Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
6,000 | <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... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Artistic Style Transfer with TensorFlow Lite
Step2: Download the content and style images, and the pre-trained TensorFlow Lite models.
Step3: ... |
6,001 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import os
from os.path import join
import sys
import json
idx = pd.IndexSlice
cwd = os.getcwd()
data_path = join(cwd, '..', 'Data storage')
file_date = '2018-03-06'
%load_ext watermark
%watermark -iv -v
# Load the "autoreload" extension
%load_ext au... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Date string for filenames
Step2: Load data
Step3: Adjusted facility emissions and generation
Step4: Extra gen/fuels from non-reporting
Step5:... |
6,002 | <ASSISTANT_TASK:>
Python Code:
n_neurons = 100
ac = AdaptiveControl(n_inputs=1, n_outputs=1, n_neurons=n_neurons, seed=1)
inputs = np.linspace(-1, 1, 100)
rates = np.zeros((len(inputs), n_neurons))
for i, input in enumerate(inputs):
current = ac.compute_neuron_input([input])
activity = ac.neuron(current)
ra... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now let's try teaching the model to just output the identity function (i.e. the output should be the same as the input). We train it over a sin... |
6,003 | <ASSISTANT_TASK:>
Python Code:
from pysap.SAPEnqueue import *
from IPython.display import display
for dest in enqueue_dest_values:
p = SAPEnqueue(dest=dest)
print(enqueue_dest_values[dest])
display(p.canvas_dump())
for opcode in enqueue_server_admin_opcode_values:
p = SAPEnqueue(dest=3, opcode=opcode)... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: SAP Enqueue packet
Step2: SAP Enqueue Server Admin opcodes
Step3: SAP Enqueue Connection Admin opcodes
Step4: SAP Enqueue Connection Admin pa... |
6,004 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'sandbox-3', 'ocnbgchem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
6,005 | <ASSISTANT_TASK:>
Python Code:
x, y, y_unc = pollute_namespace()
# complete
# complete
p = np.polyfit( # complete
# complete
# complete
# complete
p_yx = np.polyfit(y, x, 1)
p_yx_eval = np.poly1d(p_yx)
fig = plt.figure(figsize=(6,5))
ax = plt.subplot2grid((3,1), (0, 0), rowspan=2)
ax_res = plt.subplot2grid((3,1), (... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: You now have some data $x$ and $y$.
Step2: Solution 2a
Step3: There is a very good chance, though I am not specifically assuming anything, tha... |
6,006 | <ASSISTANT_TASK:>
Python Code:
meat_subset = meat[['date', 'beef', 'pork']]
df = pd.melt(meat_subset, id_vars=['date'])
df.head()
ggplot(df, aes(x='date', y='value', color='variable')) + geom_line()
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now we'll setup our aesthetics so date is the x-axis value, variable is the color of each line and value is the y-axis value.
|
6,007 | <ASSISTANT_TASK:>
Python Code:
from parse_data_to_tfrecord_lib import img_to_example, read_tfrecord, generate_tfexamples_from_detections, batch_read_write_tfrecords
from PIL import Image # used to read images from directory
import tensorflow as tf
import os
import io
import IPython.display as display
import numpy as n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Test function img_to_example()
Step2: Function Test
Step3: Test batch_read_write_tfrecords()
Step4: Read back the generated tfrecords and che... |
6,008 | <ASSISTANT_TASK:>
Python Code:
import random
deaths = 6
running = True
while running:
# Create a variable that randomly create a integer between 0 and 10.
guess = random.randint(0,10)
# if guess equals deaths,
if guess == deaths:
# then print this
print('Correct!')
# and then ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create a variable of the true number of deaths of an event
Step2: Create a variable that is denotes if the while loop should keep running
Step3... |
6,009 | <ASSISTANT_TASK:>
Python Code:
import platform
import psutil
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.facecolor']='white'
plt.rcParams['font.size']=16
import bioframe
import pyranges
print(f"Bioframe v.{bioframe.__version__}")
print(f"PyRanges v.{pyranges.__version__}"... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Below we define a function to generate random intervals with various properties, returning a dataframe of intervals.
Step2: Overlap
Step3: vs ... |
6,010 | <ASSISTANT_TASK:>
Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
from mne.filter import next_fast_len
import mne
print(__doc__)
data_path = mne.datasets.opm.dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load data, resample. We will store the raw objects in dicts with entries
Step2: Do some minimal artifact rejection just for VectorView data
Ste... |
6,011 | <ASSISTANT_TASK:>
Python Code:
import sqlite3
con = sqlite3.connect("onsgeocodes.sqlite")
import pandas as pd
#Create a function to grab a zip file from an online location and then grab a specified file from inside it
import requests, zipfile
#The following fudge copes with Python 2 and Python 3
try:
from StringIO ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The .csv file contains metadata describing the geographies listed in separate sheets in the .xlsx file.
Step2: Extracting Geography Codes
Step3... |
6,012 | <ASSISTANT_TASK:>
Python Code:
import logging # python logging module
# basic format for logging
logFormat = "%(asctime)s - [%(levelname)s] (%(funcName)s:%(lineno)d) %(message)s"
# logs will be stored in tweepy.log
logging.basicConfig(filename='tweepytopuser.log', level=logging.INFO,
format=logForma... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Authentication and Authorisation
Step3: Post this step, we will have full access to twitter api's
Step9: Streaming with tweepy
Step10: Drawba... |
6,013 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-2', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
6,014 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
matplotlib.style.use('ggplot')
%matplotlib inline
from sklearn.decomposition import PCA
mu = np.zeros(2)
C = np.array([[3,1],[1,2]])
data = np.random.multiv... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Теория
Step2: Путём диагонализации истинной матрицы ковариаций $C$, мы можем найти преобразование исходного набора данных, компоненты которого ... |
6,015 | <ASSISTANT_TASK:>
Python Code:
%load_ext sql
%sql mysql://steinam:steinam@localhost/nordwind
%%sql
select l.`Kontaktperson` , a.`Artikelname` from artikel a, lieferanten l
where a.`Kategorie-Nr` in ('1','2','3')
%%sql
select k.`Firma`,b.`BestellNr` ,p.`Nachname`
from Kunden k, bestellungen b, personal p
where p.`... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Schreiben Sie eine Abfrage, die Ihnen die Kontaktnamen der Lieferanten ausgibt, die
Step2: Schreiben Sie eine Abfrage, die Ihnen die Kundennam... |
6,016 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
bc = datasets.load_breast_cancer()
X = bc.data
y = bc.target
random_state = np.random.RandomState(0)
# shuf... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import some data to play with
Step2: Split the data and prepare data for ROC Curve
Step3: Plot ROC Curve using Matplotlib
Step4: Create ROCAU... |
6,017 | <ASSISTANT_TASK:>
Python Code:
# helper code needed for running in colab
if 'google.colab' in str(get_ipython()):
print('Downloading plot_helpers.py to util/ (only neded for colab')
!mkdir util; wget https://raw.githubusercontent.com/minireference/noBSLAnotebooks/master/util/plot_helpers.py -P util
# setup SymP... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: P4.2
Step2: So $z=s$ is a free variable, and the rest of the equation
Step3: The free variable is $z=t$.
|
6,018 | <ASSISTANT_TASK:>
Python Code:
# Put these at the top of every notebook, to get automatic reloading and inline plotting
%reload_ext autoreload
%autoreload 2
%matplotlib inline
# This file contains all the main external libs we'll use
from fastai.imports import *
from fastai.transforms import *
from fastai.conv_learner... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Here we import the libraries we need. We'll learn about what each does during the course.
Step2: making folder structure and downloading some i... |
6,019 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
x=[1,2,5]
y=np.array([[0.41,0.44,0.47],[0.25,0.22,0.21]]).T
plt.errorbar(x,y[:,0],yerr=0.7/9.3,fmt='d-b')
plt.errorbar(x,y[:,1],yerr=0.7/9.3,fmt='o-g')
plt.legend(['focal','non-focal'],loc=7)
plt.grid(False,axis='x')
plt.xlabel('Time pressure');plt.ylabel('choice probability... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The figure shows the choice probabilities for two of the options - focal and nonfocal. (Since the choice probabilities sum to one, the probabili... |
6,020 | <ASSISTANT_TASK:>
Python Code:
p = GMM([1.0], np.array([[0.5,0.05]]))
num_samples = 1000
beg = 0.0
end = 1.0
t = np.linspace(beg,end,num_samples)
num_neurons = len(p.pis)
colors = [np.random.rand(num_neurons,) for i in range(num_neurons)]
p_y = p(t)
p_max = p_y.max()
np.random.seed(110)
num_neurons = 1
neuron = Neuron(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: I can assume $q(x)$ has two forms
|
6,021 | <ASSISTANT_TASK:>
Python Code:
import mxnet as mx
from mxnet import gluon, autograd, ndarray
import numpy as np
train_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=True, transform=lambda data, label:
(data.astype(np.float32)/255, label)), batch_size=32, shuffle=... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Next, we use gluon.data.DataLoader, Gluon's data iterator, to hold the training and test data.
Step2: Now, we are ready to define the actual ne... |
6,022 | <ASSISTANT_TASK:>
Python Code:
# import the dataset
from quantopian.interactive.data.eventvestor import clinical_trials
# or if you want to import the free dataset, use:
# from quantopian.data.eventvestor import clinical_trials_free
# import data operations
from odo import odo
# import other libraries we will use
impor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let's go over the columns
Step2: Finally, suppose we want a DataFrame of GlaxoSmithKline Phase-III announcements, sorted in descending order by... |
6,023 | <ASSISTANT_TASK:>
Python Code:
import sqlalchemy
sqlalchemy.__version__
from sqlalchemy import create_engine
engine = create_engine('sqlite:///:memory:')
from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey
metadata = MetaData()
user = Table('user', metadata,
Column('id_user', Integer, prim... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Fetch an SQLite engine and create an in memory database
Step2: Now lets make a couple of tables and do some queries
Step3: ... and let's add a... |
6,024 | <ASSISTANT_TASK:>
Python Code:
const.TRAIN_FILES
const.TEST_FILES
num_data = func.load_data_file(const.TRAIN_FILES[0], ftype='bin')
cat_data = func.load_data_file(const.TRAIN_FILES[1], ftype='bin')
num_data_te = func.load_data_file(const.TEST_FILES[0], ftype='bin')
cat_data_te = func.load_data_file(const.TEST_FILES[1]... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load data
Step2: Load and adjust lookup table
Step3: Create paths per station
Step4: Create hashes based on non-zero values of num and cat da... |
6,025 | <ASSISTANT_TASK:>
Python Code:
#!pip install --user miepython
import importlib.resources
import numpy as np
import matplotlib.pyplot as plt
try:
import miepython
except ModuleNotFoundError:
print('miepython not installed. To install, uncomment and run the cell above.')
print('Once installation is successful... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: When a monochromatic plane wave is incident on a sphere, it scatters and absorbs light depending on the properties of the light and sphere. The... |
6,026 | <ASSISTANT_TASK:>
Python Code:
import os
import math
import glob
import cv2
from collections import deque
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip
%matplotlib inline
class cam_util():
util class for camera operations
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step5: Create a utility class for camera calibration
Step13: Create a class to keep track of lane detections
Step16: Use the lane pixals identified t... |
6,027 | <ASSISTANT_TASK:>
Python Code:
# Environment setup
%matplotlib inline
%cd /lang_dec
# Imports
import warnings; warnings.filterwarnings('ignore')
import hddm
import numpy as np
import matplotlib.pyplot as plt
from utils import model_tools, signal_detection
# Import control models
controls_data = hddm.load_csv('/lang_dec... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Reaction Time Distributions
Step2: Model Fitness
|
6,028 | <ASSISTANT_TASK:>
Python Code:
!cat publications.tsv
import pandas as pd
publications = pd.read_csv("publications.tsv", sep="\t", header=0)
publications
html_escape_table = {
"&": "&",
'"': """,
"'": "'"
}
def html_escape(text):
Produce entities within text.
return "".join(html_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import pandas
Step2: Import TSV
Step4: Escape special characters
Step7: Creating the markdown files
Step8: These files are in the publicatio... |
6,029 | <ASSISTANT_TASK:>
Python Code:
# import required modules
from mtpy.core.mt import MT
# Define the path to your edi file
edi_file = "C:/mtpywin/mtpy/examples/data/edi_files_2/Synth00.edi"
# Create an MT object
mt_obj = MT(edi_file)
# To see the latitude and longitude
print(mt_obj.lat, mt_obj.lon)
# To see the easting, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The mt_obj contains all the data from the edi file, e.g. impedance, tipper, frequency as well as station information (lat/long). To look at any ... |
6,030 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-1', 'ocean')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
6,031 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import mne
from mne.datasets import sample
data_path = sample.data_path()
raw_empty_room_fname = op.join(
data_path, 'MEG', 'sample', 'ernoise_raw.fif')
raw_empty_room = mne.io.read_raw_fif(raw_empty_room_fname)
raw_fname = op.join(data_path, 'MEG', 'sample', 'sa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Source estimation method such as MNE require a noise estimations from the
Step2: The definition of noise depends on the paradigm. In MEG it is ... |
6,032 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv("thwfall2017-survey.csv")
df = df[1:]
df.columns[0:3]
count = 0
for column in df.columns:
if count < 43:
df = df.rename(columns = {column:column[308:]})
print(len(column), column[... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Importing data and previewing
Step2: Oh no, these are some messy column names! Gotta clean them up, truncating the first 308 characters.
Step3:... |
6,033 | <ASSISTANT_TASK:>
Python Code:
# 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, sof... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Classification with TensorFlow
Step2: And then download the dataset.
Step3: And load the data into a DataFrame and take a peek.
Step4: We can... |
6,034 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import mne
from mne.datasets import sample
from mne.minimum_norm import re... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Compute inverse solution
Step2: View source activations
Step3: Using vector solutions
|
6,035 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import pprint
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#import cvxpy as cp
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from sklearn import preprocessing
impor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Data preprocessing
Step2: Next, we'll create partitions of the test data depending on whether the spurious feature equals the label or not.
Ste... |
6,036 | <ASSISTANT_TASK:>
Python Code:
import json
import numpy as np
from numpy import ma
import io
import re
import itertools
import random
from bokeh.charts import Histogram
import networkx as nx
from nltk.stem import WordNetLemmatizer
wnl = WordNetLemmatizer()
from sklearn.feature_extraction import DictVectorizer
from coll... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Recipe Recommender Capstone Project
Step4: Removing the Unrelated Words from Ingredients
Step5: Ingredient Analysis
Step6: Number of unique i... |
6,037 | <ASSISTANT_TASK:>
Python Code:
class newNode :
def __init__(self , x ) :
self . data = x
self . left = self . right = None
def count(root ) :
if(root == None ) :
return 0
return(count(root . left ) + count(root . right ) + 1 )
def checkRec(root , n ) :
if(root == None ) :
return False ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
6,038 | <ASSISTANT_TASK:>
Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(css_style = 'custom2.css')
os.chdir(path)
# 1. magic for inline... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Decision Tree (Classification)
Step2: Gini Index
Step12: As we can see from the plot, there is not much differences (as in they both increase ... |
6,039 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
import numpy as np
import random
import os
import glob
import cv2
import datetime
import pandas as pd
import time
import h5py
import csv
from scipy.misc import imresize, imsave
from sklearn.cross_validation import KFold, train_test_split
fro... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Configuration and Hyperparameters
Step3: Then we'll set all the relevant paths and configurations
Step4: Helper Functions For Loading Data
Ste... |
6,040 | <ASSISTANT_TASK:>
Python Code:
import nltk
abstract =
It's morning, you settle in, check your dashboards and it looks like there is an increase of load coming through on some of your web server logs. What happened? You're about to deploy code that will hopefully fix some issues; how will you know that things worked we... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Examples of text analysis
Step2: First tokenize the text and then we tag the parts of speech
Step3: Let's get a frequency distribution of the ... |
6,041 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import make_blobs
X, y = make_blobs(centers=2, random_state=0)
print('X ~ n_samples x n_features:', X.shape)
print('y ~ n_samples:', y.shape)
print('\nFirst 5 samples:\n', X[:5, :])
print('\nFirst 5 labels:', y[:5])
plt.scatter(X[y == 0, 0], X[y == 0, 1],
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: As the data is two-dimensional, we can plot each sample as a point in a two-dimensional coordinate system, with the first feature being the x-ax... |
6,042 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
from __future__ import print_function
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
def plotSequence(y):
n = np.linspace(0, y.size, y.size)
plt.scatter(n, y)
plt.plot([n,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Discrete time
Step2: Linear Difference Equations
Step3: Money exercises
Step4: 10% per year, month compound
Step5: 10% per year, day compoun... |
6,043 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
print(tf.__version__)
a = tf.constant(value = [5, 3, 8], dtype = tf.int32)
b = tf.constant(value = [3, -1, 2], dtype = tf.int32)
c = tf.add(x = a, y = b)
print(c)
with tf.Session() as sess:
result = sess.run(fetches = c)
print(result)
a = tf.placeholder(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Graph Execution
Step2: Run the Graph
Step3: Can you mix eager and graph execution together?
Step 1
Step4: Linear Regression
Step5: Loss Func... |
6,044 | <ASSISTANT_TASK:>
Python Code:
from numpy.random import randint
import matplotlib.pyplot as plt
%matplotlib inline
S = randint(low=0, high=11, size=15) # 10 random integers b/w 0 and 10
def f(x):
Dummy function - returns identity
return x
print("1. S == {}".format(S))
y1 = [f(x) for x in S]
print("2.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Reading, and writing, comprehension(s)
Step2: List comprehensions
Step3: As you can see, the translation from the math to code is natural.
Ste... |
6,045 | <ASSISTANT_TASK:>
Python Code:
%run import.ipynb
import matplotlib.pyplot as plt
isotherm = next(i for i in isotherms_n2_77k if i.material=='MCM-41')
ax = isotherm.plot()
import pygaps.graphing as pgg
ax = pgg.plot_iso(
isotherms_isosteric,
branch = 'ads',
logx = True,
x_range=(None,1),
lgd_keys=[... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Isotherm display
Step2: Isotherm plotting and comparison
Step3: A black and white (color=False) full scale graph of both adsorption and desorp... |
6,046 | <ASSISTANT_TASK:>
Python Code:
lst = [11,2,34, 4,5,5111]
len(lst)
len([11,2,'sort',4,5,5111])
sorted(lst)
lst
lst.sort()
lst
min(lst)
max(lst)
str(1212)
sum([1,2,2])
lst
lst.remove(4)
lst.append(4)
string = 'hello, wie geht, es Dir?'
string.split(',')
import urllib
import requests
import glob
import pandas
from bs4 i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2 Viel mächtigere Funktion
Step2: 3 Aber wie sind Funktion, Modules und Libraries aufgebaut?
Step3: 4 Bauen wir die eigenen Funktion
Step4: U... |
6,047 | <ASSISTANT_TASK:>
Python Code:
try:
import gi
gi.require_version('NumCosmo', '1.0')
gi.require_version('NumCosmoMath', '1.0')
except:
pass
from gi.repository import GObject
from gi.repository import NumCosmo as Nc
from gi.repository import NumCosmoMath as Ncm
import sys
import math
import numpy as np
import mat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Initializing the NumCosmo library
Step2: Initializing the objects
Step3: Computing the normalized Hubble function
Step4: Initializing the dis... |
6,048 | <ASSISTANT_TASK:>
Python Code:
import os
os.chdir(os.getcwd() + '/..')
# Run some setup code for this notebook
import random
import numpy as np
import matplotlib.pyplot as plt
from utils.data_utils import load_CIFAR10
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcPara... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps
Step2: Inline Qu... |
6,049 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
USAhousing = pd.read_csv('USA_Housing.csv')
USAhousing.head()
USAhousing.info()
USAhousing.describe()
USAhousing.columns
sns.pairplot(USAhousing)
sns.distplot(USAhousing['Pric... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Check out the Data
Step2: EDA
Step3: Training a Linear Regression Model
Step4: Train Test Split
Step5: Creating and Training the Model
Step6... |
6,050 | <ASSISTANT_TASK:>
Python Code:
from pynq.drivers.video import Frame, HDMI
from IPython.display import Image
hdmi=HDMI('in')
hdmi.start()
frame = hdmi.frame()
orig_img_path = '/home/xilinx/jupyter_notebooks/examples/data/orig.jpg'
frame.save_as_jpeg(orig_img_path)
Image(filename=orig_img_path)
from pynq.drivers.video ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2. Save frame and display JPG here
Step2: 3. Gray Scale filter
Step3: 4. Sobel filter
Step4: Step 5
|
6,051 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from stemgraphic.num import stem_graphic
import pandas as pd
import numpy as np
import math
df = pd.read_csv('../datasets/iris.csv')
df.describe()
fig, ax = stem_graphic(df['sepal_length'],
random_state=42,
title='sepal_len... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Loading the iris dataset
Step2: Let's combined both variables in one back-to-back stem-and-leaf plot
Step3: And of course, we can save a pdf. ... |
6,052 | <ASSISTANT_TASK:>
Python Code:
import ndmg
import ndmg.utils as mgu
# run small demo for experiments
print(mgu.execute_cmd('ndmg_demo-dwi', verb=True)[0])
import numpy as np
fibs = np.load('/tmp/small_demo/outputs/fibers/KKI2009_113_1_DTI_s4_fibers.npz')['arr_0']
small_fibs = fibs[1:3]
from ndmg.graph import biggraph ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The approach we will take is to take 2 fibers from our graph and verify that we end up with the appropriate voxels in our streamlines being conn... |
6,053 | <ASSISTANT_TASK:>
Python Code:
# Single word
'hello'
# Entire phrase
'This is also a string'
# We can also use double quote
"String built with double quotes"
# Be careful with quotes!
' I'm using single quotes, but will create an error'
"Now I'm ready to use the single quotes inside a string!"
# We can simply declar... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The reason for the error above is because the single quote in I'm stopped the string. You can use combinations of double and single quotes to ge... |
6,054 | <ASSISTANT_TASK:>
Python Code:
#Define inputs
filename = '../data/SERC/hyperspectral/NEON_D02_SERC_DP1_20160807_160559_reflectance.h5'
sercRefl, sercRefl_md, wavelengths = h5refl2array(filename)
clipExtDict = {}
clipExtDict['xMin'] = 367400.
clipExtDict['xMax'] = 368100.
clipExtDict['yMin'] = 4305750.
clipExtDict['yMax... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Stack NIR and VIS bands
Step2: Calculate NDVI & Plot
Step3: Extract Spectra Using Masks
Step4: Function to calculate the mean spectra for ref... |
6,055 | <ASSISTANT_TASK:>
Python Code:
def dice_samples(trials):
prob = {1: 1/2, 2: 1/4, 3: 1/8, 4: 1/16, 5: 1/32, 6: 1/32}
samples = np.zeros(trials + 1, dtype=int)
samples[0] = 1
for i in range(trials):
a = samples[i]
b = np.random.random_integers(1, 6) # uniform a priori distribution
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We change the number of MC steps to give a view to the time evolution of the M-H chain
Step2: So the constructed chains do converto our desired... |
6,056 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style("whitegrid")
from matplotlib.colors import LogNorm
%matplotlib inline
#Three component competitive binding function
#This function and its assumptions are defined in greater detail in this notebook:
##... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: In this notebook we will explore experimental design of competition assays.
Step2: We can use this function to help us decide the appropriate c... |
6,057 | <ASSISTANT_TASK:>
Python Code:
Image.fromarray(output)
Image.fromarray(grayscale_image)
native_output = image_org.filter(ImageFilter.MedianFilter(size = 3))
native_output
deviation_native = np.sqrt(np.sum(np.square(grayscale_image-np.array(rgb2gray(np.array(native_output))))))
deviation_original = np.sum(np.square(gr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Original Image (converted to grayscale)
Step2: Output with Python's native Median Filter function
Step3: As shown from the above print, AMF re... |
6,058 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import brfss
import thinkstats2
import thinkplot
df = brfss.ReadBrfss(nrows=None)
female = df[df.sex==2]
female_heights = female.htm3.dropna()
mean, std = female_heights.mean(), female_heights.std()
me... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: I'll start with the data from the BRFSS again.
Step2: Here are the mean and standard deviation of female height in cm.
Step3: NormalPdf return... |
6,059 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
data=np.load("decay_osc.npz")
T=data["tdata"]
Y=data["ydata"]
dy=data["dy"]
f=plt.figure(figsize=(15,10))
plt.errorbar(T,Y,yerr=dy,fmt='o');
assert True # leave this to grade the data impor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Fitting a decaying oscillation
Step2: Now, using curve_fit to fit this model and determine the estimates and uncertainties for the parameters
|
6,060 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import time as tm
import matplotlib.pyplot as plt
# Discretization
c1=20 # Number of grid points per dominant wavelength
c2=0.5 # CFL-Number
nx=2000 # Number of grid points
T=10 # Total propagation time
# Source Signal
f0= 10 # Center fre... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Input Parameter
Step2: Preparation
Step3: Create space and time vector
Step4: Source signal - Ricker-wavelet
Step5: Time stepping
Step6: Sa... |
6,061 | <ASSISTANT_TASK:>
Python Code:
import datetime
import os
import numpy as np
import pandas as pd
import tensorflow as tf
import time
from tensorflow import keras
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
# Fi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Grid search in Scikit-learn
Step2: To keep things simple, we'll first convert the label column to numeric and then
Step3: Next, we'll build o... |
6,062 | <ASSISTANT_TASK:>
Python Code:
%cat epochs_spec.cfg
%cat epochs.cfg
ep = burin.config.EpochParser('epochs.cfg', 'epochs_spec.cfg')
ep.is_valid()
ep.get('cal_version', date='20180101')
ep.get('cal_version', date='20180101.120000')
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Specify an epoch parser with an epoch filename and the specification filename.
|
6,063 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
g = 9.81 # m/s^2
l = 0.5 # length of pendulum, in meters
tmax = 50. # seconds
t = np.linspace(0, tmax, int(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Damped, driven nonlinear pendulum
Step4: Write a function derivs for usage with scipy.integrate.odeint that computes the derivatives for the da... |
6,064 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# -*- coding:utf-8 -*-
from __future__ import print_function
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.stats.stattools import durbin_watson
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
# デー... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 例題5-1
Step2: Durbin-Watson
Step3: DW=1.094となり、上限分布において有意水準5%でも帰無仮説を棄却することができ、自己相関が存在すると結論することができる。
|
6,065 | <ASSISTANT_TASK:>
Python Code:
# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.deep_learning.exercise_1 import *
print("Setup Complete")
horizontal_line_conv = [[1, 1],
[-1, -1]]
# load_my_image and visualize_conv are utility functions provided ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Exercise 1
Step2: Now it's your turn. Instead of a horizontal line detector, you will create a vertical line detector.
Step3: If you'd like a ... |
6,066 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
a = pd.DataFrame(np.array([[1, 2],[3, 4]]), columns=['one', 'two'])
b = pd.DataFrame(np.array([[5, 6],[7, 8],[9, 10]]), columns=['one', 'two'])
def g(a,b):
if len(a) < len(b):
a = a.append(pd.DataFrame(np.array([[np.nan, np.nan]*(len(b)-l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
6,067 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import sklearn.metrics as metrics
data = pd.read_csv('../input/mobile-price-classification/train.csv')
data.head()
data.columns
# Set variables for the targets and... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We create our feature and targets the same as before using train_test_split. This part looks like what you've already seen.
Step2: Creating and... |
6,068 | <ASSISTANT_TASK:>
Python Code:
from matplotlib.colors import ListedColormap
from sklearn import cross_validation, datasets, metrics, tree
import numpy as np
%pylab inline
classification_problem = datasets.make_classification(n_features = 2, n_informative = 2,
n_c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Генерация данных
Step2: Модель DecisionTreeClassifier
Step3: Разделяющая поверхность
|
6,069 | <ASSISTANT_TASK:>
Python Code:
# path to raw files
## CHANGE THIS!
rawFileDir = "~/perl/projects/CLdb/data/Methanosarcina/"
# directory where the CLdb database will be created
## CHANGE THIS!
workDir = "~/t/CLdb_Methanosarcina/"
# viewing file links
import os
import zipfile
import csv
from IPython.display import FileLi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The required files are in '../ecoli_raw/'
Step2: Checking that CLdb is installed in PATH
Step3: Setting up the CLdb directory
Step4: Download... |
6,070 | <ASSISTANT_TASK:>
Python Code:
from keras.applications import imagenet_utils
imagenet_utils.CLASS_INDEX_PATH
from urllib.request import urlopen
import json
with urlopen(imagenet_utils.CLASS_INDEX_PATH) as jsonf:
data = jsonf.read()
class_dict = json.loads(data.decode())
[class_dict[str(i)][1] for i in range(1000)]
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Imagenet 2012 網頁
|
6,071 | <ASSISTANT_TASK:>
Python Code:
theArray = range(0,100)
key = 101
if key in theArray:
print("The key is in the array.")
else:
print("The key is not in the array.")
def linearSearch(theValues, target):
n = len(theValues)
for i in range(n):
# If the target is in the ith element, return True
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Okay, using in operator gives us a great deal of simplicity, but we should know the behind the scenes of in operator.
Step2: Finding a specific... |
6,072 | <ASSISTANT_TASK:>
Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
import mne
from mne import io
from mne.event import define_target_events
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set parameters
Step2: Find stimulus event followed by quick button presses
Step3: View evoked response
|
6,073 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'fio-ronm', 'sandbox-3', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
6,074 | <ASSISTANT_TASK:>
Python Code:
!pip show kubeflow-fairing
# Set docker registry to store image.
# Ensure you have permission for pushing docker image requests.
DOCKER_REGISTRY = 'index.docker.io/jinchi'
# Set namespace. Note that the created PVC should be in the namespace.
my_namespace = 'hejinchi'
# You also can get... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Configure the Docker Registry for Kubeflow Fairing
Step2: Create PV/PVC to Store the Exported Model
Step3: (Optional) Skip below creating PV/P... |
6,075 | <ASSISTANT_TASK:>
Python Code:
# SQLAlchemy
from sqlalchemy import Table, Column, Integer, ForeignKey
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Date, Integer, String
Base = declarative_base()
class One(Base):
__tablename__ = 'one'... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: One To Many
Step2: To establish a bidirectional relationship in one-to-many, where the “reverse” side is a many to one, specify an additional r... |
6,076 | <ASSISTANT_TASK:>
Python Code:
#import packages
import heartpy as hp
import matplotlib.pyplot as plt
sample_rate = 250
data = hp.get_data('e0103.csv')
plt.figure(figsize=(12,4))
plt.plot(data)
plt.show()
#run analysis
wd, m = hp.process(data, sample_rate)
#visualise in plot of custom size
plt.figure(figsize=(12,4))
h... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let's look at the first file and visualise it
Step2: That is a very nice and clean signal. We don't need to do any preprocessing and can run an... |
6,077 | <ASSISTANT_TASK:>
Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
if os.environ["IS_TESTING"]:
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Step3: Before you begin
Step4: Region
Step5:... |
6,078 | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import time
from datetime import timedelta
import tarfile
from IPython... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The lab... |
6,079 | <ASSISTANT_TASK:>
Python Code:
from scipy import optimize
def f(X):
Cost function.
return (X**2).sum()
X0 = [1.,1.] # Initial guess
sol = optimize.minimize(f, X0, method = "nelder-mead")
X = sol.x
print "Solution: ", X
def func(x, omega, tau):
return np.exp(-x / tau) * np.sin(omega * x)
xdata = np.lins... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Optimization
Step2: Curve fitting using least squares
|
6,080 | <ASSISTANT_TASK:>
Python Code:
# Figure 1
Image(url="http://cntk.ai/jup/MNIST-image.jpg", width=300, height=300)
# Import the relevant modules
from __future__ import print_function # Use a function definition from future version (say 3.x from 2.7 interpreter)
import matplotlib.pyplot as plt
import numpy as np
import o... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: In this tutorial, we will use the MNIST hand-written digits data to show how images can be encoded and decoded (restored) using feed-forward net... |
6,081 | <ASSISTANT_TASK:>
Python Code:
from google.colab import auth
auth.authenticate_user()
#@markdown Please fill in the value below with your GCP project ID and then run the cell.
# Please fill in these values.
project_id = "" #@param {type:"string"}
# Quick input validations.
assert project_id, "⚠️ Please provide a Googl... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 🔗 Connect Your Google Cloud Project
Step2: ☁ Configure Your Google Cloud Project
Step3: Enable the Cloud SQL Admin API within your project.
S... |
6,082 | <ASSISTANT_TASK:>
Python Code:
1+2
1+1
1+2
print(1+2)
a = 4
b = 1.5
c = 121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212
d = 1j
e = 1/3
f = True
a+b
a*c
(b+d)*a
a+f
type(1.5)
my_name = "Roshan"
print(my_name)
my_list = [1,2,3,4,5]
my_list
my_list + [6]
my_l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The print function
Step2: Variables
Step3: Other Value Types
Step4: Selecting / Slicing
Step5: To access a single value in a list use this s... |
6,083 | <ASSISTANT_TASK:>
Python Code:
%run 'Set-up.ipynb'
%run 'Loading scenes.ipynb'
%run 'vrep_models/PioneerP3DX.ipynb'
%%vrepsim '../scenes/OU_Pioneer.ttt' PioneerP3DX
# Use the time library to set a wait duration
import time
#Tell the robot to move forward by setting both motors to speed 1
robot.move_forward(1)
#Wait fo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Driving the Robot - Forwards, Backwards, Turns
Step2: In the code cell below, see if you can write a programme that drives the robot forwards a... |
6,084 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import regularizers
from sklearn.datasets import make_moons
import numpy as np
import matplotlib.pyplot as plt
import tensorflow_probability as tfp
data = make_moons(3000, noise... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load the data
Step2: Affine coupling layer
Step4: Real NVP
Step5: Model training
Step6: Performance evaluation
|
6,085 | <ASSISTANT_TASK:>
Python Code:
from nltk.tokenize import TreebankWordTokenizer
sentence = "How does nltk tokenize this sentence?"
tokenizer = TreebankWordTokenizer()
tokenizer.tokenize(sentence)
from nltk.tokenize.casual import casual_tokenize
tweet = "OMG @twitterguy that was sooooooooo cool :D :D :D!!!!"
print(casua... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Tokenizing Social Media
Step2: N-grams
Step3: Stop-words
Step4: Sentiment
|
6,086 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import emcee
import corner
nthreads = 2
# define our true relation
m_true = 1.7
b_true = 2.7
f_true = 0.3
# generate some data
N = 30
x = np.sort(10*np.random.rand(N))
yerr = 0.2+0.6*np.random.rand(N)
y = m_true*x+b_t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Emcee has multithreadding support. Set this to the number of cores you would like to use. In this demo we will use the python multiprocessing mo... |
6,087 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-2', 'ocnbgchem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
6,088 | <ASSISTANT_TASK:>
Python Code:
! pip install -q -U xarray matplotlib
! rm -rf data-driven-discretization-1d
! git clone https://github.com/google/data-driven-discretization-1d.git
! pip install -q -e data-driven-discretization-1d
# install the seaborn bug-fix from https://github.com/mwaskom/seaborn/pull/1602
! pip inst... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Run training
Step2: Run evaluation
Step3: See also ks_spectral.nc and kdv_spectral.nc in the same directory for reference simulations with KS ... |
6,089 | <ASSISTANT_TASK:>
Python Code::
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
ohe = OneHotEncoder()
df = pd.read_csv('onehotend_data.csv')
ohe.fit(df[['town']])
ct = make_column_transformer((OneHotEncoder(categories = ohe.categories_), ['town']),... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
6,090 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from scipy import stats
from IPython.display import Image #this is for displaying the widgets in the github repo
from shaolin.dashboards.graph import GraphCalculator
forex_data = pd.read_hdf('gcalculator_data/forex_sample.h5')
forex_data.items,forex_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <a id='sample_matrices'></a>
Step3: <a id='sample_node_metrics'></a>
Step4: <a id='components'></a>
Step5: <a id='gc_parameters'></a>
Step6: ... |
6,091 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
pd.__version__
pd.show_versions()
df = pd.DataFrame(data, index=labels)
df.info()
# ...or...
df.describe()
df.iloc[:3]
# or equivalently
df.head(3)
df.loc[:, ['animal', 'age']]
# or
df[['animal', 'age']]
df.loc[df.index[[3, 4, 8]], ['animal', 'age']]
df[df['vis... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2. Print the version of pandas that has been imported.
Step2: 3. Print out all the version information of the libraries that are required by th... |
6,092 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import zipfile
with zipfile.ZipFile('../datasets/phishing.csv.zip', 'r') as z:
f = z.open('phishing.csv')
data = pd.read_csv(f, index_col=False)
data.head()
data.phishing.value_counts()
data.url[data.phishing==1].sample(50, random_state=1).tolist()
keywords =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Creating features
Step2: Contain any of the following
Step3: Lenght of the url
Step4: Create Model
Step5: Save model
Step6: Part 2
Step7: ... |
6,093 | <ASSISTANT_TASK:>
Python Code:
from smact.structure_prediction import prediction, database, mutation, probability_models, structure, utilities
import json
import itertools
from itertools import zip_longest
import smact
# An optional utility to display a progress bar
# for long-running loops. `pip install tqdm`.
from tq... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Querying the MP for garnets
Step2: Structure matching
Step3: Sorting out experimental data
Step4: Other garnet structures
Step5: Storing in ... |
6,094 | <ASSISTANT_TASK:>
Python Code:
import os
# Configure Auth and Base URL
# Planet Analytics API Base URL
PAA_BASE_URL = "https://api.planet.com/analytics/"
# API Key Config
API_KEY = os.environ['PL_API_KEY']
# Alternatively, you can just set your API key directly as a string variable:
# API_KEY = "YOUR_PLANET_API_KEY_HER... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We'll make a couple helper functions for "pretty printing" our responses
Step2: Now let's use the Requests library to get the Subscription. We'... |
6,095 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
x=np.linspace(0,50,100)
ts1=pd.Series(3.1*np.sin(x/1.5)+3.5)
ts2=pd.Series(2.2*np.sin(x/3.5+2.4)+3.2)
ts3=pd.Series(0.04*x+3.0)
#ts1.plot()
#ts2.plot()
#ts3.plot()
#plt.ylim(-2,10)
#plt.legend(['ts1','ts2','ts3'])
#plt.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: In the above example, it is clear that $ts1$ and $ts2$ are most similar (they are both $sin$ functions under different transformations). $ts3$ ... |
6,096 | <ASSISTANT_TASK:>
Python Code:
with tf.variable_scope("foo"):
with tf.variable_scope("bar"):
v = tf.get_variable("v", [1])
assert v.name == "foo/bar/v:0"
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
tf.get_variable_scope().reuse_variables()
v1 = tf.get_variable("v", [1])... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Variable scopes control variable (re)use
Step2: You’ll need to use reuse_variables() to implement Deep Networks
Step3: Case 2
Step4: <br/>
|
6,097 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn import cross_validation, metrics
from sklearn.grid_search import GridSearchCV
import matplotlib.pylab as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParam... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 载入数据
Step2: 建模与交叉验证
Step3: 第1步- 对于高的学习率找到最合适的estimators个数
Step4: Tune subsample and colsample_bytree
Step5: tune subsample
Step6: 对正则化做交叉验证... |
6,098 | <ASSISTANT_TASK:>
Python Code:
def square(x):
Square of x.
return x*x
def cube(x):
Cube of x.
return x*x*x
def root(x):
Square root of x.
return x**.5
# create a dictionary of functions
funcs = {
'square': square,
'cube': cube,
'root': root,
}
x = 2
print square(x)
print cube(x)
prin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: Functions
Step5: Functions can be passed in as arguments
Step6: $$ f(x) = 3x^2 + 5x + 3$$
Step9: Functions can also be returned by functions
... |
6,099 | <ASSISTANT_TASK:>
Python Code:
books = [
("Klein, Bernd", "Einführung in Python", "Hanser", "3", 2017),
("Sweigart, Al", "Automate the Boring Stuff with Python", "No Starch Press", "1", 2015),
("Weigend, Michael", "Python", "mitp", "6., erw. Aufl.", 2016),
("Downey, Allen B.", "Programmieren lernen mit ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Auch die
Step2: Für den Fall, dass ein Benutzer ein Buch entlehnen will, könnten wir eine
Step3: Die Sache kann schnell komplex werden, wenn
S... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.