repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
NORCatUofC/rain | flooding/FEMA and 311 Calls by Zip.ipynb | mit | fig, axs = plt.subplots(1,2)
plt.rcParams["figure.figsize"] = [15, 5]
fema_approved_zip_df[:20].plot(title='FEMA Data', ax=axs[0], kind='bar',x='zipCode',y='approvedForFemaAssistance')
flood_zip_sum[:20].plot(title='FOIA Data', ax=axs[1], kind='bar',x='Zip Code',y='Count Calls')
fema_flood_zip = pd.DataFrame()
fema_fl... |
materialsvirtuallab/matgenb | notebooks/2013-01-01-Basic functionality.ipynb | bsd-3-clause | import pymatgen.core as mg
"""
Explanation: Introduction
This notebook demostrates the core functionality of pymatgen, including the core objects representing Elements, Species, Lattices, and Structures.
Written using:
- pymatgen==2018.3.13
By convention, we import pymatgen as mg.
End of explanation
"""
si = mg.Ele... |
twschiller/frame-analysis | notebooks/Bitcoin Frame Analysis.ipynb | mit | import pandas as pd
from pandas.io import gbq
import matplotlib.pyplot as plt
import math
project_id = 'open-synthesis'
def make_rules(body):
return f"""
(REGEXP_CONTAINS({body}, "currency") or REGEXP_CONTAINS({body}, "medium of exchange")) as currency,
(REGEXP_CONTAINS({body}, "gold") and not REGEXP_CONTAINS({b... |
astarostin/MachineLearningSpecializationCoursera | course3/week4/CookingLDA_PA.ipynb | apache-2.0 | import json
with open("recipes.json") as f:
recipes = json.load(f)
print recipes[1]
"""
Explanation: Programming Assignment
Готовим LDA по рецептам
Как вы уже знаете, в тематическом моделировании делается предположение о том, что для определения тематики порядок слов в документе не важен; об этом гласит гипотеза... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 2 Supervised learning/Homework/10 1nn vs random forest/1NN против RandomForest.ipynb | mit | import numpy as np
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
"""
Explanation: 1NN против RandomForest
End of explanation
"""
#Loading digits dataset
digits = load_digits()
X = digits.data
y = digits.target
print(digits.DES... |
feststelltaste/software-analytics | notebooks/Calculating Indentation-based Complexity.ipynb | gpl-3.0 | import glob
file_list = glob.glob("../../linux/**/*.[c|h]", recursive=True)
file_list[:5]
"""
Explanation: Introduction
In this blog post, I want to show you a nice complexity metric that works for most major programming languages that we use for our software systems – the indentation-based complexity metric. Ad... |
AllenDowney/ProbablyOverthinkingIt | binomial.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
%precision 6
import matplotlib.pyplot as plt
import numpy as np
from inspect import getsourcelines
def show_code(func):
lines, _ = getsourcelines(func)
for line in lines:
print(line, end='')
"""
Explanation: The binomial distributi... |
lisitsyn/shogun | doc/ipython-notebooks/intro/Introduction.ipynb | bsd-3-clause | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
#To import all Shogun classes
from shogun import *
import shogun as sg
"""
Explanation: Machine Learning with Shogun
By Saurabh Mahindre - <a href="https://github.com/Saurabh7">github.com/Saurabh7</a> as a part of ... |
chengwliu/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb | mit | import pymc3 as pm
with pm.Model() as model:
parameter = pm.Exponential("poisson_param", 1)
data_generator = pm.Poisson("data_generator", parameter)
"""
Explanation: Chapter 2
Original content created by Cam Davidson-Pilon
Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twie... |
phoebe-project/phoebe2-docs | 2.1/tutorials/mpi.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
import phoebe
"""
Explanation: Advanced: Running PHOEBE in MPI
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation... |
fastai/fastai | nbs/examples/migrating_pytorch.ipynb | apache-2.0 | from migrating_pytorch import *
"""
Explanation: Tutorial - Migrating from pure PyTorch
Incrementally adding fastai goodness to your PyTorch models
We're going to use the MNIST training code from the official PyTorch examples, slightly reformatted for space, updated from AdaDelta to AdamW, and converted from a scrip... |
harishkrao/Python-for-Data-Analysis | Kaggle-US-Incomes/US Income Analysis Notebook.ipynb | mit | import pandas as pd
from pandas import DataFrame, Series
"""
Explanation: Analysis of U.S. Incomes by Occupation and Gender
Notebook by Harish Kesava Rao
Use of this dataset should cite the Bureau of Labor Statistics as per their copyright information: The Bureau of Labor Statistics (BLS) is a Federal government agenc... |
ajgpitch/qutip-notebooks | examples/landau-zener-stuckelberg.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
from qutip.ui.progressbar import TextProgressBar as ProgressBar
"""
Explanation: QuTiP example: Landau-Zener-Stuckelberg inteferometry
J.R. Johansson and P.D. Nation
For more information about QuTiP see http://qutip.org
End of... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/supplemental/labs/autoencoder.ipynb | apache-2.0 | import glob
import os
import time
import imageio
import matplotlib.pyplot as plt
import numpy as np
import PIL
import tensorflow as tf
from IPython import display
from tensorflow.keras import layers
"""
Explanation: Convolutional Autoencoder on MNIST dataset
Learning Objective
1. Build an autoencoder architecture (co... |
ddfabbro/ipython_tutorial | my_notebooks/facial_landmarks.ipynb | mit | import numpy as np #as always
import dlib #machine learning library
import matplotlib.pyplot as plt #to visualize things
from PIL import Image #to manipulate images
from urllib.request import urlretrieve #to download our dataset
from io import BytesIO # these libraries are used to unzip
from zipfile import Zip... |
RaspberryJamBe/ipython-notebooks | notebooks/en-gb/101 - Intro - Getting to know Python and using IPython.ipynb | cc0-1.0 | 5+11
"""
Explanation: Hm, Let's get started, shall we?
This application is called IPython and can be used to execute Python code (where Python is a programming, language; a way of explaining to a computer what you want it to do for you).
Select the cell with the sum below by clicking in it (a green border will appear ... |
scottquiring/Udacity_Deeplearning | 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 cod... |
mne-tools/mne-tools.github.io | 0.21/_downloads/f094864c4eeae2b4353a90789dd18b2b/plot_mixed_source_space_inverse.ipynb | bsd-3-clause | # Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
from nilearn import plotting
import mne
from mne.minimum_norm import make_inverse_operator, apply_inverse
# Set dir
data_path = mne.datasets.sample.data_path()
subject = 'sample'
... |
brettavedisian/phys202-2015-work | assignments/assignment10/ODEsEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
"""
Explanation: Ordinary Differential Equations Exercise 2
Imports
End of explanation
"""
def lorentz_derivs(yvec, t, sigma, rho, beta):
"""Compute the the de... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_mne_dspm_source_localization.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import (make_inverse_operator, apply_inverse,
write_inverse_operator)
# sphinx_gallery_thumbnail_number = 9
"""
Explanation: Source localization with MNE/dSPM/sLORETA
The ... |
d-k-b/udacity-deep-learning | intro-to-rnns/Anna_KaRNNa_Exercises.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is bas... |
whitead/numerical_stats | project/type1_examples/airlines.ipynb | gpl-3.0 | #loads the data from the spreadsheet
data_part1 = pd.read_excel('Flight Delays Part 1.xlsx')
FirstFourMonths = np.array(data_part1['Departure Delay (1-4, 2000)'][0:705]) #Sliced the array because pandas used cells that didn't have data in them
SecondFourMonths = np.array(data_part1['Departure Delay (5-8, 2000)'][0:850... |
balarsen/pymc_learning | pitch_angle/Cauchy1.ipynb | bsd-3-clause | # generate some data
with pm.Model() as model:
x = pm.Cauchy(name='x', alpha=0, beta=1)
trace = pm.sample(10000, njobs=4)
pm.traceplot(trace)
sampledat = trace['x']
trace.varnames, trace['x']
sns.distplot(sampledat, kde=False, norm_hist=True)
# plt.hist(sampledat, 200, normed=True);
plt.yscale('log');
np... |
LSSTDESC/Twinkles | examples/notebooks/postage_stamp_generation_inputs.ipynb | mit | import pandas as pd
from astropy.io import fits
import numpy as np
from desc.sims.GCRCatSimInterface import InstanceCatalogWriter
from lsst.sims.utils import SpecMap
import matplotlib.pyplot as plt
from lsst.utils import getPackageDir
from lsst.sims.photUtils import Sed, BandpassDict, Bandpass
from lsst.sims.catUtils.m... |
eds-uga/csci1360e-su17 | assignments/A10/A10_Q2.ipynb | mit | import sklearn.svm as svm
import numpy as np
np.random.seed(13775)
X = np.random.random((20, 2))
y = np.random.randint(2, size = 20)
m1 = train_svm(X, y, 100.0)
assert m1.C == 100.0
np.testing.assert_allclose(m1.coef_, np.array([[ 0.392707, -0.563687]]), rtol=1e-6)
import numpy as np
np.random.seed(598497)
X = np.ra... |
pikinder/nn-patterns | examples/all_methods.ipynb | mit | %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import os
import nn_patterns
import nn_patterns.utils.fileio
import nn_patterns.utils.tests.networks.imagenet
import lasagne
import theano
import imp
eutils = imp.load_source("utils", "./utils.py")
"""
Explanation: PatternNet and... |
telecombcn-dl/2017-cfis | sessions/dream.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib inline
from keras.applications import vgg16
from keras.layers import Input
from dream import *
"""
Explanation: Deep Dream
Deep Dream, or Inceptionism, was introduced by Google in this blogpost. Deep Dream is an algorithm that optimizes an input image so that it maximizes... |
oscarmore2/deep-learning-study | 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... |
rebeccabilbro/machine-learning | notebook/ML Class Session III.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
df = pd.read_csv('../data/energy/energy.csv')
df.shape
df.describe()
"""
Explanation: Energy Efficiency
What can you tell me about the data?
End of explanation
"""
import matplotlib.pyplot as plt
%matplotlib inline
from pandas.tools.plotting im... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/plots_boxplots.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: Box Plots
The following illustrates some options for the boxplot in statsmodels. These include violin_plot and bean_plot.
End of explanation
"""
data = sm.datasets.anes96.load_pandas()
party_ID = np.a... |
JoeriHermans/ml-scripts | scripts/adverserial-variational-optimization/avo-notebook.ipynb | gpl-3.0 | !date
"""
Explanation: Adverserial Variational Optimization
Gilles Louppe & Kayle Cranmer
Notebook by Joeri Hermans
End of explanation
"""
import numpy as np
import torch
import math
import matplotlib.mlab as mlab
import torch.nn.functional as F
import matplotlib.pyplot as plt
from torch.autograd import Variable
imp... |
enakai00/jupyter_ml4se_commentary | Solutions/01-Basic Calculations-solution.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import Series, DataFrame
"""
Explanation: 関数電卓として利用してみる
End of explanation
"""
data_x = np.linspace(0,1,10)
data_y = np.cos(2*np.pi*data_x)
plt.plot(data_x, data_y)
data_x = np.linspace(0,1,50)
data_y = np.cos(2*np.pi*data_x)
plt.plo... |
computational-class/computational-communication-2016 | code/04.PythonCrawlerGovernmentReport.ipynb | mit | import urllib2
from bs4 import BeautifulSoup
from IPython.display import display_html, HTML
HTML('<iframe src=http://www.hprc.org.cn/wxzl/wxysl/lczf/ width=1000 height=500></iframe>')
# the webpage we would like to crawl
"""
Explanation: 数据抓取：
抓取47年政府工作报告
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-co... |
ibmsoe/tensorflow | tensorflow/examples/tutorials/deepdream/deepdream.ipynb | apache-2.0 | # boilerplate code
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
"""
Explanation: DeepDreaming with TensorFlow
Loading and displaying the m... |
AstroHackWeek/AstroHackWeek2017 | day3/intermediate-docs.ipynb | mit | def do_something(arg1, arg2):
"""
A short sentence describing what this function does.
More description
Parameters
----------
arg1 : type1
Description of the parameter ``arg1``
arg2 : type2
Description of the parameter ``arg2``
Returns
-------
t... |
newworldnewlife/TensorFlow-Tutorials | 16_Reinforcement_Learning.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import gym
import numpy as np
import math
"""
Explanation: TensorFlow Tutorial #16
Reinforcement Learning (Q-Learning)
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
This tutorial is about so-called Reinforcement Learni... |
jorisvandenbossche/2015-EuroScipy-pandas-tutorial | solved - 01-pandas_introduction.ipynb | bsd-2-clause | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
pd.options.display.max_rows = 8
"""
Explanation: <!--<img width=700px; src="../img/logoUPSayPlusCDS_990.png"> -->
<p style="margin-top: 3em; margin-bottom: 2em;"><b><big><big><big><big>Introduction to Pandas</big></big></big></... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-1/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', 'test-institute-1', 'sandbox-2', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepp... |
tanghaibao/goatools | notebooks/relationships_change_dcnt_values.ipynb | bsd-2-clause | from goatools.base import get_godag
godag = get_godag("go-basic.obo", optional_attrs={'relationship'})
go_leafs = set(o.item_id for o in godag.values() if not o.children)
"""
Explanation: Adding optional relationships changes the dcnt value
SYNOPSIS: For GO:0019012, virion, the descendants count dcnt, is:
* 0 when u... |
fugufisch/hu_bp_python_course | 03_advanced/advanced_python_tricks.ipynb | mit | import time
t = time.localtime()
t.tm_mday
"""
Explanation: Retrospective
Interpreter
Basic Calculations
Data Structures
Control flow
Functions
Documentation
Packages
Packages are folders containing Modules. If you are lucky, these modules also work together in a way. The folder needs to contain an __init__.py file... |
ctuning/ck-math | script/explore-matrix-size-gemm-libs/explore-matrix-size-gemm-libs-analysis.ipynb | bsd-3-clause | repo_uoa = 'explore-matrix-size-gemm-libs-dvdt-prof-firefly-rk3399-001'
"""
Explanation: [PUBLIC] CLBlast vs ARM Compute Library on representative matrix sizes
Overview
Data [for developers]
Code [for developers]
Table
Plot
<a id="data"></a>
Get the experimental data
End of explanation
"""
import os
import sys
imp... |
paulovn/ml-vm-notebook | vmfiles/IPNB/Examples/a Basic/02 NumPy essentials.ipynb | bsd-3-clause | import numpy as np
"""
Explanation: NumPy essentials
NumPy is a Python library for manipulation of vectors and arrays. We import it just like any Python module:
End of explanation
"""
# From Python lists or iterators
n1 = np.array( [0,1,2,3,4,5,6] )
n2 = np.array( range(6) )
# Using numpy iterators
n3 = np.arange( 1... |
HSE-LaMBDA/modern-technologies-for-ml-and-big-data | lecture2/Sklearn_supervised_1.ipynb | mit | import numpy as np
import scipy
import sklearn
import matplotlib.pyplot as plt
%matplotlib inline
mnist = np.loadtxt("../data/mnist_train.csv", delimiter=",", skiprows=1)
X = mnist[:10000, 1:]
y = mnist[:10000, 0]
print X.shape
def plot_roc_auc(y_score, y_test):
from sklearn.metrics import roc_curve, auc
... |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-PR-raw-dir_ex_aa-fit-out-all-ph-27d.ipynb | mit | ph_sel_name = "all-ph"
data_id = "27d"
# ph_sel_name = "all-ph"
# data_id = "7d"
"""
Explanation: Executed: Mon Mar 27 11:37:43 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... |
AllenDowney/ModSim | soln/chap13.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
pedritomelenas/LMD | Naturales/Naturales.ipynb | mit | isinstance(4,int)
isinstance([3,4],int)
"""
Explanation: Números naturales. Inducción. Recursividad.
Naturales
En python podemos utilizar isinstance para determinar si un objeto es un entero
End of explanation
"""
def isnatural(n):
if not isinstance(n,int):
return false
return n>=0
isnatural(3)
is... |
maartenbreddels/ipyvolume | notebooks/demo-0.5.ipynb | mit | fig = ipv.figure()
vol_head = ipv.examples.head(max_shape=128);
vol_head.ray_steps = 800
"""
Explanation: We will render a low resolution scan of a head, which will display quite quickly (since the data size is small). If we want to see a higher resolution, we can zoom in.
End of explanation
"""
ds = ipv.datasets.aq... |
Upward-Spiral-Science/spect-team | Code/Assignment-9/SubjectSelectionExperiments.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... |
rasbt/algorithms_in_ipython_notebooks | ipython_nbs/data-structures/singly-linked-list.ipynb | gpl-3.0 | class SLLNode(object):
def __init__(self, data, next_node=None):
self.data = data
self.next_node = next_node
class SinglyLinkedList(object):
def __init__(self, head=None):
self.head = head
def __repr__(self):
s = ''
if self.head is not None:
curr... |
MIT-LCP/mimic-code-sharing | notebooks/emergency-department-exploration.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
from IPython.display import display, HTML # used to print out pretty pandas dataframes
import matplotlib.dates as dates
import matplotlib.lines as mlines
%matplotlib inline
plt.style.use('ggplot')
# specify user... |
ChrisRucker/Samples | CRUCK v1.ipynb | mit | pwd
import pandas as pd
df = pd.read_csv('data.csv')
df.tail()
"""
Explanation: <hr>
<p><center>CHRISTOPHER</center><br><font size="2"><center>ASSOCIATE DATA SCIENTIST</center></font>
<br><center>RUCKER</center></p>
<hr>
<p><font size="6"><em>-</em> LOADDATA <em>-</em></font></p>
End of explanation
"""
modelRati... |
amkatrutsa/MIPT-Opt | Spring2020/newton_quasi.ipynb | mit | import numpy as np
USE_COLAB = False
if USE_COLAB:
!pip install git+https://github.com/amkatrutsa/liboptpy
import liboptpy.unconstr_solvers as methods
import liboptpy.step_size as ss
n = 1000
m = 200
x0 = np.zeros((n,))
A = np.random.rand(n, m) * 10
"""
Explanation: Метод Ньютона
На прошлом семинаре...
... |
jseabold/statsmodels | examples/notebooks/interactions_anova.ipynb | bsd-3-clause | %matplotlib inline
from urllib.request import urlopen
import numpy as np
np.set_printoptions(precision=4, suppress=True)
import pandas as pd
pd.set_option("display.width", 100)
import matplotlib.pyplot as plt
from statsmodels.formula.api import ols
from statsmodels.graphics.api import interaction_plot, abline_plot
fr... |
4dsolutions/Python5 | Sieve of Eratosthenes.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo('V08g_lkKj6Q')
with open("primes_file.txt", "r") as primes:
output = []
for line in primes.readlines()[3:]: # skip first 4 lines
if line.strip() == 'end.':
break
for column in line.split():
num = int(column.strip())
... |
ES-DOC/esdoc-jupyterhub | notebooks/nuist/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nuist', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NUIST
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Propertie... |
aleph314/K2 | Getting and Cleaning Data/cleaning_exercises_all-regular.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
flights = pd.read_csv('flights/flights_sm_raw.csv')
airlines = pd.read_csv('flights/airlines.csv')
airports = pd.read_csv('flights/airports.csv')
f_names = [name.lower() for name in list(flights.columns)]
l_names = [name.lower() for name in list(airlines.columns)]
p_names = [nam... |
calroc/joypy | docs/0. This Implementation of Joy in Python.ipynb | gpl-3.0 | import inspect
import joy.utils.stack
print inspect.getdoc(joy.utils.stack)
"""
Explanation: Joypy
Joy in Python
This implementation is meant as a tool for exploring the programming model and method of Joy. Python seems like a great implementation language for Joy for several reasons.
We can lean on the Python immu... |
rflamary/POT | docs/source/auto_examples/plot_convolutional_barycenter.ipynb | mit | # Author: Nicolas Courty <ncourty@irisa.fr>
#
# License: MIT License
import numpy as np
import pylab as pl
import ot
"""
Explanation: Convolutional Wasserstein Barycenter example
This example is designed to illustrate how the Convolutional Wasserstein Barycenter
function of POT works.
End of explanation
"""
f1 = 1... |
StingraySoftware/notebooks | Spectral Timing/Spectral Timing Exploration.ipynb | mit | def load_and_cleanup_events(fname):
"""Load data and apply GTIs"""
events = EventList.read(fname)
lc = events.to_lc(dt=1)
lc.apply_gtis()
plt.figure()
plt.plot(lc.time, lc.counts)
new_gti = create_gti_from_condition(lc.time, lc.counts > 0, safe_interval=1)
lc.gti = new_gti
lc.apply_... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/solutions/python.BQ_explore_data.ipynb | apache-2.0 | # Run the chown command to change the ownership
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Install the Google Cloud BigQuery library
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: Exploratory Data Analysis Using Python and BigQuery
Learning Objectives
Analyze a Pandas Da... |
cloudmesh/book | notebooks/machinelearning/perceptronproblem.ipynb | apache-2.0 | # import our packages
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Write your Own Perceptron
In our examples, we have seen different algorithms and we could use scikit learn functions to get the paramters. However, do you know how is it implemented? To understand it, we c... |
gmaze/guillaumemaze | python/20190501-InverseModel.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: Demonstrate linear inverse model for the heat budget
Horizontal heat transports are non-linear terms if one assume that both temperatures and velocities have to be optimized. In order to keep the model as simple as possible, we hypothesized that only velocities require optimization.... |
flamingbear/ipython-notebooks | notebooks/nsidc0622-valid-ice-polygon-extensions.ipynb | mit | %matplotlib inline
from netCDF4 import Dataset
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
"""
Explanation: Describe the polygon extensions to the NIC climatology
This document demonstrates how we extend the possible ice regions for the nsidc-0622 valid-ice-masks.
From our documentatio... |
mitchshack/data_analysis_with_python_and_pandas | 2- IPython Notebooks and Raw Python Data Analysis/2-1 Raw Python - Maps.ipynb | apache-2.0 | from __future__ import print_function
x = range(0,10)
x
"""
Explanation: Raw Python - Maps
Mapping is basically mapping one value to another one, almost like a dictionary. This is a functional programming concept but can be useful in certain circumstances and will certainly come up in your data analysis career. This... |
melissawm/oceanobiopython | Notebooks/Aula_4.ipynb | gpl-3.0 | if (2>2):
pass
else:
print("Oi")
lista = [1,3,5,7,9]
with open("novo.txt", "w") as arquivo:
for item in lista:
arquivo.write("Elemento: {}\n".format(item))
"""
Explanation: Arquivos .csv/.xls
Relembrando um pouco da aula passada:
End of explanation
"""
import os
os.remove("novo.txt")
"""
Explan... |
MaxPowerWasTaken/MaxPowerWasTaken.github.io | jupyter_notebooks/Pandas Dont Apply _ Vectorize.ipynb | gpl-3.0 | import pandas as pd
df = pd.read_csv('datasets/quora_kaggle.csv')
df.head(3)
"""
Explanation: "You rarely want to use DataFrame.apply"
Tom Augspurger, one of the maintainers of Python's Pandas library for data analysis, has an awesome series of blog posts on writing idiomatic Pandas code. In fact you should probably ... |
mne-tools/mne-tools.github.io | stable/_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... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 4 Stats for data analysis/Homework/5 test student tests/Test Student tests.ipynb | mit | from __future__ import division
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from scipy import stats
from statsmodels.stats.weightstats import CompareMeans, DescrStatsW
... |
skidzo/pydy | examples/mass_spring_damper/mass_spring_damper.ipynb | bsd-3-clause | from IPython.display import SVG
SVG(filename='mass_spring_damper.svg')
"""
Explanation: Defining the Problem
Here we will derive the equations of motion for the classic mass-spring-damper system under the influence of gravity. The following figure gives a pictorial description of the problem.
End of explanation
"""
... |
alfkjartan/nvgimu | notebooks/Validation.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import nvg.ximu.ximudata as ximudata
%matplotlib notebook
"""
Explanation: Validation of IMU calculations using marker data
This notebook assumes that data exists in a database in the hdf5 format. For instructions how to set up the database with data see [../readme.md... |
MatteusDeloge/opengrid | notebooks/Demo_Units_and_Conversions.ipynb | apache-2.0 | import pandas as pd
import charts
from opengrid.library import misc, houseprint
"""
Explanation: This demo notebook shows how units are treated and how to apply unit conversions
Opengrid makes use of the python library pint for unit conversions
End of explanation
"""
hp = houseprint.Houseprint()
sensors = hp.search... |
Leguark/pynoddy | docs/notebooks/Test-Uncertainty-Analysis.ipynb | gpl-2.0 | reload(pynoddy.history)
reload(pynoddy.output)
reload(pynoddy.experiment.uncertainty_analysis)
reload(pynoddy)
from pynoddy.experiment.uncertainty_analysis import UncertaintyAnalysis
# the model itself is now part of the repository, in the examples directory:
history_file = os.path.join(repo_path, "examples/fold_dyk... |
gaufung/Data_Analytics_Learning_Note | python-statatics-tutorial/basic-theme/python-language/Regex.ipynb | mit | import re
m = re.match('foo', 'foo')
if m is not None: m.group()
m
m = re.match('foo', 'bar')
if m is not None: m.group()
re.match('foo', 'foo on the table').group()
# raise attributeError
re.match('bar', 'foo on the table').group()
"""
Explanation: 正则表达式
1 基础部分
管道符号（|）匹配多个正则表达式:
at | home 匹配 at,home
匹配任意单一字... |
albertfxwang/grizli | examples/Fitting-tools.ipynb | mit | %matplotlib inline
import glob
import time
import os
import numpy as np
import matplotlib.pyplot as plt
import astropy.io.fits as pyfits
import drizzlepac
import grizli
import grizli.stack
# Initialize the GroupFLT object we computed with WFC3IR_Reduction. When loaded from save files
# doesn't much matter what `r... |
diegocavalca/Studies | deep-learnining-specialization/4. Convolutional Neural Networks/resources/Convolution model - Step by Step - v1.ipynb | cc0-1.0 | import numpy as np
import h5py
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
%load_ext autoreload
%autoreload 2
np.random.seed(1)
"""
Explanation: Convolut... |
urgedata/pythondata | pyflux/Dynamic Linear Regression Models in Python.ipynb | mit | sales_df = pd.read_csv('../examples/retail_sales.csv', index_col='date', parse_dates=True)
sales_df.head()
"""
Explanation: Load the data
For this work, we're going to use the same retail sales data that we've used before. It can be found in the examples directory of this repository.
End of explanation
"""
sales_df... |
DoWhatILove/turtle | programming/python/notebooks/scikit/clustering/plot_segmentation_toy.ipynb | mit | print(__doc__)
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
l = ... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/mpi-esm-1-2-lr/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'mpi-esm-1-2-lr', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MPI-M
Source ID: MPI-ESM-1-2-LR
Topic: Ocnbgchem
Sub-Topics: Tracers. ... |
tritemio/multispot_paper | usALEX-5samples-PR-raw-AND-gate.ipynb | mit | # data_id = "7d"
"""
Explanation: usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
"""
from fretbursts import *
init_notebook()
from IPython.display import display
"""
Explanation: Load software and filenames ... |
gmaze/guillaumemaze | argo/index_file/demo_how_to_traverse_argoindex.ipynb | gpl-3.0 | import os
import pandas as pd
import numpy as np
from netCDF4 import Dataset, num2date
import multiprocessing
num_processes = multiprocessing.cpu_count()
# In[]:
def read_argoindex(index_file):
"""
Read the Argo detailled index txt file and return it as a Panda Dataframe
"""
return pd.read_csv(index_file,
... |
subhankarb/Machine-Learning-PlayGround | Machine-Learning-Specialization/machine_learning_classification/week1/module-2-linear-classifier-assignment.ipynb | apache-2.0 | from __future__ import division
import graphlab
import math
import string
"""
Explanation: Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you will use product review data from Amazon.... |
snegirigens/DLND | 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:]
text.split()[199:205]
"""
Explanation: TV Script Generation
In this project, you'll generate you... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 5 Data analysis applications/Homework/2 project wage forecast for Russia/wine.ipynb | mit | %pylab inline
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import warnings
from itertools import product
def invboxcox(y,lmbda):
if lmbda == 0:
return(np.exp(y))
else:
return(np.exp(np.log(lmbda*y+1)/lmbda))
wine = pd.read_csv('monthly-aust... |
phoebe-project/phoebe2-docs | 2.3/tutorials/building_a_system.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.Bundle()
"""
Explanation: Advanced: Building a System
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this l... |
rdhyee/nypl50 | build_ebooks_SecondFolio_Issue26.ipynb | apache-2.0 | from __future__ import print_function
from github_settings import (ry_username, ry_password,
username, password,
token,
GITENBERG_GITHUB_TOKEN,
GITENBERG_TRAVIS_ACCESS_TOKEN,
... |
P7h/FutureLearn__Learn_to_Code_for_Data_Analysis | Week#4/Week_4_exercises.ipynb | apache-2.0 | import sys
sys.version
import warnings
warnings.simplefilter('ignore', FutureWarning)
import matplotlib
matplotlib.rcParams['axes.grid'] = True # show gridlines by default
%matplotlib inline
from pandas import *
show_versions()
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Exercise-note... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n06_hyperparameter_tuning.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... |
Hyperparticle/deep-learning-foundation | lessons/autoencoder/Convolutional_Autoencoder_Solution.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
rishuatgithub/MLPy | torch/PYTORCH_NOTEBOOKS/04-RNN-Recurrent-Neural-Networks/03-RNN-Exercises-Solutions.ipynb | apache-2.0 | # RUN THIS CELL
import torch
import torch.nn as nn
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
df = pd.read_csv('../Data/TimeSeriesD... |
GuillaumeDec/machine-learning | deep-lstm-rnn-anomaly-detector/rnn_cloudmle.ipynb | gpl-3.0 | !pip install --upgrade tensorflow
import tensorflow as tf
print tf.__version__
import numpy as np
import tensorflow as tf
import seaborn as sns
import pandas as pd
SEQ_LEN = 10
def create_time_series():
freq = (np.random.random()*0.5) + 0.1 # 0.1 to 0.6
ampl = np.random.random() + 0.5 # 0.5 to 1.5
x = np.sin... |
keshr3106/ThinkStats2 | code/chap03ex.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
import chap01soln
resp = chap01soln.ReadFemResp()
"""
Explanation: Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>
Allen Downey
Read the female respondent file.
End of explanation
"""
resp_numkdhh = resp.... |
SylvainCorlay/bqplot | examples/Marks/Pyplot/Image.ipynb | apache-2.0 | import os
import ipywidgets as widgets
import bqplot.pyplot as plt
from bqplot import LinearScale
image_path = os.path.abspath('../../data_files/trees.jpg')
with open(image_path, 'rb') as f:
raw_image = f.read()
ipyimage = widgets.Image(value=raw_image, format='jpg')
ipyimage
"""
Explanation: The Image Mark
Ima... |
ChileanVirtualObservatory/DISPLAY | src/experiments/DISPLAY - 2011.0.00419.S 13CH3CN19-18.ipynb | gpl-3.0 | file_path = '../data/2011.0.00419.S/sg_ouss_id/group_ouss_id/member_ouss_2013-03-06_id/product/IRAS16547-4247_Jet_13CH3CN19-18.clean.fits'
noise_pixel = (15, 4)
train_pixels = [(135, 135), (135, 136), (136, 135), (136, 136)]
img = fits.open(file_path)
meta = img[0].data
hdr = img[0].header
# V axis
naxisv = hdr['NAX... |
metpy/MetPy | dev/_downloads/0c4dbfdebeb6fcd2f5364a69f0c6d4a8/Skew-T_Layout.ipynb | bsd-3-clause | import matplotlib.gridspec as gridspec
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, Hodograph, SkewT
from metpy.units import units
"""
Explanation: Skew-T with Complex Layout
Combine a Skew-T and a hodogra... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/06_structured/3_keras_wd.ipynb | apache-2.0 | # change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; then
gsutil mb -l ${REGION} gs://$... |
huiyi1990/maths-with-python | 03-loops-control-flow.ipynb | mit | from math import pi
def degrees_to_radians(theta_d):
"""
Convert an angle from degrees to radians.
Parameters
----------
theta_d : float
The angle in degrees.
Returns
-------
theta_r : float
The angle in radians.
"""
theta_r = pi / 180.0 *... |
davicsilva/dsintensive | notebooks/eda-miniprojects/racial_disc/sliderule_dsi_inferential_statistics_exercise_2.ipynb | apache-2.0 | import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Examining Racial Discrimination in the US Job Market
Background
Racial discrimination continues to be pervasive in cultures throughout the world. Researchers examined... |
abhinavsingh/proxy.py | tutorial/connections.ipynb | bsd-3-clause | from proxy.core.connection import TcpServerConnection
from proxy.common.utils import build_http_request
from proxy.http.methods import httpMethods
from proxy.http.parser import HttpParser, httpParserTypes
request = build_http_request(
method=httpMethods.GET,
url=b'/',
headers={
b'Host': b'jaxl.com'... |
jdhp-docs/python_notebooks | nb_dev_python/python_scipy_integrate.ipynb | mit | f = lambda x: np.power(x, 2)
result = scipy.integrate.quad(f, 0, 3)
result
"""
Explanation: https://docs.scipy.org/doc/scipy-1.3.0/reference/tutorial/integrate.html
https://docs.scipy.org/doc/scipy-1.3.0/reference/integrate.html
Integrating functions, given callable object (scipy.integrate.quad)
See:
- https://docs.... |
flaviocordova/udacity_deep_learn_project | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
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