repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
vikasgorur/notebooks | deep-learning/1_notmnist.ipynb | mit | # Config the matlotlib backend as plotting inline in IPython
%matplotlib inline
# 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... |
robcarver17/pysystemtrade | docs/notebooks/introduction_with_fxdata.ipynb | gpl-3.0 | from sysbrokers.IB.ib_connection import connectionIB
from sysbrokers.IB.ib_Fx_prices_data import ibFxPricesData
from ib_insync import util
util.startLoop() #only required when running inside a notebook
"""
Explanation: Modified introduction using forex data
This is the trading rule example shown in the introduction b... |
GoogleCloudPlatform/public-datasets-pipelines | datasets/gbif/docs/tutorials/biodiversity_map/australia.ipynb | apache-2.0 | %%capture
# Installing the required libraries:
!pip install matplotlib pandas scikit-learn tensorflow pyarrow tqdm
!pip install google-cloud-bigquery google-cloud-bigquery-storage
!pip install flake8 pycodestyle pycodestyle_magic geopandas
# Python Builtin Libraries
# Third Party Libraries
import numpy as np
import ... |
nimagh/MachineLearning | DimenstionalityReduction/DimensionalityReduction.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from scipy.io import loadmat
from scipy.stats import multivariate_normal
from scipy.linalg import det
from scipy.linalg import pinv2 as inv #pinv uses linalg.lstsq algorithm while pinv2 uses SVD
from scipy.linalg import sqrtm
from... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/robust_models_0.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
"""
Explanation: Robust Linear Models
End of explanation
"""
data = sm.datasets.stackloss.load(as_pandas=False)
data.exog = sm.add_constant(data.ex... |
metpy/MetPy | v0.6/_downloads/isentropic_example.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from netCDF4 import Dataset, num2date
import numpy as np
import metpy.calc as mcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo
from metpy.units import units
"""
Explanation: Isentropic Analy... |
4DGenome/Chromosomal-Conformation-Course | Notebooks/A1-Preparation_reference_genome.ipynb | gpl-3.0 | species = 'Homo sapiens'
taxid = '9606'
genome = 'GRCh38.p10'
refseq, dir1, dir2, dir3 = 'GCF', '000', '001', '405'
genbank = 'GCF_000001405.36'
sumurl = 'ftp://ftp.ncbi.nlm.nih.gov/genomes/all/{0}/{1}/{2}/{3}/{4}_{5}/{4}_{5}_assembly_report.txt'.format(
refseq, dir1, dir2, dir3, genbank, genome)
crmurl = 'ht... |
tensorflow/neural-structured-learning | research/third_party/denoised_smoothing/notebooks/Train_Denoiser.ipynb | apache-2.0 | !git clone https://github.com/sayakpaul/neural-structured-learning
!cd neural-structured-learning/research/denoised_smoothing && tar xf models.tar.gz
import sys
sys.path.append("neural-structured-learning/research/denoised_smoothing")
from models import dncnn
from tensorflow.keras import layers
import tensorflow as ... |
dpiparo/swanExamples | notebooks/LHCb_OpenData_Spark.ipynb | lgpl-2.1 | # This starts the Spark Session
# Note: Shift-Enter can be used to run Jupyter cells
# These instructions rely on internet access to download the spark-root package from Maven Central
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("LHCb Open Data with Spark") \
.config("s... |
mit-crpg/openmc | examples/jupyter/mgxs-part-i.ipynb | mit | from IPython.display import Image
Image(filename='images/mgxs.png', width=350)
"""
Explanation: Multigroup Cross Section Generation Part I: Introduction
This IPython Notebook introduces the use of the openmc.mgxs module to calculate multi-group cross sections for an infinite homogeneous medium. In particular, this Not... |
whitead/numerical_stats | unit_10/hw_2016/homework_9.ipynb | gpl-3.0 | from math import erf, sqrt
import numpy as np
import scipy.stats
"""
Explanation: Homework 9
CHE 116: Numerical Methods and Statistics
Prof. Andrew White
Version 1.0 (3/23/2016)
End of explanation
"""
data_1 = [3.05, 3.01, 3.20, 3.16, 3.11, 3.09]
data_2 = [3.18, 3.23, 3.19, 3.28, 3.08, 3.18]
"""
Explanation: Genera... |
certik/chess | examples_manual/Convergence2.ipynb | mit | %pylab inline
! grep "multipv 1" log3.txt | grep -v lowerbound | grep -v upperbound > log3_g.txt
def parse_info(l):
D = {}
k = l.split()
i = 0
assert k[i] == "info"
i += 1
while i < len(k):
if k[i] == "depth":
D[k[i]] = int(k[i+1])
i += 2
elif k[i] == "... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_home/2021_covid_pickle.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%load_ext pyensae
"""
Explanation: Sérialisation, pickle, COVID
Ce notebook aborde les sujets évoqués dans le titre.
End of explanation
"""
import pandas
import numpy
mat = numpy.random.randn(10000, 50)
df = pandas.DataFrame(mat)
df.shape
df.size
fo... |
tholoien/empiriciSN | Notebooks/Demo.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
import corner
import urllib
import os
from sklearn.cross_validation import train_test_split
from astroML.plotting import setup_text_plots
import empiriciSN
from demo_funcs import *
"""
Explanation: Empirical Supernova Simulation Demo
Tom Holoi... |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/30_DCGAN_Batch_Normalisation/notebooks/Batch_Normalization_Solutions.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Solutions
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a co... |
facebook/prophet | notebooks/outliers.ipynb | mit | %%R -w 10 -h 6 -u in
df <- read.csv('../examples/example_wp_log_R_outliers1.csv')
m <- prophet(df)
future <- make_future_dataframe(m, periods = 1096)
forecast <- predict(m, future)
plot(m, forecast)
df = pd.read_csv('../examples/example_wp_log_R_outliers1.csv')
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(... |
google/jax-cfd | notebooks/ml_model_inference_demo.ipynb | apache-2.0 | #@title Paths to evaluation datasets
base_path = '/content/'
kolmogorov_re_1000 = {
f'baseline_{i}x{i}': os.path.join(base_path, f'eval_{i}x{i}_64x64.nc')
for i in [64, 128, 256, 512, 1024, 2048]
}
decaying = {
f'baseline_{i}x{i}': os.path.join(base_path, f'eval_{i}x{i}_64x64.nc')
for i in [64, 128, 2... |
Diyago/Machine-Learning-scripts | time series regression/DL aproach for timeseries/Air Pressure LSTM.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
#Read the dataset into a pandas.DataFrame
df = pd.read_csv('datasets/PRSA_data_2010.1.1-2014.12.31.csv')
print('Shape of the d... |
Santana9937/Regression_ML_Specialization | Week_4_Ridge_Regression/assign_1_ridge-regression.ipynb | mit | import os
import zipfile
import numpy as np
import pandas as pd
from sklearn import linear_model
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
"""
Explanation: Regression Week 4: Ridge Regression (interpretation)
In this notebook, we will ru... |
vishaalprasad/AnimeRecommendation | notebooks/exploration/using_pretrained_cnn.ipynb | mit | import numpy as np
import tensorflow as tf
import os.path as osp
"""
Explanation: This notebook is an attempt to test and prototype using a pretrained tensorflow CNN to do classification. The end goal is to be able to take add the following as an anime-specific feature: a CNN's embeddings of the default image on MyAni... |
Benedicto/ML-Learning | Classifier_3_decision_tree_assignment_1.ipynb | gpl-3.0 | import graphlab
graphlab.canvas.set_target('ipynb')
"""
Explanation: Identifying safe loans with decision trees
The LendingClub is a peer-to-peer leading company that directly connects borrowers and potential lenders/investors. In this notebook, you will build a classification model to predict whether or not a loan pr... |
2php/CodeToolKit | 9.caffe-ssd/examples/ssd_detect.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# Make sure that caffe is on the python path:
caffe_root = '../' # this file is expected to be in {caffe_root}/examples
i... |
turbomanage/training-data-analyst | bootcamps/imagereco/fashion_cnn_batch_dropout_lab.ipynb | apache-2.0 | import os
PROJECT = 'my-project-id' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'my-bucket-name' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_TYPE='cnn_batch_norm' # 'cnn_batch_norm' or 'dnn' or 'cnn'
# do not change these
os.environ['PROJECT'] = PROJECT... |
tien-le/uranus | 04_model_training.ipynb | mit | from IPython.display import IFrame
IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)
"""
Explanation: Training a machine learning model with scikit-learn
From the video series: Introduction to machine learning with scikit-learn
Agenda
What is the K-nearest neighb... |
fullmetalfelix/ML-CSC-tutorial | ACSF.ipynb | gpl-3.0 | # --- INITIAL DEFINITIONS ---
from sklearn.neural_network import MLPRegressor
import numpy, math, random
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from visualise import view
from ase import Atoms
# parameters for the trimer
d1 = 1.0
d2 = 1.0
theta = 30
# convert angle to radians
theta *= ... |
Intel-Corporation/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_image_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... |
dataplumber/nexus | esip-workshop/other-notebooks/Nexus Deployment and Ingestion.ipynb | apache-2.0 | cd ~/nexus/esip-workshop/docker/infrastructure
docker-compose up -d cassandra1
docker logs -f cassandra1
"""
Explanation: Step 1: Sign up / get assigned EC2 instance <br>
Step 2: SSH into EC2 instance <br>
Step 3: Start up the cluster by running the following commands <br>
End of explanation
"""
docker-compose up -d... |
xMyrst/BigData | python/howto/008_Ficheros.ipynb | gpl-3.0 | %pwd
fichero = open("../datos/cuna.txt")
"""
Explanation: FICHEROS
En Python, para abrir un fichero usaremos la función open, que recibe el nombre del archivo a abrir. Por defecto, si no indicamos nada, el fichero se abre en modo lectura.
OPEN: MODO LECTURA
End of explanation
"""
ls "../datos"
fichero= open("../da... |
xiongzhenggang/xiongzhenggang.github.io | AI/ML/week2逻辑回归代码实现.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 中文乱码的处理
plt.rcParams['font.sans-serif'] =['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False
# 创建一些列数据
#随便创建一些列合适的点,然后画图观察
data = pd.DataFrame({'Exam1':np.concatenate((np.random.uniform(30,60,20),np.random.uniform(60,80,20))),'Exam2':n... |
Faris137/MachineLearningArabic | house sales project/kc_house_data/house sales project.ipynb | mit | import numpy as np
import pandas as pd
import seaborn as sb
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from sklearn.metrics import classification_report
%matplotlib inline
df = pd.read_csv("kc_h... |
janmtl/drift_qec | FullTomographyTrials.ipynb | isc | D = 0.01
N_ERRORS = 1e6
N_TRIALS = 100
N_CYCLES = np.logspace(1, 3, 10).astype(np.int)
RECORDS = []
for trial in tqdm(range(N_TRIALS)):
for n_cycles in N_CYCLES:
n = int(N_ERRORS / n_cycles)
channel = Channel(kx=0.7, ky=0.2, kz=0.1,
Q=np.linalg.qr(np.random.randn(3,3))[0],
... |
xMyrst/BigData | python/howto/004_Estructuras de datos.ipynb | gpl-3.0 | # Ejemplo de lista, los valores van entre corchetes
una_lista = [4, "Hola", 6.0, 99 ]
# Ejemplo de tupla, los valores van entre paréntesis
una_tupla = (4, "Hola", 6.0, 99)
print ("Lista: " , una_lista)
print ("Tupla: " , una_tupla)
# Las tuplas y las listas aceptan operadores de comparación y devuelven un booleano
pri... |
gangadhara691/gangadhara691.github.io | P6 data_viz/data/.ipynb_checkpoints/P2-checkpoint.ipynb | mit | import numpy as np
import pandas as pd
from IPython.display import display
%matplotlib inline
# Load the dataset
files = "titanic_data.csv"
data_titanic = pd.read_csv(files)
display(data_titanic.head())
"""
Explanation: Titanic Data Analysis By Gangadhara Naga Sai
Overview Of Titanic Dataset
In 1912, the ship RMS Ti... |
llondon6/kerr_public | notes/ns/notebooks/test_phast_shift_mmrdns_gwf.ipynb | mit | # Setup ipython environment
%load_ext autoreload
%autoreload 2
%matplotlib inline
# Import Useful things
from kerr import mmrdns
from numpy import pi
# Setup plotting backend
# ---
## MPL
import matplotlib as mpl
mpl.rcParams['lines.linewidth'] = 0.8
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['font.size'] = ... |
NYUDataBootcamp/Projects | MBA_S17/Burrell-Birth Time Frame QB Success.ipynb | mit | # import packages
import pandas as pd # data management
import matplotlib.pyplot as plt # graphics
import urllib
import numpy as np
# IPython command, puts plots in notebook
%matplotlib inline
# check Python version
import datetime as dt
import sys
#versino 2.7
#print('Today is', dt.date.t... |
stephenpardy/PythonNotebooks | astro/IntroPythonNotebook.ipynb | gpl-2.0 |
# This line is a comment -- it does nothing
# you can add comments using the '#' symbol
"""
Explanation: Intro to python
Basic commands
Hello and welcome to the wonderful world of Python. Each of these cells can be copy and pasted into your own notebook. There are code cells and text cells. The code cells execute Pyt... |
trangel/Data-Science | deep_learning_ai/Autonomous+driving+application+-+Car+detection+-+v3.ipynb | gpl-3.0 | import argparse
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy as np
import pandas as pd
import PIL
import tensorflow as tf
from keras import backend as K
from keras.layers import Input, Lambda, Conv2D
from keras.models import load_model, Mo... |
Danghor/Formal-Languages | Python/Equivalence.ipynb | gpl-2.0 | %run Regexp-2-NFA.ipynb
"""
Explanation: Checking the Equivalence of Regular Expressions
In order to check whether two regular expressions $r_1$ and $r_2$ are equivalent, perform the
following steps:
- convert $r_1$ and $r_2$ into non-deterministic <span style="font-variant:small-caps;">Fsm</span>s
$F_1$ and $F_2$ ... |
basnijholt/holoviews | examples/user_guide/17-Dashboards.ipynb | bsd-3-clause | import pandas as pd
import holoviews as hv
from bokeh.sampledata import stocks
from holoviews.operation.timeseries import rolling, rolling_outlier_std
hv.extension('bokeh')
"""
Explanation: Creating interactive dashboards
End of explanation
"""
def load_symbol(symbol, variable='adj_close', **kwargs):
df = pd.D... |
johnyquest7/Breast-Cancer-Machine-Learning-Algorithm | Breast cancer predictor.ipynb | mit | # Importing pandas to read CSV file
import pandas as pd
# Read breast cancer csv file to pandas data frame data
data = pd.read_csv('wisconsin_breast_cancer.csv')
# Display the first 5 rows of the csv file
data.head()
data.shape # It is always a good idea to understand your data
# There are 699 rows and 11 columns ... |
georgetown-analytics/envirohealth | Capstone Data Exploration.ipynb | mit | %matplotlib inline
import os
import json
import time
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.tools.plotting import scatter_matrix, radviz, parallel_coordinates
"""
Explanation: This notebook explores a sample of the SEER breast cancer data
First we import the n... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ8 legacy AutoML Natural Language Text Sentiment Analysis.ipynb | apache-2.0 | ! pip3 install google-cloud-automl
"""
Explanation: AutoML text sentiment analysis
Installation
Install the latest version of AutoML SDK.
End of explanation
"""
! pip3 install google-cloud-storage
"""
Explanation: Install the Google cloud-storage library as well.
End of explanation
"""
import os
if not os.getenv... |
markroxor/gensim | docs/notebooks/Poincare Evaluation.ipynb | lgpl-2.1 | % cd ../..
% cd docs/notebooks/
import csv
from collections import OrderedDict
import logging
import os
import pickle
import random
import re
import click
from gensim.models.poincare import PoincareModel, PoincareRelations, \
ReconstructionEvaluation, LinkPredictionEvaluation, \
LexicalEntailmentEvaluation, ... |
deepchem/deepchem | examples/tutorials/Molecular_Fingerprints.ipynb | mit | !pip install --pre deepchem
"""
Explanation: Molecular Fingerprints
Molecules can be represented in many ways. This tutorial introduces a type of representation called a "molecular fingerprint". It is a very simple representation that often works well for small drug-like molecules.
Colab
This tutorial and the rest i... |
karlstroetmann/Formal-Languages | Ply/Conflicts-Resolved.ipynb | gpl-2.0 | import ply.lex as lex
tokens = [ 'NUMBER' ]
def t_NUMBER(t):
r'0|[1-9][0-9]*'
t.value = int(t.value)
return t
literals = ['+', '-', '*', '/', '^', '(', ')']
t_ignore = ' \t'
def t_newline(t):
r'\n+'
t.lexer.lineno += t.value.count('\n')
def t_error(t):
print(f"Illegal character '{t.value[... |
idealabasu/code_pynamics | python/pynamics_examples/triple_pendulum.ipynb | mit | %matplotlib inline
"""
Explanation: title: Triple Pendulum Example
type: submodule
End of explanation
"""
use_constraints = False
"""
Explanation: Try running with this variable set to true and to false and see the difference in the resulting equations of motion
End of explanation
"""
# -*- coding: utf-8 -*-
"""
... |
JorgeDeLosSantos/nusa | docs/nusa-info/es/beam-element.ipynb | mit | %matplotlib inline
import numpy as np
from nusa import *
import itertools
import matplotlib.pyplot as plt
def pairwise(iterable):
#~ "s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = itertools.tee(iterable)
next(b, None)
return zip(a, b)
# Input data
E = 210e9 # Pa
I = 1e-5
L = 1
P = 10e3
nelm = 10
part... |
DistrictDataLabs/yellowbrick | examples/pdamodaran/feature_visualizer.ipynb | apache-2.0 | import os
import sys
# Modify the path
sys.path.append("..")
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
g = yb.anscombe()
"""
Explanation: Feature Visualizer
This notebook provides examples of visualizations done in other data studies and modifies them using the Yellowbrick libr... |
mbeyeler/opencv-machine-learning | notebooks/10.01-Understanding-Ensemble-Methods.ipynb | mit | from sklearn.ensemble import BaggingClassifier
from sklearn.neighbors import KNeighborsClassifier
bagging = BaggingClassifier(KNeighborsClassifier(), n_estimators=10)
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><... |
TimothyADavis/KinMSpy | kinms/docs/KinMS_example_notebook.ipynb | mit | from kinms import KinMS
"""
Explanation: <img style="float:top,right" src="Logo.png">
<br><br>
Welcome to the KinMS introduction
<br><br>
Here you will learn how to import and use KinMS to generate mock interferometric data cubes and gain a better understanding of using the functionalities within the package.
Copyrig... |
xianjunzhengbackup/code | python/read_pdf.ipynb | mit | def init_pdf_from_file(path):
fp=open(path,'rb')
pdfReader=PyPDF2.PdfFileReader(fp)
return pdfReader
"""
Explanation: 该方法对可以读取PDF文件，它是按行读取，如果每一行中有缺失掉VALUE，那么如何判断这些VALUE是个大问题。
在parse_items函数中碰到的就是这个问题。
End of explanation
"""
def extract_info_from_page(pdf,num_of_page,pattern):
content=pdf.getPage(num_... |
mckinziebrandon/DeepChatModels | notebooks/Analysis.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = 10, 8
BASE = '/home/brandon/Documents/seq2seq_projects/data/saved_train_data/'
path = BASE + 'cornell_03_11.csv'
df = pd.read_csv(path, index_col=0)
d... |
smharper/openmc | examples/jupyter/mg-mode-part-i.ipynb | mit | import os
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
import openmc
%matplotlib inline
"""
Explanation: This Notebook illustrates the usage of OpenMC's multi-group calculational mode with the Python API. This example notebook creates and executes the 2-D C5G7 benchmark mode... |
jbogaardt/chainladder-python | docs/tutorials/large-datasets.ipynb | mit | import chainladder as cl
import numpy as np
import pandas as pd
%matplotlib inline
print('chainladder:' + cl.__version__)
print('pandas:' + pd.__version__)
"""
Explanation: Working with Large Datasets
Getting started
All exercises rely on chainladder v0.7.5 and later. There have also been breaking changes with pandas... |
PyDataMadrid2016/Conference-Info | talks_materials/20160409_1130_Embrace_conda_packages/Embrace conda packages.ipynb | mit | !conda install -y conda-build -q -n root
"""
Explanation: Embrace conda packages
The build system we always needed, but never deserved
Juan Luis Cano Rodríguez
Madrid, 2016-04-08
Outline
Introduction
Motivation: What brought us here?
Our first conda package
Some more tricks
Working with other languages
conda-forge: a... |
cleuton/datascience | datavisualization/data_visualization_python_2.ipynb | apache-2.0 | import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d, Axes3D # Objetos que usaremos em nosso gráfico
%matplotlib inline
df = pd.read_csv('../datasets/evasao.csv') # Dados de evasão escolar que coletei
df.head()
"""
Explanation: Visualização de dados com Python
2 - Visualizações c... |
GoogleCloudPlatform/ai-platform-samples | notebooks/samples/tensorflow/prediction_auto-scaling/ai_platform_prediction_auto-scaling.ipynb | apache-2.0 | # Change these parameters!
PROJECT = 'YOUR-PROJECT-ID' # Update with your project
BUCKET = 'gs://YOUR-BUCKET-NAME' # Update with your bucket
REGION = 'us-central1' # Update with your region
# These parameters don't need to be changed
MODULE_URL = "https://tfhub.dev/google/universal-sentence-encoder-large/5"
MODEL... |
darkomen/TFG | medidas/04082015/estudio.datos.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv con los datos... |
rickiepark/openbidder | notebook/pb_server_test.ipynb | mit | from http.server import BaseHTTPRequestHandler, HTTPServer
class HTTPRequestHandler(BaseHTTPRequestHandler):
def do_POST(self):
length = self.headers['content-length']
data = self.rfile.read(int(length))
with open('sample_message.txt', 'wb') as f:
f.write(data)
print(dat... |
apozas/BIST-Python-Bootcamp | 6_Optimization_Integration_ODEs.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()
"""
Explanation: 6 Optimizing Parametric Functions, Integration, and ODEs
We will cover two things today:
Numerical integration of ODE systems
Numerical optimization
First of all we include some basic packages to ... |
usantamaria/iwi131 | ipynb/21-EjerciciosDeCertamen/Certamen2_2015_1S_CC.ipynb | cc0-1.0 | # n numero entero
def f(n):
l = []
for i in range(len(str(n))-1,-1,-1):
x = n/(10**i)
l.append(x)
n=n %(10**i)
return l
f(421)
"""
Explanation: <header class="w3-container w3-teal">
<img src="images/utfsm.png" alt="" align="left"/>
<img src="images/inf.png" alt="" align="right"/>
<... |
balarsen/pymc_learning | Logit/Boyd Logit 1.ipynb | bsd-3-clause | # http://onlinelibrary.wiley.com/doi/10.1002/2016JA022652/epdf
import pymc3
from pprint import pprint
import numpy as np
import matplotlib.pyplot as plt
import spacepy.plot as spp
import seaborn as sns
import tqdm
sns.set(font_scale=1.5)
"""
Explanation: What is the probability that core PSD rises by >2x as a functi... |
cgivre/oreilly-sec-ds-fundamentals | Notebooks/EDA/EDA Worksheet - Python Version - Answers.ipynb | apache-2.0 | data = pd.read_csv( '../../data/dailybots.csv' )
#Look at a summary of the data
data.describe()
data['botfam'].value_counts()
"""
Explanation: First, load up the data
First you're going to want to create a data frame from the dailybots.csv file which can be found in the data directory. You should be able to do this ... |
ODM2/ODM2PythonAPI | Examples/TimeSeries_RetrieveVisualize.ipynb | bsd-3-clause | import os
import datetime
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import odm2api
from odm2api.ODMconnection import dbconnection
import odm2api.services.readService as odm2rs
"{} UTC".format(datetime.datetime.utcnow())
pd.__version__
"""
Explanation: ODM2 API... |
John-Keating/ThinkStats2 | code/.ipynb_checkpoints/chap03ex-checkpoint.ipynb | gpl-3.0 | %matplotlib inline
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
"""
import thinkstats2 as ts
children_PMF = ts.Pmf(resp.numkdhh)
"""
Explanation: Make a PMF of <tt>nu... |
NiceCircuits/solderingPen | doc/design.ipynb | cc0-1.0 | #Initialization of iPython, some helper functions.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('ggplot')
from sympy import *
from IPython.display import display, Math, Latex
init_printing(use_latex="mathjax")
_Omega=u'\u03A9'
def readCsvData(fileName):
r... |
ARM-software/lisa | ipynb/deprecated/examples/android/workloads/Android_Gmaps.ipynb | apache-2.0 | from conf import LisaLogging
LisaLogging.setup()
%pylab inline
import json
import os
# Support to access the remote target
import devlib
from env import TestEnv
# Import support for Android devices
from android import Screen, Workload
# Support for trace events analysis
from trace import Trace
# Suport for FTrace... |
ZwickyTransientFacility/simsurvey-examples | simsurvey_demo.ipynb | bsd-3-clause | import os
home_dir = os.getcwd()
# Please enter the path to where you have placed the Schlegel, Finkbeiner & Davis (1998) dust map files
# You can also set the environment variable SFD_DIR to this path (in that case the variable below should be None)
sfd98_dir = os.path.join(home_dir, 'data/sfd98')
import simsurvey
i... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_dics_beamformer.ipynb | bsd-3-clause | # Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
import matplotlib.pyplot as plt
import numpy as np
from mne.datasets import sample
from mne.time_frequency import compute_epochs_csd
from mne.beamformer import dics
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path +... |
sthuggins/phys202-2015-work | assignments/assignment05/.ipynb_checkpoints/InteractEx04-checkpoint.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... |
lorgor/vulnmine | vulnmine/ipynb/170525-train-sft-match.ipynb | gpl-3.0 | # Initialize
import pandas as pd
import numpy as np
try:
df_label_software = pd.io.parsers.read_csv(
"/home/jovyan/work/shared/data/csv/label_software.csv",
error_bad_lines=False,
warn_bad_lines=True,
q... |
IST256/learn-python | content/lessons/04-Iterations/SmallGroup-Iterations.ipynb | mit | # PROMPT 4
"""
Explanation: Now You Code In Class: Gathering Data and Reporting Statistics
For this in-class exercise we demonstrate a common programming pattern for reporting statistics on data in real time as it is input. This is used for data collection and real time analyics of time-series data.
In this example w... |
johngage/Kibera-projects | content/features/notebooks.ipynb | mit | from matplotlib import rcParams, cycler
import matplotlib.pyplot as plt
import numpy as np
plt.ion()
# Fixing random state for reproducibility
np.random.seed(19680801)
N = 10
data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]
data = np.array(data).T
cmap = plt.cm.coolwarm
rcParams['axes.pr... |
victorgcapone/data-science-portifolio | Monte Carlos Experiments/Monte Carlo Demonstration.ipynb | gpl-3.0 | #First, lets plot a circle, centered arround 0 with a radius of 1
plot_circle()
"""
Explanation: The Monte Carlo method works by using random points to estimate ratios and values, the most basic example, shown here, is to calculate the value of $\pi$ by comparing the ratio of points in a circle with ones in the inscri... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/pcmdi-test-1-0/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'pcmdi-test-1-0', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: PCMDI
Source ID: PCMDI-TEST-1-0
Sub-Topics: Radiative Forcings.
Propert... |
ITAM-DS/analisis-numerico-computo-cientifico | libro_optimizacion/temas/5.optimizacion_de_codigo/5.3/Compilacion_a_C.ipynb | apache-2.0 | %%bash
lscpu
%%bash
sudo lshw -C memory
%%bash
uname -ar #r for kernel, a for all
"""
Explanation: (COMPC)=
5.3 Compilación a C
```{admonition} Notas para contenedor de docker:
Comando de docker para ejecución de la nota de forma local:
nota: cambiar <ruta a mi directorio> por la ruta de directorio que se dese... |
tdhopper/notes-on-dirichlet-processes | pages/2015-09-02-fitting-a-mixture-model.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from scipy import stats
from collections import namedtuple, Counter
"""
Explanation: Fitting a Mixture Model with Gibbs Sampling
End of explanation
"""
data = pd.Series.from_csv("clusters.csv")
_=data.hist(bins=2... |
DS-100/sp17-materials | sp17/disc/disc14/disc14.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
"""
Explanation: Gaussian Mixture Models
End of explanation
"""
from sklearn.datasets import make_classification
np.random.seed(1337)
vals = make_classification(n_features=2,
... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage6/get_started_with_matching_engine_swivel.ipynb | apache-2.0 | import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be installed with '--user'
USER_FLAG = ... |
jupyter/nbgrader | nbgrader/tests/apps/files/test.ipynb | bsd-3-clause | def squares(n):
"""Compute the squares of numbers from 1 to n, such that the
ith element of the returned list equals i^2.
"""
### BEGIN SOLUTION
if n < 1:
raise ValueError("n must be greater than or equal to 1")
return [i ** 2 for i in range(1, n + 1)]
### END SOLUTION
"""
Exp... |
spulido99/NetworksAnalysis | Ejercicios 1.1 - Graphs, Paths & Components.ipynb | mit | edges = set([(1, 2), (3, 1), (3, 2), (2, 4)])
"""
Explanation: Ejercicios Graphs, Paths & Components
Ejercicios básicos de Grafos.
Ejercicio - Número de Nodos y Enlaces
_ (resuelva en código propio y usando la librería NetworkX (python) o iGraph (R)) _
Cuente el número de nodos y enlaces con los siguientes links (asum... |
ajrichards/bayesian-examples | nlp/lda/data-munging.ipynb | bsd-3-clause | df = pd.read_csv('npr_articles.csv')
"""
Explanation: Read in Our Data
The first step is to read out data into a pandas DataFrame. For an intro to using pandas I would highly suggest looking though this 10 minute guide to pandas.
End of explanation
"""
df.head()
"""
Explanation: We can now checkout what our data c... |
UDST/activitysim | activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb | bsd-3-clause | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating Mandatory Tour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read household... |
jbpoline/cnv_analysis | CNV_dangerosite.ipynb | artistic-2.0 | CWD = osp.join(osp.expanduser('~'), 'documents','grants_projects','roberto_projects', \
'guillaume_huguet_CNV','File_OK')
filename = 'Imagen_QC_CIA_MMAP_V2_Annotation.tsv'
fullfname = osp.join(CWD, filename)
arr = np.loadtxt(fullfname, dtype='str', comments=None, delimiter='\Tab',
conv... |
mintcloud/deep-learning | embeddings/Skip-Gram word2vec_theo.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
legacysurvey/rapala | notebooks/BASS2016-DetectorAnalysis.ipynb | bsd-3-clause | for ccdNum in [2,3]:
exts = 4*(ccdNum-1) + arange(1,5)
figure(figsize=(10,6))
for pnum,ext in enumerate(exts,start=1):
subplot(2,2,pnum)
for t,lbl,clr in zip([t15,t16],#,s15[b15],s16[b16],s15[o15],s16[o16]],
['2015','2016'],#,'2015b','2016b','2015o','2016o'],
... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/05_review/labs/4_preproc.ipynb | apache-2.0 | #Ensure that we have Apache Beam version installed.
!pip freeze | grep apache-beam || sudo pip install apache-beam[gcp]==2.12.0
import tensorflow as tf
import apache_beam as beam
import shutil
import os
print(tf.__version__)
"""
Explanation: Preprocessing Using Dataflow
Learning Objectives
- Creating datasets for Mac... |
CalPolyPat/phys202-2015-work | assignments/assignment03/NumpyEx04.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Numpy Exercise 4
Imports
End of explanation
"""
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
"""
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or n... |
mromanello/SunoikisisDC_NER | participants_notebooks/Sunoikisis - Named Entity Extraction 1a-VV.ipynb | gpl-3.0 | 2 + 3
"""
Explanation: Plan of the lecture
Introduction: Information Extraction and Named Entity Recognition (NER)
NER: definitions and tasks (extraction, classification, disambiguation)
basic programming concepts in Python
Doing NER with existing libraries:
NER from Latin texts with CLTK
NER from journal articles wi... |
dbouquin/AstroHackWeek2015 | day1/day1_numericalsummaries.ipynb | gpl-2.0 | import os
import requests
# get some CSV data from the SDSS SQL server
URL = "http://skyserver.sdss.org/dr12/en/tools/search/x_sql.aspx"
cmd = """
SELECT TOP 10000
p.u, p.g, p.r, p.i, p.z, s.class, s.z, s.zerr
FROM
PhotoObj AS p
JOIN
SpecObj AS s ON s.bestobjid = p.objid
WHERE
p.u BETWEEN 0 AND 19.6 ... |
iemejia/incubator-beam | examples/notebooks/get-started/try-apache-beam-java.ipynb | apache-2.0 | # Run and print a shell command.
def run(cmd):
print('>> {}'.format(cmd))
!{cmd} # This is magic to run 'cmd' in the shell.
print('')
# Copy the input file into the local filesystem.
run('mkdir -p data')
run('gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt data/')
"""
Explanation: <a href="https://col... |
IBMDecisionOptimization/docplex-examples | examples/mp/jupyter/incremental_modeling.ipynb | apache-2.0 | import sys
try:
import docplex.mp
except:
raise Exception('Please install docplex. See https://pypi.org/project/docplex/')
"""
Explanation: Incremental modeling with decision optimization
This tutorial includes everything you need to set up decision optimization engines, build a mathematical programming model,... |
jpn--/larch | larch/doc/example/202_exville_mc_logsums.ipynb | gpl-3.0 | import larch, numpy, pandas, os
from larch import P, X
larch.__version__
"""
Explanation: 202: Exampville Mode Choice Logsums
Welcome to Exampville, the best simulated town in this here part of the internet!
Exampville is a demonstration provided with Larch that walks through some of the
data and tools that a transp... |
matmodlab/matmodlab2 | notebooks/GettingStarted.ipynb | bsd-3-clause | %pylab inline
from matmodlab2 import *
"""
Explanation: Getting Started
<a name='contents'></a>
Contents
<a href='#magic'>The <tt>matmodlab</tt> namespace</a>
<a href='#model.def'>Defining a Model</a>
<a href='#model.def.mat'>Material Model Definition</a>
<a href='#model.def.step'>Step Definitions</a>
<a href='#model... |
astarostin/MachineLearningSpecializationCoursera | course4/week1 - Доверительные интервалы - quiz.ipynb | apache-2.0 | import scipy.stats
scipy.stats.norm.ppf(0.9985)
"""
Explanation: week1 - Доверительные интервалы - quiz
1.
Давайте уточним правило трёх сигм. Утверждение: 99.7% вероятностной массы случайной величины X∼N(μ,σ2) лежит в интервале μ±c⋅σ. Чему равно точное значение константы c? Округлите ответ до четырёх знаков после дес... |
terrydolan/prem | prem-analysis.ipynb | mit | import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sys
from itertools import cycle
from collections import defaultdict
from datetime import datetime
from __future__ import division
# enable inline plotting
%matplotlib inline
"""
Explanation: English Premiership 201... |
whitead/numerical_stats | unit_13/lectures/lecture_1.ipynb | gpl-3.0 | %matplotlib inline
import random
import numpy as np
import matplotlib.pyplot as plt
from math import sqrt, pi, erf
import seaborn
seaborn.set_context("talk")
import scipy.stats
"""
Explanation: Solving Ordinary Differential Equations
Unit 13, Lecture 1
Numerical Methods and Statistics
Prof. Andrew White, April 24 201... |
StuartLittlefair/reducer | reducer/reducer-template.ipynb | bsd-3-clause | from __future__ import division
%matplotlib inline
import numpy as np
import reducer.gui
import reducer.astro_gui as astro_gui
from reducer.image_browser import ImageBrowser
import msumastro
from reducer import __version__
print __version__
"""
Explanation: Reducer: (Put your name here)
Reviewer: (Put your name he... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/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', 'bnu', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Concent... |
chemo-wakate/tutorial-6th | beginner/mario/overlook.ipynb | mit | # 数値計算やデータフレーム操作に関するライブラリをインポートする
import numpy as np
import pandas as pd
# URL によるリソースへのアクセスを提供するライブラリをインポートする。
# import urllib # Python 2 の場合
import urllib.request # Python 3 の場合
# 図やグラフを図示するためのライブラリをインポートする。
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.colors... |
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