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10,800 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MNIST MLP
You should already have gone through the GettingStartedSequentialModels notebook -- if not you'll be lost here!
Step1: We're going to use some examples from https
Step2: Typicall... | Python Code:
import numpy as np
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
Explanation: MNIST MLP
You should already have gone through the GettingStartedSequentialModels notebook -- if not you'll be lost here!
End of explanation
import keras
from keras.datasets import mnist # load up... |
10,801 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Bootcamp Final Project
Step1: Here we have displayed the most basic statistics for each of the MVP canidates, such as points, assists, steals and rebounds a game. As we can see, Westbr... | Python Code:
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics
import datetime as dt # date tools, used to note current date
import requests
from bs4 import BeautifulSoup
import urllib.request
from matplotlib.offsetbox import OffsetImage
%matplotlib inline
#per game... |
10,802 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to PyTorch
PyTorch is a Python package for performing tensor computation, automatic differentiation, and dynamically defining neural networks. It makes it particularly easy to a... | Python Code:
import numpy as np
import torch
# Create a 3 x 2 array
np.ndarray((3, 2))
# Create a 3 x 2 Tensor
torch.Tensor(3, 2)
Explanation: Introduction to PyTorch
PyTorch is a Python package for performing tensor computation, automatic differentiation, and dynamically defining neural networks. It makes it particula... |
10,803 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Unpacking a Sequence into Separate Variables
Problem
You have an N-element tuple or sequence that you would like to unpack into a collection of N variables.
Solution
Any sequence (or iterabl... | Python Code:
# Example 1
p = (4, 5)
x, y = p
print x
print y
Explanation: Unpacking a Sequence into Separate Variables
Problem
You have an N-element tuple or sequence that you would like to unpack into a collection of N variables.
Solution
Any sequence (or iterable) can be unpacked into variables using a simple assignm... |
10,804 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
El día Pi
Esta notebook fue creada originalmente como un blog post por Raúl E. López Briega en Mi blog sobre Python. El contenido esta bajo la licencia BSD.
<img alt="día Pi" title="día Pi" ... | Python Code:
# Pi utilizando el módulo math
import math
math.pi
# Pi utiizando sympy, dps nos permite variar el número de dígitos de Pi
from sympy.mpmath import mp
mp.dps = 33 # número de dígitos
print(mp.pi)
Explanation: El día Pi
Esta notebook fue creada originalmente como un blog post por Raúl E. López Briega en Mi... |
10,805 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Similarity Authors.
Step1: TensorFlow Similarity Supervised Learning Hello World
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank... | Python Code:
# @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
# dis... |
10,806 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Variational AutoEncoder
Author
Step2: Create a sampling layer
Step3: Build the encoder
Step4: Build the decoder
Step5: Define the VAE as a Model with a custom train_step
Step6: Train th... | Python Code:
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
Explanation: Variational AutoEncoder
Author: fchollet<br>
Date created: 2020/05/03<br>
Last modified: 2020/05/03<br>
Description: Convolutional Variational AutoEncoder (VAE) trained on MNIST digits.
... |
10,807 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vertex client library
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Once you've installed the Vertex client library and Google clo... | Python Code:
import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
Explanation: Vertex client library: Custom training text binary classification model with pipeline... |
10,808 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Trajectory Recommendation - Test Evaluation Protocol
Step1: Run notebook ssvm.ipynb
Step2: Sanity check for evaluation protocol
70/30 split for trajectories conform to each query. | Python Code:
% matplotlib inline
import os, sys, time
import math, random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from joblib import Parallel, delayed
Explanation: Trajectory Recommendation - Test Evaluation Protocol
End of explanation
%run 'ssvm.ipynb'
check_protoco... |
10,809 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
We will first break out a training and testing set with an 80/20 split
Step1: Random Forests
Step2: A 94% accuracy with an 95% Precision - recall - f1score is quite high, but how does it c... | Python Code:
spamTesting, spamTrain = train_test_split(
data, test_size=0.8, random_state=5)
len(spamTesting)
len(spamTrain)
Explanation: We will first break out a training and testing set with an 80/20 split
End of explanation
RndForClf = RandomForestClassifier(n_jobs=2, n_estimators=100, max_features="auto",rando... |
10,810 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chromosphere model
The chromosphere model, pytransit.ChromosphereModel, implements a transit over a thin-walled sphere, as described by Schlawin et al. (ApJL 722, 2010). The model is paralle... | Python Code:
%pylab inline
sys.path.append('..')
from pytransit import ChromosphereModel
seed(0)
times_sc = linspace(0.85, 1.15, 1000) # Short cadence time stamps
times_lc = linspace(0.85, 1.15, 100) # Long cadence time stamps
k, t0, p, a, i, e, w = 0.1, 1., 2.1, 3.2, 0.5*pi, 0.3, 0.4*pi
pvp = tile([k, t0, p, a, i... |
10,811 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sebastian Raschka, 2015
Python Machine Learning Essentials
Chapter 6 - Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Note that the optional watermark extension is a ... | Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
Explanation: Sebastian Raschka, 2015
Python Machine Learning E... |
10,812 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
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, but left the implementat... | Python Code:
%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 t... |
10,813 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to use topic model by gensim
This document will show you how to use topic model by gensim.
The data for this tutorial is from Recruit HotPepper Beauty API. So you need api key of it.
If ... | Python Code:
# enable showing matplotlib image inline
%matplotlib inline
# autoreload module
%load_ext autoreload
%autoreload 2
PROJECT_ROOT = "/"
def load_local_package():
import os
import sys
root = os.path.join(os.getcwd(), "./")
sys.path.append(root) # load project root
return root
PROJECT_ROOT... |
10,814 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Continuous training pipeline with Kubeflow Pipeline and AI Platform
Learning Objectives
Step6: The pipeline uses a mix of custom and pre-build components.
Pre-build components. The pipeline... | Python Code:
!grep 'BASE_IMAGE =' -A 5 pipeline/covertype_training_pipeline.py
Explanation: Continuous training pipeline with Kubeflow Pipeline and AI Platform
Learning Objectives:
1. Learn how to use Kubeflow Pipeline (KFP) pre-build components (BiqQuery, AI Platform training and predictions)
1. Learn how to use KFP l... |
10,815 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Got Scotch?
In this notebook, we're going to create a dashboard that recommends scotches based on their taste profiles.
Step1: Load Data <span style="float
Step2: We now define a get_simil... | Python Code:
%matplotlib widget
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import os
import ipywidgets as widgets
from traitlets import Unicode, List, Instance, link, HasTraits
from IPython.display import display, clear_output, HTML, Javascript
display(widgets.Button())... |
10,816 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Time Series Analysis
One important area of application for information theory is time series analysis. Here, we will demonstrate how to compute the modes of information flow --- intrinsic, s... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import dit
from dit.inference import binned, dist_from_timeseries
from dit.multivariate import total_correlation as I, intrinsic_total_correlation as IMI
dit.ditParams['repr.print'] = True
Explanation: Time Series Analysis
One important ... |
10,817 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute induced power in the source space with dSPM
Returns STC files ie source estimates of induced power
for different bands in the source space. The inverse method
is linear based on dSPM... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_band_induced_power
print(__doc__)
Explanation: Compute induced power... |
10,818 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exploring Climate Data
Step1: Above
Step2: One way to interface with the GDP is with the interactive web interface, shown below. In this interface, you can upload a shapefile or draw on t... | Python Code:
from IPython.core.display import Image
Image('http://www-tc.pbs.org/kenburns/dustbowl/media/photos/s2571-lg.jpg')
Explanation: Exploring Climate Data: Past and Future
Roland Viger, Rich Signell, USGS
First presented at the 2012 Unidata Workshop: Navigating Earth System Science Data, 9-13 July.
What if you ... |
10,819 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Datasets to download
Here we list a few datasets that might be interesting to explore with vaex.
New York taxi dataset
The very well known dataset containing trip infromation from the iconic... | Python Code:
import vaex
import warnings; warnings.filterwarnings("ignore")
df = vaex.open('/data/yellow_taxi_2009_2015_f32.hdf5')
print(f'number of rows: {df.shape[0]:,}')
print(f'number of columns: {df.shape[1]}')
long_min = -74.05
long_max = -73.75
lat_min = 40.58
lat_max = 40.90
df.plot(df.pickup_longitude, df.pick... |
10,820 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Übung 7
Step1: Aufbau der Basisdaten
Laden der Matlab Daten
Step2: Darstellung in einer Adjazenzmatrix
Zur Erinnerung, innerhalb einer Adjazenzmatrix wird für jede Kante zwischen zwei Knot... | Python Code:
import matplotlib
import matplotlib.pylab as pl
import matplotlib.pyplot as plt
import numpy as np
import scipy.io
import networkx as nx
def print_divider(separator='-', length=80):
print(''.join([separator for _ in range(length)]))
print()
def print_heading(msg='', separator='-'):
print('... |
10,821 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Author
Step1: First let's check if there are new or deleted files (only matching by file names).
Step2: So we have the same set of files in both versions
Step3: Let's make sure the struct... | Python Code:
import collections
import glob
import os
from os import path
import matplotlib_venn
import pandas as pd
rome_path = path.join(os.getenv('DATA_FOLDER'), 'rome/csv')
OLD_VERSION = '332'
NEW_VERSION = '333'
old_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(OLD_VERSION)))
new_version_files = f... |
10,822 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
determine_region
A notebook to determine what region (e.g. neighborhood, ward, census district) the issue is referring to
Step1: Read in neighborhood shapefiles
Step3: Now plot the shapefi... | Python Code:
import fiona
from shapely.geometry import shape
import nhrc2
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from collections import defaultdict
import numpy as np
from matplotlib.patches import Polygon
from shapely.geometry import Point
%matplotlib inline
#the project root directo... |
10,823 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
AF6UY ditDahReader Library Usage
The AF6UY ditDahReader python3 library is a morse code (CW) library with its final goal of teach the author (AF6UY) morse code by playing IRC streams in mors... | Python Code:
import ditDahReader as dd
import numpy as np
import matplotlib.pyplot as plt
Explanation: AF6UY ditDahReader Library Usage
The AF6UY ditDahReader python3 library is a morse code (CW) library with its final goal of teach the author (AF6UY) morse code by playing IRC streams in morse code. Along the way it w... |
10,824 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model
Step2: Standardized and relative regression coefficients (betas)
The relative coefficients are intended to show relative contribution of different feature and their primary purpose is... | Python Code:
Markdown('Model used: **{}**'.format(model_name))
Markdown('Number of features in model: **{}**'.format(len(features_used)))
builtin_ols_models = ['LinearRegression',
'EqualWeightsLR',
'RebalancedLR',
'NNLR',
'LassoFixe... |
10,825 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building an image retrieval system with deep features
Fire up GraphLab Create
Step1: Load the CIFAR-10 dataset
We will use a popular benchmark dataset in computer vision called CIFAR-10.
... | Python Code:
import graphlab
Explanation: Building an image retrieval system with deep features
Fire up GraphLab Create
End of explanation
image_train = graphlab.SFrame('image_train_data/')
image_test = graphlab.SFrame('image_test_data/')
Explanation: Load the CIFAR-10 dataset
We will use a popular benchmark dataset in... |
10,826 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Maxpooling Layer
In this notebook, we add and visualize the output of a maxpooling layer in a CNN.
A convolutional layer + activation function, followed by a pooling layer, and a linear lay... | Python Code:
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# TODO: Feel free to try out your own images here by changing img_path
# to a file path to another image on your computer!
img_path = 'data/udacity_sdc.png'
# load color image
bgr_img = cv2.imread(img_path)
# convert to grayscale
gray_img = cv2... |
10,827 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specif... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-3', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-3
Sub-Topics: Radiati... |
10,828 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: Get the Data
Read in the College_Data file using read_csv. Figure out how to set the first column as the index.
Step2: Check the head of the data
Step3: Check the in... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
K Means Clustering Project - Solutions
For this project we will attempt to use KMeans Clustering to c... |
10,829 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Circular Regression
Step1: Directional statistics, also known as circular statistics or spherical statistics, refers to a branch of statistics dealing with data which domain is the unit cir... | Python Code:
import arviz as az
import bambi as bmb
from matplotlib.lines import Line2D
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
az.style.use("arviz-white")
Explanation: Circular Regression
End of explanation
x = np.linspace(-np.pi, np.pi, 200)
mus = [0., 0., 0., -... |
10,830 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting Started
This tutorial is meant to get you started with writing your tests and tuning scripts using Kernel Tuner. We'll use a simple 2D Convolution kernel as an example kernel, but as... | Python Code:
%%writefile convolution_naive.cu
__global__ void convolution_kernel(float *output, float *input, float *filter) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int i, j;
float sum = 0.0;
if (y < image_height && x < image_width) {
f... |
10,831 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem
Step1: Unit Test
The following unit test is expected to fa... | Python Code:
%run ../linked_list/linked_list.py
%load ../linked_list/linked_list.py
class MyLinkedList(LinkedList):
def is_palindrome(self):
# TODO: Implement me
pass
Explanation: <small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebo... |
10,832 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
using pcolormesh to plot an x-z cross section of the cloudsat radar reflectivity
This notebook shows how to read in the reflectivity, convert it to dbZe (dbZ equivalent,
which means the dbZ ... | Python Code:
import h5py
import numpy as np
import datetime as dt
from datetime import timezone as tz
from matplotlib import pyplot as plt
import pyproj
from numpy import ma
from a301utils.a301_readfile import download
from a301lib.cloudsat import get_geo
z_file='2008082060027_10105_CS_2B-GEOPROF_GRANULE_P_R04_E02.h5'
... |
10,833 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Análise das soluções do programa Rampa
Esta página apresenta as principais soluções apresentadas no programa Rampa.
O objetivo é entender as discrepâncias entre elas e comparar suas vantagen... | Python Code:
def rr_indices( lado):
import numpy as np
r,c = np.indices( (lado, lado), dtype='uint16' )
return c
print(rr_indices(11))
Explanation: Análise das soluções do programa Rampa
Esta página apresenta as principais soluções apresentadas no programa Rampa.
O objetivo é entender as discrepâncias ... |
10,834 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hello world TensorFlow-Android
This notebook focuses on the basics of creating your first Andoird App based on TensorFlow. I've created a small DNN to classify IRIS dataset. I've discussed i... | Python Code:
#import desired packages
import tensorflow as tf
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os.path
import sys
# library for freezing the graph
from tensorflow.python.tools import freeze_graph
# library for optmising inference
from tensorflow.python.... |
10,835 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify ... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-2', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics,... |
10,836 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting PRFs in K2 TPFs from Campaign 9.1
In this simple tutorial we will show how to perform PRF photometry in a K2 target pixel file using PyKE and oktopus.
This notebook was created with ... | Python Code:
import pyke
pyke.__version__
import oktopus
oktopus.__version__
Explanation: Fitting PRFs in K2 TPFs from Campaign 9.1
In this simple tutorial we will show how to perform PRF photometry in a K2 target pixel file using PyKE and oktopus.
This notebook was created with the following versions of PyKE and oktop... |
10,837 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
자료 안내
Step1: 데이터 불러오기 및 처리
오늘 사용할 데이터는 다음과 같다.
미국 51개 주(State)별 담배(식물) 도매가격 및 판매일자
Step2: read_csv 함수의 리턴값은 DataFrame 이라는 자료형이다.
Step3: DataFrame 자료형
자세한 설명은 다음 시간에 추가될 것임. 우선은 아래 사이트를 참조... | Python Code:
import numpy as np
import pandas as pd
from datetime import datetime as dt
from scipy import stats
Explanation: 자료 안내: 여기서 다루는 내용은 아래 사이트의 내용을 참고하여 생성되었음.
https://github.com/rouseguy/intro2stats
안내사항
오늘 다루는 내용은 pandas 모듈의 소개 정도로 이해하고 넘어갈 것을 권장한다.
아래 내용은 엑셀의 스프레드시트지에 담긴 데이터를 분석하여 평균 등을 어떻게 구하는가를 알고 있다면 어렵지 ... |
10,838 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gaia DR2 variability catalogs
Part II
Step1: The catalog has many columns. What are they?
Step2: Gaia Documentation section 14.3.6 explains that some of the columns are populated with arr... | Python Code:
# %load /Users/obsidian/Desktop/defaults.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
! du -hs ../data/dr2/Gaia/gdr2/vari_rotation_modulation/csv
df0 = pd.read_csv('../data/dr2/Gaia/gdr2/vari_rotation_modulation/... |
10,839 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Let's pull it all together to do something cool.
Let's reuse a lot of our code to make a movie of our travel around San Francisco.
We'll first select a bunch of recent scenes, activate, and ... | Python Code:
# Basemap Mosaic (v1 API)
mosaicsSeries = 'global_quarterly_2017q1_mosaic'
# Planet tile server base URL (Planet Explorer Mosaics Tiles)
mosaicsTilesURL_base = 'https://tiles0.planet.com/experimental/mosaics/planet-tiles/' + mosaicsSeries + '/gmap/{z}/{x}/{y}.png'
# Planet tile server url
mosaicsTilesURL =... |
10,840 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convert our txt files to csv format (internally)
You will need to create a .\Groundwater-Composition-csv folder to store results in, which is used later on.
Step1: Single File
Step2: Loadi... | Python Code:
inFolder = r'..\Data\Groundwater-Composition2'
#os.listdir(inFolder)
csvFolder = r'..\Data\Groundwater-Composition-csv'
for file in os.listdir(inFolder):
current_file = inFolder + '\\' + file
outFile = csvFolder + '\\' + file
with open(current_file, 'r') as in_file:
lines = in_file.read... |
10,841 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convergence of Green function calculation
We check the convergence with $N_\text{kpt}$ for the calculation of the vacancy Green function for FCC and HCP structures. In particular, we will lo... | Python Code:
import sys
sys.path.extend(['../'])
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
%matplotlib inline
import onsager.crystal as crystal
import onsager.GFcalc as GFcalc
Explanation: Convergence of Green function calculation
We check the convergence with $N_\text{kpt}$ ... |
10,842 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pythonic APIs
Step1: Sizing with len()
Step2: Arithmetic
Step3: A simple but full-featured Pythonic class
String formatting mini-language | Python Code:
s = 'Fluent'
L = [10, 20, 30, 40, 50]
print(list(s)) # list constructor iterates over its argument
a, b, *middle, c = L # tuple unpacking iterates over right side
print((a, b, c))
for i in L:
print(i, end=' ')
Explanation: Pythonic APIs: the workshop notebook
Tutorial overview
Introduction
A simple b... |
10,843 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas cheat sheet
<img src="https
Step1: Thus Series can have different datatypes.
Operations on series
You can add, multiply and other numerical opertions on Series just like on numpy arr... | Python Code:
import pandas as pd
import numpy as np
#from a list
l1 = [1,2,3,4,5]
ser1 = pd.Series(data = l1) #when you dont specify labels for index, it is autogenerated
ser1
#from a numpy array
arr1 = np.array(l1)
l2 = ['a', 'b', 'c','e', 'd']
ser2 = pd.Series(data=arr1, index=l2) #indices can of any data type, here... |
10,844 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tidy Data
Thsis notebbok is designed to explore Hadley Wickman article about tidy data using pandas
The datasets are available on github
Step1: Original TB dataset. Corresponding to each ‘... | Python Code:
import pandas as pd
import numpy as np
# tuberculosis (TB) dataset
path_tb = '/Users/ericfourrier/Documents/ProjetR/tidy-data/data/tb.csv'
df_tb = pd.read_csv(path_tb)
df_tb.head(20)
Explanation: Tidy Data
Thsis notebbok is designed to explore Hadley Wickman article about tidy data using pandas
The dataset... |
10,845 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The role of dipole orientations in distributed source localization
When performing source localization in a distributed manner
(MNE/dSPM/sLORETA/eLORETA),
the source space is defined as a gr... | Python Code:
from mayavi import mlab
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
data_path = sample.data_path()
evokeds = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')
left_auditory = evokeds[0].apply_baseline()
fwd = mne.read_forward_... |
10,846 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Assignment 9
Lorenzo Biasi, Julius Vernie
Step1: 1.1
We can see in the plot the fixed points. In the central point the function has a slope bigger than one, so it is not a stable point. For... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import fsolve
%matplotlib inline
def sigmoid(x):
return 1. / (1 + np.exp(-x))
def df(x, w=0, theta=0):
return w * sigmoid(x) * (1 - sigmoid(x))
def f(x, w=0, theta=0):
return w * sigmoid(x) + theta
def g(x, w, theta):
re... |
10,847 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: TFRecord 和 tf.Example
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step5: tf.Example
tf.Example 的数据类... | Python Code:
#@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
# dist... |
10,848 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting house prices
Step1: As you can see, we have 404 training samples and 102 test samples. The data comprises 13 features. The 13 features in the input data are as
follow
Step2: Th... | Python Code:
from keras.datasets import boston_housing
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
train_data.shape
test_data.shape
Explanation: Predicting house prices: a regression example
This notebook contains the code samples found in Chapter 3, Section 6 of Deep Learning w... |
10,849 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Load Image As Greyscale
Step2: Enhance Image
Step3: View Image | Python Code:
# Load image
import cv2
import numpy as np
from matplotlib import pyplot as plt
Explanation: Title: Enhance Contrast Of Greyscale Image
Slug: enhance_contrast_of_greyscale_image
Summary: How to enhance the contrast of images using OpenCV in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags:... |
10,850 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Simple Symbolic Calculator
This file shows how a simple symbolic calculator can be implemented using Ply. The grammar for the language implemented by this parser is as follows
Step1: The... | Python Code:
import ply.lex as lex
Explanation: A Simple Symbolic Calculator
This file shows how a simple symbolic calculator can be implemented using Ply. The grammar for the language implemented by this parser is as follows:
$$
\begin{array}{lcl}
\texttt{stmnt} & \rightarrow & \;\texttt{IDENTIFIER} \;\texttt{':=... |
10,851 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Neural Network Example with Keras
(C) 2018-2019 by Damir Cavar
Version
Step1: We will use numpy as well
Step2: In his tutorial, as linked above, Jason Brownlee suggests that we initialize ... | Python Code:
from keras.models import Sequential
from keras.layers import Dense
Explanation: Neural Network Example with Keras
(C) 2018-2019 by Damir Cavar
Version: 1.1, January 2019
License: Creative Commons Attribution-ShareAlike 4.0 International License (CA BY-SA 4.0)
This is a tutorial related to the L665 course o... |
10,852 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about emb... | Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural lang... |
10,853 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LAB 5b
Step1: Lab Task #1
Step2: Check our trained model files
Let's check the directory structure of our outputs of our trained model in folder we exported the model to in our last lab. W... | Python Code:
import os
Explanation: LAB 5b: Deploy and predict with Keras model on Cloud AI Platform.
Learning Objectives
Setup up the environment
Deploy trained Keras model to Cloud AI Platform
Online predict from model on Cloud AI Platform
Batch predict from model on Cloud AI Platform
Introduction
In this notebook, ... |
10,854 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Feature
Step1: Config
Automatically discover the paths to various data folders and compose the project structure.
Step2: Identifier for storing these features on disk and referring to them... | Python Code:
from pygoose import *
import gc
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import *
from keras import backend as K
from keras.models import Model, Sequential
from keras.layers import *
from keras.optimizers import *
from keras.c... |
10,855 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
dftdecompose - Illustrate the decomposition of the image in primitive 2-D waves
This demonstration illustrates the decomposition of a step function image into cossenoidal waves of increasin... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft2
from numpy.fft import ifft2
import sys,os
ia898path = os.path.abspath('/etc/jupyterhub/ia898_1s2017/')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
f = 50 * np.ones((128,1... |
10,856 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GeometryPlot
Step1: First of all, we will create a geometry to work with
Step2: GeometryPlot allows you to quickly visualize a geometry. You can create a GeometryPlot out of a geometry ver... | Python Code:
import sisl
import sisl.viz
import numpy as np
Explanation: GeometryPlot
End of explanation
geom = sisl.geom.graphene_nanoribbon(9)
Explanation: First of all, we will create a geometry to work with
End of explanation
# GeometryPlot is the default plot of a geometry, so one can just do
plot = geom.plot()
Ex... |
10,857 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Network Biology
Coding Assignment 3
Submitted By
Step1: Question 1
Step2: Final Coexpression network (images exported from Cytoscape)
Step3: Degree Distribution for cases <br>
i. Gene Dup... | Python Code:
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import random
import copy
from Bio.PDB import *
from IPython.display import HTML, display
import tabulate
from __future__ import division
from IPython.display import Image
Explanation: Network Biology
Coding Assignment 3
Submitted By:... |
10,858 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
텐서플로우 라이브러리를 임포트 하세요.
텐서플로우에는 MNIST 데이터를 자동으로 로딩해 주는 헬퍼 함수가 있습니다. "MNIST_data" 폴더에 데이터를 다운로드하고 훈련, 검증, 테스트 데이터를 자동으로 읽어 들입니다. one_hot 옵션을 설정하면 정답 레이블을 원핫벡터로 바꾸어 줍니다.
Step1: minist.train.ima... | Python Code:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
Explanation: 텐서플로우 라이브러리를 임포트 하세요.
텐서플로우에는 MNIST 데이터를 자동으로 로딩해 주는 헬퍼 함수가 있습니다. "MNIST_data" 폴더에 데이터를 다운로드하고 훈련, 검증, 테스트 데이터를 자동으로 읽어 들입니다. one_hot 옵션을 설정하면 정답 레이블을 원핫벡터로 바꾸어 줍니다.
End of... |
10,859 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
모형 결합
모형 결합(model combining) 방법은 앙상블 방법론(ensemble methods)이라고도 한다. 이는 특정한 하나의 예측 방법이 아니라 복수의 예측 모형을 결합하여 더 나은 성능의 예측을 하려는 시도이다.
모형 결합 방법을 사용하면 일반적으로 계산량은 증가하지만 다음과 같은 효과가 있다.
단일 모형을 사용할 때 보... | Python Code:
X = np.array([[-1.0, -1.0], [-1.2, -1.4], [1, -0.5], [-3.4, -2.2], [1.1, 1.2], [-2.1, -0.2]])
y = np.array([1, 1, 1, 2, 2, 2])
x_new = [0, 0]
plt.scatter(X[y==1,0], X[y==1,1], s=100, c='r')
plt.scatter(X[y==2,0], X[y==2,1], s=100, c='b')
plt.scatter(x_new[0], x_new[1], s=100, c='g')
from sklearn.linear_mod... |
10,860 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: MNIST on TPU (Tensor Processing Unit)<br>or GPU using tf.Keras and tf.data.Dataset
<table><tr><td><img valign="middle" src="https
Step2: (you can double-ckick on collapsed cells to v... | Python Code:
import os, re, time, json
import PIL.Image, PIL.ImageFont, PIL.ImageDraw
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
print("Tensorflow version " + tf.__version__)
#@title visualization utilities [RUN ME]
This cell contains helper functions used for visualization
and down... |
10,861 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sequence alignment
Genetic material such as DNA and proteins comes in sequences made up of different blocks. The study of these sequences and their correlation is invaluable to the field of ... | Python Code:
a1 = AminoAcid("A")
print(a1)
a2 = AminoAcid(a1)
print(a1 == a2)
a3 = AminoAcid("K")
print(a3.getName("long"))
Explanation: Sequence alignment
Genetic material such as DNA and proteins comes in sequences made up of different blocks. The study of these sequences and their correlation is invaluable to the fi... |
10,862 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Experiment
Step1: Load and check data
Step2: ## Analysis
Experiment Details
Step3: Results | Python Code:
%load_ext autoreload
%autoreload 2
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 *
from nupic.research.framewo... |
10,863 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ABC calibration of $I_\text{Na}$ in Nygren model using original dataset.
Step1: Initial set-up
Load experiments used by Nygren $I_\text{Na}$ model in the publication
Step2: Load the myokit... | Python Code:
import os, tempfile
import logging
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from ionchannelABC import theoretical_population_size
from ionchannelABC import IonChannelDistance, EfficientMultivariateNormalTransition, IonChannelAcceptor
from ionchannelA... |
10,864 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
IPython Notebook for turning in solutions to the problems in the Essentials of Paleomagnetism Textbook by L. Tauxe
Problems in Chapter 1
Problem 1
Step1: Problem 2a
Step2: Problem 2b
Step3... | Python Code:
# code to calculate H_r and H_theta
import numpy as np
deg2rad=np.pi/180. # converts degrees to radians
# write code here to calculate H_r and H_theta and convert to B_r, B_theta
# This is how you print out nice formatted numbers
# floating point variables have the syntax:
# '%X.Yf'%(FP_variable) where... |
10,865 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
你的第一个神经网络
在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。
Step1: 加载和准备数据
构建神经网络的关键一步是正确地准备数据。不同尺度级别的变量使网络难以高效地掌握正确的权重。我们在下方已经提供了加载和准备数据的代码。你很快将进一步学习... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: 你的第一个神经网络
在此项目中，你将构建你的第一个神经网络，并用该网络预测每日自行车租客人数。我们提供了一些代码，但是需要你来实现神经网络（大部分内容）。提交此项目后，欢迎进一步探索该数据和模型。
End of explanation
data_path = 'Bike-Sharing-Dataset/hour.... |
10,866 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Catedra 01
Primera tarea sera anunciada hoy.
Entrega el segundo miercoles, despues de haber tenido 2 auxiliares.
La primera parte del curso
Derivadas e Integrales numericas.
Manejo de errore... | Python Code:
import matplotlib.pyplot as plt
import matplotlib as mp
import numpy as np
import math
# Esta linea hace que los graficos aparezcan en el notebook en vez de una ventana nueva.
%matplotlib inline
Explanation: Catedra 01
Primera tarea sera anunciada hoy.
Entrega el segundo miercoles, despues de haber tenido ... |
10,867 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
정규화 선형 회귀
정규화(regularized) 선형 회귀 방법은 선형 회귀 계수(weight)에 대한 제약 조건을 추가함으로써 모형이 과도하게 최적화되는 현상, 즉 과최적화를 막는 방법이다. Regularized Method, Penalized Method, Contrained Least Squares 이라고도 불리운다.
모형이 과도하게... | Python Code:
np.random.seed(0)
n_samples = 30
X = np.sort(np.random.rand(n_samples))
y = np.cos(1.5 * np.pi * X) + np.random.randn(n_samples) * 0.1
dfX = pd.DataFrame(X, columns=["x"])
dfX = sm.add_constant(dfX)
dfy = pd.DataFrame(y, columns=["y"])
df = pd.concat([dfX, dfy], axis=1)
model = sm.OLS.from_formula("y ~ x +... |
10,868 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: TensorFlowアドオンオプティマイザ：ConditionalGradient
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: モデルの構築... | Python Code:
#@title Licensed under the Apache License, Version 2.0
# 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... |
10,869 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align="center">Balance Scale Classification - UCI</h1>
Analysis of the <a href="http
Step1: Check for Class Imbalance
Step2: Feature Importances
Now we check for feature importances. H... | Python Code:
import pandas as pd
import numpy as np
%pylab inline
pylab.style.use('ggplot')
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/balance-scale/balance-scale.data'
balance_df = pd.read_csv(url, header=None)
balance_df.columns = ['class_name', 'left_weight', 'left_distance', 'right_weight', 'r... |
10,870 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Tutorial
Step1: Hello World!
Step2: # starts a comment line.
f(x,y) is a function call. f is the function name, x and y are the arguments.
Values, Types, Variables
Step3: RHS of =... | Python Code:
from __future__ import print_function
Explanation: Python Tutorial
End of explanation
# Output "Hello World!"
print("Hello, World!")
print("Hello World!", 10.0)
Explanation: Hello World!
End of explanation
# define a variable
s = "Hello World!"
x = 10.0
i = 42
# define 2 variables at once
a,b = 1,1
# outpu... |
10,871 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Index file visualization
This notebook shows an easy way to represent the In Situ data positions using the index files.<br>
For this visualization of a sample <i>index_latest.txt</i> dataset... | Python Code:
indexfile = "datafiles/index_latest.txt"
Explanation: Index file visualization
This notebook shows an easy way to represent the In Situ data positions using the index files.<br>
For this visualization of a sample <i>index_latest.txt</i> dataset of the Copernicus Marine Environment Monitoring Service.
End o... |
10,872 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load data
Step1: Semantic transformations
http
Step2: TI-IDF + LSI
Step3: Save corpora
Step4: Transform "unseen" documents
What is the best way to get LDA scores on a corpus used for tra... | Python Code:
# Load pre-saved BoW
# Save BoW
user_dir = os.path.expanduser('~/cltk_data/user_data/')
try:
os.makedirs(user_dir)
except FileExistsError:
pass
bow_path = os.path.join(user_dir, 'bow_lda_gensim.mm')
mm_corpus = gensim.corpora.MmCorpus(bow_path)
print(mm_corpus)
print(next(iter(mm_corpus)))
Explanat... |
10,873 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1.1 The Weiner Process
A Weiner process, $W(t)\,$, is a continuos-time stocastic process. By definition, the value of the process at time $t$ is
Step1: Or expressing $D$ in $\textrm{nm}^2 /... | Python Code:
import numpy as np
d = 5e-9 # particle radius in meters
eta = 1.0e-3 # viscosity of water in SI units (Pascal-seconds) at 293 K
kB = 1.38e-23 # Boltzmann constant
T = 293 # Temperature in degrees Kelvin
D = kB*T/(3*np.pi*eta*d) # [m^2 / s]
D
Explanation: 1.1 The Weiner Process
A Weiner ... |
10,874 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The role of dipole orientations in distributed source localization
When performing source localization in a distributed manner
(MNE/dSPM/sLORETA/eLORETA),
the source space is defined as a gr... | Python Code:
import mne
import numpy as np
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
data_path = sample.data_path()
evokeds = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')
left_auditory = evokeds[0].apply_baseline()
fwd = mne.read_forward_solut... |
10,875 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Load Iris Flower Data
Step2: Standardize Features
Step3: Train Support Vector Classifier
Step4: Create Previously Unseen Observation
Step5: View Predicted Probabilities | Python Code:
# Load libraries
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np
Explanation: Title: Calibrate Predicted Probabilities In SVC
Slug: calibrate_predicted_probabilities_in_svc
Summary: How to calibrate predicted probabilities in s... |
10,876 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Yellowbrick Text Examples
This notebook is a sample of the text visualizations that yellowbrick provides
Step2: Load Text Corpus for Example Code
Yellowbrick has provided a text corpus wran... | Python Code:
import os
import sys
# Modify the path
sys.path.append("..")
import yellowbrick as yb
import matplotlib.pyplot as plt
Explanation: Yellowbrick Text Examples
This notebook is a sample of the text visualizations that yellowbrick provides
End of explanation
from download import download_all
from sklearn.... |
10,877 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top
Step1: Importing data
Step2: Data preparing and cleaning | Python Code:
from skdata.data import (
SkDataFrame as DataFrame,
SkDataSeries as Series
)
import pandas as pd
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><span><a href="#SkData---Data-Specification" data-toc-modified-id=... |
10,878 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In data science, it's common to have lots of nearly duplicate data. For instance, you'll find lots of nearly duplicate web pages in crawling the internet; you'll find lots of pictures of the... | Python Code:
import itertools
import string
import functools
letters = string.ascii_lowercase
vocab = list(map(''.join, itertools.product(letters, repeat=2)))
from random import choices
def zipf_pdf(k):
return 1/k**1.07
def exponential_pdf(k, base):
return base**k
def new_document(n_words, pdf):
return set(... |
10,879 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Resolving Conflicts Using Precedence Declarations
This file shows how shift/reduce and reduce/reduce conflicts can be resolved using operator precedence declarations.
The following grammar i... | Python Code:
import ply.lex as lex
tokens = [ 'NUMBER' ]
def t_NUMBER(t):
r'0|[1-9][0-9]*'
t.value = int(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... |
10,880 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interlab from iGEM 2015
Step1: Configuring and reading your data
You first need to map each column in your plate to a colony or a control.
Step2: Then, we an instance of PlateMate
Step3: ... | Python Code:
%matplotlib inline
import pylab as pl
from math import sqrt
import sys
# importing platemate
sys.path.insert(0, '../src')
import platemate
Explanation: Interlab from iGEM 2015
End of explanation
ColumnNames = {
'C' : "Dev1",
'D' : "Dev2",
'E' : "Dev3"
}
controlNames = {
'A' : "LB",
... |
10,881 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 6
Step1: Each "letter" of a string again belongs to the string type. A string of length one is called a character.
Step2: Since computers store data in binary, the designers of early... | Python Code:
W = "Hello"
print W
for j in range(len(W)): # len(W) is the length of the string W.
print W[j] # Access the jth character of the string.
Explanation: Part 6: Ciphers and Key exchange
In this notebook, we introduce cryptography -- how to communicate securely over insecure channels. We begin with a s... |
10,882 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Theory and Practice of Visualization Exercise 1
Imports
Step1: Graphical excellence and integrity
Find a data-focused visualization on one of the following websites that is a positive examp... | Python Code:
from IPython.display import Image
Explanation: Theory and Practice of Visualization Exercise 1
Imports
End of explanation
# Add your filename and uncomment the following line:
Image(filename='graphie.JPG')
Explanation: Graphical excellence and integrity
Find a data-focused visualization on one of the follo... |
10,883 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Code Testing and CI
The notebook contains problems about code testing and continuous integration with Travis CI.
Original by E Tollerud 2017 for LSSTC DSFP Session3 and AstroHackWeek, modifi... | Python Code:
!conda install pytest pytest-cov
Explanation: Code Testing and CI
The notebook contains problems about code testing and continuous integration with Travis CI.
Original by E Tollerud 2017 for LSSTC DSFP Session3 and AstroHackWeek, modified by B Sipocz
Problem 1: Set up py.test in you repo
In this problem we... |
10,884 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Make me an Image Analyst already!
In the last lesson you learnt the basics of Python. You learnt what variables are, how loops function and how to get a list of our filenames. In this lesson... | Python Code:
import os
import glob
root_root = '/home/aneesh/Images/Source/'
dir_of_root = os.listdir(root_root)
file_paths = [glob.glob(os.path.join(root_root,dor, '*.tif')) for dor in dir_of_root]
print(file_paths[0][0])
Explanation: Make me an Image Analyst already!
In the last lesson you learnt the basics of Python... |
10,885 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
$f_{2}(c,p) = \dfrac{1}{2}r_{c}c^{2}+\dfrac{1}{4}u_{c}c^{4}+\dfrac{1}{6}v_{c}c^{6}+\dfrac{1}{2}r_{p}p^{2}+\dfrac{1}{4}u_{p}p^{4}-\gamma cp-\dfrac{1}{2}ec^{2}p^{2}-Ep$
Step1: Rescaling | Python Code:
f2 = ((1/2)*r_c*c**2+(1/4)*u_c*c**4+(1/6)*v_c*c**6+(1/2)*r_p*p**2+(1/4)*u_p*p**4-E*p-gamma*c*p-e*c**2*p**2/2)
nsimplify(f2)
Explanation: $f_{2}(c,p) = \dfrac{1}{2}r_{c}c^{2}+\dfrac{1}{4}u_{c}c^{4}+\dfrac{1}{6}v_{c}c^{6}+\dfrac{1}{2}r_{p}p^{2}+\dfrac{1}{4}u_{p}p^{4}-\gamma cp-\dfrac{1}{2}ec^{2}p^{2}-Ep$
End... |
10,886 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The NEST noise_generator
Hans Ekkehard Plesser, 2015-06-25
This notebook describes how the NEST noise_generator model works and what effect it has on model neurons.
NEST needs to be in your ... | Python Code:
import sympy
sympy.init_printing()
x = sympy.Symbol('x')
sympy.series((1-sympy.exp(-x))/(1+sympy.exp(-x)), x)
Explanation: The NEST noise_generator
Hans Ekkehard Plesser, 2015-06-25
This notebook describes how the NEST noise_generator model works and what effect it has on model neurons.
NEST needs to be in... |
10,887 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Iterative vs fragment-based mapping
Iterative mapping first proposed by <a name="ref-1"/>(Imakaev et al., 2012), allows to map usually a high number of reads. However other methodologies, le... | Python Code:
from pytadbit.mapping.full_mapper import full_mapping
Explanation: Iterative vs fragment-based mapping
Iterative mapping first proposed by <a name="ref-1"/>(Imakaev et al., 2012), allows to map usually a high number of reads. However other methodologies, less "brute-force" can be used to take into account ... |
10,888 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Code printers
The most basic form of code generation are the code printers. The convert SymPy expressions into the target language.
The most common languages are C, C++, Fortran, and Python... | Python Code:
from sympy import *
init_printing()
Explanation: Code printers
The most basic form of code generation are the code printers. The convert SymPy expressions into the target language.
The most common languages are C, C++, Fortran, and Python, but over a dozen languages are supported. Here, we will quickly go... |
10,889 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Federal Reserve Series Data
Download federal reserve series.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file exce... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: Federal Reserve Series Data
Download federal reserve series.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain... |
10,890 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specif... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-1', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcings.
Properties: 85... |
10,891 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Traveling Salesman Problem
In this assignment you will implement one or more algorithms for the traveling salesman problem, such as the dynamic programming algorithm covered in the video lec... | Python Code:
import numpy as np
file = "tsp.txt"
# file = "test2.txt"
data = open(file, 'r').readlines()
n = int(data[0])
graph = {}
for i,v in enumerate(data[1:]):
graph[i] = tuple(map(float, v.strip().split(" ")))
dist_val = np.zeros([n,n])
for i in range(n):
for k in range(n):
dist_val[i,k] = di... |
10,892 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
cxMate Service DEMO
By Ayato Shimada, Mitsuhiro Eto
This DEMO shows
1. detect communities using an igraph's community detection algorithm
2. paint communities (nodes and edges) in different ... | Python Code:
# Tested on:
!python --version
Explanation: cxMate Service DEMO
By Ayato Shimada, Mitsuhiro Eto
This DEMO shows
1. detect communities using an igraph's community detection algorithm
2. paint communities (nodes and edges) in different colors
3. perform layout using graph-tool's sfdp algorithm
End of explana... |
10,893 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'ukesm1-0-ll', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: MOHC
Source ID: UKESM1-0-LL
Topic: Atmoschem
Sub-Topics: Transport,... |
10,894 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DAT210x - Programming with Python for DS
Module3 - Lab3
Step1: Load up the wheat seeds dataset into a dataframe. We've stored a copy in the Datasets directory.
Step2: Create a new 3D subpl... | Python Code:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
# Look pretty...
# matplotlib.style.use('ggplot')
plt.style.use('ggplot')
Explanation: DAT210x - Programming with Python for DS
Module3 - Lab3
End of explanation
# .. your code here ..
Explanation: Load up the wheat seeds dataset into a ... |
10,895 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using Tensorboard in DeepChem
DeepChem Neural Networks models are built on top of tensorflow. Tensorboard is a powerful visualization tool in tensorflow for viewing your model architecture ... | Python Code:
from IPython.display import Image, display
import deepchem as dc
from deepchem.molnet import load_tox21
from deepchem.models.tensorgraph.models.graph_models import GraphConvModel
# Load Tox21 dataset
tox21_tasks, tox21_datasets, transformers = load_tox21(featurizer='GraphConv')
train_dataset, valid_dataset... |
10,896 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lambda Expressions
Lambda expressions allow us to create "anonymous" functions i.e functions without a name. This basically means we can quickly make ad-hoc functions without needing to prop... | Python Code:
# Normal function
def square(num):
result = num**2
return result
square(2)
# Simplified Version #1
def square(num):
return num**2
square(3)
# Simplified Version #1
def square(num):return num**2
square(4)
Explanation: Lambda Expressions
Lambda expressions allow us to create "anonymous" functions... |
10,897 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Problem Statement
Step1: IMDB comments dataset has been stored in the following location
Step2: There are 50000 lines in the file. Let's the first line
Step3: Total size of the file is 66... | Python Code:
spark.sparkContext.uiWebUrl
Explanation: Problem Statement: IMDB Comment Sentiment Classifier
Dataset: For this exercise we will use a dataset hosted at http://ai.stanford.edu/~amaas/data/sentiment/
Problem Statement:
This is a dataset for binary sentiment classification containing substantially more data... |
10,898 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian Data Analysis, 3rd ed
Chapter 10, demo 3
Normal approximaton for Bioassay model.
Step1: Find the mode by minimising negative log posterior. Compute gradients and Hessian analytical... | Python Code:
import numpy as np
from scipy import optimize, stats
%matplotlib inline
import matplotlib.pyplot as plt
import arviz as az
import os, sys
# add utilities directory to path
util_path = os.path.abspath(os.path.join(os.path.pardir, 'utilities_and_data'))
if util_path not in sys.path and os.path.exists(util_pa... |
10,899 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Короче функция, которая домнажать вектор на случайную матрица!
$$X_{1\times n}R_{n\times k}=C_{1\times k}$$, где
Step1: Вытаскивание даных из файла!
Ссылки | Python Code:
def mm(x, k):
if x.shape[0] > 1:
x=x.T
r = np.random.rand(x.shape[1],k)
print(r)
#print(x.dot(r))
return(x.dot(r))
mm(np.array([[1,2, 1,132, 1,2]]), 5)
Explanation: Короче функция, которая домнажать вектор на случайную матрица!
$$X_{1\times n}R_{n\times k}=C_{1\times k}$$, ... |
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