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0 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Tweedie Density Estimation
This package follows the following 2 papers to estimate the density of a presumed Tweedie distribution
Step1: And to prove ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import numpy as np
import scipy as sp
from tweedie import tweedie
import seaborn as sns
import statsmodels.api as sm
# Number of parameters for model
p = 20
# Number of simulated observations
n = 100000
np.random.seed(43)
exog = np.... |
1 | <SYSTEM_TASK:>
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Description:
syncID
Step1: We also need to import the following functions created in previous lessons
Step2: Calculate Hillshade
<img src="http
Step3: Now that w... | <ASSISTANT_TASK:>
Python Code:
from osgeo import gdal
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
Explanation: syncID: 7e916532e9fa49aeba7464350e661778
title: "Create a Hillshade from a Terrain Raster in Python"
description: "Learn how to creat... |
2 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
GLM
Step1: Local Functions
Step2: Generate Data
This dummy dataset is created to emulate some data created as part of a study into quantified self, a... | <ASSISTANT_TASK:>
Python Code:
## Interactive magics
%matplotlib inline
import sys
import warnings
warnings.filterwarnings('ignore')
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import patsy as pt
from scipy import optimize
# pymc3 libraries
import pymc3 as pm
i... |
3 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Chapter 3 - Developing Templates
Generating SoftMax distributions from normals could get quite tedious – for any sufficiently complicated shape, the nu... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
from cops_and_robots.robo_tools.fusion.softmax import SoftMax, make_regular_2D_poly
poly = make_regular_2D_poly(5, max_r=2, theta=-np.pi/4)
labels = ['Interior','Heliport Facade','Mall Terrace Entrance',
'River Terrace Entrance', 'Concou... |
4 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
.. _tut_stats_cluster_source_1samp
Step1: Set parameters
Step2: Read epochs for all channels, removing a bad one
Step3: Transform to source space
St... | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.random import randn
from scipy import stats as stats
import mne
from mne import (io, spatia... |
5 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
* Visualizing of genetic similarity with Lightning + GraphX *
Setup lightning
Step1: Load structure similarity data
Public data from http
Step2: Show... | <ASSISTANT_TASK:>
Python Code:
%libraryDependencies += "org.viz.lightning" %% "lightning-scala" % "0.1.6"
%update
import org.viz.lightning._
import org.apache.spark.graphx._
val lgn = Lightning(host="https://lightning-spark-summit.herokuapp.com" )
lgn.enableNotebook()
Explanation: * Visualizing of genetic similarity wi... |
6 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Ocean
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-2', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics... |
7 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Reframing Design Pattern
The Reframing design pattern refers to changing the representation of the output of a machine learning problem. For exa... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import seaborn as sns
from google.cloud import bigquery
import matplotlib as plt
%matplotlib inline
bq = bigquery.Client()
query =
SELECT
weight_pounds,
is_male,
gestation_weeks,
mother_age,
plurality,
mother_race
FROM
`bigquery-public-data.samples.natali... |
8 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
YouTube Spam Collection Data Set (Part 2)
Source
Step1: <a id='section3b'></a>
Read in the data from the first CSV alone
Step2: <a id='section3c'></a... | <ASSISTANT_TASK:>
Python Code:
# Import modules
import numpy as np
import pandas as pd
Explanation: YouTube Spam Collection Data Set (Part 2)
Source: UCI Machine Learning Repository
Original Source: YouTube Spam Collection v. 1
Alberto, T.C., Lochter J.V., Almeida, T.A. Filtragem Automática de Spam nos Comentários do Y... |
9 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Building-an-ANN" data-toc-modified-id="Building-an-ANN-1"><span class="toc-item-num">1 &... | <ASSISTANT_TASK:>
Python Code:
# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
# Installing Tensorflow
# pip install tensorflow
# Installing Keras
# pip install --upgrade keras
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Building-an-ANN" data-toc-m... |
10 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Enter State Farm
Step1: Create Sample
The following assumes you've already created your validation set - remember that the training and validation set... | <ASSISTANT_TASK:>
Python Code:
import theano
import os, sys
sys.path.insert(1, os.path.join(os.getcwd(), 'utils'))
%matplotlib inline
from __future__ import print_function, division
# path = "data/sample/"
path = "data/statefarm/sample/"
import utils; reload(utils)
from utils import *
from IPython.display import FileLi... |
11 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Import
Step1: Reading initial data
Step2: Remove rows with NAN from data
Step3: Add diff_pt and cos(diff_phi)
Step4: Add max, sum among PIDs
Step5:... | <ASSISTANT_TASK:>
Python Code:
import pandas
import numpy
from folding_group import FoldingGroupClassifier
from rep.data import LabeledDataStorage
from rep.report import ClassificationReport
from rep.report.metrics import RocAuc
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, ... |
12 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Feature selection
Step1: Our first step is to count up all of the words in each of the documents. This conditional frequency distribution should look ... | <ASSISTANT_TASK:>
Python Code:
documents = nltk.corpus.PlaintextCorpusReader('../data/EmbryoProjectTexts/files', 'https.+')
metadata = zotero.read('../data/EmbryoProjectTexts', index_by='link', follow_links=False)
Explanation: Feature selection: keywords
A major problem-area in text mining is determining the thematic o... |
13 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Index
Work
WorkOk
WorkSnowball
Forward Snowballing
Other
WorkUnrelated
WorkNoFile
WorkLang
Step1: Work
Since this is the default class, it is used as ... | <ASSISTANT_TASK:>
Python Code:
import database
from datetime import datetime
from snowballing.operations import load_work, reload
from snowballing.jupyter_utils import work_button, idisplay
reload()
Explanation: Index
Work
WorkOk
WorkSnowball
Forward Snowballing
Other
WorkUnrelated
WorkNoFile
WorkLang
End of explanatio... |
14 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Unity ML Agents
Environment Basics
This notebook contains a walkthrough of the basic functions of the Python API for Unity ML Agents. For instructions ... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
from unityagents import UnityEnvironment
%matplotlib inline
Explanation: Unity ML Agents
Environment Basics
This notebook contains a walkthrough of the basic functions of the Python API for Unity ML Agents. For instructions on building a ... |
15 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<div Style="text-align
Step1: Obtenemos nuestro clasificador
Step2: Obtenemos la imagen ejemplo
Step3: Definimos algunas funciones necesarias | <ASSISTANT_TASK:>
Python Code:
# Imports
import pickle
%matplotlib inline
#para dibujar en el propio notebook
import numpy as np #numpy como np
import matplotlib.pyplot as plt #matplotlib como plot
from skimage import io
from skimage.transform import rescale
from skimage.color import rgb2gray
from skimage.io import i... |
16 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Test the pretrained model
Get the game screen as the input, and use the virtual joystick to steer the wheel.
Step1: Load pretrained model, and check p... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('../')
from europilot.screen import stream_local_game_screen
from europilot.screen import Box
from europilot.joystick import LinuxVirtualJoystick
Explanation: Test the pretrained model
Get the game screen as the input, and use the virtual joystick to steer the w... |
17 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Ordinary differential equations
Euler method
Step1: To check correctness we are going to solve simple differential equation
$$y' = (x + y)^2,\
y(0) = ... | <ASSISTANT_TASK:>
Python Code:
def euler(f, x, y0):
h = x[1] - x[0]
y = np.empty_like(x)
y[0] = y0
for i in range(1, len(x)):
y[i] = y[i - 1] + h * f(x[i - 1], y[i - 1])
return y
Explanation: Ordinary differential equations
Euler method
End of explanation
dy = lambda x, y: x*x + y*y
x = np.l... |
18 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
.. _tut_io_export_pandas
Step1: Export DataFrame
Step2: Explore Pandas MultiIndex | <ASSISTANT_TASK:>
Python Code:
# Author: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
import matplotlib.pyplot as plt
import numpy as np
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'... |
19 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Committors and Shooting Point Analysis
There are several ways that we can calculate committors. This deals with two, in particular.
First, we frequentl... | <ASSISTANT_TASK:>
Python Code:
pes = toys.LinearSlope(m=[0.0], c=[0.0]) # flat line
topology = toys.Topology(n_spatial=1, masses=[1.0], pes=pes)
integrator = toys.LeapfrogVerletIntegrator(0.1)
options = {
'integ': integrator,
'n_frames_max': 1000,
'n_steps_per_frame': 1
}
engine = toys.Engine(options=option... |
20 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
1 entity referent
self ("me")
addressee ("you here")
other ("somebody else")
2+ entity referent
self, addressee ("me and you here" / inclusive we)
self... | <ASSISTANT_TASK:>
Python Code:
from itertools import combinations, combinations_with_replacement
referents = []
for i in xrange(1, len(entities) * 2):
for combo in combinations_with_replacement(entities, i):
# choral we is impossible
if combo.count('self') > 1:
continue
... |
21 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Plotting and Visualization
Step1: Landscape of Plotting Libraries
matplotlib
pandas
seaborn
mpld3
"Bringing matplotlib to the browser"
d3py
"a plottin... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib as mpl # used sparingly
import matplotlib.pyplot as plt
pd.set_option("notebook_repr_html", False)
pd.set_option("max_rows", 10)
Explanation: Plotting and Visualization
End of explanation
%matplotlib inline
Explanation: Landscape o... |
22 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Machine Learning Engineer Nanodegree
Introduction and Foundations
Project 0
Step1: From a sample of the RMS Titanic data, we can see the various featu... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few ent... |
23 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the d... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and ... |
24 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
SC57 - Working with big, multi-dimensional geoscientific datasets in Python
Step1: Basic data arrays in numpy
Step2: numpy is a powerful but "low-lev... | <ASSISTANT_TASK:>
Python Code:
# standard imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xarray as xr
import warnings
%matplotlib inline
np.set_printoptions(precision=3, linewidth=80, edgeitems=1) # make numpy less verbose
xr.set_options(display_width=70)
warnings.simplefilter('i... |
25 | <SYSTEM_TASK:>
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
I have two arrays: | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.array(
[[[ 0, 1],
[ 2, 3],
[ 4, 5]],
[[ 6, 7],
[ 8, 9],
[10, 11]],
[[12, 13],
[14, 15],
[16, 17]]]
)
b = np.array(
[[0, 1, 1],
[1, 0, 1],
[1, 1, 0]]
)
result = np.take_along_axis(a, b[..., np.newaxis], ... |
26 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Analyzing Locust Load Testing Results
This Notebook demonstrates how to analyze AI Platform Prediction load testing runs using metrics captured in Clou... | <ASSISTANT_TASK:>
Python Code:
import time
from datetime import datetime
from typing import List
import numpy as np
import pandas as pd
import google.auth
from google.cloud import logging_v2
from google.cloud.monitoring_dashboard.v1 import DashboardsServiceClient
from google.cloud.logging_v2 import MetricsServiceV2Clie... |
27 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Building interactive plots using bqplot and ipywidgets
bqplot is built on top of the ipywidgets framework
ipwidgets and bqplot widgets can be seamlessl... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import ipywidgets as widgets
import bqplot.pyplot as plt
Explanation: Building interactive plots using bqplot and ipywidgets
bqplot is built on top of the ipywidgets framework
ipwidgets and bqplot widgets can be seamlessly integrated to build interactive plots
bqplot fi... |
28 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Convolutional Neural Network (LeNet)
Update of the example of CNN given on deeplearning.net. This notebook tries to explain all the code as if reader h... | <ASSISTANT_TASK:>
Python Code:
import cPickle
import gzip
import os
import sys
import timeit
import numpy
import theano
import theano.tensor as T
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
rng = numpy.random.RandomState(23455)
# instantiate 4D tensor for input
input = T.tensor4(name... |
29 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
High level API
We recommend using tf.keras as a high-level API for building neural networks. That said, most TensorFlow APIs are usable with eager exec... | <ASSISTANT_TASK:>
Python Code:
# In the tf.keras.layers package, layers are objects. To construct a layer,
# simply construct the object. Most layers take as a first argument the number
# of output dimensions / channels.
layer = tf.keras.layers.Dense(100)
# The number of input dimensionss is often unnecessary, as it ca... |
30 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Fitting Models Exercise 2
Imports
Step1: Fitting a decaying oscillation
For this problem you are given a raw dataset in the file decay_osc.npz. This f... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
Explanation: Fitting Models Exercise 2
Imports
End of explanation
def modl(t,A,o,l,d):
return A*np.exp(-1*t)*np.cos(o*t)+d
thetabest,thetacov=opt.curve_fit(modl,tdata,ydata,np.array((6,1... |
31 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Analysis of the IAC Dataset
Step1: Preprocessing
The IAC corpus of 10,000 comments is preprocessed into a training set (80%) and a test set (20%). We ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import sklearn
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression... |
32 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Home Depot Product Search Relevance
The challenge is to predict a relevance score for the provided combinations of search terms and products. To create... | <ASSISTANT_TASK:>
Python Code:
import graphlab as gl
Explanation: Home Depot Product Search Relevance
The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product pairs to multiple human raters... |
33 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Example Usage of HDFWriter
If properties of a class needs to be saved in a hdf file, then the class should inherit from HDFWriterMixin as demonstrated ... | <ASSISTANT_TASK:>
Python Code:
from tardis.io.util import HDFWriterMixin
class ExampleClass(HDFWriterMixin):
hdf_properties = ['property1', 'property2']
hdf_name = 'mock_setup'
def __init__(self, property1, property2):
self.property1 = property1
self.property2 = property2
import num... |
34 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
2D Cylinder
Overview
The periodic shedding of laminar flow over a 2D cylinder at a Reynolds Number of 150 can be used to verify the time accuracy of th... | <ASSISTANT_TASK:>
Python Code:
remote_data = True
remote_server_auto = True
case_name = 'cylinder'
data_dir='/gpfs/thirdparty/zenotech/home/dstandingford/VALIDATION/CYLINDER'
data_host='dstandingford@vis03'
paraview_cmd='mpiexec /gpfs/cfms/apps/zCFD/bin/pvserver'
if not remote_server_auto:
paraview_cmd=None
if not ... |
35 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<span style="float
Step1: Contents
A. View the crystal structure
B. Build the biomolecular assembly
C. Isolate the DNA
D. Prep for simulation
E. Dynam... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib.pyplot import *
import moldesign as mdt
from moldesign import units as u
Explanation: <span style="float:right"><a href="http://moldesign.bionano.autodesk.com/" target="_blank" title="About">About</a> <a href="https://g... |
36 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Polynomial Surrogate Model
Consider a simple underlying function with Gaussian noise added to simulate experimental or noisy computational data.
$$y = ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
n = 20
sigma = 1.0
xdata = np.linspace(-2, 2, n)
fdata = 3*xdata**2 + 2*xdata + 1 + np.random.randn(n)*sigma
plt.figure()
plt.plot(xdata, fdata, 'o')
plt.xlabel('x')
plt.ylabel('f')
plt.show()
Explanation: Polynomial Su... |
37 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Composites simulation
Step1: We need to import here the data, modify them if needed and proceed | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from simmit import smartplus as sim
import os
dir = os.path.dirname(os.path.realpath('__file__'))
Explanation: Composites simulation : plastic plies
End of explanation
umat_name = 'MIPLN' #This is t... |
38 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Original url
Step1: For the record, we should mention that there exist many other libraries in Python to parse XML, such as minidom or BeautifulSoup w... | <ASSISTANT_TASK:>
Python Code:
from lxml import etree
Explanation: Original url: https://github.com/mikekestemont/ghent1516/blob/master/Chapter%208%20-%20Parsing%20XML.ipynb
XML in a nutshell
So far, we have primarily dealt with unstructured data in this course: we have learned to read, for example, the contents of pla... |
39 | <SYSTEM_TASK:>
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Description:
Errors and Exceptions Homework - Solution
Problem 1
Handle the exception thrown by the code below by using try and except blocks.
Step1: Problem 2
Han... | <ASSISTANT_TASK:>
Python Code:
try:
for i in ['a','b','c']:
print i**2
except:
print "An error ocurred!"
Explanation: Errors and Exceptions Homework - Solution
Problem 1
Handle the exception thrown by the code below by using try and except blocks.
End of explanation
x = 5
y = 0
try:
z = x/y
except Z... |
40 | <SYSTEM_TASK:>
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Description:
Exploring the Training Set
Author(s)
Step2: Eyes on the data!
Step4: Check out the colors at rapidtables.com/web/color/RGB_Color, but don't forget to... | <ASSISTANT_TASK:>
Python Code:
# Enter your username:
YOUR_GMAIL_ACCOUNT = '******' # Whatever is before @gmail.com in your email address
# Libraries for this section:
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import pandas as pd
import cv2
import warnings
warnings.fi... |
41 | <SYSTEM_TASK:>
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Description:
Copyright 2020 The TensorFlow Authors.
Step1: TF.Text Metrics
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="ht... | <ASSISTANT_TASK:>
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 writin... |
42 | <SYSTEM_TASK:>
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Description:
<a href="https
Step1: Computing receptive field parameters of tf.keras.applications models.
Step2: Bonus stuff | <ASSISTANT_TASK:>
Python Code:
from google.colab import drive
drive.mount('/content/gdrive')
! mkdir gdrive/MyDrive/rf_keras
%cd gdrive/MyDrive/rf_keras
! ls
! git clone https://github.com/google-research/receptive_field.git
! ls
%cd receptive_field/
! ls
! pip install .
! pip install tensorflow
Explanation: <a href="h... |
43 | <SYSTEM_TASK:>
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Description:
<br>
Weighted kernel density estimation to quickly reproduce the profile of a diffractometer
<br>
<br>
This example shows a work-arround for a quick vi... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from ImageD11.columnfile import columnfile
from ImageD11 import weighted_kde as wkde
%matplotlib inline
plt.rcParams['figure.figsize'] = (6,4)
plt.rcParams['figure.dpi'] = 150
plt.rcParams['mathtext.fontset'] = 'c... |
44 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Combining different machine learning algorithms into an ensemble model
Model ensembling is a class of techniques for aggregating together multiple diff... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
# Import the dataset
dataset_path = "spam_dataset.csv"
dataset = pd.read_csv(dataset_path, sep=",")
# Take a peak at the data
dataset.head()
Explanation: Combining different machine learning algorithms into an ensemble model
Model ensembling is a cla... |
45 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
New function to make a list and to select calibrator
I add a function to retrieve all the flux from the ALMA Calibrator list with its frequency and obs... | <ASSISTANT_TASK:>
Python Code:
file_listcal = "alma_sourcecat_searchresults_20180419.csv"
q = databaseQuery()
Explanation: New function to make a list and to select calibrator
I add a function to retrieve all the flux from the ALMA Calibrator list with its frequency and observing date, and to retrieve redshift (z) from... |
46 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2020 The TensorFlow Authors.
Step1: TensorFlow Addons 图像：运算
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href=... | <ASSISTANT_TASK:>
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 writin... |
47 | <SYSTEM_TASK:>
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Description:
<center>
<a href="http
Step1: 2.1 Valeurs propres et valeurs singulières de l'ACP non réduite
Attention Les valeurs singulières sont celles de la déco... | <ASSISTANT_TASK:>
Python Code:
# Construire la matrice de notes
import pandas as pd
note=[[6,6,5,5.5],[8,8,8,8],[6,7,11,9.5],[14.5,14.5,15.5,15],
[14,14,12,12.5],[11,10,5.5,7],[5.5,7,14,11.5],[13,12.5,8.5,9.5],
[9,9.5,12.5,12]]
dat=pd.DataFrame(note,index=["jean","alai","anni","moni","didi","andr","pier","brig","... |
48 | <SYSTEM_TASK:>
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Description:
[0] Database management
Python provides a interface with the databases GadFly, mSQL, MySQL, PostgreSQL, Microsoft SQL Server 2000, Informix, Interbase,... | <ASSISTANT_TASK:>
Python Code:
# Source: Manuel Torres. Universidad de Almería.
import pymysql
# Establecemos la conexion con la base de datos
bd = pymysql.connect("localhost", "root", "gebd", "RRHH")
# Preparamos el cursor que nos va a ayudar a realizar las operaciones con la base de datos
cursor = bd.cursor()
# E... |
49 | <SYSTEM_TASK:>
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Description:
Personality prediction from tweet
by Angelo Basile
Step1: Dataset
Step2: Baseline
For the baseline we use an SVM with a sparse feature representation... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(113) #set seed before any keras import
import pandas as pd
import random
from sklearn.model_selection import train_test_split
from collections import defaultdict
from keras.preprocessing import sequence
from collections import Counter
import pydot
Explana... |
50 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Optimization Exercise 1
Imports
Step1: Hat potential
The following potential is often used in Physics and other fields to describe symmetry breaking a... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
Explanation: Optimization Exercise 1
Imports
End of explanation
def hat(x,a,b):
return -1*a*(x**2) + b*(x**4)
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0... |
51 | <SYSTEM_TASK:>
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Description:
ES-DOC CMIP6 Model Properties - Seaice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributor... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-1', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-1
Topic: Seaice
S... |
52 | <SYSTEM_TASK:>
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Description:
Causal Inference of Lalonde
<table align="left">
<td>
<a target="_blank" href="https
Step1: Data
The data are downloaded from
Dehejia and Wahba ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import patsy
import seaborn as sns
import statsmodels.api as sm
# install and import ec
!pip install -q git+https://github.com/google/empirical_calibration
import empirical_calibration as ec
sns.set_style('whitegrid')
%config InlineBackend.figure_for... |
53 | <SYSTEM_TASK:>
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Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contribu... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-hh', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-HH
Topic: Ocnbgchem
Sub-Topi... |
54 | <SYSTEM_TASK:>
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Description:
Introduction to data analytics with pandas
Quentin Caudron
PyData Seattle, July 2017
Systems check
Do you have a working Python installation, with the ... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
Explanation: Introduction to data analytics with pandas
Quentin Caudron
PyData Seattle, July 2017
Systems check
Do you have a working Python installation, with the pandas package ?
End of explanation
import pandas as pd
%matplotlib inline
Explanation: Note : This cell ... |
55 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate n... | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
Explanation: Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the ... |
56 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Parameterizing with Continuous Variables
Step1: Continuous Factors
Base Class for Continuous Factors
Joint Gaussian Distributions
Canonical Factors
Li... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Explanation: Parameterizing with Continuous Variables
End of explanation
import numpy as np
from scipy.special import beta
# Two variable drichlet ditribution with alpha = (1,2)
def drichlet_pdf(x, y):
return (np.power(x, 1)*np.power(y, 2))/beta(x, y... |
57 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
MNIST ML Pros tutorial
This notebook is based on the tutorial found here
This tutorial is very similar to the beginners tutorial except for some increm... | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/data/MNIST/",one_hot=True)
sess = tf.InteractiveSession()
Explanation: MNIST ML Pros tutorial
This notebook is based on the tutorial found here
This tutorial is very simi... |
58 | <SYSTEM_TASK:>
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Description:
Sensor space least squares regression
Predict single trial activity from a continuous variable.
A single-trial regression is performed in each sensor a... | <ASSISTANT_TASK:>
Python Code:
# Authors: Tal Linzen <linzen@nyu.edu>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.stats.regression import linear_regression
print(__doc__)
data_path = sample.data_path()
Explana... |
59 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Model fitting with cluster-lensing & emcee
Step1: Generate a noisy measurement to fit
Step2: Write down likelihood, prior, and posterior probilities
... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn; seaborn.set()
from clusterlensing import ClusterEnsemble
import emcee
import corner
% matplotlib inline
import matplotlib
matplotlib.rcParams["axes.labelsize"] = 20
matplotlib.rcParams["legend.fontsize"] = 12
Explanation: ... |
60 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Edit this next cell to choose a different country / year report
Step1: These next few conversions don't really work. The PPP data field seems wrong.
S... | <ASSISTANT_TASK:>
Python Code:
# BGR_3_2001.json
# BRA_3_2001.json
# MWI_3_2010.23.json
# ECU_3_2014.json
# ARM_3_2010.json
# NGA_3_2009.83.json
# IDN_1_2014.json quite pointed / triangular
# PHL_3_2009.json
# ZAR_3_2012.4.json
# TZA_3_2011.77.json
# VNM_3_2008.json
# MOZ_3_2008.67.json quite rounded
# UZB_3_2003.json
... |
61 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Language Classifier
Step1: Text Classification from Folders
Step2: Footnote
Step3: Bigrams/Trigrams
Step4: specify the ngram_range - the smallest n... | <ASSISTANT_TASK:>
Python Code:
count,feature_names=text.count_letters('data/languages/E3.txt')
print((count,feature_names))
count,feature_names=text.count_letters('data/languages/E3.txt')
print((count,feature_names))
p=text.letter_freq('English',feature_names)
print(p)
print((sum(count*log10(p))))
C=text.LanguageFileCl... |
62 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributo... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-3', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MOHC
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Tr... |
63 | <SYSTEM_TASK:>
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Description:
Exercicio 1 - Caixa Eletrônico
Você está desenvolvendo o sistema de um caixa eletrônico para um banco. O cliente do banco fornece ao caixa quanto dinhe... | <ASSISTANT_TASK:>
Python Code:
def CaixaEletronico(valor):
notas50 = valor // 50
valor = valor % 50
notas20 = valor // 20
valor = valor % 20
notas10 = valor // 10
valor = valor % 10
notas5 = valor // 5
valor = valor % 5
notas1 = valor // 1
return (notas50, notas20, notas10, notas... |
64 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Module 3 Demo
What is happening under the sea surface?
Tidal Currents
A current is generated by a difference in the sea surface elevation between diffe... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image("Figures/EbbTideCurrent.jpg")
Explanation: Module 3 Demo
What is happening under the sea surface?
Tidal Currents
A current is generated by a difference in the sea surface elevation between different points in space, which makes water move back and f... |
65 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Load Data
Step1: Q
Step2: The probability of being blocked after making a personal attack and increases as a function of how many times the user has ... | <ASSISTANT_TASK:>
Python Code:
# Load scored diffs and moderation event data
d = load_diffs()
df_block_events, df_blocked_user_text = load_block_events_and_users()
df_warn_events, df_warned_user_text = load_warn_events_and_users()
moderated_users = [('warned', df_warned_user_text),
('blocked', df_blo... |
66 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Modeling and Simulation in Python
Case study
Step3: Testing make_system
Step4: Testing slope_func
Step5: Now we can run the simulation.
Step6... | <ASSISTANT_TASK:>
Python Code:
# If you want the figures to appear in the notebook,
# and you want to interact with them, use
# %matplotlib notebook
# If you want the figures to appear in the notebook,
# and you don't want to interact with them, use
# %matplotlib inline
# If you want the figures to appear in separate... |
67 | <SYSTEM_TASK:>
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Description:
If you don't care about the confidence interval of parameter
Step1: If you want the confidence intervals | <ASSISTANT_TASK:>
Python Code:
from lmfit.models import GaussianModel
# initialize the gaussian model
gm = GaussianModel()
# take a look at the parameter names
print gm.param_names
# I get RuntimeError since my numpy version is a little old
# guess parameters
par_guess = gm.guess(n,x=xpos)
# fit data
result = gm.fit(n,... |
68 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Basic Tests
Step1: I plot the error of the filtered wave. I use the absulte values of the difference between sine wave and median filtered wave and ca... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
% matplotlib inline
Explanation: Basic Tests: Error of the median filter with different window lengths and wave number 5
2015.10.09 DW
End of explanation
def ErrorPlot( waveNumber,windo... |
69 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
PMOD TIMER
In this notebook, PMOD Timer functionalities are illustrated. The Timer has two sub-modules
Step1: Instantiate Pmod_Timer class. The method... | <ASSISTANT_TASK:>
Python Code:
from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
Explanation: PMOD TIMER
In this notebook, PMOD Timer functionalities are illustrated. The Timer has two sub-modules: Timer0 and Timer1.
The Generate output and Capture Input of Timer 0 are assumed to be connected t... |
70 | <SYSTEM_TASK:>
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Description:
Moving to Shallow Neural Networks
In this tutorial, you'll implement a shallow neural network to classify digits ranging from 0 to 9. The dataset you'l... | <ASSISTANT_TASK:>
Python Code:
# Download the dataset in this directory (does that work on Windows OS ?)
! wget http://deeplearning.net/data/mnist/mnist.pkl.gz
import cPickle, gzip, numpy
import numpy as np
# Load the dataset
f = gzip.open('mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()... |
71 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
In Paramz/GPy we can implement an own optimizer in a really simple way. We need to supply GPy with an implementation of the Optimizer class.
The Optimi... | <ASSISTANT_TASK:>
Python Code:
# Get the parameters for Rprop of climin:
climin.Rprop?
class RProp(Optimizer):
# We want the optimizer to know some things in the Optimizer implementation:
def __init__(self, step_shrink=0.5, step_grow=1.2, min_step=1e-06, max_step=1, changes_max=0.1, *args, **kwargs):
su... |
72 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Creating records with date, time and timestamp fields
Step1: Let's first import fmrest, its FileMakerError and requests.
Step2: Now access the FMS an... | <ASSISTANT_TASK:>
Python Code:
import sys
print(sys.version)
Explanation: Creating records with date, time and timestamp fields
End of explanation
import fmrest
print(fmrest.__version__)
from fmrest.exceptions import FileMakerError
import requests
requests.packages.urllib3.disable_warnings()
Explanation: Let's first im... |
73 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<h3>Basic Recipe for Training a POS Tagger with SpaCy</h3>
<ol>
<li id="loaddatatitle"><a href="#-Load-Data-">Load Data </a>
<ol><li>We'll be using a s... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('/home/jupyter/site-packages/')
import requests
from spacy.syntax.arc_eager import PseudoProjectivity
def read_conllx(text):
bad_lines = 0
#t = text.strip()
#print(type(t), type('\n\n'))
# u = t.split(b'\n\n')
n_sent = 0
n_li... |
74 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
syncID
Step1: As well as our function to read the hdf5 reflectance files and associated metadata
Step2: Define the location where you are holding the... | <ASSISTANT_TASK:>
Python Code:
import h5py
import csv
import numpy as np
import os
import gdal
import matplotlib.pyplot as plt
import sys
from math import floor
import time
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
Explanation: syncID: 84457ead9b964c8d916eacde9f271ec7
title: "Assessing Spectr... |
75 | <SYSTEM_TASK:>
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Description:
Step1: Image Classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and othe... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if ... |
76 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Different definitions of momentum
By Evgenia "Jenny" Nitishinskaya
Notebook released under the Creative Commons Attribution 4.0 License.
A momentum str... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
k = 30
start = '2014-01-01'
end = '2015-01-01'
pricing = get_pricing('PEP', fields='price', start_date=start, end_date=end)
fundamentals = init_fundamentals()
num_shares = get_fundamentals(query(fundamentals.earnings_r... |
77 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2020 The Cirq Developers
Step3: Shor's algorithm
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https... | <ASSISTANT_TASK:>
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 writin... |
78 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classif... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
Explanation: Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assi... |
79 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Visualizing output from the Mass Balance workflow
This notebook is designed to work with output from the Mass Balance workflow [iceflow] developed duri... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import matplotlib.pyplot as plt
# The two statements below are used mainly to set up a plotting
# default style that's better than the default from matplotlib
#import seaborn as sns
plt.style.use('bmh')
from shapely.geometry import Point
#import pandas as pd
i... |
80 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Comparing Different Stream Environments
This Jupyter Notebook compares four streams in different environments in the U.S.
Using hydrofunctions, we are... | <ASSISTANT_TASK:>
Python Code:
import hydrofunctions as hf
%matplotlib inline
Explanation: Comparing Different Stream Environments
This Jupyter Notebook compares four streams in different environments in the U.S.
Using hydrofunctions, we are able to plot the flow duration graphs for all four streams and compare them.
... |
81 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Take the set of pings, make sure we have actual clientIds and remove duplicate pings. We collect each unique ping.
Step1: Transform and sanitize the p... | <ASSISTANT_TASK:>
Python Code:
def dedupe_pings(rdd):
return rdd.filter(lambda p: p["meta/clientId"] is not None)\
.map(lambda p: (p["meta/documentId"], p))\
.reduceByKey(lambda x, y: x)\
.map(lambda x: x[1])
Explanation: Take the set of pings, make sure we have actual clie... |
82 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Workshop Installation Guide
如何使用和开发微信聊天机器人的系列教程
A workshop to develop & use an intelligent and interactive chat-bot in WeChat
WeChat is a popular socia... | <ASSISTANT_TASK:>
Python Code:
!python --version
!pip install -U html
!pip install -U pyqrcode
!pip install -U config
!pip install -U backports.tempfile
!mv docs org_docs
Explanation: Workshop Installation Guide
如何使用和开发微信聊天机器人的系列教程
A workshop to develop & use an intelligent and interactive chat-bot in WeChat
WeChat is ... |
83 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Introduction
In the Intro to SQL micro-course, you learned how to use INNER JOIN to consolidate information from two different tables. Now you'll lear... | <ASSISTANT_TASK:>
Python Code:
#$HIDE_INPUT$
from google.cloud import bigquery
# Create a "Client" object
client = bigquery.Client()
# Construct a reference to the "hacker_news" dataset
dataset_ref = client.dataset("hacker_news", project="bigquery-public-data")
# API request - fetch the dataset
dataset = client.get_dat... |
84 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
A Nonsensical Language Model using Theano LSTM
Today we will train a nonsensical language model !
We will first collect some language data, convert it ... | <ASSISTANT_TASK:>
Python Code:
## Fake dataset:
class Sampler:
def __init__(self, prob_table):
total_prob = 0.0
if type(prob_table) is dict:
for key, value in prob_table.items():
total_prob += value
elif type(prob_table) is list:
prob_table_gen = {}
... |
85 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
In the tutorial, you learned about six different types of bias. In this exercise, you'll train a model with real data and get practice with identifyin... | <ASSISTANT_TASK:>
Python Code:
# Set up feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.ethics.ex3 import *
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
# Get the same res... |
86 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contribut... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-3', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-3
Sub-Topics: Radiati... |
87 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
W2 Lab
Step1: You can check the version of the library. Because pandas is fast-evolving library, you want to make sure that you have the up-to-date ve... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
Explanation: W2 Lab: Data processing basics and John Snow's map
Follow the contents of this notebook and answer all questions (e.g. Q1: ...)
If you are not familiar with Python yet...
Maybe you can quickly review these basics first.
numbers and strings;
if and for stat... |
88 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Looking for Correlations with Data Binned by time
Since the data was collected at slightly different times by different sensors, we decided to make uni... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np # math
import pandas as pd # manipulating data
import matplotlib.pyplot as plt # graphing
import os # useful for handling filenames etc.
from scipy.stats import pearsonr # calculat... |
89 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
You can use the single line call "analyze," which does all the available analysis simultaneously
Step1: ... or call all the methods individually | <ASSISTANT_TASK:>
Python Code:
# NOTE: This will take several minutes depending on the performance of your machine
audio_features = audioAnalyzer.analyze(audio_filename)
# plot the features
plt.rcParams['figure.figsize'] = [20, 8]
audioAnalyzer.plot(audio_features)
plt.show()
Explanation: You can use the single line ca... |
90 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
What is an Adsorption Isotherm?
In a typical isotherm analysis, a small sample of material, which one wants to determine the surface properties of, is ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from micromeritics import util
from micromeritics import isotherm_examples as ex
import matplotlib.pyplot as plt
carb = ex.carbon_black() # example isotherm of Carbon Black with N2 at 77K
sial = ex.silica_alumina() # example isotherm of Silica Alumina with N2 at 77K
m... |
91 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>
Download
Step1: Exercícios | <ASSISTANT_TASK:>
Python Code:
# 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 5</font>
Download: http://github.com/dsacademybr
End of... |
92 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Solving problems by Searching
This notebook serves as supporting material for topics covered in Chapter 3 - Solving Problems by Searching and Chapter 4... | <ASSISTANT_TASK:>
Python Code:
from search import *
from notebook import psource, heatmap, gaussian_kernel, show_map, final_path_colors, display_visual, plot_NQueens
# Needed to hide warnings in the matplotlib sections
import warnings
warnings.filterwarnings("ignore")
Explanation: Solving problems by Searching
This not... |
93 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Lecture
Step3: 2. List Methods -- 100xp, status
Step5: 3. List Methods II -- 100xp, status | <ASSISTANT_TASK:>
Python Code:
Instructions:
+ Use the upper() method on room and store the result in room_up.
Use the dot notation.
+ Print out room and room_up. Did both change?
+ Print out the number of o's on the variable room by calling count()
on room and passing the letter "o... |
94 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
robobrowser
Step1: opds
Step2: Failed attempt with requests to submit to yaml loader
Step3: travis webhooks
For https
Step4: travis webhook authent... | <ASSISTANT_TASK:>
Python Code:
from robobrowser import RoboBrowser
def post_to_yaml_loader(url, unglue_url="https://unglue.it/api/loader/yaml"):
browser = RoboBrowser(history=True)
browser.open(unglue_url)
form = browser.get_forms()[0]
form['repo_url'] = url
# weird I have to manually set r... |
95 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
<h2><span style="color
Step1: Development of the Processor class to calculate all the stats
Step2: Prototyping the dcons function to split alleles pe... | <ASSISTANT_TASK:>
Python Code:
!hostname
%load_ext autoreload
%autoreload 2
%matplotlib inline
import ipyrad
import ipyrad.analysis as ipa
import ipyparallel as ipp
from ipyrad.analysis.popgen import Popgen
from ipyrad import Assembly
from ipyrad.analysis.locus_extracter import LocusExtracter
ipyclient = ipp.Client(clu... |
96 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
9. Линейная регрессия
2. В четырехугольнике $ABCD$ независимые равные по точности измерения углов $ABD$, $DBC$, $ABC$, $BCD$, $CDB$, $BDA$, $CDA$, $DAB... | <ASSISTANT_TASK:>
Python Code:
import numpy
Explanation: 9. Линейная регрессия
2. В четырехугольнике $ABCD$ независимые равные по точности измерения углов $ABD$, $DBC$, $ABC$, $BCD$, $CDB$, $BDA$, $CDA$, $DAB$ (в градусах) дали результаты $50.78$, $30.25$, $78.29$, $99.57$, $50.42$, $40.59$, $88.87$, $89.86$ соответств... |
97 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
This note will show you how to use BigBang to investigate a particular project participant's activity.
We will focus on Fernando Perez's role within th... | <ASSISTANT_TASK:>
Python Code:
from bigbang.archive import Archive
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
Explanation: This note will show you how to use BigBang to investigate a particular project participant's activity.
We will focus on Fernando Perez's role within the IPython communit... |
98 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Update TOC trends analysis
Tore has previously written code to calculate Mann-Kendall (M-K) trend statistics and Sen's slope estimates for data series ... | <ASSISTANT_TASK:>
Python Code:
# Read data and results from the Excel macro
in_xlsx = (r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\TOC_Trends_Analysis_2015'
r'\Data\mk_sen_test_data.xlsx')
raw_df = pd.read_excel(in_xlsx, sheetname='input')
res_df = pd.read_excel(in_xlsx, sheetname='results')
raw_df
res_... |
99 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Exercise 3
We're going to switch gears a little and talk about the astrophysical part of Astrophysical Machine Learning. This exercise will have you ex... | <ASSISTANT_TASK:>
Python Code:
from astropy.io import fits as fits
fitsimage=fits.open('filename.fits')
image=np.flipud(fitsimage[0].data)
Explanation: Exercise 3
We're going to switch gears a little and talk about the astrophysical part of Astrophysical Machine Learning. This exercise will have you examine two differe... |
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