Explanation: Lab 3 - Basic Artificial Neural Network
In this lab we will build a very... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch2-Problem_2-03.ipynb | unlicense | %pylab notebook
%precision 4
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 2
Problem 2-3
End of explanation
"""
VS = 480.0 * exp(0j) # [Ohm] using polar syntax
Zline = 3.0 + 4.0j # [Ohm] using cartesian syntax
Zload = 30.0 + 40.0j # [Ohm] using cartesian syntax
"""
Explanation: Descriptio... |
dh7/ML-Tutorial-Notebooks | tf-linear-regression.ipynb | bsd-2-clause | %matplotlib notebook
import matplotlib
import matplotlib.pyplot as plt
'''
A linear regression learning algorithm example using TensorFlow library.
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''
import tensorflow as tf
import numpy as np
"""
Explanation: Linear regression w... |
jcharit1/Amazon-Fine-Foods-Reviews | code/model_building_part_3.ipynb | mit | import os
import pandas as pd
import numpy as np
import scipy as sp
import seaborn as sns
import matplotlib.pyplot as plt
import json
from IPython.display import Image
from IPython.core.display import HTML
retval=os.chdir("..")
clean_data=pd.read_pickle('./clean_data/clean_data.pkl')
clean_data.head()
kept_cols=['h... |
ZhiangChen/deep_learning | Tutorials/Udacity/1_notmnist.ipynb | mit | # 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 matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.line... |
Neuroglycerin/neukrill-net-work | notebooks/superclass_hierarchy/Using superclass predictions.ipynb | mit | with open("predictions.pkl", "rb") as f:
predictions = pickle.load(f)
"""
Explanation: Model with col_norms set to a higher value and augmentations turned on. We saved its pickle with predictions across all superclass vectors.
End of explanation
"""
superclasses = predictions[:,121:(121+38)]
s = np.sum(superclas... |
xiongzhenggang/xiongzhenggang.github.io | data-science/28-密度和轮廓图.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as np
"""
Explanation: 密度和轮廓图
有时，使用轮廓或颜色编码区域在二维中显示三维数据很有用。有三个Matplotlib函数可以帮助完成此任务：用于轮廓图的plt.contour，用于填充轮廓图的plt.contourf和用于显示图像的plt.imshow。本节介绍了使用它们的几个示例。我们将从设置笔记本开始，以绘制和导入将要使用的功能：
End of explanation
"""
def f(x, y):
... |
hktxt/MachineLearning | PyTorch Tutorials/detach.ipynb | gpl-3.0 | # a is a tensor with require grad
a = torch.tensor(2., requires_grad=True);a
b = a.detach();b # with deatch() no grad.
"""
Explanation: deatch(): https://discuss.pytorch.org/t/clone-and-detach-in-v0-4-0/16861
tensor.detach() creates a tensor that shares storage with tensor that does not require grad.
End of explana... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/statespace_concentrated_scale.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.api as sm
dta = sm.datasets.macrodata.load_pandas().data
dta.index = pd.date_range(start='1959Q1', end='2009Q4', freq='Q')
"""
Explanation: State space models - concentrating the scale out of the likelihood function
End of explanation
"""
class LocalLevel(sm... |
JDTimlin/QSO_Clustering | highz_clustering/classification/.ipynb_checkpoints/SpIESHighzQuasarPhotoz2-checkpoint.ipynb | mit | ## Read in the Training Data and Instantiating the Photo-z Algorithm
%matplotlib inline
from astropy.table import Table
import numpy as np
import matplotlib.pyplot as plt
#data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')
#JT PATH ON TRITON to training set after classification
#data = Table.... |
elektrobohemian/courses | .ipynb_checkpoints/InformationRetrieval-checkpoint.ipynb | mit | # This cell has to be run to prepare the Jupyter notebook
# The %... is an Jupyter thing, and is not part of the Python language.
# In this case we're just telling the plotting library to draw things on
# the notebook, instead of on a separate window.
%matplotlib inline
# See all the "as ..." contructs? They're just a... |
ProfessorKazarinoff/staticsite | content/code/error_bars/bar_chart_with_matplotlib.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
#if using a jupyter notebook
%matplotlib inline
"""
Explanation: Building bar charts is a useful skill for engineers.
Import matplotlib and numpy
End of explanation
"""
# Enter in the raw data
aluminum = np.array([6.4e-5 , 3.01e-5 , 2.36e-5, 3.0e-5, 7.... |
wtbarnes/aia_response | notebooks/response_function_tests.ipynb | mit | import os
import sys
import pickle
import numpy as np
import scipy
import matplotlib.pyplot as plt
import ChiantiPy.core as ch
import sunpy.instr.aia as aia
%matplotlib inline
"""
Explanation: AIA Response Function Tests
End of explanation
"""
response = aia.Response(path_to_genx_dir='../ssw_aia_response_data/')
... |
wcmckee/wcmckee.com | posts/niktrans.ipynb | mit | import os
import json
os.system('python3 nikoladu.py')
os.chdir('/home/wcmckee/nik1/')
os.system('nikola build')
os.system('rsync -azP /home/wcmckee/nik1/* wcmckee@wcmckee.com:/home/wcmckee/github/wcmckee.com/output/minedujobs')
opccschho = open('/home/wcmckee/ccschool/cctru.json', 'r')
opcz = opccschho.read()
rssc... |
walchko/soccer2 | docs/ipython/Gait_Code_Check.ipynb | mit | %matplotlib inline
from __future__ import print_function
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.insert(0, '../..')
from math import pi, sqrt
from Quadruped import Quadruped
from Gait import DiscreteRippleGait, ContinousRippleGait
"""
Explanation: Full Ga... |
the-deep-learners/TensorFlow-LiveLessons | notebooks/point_by_point_intro_to_tensorflow.ipynb | mit | import numpy as np
np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
tf.set_random_seed(42)
"""
Explanation: Introduction to TensorFlow, fitting point by point
In this notebook, we introduce TensorFlow by fitting a line of the form y=m*x+b point by point.... |
tiagoantao/bioinf-python | notebooks/01_NGS/Working_with_FASTQ.ipynb | apache-2.0 | !rm -f SRR003265.filt.fastq.gz 2>/dev/null
!wget -nd ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/NA18489/sequence_read/SRR003265.filt.fastq.gz
"""
Explanation: Getting the necessary data
You just need to download this ~28 MB file only once
End of explanation
"""
from collections import defaultdict
import gz... |
enakai00/jupyter_tfbook | Chapter03/MNIST single layer network.ipynb | gpl-3.0 | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
np.random.seed(20160612)
tf.set_random_seed(20160612)
"""
Explanation: [MSL-01] 必要なモジュールをインポートして、乱数のシードを設定します。
End of explanation
"""
mnist = input_data.read_data_sets("/tmp/data/", ... |
chipfranzen/dillinger | demos/regression_demo.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from dillinger.gaussian_process import GaussianProcess
from dillinger.kernel_functions import PeriodicKernel
%matplotlib inline
sns.set(font_scale=1.3, palette='deep', color_codes=True)
np.random.seed(0)
# setting up the objective function
def o... |
sot/aca_stats | fit_acq_prob_model-2018-04-poly-spline-tccd.ipynb | bsd-3-clause | from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from astropy.time import Time
import tables
from scipy import stats
import tables3_api
from scipy.interpolate import CubicSpline
from Chandra.Time import DateTime
%matplotlib inline
"""
Explanation: Fit... |
ChadFulton/statsmodels | examples/notebooks/discrete_choice_overview.ipynb | bsd-3-clause | from __future__ import print_function
import numpy as np
import statsmodels.api as sm
"""
Explanation: Discrete Choice Models Overview
End of explanation
"""
spector_data = sm.datasets.spector.load()
spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)
"""
Explanation: Data
Load data from Spector a... |
mromanello/SunoikisisDC_NER | participants_notebooks/Sunoikisis - Named Entity Extraction 1b_PG.ipynb | gpl-3.0 | ########
# NLTK #
########
import nltk
from nltk.tag import StanfordNERTagger
########
# CLTK #
########
import cltk
from cltk.tag.ner import tag_ner
##############
# MyCapytain #
##############
import MyCapytain
from MyCapytain.resolvers.cts.api import HttpCTSResolver
from MyCapytain.retrievers.cts5 import CTS
from M... |
ergosimulation/mpslib | scikit-mps/examples/ex_mpslib_entropy.ipynb | lgpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import mpslib as mps
"""
Explanation: MPSlib: computation of entropy and self-information
The self-information, and entropy (the average self-information), acan be commputed using MPSlib by setting
do_entropy=1
This works for all algorithms excpet when using mps_gen... |
cagaray/edubot | doc2vec/doc2vec.ipynb | apache-2.0 | #We'll need an object with questions and label like SENT_#number_of_question.
class LabeledLineSentence(object):
def __init__(self, filename):
self.filename = filename
def __iter__(self):
for uid, line in enumerate(open(utils.data_path + 'doc2vec/' + self.filename, 'r')):
yield Label... |
snucsne/CSNE-Course-Source-Code | CSNE2444-Intro-to-CS-I/jupyter-notebooks/ch07-iteration.ipynb | mit | a = 5
b = a # a and b are now equal
a = 3 # a and b are no longer equal
"""
Explanation: Chapter 7: Iteration
Contents
- Multiple assignment
- Updating variables
- The while statement
- Break statement
- Exercises
This notebook is based on "Think Python, 2Ed" by Allen B. Downey <br>
https://greenteapress.com/w... |
tclaudioe/Scientific-Computing | SC1v2/Bonus - 07-08 Weighted Least Squares.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import scipy.linalg as spla
%matplotlib inline
# https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets
from sklearn import datasets
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
import matplotlib as mpl
mpl.rcParam... |
drericstrong/Blog | 20170702_ParsevalsTheoremInPython.ipynb | agpl-3.0 | import numpy as np
import pandas as pd
from scipy.fftpack import fft
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
def calculate_fft(wf, T, N):
# xf is the frequency ("x") axis of the half-width fft,
# yf is the raw fft, yfs is the scaled fft, and yfsh is... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Concent... |
patrickmineault/xcorr-snippets | decision-making/Multi-armed bandit as a Markov decision process.ipynb | mit | import itertools
import numpy as np
from pprint import pprint
def sorted_values(dict_):
return [dict_[x] for x in sorted(dict_)]
def solve_bmab_value_iteration(N_arms, M_trials, gamma=1,
max_iter=10, conv_crit = .01):
util = {}
# Initialize every state to utility 0.
... |
dm-wyncode/zipped-code | content/posts/python-mongodb/set_creation_speed_test.ipynb | mit | import logging
import timeit
"""
Explanation: Introduction
I obtained this Fort Lauderdale Police Department data from the City of Fort Lauderdale via the Fort Lauderdale Civic Hackathon.
See my blog post about my participation in the hackathon.
This is my first Pelican blog post using a Jupyter notebook made possible... |
jupyter/docker-demo-images | notebooks/Welcome to Spark with Python.ipynb | bsd-3-clause | import pyspark
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import LogisticRegressionWithSGD
from pyspark.mllib.tree import DecisionTree
"""
Explanation: Welcome to Apache Spark with Python
Apache Spark is a fast and general-purpose cluster computing system. It provides high-l... |
JAmarel/Phys202 | Interact/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... |
ilanman/gdi | week2/02_Week2_I_functions_sol.ipynb | mit | def square(x):
"""Square of x."""
return x*x
def cube(x):
"""Cube of x."""
return x*x*x
def root(x):
"""Square root of x."""
return x**.5
# create a dictionary of functions
funcs = {
'square': square,
'cube': cube,
'root': root,
}
x = 2
print square(x)
print cube(x)
print root(x... |
relf/smt | tutorial/SMT_ExpandedLHS.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from smt.sampling_methods import LHS
import matplotlib.patches as patches
xlimits = np.array([[0.0, 4.0], [0.0, 3.0], [0.0, 3.0], [1.0, 5.0]])
sampling = LHS(xlimits=xlimits, criterion='ese', random_state=1)
num = 10
x = sampling(num)
### For visualization only
in... |
mdda/fossasia-2016_deep-learning | notebooks/2-CNN/4-ImageNet/0-modelzoo-tf-keras.ipynb | mit | import keras
#import tensorflow.contrib.keras as keras
import numpy as np
if False:
import os, sys
targz = "v0.5.tar.gz"
url = "https://github.com/fchollet/deep-learning-models/archive/"+targz
models_orig_dir = 'deep-learning-models-0.5'
models_here_dir = 'keras_deep_learning_models'
models_di... |
sainathadapa/fastai-courses | deeplearning1/nbs-custom-mine/lesson5_01_wordvectors.ipynb | apache-2.0 | def get_glove(name):
with open(path+ 'glove.' + name + '.txt', 'r') as f: lines = [line.split() for line in f]
words = [d[0] for d in lines]
vecs = np.stack(np.array(d[1:], dtype=np.float32) for d in lines)
wordidx = {o:i for i,o in enumerate(words)}
save_array(res_path+name+'.dat', vecs)
pickle... |
bmbutle2/ethereum_blockchain | notebooks/modeling2.ipynb | gpl-3.0 | train.columns
"""
Explanation: Drop some features
that might cause data leakage
that we don't have access to as inputs
End of explanation
"""
train.drop(['type',
'mv',
'blockTime',
'difficulty',
'gasLimit_b',
'gasUsed_b',
'reward',
... |
JavierVLAB/DataAnalysisScience | Titanic/Titanic_01.ipynb | gpl-3.0 | #Libraries to import
import numpy
import pandas
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style("whitegrid")
sns.set_context("notebook", font_scale=1.5)
# Import dataset
titanic_train = pandas.read_csv('train.csv')
titanic_test = pandas.read_csv('test.csv')
"""
Explanation:... |
PMEAL/OpenPNM | examples/tutorials/network/coupling_continuum_regions_with_pore_networks.ipynb | mit | import numpy as np
import scipy as sp
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
import openpnm.models.geometry as gm
import openpnm.models.physics as pm
import openpnm.models.misc as mm
import matplotlib.pyplot as plt
np.set_printoptions(precision=4)
np.random.seed(10)
ws = op.Workspace()
ws.s... |
mguerrap/tydal | Module3_TidalCurrents.ipynb | mit | from IPython.display import Image
Image("Figures/EbbTideCurrent.jpg")
"""
Explanation: Module 3 Demo
What is happening under the sea surface?
Tidal Currents
A current is generated by a difference in the sea surface elevation between different points in space, which makes water move back and forth as the surface tilt c... |
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