repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
probml/pyprobml | notebooks/misc/GCP_CC.ipynb | mit | # Hints from :
# https://medium.com/analytics-vidhya/how-to-access-files-from-google-cloud-storage-in-colab-notebooks-8edaf9e6c020
# https://stackoverflow.com/questions/57772453/login-on-colab-with-gcloud-without-service-account
"""
Explanation: <a href="https://colab.research.google.com/github/probml/probml-notebooks... |
Aniruddha-Tapas/Applied-Machine-Learning | Miscellaneous/Leaf Classification.ipynb | mit | import os
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import BernoulliNB
from sklea... |
rdhyee/webtech-learning | notebooks/qgrid_learning.ipynb | apache-2.0 | import warnings
warnings.filterwarnings('ignore')
NEED_TO_INSTALL = True
import qgrid
if NEED_TO_INSTALL:
qgrid.nbinstall(overwrite=True) # copies javascript dependencies to your /nbextensions folder
qgrid.set_defaults(remote_js=False, precision=4)
# https://github.com/quantopian/qgrid/pull/49#issue-1219... |
yl565/statsmodels | examples/notebooks/recursive_ls.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
np.set_printoptions(suppress=True)
"""
Explanation: Recursive least squares
Recursive least squares is an expanding window version of ordinary least squa... |
sraejones/phys202-2015-work | days/day13/ODEs.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
"""
Explanation: Ordinary Differential Equations
Learning Objectives: Understand the numerical solution of ODEs and use scipy.integrate.odeint to solve and explore ODEs numerically.
Imports
End of explanation
"""
tmax = 10.0 ... |
geodynamics/burnman | contrib/ipython/Compute_seismic_velocities_beginner.ipynb | gpl-2.0 | %matplotlib inline
import os, sys, numpy as np, matplotlib.pyplot as plt
sys.path.insert(1,os.path.abspath('../..'))
"""
Explanation: BurnMan - Compute seismic velocities - simple case
Copyright (C) 2012 - 2015 by the BurnMan team, released under the GNU GPL v2 or later.
Released under GPL v2 or later.
This example s... |
kgrodzicki/machine-learning-specialization | course-2-regression/notebooks/Overfitting_Demo_Ridge_Lasso.ipynb | mit | import graphlab
import math
import random
import numpy
from matplotlib import pyplot as plt
%matplotlib inline
"""
Explanation: Overfitting demo
Create a dataset based on a true sinusoidal relationship
Let's look at a synthetic dataset consisting of 30 points drawn from the sinusoid $y = \sin(4x)$:
End of explanation
... |
phoebe-project/phoebe2-docs | development/tutorials/compute.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
"""
Explanation: Compute
Now that we have datasets added to our Bundle, our next step is to run the forward model and compute a synthetic model for each of these datasets.
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running ... |
texaspse/blog | media/f16-scientific-python/week4/week4.ipynb | mit | k = np.array([1,3,5,5,6,6,7,10,23123123,31232]) # create a new array
k
k[1]
k[0]
k[4]
k[10] # even though the list has 9 items, the index actually goes from 0 to 9
k[9]
"""
Explanation: Review
Before we start let's do some quick review from last time.
Let's create a new numpy arrray. Fill it with any values yo... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Problem_6-25.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 6
Problem 6-25
End of explanation
"""
Vstart_Vrun = 1/sqrt(3)
print('''
Vstart_Vrun = {:.1f} %
===================='''.format(Vstart_Vrun*100))
"""
Explanation: Description
In this chapter, we learned that a step-down autotransforme... |
karlstroetmann/Formal-Languages | Python/FSM-2-Dot.ipynb | gpl-2.0 | def dfa2string(Fsm):
states, sigma, delta, q0, final = Fsm
result = ''
n = 0
statesToNames = {}
for q in states:
statesToNames[q] = f'S{n}'
n += 1
result += 'states: {S0, ..., ' + f'S{n-1}' + '}\n\n'
result += f'start state: {statesToNames[q0]}' + '\n\n'
result += 'sta... |
danamkaplan/py-Goldsberry | docs/0. Introduction.ipynb | mit | import goldsberry
import pandas as pd
goldsberry.__version__
"""
Explanation: An Introduction to py-Goldsberry
py-Goldsberry is a Python package that makes it easy to interface with the http://stats.nba.com and retrieve the data in a more analyzable format.
This is the first in a series of tutorials that walk through... |
TomTranter/OpenPNM | examples/extractions/Working with Extracted Networks.ipynb | mit | import warnings
import scipy as sp
import numpy as np
import porespy as ps
import openpnm as op
import matplotlib.pyplot as plt
ws = op.Workspace()
ws.settings["loglevel"] = 40
warnings.filterwarnings('ignore')
%matplotlib inline
np.random.seed(10)
#NBVAL_IGNORE_OUTPUT
im = ps.generators.overlapping_spheres(shape=[300... |
srcole/qwm | burrito/.ipynb_checkpoints/Burrito_linear models-checkpoint.ipynb | mit | %config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
import seaborn as sns
sns.set_style("white")
"""
Explanation: San Diego Burrito Analytics: Linear models
Scott Cole
21 May 2016
This... |
beeva-juanmanuelperez/beeva-best-practices | analytics/python/Ejercicio_practico_Titanic.ipynb | mit | import pandas
input_file = 'static/titanic_data.csv'
separador = ","
dataset = pandas.read_csv(filepath_or_buffer=input_file, sep=separador)
"""
Explanation: Titanic Dataset
Throughout this practice we will analyze the Titanic data set. In the [Kaggle website] (https://www.kaggle.com/c/titanic) we can find more info... |
chapmanbe/nlm_clinical_nlp | MultiSentenceDocuments.ipynb | mit | import pyConTextNLP.pyConTextGraph as pyConText
import pyConTextNLP.itemData as itemData
from textblob import TextBlob
import networkx as nx
import pyConTextNLP.display.html as html
from IPython.display import display, HTML
reports = [
"""IMPRESSION: Evaluation limited by lack of IV contrast; however, no evidence ... |
alanhdu/AccessibleML | 5. Dimensionality Reduction via Principal Components Analysis.ipynb | mit | from sklearn.decomposition import PCA
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # 3D Plotting
from scipy import stats
import seaborn as sns # prettier styling for plots
import ipywidgets # interactions
%matplotlib inlin... |
itzzthad/kaggle-exercises | rollingstones-dataset/ExploreAndVisualize.ipynb | mit | import pandas as pd
from bokeh.charts import Donut, HeatMap, Histogram, Line, Scatter, show, output_notebook, output_file
from bokeh.plotting import figure
output_notebook()
"""
Explanation: Exploring and Visualizing the Rolling Stone's 500 Greatest Albums of All Time
Data Source: https://www.kaggle.com/notgibs/500-g... |
stephank16/enes_graph_use_case | neo4j_prov/notebooks/provio_intro.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
import os, sys
# import_path = os.path.abspath('..') not necessary ?
install_path = '/home/stephan/Repos/ENES-EUDAT/enes_graph_use_case'
sys.path.append(install_path)
from neo4j_prov import provio
"""
Explanation: Example notebook illustrating W3C prov --> neo4j graph generation ba... |
kinshuk4/MoocX | misc/deep_learning_notes/Ch1 Intro to Deep Learning/004 - optimize batch training.ipynb | mit | %pylab inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
from random import random
from IPython.display import FileLink, FileLinks
def σ(z):
return 1/(1 + np.e**(-z))
def σ_prime(z):
return np.e**(z) / (np.e**z + 1)**2
def Plot(fn, *args, **kwargs):
argLength = len(args);
if ar... |
machinelearningdeveloper/lc101-kc | December 12, 2016/Covered in class.ipynb | unlicense | class Animal:
def __init__(self, name):
self.name = name
def pretty_version(self):
return self.name
def speak(self):
pass
def __str__(self):
return '{} says {}'.format(self.pretty_version(), self.speak())
class Mammal(Animal):
def __init... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session08/Day4/numba_intro.ipynb | mit | %matplotlib inline
import matplotlib.pylab as plt
import numpy as np
import math
from numba import jit, njit, vectorize
"""
Explanation: <a href="https://colab.research.google.com/github/evaneschneider/parallel-programming/blob/master/numba_intro.ipynb" target="_parent"><img src="https://colab.research.google.com/asse... |
ivastar/clear | notebooks/grizli/grizli_model_and_fit.ipynb | mit | %matplotlib inline
import time
import os
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
import drizzlepac
import grizli
import glob
from grizli import utils
import importlib
from grizli.prep import process_direct_grism_visit
from hsaquery import query, overlaps
from grizli.pipeline impor... |
enakai00/jupyter_ml4se_commentary | Solutions/04-Graph-solution.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import Series, DataFrame
"""
Explanation: グラフの描画
End of explanation
"""
data_x = np.linspace(-1,1,100)
data_sin = np.sin(2*np.pi*data_x)
data_cos = np.cos(2*np.pi*data_x)
fig = plt.figure(figsize=(6,2))
subplot = fig.add_subplot(1,1,... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/building_production_ml_systems/solutions/4b_streaming_data_inference.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
import googleapiclient.discovery
import shutil
from google.cloud import bigquery
from google.api_core.client_options import ClientOptions
from matplotlib import pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import ... |
ayush29feb/cs231n | 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
%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-reloadi... |
ReactiveX/RxPY | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | mit | reset_start_time(O.just)
stream = O.just({'answer': rand()})
disposable = subs(stream)
sleep(0.5)
disposable = subs(stream) # same answer
# all stream ops work, its a real stream:
disposable = subs(stream.map(lambda x: x.get('answer', 0) * 2))
"""
Explanation: A Decision Tree of Observable Operators
Part 1: NEW Observ... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/interactions_anova.ipynb | bsd-3-clause | %matplotlib inline
from urllib.request import urlopen
import numpy as np
np.set_printoptions(precision=4, suppress=True)
import pandas as pd
pd.set_option("display.width", 100)
import matplotlib.pyplot as plt
from statsmodels.formula.api import ols
from statsmodels.graphics.api import interaction_plot, abline_plot
... |
mne-tools/mne-tools.github.io | dev/_downloads/9a8829bce25fe2d64489ddf54437cd3c/30_info.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_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
"""
Explanation: The Info data structure
This tutori... |
majkelx/astwro | examples/gapick_analyse.ipynb | mit | resultpath = '~/tmp/gapick_fine'
"""
Explanation: Analysis of gapick tool genetic algorithm's (GA) run
Read more about PSF stars finding tool gapick in astwro documentation.
Results directory
gapick wrotes several results into directory specified by --out_dir parameter. Set path to results dir below:
End of explanatio... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/mpi-esm-1-2-lr/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'mpi-esm-1-2-lr', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MPI-M
Source ID: MPI-ESM-1-2-LR
Sub-Topics: Radiative Forcings.
Propert... |
google/starthinker | colabs/fred_series_to_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: Federal Reserve Series Data
Download federal reserve series.
License
Copyright 2020 Google LLC,
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... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/10_recommend/hybrid_recommendations/hybrid_recommendations_preproc.ipynb | apache-2.0 | %%bash
conda update -y -n base -c defaults conda
source activate py2env
pip uninstall -y google-cloud-dataflow
conda install -y pytz
pip install apache-beam[gcp]==2.9.0
"""
Explanation: Neural network hybrid recommendation system on Google Analytics data preprocessing
This notebook demonstrates how to implement a hybr... |
TurkuNLP/BINF_Programming | lectures/week-2-entrez.ipynb | gpl-2.0 | import Bio.SeqRecord as BSR
"""
Explanation: Sequence records, part 1
Instructions
Read the chapter 4 and the sections 5.1, 9.3, 9.6, 9.15, and 9.16 of the Biopython tutorial. It is also recommended to read the sections 5.4, 5.5, 9.10, and 9.12, which are touched by this notebook.
Objectives
create and manipulate Seq... |
glouppe/scikit-optimize | examples/strategy-comparison.ipynb | bsd-3-clause | import numpy as np
np.random.seed(123)
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (10, 6)
plt.set_cmap("viridis")
"""
Explanation: Comparing surrogate models
Tim Head, July 2016.
End of explanation
"""
from skopt.benchmarks import branin as _branin
def branin(x, noise_level... |
dereneaton/ipyrad | tests/ipsimdata.ipynb | gpl-3.0 | ## name for our sim data directory
DIR = "./ipsimdata"
## A mouse MT genome used to stick our data into.
INPUT_CHR = "/home/deren/Downloads/MusMT.fa"
## number of RAD loci to simulate
NLOCI = 1000
## number of inserts to reference genome and insert size
N_INSERTS = 100
INSERT_SIZE = 50
"""
Explanation: Simulate RAD... |
walkon302/CDIPS_Recommender | notebooks/.ipynb_checkpoints/Feature_Weights-checkpoint.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.getcwd()+'/../')
# our lib
from lib.resnet50 import ResNet50
from lib.imagenet_utils import preprocess_input, decode_predictions
#keras
from keras.preprocessing import image
from keras.models import Model
# sklearn
import sklearn
from sklearn.linear_model import LogisticRegr... |
jeanpat/DeepFISH | notebooks/Clean Dataset from their spurious pixels.ipynb | gpl-3.0 | import os
import h5py
from matplotlib import pyplot as plt
import numpy as np
from numpy import newaxis
from skimage import morphology as mo
from scipy.ndimage import distance_transform_bf as distance
def distanceTransform(bIm):
#from pythonvision.org
dist = distance(bIm)
dist = dist.max() - dist
dist... |
Lattecom/HYStudy | scripts/[HYStudy 27th] Decorator Pattern 2.ipynb | mit | def statically_typed(*types, return_type=None): # Decorator factory
def decorator(function): # 팩토리가 반환할 decorator 함수
@functools.wraps(function)
def wrapper(*args, **kwargs): # Decorator가 반환할 래퍼 함수
if len(args) > len(types):
raise ValueError("too many arguments")
... |
BrownDwarf/ApJdataFrames | notebooks/Pecaut2013.ipynb | mit | %pylab inline
%config InlineBackend.figure_format = 'retina'
import seaborn as sns
sns.set_context("notebook", font_scale=1.5)
import warnings
warnings.filterwarnings("ignore")
from astropy.io import ascii
"""
Explanation: ApJdataFrames Pecault2013
Title: INTRINSIC COLORS, TEMPERATURES, AND BOLOMETRIC CORRECTIONS OF... |
GoogleCloudPlatform/tf-estimator-tutorials | 08_Text_Analysis/01 - Text Classification - SMS Ham vs. Spam - Data Preparation.ipynb | apache-2.0 | import pandas as pd
import string
import re
from sklearn import model_selection
DATASET_FILE = 'data/sms-spam/SMSSpamCollection'
dataset = pd.read_csv(DATASET_FILE, sep='\t', names=['class','sms'])
dataset.head()
print("Dataset Size: {}".format(len(dataset)))
value_counts = dataset['class'].value_counts()
print(value... |
bhattacharjee/courses | CourseraDeepLearningSpecialization/2.HyperparameterTrainingRegularizationAndOptimization/Week1/Exercises/Gradient+Checking.ipynb | mit | # Packages
import numpy as np
from testCases import *
from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector
"""
Explanation: Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
You are ... |
macks22/gensim | docs/notebooks/FastText_Tutorial.ipynb | lgpl-2.1 | import gensim, os
from gensim.models.wrappers.fasttext import FastText
# Set FastText home to the path to the FastText executable
ft_home = '/home/jayant/Projects/fastText/fasttext'
# Set file names for train and test data
data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data']) + os.sep
lee_tra... |
drabastomek/learningPySpark | Chapter06/LearningPySpark_Chapter06.ipynb | gpl-3.0 | import pyspark.sql.types as typ
labels = [
('INFANT_ALIVE_AT_REPORT', typ.IntegerType()),
('BIRTH_PLACE', typ.StringType()),
('MOTHER_AGE_YEARS', typ.IntegerType()),
('FATHER_COMBINED_AGE', typ.IntegerType()),
('CIG_BEFORE', typ.IntegerType()),
('CIG_1_TRI', typ.IntegerType()),
('CIG_2_TRI'... |
AllenDowney/ProbablyOverthinkingIt | ess4.ipynb | mit | from __future__ import print_function, division
import string
import random
import cPickle as pickle
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import thinkstats2
import thinkplot
import matplotlib.pyplot as plt
import ess
# colors by colorbrewer2.org
BLUE1 = '#a6cee3'
BLUE2 = '#1... |
google-research/language | language/multiberts/coref.ipynb | apache-2.0 | #@title Import libraries and multibootstrap code
import re
import os
import numpy as np
import pandas as pd
import sklearn.metrics
import scipy.stats
from tqdm.notebook import tqdm # for progress indicator
import multibootstrap
#@title Import and configure plotting libraries
import matplotlib
from matplotlib impor... |
gsentveld/lunch_and_learn | notebooks/Probabilistic_Graphical_Model.ipynb | mit | import os
from dotenv import load_dotenv, find_dotenv
# find .env automagically by walking up directories until it's found
dotenv_path = find_dotenv()
# load up the entries as environment variables
load_dotenv(dotenv_path)
"""
Explanation: Exploring the files with Pandas
Many statistical Python packages can deal wit... |
dilanfd/fremont_bridge_bike_analysis | fremont_bridge_bike_analysis.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')
from module_code.data import get_fremont_data
df = get_fremont_data()
df.head()
df.resample('W').sum().plot() # ugly looking graphs. Change to seaborn.
# resample daily and find the rolling sum of 365 days.
ax = df.resample('D').sum().roll... |
zhouqifanbdh/liupengyuan.github.io | chapter2/homework/localization/4-5/201611680049（5）.ipynb | mit | n = int(input('请输入你想取的整数的个数'))
m = int(input('请输入取整范围的下限'))
k = int(input('请输入取整范围的上限'))
import random,math
def get_num():
number = random.randint(m,k)
return number
i = 0
total = 0
while i < n:
total = total + get_num()
i = i + 1
print(get_num())
average = total/n
math.sqrt(average)
"""
E... |
queirozfcom/python-sandbox | python3/notebooks/boosting/.ipynb_checkpoints/effect-of-categories-credit-default-checkpoint.ipynb | mit | def fix_status(current_value):
if current_value == -2: return 'no_consumption'
elif current_value == -1: return 'paid_full'
elif current_value == 0: return 'revolving'
elif current_value in [1,2]: return 'delay_2_mths'
elif current_value in [3,4,5,6,7,8,9]: return 'delay_3+_mths'
else: return 'o... |
wei-Z/Python-Machine-Learning | code/ch06/ch06.ipynb | mit | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
"""
Explanation: Sebastian Raschka, 2015
https://github.com/rasbt/python-... |
Unidata/unidata-python-workshop | notebooks/Bonus/Siphon_XARRAY_Cartopy_HRRR.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# Resolve the latest HRRR dataset
from siphon.catalog import get_latest_access_url
hrrr_catalog = "http://thredds.ucar.edu/thredds/catalog/grib/NCEP/HRRR/CONUS_2p5km/catalog.xml"
latest_hrrr_ncss = get_latest_access_url(hrrr_catalog, "NetcdfSubset"... |
KushajveerSingh/Data-Science-Libraries | Python/Advancd Python.ipynb | mit | if __name__ == "__main__":
# Do anything you want
pass
"""
Explanation: Python is a programming language. Python is often refereed as a scripting language as scripting languages are often interpreted and not compiled.<br>
End of explanation
"""
# If you want to give arguments to a function as a list
def f(x,... |
rjleveque/ptha_tutorial | Notebooks/Make_Transects_interactive.ipynb | bsd-2-clause | %pylab inline
%matplotlib notebook
from __future__ import print_function
import sys, os
from ipywidgets import interact
from IPython.display import Image, display
from ptha_paths import data_dir, events_dir
"""
Explanation: Make Transects (interactive)
This notebook illustrates how to make transects of event results... |
oditorium/blog | iPython/Various-ConvertingOsxKeychainToNotes.ipynb | agpl-3.0 | path = '/Volumes/---/keychains'
#!ls -l $path
"""
Explanation: Converting on OSX Keychain into Text Notes
The purpose of this notebook is to export the secured notes from an Apple keychain into separate files. It is pretty rough, but generally this only needs to be done once (at least for me this was the case) so no... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
P... |
snth/ctdeep | PyconZA-2016.ipynb | mit | import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
batch_size = 128
nb_classes = 10
nb_epoch = 15
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = ... |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodyna... |
AllenDowney/ModSim | soln/chap23.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
mne-tools/mne-tools.github.io | 0.23/_downloads/029173f022506572bcefa009b440732f/evoked_arrowmap.ipynb | bsd-3-clause | # Authors: Sheraz Khan <sheraz@khansheraz.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.datasets.brainstorm import bst_raw
from mne import read_evokeds
from mne.viz import plot_arrowmap
print(__doc__)
path = sample.data_path()
fname = path + '/MEG/sample/samp... |
michaelaye/planet4 | notebooks/ground_projection.ipynb | isc | from hirise_tools.downloads import download_RED_product
from hirise_tools.products import RED_PRODUCT_ID
import logging
from nbtools.logging import setup_live_logging
from planet4 import io, region_data
db = io.DBManager()
roi = region_data.Potsdam()
obsids = roi.all_obsids
obsids
obsids = db.obsids
len(obsids)
s... |
coolharsh55/advent-of-code | 2016/python3/Day20.ipynb | mit | import re
numbers = re.compile('(\d+)')
UPPER_LIMIT = 4294967295
with open('../inputs/day20.txt', 'r') as f:
data = [
tuple(map(
int, numbers.findall(line)))
for line in f.readlines()]
data.sort()
rule_index = 0
n = 0
while n <= UPPER_LIMIT:
for i in range(rule_index, le... |
rpreen/xcsf | python/notebooks/example_rmux.ipynb | gpl-3.0 | from __future__ import annotations
import random
from typing import Final
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
import xcsf
"""
Explanation: This example demonstrates XCSF (single-step) reinforcement learning applied to the real-multiplexer problem. Classifiers are composed of hyp... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/formulas.ipynb | bsd-3-clause | import numpy as np # noqa:F401 needed in namespace for patsy
import statsmodels.api as sm
"""
Explanation: Formulas: Fitting models using R-style formulas
Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas... |
h2oai/h2o-dev | h2o-py/demos/H2O_tutorial_eeg_eyestate.ipynb | apache-2.0 | import h2o
# Start an H2O Cluster on your local machine
h2o.init()
"""
Explanation: H2O Tutorial: EEG Eye State Classification
Author: Erin LeDell
Contact: erin@h2o.ai
This tutorial steps through a quick introduction to H2O's Python API. The goal of this tutorial is to introduce through a complete example H2O's capab... |
marcelomiky/PythonCodes | codes/back_to_basics/Codes_Mastering_Python.ipynb | mit | def filter_modulo(items, modulo):
output_items = []
for i in range(len(items)):
if items[i] % modulo:
output_items.append(items[i])
return output_items
def filter_modulo(items, modulo):
for item in items:
if item % modulo:
yield item
"""
Explanation: Beautiful i... |
martinjrobins/hobo | examples/toy/model-lotka-volterra.ipynb | bsd-3-clause | import pints
import pints.toy
model = pints.toy.LotkaVolterraModel()
print('Outputs: ' + str(model.n_outputs()))
print('Parameters: ' + str(model.n_parameters()))
"""
Explanation: Lotka-Volterra toy model
The LotkaVolterraModel describes the relationship between two interacting species, where one preys on the other.... |
ueapy/ueapy.github.io | content/notebooks/2016-03-11-exceptions.ipynb | mit | try:
# try executing this piece of code
x = '2' + 3 # this will throw an error, because you can't add integer and string
except:
# if there is any error above, go here
print('an error occured')
"""
Explanation: Today was an exceptional meeting. Why? Because we talked about such thing as exceptions in ... |
halexan/cs231n | assignment2/FullyConnectedNets.ipynb | mit | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
%matplotlib inline
... |
bbglab/adventofcode | 2020/claudia/2020_12_03/code.ipynb | mit | input_f = './input.txt'
"""
Explanation: Day 3
End of explanation
"""
def find_trees(input_f, move_right, move_down):
"""Find the trees in the path"""
trees = 0
pointer = 0
number_of_columns = 0
with open(input_f, 'r') as fd:
for row, line in enumerate(fd, 0):
... |
AllenDowney/ModSim | python/soln/chap14.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
simkovic/simkovic.github.io | _ipynb/No Way Anova - Logistic Regression truimphs Anova.ipynb | mit | from scipy.stats import scoreatpercentile as sap
clr=(0.2, 0.5, 0.6)
%pylab inline
plt.figure(figsize=(6,4))
plt.subplot(2,2,1);plt.title('alpha')
x=np.arange(1,9,0.1)
for a in np.arange(2,9).tolist():
y=1/(1+exp(a-1*x))*1+0
plt.plot(x,y,'b')
plt.grid(b=False,axis='x');plt.ylim([0,1]);plt.xlim([1,8])
plt.subplo... |
pbutenee/ml-tutorial | release/1/recommendation_engine.ipynb | mit | import numpy as np
import pandas as pd
import sklearn.metrics.pairwise
"""
Explanation: Recommendation Engine
In this tutorial we are going to build a simple recommender system using collaborative filtering. You'll be learning about the popular data analysis package pandas along the way.
1. The import statements
End o... |
wgm2111/ellipsoidal-outlier-detector | example_ellipsoidal_outlier_filter_new_points.ipynb | gpl-3.0 | %matplotlib inline
# Future
from __future__ import print_function
# Numpy imports
import numpy as np
import numpy.random as random
import scipy as sp
import scipy.linalg as la
# Import matplotlib.pyplot
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# My imports
from ellipsoidal_outlie... |
geoffbacon/semrep | semrep/data/evaluation/framenet/framenet.ipynb | mit | import numpy as np
import pandas as pd
from nltk.corpus import framenet as fn
"""
Explanation: FrameNet
I see three ways of getting features from FrameNet:
Does word $j$ evoke frame $i$?
Something with frame relations
Something with frame elements
End of explanation
"""
def get_lus(frame):
"""Helper to get lexe... |
jmhsi/justin_tinker | data_science/courses/deeplearning1/nbs/statefarm.ipynb | apache-2.0 | from theano.sandbox import cuda
cuda.use('gpu0')
%matplotlib inline
from __future__ import print_function, division
path = "data/state/"
#path = "data/state/sample/"
import utils; reload(utils)
from utils import *
from IPython.display import FileLink
batch_size=64
"""
Explanation: Enter State Farm
End of explanation... |
maartenbreddels/vaex | docs/source/example_arrow.ipynb | mit | !ls -alh /Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.arrow
import vaex
"""
Explanation: Arrow
Vaex supports Arrow. We will demonstrate vaex+arrow by giving a quick look at a large dataset that does not fit into memory. The NYC taxi dataset for the year 2015 contains about 150 million rows containing informati... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/art_and_science_of_ml/labs/learning_rate.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Install Sklearn
!python3 -m pip install --user sklearn
import os
import tensorflow.keras
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
from tensorflow import feature_column as fc
from tensorflow.keras import layers
fr... |
CSB-book/CSB | good_code/solutions/Jiang2013_solution.ipynb | gpl-3.0 | import csv
with open('../data/Jiang2013_data.csv') as csvfile:
# set up csv reader and specify correct delimiter
reader = csv.DictReader(csvfile, delimiter = '\t')
taxa = []
r_values = []
for row in reader:
taxa.append(row['Taxon'])
r_values.append(float(row['r']))
"""
Explanation:... |
MarsUniversity/ece387 | website/block_2_kinematics/lsn7/puma.ipynb | mit | %matplotlib inline
# Let's grab some libraries to help us manipulate symbolic equations
from __future__ import print_function
from __future__ import division
import numpy as np
import sympy
from sympy import symbols, sin, cos, pi, simplify
def makeT(a, alpha, d, theta):
# create a modified DH homogenious matrix
... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/introduction_to_tensorflow/solutions/1_core_tensorflow.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
print(tf.__version__)
"""
Explanation: Getting started with TensorFlow
Learning Objectives
1. Practice defining and performing basic operations on constant Tensors
1. Use Tensorflow's automatic differentiation capability
1. Learn ho... |
metpy/MetPy | v0.5/_downloads/upperair_soundings.ipynb | bsd-3-clause | from datetime import datetime
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
import metpy.calc as mpcalc
from metpy.io import get_upper_air_data
from metpy.plots import Hodograph, SkewT
"""
Explanation: Upper Air Sounding Tutorial
Upper air analysis is... |
mathinmse/mathinmse.github.io | Lecture-09-Matrices-and-Rotations.ipynb | mit | %matplotlib inline
import numpy as np
import sympy as sp
sp.init_session(quiet=True)
?sp.rot_axis3
x = sp.symbols('\\theta')
sp.rot_axis3(x)
"""
Explanation: Laboratory 09: Matrices and Rotations (Crystallography)
Background
The purpose of this laboratory is to understand and apply the concept of an agebraic group ... |
bearing/dosenet-analysis | Programming Lesson Modules/Module 5- Other Forms of Visualization.ipynb | mit | %matplotlib inline
import csv
import io
import urllib.request
import matplotlib.pyplot as plt
from datetime import datetime
import numpy as np
url = 'https://radwatch.berkeley.edu/sites/default/files/pictures/rooftop_tmp/weather.csv'
response = urllib.request.urlopen(url)
reader = csv.reader(io.TextIOWr... |
JustinShenk/sonic-face | PrepareData.ipynb | mit | import os
import numpy as np
import matplotlib
matplotlib.use('TkAgg') # For displaying animation
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from sklearn import svm as svm
from sklearn.externals import joblib
from sklearn import linear_model as lm
from sklearn import p... |
abhinav-upadhyay/intro-to-python | notebooks/Dictionary.ipynb | bsd-2-clause | empty_dictionary = {}
print empty_dictionary
"""
Explanation: Python Dictionary
Dictionaries can be used to store key, value pairs in Python for example: {'first_name': 'abhinav', 'last_name': 'upadhay'}
Ordering of the items in the dictionary is not guranteed
They are good for fast search or lookup operations
Creat... |
springcoil/PyDataLondonTutorial | .ipynb_checkpoints/LogisticRegScikitlearn-checkpoint.ipynb | mit | import pandas as pd
import numpy as np
from sklearn import linear_model
from sklearn.cross_validation import train_test_split
data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", header=None, names=['age', 'workclass', 'fnlwgt',
'education-categorical', 'edu... |
tlkh/Generating-Inference-from-3D-Printing-Jobs | PCA Test.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from sklearn import decomposition
from sklearn import datasets
import csv
%run 'preprocessor.ipynb' #our own preprocessor functions
"""
Explanation: PCA Test
Principal component analysis (PCA) is the linear dimensionality reduction using Singular Value Decomposition... |
marshal789/Lectures-On-Machine-Learning | K Nearest Neighbors/KNN.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
"""
Explanation: K Nearest Neighbors
For this we will use a classfied dataset.The feature column names are hidden but we are given the data and the target classes.
We will use KNN to create a model that dire... |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepping Framework,... |
nvergos/DAT-ATX-1_Project | Notebooks/2c. Supervised Learning - Text Mining for Classification.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set(rc={"axes.labelsize": 15});
# Some nice default configuration for plots
plt.rcParams['figure.figsize'] = 10, 7.5;
plt.rcParams['axes.grid'] = True;... |
atulsingh0/MachineLearning | python_DC/IntoductionToDataBase_#1.3.ipynb | gpl-3.0 | # import
"""
Explanation: Advanced SQLAlchemy Queries
End of explanation
"""
# # Import create_engine function
# from sqlalchemy import create_engine
# # Create an engine to the census database
# engine = create_engine('mysql+pymysql://student:datacamp@courses.csrrinzqubik.us-east-1.rds.amazonaws.com:3306/census')
... |
tensorflow/text | docs/tutorials/text_generation.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... |
eds-uga/csci1360e-su17 | lectures/L16.ipynb | mit | import numpy as np
np.random.seed(3908544)
# Generate two random datasets.
data1 = np.random.normal(loc = 0, scale = 58, size = 1000)
data2 = 200 * np.random.random(1000) - 100
# What are their means and variances?
print("Dataset 1 average: {:.2f} (+/- {:.2f})".format(data1.mean(), data1.std()))
print("Dataset 2 aver... |
yw-fang/readingnotes | machine-learning/handson_scikitlearn_tf_2017/ch02-notebook.ipynb | apache-2.0 | In the first example of chapter 02,we use this [dataset](./datasets/housing/housing.tgz) (Note that it's a compressed file with extension of tgz)
Cerntaily, we can download the database from the github by hand, and then decompress this file into csv file, but it's preferable to create a small function to do that. It i... |
jrossyra/adaptivemd | examples/tutorial/1_example_setup_project.ipynb | lgpl-2.1 | from adaptivemd import Project
"""
Explanation: Tutorial 1- AdaptiveMD basics
First we cover some basics demonstrating the use of AdaptiveMD objects to get you going.
We will briefly talk about
resources
files
generators
how to run a simple trajectory
All of these objects from the adaptivemd package are used to orga... |
jepegit/cellpy | dev_utils/batch_notebooks/cellpy_batch_processing_DEV_ocv.ipynb | mit | %load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cellpy
from cellpy import prms
from cellpy import prmreader
from cellpy.utils import batch
import holoviews as hv
%matplotlib inline
hv.extension("bokeh")
################################################... |
pdamodaran/yellowbrick | examples/ndanielsen/Yellowbrick in the Flower Garden.ipynb | apache-2.0 | # read the iris data into a DataFrame
import pandas as pd
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
iris = pd.read_csv(url, header=None, names=col_names)
iris.head()
"""
Explanation: Using Yello... |
tcstewar/testing_notebooks | Sparsity and SSPs.ipynb | gpl-2.0 | def sparsity_to_x_intercept(d, p):
sign = 1
if p > 0.5:
p = 1.0 - p
sign = -1
return sign * np.sqrt(1-scipy.special.betaincinv((d-1)/2.0, 0.5, 2*p))
"""
Explanation: When working with learning and neural networks, it is useful to be able to control the sparsity of the network. In this cas... |
ceteri/pytextrank | explain_algo.ipynb | apache-2.0 | import warnings
warnings.filterwarnings("ignore")
import spacy
nlp = spacy.load("en_core_web_sm")
"""
Explanation: Explain PyTextRank: the algorithm
Let's look at the TextRank algorithm used to build a graph from a raw text, and then from that extract the top-ranked phrases. This work is based on
"TextRank: Bringing... |
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