repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
hpi-epic/pricewars-merchant | docs/Working with Kafka data.ipynb | mit | import sys
sys.path.append('../')
"""
Explanation: Working with Kafka data
During a simulation, the producer and the marketplace are constantly logging sales and the activity on the market to Kafka. These information are organised in topics. In order to estimate customer demand and predict good prices, merchants can u... |
kdestasio/online_brain_intensive | nipype_tutorial/notebooks/example_normalize.ipynb | gpl-2.0 | !ls /data/ds000114/derivatives/fmriprep/sub-*/anat/*h5
"""
Explanation: Example 3: Normalize data to MNI template
This example covers the normalization of data. Some people prefer to normalize the data during the preprocessing, just before smoothing. I prefer to do the 1st-level analysis completely in subject space an... |
yaricom/goNEAT | contents/notebooks/experiments_results.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
"""
Explanation: Study of experiment results
After execution of each experiment the results of execution will be saved in Numpy NPZ format. The saved data can be used to analyse and visualize the evolutionary process.
In this ... |
relopezbriega/mi-python-blog | content/notebooks/MachineLearningOverfitting.ipynb | gpl-2.0 | # <!-- collapse=True -->
# Importando las librerías que vamos a utilizar
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_classification
from sklearn.svm import SVC
from sklearn.tree im... |
jochym/abinitio-workshop | notebooks/02_Obliczenia.ipynb | cc0-1.0 | # Import potrzebnych modułów
%matplotlib inline
from matplotlib import pyplot as plt
import matplotlib as mpl
import numpy as np
from ase.build import bulk
from ase import units
import ase.io
from IPython.core.display import Image
from __future__ import division, print_function
from ase import Atoms
from ase.units imp... |
slerch/ppnn | nn_postprocessing/notebooks/feature_importance.ipynb | mit | %load_ext autoreload
%autoreload 2
%matplotlib inline
from nn_src.imports import *
from nn_src.utils import get_datasets
#DATA_DIR = '/Users/stephanrasp/data/'
# DATA_DIR = '/scratch/srasp/ppnn_data/'
DATA_DIR = '/Volumes/SanDisk/data/ppnn_data/'
aux_train_set, aux_test_set = get_datasets(DATA_DIR, 'aux_15_16.pkl', ... |
ppham27/MLaPP-solutions | chap04/8.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
"""
Explanation: Whitening versus standardizing
End of explanation
"""
raw_data = pd.read_csv("heightWeightData.txt", header=None, names=["gender", "height", "weight"])
raw_data.info()
raw_data.head()
... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/td2a_correction_session_3A.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from jyquickhelper import add_notebook_menu
add_notebook_menu()
# Répare une incompatibilité entre scipy 1.0 et statsmodels 0.8.
from pymyinstall.fix import fix_scipy10_for_statsmodels08
fix_scipy10_for_statsmodels08()
"""
Explanation: 2A.ml - Statistiques descript... |
rashikaranpuria/Machine-Learning-Specialization | Classification/Week 2/Assignment 2/module-4-linear-classifier-regularization-assignment-blank.ipynb | mit | from __future__ import division
import graphlab
"""
Explanation: Logistic Regression with L2 regularization
The goal of this second notebook is to implement your own logistic regression classifier with L2 regularization. You will do the following:
Extract features from Amazon product reviews.
Convert an SFrame into a... |
saullocastro/pyNastran | docs/quick_start/demo/bdf_demo.ipynb | lgpl-3.0 | import os
import pyNastran
print (pyNastran.__file__)
print (pyNastran.__version__)
pkg_path = pyNastran.__path__[0]
from pyNastran.bdf.bdf import BDF, read_bdf
from pyNastran.utils import object_attributes, object_methods
print("pkg_path = %s" % pkg_path)
"""
Explanation: BDF Demo
The iPython notebook for this demo... |
gobabiertoAR/datasets-portal | audiencias/Cleaner audiencias.ipynb | mit | from data_cleaner import DataCleaner
input_path = "audiencias-raw.csv"
output_path = "audiencias-clean.csv"
dc = DataCleaner(input_path)
import pandas as pd
df = pd.read_csv("audiencias-clean.csv")
map(print, df[df.root_dependencia_descripcion == "Presidencia de la Nación"].dependencia_descripcion.unique())
map(p... |
Kaggle/learntools | notebooks/pandas/raw/ex_2.ipynb | apache-2.0 | import pandas as pd
pd.set_option("display.max_rows", 5)
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
from learntools.core import binder; binder.bind(globals())
from learntools.pandas.summary_functions_and_maps import *
print("Setup complete.")
reviews.head()
"""
Explanation: ... |
ericmjl/data-testing-tutorial | bonus-3-file-integrity.ipynb | mit | from hashlib import sha256, md5
m = sha256()
m.update('hello'.encode('utf-8'))
m.hexdigest()
"""
Explanation: File Integrity
With file integrity, the basic question we are answering is: "Has the file changed since the last time you used it?"
Hash (Browns)
File integrity can be checked by checking the "hash" of a file... |
mne-tools/mne-tools.github.io | 0.18/_downloads/4d74528a24c597c5e2cf1e334cf2a4f0/plot_compute_covariance.ipynb | bsd-3-clause | import os.path as op
import mne
from mne.datasets import sample
"""
Explanation: Computing a covariance matrix
Many methods in MNE, including source estimation and some classification
algorithms, require covariance estimations from the recordings.
In this tutorial we cover the basics of sensor covariance computations... |
paulrevere4/udacity-deep-learning | assignment1/1_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... |
JShadowMan/package | python/course/ch02-syntax-and-container/基本语法.ipynb | mit | year = 2019 # 赋值表达式, 一行可以只写一个语句
month = 7; day = 23; hour = 22; minute = 11; second = 0 # 一行也可以写多个语句, 使用 ; 进行分隔
if 1900 < year < 2100 and 1 <= month <= 12 \
and 1 <= day <= 31 and 0 <= hour < 24 \
and 0 <= minute < 60 and 0 <= second < 60: # 多个物理行组成一个逻辑行
print("时间正确")
"""
Explanation: Python中的基本语法
Pyth... |
marxav/hello-world | artificial_neural_network_101_numpy.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: <a href="https://colab.research.google.com/github/marxav/hello-world-python/blob/master/artificial_neural_network_101_numpy.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Goal: imp... |
atulsingh0/MachineLearning | scikit-learn/MatPlotLib_01.ipynb | gpl-3.0 | # Create a figure of size 8x6 inches, 80 dots per inch
plt.figure(figsize=(8, 6), dpi=80)
# Create a new subplot from a grid of 1x1
plt.subplot(1, 1, 1)
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)
# Plot cosine with a blue continuous line of width 1 (pixels)
plt.plot(X, C, color="bl... |
stevertaylor/NX01 | nanograv9yr_makehdf5.ipynb | mit | stripped_pars = list(parfiles)
for ii in range(len(stripped_pars)):
stripped_pars[ii] = stripped_pars[ii].replace('9yv1.gls.par', '9yv1.gls.strip.par')
stripped_pars[ii] = stripped_pars[ii].replace('9yv1.t2.gls.par', '9yv1.t2.gls.strip.par')
for ii in range(len(stripped_pars)):
os.system('awk \'($1 !~ /T2... |
QuantCrimAtLeeds/PredictCode | quick_start/Generate example dataset.ipynb | artistic-2.0 | import os, csv, lzma
import numpy as np
import open_cp.sources.chicago
import geopandas as gpd
import pyproj
import shapely.geometry
"""
Explanation: Generate example dataset
Using our favour source, Chicago: https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2
Geometry from https://data.cityo... |
rice-solar-physics/hot_plasma_single_nanoflares | notebooks/compute_ebtel_results.ipynb | bsd-2-clause | import sys
import os
import subprocess
import pickle
import numpy as np
sys.path.append(os.path.join(os.environ['EXP_DIR'],'ebtelPlusPlus/rsp_toolkit/python'))
from xml_io import InputHandler,OutputHandler
"""
Explanation: Compute EBTEL Results
Run the single- and two-fluid EBTEL models for a variety of inputs. This... |
nickdavidhaynes/python-data-science-intro | week_1/intro_to_python.ipynb | mit | my_variable = 10
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Welcome!" data-toc-modified-id="Welcome!-1"><span class="toc-item-num">1 </span>Welcome!</a></div><div class="lev2 toc-item"><a href="#About-me" data-toc-modified-id="About-me-11"><span class="toc-item-num">1.1 ... |
mohanprasath/Course-Work | certifications/code/boston_housing/boston_housing.ipynb | gpl-3.0 | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.cross_validation import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('hou... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/recommendation_systems/solutions/content_based_using_neural_networks.ipynb | apache-2.0 | %%bash
pip freeze | grep tensor
"""
Explanation: Content-Based Filtering Using Neural Networks
This notebook relies on files created in the content_based_preproc.ipynb notebook. Be sure to run the code in there before completing this notebook.
Also, you'll be using the python3 kernel from here on out so don't forget t... |
miykael/nipype_tutorial | notebooks/basic_joinnodes.ipynb | bsd-3-clause | from nipype import JoinNode, Node, Workflow
from nipype.interfaces.utility import Function, IdentityInterface
def get_data_from_id(id):
"""Generate a random number based on id"""
import numpy as np
return id + np.random.rand()
def merge_and_scale_data(data2):
"""Scale the input list by 1000"""
imp... |
rmsare/scarplet | docs/source/examples/multiprocessing_example.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from functools import partial
from multiprocessing import Pool
import scarplet as sl
from scarplet.datasets import load_synthetic
from scarplet.WindowedTemplate import Scarp
data = load_synthetic()
# Define parmaters for search
scale = 10
age = 10.
angles = np.lins... |
VectorBlox/PYNQ | Pynq-Z1/notebooks/examples/arduino_lcd18.ipynb | bsd-3-clause | from pynq import Overlay
Overlay("base.bit").download()
"""
Explanation: Arduino LCD Example using AdaFruit 1.8" LCD Shield
This notebook shows a demo on Adafruit 1.8" LCD shield.
End of explanation
"""
from pynq.iop import Arduino_LCD18
from pynq.iop import ARDUINO
lcd = Arduino_LCD18(ARDUINO)
"""
Explanation: 1.... |
numenta/nupic.research | projects/archive/dynamic_sparse/notebooks/ExperimentAnalysis-Comparisons.ipynb | agpl-3.0 | %load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupic.research.frameworks.dynamic... |
dkillick/courses | course_content/notebooks/cartopy_intro.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import cartopy.crs as ccrs
"""
Explanation: Cartopy in a nutshell
Cartopy is a Python package that provides easy creation of maps, using matplotlib, for the analysis and visualisation of geospatial data.
In order to create a map with cartopy and matplotlib, we typically need to import p... |
akloster/porekit-python | examples/squiggle_classifier_1/Read_Until_Efficiency.ipynb | isc | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
def sim_ru(ham_frequency, ham_duration, accuracy):
# Monte-Carlo Style
n = 1000000
ham = np.random.random(size=n)<ham_frequency
durations = np.ones(n)
accurate = np.random.random(size=n)<accuracy
durations[ham & accurate] = h... |
ES-DOC/esdoc-jupyterhub | notebooks/inm/cmip6/models/inm-cm4-8/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'inm-cm4-8', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: INM
Source ID: INM-CM4-8
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
hail-is/hail | hail/python/hail/docs/tutorials/09-ggplot.ipynb | mit | ht = hl.utils.range_table(10)
ht = ht.annotate(squared = ht.idx**2)
"""
Explanation: The Hail team has implemented a plotting module for hail based on the very popular ggplot2 package from R's tidyverse. That library is very fully featured and we will never be quite as flexible as it, but with just a subset of its fun... |
markovmodel/adaptivemd | examples/tutorial/6_example_multi_traj_type.ipynb | lgpl-2.1 | import sys, os
"""
Explanation: AdaptiveMD
Example 6 - Multi-traj
0. Imports
End of explanation
"""
from adaptivemd import Project
"""
Explanation: Alright, let's load the package and pick the Project since we want to start a project
End of explanation
"""
# Use this to completely remove the example-worker projec... |
lilleswing/deepchem | examples/tutorials/11_Putting_Multitask_Learning_to_Work.ipynb | mit | !curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
import deepchem
deepchem.__version__
"""
Explanation: Tutorial Part 11: Putting Multitask Lea... |
ericmjl/systems-microbiology-hiv | 02 Train and Test - Protease.ipynb | mit | # Read in the protease inhibitor data
data = pd.read_csv('drug_data/hiv-protease-data.csv', index_col='SeqID')
drug_cols = data.columns[0:8]
feat_cols = data.columns[8:]
# Read in the consensus data
consensus = SeqIO.read('sequences/hiv-protease-consensus.fasta', 'fasta')
consensus_map = {i:letter for i, letter in en... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-mh/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-mh', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-MH
Topic: Ocean
Sub-Topics: Timestepping Framewor... |
swirlingsand/deep-learning-foundations | gans/batch-norm/Batch_Normalization_Exercises.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 – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
sanchestm/gm-mosquito-sim | testing other features/.ipynb_checkpoints/randomwalk2d-checkpoint.ipynb | mit | def findquadrant(point,size):
y,x = point
halfsize = size/2
if x < -halfsize:
if y > halfsize: return [0,0]
if y < -halfsize: return [2,0]
return [1,0]
if x > halfsize:
if y > halfsize: return [0,2]
if y < -halfsize: return [2,2]
return [1,2]
if y > ha... |
jmhsi/justin_tinker | data_science/courses/temp/courses/dl1/nlp.ipynb | apache-2.0 | sl=1000
vocab_size=200000
PATH='data/aclImdb/'
names = ['neg','pos']
trn,trn_y = texts_from_folders(f'{PATH}train',names)
val,val_y = texts_from_folders(f'{PATH}test',names)
"""
Explanation: IMBD dataset and the sentiment classification task
The large movie view dataset contains a collection of 50,000 reviews from I... |
deeplycloudy/lmaworkshop | TRACER-2021/FirstLMAplots.ipynb | bsd-2-clause | # We could tediously build a list …
# filenames = ['/data/Houston/realtime-tracer/LYLOUT_200524_210000_0600.dat.gz',]
# Instead, let's read a couple hours at the same time.
import sys, glob
filenames = glob.glob('/data/Houston/130619/LYLOUT_130619_2[0-1]*.dat.gz')
for filename in filenames:
print(filename)
import... |
jseabold/statsmodels | examples/notebooks/autoregressions.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as pdr
import seaborn as sns
from statsmodels.tsa.ar_model import AutoReg, ar_select_order
from statsmodels.tsa.api import acf, pacf, graphics
"""
Explanation: Autoregressions
This notebook introduces autoregression modelin... |
armgilles/presentation | EPSI/I5/Projet Big Data/EP3/Regression.ipynb | mit | features = [col for col in data.columns if col not in "SalePrice"]
features
train = data[features]
y = data.SalePrice
#y = data['SalePrice']
train.head()
y.head()
sns.distplot(y)
# Modele pour la regression
from sklearn.linear_model import Ridge
import sklearn
sklearn.__version__
# Initialisation du model
mod... |
ProfessorKazarinoff/staticsite | content/code/statics/simple_statics_problem.ipynb | gpl-3.0 | import numpy as np
from numpy.linalg import inv
np.set_printoptions(precision=3)
"""
Explanation: A Statics Problem
Given:
A weight of 22lbs is hung by a ring. The ring is held by two cords pulled apart.
The cord A on the left is at an angle $\alpha$ = 45° CW relative to the -x-axis (45° above horazontal)
The... |
zhuangjun1981/retinotopic_mapping | retinotopic_mapping/examples/analysis_retinotopicmapping/Retinotopic_Mapping_Analysis_Template/2015-10-31_RetinotopicMappingAnalysisTemplate.ipynb | gpl-3.0 | from IPython.display import Javascript,display
from corticalmapping.ipython_lizard.html_widgets import raw_code_toggle
raw_code_toggle()
display(Javascript("""var nb = IPython.notebook;
//var is_code_cell = (nb.get_selected_cell().cell_type == 'code')
//var curr_idx = (nb.get... |
robertutterback/robertutterback.github.io | courses/comp347/f20/hwk1-sol.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: Homework 1
Due Wednesday, September 5 by 2:00 PM. Submit via handin as hwk1.
Some helpful setup code. Feel free to add whatever else you might need.
End of explanation
"""
df = pd.read_csv('1-1.csv', comment='... |
justanr/notebooks | fizzbuzz_with_pynads.ipynb | mit | from pynads import Container
class Person(Container):
__slots__ = ('name', 'age')
def __init__(self, name, age):
self.name = name
self.age = age
def _get_val(self):
return {'name': self.name, 'age': self.age}
def __repr__(self):
return "Person(name={!s}, age={!... |
LimeeZ/phys292-2015-work | assignments/assignment05/InteractEx04.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 4
Imports
End of explanation
"""
def random_line(m, b, sigma, size=10):
"""Create a line y = m*x + b + N(0,si... |
csantill/AustinSIGKDD-DecisionTrees | notebooks/Decision Trees.ipynb | bsd-3-clause | from __future__ import print_function
import os
from IPython.display import Image
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.cross_validation import cross_val_score
from sklearn import tree
from sklearn.externals.six import ... |
dnc1994/MachineLearning-UW | ml-foundations/backup/house-price/Predicting house prices.ipynb | mit | import graphlab
"""
Explanation: Fire up graphlab create
End of explanation
"""
sales = graphlab.SFrame('home_data.gl/')
sales
"""
Explanation: Load some house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
End of explanation
"""
graphlab.canvas.set_ta... |
CommonClimate/teaching_notebooks | GEOL351/ENSO_recharge.ipynb | mit | %matplotlib inline
import numpy as np
from scipy import integrate
import nitime.algorithms as tsa
import nitime.utils as utils
from nitime.viz import winspect
from nitime.viz import plot_spectral_estimate
import seaborn as sns
sns.set_palette("Dark2")
# define model parameters
Tscale = 7.5 # in Kelvins
tscale = 1/6.... |
kastnerkyle/kastnerkyle.github.io-nikola | blogsite/posts/introduction-to-gaussian-processes.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
rng = np.random.RandomState(1999)
n_samples = 1000
X = rng.rand(n_samples)
y = np.sin(20 * X) + .05 * rng.randn(X.shape[0])
X_t = np.linspace(0, 1, 100)
y_t = np.sin(20 * X_t)
plt.scatter(X, y, color='steelblue', label='measured y')
plt.plot(X_t, y_... |
choderalab/yank | Yank/reports/YANK_Health_Report_Template.ipynb | mit | # Mandatory Settings
store_directory = 'STOREDIRBLANK'
analyzer_kwargs = ANALYZERKWARGSBLANK
# Optional Settings
decorrelation_threshold = 0.1
mixing_cutoff = 0.05
mixing_warning_threshold = 0.90
phase_stacked_replica_plots = False
"""
Explanation: YANK Simulation Health Report
General Settings
Mandatory Settings
st... |
google/starthinker | colabs/dv360_api_patch_from_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: 1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
End of explanation
"""
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" % CLOUD_PROJECT)
... |
Kaggle/learntools | notebooks/ml_explainability/raw/ex5_shap_advanced.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import shap
# Environment Set-Up for feedback system.
from learntools.core import binder
binder.bind(globals())
from learntools.ml_explainability.ex5 import *
print("Setup Comp... |
NEONInc/NEON-Data-Skills | code/Python/uncertainty/lidar-uncertainty .ipynb | gpl-2.0 | import sys
sys.version
import gdal
import h5py
import numpy as np
from math import floor
import os
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Background
In 2016 the NEON AOP flew the PRIN site in D11 on a poor weather day to ensure coverage of the site. The following day, the weather improved... |
wcmitchell/insights-core | notebooks/Insights Core Tutorial.ipynb | apache-2.0 | from insights.core import dr
# Here's our component type with the clever name "component."
# We could have named it anything. Insights Core provides several types
# that we'll come to later.
component = dr.new_component_type("component")
"""
Explanation: Red Hat Insights Core
Insights Core is a framework for collect... |
OpenWeavers/openanalysis | doc/Langauge/15 - Exception and Exception handling.ipynb | gpl-3.0 | div = lambda x,y : x/y
div(8,2)
div(0/0)
"""
Explanation: Exceptions
In an ideal situation, our program runs smoothly without any errors. However it is not always the case. Errors may be due to developer's fault or programmer's mistake or of computer. Source of some errors might be hard to undertsand. However it is ... |
herruzojm/udacity-deep-learning | sentiment-rnn/.ipynb_checkpoints/Sentiment RNN Solution-checkpoint.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... |
tjwei/HackNTU_Data_2017 | Week03/04-Speed-Limit.ipynb | mit | import tqdm
import tarfile
import pandas
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import PIL
import gzip
from urllib.request import urlopen
%matplotlib inline
matplotlib.style.use('ggplot')
# progress bar
tqdm.tqdm.pandas()
# 檔案名稱格式
filename_format="M06A_{year:04d}{month:02d}{day:02d}.tar.... |
jonathanmorgan/msu_phd_work | methods/data_creation/prelim_month-create_Reliability_Names_data.ipynb | lgpl-3.0 | import datetime
print( "packages imported at " + str( datetime.datetime.now() ) )
"""
Explanation: prelim_month - create Reliability_Names data
2016.12.04 - work log - prelim_month - create Reliability_Names
original file name: 2016.12.04-work_log-prelim_month-create_Reliability_Names.ipynb
This is the notebook where... |
tensorflow/hub | examples/colab/tf2_arbitrary_image_stylization.ipynb | apache-2.0 | # Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
tensorflow/docs-l10n | site/ja/r1/tutorials/keras/basic_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... |
Cyianor/smc2017 | solutions/code/Python/fheld/exI.ipynb | mit | import numpy as np
from numpy.random import randn, choice, multinomial
from scipy import stats
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style()
"""
Explanation: SMC2017: Exercise set I
Setup
End of explanation
"""
class TooLittleSampleCoverage(Exception):... |
jldinh/multicell | examples/06 - Growth and divisions.ipynb | mit | %matplotlib notebook
"""
Explanation: Preparation
End of explanation
"""
import multicell
import numpy as np
"""
Explanation: Imports
End of explanation
"""
sim = multicell.simulation_builder.generate_cell_grid_sim(20, 20, 1, 1e-3)
"""
Explanation: Problem definition
Simulation and tissue structure
End of explan... |
jdsanch1/SimRC | 01. Parte 1/05. Clase 5/.ipynb_checkpoints/05Class NB-checkpoint.ipynb | mit | #importar los paquetes que se van a usar
import pandas as pd
import pandas_datareader.data as web
import numpy as np
from sklearn.cluster import KMeans
import datetime
from datetime import datetime
import scipy.stats as stats
import scipy as sp
import scipy.optimize as optimize
import scipy.cluster.hierarchy as hac
imp... |
mahieke/maschinelles_lernen | a2/excercise1.ipynb | mit | import pandas as pd
import numpy as np
import util
import scipy.stats as scs
%matplotlib inline
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'
cols =["CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B","LSTAT","TGT"]
boston = pd.read_csv(url, sep=" ", ski... |
saga-survey/saga-code | ipython_notebooks/Spectra Combining.ipynb | gpl-2.0 | spec_data_raw = table.Table.read('SAGADropbox/data/saga_spectra_raw.fits.gz')
spec_data_raw
"""
Explanation: Load the spectroscopic data
End of explanation
"""
# Just setting the dtype does *not* do the conversion of the values. It instead tells numpy to
# re-interpret the same set of bits as thought they were int... |
Upward-Spiral-Science/the-vat | Code/inferential_simulation_AL.ipynb | apache-2.0 | # Import Necessary Libraries
import numpy as np
import os, csv, json
from matplotlib import *
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.axes_grid1 import make_axes_locatable
import scipy
import itertools
from sklearn.decomposition import PCA
import skimage.measure... |
jtwhite79/pyemu | examples/pstfrom_mf6.ipynb | bsd-3-clause | import os
import shutil
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pyemu
import flopy
"""
Explanation: Setting up a PEST interface from MODFLOW6 using the PstFrom class
The PstFrom class is a generalization of the prototype PstFromFlopy class. The generalization in PstFrom means user... |
mathnathan/notebooks | dissertation/GNN - 1D GMM Example.ipynb | mit | #p = GMM([0.1,0.3,0.6], np.array([[0.2,.01],[0.5,0.01],[0.8,0.01]]))
p = GMM([0.4,0.6], np.array([[0.2,0.05],[0.65,.015]]))
num_samples = 1000
beg = 0.0
end = 1.0
t = np.linspace(beg,end,num_samples)
num_neurons = len(p.pis)
colors = [np.random.rand(num_neurons,) for i in range(num_neurons)]
p_y = p(t)
p_max = p_y.max... |
testedminds/sand | docs/Matrix visualization with Bokeh.ipynb | apache-2.0 | from bokeh.sampledata.les_mis import data
data.keys()
len(data['nodes'])
data['nodes'][0:5]
"""
Explanation: Introduction to Bokeh
Bokeh is an open-source Python interactive visualization library from Continuum Analytics that targets modern web browsers for presentation.
Bokeh includes an example of network visuali... |
vascotenner/holoviews | doc/Tutorials/Columnar_Data.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import holoviews as hv
from IPython.display import HTML
hv.notebook_extension()
"""
Explanation: In this Tutorial we will explore how to work with columnar data in HoloViews. Columnar data has a fixed list of column headings, with values stored in an arbitrarily long list of rows... |
gully/starfish-demo | demo4/notebooks/Cholesky_errors.ipynb | mit | import numpy as np
CC_1d = np.fromfile('CC_test.npy')
CC_1d.shape
"""
Explanation: Cholesky decomposition errors.
gully
February 2016
Starfish error #26 shows that there is some strange Cholesky-decomposition rounding error problem. In this demo, we will try to recreate the problem, characterize it, and solve it.
W... |
geoscixyz/computation | docs/case-studies/TDEM/TKC_ATEM.ipynb | mit | import numpy as np
from scipy.constants import mu_0
import matplotlib.pyplot as plt
import ipywidgets
from SimPEG import EM, Mesh, Utils, Maps
%matplotlib inline
# import a solver. If you want to re-run the forward simulation or inversion,
# make sure you have pymatsolver (https://github.com/rowanc1/pymatsolver)
#... |
mne-tools/mne-tools.github.io | 0.20/_downloads/2be4fb4bf7f4e0825af6c222c396d97a/plot_compute_csd.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# License: BSD (3-clause)
from matplotlib import pyplot as plt
import mne
from mne.datasets import sample
from mne.time_frequency import csd_fourier, csd_multitaper, csd_morlet
print(__doc__)
"""
Explanation: Compute a cross-spectral density (CSD) matrix
A cross-sp... |
edwardd1/phys202-2015-work | assignments/assignment12/FittingModelsEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Fitting Models Exercise 1
Imports
End of explanation
"""
a_true = 0.5
b_true = 2.0
c_true = -4.0
"""
Explanation: Fitting a quadratic curve
For this problem we are going to work with the following mod... |
ambonip/Analisi-Trab | Analisi thibya Brahms.ipynb | gpl-3.0 | %matplotlib inline
#importo le librerie
import pandas as pd
import os
from __future__ import print_function,division
import numpy as np
import seaborn as sns
os.environ["NLS_LANG"] = "ITALIAN_ITALY.UTF8"
"""
Explanation: <h2>Analisi comparativa dei metodi di dosaggio degli anticorpi anti recettore del TSH</h2>
<h3>Met... |
fullmetalfelix/ML-CSC-tutorial | MBTR.ipynb | gpl-3.0 | # --- INITIAL DEFINITIONS ---
from dscribe.descriptors import MBTR
import numpy as np
from visualise import view
from ase import Atoms
import matplotlib.pyplot as mpl
"""
Explanation: Many Body Tensor Representation
MBTR is a global descriptor for a molecule/unit cell. It eliminates rotational, translational, and perm... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session03/Day4/Profiling_solns.ipynb | mit | import random
import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: Profiling and Optimizing
By C Hummels (Caltech)
End of explanation
"""
string_list = ['the ', 'quick ', 'brown ', 'fox ', 'jumped ', 'over ', 'the ', 'lazy ', 'dog']
%%timeit
output = ""
for string in string_list:
output+=st... |
tcmoore3/mbuild | docs/tutorials/tutorial_polymers.ipynb | mit | import mbuild as mb
class CH2(mb.Compound):
def __init__(self):
super(CH2, self).__init__()
self.add(mb.Particle(name='C', pos=[0,0,0]), label='C[$]')
# Add hydrogens
self.add(mb.Particle(name='H', pos=[-0.109, 0, 0.0]), label='HC[$]')
self.add(mb.Particle(name... |
arasdar/DL | udacity-dl/CNN/cnn_bp-learning-curves.ipynb | unlicense | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hoo... |
mne-tools/mne-tools.github.io | 0.18/_downloads/9460321824116e4964fbe6d88d27462e/plot_cluster_stats_evoked.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Permutation F-test on sensor data with 1D c... |
maxalbert/paper-supplement-nanoparticle-sensing | notebooks/fig_7_frequency_change_vs_lateral_particle_position.ipynb | mit | import matplotlib.pyplot as plt
import pandas as pd
from style_helpers import style_cycle_fig7
%matplotlib inline
plt.style.use('style_sheets/fig7.mplstyle')
"""
Explanation: Fig. 7: Frequency Change $\Delta f$ vs. Lateral Particle Position
This notebook reproduces Fig. 7 in the paper, which shows the the frequency c... |
metpy/MetPy | v0.12/_downloads/62a1acd718d4c5b9717787544d4cf09f/Gradient.ipynb | bsd-3-clause | import numpy as np
import metpy.calc as mpcalc
from metpy.units import units
"""
Explanation: Gradient
Use metpy.calc.gradient.
This example demonstrates the various ways that MetPy's gradient function
can be utilized.
End of explanation
"""
data = np.array([[23, 24, 23],
[25, 26, 25],
... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 17-12-11-workcamp-ml/2017-12-11-arbeiten-mit-dictionaries-10.ipynb | gpl-3.0 | mktcaps = {'AAPL':538.7,'GOOG':68.7,'IONS':4.6}# Dictionary wird initialisiert
print(type(mktcaps))
print(mktcaps)
print(mktcaps.values())
print(mktcaps.keys())
print(mktcaps.items())
c=mktcaps.items()
print c[0]
mktcaps['AAPL'] #Gibt den Wert zurück der mit dem Schlüssel "AAPL" verknüpft ist
mktcaps['GS'] #Fehler w... |
datactive/bigbang | examples/experimental_notebooks/Collaboration Robustness.ipynb | mit | %matplotlib inline
"""
Explanation: This notebook explores how collaborative relationships form between mailing list participants over time.
The hypothesis, loosely put, is that early exchanges are indicators of growing relationships or trust that should be reflected in information flow at later times.
End of explanat... |
thewtex/SimpleITK-Notebooks | 65_Registration_FFD.ipynb | apache-2.0 | import SimpleITK as sitk
import registration_utilities as ru
import registration_callbacks as rc
from __future__ import print_function
import matplotlib.pyplot as plt
%matplotlib inline
from ipywidgets import interact, fixed
#utility method that either downloads data from the MIDAS repository or
#if already downloa... |
aufziehvogel/kaggle | two-sigma-rental-listing/notebooks/2.1-sk-engineering-numerical-features.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
df = pd.read_json('../data/raw/train.json')
df['created'] = df['created'].apply(lambda row: pd.to_datetime(row))
"""
Explanation: Numerical Features Engineering
In this notebook we want to try to engineer... |
NervanaSystems/neon_course | 01 MNIST example.ipynb | apache-2.0 | from neon.backends import gen_backend
be = gen_backend(batch_size=128)
"""
Explanation: To explore this ipython notebook, press SHIFT+ENTER to progress to the next cell. Feel free to make changes, enter code, and hack around. You can create new code cells by selecting INSERT->Insert Cell Below
MNIST Example
MNIST ... |
rishuatgithub/MLPy | hugging-face/3. Behind the pipeline.ipynb | apache-2.0 | from transformers import AutoTokenizer
checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
raw_inputs = [
"I've been waiting for a HuggingFace course my whole life.",
"I hate this so much!",
]
inputs = tokenizer(raw_inputs, padding=True, trunc... |
spacedrabbit/PythonBootcamp | Milestone Project 1- Walkthrough Steps Workbook.ipynb | mit | # For using the same code in either Python 2 or 3
from __future__ import print_function
## Note: Python 2 users, use raw_input() to get player input. Python 3 users, use input()
"""
Explanation: Milestone Project 1: Walk-through Steps Workbook
Below is a set of steps for you to follow to try to create the Tic Tac To... |
palrogg/foundations-homework | Data_and_databases/Homework_3_Paul_Ronga.ipynb | mit | !pip3 install bs4
from bs4 import BeautifulSoup
from urllib.request import urlopen
html_str = urlopen("http://static.decontextualize.com/widgets2016.html").read()
document = BeautifulSoup(html_str, "html.parser")
"""
Explanation: Homework assignment #3
These problem sets focus on using the Beautiful Soup library to sc... |
tommyogden/maxwellbloch | docs/usage/structure.ipynb | mit | import numpy as np
"""
Explanation: Structure and Angular Momentum
End of explanation
"""
print(np.sqrt(1/6/3))
print(np.sqrt(1/2/3))
"""
Explanation: Adding Structure
So far we've looked at simple 2 and 3 level systems, but to accurately model a physical system we may need to consider complex structures. For examp... |
verdverm/pypge | notebooks/Dissertation/data_gen/nist_convert.ipynb | mit | from pypge.benchmarks import explicit
import numpy as np
import pandas as pd
# visualization libraries
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# plot the visuals in ipython
%matplotlib inline
"""
Explanation: Explicit 1D Benchmarks
This file demonstrates how to generate, plot, and o... |
YeoLab/single-cell-bioinformatics | notebooks/6.2_Batch_Correction.ipynb | bsd-3-clause | from __future__ import print_function
# Interactive Python (IPython - now Jupyter) widgets for interactive exploration
import ipywidgets
# Numerical python library
import numpy as np
# PLotting library
import matplotlib.pyplot as plt
# Dataframes in python
import pandas as pd
# Linear model correction
import patsy... |
alephcero/adsProject | olds/modelosFinales.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import os
import sys
import simpledbf
%pylab inline
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
from sklearn import linear_model
"""
Explanation: Modelos finales
Libraries
End of explanation
"""
def runModel(... |
feststelltaste/software-analytics | notebooks/Committer Distribution.ipynb | gpl-3.0 | import py2neo
import pandas as pd
import matplotlib.pyplot as plt
# display graphics directly in the notebook
%matplotlib inline
"""
Explanation: Introduction
In the last notebook, I showed you how easy it is to connect jQAssistant/neo4j with Python Pandas/py2neo. In this notebook, I show you a (at first glance) simpl... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/custom/showcase_custom_text_binary_classification_batch.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: Custom training text binary classification model for batch predictio... |
evanmiltenburg/python-for-text-analysis | Chapters/Chapter 20 - Visualization and Statistics.ipynb | apache-2.0 | # This is special Jupyter notebook syntax, enabling interactive plotting mode.
# In this mode, all plots are shown inside the notebook!
# If you are not using notebooks (e.g. in a standalone script), don't include this.
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Chapter 19 - Visualization and ... |
phoebe-project/phoebe2-docs | 2.1/tutorials/meshes.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Accessing and Plotting Meshes
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
"""
%matplotli... |
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