repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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neuro-data-science/neuro_data_science | python/modeling/connectivity.ipynb | gpl-3.0 | import numpy as np
import scipy.io as si
import networkx as nx
import matplotlib.pyplot as plt
import bct
import sys
sys.path.append('../src/')
import opencourse.bassett_funcs as bf
plt.rcParams['image.cmap'] = 'viridis'
plt.rcParams['image.interpolation'] = 'nearest'
%matplotlib inline
"""
Explanation: This code i... |
KEHANG/AutoFragmentModeling | ipython/1. frag_mech_generation/.ipynb_checkpoints/generate_fragment_mechanism-checkpoint.ipynb | mit | import os
from tqdm import tqdm
from rmgpy import settings
from rmgpy.data.rmg import RMGDatabase
from rmgpy.kinetics import KineticsData
from rmgpy.rmg.model import getFamilyLibraryObject
from rmgpy.data.kinetics.family import TemplateReaction
from rmgpy.data.kinetics.depository import DepositoryReaction
from rmgpy.d... |
dtamayo/rebound | ipython_examples/TransitTimingVariations.ipynb | gpl-3.0 | import rebound
import numpy as np
"""
Explanation: Calculating Transit Timing Variations (TTV) with REBOUND
The following code finds the transit times in a two planet system. The transit times of the inner planet are not exactly periodic, due to planet-planet interactions.
First, let's import the REBOUND and numpy pac... |
martinjrobins/hobo | examples/sampling/nested-rejection-sampling.ipynb | bsd-3-clause | import pints
import pints.toy as toy
import numpy as np
import matplotlib.pyplot as plt
# Load a forward model
model = toy.LogisticModel()
# Create some toy data
r = 0.015
k = 500
real_parameters = [r, k]
times = np.linspace(0, 1000, 100)
signal_values = model.simulate(real_parameters, times)
# Add independent Gauss... |
jdnz/qml-rg | Tutorials/Python_Introduction.ipynb | gpl-3.0 | from __future__ import print_function, division
"""
Explanation: 1. Introduction
Perhaps instead of telling you how to write a loop or a conditional in Python, it might be a better option to put Python in context, tell a bit about how programming languages are designed, and why certain trade-offs are chosen. A program... |
deculler/DataScienceTableDemos | Clicks.ipynb | bsd-2-clause | clicks = Table.read_table("http://stat.columbia.edu/~rachel/datasets/nyt1.csv")
clicks
"""
Explanation: This workbook shows a example derived from the EDA exercise in Chapter 2 of Doing Data Science, by o'Neil abd Schutt
End of explanation
"""
age_upper_bounds = [18, 25, 35, 45, 55, 65]
def age_range(n):
if n =... |
SHDShim/pytheos | examples/6_p_scale_test_Dorogokupets2007_Au.ipynb | apache-2.0 | %config InlineBackend.figure_format = 'retina'
"""
Explanation: For high dpi displays.
End of explanation
"""
import matplotlib.pyplot as plt
import numpy as np
from uncertainties import unumpy as unp
import pytheos as eos
"""
Explanation: 0. General note
This example compares pressure calculated from pytheos and o... |
DJCordhose/ai | notebooks/rl/berater-v4.ipynb | mit | # !pip install git+https://github.com/openai/baselines >/dev/null
# !pip install gym >/dev/null
import numpy
import gym
from gym.utils import seeding
from gym import spaces
def state_name_to_int(state):
state_name_map = {
'S': 0,
'A': 1,
'B': 2,
'C': 3,
}
return state_name_... |
tensorflow/docs-l10n | site/ja/guide/upgrade.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... |
FRBs/FRB | docs/nb/DM_Halos and DM_IGM.ipynb | bsd-3-clause | # imports
from importlib import reload
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline as IUS
from astropy import units as u
from frb.halos.models import ModifiedNFW
from frb.halos import models as frb_halos
from frb.halos import hmf as frb_hmf
from frb.dm import igm as frb_igm
from frb.... |
supergis/git_notebook | geospatial/geojson/pygeojson.ipynb | gpl-3.0 | from pprint import *
"""
Explanation: GeoJSON的python支持库。
openthings@163.com, 2016-04.
IETF标准项目：https://github.com/geojson
PyPi支持库: https://pypi.python.org/pypi/geojson
* 其它的支持库包括：GeoPandas, Shaply, GDAL, GIScript
End of explanation
"""
from geojson import Point
Point((-115.81, 37.24)) # doctest: +ELLIPSIS
"""... |
authman/DAT210x | Module5/Module5 - Lab9.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
matplotlib.style.use('ggplot') # Look Pretty
"""
Explanation: DAT210x - Programming with Python for DS
Module5- Lab9
End of explanation
"""
def drawLine(model, X_test, y_test, title, R2):... |
gregcaporaso/sketchbook | 2015.07.12-species-classifiers.ipynb | bsd-3-clause | %pylab inline
from __future__ import division
import numpy as np
import pandas as pd
import skbio
import qiime_default_reference
"""
Explanation: In this recipe, we're going to build taxonomic classifiers for amplicon sequencing. We'll do this for 16S using some scikit-learn classifiers.
End of explanation
"""
###
#... |
tensorflow/docs-l10n | site/en-snapshot/lite/performance/quantization_debugger.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... |
kubeflow/pipelines | components/google-cloud/google_cloud_pipeline_components/experimental/dataflow/python_job/DataflowPythonJobOp_sample.ipynb | apache-2.0 | !pip3 install -U google-cloud-pipeline-components -q
"""
Explanation: Vertex Pipelines: Dataflow Python Job OP
Overview
This notebook shows how to use the DataflowPythonJobOp to create a Python Dataflow Job component. DataflowPythonJobOp creates a pipeline component that prepares data by submitting an Apache Beam job... |
chinskiy/KDD-99 | exploratory_analysis.ipynb | mit | %matplotlib inline
#%matplotlib notebook
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import constants
data_10_percent = 'kddcup.data_10_percent'
data_full = 'kddcup.data'
data = pd.read_csv(data_10_percent, names=constants.names)
# Remove Traffic featur... |
jswoboda/SimISR | ExampleNotebooks/SingleBeamExample.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import os,inspect
from SimISR import Path
import scipy as sp
from SimISR.utilFunctions import readconfigfile,makeconfigfile
from SimISR.IonoContainer import IonoContainer,MakeTestIonoclass
from SimISR.runsim import main as runsim
from SimISR.analysisplots import analy... |
rjenc29/numerical | tensorflow/5_word2vec.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
from __future__ import print_function
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import zipfile
from matplotlib import pylab
from six.mov... |
empet/Math | Plotly-interactive-visualization-of-complex-valued-functions.ipynb | bsd-3-clause | import numpy as np
import numpy.ma as ma
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.colors
def hsv_colorscale(S=1, V=1):
if S < 0 or S > 1 or V < 0 or V > 1:
raise ValueError('Parameters S (saturation), V (value, brightness) must be in [0,1]')
argument = np.array([-pi, ... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/nicam16-7s/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'nicam16-7s', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MIROC
Source ID: NICAM16-7S
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, ... |
johnhw/summerschool2016 | classifying_audio_streams/audio_1.ipynb | mit | import numpy as np
import sklearn.datasets, sklearn.linear_model, sklearn.neighbors
import matplotlib.pyplot as plt
#import seaborn as sns
import sys, os, time
import scipy.io.wavfile, scipy.signal
%matplotlib inline
import matplotlib as mpl
from IPython.core.display import HTML
mpl.rcParams['figure.figsize'] = (18.0, ... |
shengqiu/renthop | xgboost.ipynb | gpl-2.0 | import os
import sys
import operator
import numpy as np
import pandas as pd
from scipy import sparse
import xgboost as xgb
from sklearn import model_selection, preprocessing, ensemble
from sklearn.metrics import log_loss
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
"""
Explanation: It s... |
NORCatUofC/rain | n-year/notebooks/N-Year Storms.ipynb | mit | from __future__ import absolute_import, division, print_function, unicode_literals
import pandas as pd
from datetime import datetime, timedelta
import operator
import matplotlib.pyplot as plt
from collections import namedtuple
%matplotlib notebook
# The following code is adopted from Pat's Rolling Rain N-Year Thresho... |
ericfourrier/pandas-patch | examples/Pandas Patch In Action.ipynb | mit | from pandas_patch import *
%psource structure
def get_test_df_complete():
""" get the full test dataset from Lending Club open source database,
the purpose of this fuction is to be used in a demo ipython notebook """
import requests
from zipfile import ZipFile
from StringIO import StringIO
zip... |
csaladenes/aviation | code/.ipynb_checkpoints/airport_dest_parser-checkpoint.ipynb | mit | L=json.loads(file('../json/L.json','r').read())
M=json.loads(file('../json/M.json','r').read())
N=json.loads(file('../json/N.json','r').read())
import requests
AP={}
for c in M:
if c not in AP:AP[c]={}
for i in range(len(L[c])):
AP[c][N[c][i]]=L[c][i]
"""
Explanation: Load airports of each country
En... |
do-mpc/do-mpc | documentation/source/mhe_example.ipynb | lgpl-3.0 | import numpy as np
from casadi import *
# Add do_mpc to path. This is not necessary if it was installed via pip.
import sys
sys.path.append('../../')
# Import do_mpc package:
import do_mpc
"""
Explanation: Getting started: MHE
Open an interactive online Jupyter Notebook with this content on Binder:
In this Jupyter ... |
mohanprasath/Course-Work | coursera/data_visualization_with_python/DV0101EN-3-4-1-Waffle-Charts-Word-Clouds-and-Regression-Plots-py-v2.0.ipynb | gpl-3.0 | import numpy as np # useful for many scientific computing in Python
import pandas as pd # primary data structure library
from PIL import Image # converting images into arrays
"""
Explanation: <a href="https://cognitiveclass.ai"><img src = "https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png" width ... |
gautam1858/tensorflow | tensorflow/lite/g3doc/performance/post_training_integer_quant_16x8.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... |
pwer21c/pwer21c.github.io | python/pythoncodes/3_preview_while_ifelse_samedi.ipynb | mit | a=5
if a>3:
print("a는 3보다 큽니다.")
a=1
if a>3:
print("a는 3보다 큽니다.")
a=330
b=200
if b > a:
print("b는 a보다 커요")
elif b==a:
print("b와 a는 같은 숫자에요")
else:
print("b는 a보다 작아요")
while 문을 이용하여 1에서 10까지 출력하세요
i=2
while i<11:
print(i)
i=i+2
i=0
while i<11:
if i!=0:
print(i)
i=i+... |
ucsd-ccbb/visJS2jupyter | notebooks/multigraph_example/.ipynb_checkpoints/multigraph_example-checkpoint.ipynb | mit | import matplotlib as mpl
import networkx as nx
import pandas as pd
import random
import visJS2jupyter.visJS_module
"""
Explanation: Multigraph Network Styling for visJS2jupyter
Authors: Brin Rosenthal (sbrosenthal@ucsd.edu), Mikayla Webster (m1webste@ucsd.edu), Julia Len (jlen@ucsd.edu)
Import packages
End of expl... |
sorig/shogun | doc/ipython-notebooks/metric/LMNN.ipynb | bsd-3-clause | import numpy
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
x = numpy.array([[0,0],[-1,0.1],[0.3,-0.05],[0.7,0.3],[-0.2,-0.6],[-0.15,-0.63],[-0.25,0.55],[-0.28,0.67]])
y = numpy.array([0,0,0,0,1,1,2,2])
"""
Explanation: Metric Learning with the Shogun Machine Learning Toolbox
By Fernando J. I... |
satishgoda/learning | web/jquery_slide.ipynb | mit | from IPython.display import HTML
%%writefile jquery_slide_toggle.html
<script>
$(document).ready(function(){
$("#flip").click(function(){
$("#panel").slideToggle("fast");
});
});
</script>
"""
Explanation: Sliding in jQuery
https://www.w3schools.com/jquery/jquery_slide.asp
https://www.w3schools.c... |
sot/aca_stats | fit_acq_model-2018-11-dev/fit_acq_model-2018-11-binned-poly-binom.ipynb | bsd-3-clause | import sys
import os
from itertools import count
from pathlib import Path
sys.path.insert(0, str(Path(os.environ['HOME'], 'git', 'skanb', 'pea-test-set')))
import utils as asvt_utils
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table, vstack
from astropy.time import Time
import tables
fr... |
edwardd1/phys202-2015-work | 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... |
karlnapf/shogun | doc/ipython-notebooks/multiclass/KNN.ipynb | bsd-3-clause | import numpy as np
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat, savemat
from numpy import random
from os import path
mat = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = mat['data']
Yall = np.array(mat['label'].squeeze(), dtype=n... |
NeuroDataDesign/pan-synapse | pipeline_1/background/Precision_Recall_2.0.ipynb | apache-2.0 | def generatePointSet():
center = (rand(0, 9), rand(0, 999), rand(0, 999))
toPopulate = []
for z in range(-3, 2):
for y in range(-3, 2):
for x in range(-3, 2):
curPoint = (center[0]+z, center[1]+y, center[2]+x)
#only populate valid points
va... |
random-forests/tensorflow-workshop | archive/extras/estimators-comparison.ipynb | apache-2.0 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import collections
from sklearn.datasets import make_moons, make_circles, make_blobs
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
i... |
samkennerly/TruckVotes | books/model.ipynb | bsd-2-clause | %load_ext autoreload
%autoreload 2
%autosave 0
from truckvotes import *
"""
Explanation: choose a model
End of explanation
"""
def show_error(predicted,actual):
fTrueRed = (predicted > 0.5) & (actual > 0.5)
fTrueBlue = (predicted < 0.5) & (actual < 0.5)
fCorrect = fTrueRed | fTrueBlue
fClose = (pre... |
anhiga/poliastro | docs/source/examples/Propagation using Cowell's formulation.ipynb | mit | import numpy as np
from astropy import units as u
from matplotlib import ticker
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
plt.ion()
from scipy.integrate import ode
from poliastro.bodies import Earth
from poliastro.twobody import Orbit
from poliastro.examples import iss
from polias... |
TheMitchWorksPro/DataTech_Playground | PY_Basics/Walkthroughs/TMWP_Num_Seq_As_Num_Experiment.ipynb | mit | from __future__ import print_function
# only need this line for Python 2.7 ... by importing print() we also get support for unpacking within print
# * for unpacking is not recognized in this context in Python 2.7 normally
# arguments on print and behavior of print in this example is also Python 3.x which "f... |
hashiprobr/redes-sociais | encontro10.ipynb | gpl-3.0 | import sys
sys.path.append('..')
import socnet as sn
"""
Explanation: Encontro 10: Lacunas Estruturais
O enunciado da Escrita 4 continua ao longo deste notebook.
Preste atenção nas partes em negrito.
Importando a biblioteca:
End of explanation
"""
sn.node_size = 10
sn.node_color = (0, 0, 0)
sn.edge_width = 1
"""
... |
bosscha/alma-calibrator | notebooks/selecting_source/alma_database_selection5.ipynb | gpl-2.0 | file_listcal = "alma_sourcecat_searchresults_20180419.csv"
q = databaseQuery()
"""
Explanation: New function to make a list and to select calibrator
I add a function to retrieve all the flux from the ALMA Calibrator list with its frequency and observing date, and to retrieve redshift (z) from NED.
End of explanation
... |
Merinorus/adaisawesome | Homework/01 - Pandas and Data Wrangling/Intro to Pandas.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None # default='warn'
"""
Explanation: Table of Contents
<p><div class="lev1"><a href="#Introduction-to-Pandas"><span class="toc-item-num">1 </span>Introduction to Pandas</a></div><div class="lev2"><a href="#Pandas-Data-Structures"... |
liupengyuan/python_tutorial | chapter3/python正则表达式.ipynb | mit | import re
"""
Explanation: python正则表达式快速基础教程
正则表达式，这个术语不太容易望文生义(没有去考证是如何被翻译为正则表达式的)，其实其英文为Regular Expression，直接翻译就是：有规律的表达式。这个表达式其实就是一个字符序列，反映某种字符规律，用(字符串模式匹配)来处理字符串。很多高级语言均支持利用正则表达式对字符串进行处理的操作。
python提供的正则表达式文档可参见：https://docs.python.org/3/library/re.html
End of explanation
"""
s = 'Blow low, follow in of which low... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/05_review/labs/5_train_bqml.ipynb | apache-2.0 | PROJECT = 'cloud-training-demos' # Replace with your PROJECT
BUCKET = 'cloud-training-bucket' # Replace with your BUCKET
REGION = 'us-central1' # Choose an available region for Cloud MLE
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
gcloud co... |
oscarmore2/deep-learning-study | 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... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3
Topic: Atmos
Sub-Topics: Dyn... |
ghvn7777/ghvn7777.github.io | content/fluent_python/20_describe.ipynb | apache-2.0 | class Quantity: # 描述符类
def __init__(self, storage_name): # storage_name 是托管实例中存储值的属性的名称
self.storage_name = storage_name
# 设置托管属性赋值会调用 __set__方法
# 这里的 self 是描述符实例，即 LineItem.weight 或 LineItem.price
# instance 是托管实例（LineItem 实例），value 是要设定的值
def __set__(self, instance, value):
... |
ellisonbg/talk-2014 | Visualization.ipynb | mit | from IPython.display import display, Image, HTML
from talktools import website, nbviewer
"""
Explanation: Plotting and visualization
End of explanation
"""
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import numpy as np
"""
Explanation: One of the main usage cases for this displ... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/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', 'mpi-m', 'sandbox-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MPI-M
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turb... |
datastax-demos/Muvr-Analytics | ipython-analysis/exercise-cnn.ipynb | bsd-3-clause | %matplotlib inline
import logging
logging.basicConfig(level=10)
logger = logging.getLogger()
import shutil
from os import remove
import cPickle as pkl
from os.path import expanduser, exists
"""
Explanation: CNN Experiments on muvr data
First we need to setup the environment and import all the necessary stuff.
End o... |
batfish/pybatfish | docs/source/notebooks/routingProtocols.ipynb | apache-2.0 | bf.set_network('generate_questions')
bf.set_snapshot('generate_questions')
"""
Explanation: Routing Protocol Sessions and Policies
This category of questions reveals information regarding which routing
protocol sessions are compatibly configured and which ones are
established. It also allows to you analyze BGP routi... |
mne-tools/mne-tools.github.io | 0.21/_downloads/f01121873dbae065a1740e6c0c20d1d5/plot_eeg_no_mri.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.
import os.path as op
import mne
from mne.datasets import eegbci
from mne.datasets import fetch_fsaverage
# Download fsaverage files
fs_dir = fetch_fsaverage(verbose=True)
subjects_dir = op.... |
danielwe/gridcell | example.ipynb | apache-2.0 | %matplotlib inline
"""
Explanation: Example usage of the gridcell package
End of explanation
"""
# Select data source
datafile = '../../data/FlekkenBen/data.mat'
# Load raw data from file
from scipy import io
raw_data = io.loadmat(datafile, squeeze_me=True)
#print(raw_data)
# Create sessions dict from the data
fr... |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, Em... |
zzsza/Datascience_School | 10. 기초 확률론3 - 확률 분포 모형/03. 이항 확률 분포 (파이썬 버전).ipynb | mit | N = 10
theta = 0.6
rv = sp.stats.binom(N, theta)
rv
"""
Explanation: 이항 확률 분포
성공확률이 $\theta$ 인 베르누이 시도를 $N$번 하는 경우를 생각해 보자. 가장 운이 좋을 때에는 $N$번 모두 성공할 것이고 가장 운이 나쁜 경우에는 한 번도 성공하지 못할 겻이다. $N$번 중 성공한 횟수를 확률 변수 $X$ 라고 한다면 $X$의 값은 0 부터 $N$ 까지의 정수 중 하나가 될 것이다.
이러한 확률 변수를 이항 분포(binomial distribution)를 따르는 확률 변수라고 하며 다음과 같이 표... |
cochoa0x1/integer-programming-with-python | 05-routes-and-schedules/traveling_salesman2_vehicle_routing.ipynb | mit | from pulp import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sn
"""
Explanation: Multiple Traveling Salesman and the Problem of routing vehicles
Imagine we have instead of one salesman traveling to all the sites, that instead the workload is shared among many salesman. Thi... |
mumuwoyou/vnpy-master | sonnet/contrib/examples/CartPole_policy_gradient.ipynb | mit | import gym
import numpy as np, pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
env = gym.make("CartPole-v0")
#gym compatibility: unwrap TimeLimit
if hasattr(env,'env'):
env=env.env
env.reset()
n_actions = env.action_space.n
state_dim = env.observation_space.shape
plt.imshow(env.render("rgb_array... |
amcdawes/QMlabs | Lab 1 - Vectors and Matrices Solutions.ipynb | mit | from numpy import array, dot, outer, sqrt, matrix
from numpy.linalg import eig, eigvals
from matplotlib.pyplot import hist
%matplotlib inline
rv = array([1,2]) # a row vector
rv
cv = array([[3],[4]]) # a column vector
cv
"""
Explanation: Lab 1 - Vectors and Matrices
This notebook demonstrates the use of vectors a... |
andreabduque/GAFE | GAFE tutorial.ipynb | mit | #Implements functional expansions
from functions.FE import FE
#Evaluates accuracy in a dataset for a particular classifier
from fitness import Classifier
#Implements gafe using DEAP toolbox
import ga
"""
Explanation: Import modules from GAFE
End of explanation
"""
from sklearn.preprocessing import MinMaxScaler
impor... |
blackjax-devs/blackjax | examples/SGLD.ipynb | apache-2.0 | import jax
import jax.nn as nn
import jax.numpy as jnp
import jax.scipy.stats as stats
import numpy as np
"""
Explanation: MNIST digit recognition with a 3-layer Perceptron
This example is inspired form this notebook in the SGMCMCJax repository. We try to use a 3-layer neural network to recognise the digits in the MNI... |
james-prior/cohpy | 20170424-cohpy-lbyl-v-eafp.ipynb | mit | numbers = (3, 1, 0, -1, -2)
def foo(x):
return 10 // x
for x in numbers:
y = foo(x)
print(f'foo({x}) --> {y}')
"""
Explanation: LBYL versus EAFP
In some other languages,
one can not recover from an error,
or it is difficult to recover from an error,
so one tests input before doing something that could p... |
phoebe-project/phoebe2-docs | 2.3/tutorials/l3.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: "Third" Light
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import numpy as np
import m... |
AllenDowney/ModSimPy | notebooks/oem.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... |
aschaffn/phys202-2015-work | assignments/assignment03/NumpyEx03.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 3
Imports
End of explanation
"""
def brownian(maxt, n):
"""Return one realization of a Brownian (Wiener) process with n steps... |
DJCordhose/ai | notebooks/workshops/d2d/cnn-intro.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
t... |
KshitijT/fundamentals_of_interferometry | 3_Positional_Astronomy/3_1_equatorial_coordinates.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
from IPython.display import HTML
HTML('../style/code_toggle.html')
import healpy as hp
%pylab inline
pylab.rcParams['figure.figsize'] = (15, 10)
import matplotlib
impor... |
google/data-pills | pills/Google Ads/[DATA_PILL]_[Google_Ads]_Frequency_and_Audience_Analysis_(ADH).ipynb | apache-2.0 | # The Developer Key is used to retrieve a discovery document containing the
# non-public Full Circle Query v2 API. This is used to build the service used
# in the samples to make API requests. Please see the README for instructions
# on how to configure your Google Cloud Project for access to the Full Circle
# Query v2... |
chetan51/nupic.research | projects/dynamic_sparse/notebooks/ExperimentAnalysis-Neurips-debug-hebbianANDmagnitude.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
import sys
sys.path.append("../../")
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
... |
sofianehaddad/gosa | doc/example_gosa.ipynb | lgpl-3.0 | import openturns as ot
import numpy as np
import pygosa
%pylab inline
"""
Explanation: Example of using pygosa
We illustrate hereafter the use of the pygosa module.
End of explanation
"""
model = ot.SymbolicFunction(["x1","x2","x3"], ["sin(x1) + 7*sin(x2)^2 + 0.1*(x3^4)*sin(x1)"])
dist = ot.ComposedDistribution( 3 *... |
bbalasub1/glmnet_python | docs/glmnet_vignette.ipynb | gpl-3.0 | # Jupyter setup to expand cell display to 100% width on your screen (optional)
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# Import relevant modules and setup for calling glmnet
%reset -f
%matplotlib inline
import sys
sys.path.append('../test')
... |
JamesLuoau/deep-learning-getting-started | first-neural-network/Your_first_neural_network.ipynb | apache-2.0 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
rasbt/python-machine-learning-book | code/ch06/ch06.ipynb | mit | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,sklearn
"""
Explanation: Copyright (c) 2015 - 2017 Sebastian Raschka
https://github.com/rasbt/python-machine-learning-book
MIT License
Python Machine Learning - Code Examples
Chapter 6 - Learning Best Practices for Model Evaluati... |
whitead/numerical_stats | unit_4/hw_2017/problem_set_2.ipynb | gpl-3.0 | if 10**5 > 3**9:
print('10^5 is greater')
else:
print('3^9 is greater')
"""
Explanation: Answer the following questions in Python. Do all calculations in Python. Your answers should have a pattern similar to this:
python
if 3 < (5 * 2):
print('3 is less than 5 times 2')
else:
print('3 is not less th... |
chengjun/iching | iching.ipynb | mit | import random
def sepSkyEarth(data):
sky = random.randint(1, data-2)
earth = data - sky
earth -= 1
return sky , earth
def getRemainder(num):
rm = num % 4
if rm == 0:
rm = 4
return rm
def getChange(data):
sky, earth = sepSkyEarth(data)
skyRemainder = getRemainder(sky)
... |
psychemedia/parlihacks | notebooks/Apache Drill - Hansard Demo.ipynb | mit | #Download data file
!wget -P /Users/ajh59/Documents/parlidata/ https://zenodo.org/record/579712/files/senti_post_v2.csv
#Install some dependencies
!pip3 install pydrill
!pip3 install pandas
!pip3 install matplotlib
#Import necessary packages
import pandas as pd
from pydrill.client import PyDrill
#Set the notebooks u... |
IST256/learn-python | content/lessons/09-Dictionaries/LAB-Dictionaries.ipynb | mit | stock = {} # empty dictionary
stock['symbol'] = 'AAPL'
stock['name'] = 'Apple Computer'
print(stock)
print(stock['symbol'])
print(stock['name'])
"""
Explanation: In-Class Coding Lab: Dictionaries
The goals of this lab are to help you understand:
How to use Python Dictionaries
Basic Dictionary methods
Dealing with Key... |
Patri-meteocat/Meteocat_ANL_collaboration | notebooks/Edges_dualPRF_example.ipynb | bsd-2-clause | import matplotlib.pyplot as plt
import pylab as plb
import matplotlib as mpl
import pyart
import numpy as np
import scipy as sp
import numpy.ma as ma
from pylab import *
from scipy import ndimage
from matplotlib.backends.backend_pdf import PdfPages
def local_valid(mask, dim, Nmin=None, **kwargs):
if Nmin is ... |
karlstroetmann/Formal-Languages | Ply/Mysterious-Conflicts.ipynb | gpl-2.0 | import ply.lex as lex
tokens = [ 'X' ]
def t_X(t):
r'x'
return t
literals = ['v', 'w', 'y', 'z']
t_ignore = ' \t'
def t_newline(t):
r'\n+'
t.lexer.lineno += t.value.count('\n')
def t_error(t):
print(f"Illegal character '{t.value[0]}'")
t.lexer.skip(1)
__file__ = 'main'
lexer = lex.lex()... |
miky-kr5/Presentations | EVI - 2018/EVI 04/Modulo2.ipynb | cc0-1.0 | import pandas as pd
pd.Series?
"""
Explanation: Introducción a Pandas en los cuadernos de Jupyter
La estructura de Datos Serie
Arreglo unidimensional con etiquetas en los ejes (incluidas series de tiempo). Los parámetros de una Serie son: data (matriz, diccionario o escalar), index (arreglo de índices), dtype (numpy.... |
pastas/pasta | examples/notebooks/10_multiple_wells.ipynb | mit | import numpy as np
import pandas as pd
import pastas as ps
import matplotlib.pyplot as plt
ps.show_versions()
"""
Explanation: Adding Multiple Wells
This notebook shows how a WellModel can be used to fit multiple wells with one response function. The influence of the individual wells is scaled by the distance to the ... |
jhjungCode/pytorch-tutorial | 06_MINIST_Save_and_Restore.ipynb | mit | %matplotlib inline
"""
Explanation: Save & Restore with a minist example
Minist예제를 수행하면 알겠지만, Train에 생각보다는 꽤 많은 시간이 소요됩니다.
이 이유만이 아니라 평가시에는 trainnig후에 model의 parameter를 저장했다가 평가시에는 그 parameter를 불러들여서 사용하는 것이 일반적입니다.
여기에 사용되는 함수는 torch.save, torch.load와 model.state_dict(), model.load_state_dict()입니다.
사실 4장의 tutorial의 마... |
fangohr/oommf-python | new/notebooks/standard_problem3.ipynb | bsd-2-clause | !rm -rf standard_problem3/ # Delete old result files (if any).
"""
Explanation: Micromagnetic standard problem 3
Author: Marijan Beg, Ryan Pepper
Date: 11 May 2016
Problem specification
This problem is to calculate the single domain limit of a cubic magnetic particle. This is the size $L$ of equal energy for the so-c... |
NervanaSystems/coach | tutorials/0. Quick Start Guide.ipynb | apache-2.0 | # Adding module path to sys path if not there, so rl_coach submodules can be imported
import os
import sys
import tensorflow as tf
module_path = os.path.abspath(os.path.join('..'))
resources_path = os.path.abspath(os.path.join('Resources'))
if module_path not in sys.path:
sys.path.append(module_path)
if resources_p... |
henchc/Rediscovering-Text-as-Data | 08-Classification/01-Classification.ipynb | mit | demo_tb = Table()
demo_tb['Study_Hours'] = [2.0, 6.9, 1.6, 7.8, 3.1, 5.8, 3.4, 8.5, 6.7, 1.6, 8.6, 3.4, 9.4, 5.6, 9.6, 3.2, 3.5, 5.9, 9.7, 6.5]
demo_tb['Grade'] = [67.0, 83.6, 35.4, 79.2, 42.4, 98.2, 67.6, 84.0, 93.8, 64.4, 100.0, 61.6, 100.0, 98.4, 98.4, 41.8, 72.0, 48.6, 90.8, 100.0]
demo_tb['Pass'] = [0, 1, 0, 1, 0,... |
JoseGuzman/myIPythonNotebooks | Stochastic_systems/Fit_real_histogram.ipynb | gpl-2.0 | %pylab inline
from scipy.stats import norm
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#-Fit-real-histogram" data-toc-modified-id="-Fit-real-histogram-1"><span class="toc-item-num">1 </span> Fit real histogram</a></span><... |
bhattacharjee/courses | CourseraDeepLearningSpecialization/1.NeuralNetworksAndDeepLearning/Week2/Exercises/.ipynb_checkpoints/Python+Basics+With+Numpy+v3-Copy1-checkpoint.ipynb | mit | ### START CODE HERE ### (≈ 1 line of code)
test = "Hello World"
### END CODE HERE ###
print ("test: " + test)
"""
Explanation: Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help fami... |
EdwardDixon/deeplearning | facepaint/main/Image modelling with H2O.ipynb | apache-2.0 | data
y = "b"
x = ["x","y"]
train, valid, test = data.split_frame([0.75, 0.15])
from h2o.estimators import H2ODeepLearningEstimator
m = H2ODeepLearningEstimator(model_id="DL_defaults", hidden=[20,20,20,20,20,20,20,20,20,20], activation='tanh',epochs=10000)
m.train(x,y,train)
m
"""
Explanation: Our Data
To use it wit... |
jegibbs/phys202-2015-work | assignments/assignment08/InterpolationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style('white')
from scipy.interpolate import griddata
"""
Explanation: Interpolation Exercise 2
End of explanation
"""
x=np.array(-5,5)
x
"""
Explanation: Sparse 2d interpolation
In this example the values of a scal... |
vinitsamel/udacitydeeplearning | 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... |
aleph314/K2 | EDA/EDA_MTA_Exercises.ipynb | gpl-3.0 | import csv
import os
"""
Explanation: Exploratory Data Analysis with Python
We will explore the NYC MTA turnstile data set. These data files are from the New York Subway. It tracks the hourly entries and exits to turnstiles (UNIT) by day in the subway system.
Here is an example of what you could do with the data. Jame... |
karlstroetmann/Artificial-Intelligence | Python/7 Neural Networks/Neural-Network-Keras.ipynb | gpl-2.0 | import gzip
import pickle
import numpy as np
import keras
import tensorflow as tf
"""
Explanation: Building a Neural Network with Keras
End of explanation
"""
%env KMP_DUPLICATE_LIB_OK=TRUE
"""
Explanation: The following magic command is necessary to prevent the Python kernel to die because of linkage problems.
En... |
BorisPolonsky/LearningTensorFlow | RNN101/Customized RNN.ipynb | mit | import tensorflow as tf
import numpy as np
"""
Explanation: Customized RNN
Brief
Learning to define operations in rnn cells under TensorFlow API r1.3.
End of explanation
"""
class MyRnnCell(tf.nn.rnn_cell.RNNCell):
def __init__(self, state_size, dtype):
self._state_size = state_size
self._dtype =... |
charlesll/RamPy | examples/Mixing_spectra.ipynb | gpl-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import rampy as rp
"""
Explanation: Example of the mixing_sp() function
Author: Charles Le Losq
This function allows one to mix two endmembers spectra, $ref1$ and $ref2$, to an observed one $obs$:
$obs = ref1 * F1 + ref2 * (1-F1)$ .
The calculation ... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/64_Registration_Memory_Time_Tradeoff.ipynb | apache-2.0 | import SimpleITK as sitk
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# utility method that either downloads data from the Girder repository or
# if already downloaded returns the file name for reading from disk (cached data)
%run update_path_to_download_script
from downloaddata import fetch... |
ggljzr/mi-ddw | Task 3 - Text Mining/task3.ipynb | mit | import nltk
import numpy as np
import wikipedia
import re
"""
Explanation: Text mining
In this task we will use nltk package to recognize named entities and classify in a given text (in this case article about American Revolution from Wikipedia).
nltk.ne_chunk function can be used for both recognition and classificati... |
saudijack/unfpyboot | TestInstall/BootCampTestInstall.ipynb | mit | success = True # We'll use this to keep track of the various tests
failures = []
try:
import numpy as np
import scipy
print "numpy and scipy imported -- success!"
except:
success = False
msg = "* There was a problem importing numpy or scipy. You will definitely need these!"
print msg
failu... |
dsevilla/jisbd17-nosql | talk.ipynb | mit | %load extra/utils/functions.py
ds(1,2)
ds(3)
yoda(u"Una guerra SQL vs. NoSQL no debes empezar")
"""
Explanation: Tecnologías NoSQL -- Tutorial en JISBD 2017
Toda la información de este tutorial está disponible en https://github.com/dsevilla/jisbd17-nosql.
Diego Sevilla Ruiz, dsevilla@um.es.
End of explanation
"""
... |
smorton2/think-stats | code/chap11ex.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import pandas as pd
import random
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org... |
ozak/CompEcon | notebooks/IntroPython.ipynb | gpl-3.0 | 1+1-2
3*2
3**2
-1**2
3*(3-2)
3*3-2
"""
Explanation: Introduction to <img src="https://www.python.org/static/community_logos/python-logo-inkscape.svg" alt="Python" width=200/> and <img src="https://ipython.org/_static/IPy_header.png" alt="IPython" width=250/> using <img src="https://raw.githubusercontent.com/adeba... |
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