repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
chrismcginlay/crazy-koala | jupyter/04_making_decisions_introduction.ipynb | gpl-3.0 | temperature = float(input("Please enter the temperature: "))
if temperature<15:
print("It is too cold.")
print("Turn up the heating.")
"""
Explanation: Making Decisions - Introduction
Your programs so far always carry out the same commands every time the programs are run. Most programs need to be able to carry... |
gsentveld/lunch_and_learn | notebooks/Data_Exploration.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... |
qutip/qutip-notebooks | examples/control-pulseoptim-Lindbladian.ipynb | lgpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import datetime
from qutip import Qobj, identity, sigmax, sigmay, sigmaz, sigmam, tensor
from qutip.superoperator import liouvillian, sprepost
from qutip.qip import hadamard_transform
import qutip.logging_utils as logging
logger = logging.get_logger... |
dismalpy/dismalpy | doc/notebooks/local_linear_trend.ipynb | bsd-2-clause | %matplotlib inline
import numpy as np
import pandas as pd
from scipy.stats import norm
import dismalpy as dp
import matplotlib.pyplot as plt
"""
Explanation: State space modeling: Local Linear Trends
This notebook describes how to extend the state space classes to create and estimate a custom model. Here we develop a... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/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', 'dwd', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection, ... |
basp/notes | 3dgfx.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: 3dgfx the math
This is pretty much a collection of notes mostly inspired by Computer Graphics, Fall 2009. Yeah it's an old course but it's very good and covers a lot of essentials in a fast pace.
This is by no means a substitute for... |
deepmind/acme | examples/quickstart.ipynb | apache-2.0 | environment_library = 'gym' # @param ['dm_control', 'gym']
"""
Explanation: Acme: Quickstart
Guide to installing Acme and training your first D4PG agent.
<a href="https://colab.research.google.com/github/deepmind/acme/blob/master/examples/quickstart.ipynb" target="_parent"><img src="https://colab.research.google.com/... |
analysiscenter/dataset | examples/tutorials/05_creating_CNN.ipynb | apache-2.0 | import sys
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import PIL
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
# the following line is not required if BatchFlow is installed as a python package.
sys.path.append('../..')
from batchflow import D, B, V, C, R, P
... |
BryanCutler/spark | python/docs/source/getting_started/quickstart.ipynb | apache-2.0 | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
"""
Explanation: Quickstart
This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of RDDs. When Spark transforms data, it does not immediately compu... |
rainyear/pytips | Tips/2016-04-08-Descriptor.ipynb | mit | a = 1
b = 2
print("a + b = {}".format(a+b))
# 相当于
print("a.__add__(b) = {}".format(a.__add__(b)))
"""
Explanation: Python 描述符
本篇主要关于三个常用内置方法：property()，staticmethod()，classmethod()
在 Python 语言的设计中，通常的语法操作最终都会转化为方法调用，例如：
End of explanation
"""
class Int:
ctype = "Class::Int"
def __init__(self, val):
... |
wmfschneider/CHE30324 | Homework/HW5-soln.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
E = []
l = 1.4e-10 #m
hbar = 1.05457e-34 #J*s
m = 9.109e-31 #kg
N = [1,3,5,7,9] #N = number of C-C bonds
for n in range (1,7):
for i in N:
e = (n**2*np.pi**2*hbar**2*6.2415e18)/(2*m*(i*l)**2)
E.append(e)
plt.scatter(N,E[0:5], label = "n=1")
plt.scatter(N,... |
tensorflow/docs-l10n | site/ja/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
mne-tools/mne-tools.github.io | 0.20/_downloads/d52b5321a00f5cf4d4be975019fb541b/plot_morph_surface_stc.ipynb | bsd-3-clause | # Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import mne
from mne.datasets import sample
print(__doc__)
"""
Explanation: Morph surface source estimate
This example demonstrates how to morph an individual subject's
:class:mne.SourceEstimate to a common reference space. We a... |
nbokulich/short-read-tax-assignment | ipynb/simulated-community/taxonomy-assignment.ipynb | bsd-3-clause | from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.simulated_communities import copy_expected_composition
from tax_credit.framework_functions import (parameter_sweep,
generate_per_method_biom_... |
tpin3694/tpin3694.github.io | machine-learning/flatten_a_matrix.ipynb | mit | # Load library
import numpy as np
"""
Explanation: Title: Flatten A Matrix
Slug: flatten_a_matrix
Summary: How to flatten a matrix in Python.
Date: 2017-09-02 12:00
Category: Machine Learning
Tags: Vectors Matrices Arrays
Authors: Chris Albon
Preliminaries
End of explanation
"""
# Create matrix
matrix = np.a... |
tensorflow/lucid | notebooks/feature-visualization/any_number_channels.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
import lucid.modelzoo.vision_models as models
from lucid.misc.io import show
import lucid.optvis.objectives as objectives
import lucid.optvis.param as param
import lucid.optvis.render as render
import lucid.optvis.transform as transform
model = models.InceptionV1()
model.loa... |
ericmjl/systems-microbiology-hiv | Problem Set (Solutions).ipynb | mit | # This cell loads the data and cleans it for you, and log10 transforms the drug resistance values.
# Remember to run this cell if you want to have the data loaded into memory.
DATA_HANDLE = 'drug_data/hiv-protease-data.csv' # specify the relative path to the protease drug resistance data
N_DATA = 8 # specify the numb... |
grfiv/MNIST | svm.scikit/svm_rbf.scikit_random_gridsearch.ipynb | mit | from __future__ import division
import os, time, math, csv
import cPickle as pickle
import matplotlib.pyplot as plt
import numpy as np
from print_imgs import print_imgs # my own function to print a grid of square images
from sklearn.preprocessing import StandardScaler
from sklearn.utils import shuffle... |
ES-DOC/esdoc-jupyterhub | notebooks/thu/cmip6/models/sandbox-2/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'sandbox-2', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: THU
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: Glaciers, Ice.
Properties: 3... |
drgmk/sdf | examples/explore_results.ipynb | mit | import requests
import pickle
"""
Explanation: Explore sdf output
sdf generates a large amount of information during fitting. Most of this is saved in a database that isn't visible on the web, and also in pickle files that can be found for each model under the "..." link.
A simpler output is the json files under the "... |
AllenDowney/ModSim | soln/chap20.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/'
... |
DamienIrving/ocean-analysis | development/dask_iris.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
import glob
import iris
from iris.experimental.equalise_cubes import equalise_attributes
import iris.coord_categorisation
infiles = glob.glob('/g/data/ua6/DRSv3/CMIP5/CCSM4/historical/mon/ocean/r1i1p1/thetao/latest/thetao_Omon_CCSM4_historical_r1i1p1_??????-??????.nc'... |
antoinecarme/sklearn_explain | doc/sklearn_reason_codes.ipynb | bsd-3-clause | from sklearn import datasets
import pandas as pd
ds = datasets.load_breast_cancer();
NC = 4
lFeatures = ds.feature_names[0:NC]
df = pd.DataFrame(ds.data[:,0:NC] , columns=lFeatures)
df['TGT'] = ds.target
df.sample(6, random_state=1960)
"""
Explanation: Model Explanation for Classification Models
This document descri... |
snowicecat/umich-eecs445-f16 | handsOn_lecture17_clustering-mixtures-em/handsOn_lecture17_clustering-mixtures-em.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt;
import matplotlib as mpl;
import numpy as np;
"""
Explanation: $$ \LaTeX \text{ command declarations here.}
\newcommand{\R}{\mathbb{R}}
\renewcommand{\vec}[1]{\mathbf{#1}}
\newcommand{\X}{\mathcal{X}}
\newcommand{\D}{\mathcal{D}}
\newcommand{\G}{\mathcal{G}}
\n... |
ajfriend/cyscs | tutorial_parallel.ipynb | mit | import scs
from concurrent import futures
num_problems = 20
m = 1000 # size of L1 problem
data = [scs.examples.l1(m, seed=i) for i in range(num_problems)]
"""
Explanation: Calling SCS in Parallel
In this notebook, we set up a list of several SCS problems and map scs.solve over that list
to solve each of the problems... |
shreyas111/Multimedia_CS523_Project1 | Style_Transfer_Saving_Input_Output_Images.ipynb | mit | from IPython.display import Image, display
Image('images/15_style_transfer_flowchart.png')
"""
Explanation: Style Transfer
Our Changes:
Added code for saving the input content and style images. Also added code for saving the output mixed image
End of explanation
"""
%matplotlib inline
import matplotlib.pyplot as plt... |
srcole/qwm | burrito/.ipynb_checkpoints/Burrito_bootcamp-checkpoint.ipynb | mit | # These commands control inline plotting
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np # Useful numeric package
import scipy as sp # Useful statistics package
import matplotlib.pyplot as plt # Plotting package
"""
Explanation: San Diego Burrito Analytics: Bootcamp 2016
Scott Col... |
bundgus/python-playground | jupyter-notebook-playground/P4DS4D; 16; Outliers.ipynb | mit | import numpy as np
from scipy.stats.stats import pearsonr
np.random.seed(101)
normal = np.random.normal(loc=0.0, scale= 1.0, size=1000)
print 'Mean: %0.3f Median: %0.3f Variance: %0.3f' % (np.mean(normal), np.median(normal), np.var(normal))
outlying = normal.copy()
outlying[0] = 50.0
print 'Mean: %0.3f Median: %0.3f V... |
adico-somoto/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... |
GoogleCloudPlatform/ai-platform-samples | ai-explanations-local-experience.ipynb | apache-2.0 | PROJECT_ID = "[your-project-id]" #@param {type:"string"}
if PROJECT_ID == "" or PROJECT_ID is None or PROJECT_ID == "[your-project-id]":
# Get your GCP project id from gcloud
shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null
PROJECT_ID = shell_output[0]
print("Project ID:", ... |
pyzos/pyzos | Examples/jupyter_notebooks/00_Enhancing_the_ZOS_API_Interface.ipynb | mit | from __future__ import print_function
import os
import sys
import numpy as np
from IPython.display import display, Image, YouTubeVideo
import matplotlib.pyplot as plt
# Imports for using ZOS API in Python directly with pywin32
# (not required if using PyZOS)
from win32com.client.gencache import EnsureDispatch, EnsureM... |
philmui/datascience | lecture07.big.data/lecture07.3.trends.ipynb | mit | yrs = [str(yr) for yr in range(2002, 2016)]
"""
Explanation: We are only interested the year range from 2002 - 2006
End of explanation
"""
export_df = df[(df['trade_type'] == 'Export') &
(df['partner'] == 'EXT_EU28')
].loc[['EU28', 'UK']][yrs]
export_df.head(4)
"""
Explanation: Let's f... |
Jim00000/Numerical-Analysis | 7_Boundary_Value_Problems.ipynb | unlicense | # Import modules
import numpy as np
import scipy
"""
Explanation: ★ Boundary Value Problems ★
End of explanation
"""
def ode_rkf45(f, a, b, y0, h = 1e-3, tol = 1e-6):
w = y0.astype(np.float64)
t = a
while(t < b):
w_this, t_this = w, t
s1 = f(t, w)
hs1 = h * s1
s2 = f(t + h... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session02/Day5/PracticalMachLearnWorkflowSolutions.ipynb | mit | import numpy as np
from astropy.table import Table
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: A Practical Guide to the Machine Learning Workflow:
Separating Stars and Galaxies from SDSS
Version 0.1
By AA Miller 2017 Jan 22
We will now follow the steps from the machine learning workflow lectur... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dyn... |
sbu-python-summer/python-tutorial | day-1/python-day1-exercises1.ipynb | bsd-3-clause | import random
random_number = random.randint(0,9)
"""
Explanation: Exercises
Q 1
When talking about floating point, we discussed machine epsilon, $\epsilon$—this is the smallest number that when added to 1 is still different from 1.
We'll compute $\epsilon$ here:
Pick an initial guess for $\epsilon$ of eps = ... |
deeplook/notebooks | mapping/geodesic_polylines.ipynb | mit | %matplotlib inline
import math
import folium
la = 34.05351, -118.24531
nyc = 40.71453, -74.00713
berlin = 52.516071, 13.37698
potsdam = 52.39962, 13.04784
singapore = 1.29017, 103.852
sydney = -33.86971, 151.20711
"""
Explanation: Using truly geodesic polylines with Folium
This notebook shows how long straight lin... |
w4zir/ml17s | lectures/lec03-gradient-descent.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
# read data in pandas frame
dataframe = pd.read_csv('datasets/house_dataset1.csv')
# assign x and y
X = np.array(dataframe[['Size']])
y = np.array(dataframe[['Price']])
m = y.size # number of tr... |
SKA-ScienceDataProcessor/algorithm-reference-library | workflows/notebooks/imaging-fits_arlexecute.ipynb | apache-2.0 | %matplotlib inline
import os
import sys
sys.path.append(os.path.join('..', '..'))
from data_models.parameters import arl_path
results_dir = arl_path('test_results')
from matplotlib import pylab
pylab.rcParams['figure.figsize'] = (10.0, 10.0)
pylab.rcParams['image.cmap'] = 'rainbow'
from matplotlib import pyplot ... |
GoogleCloudPlatform/nvidia-merlin-on-vertex-ai | 02-model-training-hugectr.ipynb | apache-2.0 | import json
import os
import time
from google.cloud import aiplatform as vertex_ai
from google.cloud.aiplatform import hyperparameter_tuning as hpt
"""
Explanation: Training Large Recommender Models with NVIDIA Merlin HugeCTR and Vertex AI
This notebook demonstrates how to use Vertex AI Training to operationalize tra... |
arcyfelix/Courses | 18-11-22-Deep-Learning-with-PyTorch/05-Recurrent Neural Networks/02 - Character_Level_RNN.ipynb | apache-2.0 | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
"""
Explanation: Character-Level LSTM in PyTorch
In this notebook, I'll construct a character-level LSTM with PyTorch. The network will train character by character on some text, then generate new text character by character. As an ex... |
Ecotrust/growth-yield-batch | notebooks/QAQC on Ridge Property.ipynb | bsd-3-clause | %matplotlib inline
from matplotlib.pylab import plt
import pandas as pd
from sqlalchemy import create_engine
from matplotlib import cm
import seaborn as sns
"""
Explanation: This notebook will explore the Ridge property data as modeled by FVS and the Ecotrust Growth-Yield-Batch system. Also serves as a demonstration o... |
anugrah-saxena/pycroscopy | docs/auto_examples/microdata_example.ipynb | mit | # Code source: Chris Smith -- cq6@ornl.gov
# Liscense: MIT
import numpy as np
import pycroscopy as px
"""
Explanation: Writing to hdf5 using the Microdata objects
End of explanation
"""
# First create some data
data1 = np.random.rand(5, 7)
"""
Explanation: Create some MicroDatasets and MicroDataGroups that will be... |
sangheestyle/ml2015project | howto/make_data_a_serialized_object.ipynb | mit | import csv
import gzip
import cPickle as pickle
from collections import defaultdict
import yaml
question_reader = csv.reader(open("../data/questions.csv"))
question_header = ["answer", "group", "category", "question", "pos_token"]
questions = defaultdict(dict)
for row in question_reader:
question = {}
row[-1... |
eneskemalergin/Data_Structures_and_Algorithms | Chapter4/4-Algorithm_Analysis.ipynb | gpl-3.0 | def ex1(n):
total = 0
for i in range(n):
total += i
return total
print ex1(10)
"""
Explanation: Algorithm Analysis
We can solve a problem with different solutions, but which one is better/best solution? We can answer this question by measuing the execution time, measuring the memory usage, and so... |
NeuroDataDesign/pan-synapse | pipeline_3/background/GabaExploration.ipynb | apache-2.0 | def otsuVox(argVox):
probVox = np.nan_to_num(argVox)
bianVox = np.zeros_like(probVox)
for zIndex, curSlice in enumerate(probVox):
#if the array contains all the same values
if np.max(curSlice) == np.min(curSlice):
#otsu thresh will fail here, leave bianVox as all 0's
... |
Bowenislandsong/Distributivecom | Archive/Actors.ipynb | gpl-3.0 | import ray
ray.init(num_gpus=2)
"""
Explanation: Remote functions in Ray should be thought of as functional and side-effect free. Restricting ourselves only to remote functions gives us distributed functional programming, which is great for many use cases, but in practice is a bit limited.
Ray extends the dataflow mod... |
mathLab/RBniCS | tutorials/05_gaussian/tutorial_gaussian_exact.ipynb | lgpl-3.0 | from dolfin import *
from rbnics import *
"""
Explanation: TUTORIAL 05 - Exact Parametrized Functions for non-affine elliptic problems
Keywords: exact parametrized functions
1. Introduction
In this Tutorial, we consider steady heat conduction in a two-dimensional square domain $\Omega = (-1, 1)^2$.
The boundary $\part... |
nicolasfauchereau/ICU | indices/plot_real_time_indices.ipynb | bsd-3-clause | %matplotlib inline
import os, sys
import pandas as pd
from datetime import datetime, timedelta
from cStringIO import StringIO
import requests
import matplotlib as mpl
from matplotlib import pyplot as plt
from IPython.display import Image
"""
Explanation: Plots the NINO Sea Surface Temperature indices (data from th... |
w4zir/ml17s | lectures/lec02-regression-single-variable.ipynb | mit | import pandas as pd
from sklearn import linear_model
import matplotlib.pyplot as plt
# read data in pandas frame
dataframe = pd.read_csv('datasets/house_dataset1.csv')
# assign x and y
x_feature = dataframe[['Size']]
y_labels = dataframe[['Price']]
# check data by printing first few rows
dataframe.head()
"""
Explan... |
mattilyra/gensim | docs/notebooks/annoytutorial.ipynb | lgpl-2.1 | # pip install watermark
%reload_ext watermark
%watermark -v -m -p gensim,numpy,scipy,psutil,matplotlib
"""
Explanation: Similarity Queries using Annoy Tutorial
This tutorial is about using the (Annoy Approximate Nearest Neighbors Oh Yeah) library for similarity queries with a Word2Vec model built with gensim.
Why use ... |
esa-as/2016-ml-contest | dagrha/KNN_submission_1_dagrha.ipynb | apache-2.0 | import pandas as pd
import numpy as np
from sklearn import neighbors
from sklearn import preprocessing
from sklearn.model_selection import LeaveOneGroupOut
import inversion
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Facies classification using KNearestNeighbors
<a rel=... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/cmip6/models/sandbox-1/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'sandbox-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CAS
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
konstantinstadler/country_converter | doc/country_converter_examples.ipynb | gpl-3.0 | import country_converter as coco
converter = coco.CountryConverter()
"""
Explanation: Country Converter
The country converter (coco) is a Python package to convert country names into different classifications and between different naming versions. Internally it uses regular expressions to match country names.
Install... |
a301-teaching/a301_code | notebooks/ground_track.ipynb | mit | from a301utils.a301_readfile import download
from a301lib.cloudsat import get_geo
import glob
import os
from pathlib import Path
import sys
import json
import numpy as np
import h5py
from matplotlib import pyplot as plt
from mpl_toolkits.basemap import Basemap
rad_file='MYD021KM.A2006303.2220.006.2012078143305.h5'
g... |
laisee/bitfinex | examples/Backtest.ipynb | mit | import sys
sys.path.append('..')
from bitfinex.backtest import data
%pylab inline
"""
Explanation: Backtesting example
This notebook assumes you have the bitfinex library installed
End of explanation
"""
with open('quandl.key', 'r') as f:
key = f.read().strip()
data.Quandl.search('bitfinex')
"""
Explanation: fe... |
pydata/xarray | doc/examples/monthly-means.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
"""
Explanation: Calculating Seasonal Averages from Time Series of Monthly Means
Author: Joe Hamman
The data used for this example can be found in the xarray-data repository. You may need to change the path to... |
edwardd1/phys202-2015-work | 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
----------
... |
Piezoid/pyGATB | samples/notebook.ipynb | agpl-3.0 | from gatb import Graph
graph = Graph('-in ../../DiscoSnp/test/large_test/discoRes_k_31_c_auto.h5') # chr1 with simulated variants
graph
help(graph)
"""
Explanation: pyGATB: presentation and usage
bash
git clone --recursive https://github.com/GATB/pyGATB
cd pyGATB
mkdir build && cd build
cmake . .. -DCMAKE_BU... |
inncretech/datascience | projects/data_clean/notebook/blog_data-cleaning.ipynb | mit | import pandas as pd
"""
Explanation: <html>
<body>
<img src="logo.png">
<B><p style="text-align:center; color: blue; font-size: 30px "> Inncretech Project
<br><br>
<I style= "text-align:center;color:black; font-size: 20px"><B style = "color:red">Inn</B>ovation <B style = "color:red">Cre</B>ativit... |
scidash/sciunit | docs/chapter4.ipynb | mit | !pip install -q sciunit
"""
Explanation: <a href="https://colab.research.google.com/github/scidash/sciunit/blob/master/docs/chapter4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Chapter 4. Example of RunnableModel and Backend
(or back to Chapter ... |
Chiroptera/QCThesis | notebooks/cuda sorting test.ipynb | mit | 4e7*4/1024/1024
a=np.random.randint(0,1e4,1e6)
dA = cuda.to_device(a)
del a
@cuda.reduce
def argmax_gpu(a,b):
if a >= b:
return a
else:
return b
%time a.max()
%time argmax_gpu(dA)
sorter = RadixSort(maxcount=dA.size, dtype=dA.dtype)
dRes = sorter.argsort(dA)
res = dRes.copy_to_host()
a
... |
xdze2/thermique_appart | drafts/Model03_old.ipynb | mit | filename = './results/model02results.csv'
Ttuile = pd.read_csv( filename, index_col=0, parse_dates=True )
Ttuile.plot(figsize=(14, 5) ); plt.ylabel('T_tuile °C');
"""
Explanation: Modèle 03 -old-
Utilise le Model02 pour prédire la température intérieure de l'appartement
<img src="images/sch_model03.jpg" width="500p... |
metpy/MetPy | dev/_downloads/87fd6ee8be4ea1587fa2ad7f4206407a/Combined_plotting.ipynb | bsd-3-clause | import xarray as xr
from metpy.cbook import get_test_data
from metpy.plots import ContourPlot, ImagePlot, MapPanel, PanelContainer
from metpy.units import units
# Use sample NARR data for plotting
narr = xr.open_dataset(get_test_data('narr_example.nc', as_file_obj=False))
"""
Explanation: Combined Plotting
Demonstra... |
lyoung13/deep-learning-nanodegree | p4-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... |
enbanuel/phys202-2015-work | assignments/midterm/InteractEx06.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
"""
Explanation: Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
End of explan... |
JJINDAHOUSE/deep-learning | transfer-learning/Transfer_Learning_Solution.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
CompPhysics/MachineLearning | doc/pub/week35/ipynb/.ipynb_checkpoints/week35-checkpoint.ipynb | cc0-1.0 | %matplotlib inline
# Common imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import display
import os
# Where to save the figures and data files
PROJECT_ROOT_DIR = "Results"
FIGURE_ID = "Results/FigureFiles"
DATA_ID = "DataFiles/"
if not os.path.exists(PROJECT_ROOT_... |
marshal789/Lectures-On-Machine-Learning | Support Vector Machines/SVM.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Support Vector Machines
Import Libraries
End of explanation
"""
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
"""
Explanation: Get the Data
Using the buil... |
hongguangguo/shogun | doc/ipython-notebooks/clustering/GMM.ipynb | gpl-3.0 | %pylab inline
%matplotlib inline
# import all Shogun classes
from modshogun import *
from matplotlib.patches import Ellipse
# a tool for visualisation
def get_gaussian_ellipse_artist(mean, cov, nstd=1.96, color="red", linewidth=3):
"""
Returns an ellipse artist for nstd times the standard deviation of this
... |
drericstrong/Blog | 20170106_DQ0TransformInPython.ipynb | agpl-3.0 | import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# User configurable
freq = 1/60
end_time = 180
v_peak = 220
step_size = 0.01
# Find the three-phase voltages
v1 = []
v2 = []
v3 = []
thetas = 2 * np.pi * freq * np.arange(0,end_time,step_size)
for ii, t in enumerate(thetas):
... |
jellis18/enterprise | tests/data.ipynb | mit | % matplotlib inline
%config InlineBackend.figure_format = 'retina'
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from enterprise.pulsar import Pulsar
import enterprise.signals.parameter as parameter
from enterprise.signals import utils
from enterprise.signals import signal_base
f... |
KirtoXX/Security_Camera | ssd_mobilenet/object_detection/object_detection_tutorial.ipynb | apache-2.0 | import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
"""
Explanation: Object Detection Demo
Welcome to the object detection ... |
feststelltaste/software-analytics | demos/20181213_EuregJUG_Aachen/No Go Areas.ipynb | gpl-3.0 | import pandas as pd
log = pd.read_csv("../../../software-data/projects/linux/linux_blame_log.csv.gz")
log.head()
log.info()
top10 = log['author'].value_counts().head(10)
top10
%matplotlib inline
top10.plot.pie();
"""
Explanation: Versionskontrollsysteme sind eine unglaubliche Informationsquelle um Softwaresysteme... |
sbenthall/bigbang | examples/experimental_notebooks/Analyze Senders.ipynb | agpl-3.0 | %matplotlib inline
"""
Explanation: This notebook shows how BigBang can help you analyze the senders in a particular mailing list archive.
First, use this IPython magic to tell the notebook to display matplotlib graphics inline. This is a nice way to display results.
End of explanation
"""
import bigbang.mailman as ... |
ndanielsen/dc_parking_violations_data | notebooks/Top 15 Violations by Revenue And Total for MD.ipynb | mit | dc_df = df[(df.rp_plate_state.isin(['MD']))]
dc_fines = dc_df.groupby(['violation_code']).fine.sum().reset_index('violation_code')
fine_codes_15 = dc_fines.sort_values(by='fine', ascending=False)[:15]
top_codes = dc_df[dc_df.violation_code.isin(fine_codes_15.violation_code)]
top_violation_by_state = top_codes.groupby... |
wbinventor/openmc | examples/jupyter/mgxs-part-ii.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-dark')
import openmoc
import openmc
import openmc.mgxs as mgxs
import openmc.data
from openmc.openmoc_compatible import get_openmoc_geometry
%matplotlib inline
"""
Explanation: This IPython Notebook illustrates the use of the openmc.mgxs modu... |
brandoncgay/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... |
diging/tethne-notebooks | 5. Co-citation analysis.ipynb | gpl-3.0 | %pylab inline
import matplotlib.pyplot as plt
"""
Explanation: Introduction to Tethne: Co-citation analysis
In this workbook we will conduct a co-citation analysis using the approach outlined in Chen (2009). If you have used the Java-based desktop application CiteSpace II, this should be familiar: this is the same me... |
AndreySheka/dl_ekb | hw6/Seminar 6 - segmentation.ipynb | mit | ! wget https://www.dropbox.com/s/o8loqc5ih8lp2m9/weights.pkl?dl=0
! wget https://www.dropbox.com/s/jy34yowcf85ydba/data.zip?dl=0
! unzip -q data.zip
import scipy as sp
import scipy.misc
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
"""
Explanation: Seminar 6 - Neural networks for segmentation... |
corochann/deep-learning-tutorial-with-chainer | src/04_cifar_cnn/image_processing_basic.ipynb | mit | import os
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
def readRGBImage(imagepath):
image = cv2.imread(imagepath) # Height, Width, Channel
(major, minor, _) = cv2.__version__.split(".")
if major == '3':
# version 3 is used, need to convert
image = cv2.cvtColor(image, cv2... |
ling7334/tensorflow-get-started | mnist/Getting_Started_With_TensorFlow.ipynb | apache-2.0 | import tensorflow as tf
"""
Explanation: 开始使用Tensorflow
本教程帮助你使用TensorFlow编程, 开始之前，确保你安装了Tensorflow。使用 TensorFlow，你必须了解：
* 如何使用Python编程。
* 至少了解数组的概念。
* 最好了解过机器学习。但不了解的话，本教程仍不失为一个很好的开始。
Tensorflow提供多种API。最底层API——Tensorflow核心——提供了完全的编程控制。我们建议机器学习研究人员以及需要精细控制他们模型的人使用Tensorflow核心。最高层API是建立在Tensorflow核心上的。这些高层API通常比Tensorf... |
y2ee201/Deep-Learning-Nanodegree | my-experiments/reinforcement learning/Frozen Lake.ipynb | mit | import gym
import tensorflow as tf
from collections import deque
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from gym import wrappers
import shutil
shutil.rmtree('./monitor')
env = gym.make('FrozenLake-v0')
env = wrappers.Monitor(env,'./monito... |
yy/dviz-course | m06-data/m06-lab.ipynb | mit | import pandas as pd
pew_df = pd.read_csv('https://raw.githubusercontent.com/tidyverse/tidyr/4c0a8d0fdb9372302fcc57ad995d57a43d9e4337/vignettes/pew.csv')
pew_df
"""
Explanation: Module 6: Data types and tidy data
Tidy data
Let's do some tidy exercise first. This is one of the non-tidy dataset assembled by Hadley Wickh... |
cdt15/lingam | examples/RESIT.ipynb | mit | import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import print_causal_directions, print_dagc, make_dot
import warnings
warnings.filterwarnings('ignore')
print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])
np.set_printoptions(precision=3, suppress=Tru... |
ThunderShiviah/code_guild | interactive-coding-challenges/stacks_queues/queue_list/queue_list_challenge.ipynb | mit | class Node(object):
def __init__(self, data):
# TODO: Implement me
pass
class Queue(object):
def __init__(self):
# TODO: Implement me
pass
def enqueue(self, data):
# TODO: Implement me
pass
def dequeue(self):
# TODO: Implement me
pass... |
deepmind/dm_alchemy | examples/AlchemyGettingStarted.ipynb | apache-2.0 | import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import dm_alchemy
from dm_alchemy import io
from dm_alchemy import symbolic_alchemy
from dm_alchemy import symbolic_alchemy_bots
from dm_alchemy import symbolic_alchemy_trackers
from dm_alchemy import symbolic_alchemy_wrapper
from dm_al... |
saketkc/hatex | 2015_Fall/MATH-578B/Homework5/Homework5.ipynb | mit | %matplotlib inline
from __future__ import division
import pandas as pd
import matplotlib
import itertools
matplotlib.rcParams['figure.figsize'] = (16,12)
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1)
def propose(S):
r = np.random.choice(len(S), 2)
rs = np.sort(r)
j,k=rs[0],rs[1]
... |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/discrete_choice_overview.ipynb | bsd-3-clause | import numpy as np
import statsmodels.api as sm
"""
Explanation: Discrete Choice Models Overview
End of explanation
"""
spector_data = sm.datasets.spector.load()
spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)
"""
Explanation: Data
Load data from Spector and Mazzeo (1980). Examples follow Gree... |
googlegenomics/datalab-examples | datalab/genomics/Explore 1000 Genomes Samples.ipynb | apache-2.0 | import gcp.bigquery as bq
samples_table = bq.Table('genomics-public-data:1000_genomes.sample_info')
samples_table.schema
"""
Explanation: <!-- Copyright 2015 Google Inc. All rights reserved. -->
<!-- Licensed under the Apache License, Version 2.0 (the "License"); -->
<!-- you may not use this file except in complianc... |
ueapy/ueapy.github.io | content/notebooks/2018-02-19-debugging-profiling.ipynb | mit | from IPython.core.debugger import set_trace
"""
Explanation: Today we went through some basic tools to inspect Python scripts for errors and performance bottlenecks.
Debugging
Python DeBugger (PDB)
The standard Python tool for interactive debugging is pdb, the Python debugger.
This debugger lets the user step throug... |
Petr-By/qtpyvis | notebooks/caffe/train.ipynb | mit | solver = caffe.SGDSolver('mnist_solver.prototxt')
solver.net.forward()
niter = 2500
test_interval = 100
# losses will also be stored in the log
train_loss = np.zeros(niter)
test_acc = np.zeros(int(np.ceil(niter / test_interval)))
output = np.zeros((niter, 8, 10))
# the main solver loop
for it in range(niter):
sol... |
AllenDowney/ModSimPy | soln/throwingaxe_soln.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... |
xdze2/thermique_appart | drafts/get_sun_position.ipynb | mit | map_coords = (45.1973288, 5.7103223) #( 45.166672, 5.71667 )
import pysolar.solar as solar
import datetime as dt
d = dt.datetime.now()
#d = dt.datetime(2017, 6, 20, 13, 30, 0, 130320)
solar.get_altitude( *map_coords, d)
solar.get_azimuth(*map_coords, d)
Alt = [ solar.get_altitude(*map_coords, dt.datetime(2017, 12... |
dolittle007/dolittle007.github.io | notebooks/GLM-poisson-regression.ipynb | gpl-3.0 | ## Interactive magics
%matplotlib inline
import sys
import warnings
warnings.filterwarnings('ignore')
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import patsy as pt
from scipy import optimize
# pymc3 libraries
import pymc3 as pm
import theano as thno
import ... |
weleen/mxnet | example/notebooks/basic/image_io.ipynb | apache-2.0 | %matplotlib inline
import os
import subprocess
import mxnet as mx
import numpy as np
import matplotlib.pyplot as plt
# change this to your mxnet location
MXNET_HOME = '/scratch/mxnet'
"""
Explanation: Image Data IO
This tutorial explains how to prepare, load and train with image data in MXNet. All IO in MXNet is hand... |
ES-DOC/esdoc-jupyterhub | notebooks/inm/cmip6/models/inm-cm4-8/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'inm-cm4-8', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: INM
Source ID: INM-CM4-8
Topic: Ocean
Sub-Topics: Timestepping Framework, Advection, ... |
akshayrangasai/akshayrangasai.github.io | Blog Post Content/.ipynb_checkpoints/Airport Waiting Time-checkpoint.ipynb | mit | %matplotlib inline
#Imports for solution
import numpy as np
import scipy.stats as sp
from matplotlib.pyplot import *
#Setting Distribution variables
##All rates are in per Minute.
"""
Explanation: Airport Wait Time Simulation
End of explanation
"""
#Everything will me modeled as a Poisson Process
SIM_TIME = 180
Q... |
MaximMalakhov/coursera | Learning on marked data/Week 5/task_nn.ipynb | mit | # Выполним инициализацию основных используемых модулей
%matplotlib inline
import random
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
import numpy as np
"""
Explanation: В этом задании вы будете настраивать двуслойную нейронную сеть для решения задачи многоклассовой классификации. Предла... |
Zhenxingzhang/AnalyticsVidhya | Articles/Parameter_Tuning_GBM_with_Example/GBM model.ipynb | apache-2.0 | import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn import cross_validation, metrics
from sklearn.grid_search import GridSearchCV
import matplotlib.pylab as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 4
"""
Expla... |
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