repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
agile-geoscience/striplog | docs/tutorial/03_Display_objects.ipynb | apache-2.0 | from striplog import Decor
"""
Explanation: Display objects
A striplog depends on a hierarchy of objects. This notebook shows the objects related to display:
Decor: One element from a legend — describes how to display a Rock.
Legend: A set of Decors — describes how to display a set of Rocks or a Striplog.
<hr />
De... |
Hexiang-Hu/mmds | week6/Quiz-Week6.ipynb | mit | import numpy as np
p1 = (5, 4)
p2 = (8, 3)
p3 = (7, 2)
p4 = (3, 3)
def calc_wb(p1, p2):
dx = ( p1[0] - p2[0] )
dy = ( p1[1] - p2[1] )
return ( ( float(dy) *2 / float(dy - dx), float(-dx)*2 / float(dy - dx) ),\
(dx*p2[1] - dy * p2[0])*2 / float(dy - dx) + 1) # b = dx*y1 - dy*x1
def cal_margin(... |
gwulfs/research_public | lectures/long_short_equity/Long-Short Equity Strategies.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# We'll generate a random factor
current_factor_values = np.random.normal(0, 1, 10000)
equity_names = ['Equity ' + str(x) for x in range(10000)]
# Put it into a dataframe
factor_data = pd.Series(current_factor_values, index = equity_names)
factor_d... |
kit-cel/wt | nt1/vorlesung/7_entzerrung/isi.ipynb | gpl-2.0 | # importing
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 22}
plt.rc('font', **font)
plt.rc('text', usetex=matplotlib.checkdep_usetex(True))
matplotlib.rc('figure', figsize=(18, 6) )
""... |
zhaojijet/UdacityDeepLearningProject | language-translation/dlnd_language_translation.ipynb | apache-2.0 | """
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... |
linan7788626/tutmom | mystic.ipynb | bsd-3-clause | %matplotlib inline
"""
Explanation: Optimiztion with mystic
End of explanation
"""
"""
Example:
- Minimize Rosenbrock's Function with Nelder-Mead.
- Plot of parameter convergence to function minimum.
Demonstrates:
- standard models
- minimal solver interface
- parameter trajectories using retall... |
Danghor/Formal-Languages | Ply/Conflicts.ipynb | gpl-2.0 | import ply.lex as lex
tokens = [ 'NUMBER' ]
def t_NUMBER(t):
r'0|[1-9][0-9]*'
t.value = float(t.value)
return t
literals = ['+', '-', '*', '/', '(', ')']
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]}... |
deepakgupta1313/models | slim/slim_walkthough.ipynb | apache-2.0 | import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import math
import numpy as np
import tensorflow as tf
import time
from datasets import dataset_utils
# Main slim library
slim = tf.contrib.slim
"""
Explanation: TF-Slim Walkthrough
This notebook will walk you through the basics of using TF-Slim to... |
zlxs23/Python-Cookbook | data_structure_and_algorithm_py2_1.ipynb | apache-2.0 | p = (1,2,3)
x,y,z = p
x
y
z
da = [1,2,'a',p]
a,b,c,_ = da
a
b
c
_
"""
Explanation: 1.1 解压序列赋值给多个变量
任何的序列(或者是可迭代对象)可以通过一个简单的赋值语句解压并赋值给多个变量，唯一的前提就是变量的数量必须跟序列元素的数量是一样的
End of explanation
"""
s = 'acfun'
a,b,c,d,e = s
a
e
"""
Explanation: 这里本身我要输出(1,2,3 )但是在ipython中'_'自动识别成最新的（上一个值） 类似于matlab 中的ans
当然在pyth... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/05-Pandas-with-Time-Series/02 - Time Shifting.ipynb | apache-2.0 | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('time_data/walmart_stock.csv',
index_col = 'Date')
df.index = pd.to_datetime(df.index)
df.head()
df.tail()
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
<cen... |
thombashi/sqlitebiter | test/data/pytablewriter_examples.ipynb | mit | writer = pytablewriter.LatexMatrixWriter()
writer.table_name = "B"
writer.value_matrix = [
["a_{11}", "a_{12}", "\\ldots", "a_{1n}"],
["a_{21}", "a_{22}", "\\ldots", "a_{2n}"],
[r"\vdots", "\\vdots", "\\ddots", "\\vdots"],
["a_{n1}", "a_{n2}", "\\ldots", "a_{nn}"],
]
writer.write_table()
"""
Explanatio... |
samuxiii/notebooks | simpsons/Simpsons_SPP_Approach-PyTorch.ipynb | apache-2.0 | !pip install -q kaggle
!mkdir -p ~/.kaggle
!echo '{"username":"XXXX","key":"XXXX"}' > ~/.kaggle/kaggle.json
!kaggle datasets download -d alexattia/the-simpsons-characters-dataset
!unzip -qo the-simpsons-characters-dataset.zip -d the-simpsons-characters-dataset
!unzip -qo ./the-simpsons-characters-dataset/simpsons_data... |
phoebe-project/phoebe2-docs | 2.3/tutorials/limb_darkening.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Limb Darkening
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
logger = phoebe.logger()
b = phoebe.default_binary()
... |
regisDe/compagnons | Calcul symbolique.ipynb | gpl-2.0 | %matplotlib inline
"""
Explanation: Calcul symbolique en Python
End of explanation
"""
from sympy import *
"""
Explanation: Introduction
Ce notebook est la traduction française du cours sur SymPy disponible entre autre sur Wakari avec quelques modifications et compléments notamment pour la résolution d'équations di... |
whitead/numerical_stats | unit_10/hw_2017/problem_set_2.ipynb | gpl-3.0 | import scipy.stats as ss
import numpy as np
Z = (1070 - 1064) / 7
p = 1 - (ss.norm.cdf(Z - ss.norm.cdf(-Z)))
print(p)
"""
Explanation: General Instructions
For full credit, you must have the following items for each problem:
[1 point] Describe what and why the method you're using is applicable. For example, 'I cho... |
comp-journalism/Baseline_Problem_for_Algorithm_Audits | BASELINE/Google_Images_Baseline.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (15, 3)
plt.rcParams['font.family'] = 'sans-serif'
pd.set_option('display.width', 5000)
pd.set_option('display.max_columns', 60)
"""
Explanation: Google Image Search Ba... |
alexiusacademia/Masteral-Theory-of-Plates-and-Shells | Assignments/01 - 08-27-2016/Assignment 01 - Plates and Shells.ipynb | gpl-3.0 | import sympy as sp
from sympy import init_printing
init_printing(use_unicode=True)
# Declare the symbols
EPx, EPy, EPz = sp.symbols("\u03B5x \u03B5y \u03B5z")
Qx, Qy, Qz = sp.symbols("\u03C3x \u03C3z \u03C3z")
E, v = sp.symbols("E \u03C5")
"""
Explanation: <div style="font-size:24px;background-color:blue;color:#fff;... |
solowPy/solowPy | examples/4 Solving the model.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sympy as sym
import solowpy
"""
Explanation: <div align='center' ><img src='https://raw.githubusercontent.com/davidrpugh/numerical-methods/master/images/sgpe-logo.jpg' width="1200" height="100"></div>
<div align='right'>... |
rongchuhe2/workshop_data_analysis_python | example_loan_prediction.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv("data/loan_prediction_train.csv")
df.head()
"""
Explanation: Problem Statement
About Company
Company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first appl... |
henriquefacioli/mc855-proj2 | Projeto.ipynb | gpl-3.0 | # Import findspark
import findspark
# Initialize and provide path
findspark.init("/home/henrique/Downloads/spark")
# Or use this alternative
#findspark.init()
# Import SparkSession
from pyspark.sql import SparkSession
# Build the SparkSession
spark = SparkSession.builder \
.master("local") \
.appName... |
olgabot/cshl-singlecell-2017 | notebooks/2.1_explore_clustering.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
# use seaborn plotting defaults
import seaborn as sns; sns.set()
"""
Explanation: <small><i>The K-means section of this notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Clusteri... |
mne-tools/mne-tools.github.io | stable/_downloads/91078106f2c04f1e09c01a2fa07e9d27/10_raw_overview.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import mne
"""
Explanation: The Raw data structure: continuous data
This tutorial covers the basics of working with raw EEG/MEG data in Python. It
introduces the :class:~mne.io.Raw data structure in detail, including how to
load, query, subselect, export, an... |
tleonhardt/LearningCython | Learning_Cython_video/Chapter06/opoverload/opoverload.ipynb | mit | %load_ext cython
"""
Explanation: Operator Overloading Playground!
End of explanation
"""
%%cython
from libc.math cimport sqrt
cdef class Unsure:
cdef double value
cdef double error
def __init__(self, double value, double error=0):
self.value = value
self.error = error
def _... |
metpy/MetPy | v0.6/_downloads/upperair_soundings.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import Hodograph, SkewT
from metpy.units import units
"""
Explanation: Upper Air Sounding Tutorial
Upper... |
FRBs/FRB | docs/nb/Halo_Scattering.ipynb | bsd-3-clause | # imports
import numpy as np
from importlib import reload
from astropy import units
from astropy import constants
from frb import turb_scattering as frb_scatt
"""
Explanation: Halo Scattering
v1 -- Mainly in the context of the FRB 181112 paper
End of explanation
"""
reload(frb_scatt)
z_FRB = 0.4755
z_halo = 0.367
... |
y2ee201/Deep-Learning-Nanodegree | my-experiments/.ipynb_checkpoints/Linear Regression-checkpoint.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
"""
Explanation: Linear regression using Batch Gradient Descent
Building linear regression from ground up
End of explanation
"""
class linear_regression():
def __init__(self):
self.weights = None
self.learning_rate = N... |
boffi/boffi.github.io | dati_2017/wt05/MassMatrix.ipynb | mit | m, L, x1, x2 = symbols('m L x_1 x_2')
"""
Explanation: Mass Matrix
<img src="figures/trab01_conv.svg" alt="Dynamic System" style="width:95%;"/>
The 2 DOF dynamical system in figure is composed of two massless rigid bodies and a massive one.
Compute the mass matrix of the system with reference to the degrees of freedom... |
slock83/FaceDetect | .ipynb_checkpoints/NN Playground-VariX-checkpoint.ipynb | bsd-3-clause | import numpy as np
from cStringIO import StringIO
import matplotlib.pyplot as plt
import caffe
from IPython.display import clear_output, Image, display
import cv2
import PIL.Image
import os
os.chdir("start_deep/")
"""
Explanation: Neural network "playground"
imports
End of explanation
"""
caffe.set_mode_cpu()
"""
... |
hpanderson/dexpy-pymntos | dexpy-demo.ipynb | apache-2.0 | dot = Digraph(comment='Design of Experiments')
dot.body.extend(['rankdir=LR', 'size="10,10"'])
dot.node_attr.update(shape='rectangle', style='filled', fontsize='20', fontname="helvetica")
dot.node('X', 'Controllable Factors', color='mediumseagreen', width='3')
dot.node('Z', 'Noise Factors', color='indianred2', width='... |
reynoldsk/pySCA | SCA_betalactamase.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import division
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import copy
import scipy.cluster.hierarchy as sch
from scipy.stats import scoreatpercentile
import scaTools as sca
import colorsys
import mpld3
import cPickle as pickle
from optparse import Opti... |
trangel/Data-Science | deep_learning_ai/Neural+machine+translation+with+attention+-+v4.ipynb | gpl-3.0 | from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply
from keras.layers import RepeatVector, Dense, Activation, Lambda
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.models import load_model, Model
import keras.backend as K
import numpy as np
from... |
INM-6/Python-Module-of-the-Week | session20_NEST/jupyter_notebooks/2_brunel_network.ipynb | mit | # populate namespace with pylab functions and stuff
%pylab inline
# import NEST & NEST rasterplot
import nest
import nest.raster_plot
"""
Explanation: PyNEST - Brunel Network
Modeling networks of spiking neurons using NEST
Python Module of the Week, 03.05.2019
Alexander van Meegen
<img src="img/erdos-renyi-ei.png" alt... |
jeffzhengye/pylearn | tensorflow_learning/tf2/notebooks/.ipynb_checkpoints/training_keras_models_on_cloud-checkpoint.ipynb | unlicense | !pip install -q tensorflow_cloud
import tensorflow as tf
import tensorflow_cloud as tfc
from tensorflow import keras
from tensorflow.keras import layers
"""
Explanation: Training Keras models with TensorFlow Cloud
Author: Jonah Kohn<br>
Date created: 2020/08/11<br>
Last modified: 2020/08/11<br>
Description: In-depth... |
alexbarcelo/pythoncoursetgk | notebook/25-primercaspractic_solved.ipynb | mit | prices = {'apple': 0.40, 'banana': 0.50, 'entrada_promocional': 10, 'entrada_simple': 17}
"""
Explanation: Python Course - Primer cas pràctic
<img src="http://www.telecogresca.com/logo_mail.png"></img>
Exercici fortament sintètic
(En part de https://wiki.python.org/moin/SimplePrograms, en part collita pròpia)
Consider... |
tensorflow/workshops | tfx_labs/Lab_1_Pipeline_in_Colab.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... |
goujou/LAPM | notebooks/Intro to LAPM.ipynb | mit | from sympy import *
from LAPM import *
from LAPM.linear_autonomous_pool_model import LinearAutonomousPoolModel
"""
Explanation: Introduction to LAPM
LAPM is a python package for the analysis of linear autonomous pool (compartmental) models. It can be used to obtain a large set of different system-level diagnostics of ... |
mbeyeler/opencv-machine-learning | notebooks/05.03-Using-Decision-Trees-for-Regression.ipynb | mit | import numpy as np
rng = np.random.RandomState(42)
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: 100px; background: white; padding: 1px; border: 1p... |
piscataway/datascience | lab/01 Introduction-to-Python.ipynb | mit | # COMMENTS begin with a pound sign (#) and extend to the end of the line.
# Comments are ignored by the computer. They are used to explain what the code is supposed to do.
# It is best practice to use LOTS of comments.
# That way, when you look back at your code, you can more quickly understand what you meant to do.
... |
dsiufl/2015-Fall-Hadoop | instructor-notes/1-hadoop-streaming-py-wordcount.ipynb | mit | hadoop_root = '/home/ubuntu/shortcourse/hadoop-2.7.1/'
hadoop_start_hdfs_cmd = hadoop_root + 'sbin/start-dfs.sh'
hadoop_stop_hdfs_cmd = hadoop_root + 'sbin/stop-dfs.sh'
# start the hadoop distributed file system
! {hadoop_start_hdfs_cmd}
# show the jave jvm process summary
# You should see NamenNode, SecondaryNameNod... |
Jeff-Meadows/box-python-sdk-examples | Box Python SDK.ipynb | apache-2.0 | # Import two classes from the boxsdk module - Client and OAuth2
from boxsdk import Client, OAuth2
# Define client ID, client secret, and developer token.
CLIENT_ID = None
CLIENT_SECRET = None
ACCESS_TOKEN = None
# Read app info from text file
with open('app.cfg', 'r') as app_cfg:
CLIENT_ID = app_cfg.readline()
... |
petrs/ECTester | util/plot_gen.ipynb | mit | %matplotlib notebook
import numpy as np
from scipy.stats import describe
from scipy.stats import norm as norm_dist
from scipy.stats.mstats import mquantiles
from math import log, sqrt
import matplotlib.pyplot as plt
from matplotlib import ticker, colors, gridspec
from copy import deepcopy
from utils import plot_hist, m... |
AllenDowney/ModSim | soln/chap05.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/'
... |
govinda-kamath/clustering_on_transcript_compatibility_counts | Timing_pipeline/Timing_Analysis.ipynb | mit | # Some modules used in this notebook
import numpy as np
import os
import re
import colorsys
import matplotlib.pyplot as plt
"""
Explanation: Runtime comparison of alignment/quantification methods
This notebook reviews how we ran other tools and obtained Figure 3 in our paper. Before clustering cells, we need to obtai... |
google/applied-machine-learning-intensive | content/03_regression/02_regression_in_sklearn/colab.ipynb | apache-2.0 | # 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 the L... |
wubin7019088/lightfm | examples/movielens/example.ipynb | apache-2.0 | import data
"""
Explanation: Getting the data
The first step is to get the movielens data.
Let's import the utility functions from data.py:
End of explanation
"""
import inspect
print(inspect.getsource(data._build_interaction_matrix))
"""
Explanation: The following functions get the dataset, and save it to a local... |
tjctw/PythonNote | 6.041/A Trivial A Day - Bayes's rule - 1 -zh-TW.ipynb | cc0-1.0 | pa = 0.001
pbga = 0.95
pac = 1-pa
pbgac = 0.05
print "Total probability of P(B) is " + \
str(0.001*0.95 + 0.05* 0.999)
"""
Explanation: 前言
大學時候一直沒辦法學好這個被教授屢屢稱為trivial的科目。
這個系列文章我們將以淺入淺出的原則幫作者複習一些機率的概念。
題1 我是不是該去看醫生
這個是個簡單的機率問題。假定有個醫學界阿宅檢定號稱通過考試的阿宅有95%的機率是個阿宅，而當前人口有99.9%是正常人。
請問
1.）隨便抓一個人去檢驗而出現陽性反應的機率有多少？
$${ P... |
DJCordhose/speed-limit-signs | notebooks/retrain-cnn-step-2-2-using-bottleneck-features.ipynb | apache-2.0 | 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... |
phoebe-project/phoebe2-docs | 2.3/tutorials/fti.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Finite Time of Integration (fti)
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 n... |
GoogleCloudPlatform/mlops-on-gcp | examples/tfdv-structured-data/tfdv-covertype.ipynb | apache-2.0 | import os
import tempfile
import tensorflow as tf
import tensorflow_data_validation as tfdv
import time
from apache_beam.options.pipeline_options import PipelineOptions, GoogleCloudOptions, StandardOptions, SetupOptions, DebugOptions, WorkerOptions
from google.protobuf import text_format
from tensorflow_metadata.proto... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/05_artandscience/d_customestimator_linear.ipynb | apache-2.0 | import math
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format
"""
Explanation: Custom Estimator
Learning Objectives:
* Use a custom estimator of the Estimator class... |
google/empirical_calibration | notebooks/survey_calibration_simulated.ipynb | apache-2.0 | #@title Copyright 2019 The Empirical Calibration Authors.
# 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 l... |
metpy/MetPy | v0.10/_downloads/3aec65fc693ccd0216a40e663bc10ddb/Hodograph_Inset.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, Hodograph, SkewT
from metpy.units import units
"""
Explanation: Hodograph Inset
... |
darkomen/TFG | modelado/temperatura/.ipynb_checkpoints/modelado-checkpoint.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Mostramos todos los gráficos en el n... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topic... |
sameersingh/ml-discussions | week1/loading_data_and_plotting.ipynb | apache-2.0 | from __future__ import division
"""
Explanation: As a rule, I always import division from future when using python 2.*. By doing so, any floating number division will work correctly without the use of '.' or numpy (e.g. 3/2 will prduce 1.5 instead of 1)
End of explanation
"""
import numpy as np
# setting a random s... |
antoniomezzacapo/qiskit-tutorial | qiskit/aqua/chemistry/advanced_howto.ipynb | apache-2.0 | # import common packages
import numpy as np
from collections import OrderedDict
# lib from Qiskit Aqua Chemistry
from qiskit_aqua_chemistry import FermionicOperator
# lib from Qiskit Aqua
from qiskit_aqua import Operator
from qiskit_aqua import (get_algorithm_instance, get_optimizer_instance,
... |
AllenDowney/ThinkBayes2 | soln/chap18.ipynb | mit | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename... |
cliburn/sta-663-2017 | notebook/11B_Threads_Processses_Concurrency.ipynb | mit | %load_ext cython
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import multiprocessing as mp
from multiprocessing import Pool, Value, Array
import os
import time
import numpy as np
from numba import njit
"""
Explanation: Multi-Core Parallelism
End of explanation
"""
def mc_pi(n):
s = 0
... |
mne-tools/mne-tools.github.io | 0.23/_downloads/7ba58cd4e9bc2622d60527d21fc13577/decoding_spatio_temporal_source.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Jean-Remi King <jeanremi.king@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline impo... |
LFPy/LFPy | examples/LFPy-example-02.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import LFPy
"""
Explanation: Example 2: Extracellular response of synaptic input
This is an example of LFPy running in a Jupyter notebook. To run through this example code and produce output, press <shift-Enter> in each c... |
mne-tools/mne-tools.github.io | dev/_downloads/91078106f2c04f1e09c01a2fa07e9d27/10_raw_overview.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import mne
"""
Explanation: The Raw data structure: continuous data
This tutorial covers the basics of working with raw EEG/MEG data in Python. It
introduces the :class:~mne.io.Raw data structure in detail, including how to
load, query, subselect, export, an... |
Unidata/unidata-python-workshop | notebooks/NumPy/NumPy Broadcasting and Vectorization.ipynb | mit | import numpy as np
a = np.array([10, 20, 30, 40])
a + 5
"""
Explanation: <a name="top"></a>
<div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_static/unidata_150x150.png" alt="Unidata Logo" style="height: 98px... |
XPRIZE/GLEXP-Team-SlideSpeech | FitNeuralNets/testFitNeuralNet.ipynb | apache-2.0 | dirName = "../lettersketch/assets/train_images/UpperCase/StraightLines/"
fileNames = []
fileLetters = []
for fileName in os.listdir(dirName):
if fileName.endswith(".png") and (not "__" in fileName):
fileNames.append(dirName+fileName)
letter = fileName.split("_")[1]
fileLetters.append(letter)
... |
ecabreragranado/OpticaFisicaII | Trabajo Anillos de Newton/Anillos_de_Newton_Ejercicio.ipynb | gpl-3.0 | # MODIFICAR EL NOMBRE DEL FICHERO IMAGEN. LUEGO EJECUTAR
########################################################
nombre_fichero_imagen="nombre.jpg" # Incluir el nombre completo con extensión del fichero imagen dentro de las comillas
# DESDE AQUÍ NO TOCAR
############################################... |
csdms/coupling | docs/demos/hydrotrend.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: HydroTrend
Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/hydrotrend.ipynb
Package installation command: $ conda install notebook pymt_hydrotrend
Command to download a local copy:
$ curl -O https://raw.githubusercontent.... |
CCI-Tools/ect-core | notebooks/cate-uc09.ipynb | mit | from cate.core.ds import DATA_STORE_REGISTRY
import cate.ops as ops
from cate.util import ConsoleMonitor
monitor = ConsoleMonitor()
data_store = DATA_STORE_REGISTRY.get_data_store('esa_cci_odp')
local_store = DATA_STORE_REGISTRY.get_data_store('local')
oz_remote_sources = data_store.query('esacci.OZONE.mon.L3.NP.mul... |
wd15/chimad-phase-field | hackathons/hackathon1/problems.ipynb | mit | from IPython.display import SVG
SVG(filename='../images/block1.svg')
"""
Explanation: Table of Contents
Challenge Problems
1. Spinodal Decomposition - Cahn-Hilliard
1.1 Parameter Values
1.2 Initial Conditions
1.3 Domains
1.a Square Periodic
1.b No Flux
1.c T-Shape No Flux
1.d Sphere
1.4 Tasks
2. Ostwald Ripening ... |
kabrapratik28/Stanford_courses | cs231n/assignment1/knn.ipynb | apache-2.0 | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.... |
basp/notes | linear_algebra_lectures.ipynb | mit | f1 = lambda x: 2*x
f2 = lambda x: (1/2*x) + 1 + (1/2)
x = np.linspace(0, 3, 100)
plt.plot(x, f1(x), label=r'$y = 2x$')
plt.plot(x, f2(x), label=r'$y = \frac{1}{2}x + 1\frac{1}{2}$')
plt.legend(loc=4)
"""
Explanation: linear algebra
Most of these notes correspond to the video lectures by Professor Gilbert Strang of MIT... |
claudiuskerth/PhDthesis | Data_analysis/SNP-indel-calling/dadi/01_dadi_1D_exp_growth.ipynb | mit | from ipyparallel import Client
cl = Client()
cl.ids
cl[:].targets
%%px --noblock
# run a whole cell in non-blocking mode, by default on all engines
# note: the magic has to be at the top of the cell
import time
time.sleep(1)
time.time()
%pxresult
# get the result from the AsyncResult object
%%px --local
# run ... |
WoodResourcesGroup/RoundwoodHarvestGHG | .ipynb_checkpoints/wood_fates-checkpoint.ipynb | mit | HWu = pd.read_sql('''SELECT *
FROM so4
WHERE harvestslash = "X"
AND processingresidues = "X"
AND "post-usewoodproduct" = "X"
AND stumps is null''', sqdb['cx'],index_col = 'index')
HWo = pd.read_sql('... |
dsacademybr/PythonFundamentos | Cap02/Notebooks/DSA-Python-Cap02-03-Strings.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 2</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Uma ú... |
openfisca/openfisca-france-indirect-taxation | openfisca_france_indirect_taxation/examples/notebooks/compare_final.ipynb | agpl-3.0 | # Import de modules généraux
from __future__ import division
import pkg_resources
import os
import pandas as pd
from pandas import concat
import seaborn
# modules spécifiques
from openfisca_france_indirect_taxation.examples.utils_example import graph_builder_line
# from ipp_macro_series_parser.agregats_transports.tr... |
danresende/deep-learning | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
marcinofulus/PR2014 | MPI/PR_cython_numpy_fortran_diff2d.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
%load_ext Cython
%%cython
cimport cython
cimport numpy as np
@cython.wraparound(False)
@cython.boundscheck(False)
def cython_diff2d(np.ndarray[double, ndim=2] u,np.ndarray[double, ndim=2] v, double dx2, double dy2, double c):
cdef unsigned int... |
cshankm/rebound | ipython_examples/Churyumov-Gerasimenko.ipynb | gpl-3.0 | import rebound
sim = rebound.Simulation()
sim.add("Sun")
sim.add("Jupiter")
sim.add("Saturn")
"""
Explanation: 67P/Churyumov–Gerasimenko
This tutorial teaches you how to use the IAS15 integator (Rein and Spiegel, 2015) to simulate the orbit of 67P/Churyumov–Gerasimenko. We will download the data from NASA Horizons and... |
mwickert/scikit-dsp-comm | docs/source/nb_examples/Multirate_Processing.ipynb | bsd-2-clause | Image('300ppi/Interpolator_Top_Level@300ppi.png',width='60%')
Image('300ppi/Decimator_Top_Level@300ppi.png',width='60%')
"""
Explanation: Multirate Signal Processing Using multirate_helper
In this section the classes multirate_FIR and multirate_IIR, found in the module sk_dsp_comm.multirate_helper, are discussed with... |
lknelson/text-analysis-2017 | 03-Pandas_and_DTM/01-DTM_DistinctiveWords_ExerciseSolutions.ipynb | bsd-3-clause | #First recreate our data
import pandas
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
#create a dataframe called "df"
df = pandas.read_csv("../Data/BDHSI2016_music_reviews.csv", sep = '\t', encoding = 'utf-8')
#counte vectorizer
countvec = Coun... |
massimo-nocentini/on-python | fdg/intro.ipynb | mit | from operator import attrgetter
from sympy import *
init_printing()
"""
Explanation: <p>
<img src="http://www.cerm.unifi.it/chianti/images/logo%20unifi_positivo.jpg"
alt="UniFI logo" style="float: left; width: 20%; height: 20%;">
<div align="right">
Massimo Nocentini<br>
<small>
<br>May 2018: intro
</small>
<... |
dwhswenson/openpathsampling | examples/toy_model_mstis/toy_mstis_A1_split.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import openpathsampling as paths
import numpy as np
"""
Explanation: Splitting a simulation
Included in this notebook:
Split a full simulation file into trajectories and the rest
End of explanation
"""
%%time
storage = paths.AnalysisStorage("mstis.nc")
st_split = ... |
TakayukiSakai/tensorflow | tensorflow/examples/udacity/2_fullyconnected.ipynb | apache-2.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
"""
Explanation: Deep Learning
Assignment 2
Previously in 1_n... |
hannorein/rebound | ipython_examples/User_Defined_Collision_Resolve.ipynb | gpl-3.0 | import rebound
import numpy as np
import matplotlib.pyplot as plt
def setupSimulation():
''' Setup the 3-Body scenario'''
sim = rebound.Simulation()
sim.integrator = "ias15" # IAS15 is the default integrator, so we don't need this line
sim.add(m=1.)
sim.add(m=1e-3, a=1., r=np.sqrt(1e-3/3.)) # we no... |
AllenDowney/ProbablyOverthinkingIt | ess3.ipynb | mit | from __future__ import print_function, division
import string
import random
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
%matplotlib inline
"""
Explanation: Internet use and religion in Europe, part thre... |
kit-cel/wt | mloc/ch4_Autoencoders/Autoencoder_AWGN_variableBatchSize.ipynb | gpl-2.0 | import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from ipywidgets import interactive
import ipywidgets as widgets
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("We are using the following device for learning:",device)
""... |
fluxcapacitor/source.ml | jupyterhub.ml/notebooks/train_deploy/zz_under_construction/tensorflow/optimize/05_Train_Model_Distributed.ipynb | apache-2.0 | import tensorflow as tf
cluster = tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223"]})
"""
Explanation: Train Model on Distributed Cluster
IMPORTANT: You Must STOP All Kernels and Terminal Session
The GPU is wedged at this point. We need to set it free!!
Define ClusterSpec
End of explanation
"""
... |
ewulczyn/talk_page_abuse | src/modeling/Clean Annotations Exploration.ipynb | apache-2.0 | df['is_harassment_or_attack'].value_counts(dropna=False)
def attack_and_not_attack(s):
return 'not_attack' in s and s!= 'not_attack'
df[df['is_harassment_or_attack'].apply(attack_and_not_attack)]['_worker_id'].value_counts().head()
"""
Explanation: Explore ambivalent is_harassment_or_attack labels
It is incorrec... |
scidash/sciunit | docs/chapter2.ipynb | mit | !pip install -q sciunit
import sciunit
"""
Explanation: <a href="https://colab.research.google.com/github/scidash/sciunit/blob/master/docs/chapter2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Chapter 2. Writing a model and test in SciUnit from ... |
amanikamail/flexx | examples/notebooks/flexx_tutorial_react.ipynb | bsd-2-clause | from flexx import react
"""
Explanation: Tutorial for flexx.react - reactive programming
Also see http://flexx.readthedocs.org/en/latest/react/
Where classic event-driven programming is about reacting to things that happen, RP is about staying up to date with changing signals. Signals are objects that have a value whi... |
mbeyeler/opencv-machine-learning | notebooks/02.04-Visualizing-Data-from-an-External-Dataset.ipynb | mit | import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
% matplotlib inline
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: 1... |
jasonjgy2000/Cuny | Data 608/Homework 4/Homeword 4 Notebook.ipynb | gpl-3.0 | data.dtypes
"""
Explanation: Data Cleansing
End of explanation
"""
data['Date'] = pd.to_datetime(data["Date"])
data['Site'] = data['Site'].astype('category')
data['EnteroCount'] = data['EnteroCount'].str.replace(r'\<|>', '').astype('int64')
# remove Na
data = data.dropna()
data[3230:3234]
data.dtypes
"""
Explanat... |
jljones/portfolio | ds/Webscraping_Craigslist_multi.ipynb | apache-2.0 | %pylab inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import requests
from bs4 import BeautifulSoup as bs4
"""
Explanation: Webscraping Craigslist for Housing Listings in the East Bay
Jennifer Jones
End of explanation
"""
# Get the data using the requests module
npgs = np.arange(0,10,... |
egodat/chaos_game_fractal | fractal_from_random.ipynb | mit | import pickle,glob
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%pylab inline
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Generating-Fractal-From-Random-Points---The-Chaos-Game" data-toc-modified-id="Generating-Fractal-From-Random-Points---The-Chaos-Game-1"><sp... |
CalPolyPat/phys202-2015-work | assignments/assignment09/IntegrationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
"""
Explanation: Integration Exercise 1
Imports
End of explanation
"""
def trapz(f, a, b, N):
"""Integrate the function f(x) over the range [a,b] with N points."""
h = (b-a)/N
I = 0
for i in range(N):
... |
jeffzhengye/pylearn | jpx-tokyo-simple-lstm-network-scuec.ipynb | unlicense | # check gpu env with torch
import torch
print(torch.__version__) # 查看torch当前版本号
print(torch.version.cuda) # 编译当前版本的torch使用的cuda版本号
print("is_cuda_available:", torch.cuda.is_available()) # 查看当前cuda是否可用于当前版本的Torch，如果输出
print('gpu count:', torch.cuda.device_count())
# 查看指定GPU的容量、名称
device = "cuda:0"
print(f"{devic... |
ccasotto/rmtk | rmtk/vulnerability/derivation_fragility/NLTHA_on_SDOF/2MSA_on_SDOF.ipynb | agpl-3.0 | from rmtk.vulnerability.common import utils
import double_MSA_on_SDOF
import numpy
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF.read_pinching_parameters import read_parameters
import MSA_utils
%matplotlib inline
"""
Explanation: Double Multiple Stripe Analysis (2MSA) for Single Degree of Freedom (SDOF) O... |
GoogleCloudPlatform/training-data-analyst | courses/fast-and-lean-data-science/colab_intro.ipynb | apache-2.0 | import math
import tensorflow as tf
from matplotlib import pyplot as plt
print("Tensorflow version " + tf.__version__)
a=1
b=2
a+b
"""
Explanation: <img alt="Colaboratory logo" height="45px" src="https://colab.research.google.com/img/colab_favicon.ico" align="left" hspace="10px" vspace="0px">
<h1>Welcome to Colabora... |
DJCordhose/ai | notebooks/workshops/tss/cnn-imagenet-retrain.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... |
shunliz/test | python/Pandas.ipynb | apache-2.0 | s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd'])
data = {'Country': ['Belgium', 'India', 'Brazil'],
'Capital': ['Brussels', 'New Delhi', 'Brasília'],
'Population': [11190846, 1303171035, 207847528]}
df = pd.DataFrame(data,
columns=['Country', 'Capital', 'Population'])
#Pivvot,
data = {'Date': ['2016-03-01', '... |
CUBoulder-ASTR2600/lectures | lecture_15_ndarraysII.ipynb | isc | %matplotlib inline
import numpy as np
import matplotlib.pyplot as pl
"""
Explanation: Some comments from homework 4
Don't do lines too long, it's considered bad style as horizontal
scrolling is awkward. Most projects demand lines < 80 or, more rarely, < 100 chars.
This also helps the case when you want to compare tw... |
ngcm/training-public | FEEG6016 Simulation and Modelling/03-Molecular-Dynamics-Lab-1.ipynb | mit | from IPython.core.display import HTML
css_file = 'https://raw.githubusercontent.com/ngcm/training-public/master/ipython_notebook_styles/ngcmstyle.css'
HTML(url=css_file)
"""
Explanation: Molecular Dynamics: Lab 1
In part based on Fortran code from Furio Ercolessi.
End of explanation
"""
%matplotlib inline
import num... |
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