repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
arnaldog12/Manual-Pratico-Deep-Learning | Rede Neural.ipynb | mit | import numpy as np
import _pickle as pkl
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.datasets.samples_generator import make_blobs, make_circles, make_moons, make_classification
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MinMaxScaler, OneHotEncode... |
aaai2018-paperid-62/aaai2018-paperid-62 | evaluation_analysis.ipynb | mit | import pandas as pd
pd.options.mode.chained_assignment = None # default='warn', hides SettingWithCopyWarning
file = 'data/evaluations.csv'
conversion_dict = {'research_type': lambda x: int(x == 'E')}
evaluation_data = pd.read_csv(file, sep=',', header=0, index_col=0, converters=conversion_dict)
print('Samples per co... |
wasit7/PythonDay | notebook/.ipynb_checkpoints/Somkiat's Basic Python-checkpoint.ipynb | bsd-3-clause | x=1
print x
type(x)
x.conjugate()
type(1+2j)
z=1+2j
print z
(1,2)
t=(1,2,"text")
t
t
def foo():
return (1,2)
x,y=foo()
print x
print y
def swap(x,y):
return (y,x)
x=1;y=2
print "{0:d} {1:d}".format(x,y)
x,y=swap(x,y)
print "{:f} {:f}".format(x,y)
dir(1)
x=[]
x.append("text")
x
x.append(1)
x.p... |
molgor/spystats | notebooks/Sandboxes/TensorFlow/Biospytial Models in GPFlow.ipynb | bsd-2-clause | bees = pickNode(Family,name="Apidae")
# Extract the cells
bcells = list(bees.cells)
len(bcells)
"""
Explanation: Let's start by picking a Family Node, e.g. The Bees in Mexico.
End of explanation
"""
### In the form of a anonymous function
getNeighbours = lambda cells : list(set(reduce(lambda a,b : a+b , map(lambd... |
JuBra/cobrapy | documentation_builder/gapfilling.ipynb | lgpl-2.1 | import cobra.test
model = cobra.test.create_test_model("salmonella")
"""
Explanation: Gapfillling
GrowMatch and SMILEY are gap-filling algorithms, which try to to make the minimal number of changes to a model and allow it to simulate growth. For more information, see Kumar et al.. Please note that these algorithms ar... |
blbradley/subset-selector | examples/example.ipynb | mit | from subset_selector import SubsetSelector
ss = SubsetSelector(x, y)
ss.plot()
"""
Explanation: Using subset-selector
Create SubsetSelector with data and plot. y should be a multidimensional array. Each sample from a subset of y is graphed. You can scroll through subsets using the figure's toolbar, select graphs of in... |
d125q/Internet_Robustness | analysis.ipynb | gpl-3.0 | import pickle
from collections import OrderedDict
import datetime
with open('robustness.pickle', 'rb') as handle:
robustness = pickle.load(handle)
robustness = OrderedDict({
attack_type: {
timestamp: OrderedDict(result)
for timestamp, result in sorted(timestamped_results.items()... |
cosmodesi/desibgsdev | Redshift_Efficiency_Study/BGS_moonsep_sanitycheck.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table, vstack
from astropy.io import fits
import seaborn as sns
sns.set(style='white', font_scale=1.1, palette='deep')
from desiutil.log import get_logger, DEBUG
log = get_logger()
from desistudy_qa import qa_zmag, qa_efficiency, q... |
linsalrob/PyFBA | iPythonNotebooks/PATRIC to FBA.ipynb | mit | import sys
import os
import copy
import PyFBA
import re
import inspect
inspect.getfile(PyFBA)
"""
Explanation: How to create and run a gap-filled FBA from PATRIC
The PATRIC (the Pathosystems Resource Integration Center) contains the best collection of well annotated genomes. They also happen to have been annotated by... |
net-titech/CREST-Deep-M | notebooks/JPEG Compression.ipynb | mit | from scipy.misc import imsave, toimage
from os import listdir
from os.path import basename, splitext
import glob
import numpy as np
npy_path = '../compressed-models/alexnet/npy/'
jpg_path = '../compressed-models/alexnet/jpegs/'
gif_path = '../compressed-models/alexnet/gifs/'
png_path = '../compressed-models/alexnet/pn... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.ipynb | bsd-3-clause | %matplotlib inline
from importlib import reload
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy.stats import invwishart, invgamma
# Get the macro dataset
dta = sm.datasets.macrodata.load_pandas().data
dta.index = pd.date_range('1959Q1', '2009Q3', freq='Q... |
SnShine/aima-python | rl.ipynb | mit | from rl import *
"""
Explanation: Reinforcement Learning
This IPy notebook acts as supporting material for Chapter 21 Reinforcement Learning of the book Artificial Intelligence: A Modern Approach. This notebook makes use of the implementations in rl.py module. We also make use of implementation of MDPs in the mdp.py m... |
mne-tools/mne-tools.github.io | 0.23/_downloads/8eeab316a5e011839687fbb76fb65f29/cwt_sensor_connectivity.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne import io
from mne.connectivity import spectral_connectivity, seed_target_indices
from mne.datasets import sample
from mne.time_frequency import AverageTFR
print(__doc__)
"""
Explanation: Compute... |
ptkool/spark | python/docs/source/getting_started/quickstart.ipynb | apache-2.0 | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
"""
Explanation: Quickstart
This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of RDDs. When Spark transforms data, it does not immediately compu... |
ispamm/tensorflow-tutorial | 3-TensorBoard.ipynb | gpl-3.0 | from sklearn import datasets, model_selection
data = datasets.load_breast_cancer()
X = data['data']
y = data['target'].reshape(-1, 1)
"""
Explanation: Visualizzare statistiche con la TensorBoard
Prerequisiti per il tutorial:
* T1 - Reti neurali feedforward
Contenuti del tutorial:
1. Struttura della TensorBoard.
2. Ese... |
pvalienteverde/MeetUpIntroMLySistemasRecomendacion | Sistemas De Recomendacion.ipynb | mit | import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.neighbors import NearestNeighbor... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/tutorials/pipeline/pipeline_tutorial_lesson_7.ipynb | unlicense | # Dollar volume factor
dollar_volume = AverageDollarVolume(window_length=30)
# High dollar volume filter
high_dollar_volume = (dollar_volume > 10000000)
# Average close price factors
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10, mask=high_dollar_volume)
mean_close_30 = SimpleMo... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Python-for-Data-Analysis/NumPy/Numpy Exercise - Solutions.ipynb | mit | import numpy as np
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
NumPy Exercises - Solutions
Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks and then you'll be asked some more complicated questions.
Import NumPy ... |
RoebideBruijn/datascience-intensive-course | exercises/linear_regression/Mini_Project_Linear_Regression.ipynb | mit | # special IPython command to prepare the notebook for matplotlib and other libraries
%pylab inline
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import sklearn
import seaborn as sns
# special matplotlib argument for improved plots
from matplotlib import rcParams
... |
Upward-Spiral-Science/the-vat | Code/exploratory_analysis_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
import scipy
from sklearn.decomposition import PCA
import skimage.measure
# pretty charting
import seaborn as sns
sns.set_palette('muted')
sns.set... |
BDannowitz/polymath-progression-blog | book-tracking/book-tracking-2019-07.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
"""
Explanation: 2019 Book Update
End of explanation
"""
book_df = pd.read_csv('goodreads_library_export.csv')
book_df['Date Added'] = pd.to_datetime(book_df['Date Added'],
format="%Y/%m/%d")
book_df.columns
"""
Explanation: Read in my G... |
ES-DOC/esdoc-jupyterhub | notebooks/snu/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/emac-2-53-vol/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'emac-2-53-vol', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: EMAC-2-53-VOL
Sub-Topics: Radiativ... |
chesters99/ghpages | content/holybooks.ipynb | gpl-3.0 | import re
import glob
import lxml.html
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
%matplotlib inline
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.deco... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex29-Identify Patterns in the Coupled Fields of SLP and SST through Canonical Correlation Analysis.ipynb | mit | %matplotlib inline
import numpy as np
import xarray as xr
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import CCA
import cartopy.crs as ccrs
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.gridspec as gridspec
plt.rcParams['figure.fig... |
cruzpmmq/fmp-soma-vadis | tutorial.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import time
plt.style.use('ggplot')
np.random.seed(1)
"""
Explanation: Função Massa de Probabilidade da soma (e diferença) de Variáveis Aleatórias Discretas Independentes
2017 Pedro Cruz up200506513@fc.up.pt
End of explanation
"""
# Variável Aleatória X1
S_X1 = np... |
dereneaton/ipyrad | tests/cookbook-treemix-pedicularis.ipynb | gpl-3.0 | ## conda install -c ipyrad ipyrad
## conda install -c ipyrad treemix
## conda install -c eaton-lab toytree
import ipyrad.analysis as ipa
import toytree
import toyplot
import numpy as np
print 'ipyrad', ipa.__version__
print 'toytree', toytree.__version__
print 'toyplot', toyplot.__version__
! treemix --version | grep... |
PedramNavid/MachineLearningNanodegree | 3_Titanic/titanic_survival_exploration.ipynb | gpl-3.0 | import sys
print(sys.version)
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few ... |
infotranecon/IEtools | Dynamic Equilibrium Example-Bitcoin.ipynb | mit | import numpy as np
import pylab as pl
from IEtools import *
import xlrd
%pylab inline
"""
Explanation: These were run in Python 3 using the Anaconda distribution.
The dynamic equilibrium model is described in more detail here:
http://informationtransfereconomics.blogspot.com/2017/01/dynamic-equilibrium-presentation.ht... |
GoogleCloudPlatform/mlops-on-gcp | continuous_training/composer-mlflow/chicago_taxi_fare_experiment.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
from scipy import stats
from sklearn.ensemble import RandomForestRegressor
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.pipeline import Pip... |
joommf/tutorial | workshops/Durham/.ipynb_checkpoints/tutorial0_first_notebook-checkpoint.ipynb | bsd-3-clause | import oommfc as oc
import discretisedfield as df
%matplotlib inline
print(df.__file__)
"""
Explanation: Tutorial 0 - First JOOMMF notebook
The goal of this tutorial is for all participants to familiarise themselves with running JOOMMF simulations in Jupyter notebook. The only thing you need to know for this tutorial ... |
ADicksonLab/wepy | info/tutorials/data_analysis/README.ipynb | mit | import os
import os.path as osp
import random as rand
from pathlib import Path
import shutil as sh
import warnings
from PIL import Image
warnings.filterwarnings("ignore")
rand.seed(33)
## Inputs and Outputs
input_dir = Path("input")
outputs_dir = Path("_output")
media_dir = input_dir / 'media'
sh.rmtree(outputs_... |
rishuatgithub/MLPy | nlp/UPDATED_NLP_COURSE/04-Semantics-and-Sentiment-Analysis/03-Sentiment-Analysis-Assessment.ipynb | apache-2.0 | # Import spaCy and load the language library. Remember to use a larger model!
# Choose the words you wish to compare, and obtain their vectors
# Import spatial and define a cosine_similarity function
# Write an expression for vector arithmetic
# For example: new_vector = word1 - word2 + word3
# List the to... |
ZhukovGreen/UMLND | 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 matplotlib.pyplot as plt
# Import supplementary visualizations code visuals.py
import visuals as vs
# Load the Boston housing dataset
data = pd.read_csv('housing.csv')
prices =... |
Mathtodon/Mathtodon.github.io | Final_Version_With_Decision_Tree.ipynb | cc0-1.0 | tree1 = tree.DecisionTreeClassifier( criterion ='entropy',random_state = 0)
fittedtree=tree1.fit( X_train, y_train)
#print metrics.confusion_matrix(y_train, fittedtree.predict(X_train))
#print metrics.accuracy_score(y_train, fittedtree.predict(X_train))
cross_validation.cross_val_score(tree1, X_train, y_train, cv = ... |
jorisvandenbossche/DS-python-data-analysis | notebooks/python_recap/04-reusing_code.ipynb | bsd-3-clause | %%file test.py
message = "Hello how are you?"
for word in message.split():
print(word)
"""
Explanation: Python the basics: Reusing code
DS Data manipulation, analysis and visualization in Python
May/June, 2021
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvand... |
nehal96/Deep-Learning-ND-Exercises | Weight Initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
deepmind/educational | colabs/summer_schools/intro_to_unsupervised_learning.ipynb | apache-2.0 | import numpy as np
# Basic plotting in style:
import matplotlib.pyplot as plt
import seaborn as sns
# Animations:
from matplotlib import animation
from IPython.display import HTML
#@title Data generating functions
# 0, 2, 5, 10, 80
#https://tall.life/height-percentile-calculator-age-country/
HIDDEN_MU = [49.3, 50, 86.... |
simmy88/UdacityMLND | Customer Segments/customer_segments.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the wholesale custo... |
jaredvonhalle/CSC478WorldFoodFacts | Regression.ipynb | mit | x = data.as_matrix()
y = target.as_matrix()
x = np.array([np.concatenate((v,[1])) for v in x]) #add column of ones to the end of the data set
print x
linreg = LinearRegression()
linreg.fit(x,y)
p = linreg.predict(x)
p
err = abs(p-y)
err
total_error = np.dot(err,err)
rmse_train = np.sqrt(total_error/len(p))
rmse_tr... |
UWSEDS/LectureNotes | PreFall2018/Python Functions.ipynb | bsd-2-clause | import numpy as np
# Determine if num is prime
# This code has a bug. What is it?
# Also, the efficiency of the code can be improved. How?
num = 8
upper = int(np.sqrt(num)) + 1:
if num % integer == 0:
print("Not prime!")
is_prime = False
import pdb; pdb.set_trace()
if is_prime:
print("Is... |
armgilles/presentation | EPSI/I5/Qualite des donnees/EP3/analyse_commune.ipynb | mit | commune_metropole = pd.read_csv('data/commune_metropole.csv', encoding='utf-8')
commune_metropole.shape
commune_metropole.head()
insee = pd.read_csv('data/insee.csv',
sep=";", # séparateur du fichier
dtype={'COM' : np.dtype(str)}, # On force la colonne CO... |
Amarchuk/2FInstability | notebooks/2f/.ipynb_checkpoints/instabilities-checkpoint.ipynb | gpl-3.0 | from IPython.display import HTML
from IPython.display import Image
from PIL import Image as ImagePIL
%pylab
%matplotlib inline
"""
Explanation: Неустойчивости
Тут будут все функции, имеющие отношение к неустойчивостям, чтобы не копировать их каждый раз.
Тесты на эти функции в соседнем ноутбуке.
End of explanation
"""... |
MBARIMike/biofloat | notebooks/explore_surface_oxygen_and_WOA.ipynb | mit | from biofloat import ArgoData
from os.path import join, expanduser
ad = ArgoData(cache_file = join(expanduser('~'),
'biofloat_fixed_cache_variablesDOXY_ADJUSTED-PSAL_ADJUSTED-TEMP_ADJUSTED_wmo1900650-1901157-5901073.hdf'))
"""
Explanation: Explore surface Argo oxygen float and World Ocean Atlas data
Get cached s... |
tsob/cnn-music-structure | src/simple_cnn.ipynb | mit | # A bit of setup
# Usual imports
import time
import numpy as np
import matplotlib.pyplot as plt
# Notebook plotting magic
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# For auto-reloadin... |
HazyResearch/dd-genomics | dsr/DSR_search.ipynb | apache-2.0 | def root_tree_at(new_root):
"""
Given a node, remove all parents and add as children
so that this node becomes the new root
"""
# Check to see if the new root has any parents...
parents = new_root.xpath("..")
if len(parents) > 0:
p = root_tree_at(parents[0])
p.remove(new_root... |
sumedhasingla/TubeTK | Examples/MergeAdjacentImages/MergeAdjacentImages.ipynb | apache-2.0 | import os
import sys
# Path for TubeTK libs
TubeTK_BUILD_DIR=None
if 'TubeTK_BUILD_DIR' in os.environ:
TubeTK_BUILD_DIR = os.environ['TubeTK_BUILD_DIR']
if not os.path.exists(TubeTK_BUILD_DIR):
print('TubeTK_BUILD_DIR not found!')
print(' Set environment variable')
sys.exit(1)
"""
Explanation: MergeA... |
maciejkula/lightfm | examples/ann/annoy_nsmlib_example.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
from sklearn.metrics import pairwise_distances
import time
from lightfm.datasets import fetch_movielens
movielens = fetch_movielens()
for key, value in movielens.items():
print(key, type(value), value.shape)
train = movielens['train']
test = movielens['te... |
mne-tools/mne-tools.github.io | 0.19/_downloads/e6f2b45ae501bec73cddc70f08093041/plot_40_visualize_raw.ipynb | bsd-3-clause | import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(tmax=60).load_data()
"""
Explanation: Built-in plotti... |
GoogleCloudPlatform/training-data-analyst | blogs/batch_predictions/batch_predictions_keras.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
from tensorflow import keras
tf.__version__
# Set GCP configs if using Cloud AI Platform
import os
PROJECT = "your-gcp-project-here" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
BUCKET = "your-gcp-bucket-here"
... |
poppy-project/community-notebooks | tutorials-education/poppy_ergo_jr__decouverte_du_robot/TP1_mouvement_et_fonctions_eleve_vf.ipynb | lgpl-3.0 | def f_init(bot, pos_initiale):
#debut du corps de la procédure
"""
Explanation: Initialisation
Ecrire un jeu d'instructions permettant
d'initialiser la vitesse de chacun des moteurs à 30°/s.
de mettre les moteurs poppy.m1 à poppy.m6 dans les positions données par la liste suivante pos_init = [0, -90, 30, 0, 60,... |
setiQuest/ML4SETI | tutorials/Step_4_Classify_with_WatsonVR_old.ipynb | apache-2.0 | #!pip install --user --upgrade watson-developer-cloud
#Making a local folder to put my data.
#NOTE: YOU MUST do something like this on a Spark Enterprise cluster at the hackathon so that
#you can put your data into a separate local file space. Otherwise, you'll likely collide with
#your fellow participants.
my_tea... |
trangel/Insight-Data-Science | general-docs/nlp-preprocessing/notebook_drafts/NLP-preprocessing-NYT-opeds.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import nltk
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.snowball import SnowballStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
"""
Explanation: Notebook for prepr... |
craigrshenton/home | notebooks/notebook10.ipynb | mit | import pandas as pd
from pandas import read_csv
pd.set_option('precision', 3) # set display precision to 3 significant figures
filename = 'C:/Users/craigrshenton/Desktop/Dropbox/python/python_pro/machine_learning_mastery_with_python/machine_learning_mastery_with_python_code/chapter_07/pima-indians-diabetes.data.csv'
n... |
theandygross/HIV_Methylation | Figure3.ipynb | mit | import NotebookImport
from Setup.Imports import *
from Setup.Read_HIV_Data import *
import Setup.DX_Imports as dx
"""
Explanation: Figure 4
End of explanation
"""
import Parallel.Age_HIV_Features as fx
sns.set_context("paper", rc={"lines.linewidth": 2.5,
"font_size":14})
sns.set_styl... |
mauroalberti/geocouche | pygsf/checks/Test divergence and curl module.ipynb | gpl-2.0 | from pygsf.mathematics.arrays import *
from pygsf.spatial.rasters.geotransform import *
from pygsf.spatial.rasters.fields import *
"""
Explanation: Test divergence and curl module
This document present tests on divergence and curl module calculation using pygsf.
Preliminary settings
The modules to import for dealing... |
mne-tools/mne-tools.github.io | 0.21/_downloads/e807ecd348331c3d789aa11b874576a5/plot_decoding_time_generalization_conditions.ipynb | bsd-3-clause | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
f... |
bearing/dosenet-analysis | calibration/Detector_Comparison.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
#from detector 1:
d1_U = np.genfromtxt('uranium_test_2019-02-19_D3S.csv', delimiter= ",").T
d1_Th = np.genfromtxt('thorium_test_2019-02-19_D3S.csv', delimiter= ",").T
#from detector 2:
d2_U = np.genfromtxt('Uranium_102566_2019-03-2... |
flamingbear/ipython-notebooks | notebooks/EASE-Grid Sea Ice Age Python Example.ipynb | mit | !wget -qN ftp://sidads.colorado.edu/pub/DATASETS/nsidc0611_seaice_age/data/2012/iceage-2012w19.bin
"""
Explanation: EASE-Grid Sea Ice Age, Version 2
Short Jupyter notebook describing how to read the Sea Ice Age Dataset.
The dataset documentation and data can be found here: http://nsidc.org/data/nsidc-0611
To begin: I ... |
estnltk/episode-miner | docs/episode-miner.ipynb | gpl-2.0 | from episode_miner import EventText, EventSequences, Episode, Episodes
from estnltk.taggers import EventTagger
from IPython.display import HTML, FileLink
"""
Explanation: Usage
End of explanation
"""
event_vocabulary = [{'term': 'üks'},
{'term': 'kaks'}]
event_tagger = EventTagger(event_voca... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/dev/n16_hallucinating_with_predictor.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... |
Lstyle1/Deep_learning_projects | weight-initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
w4zir/ml17s | .ipynb_checkpoints/python-tutorial-checkpoint.ipynb | mit | def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[int(len(arr) / 2)]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
print (quicksort([3,6,8,10,1,2]))
"""
Ex... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/nicam16-7s/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'nicam16-7s', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MIROC
Source ID: NICAM16-7S
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Tu... |
sarathid/Learning | Deep_learning_ND/dlnd_image_classification.ipynb | gpl-3.0 | """
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'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
rahulbakshee/TensorFlow-Basics | 03_tf_mnist_01_softmax_flattened_input .ipynb | mit | # import
import tensorflow as tf
# import mnist data
# data hosted at http://yann.lecun.com/exdb/mnist/
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
"""
Explanation: inspired from https://www.tensorflow.org/get_started/mnist/beginners
End of ... |
CELMA-project/CELMA | derivations/laplacianInversion/ForwardsBackwardsNonSymmetric.ipynb | lgpl-3.0 | from IPython.display import display
from sympy import init_printing
from sympy import symbols, expand, together, as_finite_diff, collect
from sympy import Function, Eq, Subs
from collections import deque
init_printing()
def finiteDifferenceOfOneTerm(factors, wrt, stencil):
"""
Finds the finite different appro... |
swirlingsand/self-driving-car-nanodegree-nd013 | play/CarND-Camera-Calibration-master/CarND-Camera-Calibration-master/camera_calibration.ipynb | mit | %matplotlib inline
import matplotlib
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
# %matplotlib qt
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*8,3), np.float32)
objp[:,:2] = np.mgrid[0:8, 0:6].T.reshape(-1,2)
# Arrays to store object points and... |
fionapigott/Data-Science-45min-Intros | kernel_smoothing/Regression with kernel smoothing.ipynb | unlicense | import sys
import heapq
import sklearn
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
%matplotlib inline
"""
Explanation: Regression with kernel smoothing
Or
"Mr. Smoothie's Smoothie machine Smoother", a Story in Seven Parts
What are we doing here?
We're doing regression!
start with... |
chrander/pygameday | examples/example.ipynb | mit | from pygameday import GameDayClient
from datetime import datetime
"""
Explanation: pygameday Example Notebook
This Jupyter notebook walks through basic pygameday usage.
First, import the GameDayClient object. We've also imported dateutil.parser to help with converting date strings to date objects.
End of explanation
... |
qingshuimonk/LolStats | getStarted.ipynb | mit | from lolcrawler_util import read_key
api_key = read_key()
# try hello world
import urllib2
import json
response = urllib2.urlopen('https://na.api.pvp.net/api/lol/na/v1.4/summoner/by-name/RiotSchmick?api_key=' + api_key)
data = json.load(response)
print(json.dumps(data, indent=2))
"""
Explanation: Get API_KEY
API_KEY c... |
jinzishuai/learn2deeplearn | deeplearning.ai/C5.SequenceModel/Week2_NLP_WordEmbeddings/assignment/Emojify/Emojify - learners.ipynb | gpl-3.0 | import numpy as np
from emo_utils import *
import emoji
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Emojify!
Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.
Have you ever wanted to make your text messages more expressive? You... |
bashtage/statsmodels | examples/notebooks/statespace_cycles.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
endog = DataReader('UNRATE', 'fred', start='1954-01-01')
endog.index.freq = endog.index.inferred_freq
"""
Explanation: Trends and cycles in unemployment... |
sshleifer/object_detection_kitti | object_detection/kitti-inference.ipynb | apache-2.0 | import numpy as np
import os
import pickle
import six.moves.urllib as urllib
import sys
sys.path.append("..")
import tarfile
import tensorflow as tf
import zipfile
from object_detection.eval_util import evaluate_detection_results_pascal_voc
from collections import defaultdict
from io import StringIO
from matplotlib im... |
yunfeiz/py_learnt | sample_code/tushare_pyecahrts_v0.1.ipynb | apache-2.0 | import tushare as ts
import pandas as pd
from IPython.display import HTML
stock_selected='600487'
#历年前十大股东持股情况
#df1为季度统计摘要，data1为前十大持股明细统计
df1, data1 = ts.top10_holders(code=stock_selected, gdtype='0') #gdtype等于1时表示流通股，默认为0
#df1, data1 = ts.top10_holders(code='002281', year=2015, quarter=1, gdtype='1')
df1 = df1.sort... |
chris1610/pbpython | notebooks/Learn_Pandas-Weighted_Average.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
"""
Explanation: Learning More About Pandas by Building and Using a Weighted Average Function
This notebook is based on the article on Pbpython.com. Please reference it for the background and additional details
End of explanation
"""
sales = pd.read_excel("https://github.com/ch... |
bashtage/statsmodels | examples/notebooks/deterministics.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.rc("figure", figsize=(16, 9))
plt.rc("font", size=16)
"""
Explanation: Deterministic Terms in Time Series Models
End of explanation
"""
from statsmodels.tsa.deterministic import DeterministicProcess
index = pd.RangeIndex(0, 100)
det_proc = ... |
psiq/gdsfactory | notebooks/10_YAML_component_connections.ipynb | mit | import pp
gap = 0.2
wg_width = 0.5
length = 10
yaml = f"""
instances:
sl:
component: coupler_symmetric
settings:
gap: {gap}
wg_width: {wg_width}
sr:
component: coupler_symmetric
settings:
gap: {gap}
wg_width: {wg_width}
cs:
component: coupler_s... |
ioos/notebooks_demos | notebooks/2017-07-25-HFRadar_currents.ipynb | mit | from IPython.display import HTML
url = (
"https://cordc.ucsd.edu/projects/mapping/maps/fullpage.php?"
"ll=29.061888,-87.373643&"
"zm=7&"
"mt=&"
"rng=0.00,50.00&"
"us=1&"
"cs=4&"
"res=6km_h&"
"ol=3&"
"cp=1"
)
iframe = (
'<iframe src="{src}" width="750" height="450" style="b... |
rigetticomputing/grove | examples/JordanGradient.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
from mock import patch
from grove.alpha.jordan_gradient.jordan_gradient import estimate_gradient
"""
Explanation: Estimating the gradient of a quadratic potential function using Jordan's quantum gradient estimation algorithm (10.1103/PhysRevLett.95.050501).
First, im... |
kimegitee/deep-learning | tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
omarcostahamido/One-Quick-Solution_Patches | Other/las-vegas-dice-with-comments.ipynb | agpl-3.0 | # begin by importing the essential libraries from qiskit
from qiskit import IBMQ, Aer
from qiskit import ClassicalRegister, QuantumRegister, QuantumCircuit
# The following options are recomended for Jupter notebook visualization using matplotlib
%matplotlib inline
"""
Explanation: Challenge 1: Break the Bank - Las Ve... |
wd15/chimad-phase-field | hackathons/hackathon1/fipy/1b.ipynb | mit | %matplotlib inline
import sympy
import fipy as fp
import numpy as np
A, c, c_m, B, c_alpha, c_beta = sympy.symbols("A c_var c_m B c_alpha c_beta")
f_0 = - A / 2 * (c - c_m)**2 + B / 4 * (c - c_m)**4 + c_alpha / 4 * (c - c_alpha)**4 + c_beta / 4 * (c - c_beta)**4
print f_0
sympy.diff(f_0, c, 2)
"""
Explanation: Ta... |
pierresendorek/tensorflow_crescendo | second_simplest_working_tensorflow_notebook_ever.ipynb | lgpl-3.0 | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: (Second) Simplest Working Tensorflow Notebook Ever
In this notebook, we show how to use TensorFlow on a very simple task : estimating the coefficients $a$ and $b$ of an affine function $y = ax + b$.
Here, in... |
hungiyang/StatisticalMethods | examples/XrayImage/FirstLook.ipynb | gpl-2.0 | from __future__ import print_function
import astropy.io.fits as pyfits
import numpy as np
import os
import urllib
import astropy.visualization as viz
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 10.0)
"""
Explanation: A First Look at an X-ray Image Dataset
Images are data.... |
seg/2016-ml-contest | BGC_Team/Facies Prediction_submit6.ipynb | apache-2.0 | ##### import basic function
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
##### import stuff from scikit learn
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.metrics import confusion_matrix, make_scorer, f1_score,... |
enbanuel/phys202-2015-work | assignments/assignment08/InterpolationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp1d
"""
Explanation: Interpolation Exercise 1
End of explanation
"""
# YOUR CODE HERE
with np.load('trajectory.npz') as data:
t = data['t']
x = data['x']
y = data['y']
print(x)
... |
cleemesser/eeg-hdfstorage | notebooks/vizAbsenceSz.ipynb | bsd-3-clause | # import libraries
from __future__ import print_function, division, unicode_literals
%matplotlib inline
# %matplotlib notebook
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import h5py
from pprint import pprint
import stacklineplot # local copy
# matplotlib.rcParams['figure... |
geography-munich/sciprog | material/sub/jrjohansson/Lecture-3-Scipy.ipynb | apache-2.0 | # what is this line all about? Answer in lecture 4
%matplotlib inline
import matplotlib.pyplot as plt
from IPython.display import Image
"""
Explanation: SciPy - Library of scientific algorithms for Python
J.R. Johansson (jrjohansson at gmail.com)
The latest version of this IPython notebook lecture is available at http... |
sastels/Onboarding | 4.5 - Sorting exercises.ipynb | mit | # Provided simple test() function used in main() to print
# what each function returns vs. what it's supposed to return.
def test(got, expected):
if got == expected:
prefix = ' OK '
else:
prefix = ' X '
print '%s got: %s expected: %s' % (prefix, repr(got), repr(expected))
"""
Explanation: ... |
joashxu/NYCSubwayDataAnalysis | NYCSubway.ipynb | mit | %matplotlib inline
import numpy as np
import pandas
import matplotlib.pyplot as plt
try:
import seaborn as sns
sns.set_palette("deep", desat=.6)
sns.set_context(rc={"figure.figsize": (10, 6)})
except ImportError:
sns = None
df = pandas.read_csv('turnstile_data_master_with_weather.csv')
"""
Expl... |
karlstroetmann/Algorithms | Python/Chapter-05/Quick-Sort-Array.ipynb | gpl-2.0 | def logging(f):
def logged_f(*a):
print(f'{f.__name__}{a}')
r = f(*a)
print(f'{f.__name__}{a} = {r}')
return r
return logged_f
"""
Explanation: The function logging is used as a decorator. It takes a function
f as its argument and returns a new function logged_f that returns
... |
JannesKlaas/MLiFC | Week 2/Ch. 11 - Evaluating and deploying the model.ipynb | mit | import keras
from keras.models import load_model
model = load_model('./support_files/Ch11_model.h5')
"""
Explanation: Ch. 11 - Evaluating and deploying the model
In chapter 10 we have learned a lot of new tricks and tools to build neural networks that can deal with stuructured data such as the bank marketing dataset.... |
WomensCodingCircle/CodingCirclePython | Lesson07_ListsandTuples/Lists and Tuples - after class.ipynb | mit | sushi_order = ['unagi', 'hamachi', 'otoro']
prices = [6.50, 5.50, 15.75]
print(sushi_order)
print(prices)
"""
Explanation: Lists and Tuples
Lists Recap
A list is a sequence of values. These values can be anything: strings, numbers, booleans, even other lists.
To make a list you put the items separated by commas betwee... |
tritemio/multispot_paper | realtime kinetics/Simulated Kinetic Curve Fit - Run-All.ipynb | mit | from nbrun import run_notebook
"""
Explanation: Kinetic Curve Simulation Fit
<p class=lead>This notebook performs fits of simulated Kinetic Curves for different kinetics parameters. A single [template notebook](Simulated Kinetic Curve Fit - Template.ipynb) is executed several times, once for each set of parameters.
<p... |
aemreunal/15688-project | NYC Traffic Collision Data.ipynb | mit | import os
from os import path
from bs4 import BeautifulSoup
import collections
import numpy as np
import pandas as pd
import re
import requests
import scipy
from scipy.cluster.vq import kmeans, kmeans2, vq
from scipy.spatial.distance import cdist
from IPython.core.display import display, HTML
from colour import Color
... |
sarathid/Learning | Deep_learning_ND/Week 1/dlnd-your-first-network/DLND-your-first-network/dlnd-your-first-neural-network.ipynb | gpl-3.0 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
kfollette/ASTR200-Spring2017 | Labs/Lab8/Lab 8.ipynb | mit | from numpy import *
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: <small><i>This notebook is based on one put together by Mark Krumholz and has been modified to suit the purposes of this course, including expansion/modification of explanations and additional exercises. The original can be found a... |
gtzan/mir_book | data_mining_naive_bayes.ipynb | cc0-1.0 | %matplotlib inline
import matplotlib.pyplot as plt
import pickle
import numpy as np
# load some lyrics bag of words data, binarize, separate matrix rows by genre
data = np.load('data.npz')
a = data['arr_0']
a[a > 0] = 1
labels = np.load('labels.npz')
labels = labels['arr_0']
dictionary = pickle.load(open('dictiona... |
2015fallhw/user9999 | content/notebook/SIGEvolution.ipynb | agpl-3.0 | from deap import base, creator
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
"""
Explanation: DEAP - Enabling Nimbler Evolutions
by François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau, and Christian Gagné
<img src="files/images/deap_highlights.png" width="400px" style="f... |
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