repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
ceholden/ceholden.github.io | _drafts/2016-09-09-Landsat-Metadata-Dask.ipynb | mit | import dask.dataframe as ddf
columns = {
'sceneID': str,
'sensor': str,
'path': int,
'row': int,
'acquisitionDate': str,
'cloudCover': float,
'cloudCoverFull': float,
'sunElevation': float,
'sunAzimuth': float,
'DATA_TYPE_L1': str,
'GEOMETRIC_RMSE_MODEL': float,
'GEOMETR... |
Jackporter415/phys202-2015-work | assignments/assignment03/NumpyEx03.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 3
Imports
End of explanation
"""
def brownian(maxt, n):
"""Return one realization of a Brownian (Wiener) process with n steps... |
pierre-rouanet/aupyom | examples/Live modification of the pitch and time-scale of sounds.ipynb | gpl-3.0 | from aupyom import Sampler, Sound
from aupyom.util import example_audio_file
sampler = Sampler()
audio_file = example_audio_file()
s1 = Sound.from_file(audio_file)
"""
Explanation: Live modification of the pitch and time-scale of sounds
Aupyom was mainly designed so it is really easily to modify the pitch ant time-s... |
karlstroetmann/Formal-Languages | ANTLR4-Python/Earley-Parser/Earley-Parser.ipynb | gpl-2.0 | !type simple.g
!cat simple.g
"""
Explanation: Implementing an Earley Parser
A Grammar for Grammars
Earley's algorithm has two inputs:
- a grammar $G$ and
- a string $s$.
It then checks whether the string $s$ can be parsed with the given grammar.
In order to input the grammar in a natural way, we first have to develop... |
nholtz/structural-analysis | Devel/Old/v04-old/Milestones/Frame2D-v04-Milestone1.ipynb | cc0-1.0 | from __future__ import print_function
import salib as sl
sl.import_notebooks()
from Tables import Table
from Nodes import Node
from Members import Member
from LoadSets import LoadSet, LoadCombination
from NodeLoads import makeNodeLoad
from FixedEndForces import makeMemberLoad
from collections import OrderedDict, defau... |
Olsthoorn/TransientGroundwaterFlow | exercises_notebooks/Sudden_head_ change_section_54.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
from scipy.special import erfc # scipy.special has numerous special mathematical functions
"""
Explanation: Sudden head change at $x=0$
IHE, Delft, Transient Groundwater
Exercises in class, 2012-01-07
@T.N.Olsthoorn
dddk
Loading modules
End of explanation
"""
x = n... |
rlopc/datcom-labs | ugr-datcom-ncc_ni-labs/ugr-datcom-ncc_ni-lab_00/ex_06-numerical_integration_hh_squid_axon.ipynb | gpl-3.0 | %matplotlib inline
import brian2 as b2
import matplotlib.pyplot as plt
from neurodynex.hodgkin_huxley import HH
from neurodynex.tools import input_factory
import jupyterthemes as jt
jt.get_themes()
jt.
HH.getting_started()
"""
Explanation: Adaptative Integrate And Fire Model
End of explanation
"""
I_min = 2.30
cu... |
harpolea/r3d2 | docs/states.ipynb | mit | from r3d2 import eos_defns, State
eos = eos_defns.eos_gamma_law(5.0/3.0)
U = State(1.0, 0.1, 0.0, 2.0, eos)
"""
Explanation: States
A Riemann Problem is specified by the state of the material to the left and right of the interface. In this hydrodynamic problem, the state is fully determined by an equation of state an... |
zzsza/Datascience_School | 30. 딥러닝/07. RNN 기본 구조와 Keras를 사용한 RNN 구현.ipynb | mit | s = np.sin(2 * np.pi * 0.125 * np.arange(20))
plt.plot(s, 'ro-')
plt.xlim(-0.5, 20.5)
plt.ylim(-1.1, 1.1)
plt.show()
"""
Explanation: RNN 기본 구조와 Keras를 사용한 RNN 구현
신경망을 사용하여 문장(sentence)이나 시계열(time series) 데이터와 같은 순서열(sequence)를 예측하는 문제를 푸는 경우, 예측하고자 하는 값이 더 오랜 과거의 데이터에 의존하게 하려면 시퀀스를 나타내는 벡터의 크기를 증가시켜야 한다. 예를 들어 10,000... |
plipp/informatica-pfr-2017 | nbs/2/2-Numerical-Data-Pandas-Self-Employment-Rates-DF-Exercise.ipynb | mit | countries = ['AUS', 'AUT', 'BEL', 'CAN', 'CZE', 'FIN', 'DEU', 'GRC', 'HUN', 'ISL', 'IRL', 'ITA', 'JPN',
'KOR', 'MEX', 'NLD', 'NZL', 'NOR', 'POL', 'PRT', 'SVK', 'ESP', 'SWE', 'CHE', 'TUR', 'GBR',
'USA', 'CHL', 'COL', 'EST', 'ISR', 'RUS', 'SVN', 'EU28', 'EA19', 'LVA']
male_selfemployment_rate... |
Housebeer/Natural-Gas-Model | backup/Matching Market v1.ipynb | mit | import random as rnd
class Supplier():
def __init__(self):
self.wta = []
# the supplier has n quantities that they can sell
# they may be willing to sell this quantity anywhere from a lower price of l
# to a higher price of u
def set_quantity(self,n,l,u):
for i in range(n):
... |
bjornaa/ladim | examples/line/holoviews.ipynb | mit | import numpy as np
from netCDF4 import Dataset
import holoviews as hv
from postladim import ParticleFile
hv.extension('bokeh')
"""
Explanation: Plotting particle distributions with holoviews
End of explanation
"""
# Read bathymetry and land mask
with Dataset('../data/ocean_avg_0014.nc') as ncid:
H = ncid.variabl... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day08-modeling-viral-load-2/Day_8_Pre_Class_Notebook_SOLUTIONS.ipynb | agpl-3.0 | # Imports the functionality that we need to display YouTube videos in a Jupyter Notebook.
# You need to run this cell before you run ANY of the YouTube videos.
from IPython.display import YouTubeVideo
# WATCH THE VIDEO IN FULL-SCREEN MODE
YouTubeVideo("8_wSb927nH0",width=640,height=360) # Complex 'if' statements... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/building_production_ml_systems/solutions/0_export_data_from_bq_to_gcs.ipynb | apache-2.0 | from google import api_core
from google.cloud import bigquery
"""
Explanation: Exporting data from BigQuery to Google Cloud Storage
In this notebook, we export BigQuery data to GCS so that we can reuse our Keras model that was developed on CSV data.
End of explanation
"""
# Change below if necessary
PROJECT = !gclou... |
chesters99/ghpages | content/loan-default-prediction.ipynb | gpl-3.0 | %%time
print('Reading: loan_stat542.csv into loans dataframe...')
loans = pd.read_csv('loan_stat542.csv')
print('Loans dataframe:', loans.shape)
test_ids = pd.read_csv('Project3_test_id.csv', dtype={'test1':int,'test2':int, 'test3':int,})
print('ids dataframe:', test_ids.shape)
trains = []
tests = []
labels = []
for... |
SylvainCorlay/bqplot | examples/Applications/Visualizing the US Elections.ipynb | apache-2.0 | from __future__ import print_function
import pandas as pd
import numpy as np
from ipywidgets import VBox, HBox
import os
codes = pd.read_csv(os.path.abspath('../data_files/state_codes.csv'))
try:
from pollster import Pollster
except ImportError:
print('Pollster not found. Installing Pollster..')
try:
... |
metpy/MetPy | v0.8/_downloads/Skew-T_Layout.ipynb | bsd-3-clause | import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, Hodograph, SkewT
from metpy.units import units
"""
Explanation: Skew-T with Complex Layout
Combine a S... |
4DGenome/Chromosomal-Conformation-Course | Notebooks/A5-Modeling_-_analysis_of_3D_models.ipynb | gpl-3.0 | from pytadbit import load_structuralmodels
models_t0 = load_structuralmodels('T0.models')
model= models_t0[0]
"""
Explanation: Descriptive statisitcs on a sinlge model
End of explanation
"""
print model.radius_of_gyration()
"""
Explanation: Calculate the radius of gyration of a model (median distance of all part... |
drericstrong/Blog | 20161220_Dice Advantage and Disadvantage.ipynb | agpl-3.0 | import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
#Remember that Python is zero-indexed, and the range function will return
#up to one value less than the second parameter
roll1poss = list(range(1,21))
roll2poss = list(range(1,21))
#This next line might... |
ilogue/pyrsa | demos/exercise_all.ipynb | lgpl-3.0 | import numpy as np
from scipy import io
import matplotlib.pyplot as plt
import pyrsa
"""
Explanation: Getting started exercise for RSA3.0
Introduction
In these three exercises you will get an introduction to the functionality of the new pyRSA-toolbox for inferring the underlying model representation based on measured ... |
rmenegaux/bqplot | examples/Mark Interactions.ipynb | apache-2.0 | x_sc = LinearScale()
y_sc = LinearScale()
x_data = np.arange(20)
y_data = np.random.randn(20)
scatter_chart = Scatter(x=x_data, y=y_data, scales= {'x': x_sc, 'y': y_sc}, default_colors=['dodgerblue'],
interactions={'click': 'select'},
selected_style={'opacity': 1.0, 'fil... |
abevieiramota/data-science-cookbook | 2017/07-decision-tree/decision_tree.ipynb | mit | import os
import pandas as pd
import math
import numpy as np
from sklearn.tree import DecisionTreeClassifier
headers = ["buying", "maint", "doors", "persons","lug_boot", "safety", "class"]
data = pd.read_csv("car_data.csv", header=None, names=headers)
data = data.sample(frac=1).reset_index(drop=True) # shuffle
"""
E... |
badlands-model/BayesLands | Examples/mountain/Hydrometrics.ipynb | gpl-3.0 | %matplotlib inline
from matplotlib import cm
# Import badlands grid generation toolbox
import pybadlands_companion.hydroGrid as hydr
# display plots in SVG format
%config InlineBackend.figure_format = 'svg'
"""
Explanation: Hydrometrics
In this notebook, we show how to compute several hydrometics parameters based ... |
arne-cl/alt-mulig | python/pocores-vs-markus-conll-scoring.ipynb | gpl-3.0 | import sys
def has_valid_annotation(mmax_file, scorer_path, metric, verbose=False):
"""
Parameters
----------
metric : str
muc, bcub, ceafm, ceafe, blanc
verbose : bool or str
True, False or 'very'
"""
scorer = sh.Command(scorer_path)
mdg = MMAXDocumentGraph(mmax_file)
... |
SJSlavin/phys202-2015-work | assignments/midterm/AlgorithmsEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
"""
Explanation: Algorithms Exercise 3
Imports
End of explanation
"""
def char_probs(s):
"""Find the probabilities of the unique characters in the string s.
Parameters
----------
... |
flsantos/startup_acquisition_forecast | modelling.ipynb | mit | #All imports here
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import display, HTML
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.... |
paris-saclay-cds/ramp-workflow | rampwf/tests/kits/titanic_no_test_old/titanic_no_test_old_starting_kit.ipynb | bsd-3-clause | %matplotlib inline
import os
import glob
import numpy as np
from scipy import io
import matplotlib.pyplot as plt
import pandas as pd
from rampwf.utils.importing import import_module_from_source
"""
Explanation: Paris Saclay Center for Data Science
Titanic RAMP: survival prediction of Titanic passengers
Benoit Playe (I... |
JakeColtman/BayesianSurvivalAnalysis | .ipynb_checkpoints/Full done-checkpoint.ipynb | mit | running_id = 0
output = [[0]]
with open("E:/output.txt") as file_open:
for row in file_open.read().split("\n"):
cols = row.split(",")
if cols[0] == output[-1][0]:
output[-1].append(cols[1])
output[-1].append(True)
else:
output.append(cols)
output = out... |
Almaz-KG/MachineLearning | ml-for-finance/python-for-financial-analysis-and-algorithmic-trading/01-Python-Crash-Course/Python Crash Course Exercises - Solutions.ipynb | apache-2.0 | price = 300
price**0.5
import math
math.sqrt(price)
"""
Explanation: Python Crash Course Exercises - Solutions
This is an optional exercise to test your understanding of Python Basics. The questions tend to have a financial theme to them, but don't look to deeply into these tasks themselves, many of them don't hold ... |
tanmay987/deepLearning | sentiment-rnn/Sentiment_RNN_Solution.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... |
post2web/nbloader | .ipynb_checkpoints/tutorial-checkpoint.ipynb | mit | from nbloader import Notebook
loaded_notebook = Notebook('test.ipynb')
"""
Explanation: Importing Jupyter Notebooks as "Objects"
Jupyter Notebooks are great for data exploration, visualizing, documenting, prototyping and iteracting with the code, but when it comes to creating an actual program out of a notebook they ... |
radu941208/DeepLearning | Hyperparameter_Tuning_Regularization_Optimization/Regularization.ipynb | mit | # import packages
import numpy as np
import matplotlib.pyplot as plt
from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec
from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters
import sklearn
import sklearn.da... |
CNS-OIST/STEPS_Example | user_manual/source/diffusion_boundary.ipynb | gpl-2.0 | import steps.model as smodel
import steps.geom as sgeom
import steps.rng as srng
import steps.solver as solvmod
import steps.utilities.meshio as meshio
import numpy
import pylab
"""
Explanation: Diffusion Boundary
The simulation script described in this chapter is available at STEPS_Example repository.
In some systems... |
xtr33me/deep-learning | autoencoder/Simple_Autoencoder.ipynb | mit | img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits.
End of explanation
"""
# Size of the encoding layer (the hidden layer)
encoding_dim = 32 # feel free to ch... |
lowRISC/ot-sca | jupyter/otbn_attack_100M.ipynb | apache-2.0 | import numpy as np
wave = np.load('waves_p256_100M_2s.npy')
#wave = np.load('waves_p256_100M_2s_12bits.npy')
#wave = np.load('waves_p256_100M_2s_12bits830.npy')
#wave = np.load('waves_p256_100M_2s_12bitsf0c.npy')
import numpy as np
import pandas as pd
from scipy import signal
def butter_highpass(cutoff, fs, order=5):... |
mne-tools/mne-tools.github.io | 0.23/_downloads/9bd293f49554a21d68d4f2a842cc6cc2/59_head_positions.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from os import path as op
import mne
print(__doc__)
data_path = op.join(mne.datasets.testing.data_path(verbose=True), 'SSS')
fname_raw = op.join(data_path, 'test_move_anon_raw.fif')
raw = mne.io.read_raw_fif(fname_raw, allow_maxshield='yes... |
brclark-usgs/flopy | examples/Notebooks/flopy3_gridgen.ipynb | bsd-3-clause | %matplotlib inline
import os
import sys
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import flopy
from flopy.utils.gridgen import Gridgen
print(sys.version)
print('numpy version: {}'.format(np.__version__))
print('matplotlib version: {}'.format(mpl.__version__))
print('flopy version: {... |
sudhanshuptl/Machine-Learning | Data Analysis learning/Data_Analysis_2(Numpy Pandas).ipynb | gpl-2.0 | import numpy as np
"""
Explanation: Basics of NUmpy & Pandas
Numpy
Numpy uses array whereas pandas used scaler <br />
End of explanation
"""
num = np.array([3,4,2,5,7,23,56,23,7,23,89,43,676,43])
num
"""
Explanation: Array are similar to python list , but it all element must be of same data type, and it faster than... |
jrrembert/cybernetic-organism | dato/recommendations/Analyzing product sentiment.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Predicting sentiment from product reviews
Fire up GraphLab Create
End of explanation
"""
products = graphlab.SFrame('amazon_baby.gl/')
"""
Explanation: Read some product review data
Loading reviews for a set of baby products.
End of explanation
"""
products.head()
"""
Explanation... |
AbhilashReddyM/GeometricMultigrid | notebooks/Making_a_Preconditioner-vectorized.ipynb | mit | import numpy as np
"""
Explanation: This is functionally similar to the the other notebook. All the operations here have been vectorized. This results in much much faster code, but is also much unreadable. The vectorization also necessitated the replacement of the Gauss-Seidel smoother with under-relaxed Jacobi. That ... |
walkon302/CDIPS_Recommender | notebooks/Create_Datasets_for_Evaluation.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# get data
user_profile = pd.read_csv('../data_user_view_buy/user_profile.csv',sep='\t',header=None)
user_profile.columns = ['user_id','buy_spu','buy_sn','buy_ct3','view_spu','view_sn','view_ct3','time_interval','view_cnt','view_... |
dereneaton/ipyrad | tests/cookbook-quartet-species-tree.ipynb | gpl-3.0 | ## conda install ipyrad -c ipyrad
## conda install toytree -c eaton-lab
import ipyrad.analysis as ipa
import ipyparallel as ipp
import toytree
"""
Explanation: Inferring species trees with tetrad
When you install ipyrad a number of analysis tools are installed as well. This includes the program tetrad, which applies ... |
kimkipyo/dss_git_kkp | Python 복습/05일차.화_디버깅,예외,예외처리,우분투,숙제_하노이의탑/5일차_디버깅,예외,예외처리,우분투.ipynb | mit | for i in range(3):
a = i * 7 #0, 7, 14
b = i + 2 #2, 3, 4
c = a * b # 0, 21, 56
#만약 이 range값이 3017, 5033일 경우에는 무슨 값인지 알 수 없다. 이 때 쉽게 a,b,c값이 무엇인지 찾는 방법을 소개
"""
Explanation: 1T_디버깅(Debugging), 오류(errors), 예외(Exceptions)처리(Handling)
End of explanation
"""
name = "KiPyo Kim"
age = 29
from IPython import em... |
jalabort/templatetracker | notebooks/KCF Tracker.ipynb | bsd-3-clause | video_path = '../data/video.mp4'
cam = cv2.VideoCapture(video_path)
print 'Is video capture opened?', cam.isOpened()
n_frames = 1000
resolution = (640, 360)
frames = []
for _ in range(n_frames):
# read frame
frame = cam.read()[1]
# scale down
frame = cv2.resize(frame, resolution)
# bgr to rgb
... |
yunqu/PYNQ | boards/Pynq-Z1/base/notebooks/board/asyncio_buttons.ipynb | bsd-3-clause | from pynq import PL
from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
"""
Explanation: Using Interrupts and asyncio for Buttons and Switches
This notebook provides a simple example for using asyncio I/O to interact asynchronously with multiple input devices. A task is created for each input de... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
tkzeng/molecular-design-toolkit | moldesign/_notebooks/Example 4. HIV Protease bound to an inhibitor.ipynb | apache-2.0 | import moldesign as mdt
import moldesign.units as u
"""
Explanation: <span style="float:right"><a href="http://moldesign.bionano.autodesk.com/" target="_blank" title="About">About</a> <a href="https://forum.bionano.autodesk.com/c/Molecular-Design-Toolkit" target="_blank" title="Forum... |
myfunprograms/machine-learning | finding_donors/finding_donors_original.ipynb | apache-2.0 | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualization code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Loa... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/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', 'hammoz-consortium', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: T... |
gdsfactory/gdsfactory | docs/notebooks/06_yaml_component.ipynb | mit | # %matplotlib widget
import ipywidgets
from IPython.display import clear_output
import matplotlib.pyplot as plt
import gdsfactory as gf
x = ipywidgets.Textarea(rows=20, columns=480)
x.value = """
name: sample_different_factory
instances:
bl:
component: pad
tl:
component: pad
br:
compon... |
maxrose61/GA_DS | FInal_Project/.ipynb_checkpoints/Quantifying_Influence_Analysis_maxrose_DSFinal-checkpoint.ipynb | gpl-3.0 | ### Import as many items as possible to have available.
### Import data from CSV
%matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegres... |
tensorflow/workshops | tfx_labs/Lab_6_Model_Analysis.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... |
dataworkshop/titanic | vladimir/src/Titanic.ipynb | mit | train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
all_df = train_df.append(test_df)
all_df['is_test'] = all_df.Survived.isnull()
all_df.index = all_df.Survived
del all_df['Survived']
all_df.head()
"""
Explanation: Read data
End of explanation
"""
train_df.describe()
"""
Expla... |
Neuroglycerin/neukrill-net-work | notebooks/model_run_and_result_analyses/Analyse alexnet_learning_rate model.ipynb | mit | cd ..
%run check_test_score.py -v run_settings/alexnet_based_norm_global.json
"""
Explanation: This notebook investigates alexnet-based model with normalisation and a new learning rate schedule.
The changes made include: increasing the number of epochs on which learning rate and momentum saturate to 250 (instead of o... |
google-research/google-research | domain_conditional_predictors/validation_experiment.ipynb | apache-2.0 | #@test {"skip": true}
!pip install dm-sonnet==2.0.0 --quiet
!pip install tensorflow_addons==0.12 --quiet
#@test {"output": "ignore"}
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_addons as tfa
try:
import sonnet.v2 as snt
except ModuleNotFoundError:
import sonne... |
adukic/nd101 | intro-to-tflearn/Sentiment Analysis with TFLearn.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/Distributed/Distributed_Tensorflow_Training_HybridCloud.ipynb | apache-2.0 | import tensorflow as tf
!pip install version_information
%load_ext version_information
%version_information numpy, scipy, matplotlib, pandas, tensorflow, sklearn, skflow
"""
Explanation: Distributed Tensorflow
End of explanation
"""
!kubectl get pod
CLUSTER_SPEC= """
{
'ps' : ['clustered-tensorflow-ps0:2222',... |
mhdella/scipy_2015_sklearn_tutorial | notebooks/04.3 Analyzing Model Capacity.ipynb | cc0-1.0 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.svm import SVR
from sklearn import cross_validation
rng = np.random.RandomState(42)
n_samples = 200
kernels = ['linear', 'poly', 'rbf']
true_fun = lambda X: X ** 3
X = np.sort(5 * (rng.rand(n_samples) - .5))
y = tru... |
mrcinv/matpy | 02b_vrste.ipynb | gpl-2.0 | from sympy import *
init_printing()
n = Symbol('n')
a = lambda n: 1/(n*(n+2))
Sum(a(n),(n,1,oo))
"""
Explanation: ^ gor: Uvod
Številske vrste
Vrsta je neskončna vsota
$$a_0+a_1+a_2+\ldots = \sum_{n=0}^\infty a_n.$$
Vsoto vrste definiramo z zaporedjem delnih vsot
$$S_n=a_0+a_1+\ldots a_n =\sum_{k=0}^n a_k$$
in
$$\sum_{... |
bmeaut/python_nlp_2017_fall | course_material/05_Generator_expressions_list_comprehension/05_OOP_Comprehension_ContextM_lab_solution.ipynb | mit | from math import gcd
class RationalNumber(object):
# TODO
r = RationalNumber(43, 2)
assert r + r == RationalNumber(43) # q = 1 in this case
assert r * 2 == r + r
r1 = RationalNumber(3, 2)
r2 = RationalNumber(4, 3)
assert r1 * r2 == RationalNumber(12, 6)
assert r1 / r2 == RationalNumber(9, 8)
as... |
unapiedra/BBChop | analysis/Example Run.ipynb | gpl-2.0 | with open('example_run.csv') as f: s = f.read()
N = 10
runs = [[1/N for _ in range(N)]]
for line in s.split('\n'):
line = line.strip('[]')
if len(line) > 0:
li = [float(i) for i in line.split(',')]
runs.append(li)
"""
Explanation: In this little experiment, I printed the likelihoods after each... |
chivalrousS/word2vec | examples/doc2vec.ipynb | apache-2.0 | from __future__ import unicode_literals
import os
import nltk
directories = ['train/pos', 'train/neg', 'test/pos', 'test/neg', 'train/unsup']
input_file = open('/Users/drodriguez/Downloads/alldata.txt', 'w')
id_ = 0
for directory in directories:
rootdir = os.path.join('/Users/drodriguez/Downloads/aclImdb', dire... |
hagne/atm-py | examples/instruments_POPS_mie.ipynb | mit | from atmPy.aerosols.instruments.POPS import mie
%matplotlib inline
import matplotlib.pylab as plt
plt.rcParams['figure.dpi'] = 200
"""
Explanation: Introduction
This module provides tools to simulate scattering intensities detected by POPS as a function of particle size, refractive index, and some more less obvious p... |
European-XFEL/h5tools-py | docs/parallel_example.ipynb | bsd-3-clause | from karabo_data import RunDirectory
import multiprocessing
import numpy as np
"""
Explanation: Parallel processing with a virtual dataset
This example demonstrates splitting up some data to be processed by several worker processes, and collecting the results back together.
For this example, we'll use data from an XGM... |
uwoseis/anemoi | notebooks/Compare Solutions Homogeneous.ipynb | mit | import sys
sys.path.append('../')
import numpy as np
from anemoi import MiniZephyr, SimpleSource, AnalyticalHelmholtz
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('png')
matplotlib.rcParams['... |
datascienceguide/datascienceguide.github.io | tutorials/.ipynb_checkpoints/Linear-Regression-Tutorial-Copy1-checkpoint.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from math import log
from sklearn import linear_model
#comment below if not using ipython notebook
%matplotlib inline
"""
Explanation: Linear Regression Tutorial
Author: Andrew Andrade (andrew@andrewandrade.ca)
This is part one of a series of tu... |
sempwn/ABCPRC | Tutorial_Ecology.ipynb | mit | %matplotlib inline
import ABCPRC as prc
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
"""
Explanation: Ecology Example
We use here the example of migratory birds in order to demonstrate the model fitting of this package
End of explanation
"""
def ibm(*ps):
m0,k = ps[0],ps[1]
... |
tingelst/pymanopt | examples/MoG_singularity_heuristic.ipynb | bsd-3-clause | import autograd.numpy as np
np.set_printoptions(precision=2)
import matplotlib.pyplot as plt
%matplotlib inline
# Number of data
N = 1000
# Dimension of data
D = 2
# Number of clusters
K = 3
pi = [0.1, 0.6, 0.3]
mu = [np.array([-4, 1]), np.array([0, 0]), np.array([2, -1])]
Sigma = [np.array([[3, 0],[0, 1]]), np.arr... |
wmfschneider/CHE30324 | Homework/HW4-soln.ipynb | gpl-3.0 | import sympy as sy
from sympy import *
x=Symbol('x')
a=Symbol('a',positive=True)
b=Symbol('b',positive=True)
Wavefunction=a*exp(-x**2/2/b**2)
A=integrate(Wavefunction**2,(x,-oo,+oo)) # calculate the integral of (wavefunc) * (wavefunc*) from -oo to +oo
Wavefunction_normalized=Wavefunction/sqrt(A)
pprint(Wavefunct... |
Jackie789/JupyterNotebooks | Naive+Bayes+for+Classification+of+Positive-Negative+reviews.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
import scipy
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
# Grab and process the raw data.
data_path = ("/Users/jacquelynzuker/Desktop/sentiment labelled sentences/amazon_cells_labelled.txt"
)
amazon_raw = pd.read_csv(data_pa... |
vyvojer/ploev | notebooks/Board matching.ipynb | gpl-3.0 | odds_oracle = OddsOracle()
calc = Calc(odds_oracle)
"""
Explanation: Сначала присоединяемся к серверу OddsOracle
End of explanation
"""
# Префлоп диапазоны (используем сохранненные диапазоны PokerJuice)
main_ranges = ['$FI12', '$FI20', '$FI25', '$FI30', '$FI40', '$FI50',
'$3b4i', '$3b6i', '$3b8i', '$... |
kit-cel/wt | wt/vorlesung/ch7_9/weakly_stationary.ipynb | gpl-2.0 | # importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Content and Objective
Checking for (... |
tyamamot/h29iro | codes/5_Learning_to_Rank.ipynb | mit | ! ../bin/svm_rank_learn -c 0.03 ../data/svmrank_sample/train.dat ../data/svmrank_sample/model
"""
Explanation: 第5回 ランキング学習（Ranking SVM）
この演習課題ページでは，Ranking SVMの実装であるSVM-rankの使い方を説明します．この演習ページの目的は，SVM-rankを用いてモデルの学習，テストデータに対するランク付けが可能になることです．
この演習ページでは以下のツールを使用します．
- SVM-rank (by Prof. Thorsten Joachims)
- https://w... |
tuanavu/coursera-university-of-washington | machine_learning/1_machine_learning_foundations/assignment/week4/Document retrieval.ipynb | mit | import graphlab
"""
Explanation: Document retrieval from wikipedia data
Fire up GraphLab Create
End of explanation
"""
people = graphlab.SFrame('people_wiki.gl/')
"""
Explanation: Load some text data - from wikipedia, pages on people
End of explanation
"""
people.head()
len(people)
"""
Explanation: Data contain... |
nansencenter/nansat-lectures | notebooks/03 object oriented programming.ipynb | gpl-3.0 | class A():
pass
"""
Explanation: Intorduction to Object Oriented Programming in Python
Definition of the minimal class in two lines
Define class
End of explanation
"""
a = A() # create an instance of class A
print (a)
print (type(a))
"""
Explanation: Use class
End of explanation
"""
class Human(object):
... |
robertoalotufo/ia898 | 2S2018/13 Correlacao de fase.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from numpy.fft import *
import sys,os
ia898path = os.path.abspath('../../')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
f = mpimg.imread('../data/cameraman.tif')
# Transladan... |
dipanjank/ml | data_analysis/abalone.ipynb | gpl-3.0 | %pylab inline
pylab.style.use('ggplot')
import pandas as pd
import numpy as np
import seaborn as sns
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'
data_df = pd.read_csv(url, header=None)
data_df.head()
"""
Explanation: Abalone - UCI
End of explanation
"""
data_df.columns = ... |
diegocavalca/Studies | books/deep-learning-with-python/5.2-using-convnets-with-small-datasets.ipynb | cc0-1.0 | import os, shutil
# The path to the directory where the original
# dataset was uncompressed
original_dataset_dir = '/Users/fchollet/Downloads/kaggle_original_data'
# The directory where we will
# store our smaller dataset
base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'
os.mkdir(base_dir)
# Directories for... |
gbtimmon/ase16GBT | code/6/magoff2_pom3_ga.ipynb | unlicense | %matplotlib inline
# All the imports
from __future__ import print_function, division
from math import *
import random
import sys
import matplotlib.pyplot as plt
# TODO 1: Enter your unity ID here
__author__ = "magoff2"
class O:
"""
Basic Class which
- Helps dynamic updates
- Pretty Prints
... |
betoesquivel/comment_summarization | Lab1 Text processing with python.ipynb | mit | import sklearn
import numpy as np
import matplotlib.pyplot as plt
data = np.array([[1,2], [2,3], [3,4], [4,5], [5,6]])
x = data[:,0]
y = data[:,1]
data, x, y
"""
Explanation: Basic usage of Sklearn
End of explanation
"""
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_d... |
d-k-b/udacity-deep-learning | embeddings/Skip-Grams-Solution.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... |
brian-rose/ClimateModeling_courseware | Lectures/Lecture10 -- Who needs spectral bands.ipynb | mit | # Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
"""
Explanation: ATM 623: Climate Modeling
Brian E. J. Rose, University at Albany
Lecture 10: Who needs spectral bands? We do. Some baby steps...
Warning: content out of date and not maintained
You really should be looking at T... |
yl565/statsmodels | examples/notebooks/interactions_anova.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
from statsmodels.compat import urlopen
import numpy as np
np.set_printoptions(precision=4, suppress=True)
import statsmodels.api as sm
import pandas as pd
pd.set_option("display.width", 100)
import matplotlib.pyplot as plt
from statsmodels.formula.api import ols... |
csdms/pymt | notebooks/gipl_and_ecsimplesnow.ipynb | mit | import pymt.models
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import matplotlib.colors as mcolors
from matplotlib.colors import LinearSegmentedColormap
sns.set(style='whitegrid', font_scale= 1.2)
"""
Explanation: Coupling GIPL and ECSimpleSnow models
Before you begin, install:
conda insta... |
DJCordhose/ai | notebooks/ai/Play.ipynb | mit | terrain = [
["_", "R", "_", "_"],
["H", "_", "B", "_"],
["_", "_", "B", "_"],
["B", "_", "G", "_"]
]
"""
Explanation: Robot Run
The Game
In a certain terrain a Robot (R) plays against a Human player (H)
* Both Human and Robot try to reach a goal which is at the same distance from both of them
* Blocks ... |
tlhr/plumology | examples/example.ipynb | mit | from plumology import vis, util, io
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: PLUMOLOGY
vis: Visualization and plotting functions
util: Various utilities and calculation functions
io: Functions to read certain output files and an HDF interface
End of e... |
metpy/MetPy | v0.9/_downloads/d02fda82caa4290e31f980126221b2a4/Wind_SLP_Interpolation.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from metpy.calc import wind_components
from metpy.cbook import get_test_data
from metpy.interpolate import interpolate_to_grid, remove_nan_obse... |
letsgoexploring/linearsolve-package | examples/cia_model.ipynb | mit | # Import numpy, pandas, linearsolve, scipy.optimize, matplotlib.pyplot
import numpy as np
import pandas as pd
import linearsolve as ls
from scipy.optimize import root,fsolve,broyden1,broyden2
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
"""
Explanation: A Cash-in-Advance Model
Replicate ... |
mne-tools/mne-tools.github.io | 0.22/_downloads/1935e973eb220e31cb4a6a6541231eb1/plot_background_statistics.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
from functools import partial
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa, analysis:ignore
import mne
from mne.stats import (ttest_1samp_no_p, bonferroni_correctio... |
mbinkowski/DeepSpeechDistances | deep_speech_distances.ipynb | apache-2.0 | !pip install python_speech_features
!pip install resampy
!pip install scipy
!pip install gdown
!pip install tqdm -U
"""
Explanation: This notebook provides a demo for the use of DeepSpeech Distances proposed in High Fidelity Speech Synthesis with Adversarial Networks as new evaluation metrics for neural speech synthes... |
CivicTechTO/ttc_subway_times | doc/Single_station_all_day_analysis.ipynb | gpl-3.0 | import datetime
from psycopg2 import connect
import configparser
import pandas as pd
import pandas.io.sql as pandasql
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
%matplotlib qt
try:
con.close()
except:
print("No existing connection... moving on")... |
vvishwa/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
ogoann/StatisticalMethods | examples/SDSScatalog/GalaxySizes.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
import numpy as np
import SDSS
import pandas as pd
import matplotlib
%matplotlib inline
galaxies = "SELECT top 1000 \
petroR50_i AS size, \
petroR50Err_i AS err \
FROM PhotoObjAll \
WHERE \
(type = '3' AND petroR50Err_i > 0)"
print galaxies
# Download data. This can take a few mome... |
thewtex/SimpleITK-Notebooks | 02_Pythonic_Image.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rc('image', aspect='equal')
%matplotlib inline
import SimpleITK as sitk
# Download data to work on
from downloaddata import fetch_data as fdata
"""
Explanation: Pythonic Syntactic Sugar
The Image Basics Notebook was straight forward and closely follows ITK's... |
TomTranter/OpenPNM | examples/percolation/Part A - Ordinary Percolation.ipynb | mit | import openpnm as op
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(10)
from ipywidgets import interact, IntSlider
%matplotlib inline
ws = op.Workspace()
ws.settings["loglevel"] = 40
"""
Explanation: Part A: Ordinary Percolation
OpenPNM contains several percolation algorithms which are central to th... |
mathLab/RBniCS | tutorials/08_nonlinear_parabolic/tutorial_nonlinear_parabolic_exact.ipynb | lgpl-3.0 | from dolfin import *
from rbnics import *
from utils import *
"""
Explanation: Tutorial 08 - Non linear Parabolic problem
Keywords: exact parametrized functions, POD-Galerkin
1. Introduction
In this tutorial, we consider the FitzHugh-Nagumo (F-N) system. The F-N system is used to describe neuron excitable systems. The... |
sameersingh/ml-discussions | week1/using_mltools_package.ipynb | apache-2.0 | from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(0)
"""
Explanation: I combined all the code lines I said should be at the begining of your code.
End of explanation
"""
!ls
"""
Explanation: Importing mltools
First you want to make sure it sits in ... |
gee-community/gee_tools | notebooks/image/addConstantBand.ipynb | mit | import ee
ee.Initialize()
from geetools import tools
col = ee.ImageCollection('COPERNICUS/S2').select(['B1', 'B2', 'B3']).limit(10)
"""
Explanation: addConstantBands(value, names, *pairs)
Adds bands with a constant value
names: final names for the additional bands
value: constant value
pairs: keywords for the bands... |
JonasHarnau/apc | apc/vignettes/vignette_over_dispersed_apc.ipynb | gpl-3.0 | import apc
# Turn off a FutureWarnings
import warnings
warnings.simplefilter('ignore', FutureWarning)
"""
Explanation: Over-dispersed Age-Period-Cohort Models
We replicate the data example in Harnau and Nielsen (2017) in Section 6.
The work on this vignette was supported by the European Research Council, grant AdG 69... |
tensorflow/docs-l10n | site/ja/tutorials/generative/dcgan.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... |
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