repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
kiritisai/deep-learning-udacity | image-classification/dlnd_image_classification.ipynb | mit | """
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... |
google-research/robustness_metrics | robustness_metrics/projects/revisiting_calibration/revisiting_calibration.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# 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 ap... |
DavidQiuChao/CS231nHomeWorks | assignment1/two_layer_net.ipynb | mit | # A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
from __future__ import print_function
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['im... |
feststelltaste/software-analytics | prototypes/Linux Git Log Performance.ipynb | gpl-3.0 | first_commit = git_log.index[-1]
first_commit
today = pd.to_datetime('today')
type(today)
"""
Explanation: Note: We are using the UNIX timestamp here because it's superfast to convert it to a real datatime64 data type.
Cleaning up wrong timestamps
_Note: 'today'is suboptimal
End of explanation
"""
git_log[(git_log ... |
mne-tools/mne-tools.github.io | 0.22/_downloads/ff83425ee773d1d588a6994e5560c06c/plot_mne_dspm_source_localization.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
"""
Explanation: Source localization with MNE/dSPM/sLORETA/eLORETA
The aim of this tutorial is to teach you how to compute and apply a linear
minimum-norm inv... |
prihandoko/prihandoko.github.io | _pages/learn/python/4. Data Structure.ipynb | mit | import sys
fishes = ["teri", "kakap", "tongkol"]
items = (" sword"," halberd"," axe"," knife"," blade")
for fish in fishes:
print("ini ikan "+fish)
for item in items:
print(item)
print(fishes[0])
print(items[-1])
del(fishes[-1])
for fish in fishes:
print("ini ikan "+fish)
"""
Explanation: Data Structur... |
quantumlib/Cirq | docs/tutorials/pasqal/getting_started.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... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.15 group records based on field.ipynb | gpl-3.0 | rows = [
{'address': '5412 N CLARK', 'date': '07/01/2012'},
{'address': '5148 N CLARK', 'date': '07/04/2012'},
{'address': '5800 E 58TH', 'date': '07/02/2012'},
{'address': '2122 N CLARK', 'date': '07/03/2012'},
{'address': '5645 N RAVENSWOOD', 'date': '07/02/2012'},
{'address': '1060 W ADDISON'... |
eldritchideen/weekly-trend-trader | Trend-Trader.ipynb | mit | import pandas as pd
import pandas.io.data as web
import datetime
"""
Explanation: Weekend Trend Trader
This notebook outlines experiments in getting my weekend trend trader code implemented.
End of explanation
"""
# date ranges
end = datetime.date(2015, 1, 26)
start = end + datetime.timedelta(weeks=-21)
start
# Get... |
neuro-ml/reskit | tutorials/.ipynb_checkpoints/Getting Started-checkpoint.ipynb | bsd-3-clause | from sklearn.datasets import make_classification
X, y = make_classification()
data = {'X': X, 'y': y}
"""
Explanation: Let's say we want to prepare data and try some scalers and classifiers for prediction in a classification problem. We will tune paramaters of classifiers by grid search technique.
Data preparing:
En... |
mne-tools/mne-tools.github.io | 0.19/_downloads/b44431cb3a9d20511482e4da8b41ff49/plot_gamma_map_inverse.ipynb | bsd-3-clause | # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import gamma_map, make_stc_from_dipoles
from mne.viz import (plot_sparse_source_estimate... |
sarahmid/programming-bootcamp-v2 | lab7_exercises_ANSWERS.ipynb | mit | import imp
my_utils = imp.load_source('my_utils', '../utilities/my_utils.py') #CHANGE THIS PATH
# test that this worked
print "Test my_utils.gc():", my_utils.gc("ATGGGCCCAATGG")
print "Test my_utils.reverse_compl():", my_utils.reverse_compl("GGGGTCGATGCAAATTCAAA")
print "Test my_utils.read_fasta():", my_utils.read_fas... |
elsuizo/Charlas_presentaciones | Python_scientific/.ipynb_checkpoints/numpy_matplot_ejemplos-checkpoint.ipynb | gpl-2.0 | #Importamos el modulo numpy con el alias np
import numpy as np
#Creo un array
a = np.array([1,0,0])
a
type(a)
"""
Explanation: <img src="files/Images/Numpylogo.png" style="float: left;"/>
<div style="clear: both;">
## Numpy
### Biblioteca(modulo) numérico de Python
* Arrays multimensionales (ndarray)
* Más cerca... |
QuLogic/folium | examples/MarkerCluster.ipynb | mit | icon_create_function = """\
function(cluster) {
return L.divIcon({
html: '<b>' + cluster.getChildCount() + '</b>',
className: 'marker-cluster marker-cluster-large',
iconSize: new L.Point(20, 20)
});
}"""
from folium.plugins import MarkerCluster
m = folium.Map(
location=[np.mean(lats), np.mean... |
datawagovau/harvesters | Implicit Georeferencing.ipynb | mit | import ckanapi
from harvest_helpers import *
from secret import CKAN
ckan = ckanapi.RemoteCKAN(CKAN["dpaw-internal"]["url"], apikey=CKAN["dpaw-internal"]["key"])
print("Using CKAN {0}".format(ckan.address))
"""
Explanation: Implicit Georeferencing
This workbook sets explicit georeferences from implicit georeferencing... |
kubeflow/code-intelligence | Issue_Embeddings/notebooks/04b_Inference-Batch.ipynb | mit | from inference import InferenceWrapper, pass_through
from IPython.display import display, Markdown
import pandas as pd
from torch.nn.utils.rnn import pad_sequence
from torch import Tensor, cat, device
from torch.cuda import empty_cache
from typing import List
from tqdm import tqdm
from numpy import concatenate as cat
i... |
dpiparo/swanExamples | notebooks/CMSDimuon_py.ipynb | lgpl-2.1 | import ROOT
"""
Explanation: Dimuon spectrum
<hr style="border-top-width: 4px; border-top-color: #34609b;">
This ROOTbook produces a plot of the dimuon spectrum starting from a subset of the CMS collision events of Run2010B.
Dataset Reference:<br>
McCauley, T. (2014). Dimuon event information derived from the Run2010... |
jerrythomas/data-science-at-scale | People most likely to go missing in Mission District between 10am & 11am on Fridays.ipynb | mit | import vincent
import pandas as pd
from vincent import AxisProperties, PropertySet, ValueRef
from vincent import Map
vincent.core.initialize_notebook()
"""
Explanation: People most likely to go missing in Mission district between 10am & 11am on Fridays
Summary of findings
Mission district has highest rate of missing... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a/td1a_correction_session1.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.0 - Premiers pas en Python (correction)
La partie 1 ne nécessite pas de correction.
End of explanation
"""
a = 5
a = a + 4 # a + 4
print (a)
"""
Explanation: Partie 2
Les erreurs sont corrigées de telle sorte que la ligne fautive e... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_compute_mne_inverse_raw_in_label.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname_inv = data_path + '/... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/tensorflow_extended/labs/Vertex_AI_Training_and_Serving_with_TFX_and_Vertex_Pipelines.ipynb | apache-2.0 | # Use the latest version of pip.
!pip install --upgrade pip
!pip install --upgrade "tfx[kfp]<2"
"""
Explanation: Training and Serving with TFX and Vertex Pipelines
Learning objectives
Prepare example data.
Create a pipeline.
Run the pipeline on Vertex Pipelines.
Test with a prediction request.
Introduction
In this n... |
gmonce/datascience | src/2. - Matrices for data scientists.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np, scipy as sp, matplotlib.pyplot as plt
"""
Explanation: <a href="https://colab.research.google.com/github/gmonce/datascience/blob/master/src/2.%20-%20Matrices%20for%20data%20scientists.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt=... |
liang-chen/my-notes | miscellaneous/python_generator.ipynb | mit | def firstn(n):
num = 0
while num < n:
yield num
num += 1
print(sum(firstn(1000000)))
"""
Explanation: A genertor function, which acts as an iterator, provides memory-efficient way to generate a large sequence of data, such as an array with size of 100,000. We have 3 different methods t... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/.ipynb_checkpoints/3 - Pandas Exercises-checkpoint.ipynb | apache-2.0 | import pandas as pd
df = pd.read_csv('./data/banklist.csv')
"""
Explanation: Pandas Exercises
Time to test your new pandas skills! Use the two csv files in this folder to complete the tasks in bold below!
NOTE: ALL TASKS MUST BE DONE IN ONE LINE OF PANDAS CODE. GOT STUCK? NO PROBLEM! CHECK OUT THE SOLUTIONS LECTURE!... |
AstroHackWeek/AstroHackWeek2014 | day1/ipython/Converting Notebooks With nbconvert.ipynb | bsd-3-clause | !ipython nbconvert 'Working With Markdown Cells.ipynb'
"""
Explanation: NbConvert
Command line usage
NbConvert is the library, and the command line tool that allow to convert from notebook to other formats.
It is a technological preview in 1.0 but is already usable and highly configurable.
It ships already with many ... |
phoebe-project/phoebe2-docs | 2.1/tutorials/pblum.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Passband Luminosity
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
"""
%matplotlib inline
... |
spacedrabbit/PythonBootcamp | Objects and Data Structures Assessment Test-Copy1.ipynb | mit | ((((10 + 10) * 5) / 10)**2) + 5.00 - 4.75
"""
Explanation: Objects and Data Structures Assessment Test
Test your knowledge.
Answer the following questions
Write a brief description of all the following Object Types and Data Structures we've learned about:
Numbers: represent a whole or floating point value, pos/neg.... |
JoeriHermans/dist-keras | examples/kafka_spark_high_throughput_ml_pipeline.ipynb | gpl-3.0 | !(date +%d\ %B\ %G)
"""
Explanation: Kafka and Spark High Throughput Deep Learning Production Pipeline
Joeri Hermans (Technical Student, IT-DB-SAS, CERN)
Departement of Knowledge Engineering
Maastricht University, The Netherlands
End of explanation
"""
import json
from keras.models import Sequenti... |
dianafprieto/SS_2017 | 01_NB_IntroductionPython.ipynb | mit | print('Hello World')
"""
Explanation: <!-- <img src="files/images/python-screenshot.jpg" width="600"> -->
<img src="imgs/header.png">
A first glance at Python and Jupyter
1. Hello World
End of explanation
"""
help(random)
"""
Explanation: 2. Print the 'help' for a particular function (try also using the IPython she... |
slundberg/shap | notebooks/tabular_examples/tree_based_models/tree_shap_paper/Figure 1 - Simple Inconsistency Example.ipynb | mit | import matplotlib.pyplot as pl
import numpy as np
import shap
import xgboost as xgb
"""
Explanation: Figure 1 - Simple Inconsistency Example
Here we create synthetic data to make XGBoost build the Model A and Model B from Figure 1 of the Tree SHAP paper. We then compute both individualized feature importances using Tr... |
tensorflow/docs-l10n | site/ko/xla/tutorials/compile.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... |
AllenDowney/ModSimPy | notebooks/chap24.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
paris-saclay-cds/python-workshop | Day_1_Scientific_Python/scikit-learn/14-Model-Complexity-and-GridSearchCV.ipynb | bsd-3-clause | from sklearn.model_selection import cross_val_score, KFold
from sklearn.neighbors import KNeighborsRegressor
# generate toy dataset:
x = np.linspace(-3, 3, 100)
rng = np.random.RandomState(42)
y = np.sin(4 * x) + x + rng.normal(size=len(x))
X = x[:, np.newaxis]
cv = KFold(shuffle=True)
# for each parameter setting do... |
mdda/fossasia-2016_deep-learning | notebooks/2-CNN/5-TransferLearning/5-ImageClassifier-keras.ipynb | mit | import os
from tensorflow import keras # Works with TF 1.12
#import keras
import numpy as np
import scipy
import matplotlib.pyplot as plt
%matplotlib inline
import time
CLASS_DIR='./images/cars'
#CLASS_DIR='./images/seefood' # for HotDog vs NotHotDog
"""
Explanation: Re-Purposing a Pretrained Network
Since a la... |
Contiamo/contiamo-client-python | demo-notebooks/customer-segmentation.ipynb | mit | import pandas as pd
import contiamo
import datetime
import seaborn as sns
import numpy as np
from sklearn.cluster import KMeans
from sklearn import datasets
"""
Explanation: Customer segmentation
_Suppose we want to cluster our customers according to their buying behavior, e.g. their purchase frequency. The Kmeans f... |
RadoslawDryzner/LeRepoDuGuerrier | project/project.ipynb | mit | # Explore the Million Song Dataset directory structure to grab the filenames of the songs
all_files = []
for (dirpath, dirnames, filenames) in os.walk("million-song/data"):
all_files.extend([dirpath + "/" + filename for filename in filenames if filename.endswith(".h5")])
all_files_num = len(all_files)
all_files_num... |
DataFighter/Lasagne-tutorial | examples/tutorial.ipynb | mit | # Lasagne is pre-release, so it's interface is changing.
# Whenever there's a backwards-incompatible change, a warning is raised.
# Let's ignore these for the course of the tutorial
import warnings
warnings.filterwarnings('ignore', module='lasagne')
import theano
import theano.tensor as T
import lasagne
import numpy a... |
ashwindeo/dataschoolers | 6_VisualizationSchoolGrouping.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns
sns.set_s... |
pastas/pastas | concepts/pastas_synthetic.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from scipy.special import gammainc, gammaincinv
import pandas as pd
import pastas as ps
ps.show_versions()
"""
Explanation: Synthetic Data
Developed by Stijn Klop and Mark Bakker
This Notebook contains a number of examples and tests with synthetic data. The purpose o... |
navierula/Research-Fall-2017 | minMaxCalc/dataExploration.ipynb | mit | # append data from time column to list
time = []
for item in df.index:
time.append(df["Time"][item])
# append data from load column to list
load = []
for item in df.index:
load.append(df["Load"][item])
# convert time array to np array for further processing
np_time = array(time)
# for local maxima
max = argr... |
bashtage/statsmodels | examples/notebooks/statespace_concentrated_scale.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.api as sm
dta = sm.datasets.macrodata.load_pandas().data
dta.index = pd.date_range(start='1959Q1', end='2009Q4', freq='Q')
"""
Explanation: State space models - concentrating the scale out of the likelihood function
End of explanation
"""
class LocalLevel(sm... |
infilect/ml-course1 | keras-notebooks/RNN/7.1 RNN and LSTM.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
#import theano
#import theano.tensor as T
import keras
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
# -... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/feature_engineering/solutions/2_bqml_adv_feat_eng.ipynb | apache-2.0 | PROJECT = !gcloud config get-value project
PROJECT = PROJECT[0]
%env PROJECT=$PROJECT
"""
Explanation: Advanced Feature Engineering in BQML
Learning Objectives
Evaluate the model
Extract temporal features, feature cross temporal features
Apply ML.FEATURE_CROSS to categorical features
Create a Euclidian feature colum... |
voytekresearch/psd_fit | voytek_PSD_peak_finding_code_3.5.15.ipynb | mit | %pylab inline
from __future__ import division
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
import random
import os
import scipy.io
import scipy.signal
from sklearn.metrics import r2_score
from sklearn import cross_validation
from sklearn import cluster
import sys
import warnings
warnings.filte... |
mne-tools/mne-tools.github.io | 0.23/_downloads/141ddce18e923e8220337b357ba3dc45/ssd_spatial_filters.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
# Victoria Peterson <victoriapeterson09@gmail.com>
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import Epochs
from mne.datasets.fieldtrip_cmc import data_path
from mne.decoding import SSD
"""
Explanation: Compute Spectro-... |
johanvdw/niche_vlaanderen | docs/getting_started.ipynb | mit | import niche_vlaanderen as nv
"""
Explanation: Getting started
Interactive usage
Niche Vlaanderen can be used interactively in Python. For interactive use, we recommend using the Jupyter notebook. This allows one to save documentation, code and results in the same file.
This file itself is a notebook, and if you want ... |
trsumit/sumit-udacity | nanodegree/c-ud032-nd/FinalProject/OpenstreetMapData.ipynb | mit | OSM_FILE = 'data/map.osm'
"""
Explanation: OpenStreetMap is an open project, which means it's free and everyone can use it and edit as they like. OpenStreetMap is direct competitor of Google Maps. How OpenStreetMap can compete with the giant you ask? It's depend completely on crowd sourcing. There's lot of people will... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/cnrm-esm2-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-esm2-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: CNRM-ESM2-1
Topic: Atmoschem
Sub-Topics: T... |
drabastomek/learningPySpark | Chapter02/LearningPySpark_Chapter02.ipynb | gpl-3.0 | data = sc.parallelize(
[('Amber', 22), ('Alfred', 23), ('Skye',4), ('Albert', 12),
('Amber', 9)])
"""
Explanation: Chapter 3 - Learning PySpark
Resilient Distributed Datasets
Creating RDDs
There are two ways to create an RDD in PySpark. You can parallelize a list
End of explanation
"""
data_from_file = sc.... |
bashtage/statsmodels | examples/notebooks/ols.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.api as sm
np.random.seed(9876789)
"""
Explanation: Ordinary Least Squares
End of explanation
"""
nsample = 100
x = np.linspace(0, 10, 100)
X = np.column_stack((x, x ** 2))
beta = np.array([1, 0.1, 10])
e = ... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/sandbox-1/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timestepping Framework, Advecti... |
ralphhaygood/sklearn-gbmi | example.ipynb | mit | import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.inspection import plot_partial_dependence
from sklearn_gbmi import *
DATA_COUNT = 10000
RANDOM_SEED = 137
TRAIN_FRACTION = 0.9
"""
Explanation: Example
A complete example of how to use the sklearn_gbmi mod... |
haanjack/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
bjshaw/phys202-2015-work | days/day19/FittingModels.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize as opt
from IPython.html.widgets import interact
"""
Explanation: Fitting Models
Learning Objectives: learn to fit models to data using linear and non-linear regression.
This material is licensed under the MIT license and... |
deepfield/ibis | docs/source/notebooks/tutorial/7-Advanced-Topics-ComplexFiltering.ipynb | apache-2.0 | import ibis
import os
hdfs_port = os.environ.get('IBIS_WEBHDFS_PORT', 50070)
hdfs = ibis.hdfs_connect(host='quickstart.cloudera', port=hdfs_port)
con = ibis.impala.connect(host='quickstart.cloudera', database='ibis_testing',
hdfs_client=hdfs)
ibis.options.interactive = True
"""
Explanation: A... |
karlstroetmann/Formal-Languages | Exercises/Grammar2HTML-Antlr/Grammar-2-HTML.ipynb | gpl-2.0 | !cat Grammar.g4
!type Grammar.g4
"""
Explanation: Converting a Grammar into <span style="font-variant:small-caps;">Html</span>
You should store the grammar in the file Grammar.g4. This grammar should describe the lexical structure of the grammar for the language
C that is contained in the file
<a href="https://git... |
jakevdp/sklearn_tutorial | notebooks/04.1-Dimensionality-PCA.ipynb | bsd-3-clause | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('seaborn')
"""
Explanation: <small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Dimensionality Reducti... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_compute_covariance.ipynb | bsd-3-clause | import os.path as op
import mne
from mne.datasets import sample
"""
Explanation: .. _tut_compute_covariance:
Computing covariance matrix
End of explanation
"""
data_path = sample.data_path()
raw_empty_room_fname = op.join(
data_path, 'MEG', 'sample', 'ernoise_raw.fif')
raw_empty_room = mne.io.read_raw_fif(raw_e... |
WomensCodingCircle/CodingCirclePython | Lesson13_GUIs/GUI.ipynb | mit | import tkinter
"""
Explanation: GUI
Python has a few options for creating GUIs (Graphical User Interfaces). We will learn Tkinter because it is part of the standard library and easy to use.
GUIs are a type of user interface that allows users to run your program using viusal indicators like buttons, text boxes, and sc... |
HarshaDevulapalli/foundations-homework | 05/05-Homework-Devulapalli-Spotify_graded.ipynb | mit | import requests
lil_response = requests.get('https://api.spotify.com/v1/search?query=lil&type=artist&type=track&market=US&limit=50')
#print(response.text)
lil_data = lil_response.json()
lil_data.keys()
#print(lil_data)
lil_data['artists'].keys()
lil_artists = lil_data['artists']['items']
for artist in lil_artists:... |
open-forcefield-group/openforcefield | examples/using_smirnoff_in_amber_or_gromacs/convert_to_amber_gromacs.ipynb | mit | from simtk.openmm.app import PDBFile
from openff.toolkit.topology import Molecule, Topology
from openff.toolkit.typing.engines.smirnoff import ForceField
ethanol = Molecule.from_smiles("CCO")
cyclohexane = Molecule.from_smiles("C1CCCCC1")
# Obtain the OpenMM Topology object from the PDB file.
pdbfile = PDBFile('1_cyc... |
ryneches/SuchTree | examples.ipynb | bsd-3-clause | %pylab inline
%config InlineBackend.figure_format='retina'
from SuchTree import SuchTree, SuchLinkedTrees
import seaborn
import pandas
from scipy.cluster.hierarchy import ClusterWarning
warnings.simplefilter( 'ignore', UserWarning )
warnings.simplefilter( 'ignore', FutureWarning )
"""
Explanation: SuchTree
There are... |
jlawman/jlawman.github.io | content/numpy/Basics - Attributes, array creation, and basic operations on arrays.ipynb | mit | #"NumPy's main object is the homogeneous multidimensional array"
import numpy as np
arr = np.array([[0,1,2],[3,4,5]])
arr
"""
Explanation: Some Basic NumPy functionality (attributes, array creation, basic operations between arrays, and basic operations on one array). Adapted from https://docs.scipy.org/doc/numpy-dev... |
ryanpdwyer/teensyio | examples/LED testing.ipynb | mit | np.std(df.y_scaled[np.logical_and(df.x_scaled < 1, df.x_scaled > 0.5)], ddof=1)*1000
"""
Explanation: Tiny offset from zero here, but overall it looks pretty good.
End of explanation
"""
(4./10000)**-1
"""
Explanation: With default analogRead settings, 12-bit resolution, we see 4 µA measured current noise on 10 mA ... |
texaspse/blog | media/f16-scientific-python/week2/Scientific Python Workshop 2.ipynb | mit | import pandas as pd
import numpy as np
#Dont import matplotlib until we get to histogram example
import matplotlib.pyplot as plt
#This next line tells jupyter to plot it in the same space
%matplotlib inline
"""
Explanation: First import pandas and numpy
End of explanation
"""
table = pd.read_excel("GASISData.xls")
... |
tensorflow/docs | site/en/r1/tutorials/eager/custom_training.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... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session12/Day2/TellingStoriesWithData.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
%matplotlib notebook
"""
Explanation: Telling Stories With Data
Version 0.1
By Kalina Borkiewicz
8 Jan 2021
Find a dataset of your choice. This can be one you have visualized before, one you found online, or the sample d... |
nelson-liu/lectures | 2016-2017.Meetings/fall/05.DIY_naive_bayes/naive_bayes_classifier_solution/naive_bayes_tweet_classifier.ipynb | mit | # setting up some imports and some miscellaneous helper methods we'll be using
import re
import string
from math import log
from __future__ import print_function
# our default smoothing value, we pretend we see each word at
# least once so all our probabilities are well-formed
# (so no multiplying by 0 if a word isn'... |
edhenry/notebooks | Algorithms Toolbox - Week 2.ipynb | mit | def ex_run_time(coef, n):
rt = []
for i in range(n):
rt.append((i, (coef*(i**2))))
return rt
def decomp_run_time(coef_a, coef_b, n):
rt = []
for i in range(n):
rt.append((i, ((coef_a*i) + coef_b)))
return rt
ex_rt = ex_run_time(6, 100)
decomp_rt = decomp_run_time(100, 300, 100... |
ethen8181/machine-learning | keras/cnn_image_keras.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# 1. magic to print version
# 2. magic so that the notebo... |
debsankha/network_course_python | exercises/02-exercise-graphs-basic-sol.ipynb | gpl-2.0 | AG.nodes()
AG.edges()
nx.draw(AG)
"""
Explanation: E1.1 Explore the graph AG using the methods:
* AG.nodes()
* AG.edges()
* AG.number_of_nodes()
etc.
Draw the graph using nx.draw
End of explanation
"""
np.average([nx.shortest_path_length(AG, source=u, target=v) for u in AG.nodes() for v in AG.nodes() if u!=v])
... |
evangelistalab/forte | tutorials/Tutorial_02.01_mo_localization.ipynb | lgpl-3.0 | import psi4
import forte
import forte.utils
"""
Explanation: Forte Tutorial 2.02: Orbital localization
In this tutorial we are going to explore how to localize orbitals and visualize them in Jupyter notebooks using the Python API.
Let's import the psi4 and forte modules, including the forte.utils submodule
End of exp... |
QuantEcon/econometrics | Notebook_02_asymptotics/asymptotic_analysis_code.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import ConnectionPatch
import warnings
warnings.filterwarnings('ignore')
colors = sns.color_palette("Blues")
plt.rc('text', usetex=Tr... |
foreignOwl/data-analysis-notebooks | thanksgivingSurvey.ipynb | mit | # this line is required to see visualizations inline for Jupyter notebook
%matplotlib inline
# importing modules that we need for analysis
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# read the data from file and print out first few rows and columns
thanksgiving = pd.read_csv("thanksgiving.... |
lpsinger/healpy | doc/healpy_tutorial.ipynb | gpl-2.0 | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import healpy as hp
"""
Explanation: healpy tutorial
See the Jupyter Notebook version of this tutorial at https://github.com/healpy/healpy/blob/master/doc/healpy_tutorial.ipynb
See a executed version of the notebook with embedded plots at https://... |
nanoos-pnw/ioos-ws | SensorMLparsing_IOOSSOS.ipynb | mit | from datetime import datetime
from urllib import urlencode
from collections import OrderedDict
import numpy as np
import pandas as pd
from shapely.geometry import Point
import geopandas as gpd
from owslib.sos import SensorObservationService
from owslib.swe.sensor.sml import SensorML, Contact, Documentation
from owsli... |
tobydriscoll/fnc-extras | python/Chapter03.ipynb | mit | year = arange(1955,2005,5)
y = array([ -0.0480, -0.0180, -0.0360, -0.0120, -0.0040,
0.1180, 0.2100, 0.3320, 0.3340, 0.4560 ])
fig,ax = subplots()
ax.scatter(year,y,color="k",label="data")
xlabel("year")
ylabel("anomaly (degrees C)")
title("World temperature anomaly");
"""
Explanation: Example 3.1.1
Here are 5-yea... |
mne-tools/mne-tools.github.io | 0.18/_downloads/ac6935fd2f56d2a7d16c589c65a46140/plot_artifacts_correction_maxwell_filtering.ipynb | bsd-3-clause | import mne
from mne.preprocessing import maxwell_filter
data_path = mne.datasets.sample.data_path()
"""
Explanation: Artifact correction with Maxwell filter
This tutorial shows how to clean MEG data with Maxwell filtering.
Maxwell filtering in MNE can be used to suppress sources of external
interference and compensat... |
greg-ashby/deep-learning-nanodegree | image-classification/dlnd_image_classification.ipynb | mit | """
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... |
rileyrustad/pdxapartmentfinder | analysis/.ipynb_checkpoints/Munge-checkpoint.ipynb | mit | with open('../pdxapartmentfinder/data/MasterApartmentData.json') as f:
my_dict = json.load(f)
dframe = DataFrame(my_dict)
dframe = dframe.T
dframe.shape
"""
Explanation: Load the data from our JSON file.
The data is stored as a dictionary of dictionaries in the json file. We store it that way beacause it's easy t... |
klin90/missinglink | Analysis.ipynb | gpl-3.0 | from collections import Counter
from ast import literal_eval
import pandas as pd
import numpy as np
# import and cleaning
rests = pd.read_csv('cleaned.csv')
rests['categories'] = rests['categories'].apply(literal_eval)
# American cities only
rests = rests[rests['state'].isin(['PA', 'NC', 'IL', 'AZ', 'NV', 'WI', 'OH']... |
ichbinjakes/CodeExamples | Road_fatalities.ipynb | gpl-3.0 | data = pd.read_csv(r'data/Fatalities_July_2016_II.csv')
filter_age = data['Age']>=0
data = data[filter_age]
data.head()
"""
Explanation: Federal Office of Road Saftey:
THE HISTORY OF ROAD FATALITIES IN AUSTRALIA
https://www.monash.edu/__data/assets/pdf_file/0020/216452/muarc237.pdf
THE VICTORIAN PARLIAMENTARY ROAD SA... |
turbomanage/training-data-analyst | self-paced-labs/tensorflow-2.x/synthetic_features_and_outliers.ipynb | apache-2.0 | !pip install tensorflow==2.0.0-beta1
from matplotlib import cm
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import logging
from packaging import version
from IPython.display import display
pd.options.display.max_rows = 10
pd.options.display.float_format =... |
phanrahan/magmathon | notebooks/tutorial/icestick/Getting Started.ipynb | mit | import magma as m
m.set_mantle_target("ice40")
"""
Explanation: Getting Started
Magma is a hardware construction language written in Python 3. The central abstraction in Magma is a Circuit, which is analagous to a verilog module. A circuit is a set of functional units that are wired together.
Magma is designed to work... |
mne-tools/mne-tools.github.io | 0.22/_downloads/6f729e8febc223b1d7003304f207eea9/plot_40_epochs_to_data_frame.ipynb | bsd-3-clause | import os
import seaborn as sns
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
"""
Explanation... |
drcgw/IPython-Big-Data | Project-2/Project-2-Master/Project-2-Master.ipynb | bsd-3-clause | from Bio import SeqIO, AlignIO
from Bio.Align.Applications import ClustalwCommandline
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.Graphics import GenomeDiagram
from reportlab.lib import colors as rlc
from reportlab.lib.units import cm
#Make a variable point to where ClustalW2 is on your computer
cl... |
samstav/scipy_2015_sklearn_tutorial | notebooks/03.1 Case Study - Supervised Classification of Handwritten Digits.ipynb | cc0-1.0 | from sklearn.datasets import load_digits
digits = load_digits()
"""
Explanation: Supervised Learning: Classification of Handwritten Digits
In this section we'll apply scikit-learn to the classification of handwritten
digits. This will go a bit beyond the iris classification we saw before: we'll
discuss some of the me... |
mliu49/RMG-stuff | Thermo/sdata134k_large_polycyclic.ipynb | mit | from rmgpy.data.rmg import RMGDatabase
from rmgpy import settings
from rmgpy.species import Species
from rmgpy.molecule import Group
from rmgpy.rmg.main import RMG
from IPython.display import display
import numpy as np
import os
import pandas as pd
from pymongo import MongoClient
import logging
logging.disable(logging.... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_mne_solutions.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
# Read data
fname_evoked = data_path + '/MEG/s... |
khalido/nd101 | nd101_wk2.ipynb | gpl-3.0 | y = np.array([0.72*i*np.random.randint(1,20) + 2*i for i in range(50)])
x = np.array([i for i in range(len(y))])
plt.scatter(x,y);
"""
Explanation: Linear Regression refresher
Some random data
End of explanation
"""
from sklearn.linear_model import LinearRegression
a = LinearRegression()
#putting the input data int... |
mne-tools/mne-tools.github.io | 0.23/_downloads/99798f35196ac117a93fa936a5c48a93/20_visualize_evoked.ipynb | bsd-3-clause | import os
import numpy as np
import mne
"""
Explanation: Visualizing Evoked data
This tutorial shows the different visualization methods for
~mne.Evoked objects.
As usual we'll start by importing the modules we need:
End of explanation
"""
sample_data_folder = mne.datasets.sample.data_path()
sample_data_evk_file = o... |
hyperion-rt/paper-2017-sed-models | notebook_raw/reading_raw_files.ipynb | bsd-2-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import au
"""
Explanation: Using the Hyperion files from the Robitaille (2017) YSO SED models
This notebook demonstrates how to use the Hyperion files for the YSO SED models publi... |
ES-DOC/esdoc-jupyterhub | notebooks/cccma/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccma', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CCCMA
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Bal... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n11_results-checkpoint.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sys
from time import time
import pickle
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (20.0, 10.0)
%load_ext autoreload
%autoreload 2
sys.path.append('../../')
"""
Explanation: This noteboo... |
DaveBackus/Data_Bootcamp | Projects/Summer_15/Employment-Population-Ratio_DavidCai_Jul_15.ipynb | mit | """
Creates a figure using FRED data
Uses pandas Remote Data Access API
Documentation can be found at http://pandas.pydata.org/pandas-docs/stable/remote_data.html
"""
%matplotlib inline
import pandas as pd
import pandas.io.data as web
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
from dateut... |
chris1610/pbpython | notebooks/Pandas_Data_Types.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
df = pd.read_csv("https://github.com/chris1610/pbpython/blob/master/data/sales_data_types.csv?raw=True")
df
"""
Explanation: Pandas Data Types
Accompanying the PB Python article here
End of explanation
"""
df.info()
"""
Explanation: Use df.info and df.dtypes to look at the t... |
irockafe/revo_healthcare | notebooks/MTBLS315/exploratory/Old_notebooks/MTBLS315_uhplc_pos_classifer-4.5ppm.ipynb | mit | import time
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import cross_val_score
#from sklearn.... |
astroumd/GradMap | notebooks/Lectures2021/Lecture2/challenge_problem2_instructor_version.ipynb | gpl-3.0 | #Helper function
def projection(t, v0, g):
return v0*t - 0.5*g*(t**2)
def projectile_X(t, v0, theta, g):
vx0 = v0*np.cos(theta)
return projection(t, vx0, 0)
def projectile_Y(t,v0, theta, g):
vy0 = v0*np.sin(theta)
return projection(t, vy0, g)
"""
Explanation: Welcome to your projectile motion cha... |
aleph314/K2 | Data Mining/Association Rules/assoc_mining_problems.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
with open('itemsets.dat') as f:
transactions = []
for row in f:
transactions.append(row.strip().split(','))
transactions[0:5]
"""
Explanation: Association Mining
The goal in this problem set is to design and cod... |
quantopian/research_public | notebooks/lectures/Means/questions/notebook.ipynb | apache-2.0 | # Useful Functions
def mode(l):
# Count the number of times each element appears in the list
counts = {}
for e in l:
if e in counts:
counts[e] += 1
else:
counts[e] = 1
# Return the elements that appear the most times
maxcount = 0
modes = {}
... |
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