Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
5,500 | <ASSISTANT_TASK:>
Python Code:
from os.path import join
hot_dog_image_dir = 'inputs/seefood/train/hot_dog'
hot_dog_paths = [join(hot_dog_image_dir, filename) for filename in
['1000288.jpg',
'127117.jpg']]
not_hot_dog_image_dir = 'inputs/seefood/train/not_hot_dog'
not_hot_dog_paths = [jo... | <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) Set Up Preprocessing
Step2: 3) Modeling
Step3: 4) Visualize Your Results
Step4: Now you are ready to move on to transfer learning, which a... |
5,501 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
def big_list1(n):
l = []
for i in range(n):
l.append(i)
return l
def big_list2(n):
return list(range(n))
def big_list(n):
big_list1(n)
big_list2(n)
%prun -q -T profile_example.txt -D profile_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: profiling with cProfile
Step2: Memory profile
Step3: Thz functions to test must be part of file and cannot be implemented in the notebook. So ... |
5,502 | <ASSISTANT_TASK:>
Python Code:
import gzip
import requests
import zipfile
url = "https://dl.dropbox.com/s/lnly9gw8pb1xhir/overfitting.zip"
results = requests.get(url)
import StringIO
z = zipfile.ZipFile(StringIO.StringIO(results.content))
# z.extractall()
z.extractall()
z.namelist()
d = z.open('overfitting.csv')
d.read... | <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: Implementation of Salisman's Don't Overfit submission
Step2: Develop Tim's model
Step3: looks pretty right
|
5,503 | <ASSISTANT_TASK:>
Python Code:
p_hi = 0.8 # probability of success in the high probability subpopulation
p_lo = 0.2 # probability of success in the low probability subpopulation
delta_p = 0.05 # effect size
# probability of success under treatment
P_T_additive = delta_p + 0.5*p_hi+0.5*p_lo
# probability of success unde... | <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: Example 2
|
5,504 | <ASSISTANT_TASK:>
Python Code:
import os
import pandas as pd
from sqlalchemy import create_engine
with open(os.environ["PGPASS"], "rb") as f:
content = f.readline().decode("utf-8").replace("\n", "").split(":")
engine = create_engine("postgresql://{user}:{passwd}@{host}/{db}".format(user=content[3],
... | <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: frontpage_examples
Step2: Grab some examples
Step3: crawls
Step4: Remove VPS related info
Step5: onion services
Step6: Probably unnecessary... |
5,505 | <ASSISTANT_TASK:>
Python Code:
# boilerplate code
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
#!wget https://storage.googleapis.com/downloa... | <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='loading'></a>
Step6: To take a glimpse into the kinds of patterns that the network learned to recognize, we will try to generate images ... |
5,506 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import ipyparallel as ipp
import qinfer as qi
from functools import partial
%matplotlib inline
import matplotlib.pyplot as plt
try:
plt.style.use('ggplot')
except:
pass
client = ipp.Client()
print(client)
dview = client[:]
print(dview)
serial_m... | <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 import the IPython parallelization library ipyparallel, as well as QInfer itself and some useful things from the Python standard librar... |
5,507 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pyasf
import pylab as pl
import sympy as sp
pl.rcParams.update({'font.size':14})
from IPython.display import display, Math
print_latex = lambda x: display(Math(sp.latex(x)))
sto = pyasf.unit_cell("sto_bulk_80873.cif") # init the cif file, this one has quite OK De... | <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: From databases we can only calculate the smooth part of the resonant corrections $f_1(E)$ and $f_2(E)$. The fine structure oscillations I obtain... |
5,508 | <ASSISTANT_TASK:>
Python Code:
documents = nltk.corpus.PlaintextCorpusReader('../data/EmbryoProjectTexts/files', 'https.+')
metadata = zotero.read('../data/EmbryoProjectTexts', index_by='link', follow_links=False)
wordcounts_per_document = nltk.ConditionalFreqDist([
(fileid, normalize_token(token))
for fileid... | <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: Our first step is to count up all of the words in each of the documents. This conditional frequency distribution should look familiar by now.
|
5,509 | <ASSISTANT_TASK:>
Python Code:
# %load ../data/melanoma_data.py
from numpy import reshape, sum
melanoma_data = reshape([1.57808, 0.00000, 2, 1.27, 35.9945, 1, 1.48219,
0.00000, 2, 0.76, 41.9014, 1, 0.0, 7.33425, 1, 35.00, 70.2164, 2, 2.23288,
0.00000, 1, 1.70, 33.7096, 1, 0.0, 9.38356, 2, 1.00, 47.9726, 1, 3.... | <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 MAP estimate can be obtained in PyMC3 via the find_MAP function. As with sample, we run find_MAP inside a model context, or pass the model e... |
5,510 | <ASSISTANT_TASK:>
Python Code:
from lxml import etree
tree = etree.parse("data/TEI/sonnet18.xml")
print(tree)
print(etree.tostring(tree))
print(etree.tostring(tree).decode())
print(etree.tostring(tree, pretty_print=True).decode())
for node in tree.iterfind("//rhyme"):
print(node)
for node in tree.iterfind("//... | <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: For the record, we should mention that there exist many other libraries in Python to parse XML, such as minidom or BeautifulSoup which is an int... |
5,511 | <ASSISTANT_TASK:>
Python Code:
import ctcsound
cs = ctcsound.Csound()
csd = '''
<CsoundSynthesizer>
<CsOptions>
-d -o dac -m0
</CsOptions>
<CsInstruments>
sr = 48000
ksmps = 100
nchnls = 2
0dbfs = 1
instr 1
idur = p3
iamp = p4
icps = cpspch(p5)
irise = ... | <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: Then, let's start a new thread, passing the opaque pointer of the Csound instance as argument
Step2: Now, we can send messages to the performan... |
5,512 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.stats as st
from sci_analysis import analyze
%matplotlib inline
# Create x-sequence and y-sequence from random variables.
np.random.seed(987654321)
x_sequence = st.norm.rvs(2, size=2000)
y_sequence = np.array([x + st.norm.rvs(0, 0.5, size=1) for x in x_sequ... | <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: Scatter Plot
Step2: Boxplot Borders
Step3: Contours
Step4: Grouped Scatter Plot
Step5: Interpreting the Statistics
Step6: fit
Step7: point... |
5,513 | <ASSISTANT_TASK:>
Python Code:
data = pd.read_csv( '../../data/dailybots.csv' )
#Look at a summary of the data
data.describe()
data['botfam'].value_counts()
grouped_df = data[data.botfam == "Ramnit"].groupby(['industry'])
grouped_df.sum()
group2 = data[['botfam','orgs']].groupby( ['botfam'])
summary = group2.agg([np.... | <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: Exercise 2
Step3: Exercise 3
Step4: Exercise 4
Step5: Exercise 5
|
5,514 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import graphlab
import math
import string
import numpy
products = graphlab.SFrame('amazon_baby.gl/')
products
products[269]
def remove_punctuation(text):
import string
return text.translate(None, string.punctuation)
review_without_punctuation =... | <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 preparation
Step2: Now, let us see a preview of what the dataset looks like.
Step3: Build the word count vector for each review
Step4: N... |
5,515 | <ASSISTANT_TASK:>
Python Code:
# Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
import pickle
%... | <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 show the symbols data, to see how good the recommender has to be.
Step2: Let's run the trained agent, with the test set
Step3: And now a... |
5,516 | <ASSISTANT_TASK:>
Python Code:
# your code here
# your code here
plt.close() # leave this here. it makes sure that if you run this cell again, the plot appears below
# parameters
# calculate the trajectory
# plot-don't forget to label your axes!
plt.close() # keep this here
# your code here
# your code 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: Now write a function that returns the total time the projectile will stay in the air (which means return to $y = 0$), in units of seconds, given... |
5,517 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import pandas as pd
import random
import thinkstats2
import thinkplot
import scipy.stats
def EvalNormalCdfInverse(p, mu=0, sigma=1):
return scipy.stats.norm.ppf(p, loc=mu, scale=sigma)
EvalNormalCd... | <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: Analytic methods
Step2: Here's the confidence interval for the estimated mean.
Step3: normal.py provides a Normal class that encapsulates what... |
5,518 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
from __future__ import unicode_literals
from matplotlib.gridspec import GridSpec
# %matplotlib notebook
my_... | <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 Dataset
Step2: Redshift volume on which I intend to focus my analysis
Step3: Selecting the subsample
Step4: Characterizing the UV emi... |
5,519 | <ASSISTANT_TASK:>
Python Code:
def word_count(document, search_term):
Count how many times search_term appears in document.
words = document.split()
answer = 0
for word in words:
if word == search_term:
answer += 1
return answer
def nearest_square(limit):
Find the larg... | <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: Python Advanced
Step3: Since the variable answer here is defined within each function seperately, you can reuse the same name of the variable, ... |
5,520 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from helpers.models import fit_model
from helpers.helpers import make_binary, class_info
# set random state for camparability
random_state = np.random.RandomState... | <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: Preprocesamiento
Step2: Obtener los datos como np.array y separar los datos en predictor (X) y objetivo (Y)
Step3: Eliminar los datos con etiq... |
5,521 | <ASSISTANT_TASK:>
Python Code:
import csv
import yaml
reader = csv.reader(open("../data/questions.csv"))
question_1 = reader.next()
question_1
yaml.load(question_1[-1].replace(": u'", ": '"))
reader = csv.reader(open("../data/train.csv"))
reader.next()
train_set = []
for row in reader:
train_set.append(row)
pr... | <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: Read the first line and see the structure.
Step2: Yes, each line is converted into list and it has 6 items as expected. However, how can we use... |
5,522 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import matplotlib.pyplot as plt
Univariate Local Linear Trend Model
class LocalLinearTrend(sm.tsa.statespace.MLEModel):
def __init__(self, endog):
# Model order... | <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: To take advantage of the existing infrastructure, including Kalman filtering and maximum likelihood estimation, we create a new class which exte... |
5,523 | <ASSISTANT_TASK:>
Python Code:
# Install datacommons_pandas
!pip install datacommons_pandas --upgrade --quiet
# Import Data Commons
import datacommons_pandas as dc
# Import other required libraries
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import pandas as pd
import json
# In the browser, w... | <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: Example
Step2: Using get_places_in to Query Administrative Areas
Step3: Let's see what states are in the USA
Step4: Great! With the place dci... |
5,524 | <ASSISTANT_TASK:>
Python Code:
import cvxpy as cp
import numpy as np
# Ensure repeatably random problem data.
np.random.seed(0)
# Generate random data matrix A.
m = 10
n = 10
k = 5
A = np.random.rand(m, k).dot(np.random.rand(k, n))
# Initialize Y randomly.
Y_init = np.random.rand(m, k)
# Ensure same initial random Y, ... | <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: Perform alternating minimization
Step2: Output results
|
5,525 | <ASSISTANT_TASK:>
Python Code:
# Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.computer_vision.ex3 import *
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from matplotlib import gridspec
import learntools.computer_vision.visiontools as vision... | <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 this cell to get back to where you left off in the previous lesson. We'll use a predefined kernel this time.
Step2: 1) Apply Pooling to Con... |
5,526 | <ASSISTANT_TASK:>
Python Code:
import sys
print sys.executable
%load_ext autoreload
%autoreload 2
%reload_ext autoreload
import sonnet as snt
import tensorflow as tf
import tflearn
import numpy as np
import dataset_utils
data = dataset_utils.load_data(filename="../synthetic_data/toy.pickle")
input_data_, output_mask_,... | <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 toy data set
Step2: Build RNN Model
|
5,527 | <ASSISTANT_TASK:>
Python Code:
%%script bash
# Ignore this boring cell.
# It allows one to do C in Jupyter notebook.
cat >20170706_head.c <<EOF
#include <stdlib.h>
#include <stdio.h>
#define LINES (3)
#define COLUMNS (4)
void print_buf(char buf[LINES][COLUMNS])
{
for (int row = 0; row < LINES; row++) {
for ... | <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 above is what the output should look like.
Step2: Now for some Python.
|
5,528 | <ASSISTANT_TASK:>
Python Code:
u1 = ["green", "green", "blue", "green"]
a1 = set({("green", 3), ("blue", 1)})
assert a1 == set(urn_to_dict(u1).items())
u2 = ["red", "blue", "blue", "green", "yellow", "black", "black", "green", "blue", "yellow", "red", "green", "blue", "black", "yellow", "yellow", "yellow", "green", "bl... | <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: Part B
Step2: Part C
Step3: Part D
Step4: Part E
|
5,529 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import math
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.mgxs
# 1.6 enriched fuel
fuel = openmc.Material(name='1.6% Fuel')
fuel.set_density('g/cm3', 10.31341)
fuel.add_nuclide('U235', 3.7503e-4)
fuel.add_nuclide('U238', 2.2625e-2)
fuel... | <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: First we need to define materials that will be used in the problem
Step2: With our three materials, we can now create a Materials object that c... |
5,530 | <ASSISTANT_TASK:>
Python Code:
a=5
a==6
i=6
i>5
i=2
i>5
i=2
i!=6
i=6
i!=6
"ACDC"=="Michael Jackson"
"ACDC"!="Michael Jackson"
'+'>'!'
'B'>'A'
'BA'>'AB'
age=19
#age=18
#expression that can be true or false
if age>18:
#within an indent, we have the expression that is run if the condition is true
pr... | <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 result is false, as 5 does not equal 6.
Step2: If we set i=2 the condition is false as 2 is less than 5
Step3: Let's display some values f... |
5,531 | <ASSISTANT_TASK:>
Python Code:
import csv
import numpy as np
import scipy as sp
import pandas as pd
import sklearn as sk
import matplotlib.pyplot as plt
from IPython.display import Image
print('csv: {}'.format(csv.__version__))
print('numpy: {}'.format(np.__version__))
print('scipy: {}'.format(sp.__version__))
print('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: The easiest way to learn how regression works is by thinking about an example. Consider an imaginary dataset of buildings built in Denver contai... |
5,532 | <ASSISTANT_TASK:>
Python Code:
imcontroller = ImageController(demo.image_info)
demo.image_info.items()
imcontroller.generate_image_obj()
imcontroller.channels
imcontroller
print('Numerical Labels that index image: ')
print(imcontroller.image_obj.labels)
print('Channels: ')
print(imcontroller.channels)
print('Image ty... | <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: Normally the image controller would be passed images directly, but for now, we have to load them from disk by calling generate_im_obj
Step2: Fr... |
5,533 | <ASSISTANT_TASK:>
Python Code:
import urllib, time, hashlib
hosts = ['http://www.scikit-learn.org', 'http://www.numpy.org', 'http://www.scipy.org', 'http://pandas.pydata.org']
start = time.time()
for host in hosts:
f = urllib.request.urlopen(host)
print(f.read().upper()[:20], host)
print("Elapsed time: {}".form... | <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: Multi-threaded
Step2: Asynchronous
Step3: Some thoughts about callbacks
Step4: What should our MedBot do?
Step5: Sending the alarm
Step6: H... |
5,534 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
from sklearn.pipeline import make_pipeline
from sklearn import preprocessing
from sklearn import model_selection
from sklearn import svm
# load iris data
iris = load_iris()
X = iris.data
y = iris.target
# Create a pipeline that scales the data 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 classifier pipeline
Step2: Cross validation
Step3: Model evaluation
|
5,535 | <ASSISTANT_TASK:>
Python Code:
from causalinfo import *
from numpy import log2
from numpy.testing import assert_allclose
# You only need this if you want to draw pretty pictures of the Networksa
from nxpd import draw, nxpdParams
nxpdParams['show'] = 'ipynb'
w, x, y, z = make_variables("W X Y Z", 2)
wdist = UniformDist(... | <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: Ay & Polani, Example 3
Step2: Ay & Polani, Example 5.1
Step3: Ay & Polani, Example 5.2
|
5,536 | <ASSISTANT_TASK:>
Python Code:
names = {}
for node in graph:
for edge in node:
if edge.guid == "169a81aefca74e92b45e3fa03c7021df":
value = node[edge].value
if value in names:
raise ValueError('name: "{}" defined twice'.format(value))
names[value] = node
... | <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: Pikov Classes
Step2: Gamekitty
Step3: Create frames for each "clip"
Step4: Create the root node
Step5: More clips and transitions
|
5,537 | <ASSISTANT_TASK:>
Python Code:
# Update the PIP version.
!python -m pip install --upgrade pip
!pip install kfp==1.1.1
!pip install kubeflow-katib==0.10.1
from IPython.display import display_html
display_html("<script>Jupyter.notebook.kernel.restart()</script>",raw=True)
import kfp
import kfp.dsl as dsl
from kfp 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: Restart the Notebook kernel to use the SDK packages
Step2: Import required packages
Step3: Define an Experiment
Step4: Define a Trial templat... |
5,538 | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator
from mne.minimum_norm 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: View activation time-series to illustrate the benefit of aligning/flipping
Step2: Viewing single trial dSPM and average dSPM for unflipped pool... |
5,539 | <ASSISTANT_TASK:>
Python Code:
import os
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
from sklearn.model_selection import train_test_split
import matplotlib.pylab as 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: Load Data
Step2: Define a function for modeling and cross-validation
Step3: Step 1- Find the number of estimators for a high learning rate
Ste... |
5,540 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
def well2d(x, y, nx, ny, L=1.0):
Compute the 2d quantum well wave function.
psi=(2/L)*np.sin((nx*np.pi*x)/L)*np.sin((ny*np.pi*y)/L)
return psi
psi = well2d(np.linspace(0,1,10), np.linspace(0,1,10), 1, 1)
as... | <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: Contour plots of 2d wavefunctions
Step3: The contour, contourf, pcolor and pcolormesh functions of Matplotlib can be used for effective visuali... |
5,541 | <ASSISTANT_TASK:>
Python Code:
import pypot.dynamixel
ports = pypot.dynamixel.get_available_ports()
if not ports:
raise IOError('no port found!')
print 'ports found', ports
my_port = "/dev/ttyACM1" #Change this value to match your setup
using_XL320 = False #Change this value to True if you use XL-320 motors
... | <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: From the available ports, select the port where the new motor is pluggued.
Step2: Select the new ID and the new baudrate you wish for your moto... |
5,542 | <ASSISTANT_TASK:>
Python Code:
import math
import torch
import tqdm
import gpytorch
from matplotlib import pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
# Training data is 100 points in [0,1] inclusive regularly spaced
train_x_mean = torch.linspace(0, 1, 20)
# We'll assume the variance shrinks th... | <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 up training data
Step2: Setting up the model
Step3: Training the model with uncertain features
|
5,543 | <ASSISTANT_TASK:>
Python Code:
from datetime import date
from organizer.models import Tag, Startup, NewsLink
from blog.models import Post
edut = Tag(name='Education', slug='education')
edut
edut.save()
edut.delete()
edut # still in memory!
type(Tag.objects) # a model manager
Tag.objects.create(name='Video Games', s... | <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: Interacting With the Database
Step2: Creation and Destruction with Managers
Step3: Methods of Data Retrieval
Step4: The get method
Step5: Th... |
5,544 | <ASSISTANT_TASK:>
Python Code:
pudl_settings = pudl.workspace.setup.get_defaults()
settings_file_name= 'etl_full.yml'
etl_settings = EtlSettings.from_yaml(
pathlib.Path(pudl_settings['settings_dir'],
settings_file_name))
validated_etl_settings = etl_settings.datasets
datasets = validated_etl_settin... | <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 can skip the settings step above and set these years/tables yourself here without using the settings files... just know they are not validat... |
5,545 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
%matplotlib notebook
# use the 'seaborn-colorblind' style
plt.style.use('seaborn-colorblind')
# SOURCE: 2018: https://www.officeholidays.com/countries/usa/michigan/2018
# scrap tool: ... | <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: Getting Dataset
Step2: Processing Data
Step3: Plotting
|
5,546 | <ASSISTANT_TASK:>
Python Code:
import math
x = math.sin(1.2)
x
from math import pi
theta_d = 30.0
theta_r = pi / 180.0 * theta_d
print(theta_r)
from math import pi
def degrees_to_radians(theta_d):
Convert an angle from degrees to radians.
Parameters
----------
theta_d : float
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: Go to the editor in spyder and enter those commands in a file
Step2: Also, in the top right of the spyder window, select the "Variable explorer... |
5,547 | <ASSISTANT_TASK:>
Python Code:
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"]... | <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: Create raw dataset
Step3: Create dataset for WALS
Step4: Creating rows and columns datasets
Step5: To summarize, we created the following dat... |
5,548 | <ASSISTANT_TASK:>
Python Code:
from qrays import Qvector, Vector
a = Qvector((1,0,0,0))
a.length()
b = Qvector((0,1,0,0))
(a-b).length()
from tetvols import make_tet
import unittest
class TestQuadrays(unittest.TestCase):
def test_martian(self):
p = Qvector((2,1,0,1))
q = Qvector((2,1,1,0))
... | <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 Quadray coordinate system stands on its own without merging with 20th Century Neoplatonist esoterica. Some of the shoptalk which follows is ... |
5,549 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
def V_vdW(p, kT, N, a=0, b=0):
Solve the van der Waals equation for V.
coeffs = [p, - (kT * N + p * N *b), a * N**2, - a * N**3 * b]
V = sorted(np.roots(coeffs))
return np.real(V).tolist()
print(V_vdW(1.0, 1.0, 1000))
import signac
project = signac.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: 1.4 Modifying the Data Space
Step2: You will notice that this equation is a cubic polynomial and therefore has 3 possible solutions instead of ... |
5,550 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'emac-2-53-aerchem', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_co... | <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: 2... |
5,551 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import platform
from utils import *
from mesh import *
from deformation import *
import numpy as np
import os
from sklearn import preprocessing, decomposition, neighbors, cluster
from scipy import s... | <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. Introduction
Step2: 3.2 Classification approach - Supervised
Step3: 3.2.2 Compute Laplacian, eigenvectors and eigenvalues.
Step4: Calculat... |
5,552 | <ASSISTANT_TASK:>
Python Code:
import numba
import numexpr as ne
import numpy as np
import matplotlib.pyplot as roberplot
import matplotlib.image as mpimg
%load_ext line_profiler
%load_ext memory_profiler
def rgb2gray(rgb):
return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])
def image_plot(img):
roberplot.figure(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step4: Local Binary Pattern Representation
Step5: Just an example of usage
|
5,553 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import visa
rm = visa.ResourceManager() # Creamos al Resource Manager
rm.list_resources() # Esto les permitirá ver qué es lo que pyvisa reconoce conectado a la PC
resource_name = 'USB0::0x0699::0x0346::C033250::INSTR' # Este es un nombre ejemplo con el cual ... | <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: Es importante en los pasos que acabamos de dar que reconozcamos cuando Pyvisa reconoce nuestro instrumental y cuando no. Si nos conectamos por U... |
5,554 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pytz
import matplotlib.pyplot as plt
import pandas as pd
import ulmo
from ulmo.util import convert_datetime
print(ulmo.cuahsi.wof.__doc__)
print([obj for obj in dir(ulmo.cuahsi.wof) if not obj.startswith('__')])
# WaterML/WOF WSDL endpoints
wsdlurl = 'http://54.... | <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: CUAHSI WaterOneFlow
Step2: Get site information
Step3: Get Values
Step4: 'odm2timeseries
Step5: site_values['values'] is a list of individua... |
5,555 | <ASSISTANT_TASK:>
Python Code:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(tmax=60).load_data()
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: We've seen in a previous tutorial <tut-raw-class> how to plot data
Step2: It may not be obvious when viewing this tutorial online, but by... |
5,556 | <ASSISTANT_TASK:>
Python Code:
def DH_simbolico(a, d, α, θ):
from sympy import Matrix, sin, cos
# YOUR CODE HERE
raise NotImplementedError()
from sympy import Matrix, sin, cos, pi
from nose.tools import assert_equal
assert_equal(DH_simbolico(0,0,0,pi/2), Matrix([[0,-1,0,0],[1,0,0,0], [0,0,1,0],[0,0,0,1]]))
... | <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: Cree una función que tome como argumentos los parametros de los grados de libertad de un manipulador tipo PUMA y devuelva las matrices de transf... |
5,557 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-3', 'landice')
# 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... |
5,558 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(filename='images/mdgxs.png', width=350)
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import openmc
import openmc.mgxs as mgxs
# Instantiate some Nuclides
h1 = openmc.Nuclide('H1')
o16 = openmc.Nuclide('O16')
u235 = openmc.... | <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 variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applic... |
5,559 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nuist', 'sandbox-2', 'landice')
# 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... |
5,560 | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame('kc_house_data_small.gl/')
import numpy as np # note this allows us to refer to numpy as np instead
def get_numpy_data(data_sframe, features, output):
data_sframe['constant'] = 1 # this is how you add a constant column to an SFrame
# add 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: Load in house sales data
Step2: Import useful functions from previous notebooks
Step3: We will also need the normalize_features() function fro... |
5,561 | <ASSISTANT_TASK:>
Python Code:
from pyspark.sql import SQLContext
from pyspark.sql.types import *
sqlContext = SQLContext(sc)
schema = StructType([ \
StructField("state", StringType(), True), \
StructField("account_length", DoubleType(), True), \
StructField("area_code", StringType(), True), \
StructFie... | <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: Basic DataFrame operations
Step2: Exercise
Step3: Feature Visualization
Step4: DataTypes
Step5: Seaborn
Step6: We can examine feature diffe... |
5,562 | <ASSISTANT_TASK:>
Python Code:
T = 120
Tr = 2.2*120
print [Tr/10, Tr/4]
h = 40.0
pc = -1.0/120
pd = np.exp(pc*h)
print pd
s,z = sy.symbols('s, z')
h = sy.symbols('h', positive=True)
F = (16*s+1)/(100*s+1)
H = sy.simplify(F.subs(s, (z-1)/(z*h)))
print H
p1,p2,p3,p4 = sy.symbols('p1, p2, p3, p4')
sy.expand((z-0.7+sy.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: (b)
Step2: Problem 2
Step3: Problem 3
Step4: Set up the state-space model. Make sure it is correct.
Step5: (c) Finding the feedback gain
Ste... |
5,563 | <ASSISTANT_TASK:>
Python Code:
from serial import Serial
from Servo import Servo
from IPython.html.widgets import interact
sp = Serial("/dev/ttyUSB0", 19200)
a = Servo(sp, dir = 'a')
w1 = interact(a.set_pos, pos = (-90, 90))
import time
#-- Sequence of angles
seq = [40, 0, 20, -40, -80, 0]
#-- Repeat the sequence... | <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 the Servo class. It is needed for creating the Servo objects
Step2: Import the IPython 3 interact function. It is needed for creating th... |
5,564 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
fe_25 = 25 # [Hz]
fe_60 = 60 # [Hz]
P = array([2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0])
n = 120*fe_25 / P
print('''
|-----------------+--------------|
| Number of Poles | n_m |
|-----------------+--------------|''')
# We use a simple for-loop to print a row per 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: Description
Step2: (a)
Step3: Alternatively (and much simpler) you can use the "max()" function
Step4: (b)
|
5,565 | <ASSISTANT_TASK:>
Python Code:
workDir = '/home/nick/notebook/SIPSim/dev/bac_genome3/validation/'
R_dir = '/home/nick/notebook/SIPSim/lib/R/'
figDir = '/home/nick/notebook/SIPSim/figures/'
nprocs = 3
import os
import numpy as np
import dill
import pandas as pd
%load_ext rpy2.ipython
%%R
library(ggplot2)
library(plyr)
... | <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: Init
Step2: Determining the probability of detecting the taxa across the entire gradient
Step3: skewed normal distribution
Step4: small unifo... |
5,566 | <ASSISTANT_TASK:>
Python Code:
%%capture
!pip install git+https://github.com/biothings/biothings_explorer#egg=biothings_explorer
# import modules from biothings_explorer
from biothings_explorer.hint import Hint
from biothings_explorer.user_query_dispatcher import FindConnection
import nest_asyncio
nest_asyncio.apply()... | <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, import the relevant modules
Step2: Step 1
Step3: Step 2
Step4: Here, we formulate a FindConnection query with "hyperphenylalaninemia" a... |
5,567 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.io import loadmat, savemat
from numpy import random
from os import path
mat = loadmat('../../../data/multiclass/usps.mat')
Xall = mat['data']
Yall = np.array(mat['label'].squeeze(), dtype=np.double)
# map from 1..10 to 0..9, since shogun
# requires ... | <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 us plot the first five examples of the train data (first row) and test data (second row).
Step2: Then we import shogun components and conve... |
5,568 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
# import all Shogun classes
from shogun import *
from matplotlib.patches import Ellipse
# a tool for visualisation
def get_gaussian_ellipse_artist(mean, cov, nstd=1.96, color="red", li... | <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: Gaussian Mixture Models and Expectation Maximisation in Shogun
Step2: Set up the model in Shogun
Step3: Sampling from mixture models
Step4: E... |
5,569 | <ASSISTANT_TASK:>
Python Code:
def addFunction(inputNumber):
result = inputNumber + 2
return result
print addFunction(2)
var = 2
print addFunction(var)
def addFunction(inputNumber):
if inputNumber < 0:
return 'Number must be positive!'
result = inputNumber + 2
return result
print addFunct... | <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: On its own, this code will only define what the function does, but will not actually run any code. To execute the code inside the function you h... |
5,570 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import scipy as sp
zz = np.loadtxt('wiggleZ_DR1_z.dat',dtype='float'); # Load WiggleZ redshifts
np.min() # Check bounds
np.max()
nbins = 50; # Is this a good choice?
n, bins, patches = hist() # With hist, one needs to (spuriously) request the patch objects as well
x = bi... | <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 from file
Step2: Check bounds
Step3: Construct histogram from data
Step4: Interpolate histogram output -> p(z); n.b. that you can a... |
5,571 | <ASSISTANT_TASK:>
Python Code:
### Get Gradient Jacobians (Change in h(x) i.e. ground level/ Change in x/y)
grad_lat = (np.gradient(map_terrain, axis = 0))/75
grad_lon = (np.gradient(map_terrain, axis = 1))/75
grid_points = np.array(list(product(map_lat_range, map_lon_range)))
map_grad_stack_lat = grad_lat.reshape(-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: Terrain Altimeter Sensor
Step2: Set Up Navigation Filter
Step3: Plot Results
|
5,572 | <ASSISTANT_TASK:>
Python Code:
# Print platform info of Python exec env.
import sys
sys.version
import warnings
warnings.simplefilter('ignore', FutureWarning)
from pandas import *
show_versions()
data = read_excel('WHO POP TB some.xls')
data.head()
data.tail()
data.info()
data.describe()
tbColumn = data['TB deaths']
... | <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 data
Step2: The range of the problem
Step3: The total number of deaths in 2013 is
Step4: The largest and smallest number of deaths in a s... |
5,573 | <ASSISTANT_TASK:>
Python Code:
!pip install arviz
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import pandas as pd
import theano
import seaborn as sns
sns.set_style("whitegrid")
np.random.seed(123)
url = "https://github.com/twiecki/WhileMyMCMCGentlySamples/blob/master/content... | <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 intuitive specification
Step2: I have seen plenty of traces with terrible convergences but this one might look fine to the unassuming eye. ... |
5,574 | <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... | <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: Text Classification from Folders
Step2: Footnote
Step3: Bigrams/Trigrams
Step4: specify the ngram_range - the smallest ngram to use, and the ... |
5,575 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
xv=[1,2,3,4]; yv=[5,1,4,0]
plt.plot(xv,yv);
plt.plot(xv,yv,'ro');
myplot=plt.plot(xv,yv,'k--');
plt.setp(myplot,linewidth=3.0,marker='+',markersize=30);
myplot=plt.plot(xv,yv,'k--');
plt.setp(myplot,'linewidth',... | <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: Above commands enable pylab environment => direct access to numpy, scipy and matplotlib. The option 'inline' results in plot outputs to be direc... |
5,576 | <ASSISTANT_TASK:>
Python Code:
sexual_mean, sexual_standard_deviation = 1.1, 0.15
asexual_mean, asexual_standard_deviation = 1.2, 0.3
pod_sexual = norm.cdf(0, loc=sexual_mean, scale=sexual_standard_deviation)
pod_asexual = norm.cdf(0, loc=asexual_mean, scale=asexual_standard_deviation)
print("The probability that the ... | <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 way to figure out the expected extinction time is to figure out the expected number of generations until the mean population growth rate dip... |
5,577 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# importamos bibliotecas para plotear
import matplotlib
import matplotlib.pyplot as plt
# para desplegar los plots en el notebook
%matplotlib inline
# para cómputo simbólico
from sympy import *
init_printing()
x, y = symbols('x y')
f = (1-x-y)*x
f
g = (4-7*x-3*y)*y
g
s... | <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: Equilibrios
Step2: Jacobiana
Step3: Evaluada en un punto de equilibrio
|
5,578 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input 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: The base catalog
Step2: Target_Name is the name of the (central) object at each observation, from that we see we have 681 unique sources out of... |
5,579 | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (6, 6, 3)
L = AveragePooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.r... | <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: [pooling.AveragePooling2D.1] input 6x6x3, pool_size=(2, 2), strides=(1, 1), padding='valid', data_format='channels_last'
Step2: [pooling.Averag... |
5,580 | <ASSISTANT_TASK:>
Python Code:
!pip install lxml
!pip install BeautifulSoup4
import urllib.request
from lxml import html
from bs4 import BeautifulSoup
# Scrape all HTML from webpage.
def scrapewebpage(url):
# Open URL and get HTML.
web = urllib.request.urlopen(url)
# Make sure there wasn't any errors opening the UR... | <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. Define functions for scraping
Step2: 3. Scrape Internet Movie Database
Step3: 4. Scrape Washington Post
Step4: 5. Scrape Wikipedia
Step5: ... |
5,581 | <ASSISTANT_TASK:>
Python Code:
from neon.backends import gen_backend
be = gen_backend(backend='gpu', batch_size=1)
print be
import pickle as pkl
sentence_length = 128
vocab_size = 20000
# we have some special codes
pad_char = 0 # padding character
start = 1 # marker for start of review
oov = 2 # when the word is ou... | <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 also define a few parameters, and the load the vocabulary. The vocab is a 1
Step2: Load Model
Step3: Inference
Step5: Now we write our new... |
5,582 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import GPy
import pods
from IPython.display import display
data = pods.datasets.olympic_sprints()
X = data['X']
y = data['Y']
print data['info'], data['details']
print data['citation']
print data['output_info']
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:
Step1: Running Example
Step2: When using data sets it's good practice to cite the originators of the data, you can get information about the source of... |
5,583 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
reviews = pd.read_csv('reviews.txt', header=None)
labels = pd.read_csv('labels.txt', header=None)
from collections import Counter
total_counts = Counter()
for idx,... | <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: Preparing the data
Step2: Counting word frequency
Step3: Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in... |
5,584 | <ASSISTANT_TASK:>
Python Code:
import copy
import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
% matplotlib inline
#%matplotlib auto
# Observation
obs = fits.getdata("/home/jneal/.handy_spectra/HD211847-1-mixavg-tellcorr_1.fits")
plt.plot(obs["wavelength"], obs["flux"])
plt.hlines(1, 2111, 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: The obeservatios were originally automatically continuum normalized in the iraf extraction pipeline.
Step2: The two PHOENIX ACES spectra here ... |
5,585 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn', hides SettingWithCopyWarning
file = 'data/evaluations.csv'
conversion_dict = {'research_type': lambda x: int(x == 'E')}
evaluation_data = pd.read_csv(file, sep=',', header=0, index_col=0, converters=conversio... | <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 dataset has 400 samples with 27 columns. Some of these columns are not necessary for further analysis
Step2: The above two rows exemplify a... |
5,586 | <ASSISTANT_TASK:>
Python Code:
display('Number of rows: {}'.format(len(df)))
display('Unique SSIDs: {}'.format(len(df['SSID'].unique())))
display('Unique MACs: {}'.format(len(df['MAC'].unique())))
display('Number of Auth Mode types: {}'.format(len(df['AuthMode'].unique())))
def auth_filter(x):
if 'WPA2' in x:
... | <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: So there are a significant number of open networks, but the overall majority use WPA2. That's good for the University but not so great for attac... |
5,587 | <ASSISTANT_TASK:>
Python Code:
import mltoolbox.image.classification as model
from google.datalab.ml import *
import os
bucket = 'gs://' + datalab_project_id() + '-coast'
preprocessed_dir = bucket + '/preprocessed'
staging_dir = bucket + '/staging'
model_dir = bucket + '/model'
train_set = BigQueryDataSet('SELECT image... | <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: Training
Step2: Check your job status. You can run
Step3: Evaluation
Step4: Model Deployment
Step5: Online Prediction
Step6: Batch Predicti... |
5,588 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
#@title General setup
import os
import tempfile
ARTIFACTS_DIR = os.path.join(tempfile.gettempdir(), "... | <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: Dynamic Shapes
Step2: Create a program using TensorFlow and import it into IREE
Step3: Test the imported program
Step4: Download compilation ... |
5,589 | <ASSISTANT_TASK:>
Python Code:
# Imports
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute
from qiskit.tools.visualization import matplotlib_circuit_drawer as circuit_drawer
from qiskit.tools.visualization import plot_his... | <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: Recall that to make the Bell state $|\psi\rangle= (|00\rangle+|11\rangle)/\sqrt{2}$ from the initial state $|00\rangle$, the quantum circuit fir... |
5,590 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
from pypot.creatures import PoppyTorso
poppy = PoppyTorso()
for m in poppy.motors:
m.goto_position(0, 2)
# Left arm is compliant, right arm is active
for m in poppy.l_arm:
m.compliant = False
for m in poppy.r_arm:
m.compliant = False
# The tor... | <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: Then, create your Pypot robot
Step2: Initialize your robot positions to 0
Step3: The left arm must be compliant (so you can move it), and th... |
5,591 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'product': [1179160, 1066490, 1148126, 1069104, 1069105, 1160330, 1069098, 1077784, 1193369, 1179741],
'score': [0.424654, 0.424509, 0.422207, 0.420455, 0.414603, 0.168784, 0.168749, 0.168738, 0.168703, 0.168684]})
products = [1066... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
5,592 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy
import matplotlib.pyplot as plt
t = numpy.linspace(0.0, 1.6e3, 100)
c_0 = 1.0
decay_constant = numpy.log(2.0) / 1600.0
fig = plt.figure()
axes = fig.add_subplot(1, 1, 1)
axes.plot(t, 1.0 * numpy.exp(-decay_constant * t))
axes.set_title("Radioactive Decay w... | <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: Numerical Methods for Initial Value Problems
Step2: Examples
Step3: Examples
Step4: A similar method can be derived if we consider instead us... |
5,593 | <ASSISTANT_TASK:>
Python Code:
baseDir = '/home/nick/notebook/SIPSim/dev/priming_exp/'
workDir = os.path.join(baseDir, 'exp_info')
otuTableFile = '/var/seq_data/priming_exp/data/otu_table.txt'
otuTableSumFile = '/var/seq_data/priming_exp/data/otu_table_summary.txt'
metaDataFile = '/var/seq_data/priming_exp/data/allsamp... | <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: Init
Step2: Loading OTU table (filter to just bulk samples)
Step3: Which gradient(s) to simulate?
Step4: Notes
Step5: Total richness of star... |
5,594 | <ASSISTANT_TASK:>
Python Code:
from openhunt.mordorutils import *
spark = get_spark()
mordor_file = "https://raw.githubusercontent.com/OTRF/mordor/master/datasets/small/windows/lateral_movement/host/empire_psexec_dcerpc_tcp_svcctl.zip"
registerMordorSQLTable(spark, mordor_file, "mordorTable")
df = spark.sql(
'''
SELE... | <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: Download & Process Mordor Dataset
Step2: Analytic I
|
5,595 | <ASSISTANT_TASK:>
Python Code:
!pip install cython https://github.com/SciTools/cartopy/archive/v0.18.0b1.zip
import cartopy.io.img_tiles as cimgt
request = cimgt.OSM()
import cartopy.crs as ccrs
carto_proj = ccrs.GOOGLE_MERCATOR
%matplotlib notebook
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_sub... | <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: Selecting a Tileset
Step2: Projection
Step3: Rendering Tiles
Step4: To render a specific region, call set_extent with the geodetic (lat/lon) ... |
5,596 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from astropy.table import Table
import numpy as np
import matplotlib.pyplot as plt
data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')
# X is in the format need for all of the sklearn tools, it just has the colors
# X = np.vstack([ data['ug'], d... | <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: Second, load the test data
Step2: I had some problems with GTR-ADM-QSO-ir_good_test_2016n.fits because it thought that there were blank entries... |
5,597 | <ASSISTANT_TASK:>
Python Code:
reset_start_time(O.map, title='map') # alias is "select"
# warming up:
d = subs(O.from_((1, 2 , 3)).map(lambda x: x * 2))
rst(O.pluck, title='pluck')
d = subs(O.from_([{'x': 1, 'y': 2}, {'x': 3, 'y': 4}]).pluck('y'))
class Coord:
def __init__(self, x, y):
self.x = x
s... | <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: ...by emitting all of the items emitted by corresponding Observables
Step2: flat_map_latest(select_switch)
Step3: concat_map
Step4: many_sele... |
5,598 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import gym
import numpy as np
import random
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow.contrib.slim as slim
env = gym.make('CartPole-v0')
class experience_buffer():
def __init__(self, buffer_size = 100... | <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 environment
Step2: The Deep Q-Network
Step3: Implementing the network itself
Step4: Training the network
Step5: Some statistics on ... |
5,599 | <ASSISTANT_TASK:>
Python Code:
# location is relative or absolute path to the file
sne = ans.snanaSims.SnanaSims.fromSNANAfileroot('snana_fits', location=ans.example_data, n=1)
sn = ans.snanaSims.SnanaSims.reformat_SNANASN(sne.snList[0])
ans.snanaSims.SnanaSims.matchSNANAbandnamesinregistry()
sn[:5]
sn.meta
model ... | <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: Pick out the very first SN and reformat it to look the way sncosmo expects SN to look
Step2: Make sure SNCosmo can understand band names
Step3... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.