Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
4,000 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
data = pd.read_csv('test_user_data.csv')
print(data.head(10))
apply_ex = data.groupby('user_id').apply(len)
print(apply_ex.head())
transform_ex = data.groupby('user_id').transform(len)
print(transform_ex.head())
data['event_count'] = data.groupby('user_id')['user_id... | <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 contains one event per row and has 5 variables
Step2: The output here is a pandas Series with each user_id as the index and the count ... |
4,001 | <ASSISTANT_TASK:>
Python Code:
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import xarray as xr
%matplotlib inline
ds = xr.tutorial.load_dataset("air_temperature")
air = ds.air.isel(time=[0, 724]) - 273.15
# This is the map projection we want to plot *onto*
map_proj = ccrs.LambertConformal(central_longi... | <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 example dataset
Step2: Multiple plots and map projections
Step3: Centered colormaps
Step4: Control the plot's colorbar
Step5: Multiple ... |
4,002 | <ASSISTANT_TASK:>
Python Code:
import random
gameStake = 50
cards = range(10)
class Player:
# in the __init__() function, use the two input variables to initialize the ID and starting pot of each player
def __init__(self, inputID, startingPot):
# [CREATE YOUR INITIALIZATIONS HERE]
... | <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 will establish some general variables for our game, including the 'stake' of the game (how much money each play is worth), as well as a... |
4,003 | <ASSISTANT_TASK:>
Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1., x=1., vz = 2.)
sim.add(m=1., a=1.)
sim.status()
sim.add(m=1.e-3, a=100.)
sim.add(primary=sim.particles[1], a=0.01)
orbits = sim.calculate_orbits()
for orbit in orbits:
print(orbit)
print(sim.particles[3].calculate_orbit(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: Any components not passed automatically default to 0. REBOUND can also accept orbital elements.
Step2: We always have to pass a semimajor ax... |
4,004 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import exatomic
u = exatomic.Universe()
u
#myxyz = exatomic.XYZ('../data/examples/porphyrin.xyz')
myxyz = exatomic.XYZ('porphyrin.xyz')
myxyz.head()
myxyz.atom.head() # Atomic units are used throughout the exatomic package
myuni = myxyz.to_univ... | <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 are some test demo containers to play around with
Step2: exatomic universes in principle contain a QM/MD calculation or set of calculation... |
4,005 | <ASSISTANT_TASK:>
Python Code:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_... | <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: Flower power
Step2: ConvNet Codes
Step3: Below I'm running images through the VGG network in batches.
Step4: Building the Classifier
Step5: ... |
4,006 | <ASSISTANT_TASK:>
Python Code:
# Authors: Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import mne
from mne.cov import compute_covariance
from mne.datasets import somato
from mne.time_frequency import csd_morlet
from ... | <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 the raw data and creating epochs
Step2: Compute covariances
Step3: Compute some source estimates
Step4: Plot source estimates
Step5: ... |
4,007 | <ASSISTANT_TASK:>
Python Code:
from math import exp
# Could avoid this by using our constants.py module!
h = 6.626e-34 # MKS
k = 1.38e-23
c = 3.00e8
def intensity(wave, temp, mydefault=0):
wavelength = wave / 1e10
B = 2 * h * c**2 / (wavelength**5 * (exp(h * c / (wavelength * k * temp)) - 1))
return B
myw... | <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: Q. Is the following call sequence acceptable?
Step2: No! The following are all OK!
Step3: Keyword arguments
Step4: The first two arguments in... |
4,008 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
df1 = pd.DataFrame({'A':['A0','A1','A2','A3'],
'B':['B0','B1','B2','B3'],
'C':['C0','C1','C2','C3'],
'D':['D0','D1','D2','D3']},
index=[0,1,2,3])
df1
df2 = pd.DataFrame({'... | <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: 使用join='inner' 查看结果 ,和outer一样！
Step2: 修改df3的index，看看结果
Step3: 修改df5，使得某几行和df2一样，再看看结果
Step4: 如同numpy.concatenate()方法, pandas.concat方法接收一个列表或者... |
4,009 | <ASSISTANT_TASK:>
Python Code:
# Run some setup code for this notebook.
from __future__ import division
import sys
import os
sys.path.append('..')
import graphlab
import numpy as np
products = graphlab.SFrame('datasets/')
# feature processing
# ---------------------------------------------------------------
import j... | <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 and process review dataset
Step2: Just like we did previously, we will work with a hand-curated list of important words extracted from the... |
4,010 | <ASSISTANT_TASK:>
Python Code:
%load_ext brythonmagic
from brythonmagic import load_brython_dev
load_brython_dev()
from brythonmagic import load_js_lib
load_js_lib("https://cdnjs.cloudflare.com/ajax/libs/highcharts/5.0.7/highcharts.js")
html = <div id="hc_ex1" style="width: 700px; height: 300px;"></div>
%%brython -... | <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: And the brython js lib
Step2: [It is highly recommended that, at least, you read the brythonmagic docs to understand what it does. It is also r... |
4,011 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.datasets impo... | <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 Note about GridSearchCV's best_score_ attribute
Step2: By executing the code above, we created a simple data set of random integers that shal... |
4,012 | <ASSISTANT_TASK:>
Python Code:
import os
from io import BytesIO
import tarfile
import tempfile
from six.moves import urllib
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
import tensorflow as tf
class DeepLabModel(object):
Class to load deeplab model 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:
Step7: Import helper methods
Step8: Select a pretrained model
Step10: Run on sample images
|
4,013 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sympy import *
init_printing()
from myhdl import *
from myhdlpeek import *
import random
#python file of convince tools. Should be located with this notebook
from sympy_myhdl_tools import *
def DFFSyncCenter(D_in, Q_out, Qn_out, clk):
@alwa... | <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: Flip-Flops vs Latches
Step2: Sythinsis
Step3: !? clock in not hooked up on the wiring in this code need to figure out why
Step4: Sythinsis
St... |
4,014 | <ASSISTANT_TASK:>
Python Code:
import pandas
imdb = pandas.read_csv('data/imdb_labelled.txt', sep="\t", names=["sentences", "polarity"])
yelp = pandas.read_csv('data/yelp_labelled.txt', sep="\t", names=["sentences", "polarity"])
amazon = pandas.read_csv('data/amazon_cells_labelled.txt', sep="\t", names=["sentences", "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: 1. Tokenization
Step2: 1. Dicionário
Step3: 1. Vetorização
Step4: 1. Word normalization
|
4,015 | <ASSISTANT_TASK:>
Python Code:
# Import matplotlib
import matplotlib
# Import pandas
import pandas as pd
# Tell matplotlib to plot in this window instead of a separate window.
%matplotlib inline
# Load data into dataframe (we will get to this later)
df = pd.read_csv('data/simple.csv')
# Plot data as line plot using sim... | <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 you get more experienced you may want to use matplotlib directly
|
4,016 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
from LogGabor import LogGabor
parameterfile = 'https://raw.githubusercontent.com/bicv/LogGabor/master/default_param.py'
lg = LogGabor(parameterfile)
lg.set_size((32, 32))
import os
import numpy as np
np.set_printoptions(formatter={'float': '{: 0.3f}'.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: To install the dependencies related to running this notebook, see Installing notebook dependencies.
Step2: Perspectives
Step3: Back to top
Ste... |
4,017 | <ASSISTANT_TASK:>
Python Code:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import classes from thinkbayes2
from thinkbayes2 import Pmf, Cdf, Suite, J... | <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: The Weibull distribution
Step5: SciPy also provides functions to evaluate the Weibull distribution, which I'll use to check my implementation.
... |
4,018 | <ASSISTANT_TASK:>
Python Code:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim library
from modsim import *
# set the 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:
Step5: More than one State object
Step7: And here's run_simulation, which is a solution to the exercise at the end of the previous notebook.
Step8: N... |
4,019 | <ASSISTANT_TASK:>
Python Code:
import pyisc;
import numpy as np
from scipy.stats import poisson
%matplotlib inline
from pylab import hist, plot, figure
po_normal = poisson(10)
po_anomaly = poisson(25)
freq_normal = po_normal.rvs(10000)
freq_anomaly = po_anomaly.rvs(15)
data = np.column_stack([
list(freq_norma... | <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 Creation
Step2: Create an 2D array with two columns that combines random frequency and time period equal to 1.
Step3: If we plot the hist... |
4,020 | <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... |
4,021 | <ASSISTANT_TASK:>
Python Code:
def aquire_audio_data():
D, T = 4, 10000
y = np.random.normal(size=(D, T))
return y
y = aquire_audio_data()
Y = stft(y, **stft_options).transpose(2, 0, 1)
with tf.Session() as session:
Y_tf = tf.placeholder(
tf.complex128, shape=(None, None, None))
Z_tf = wpe(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: Example with real audio recordings
Step2: Audio data
Step3: STFT
Step4: iterative WPE
Step5: Power spectrum
|
4,022 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
import time
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
#import reader
impo... | <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: MSCOCO Captions
Step2: How can you look at feature maps from the first convolutional layer? Look here if you need a hint.
Step3: How can you l... |
4,023 | <ASSISTANT_TASK:>
Python Code:
import csv
import requests
response = requests.get("http://api.open-notify.org/iss-now.json")
response.status_code
# Set up the parameters we want to pass to the API.
# This is the latitude and longitude of New York City.
parameters = {"lat": 40.71, "lon": -74}
# Make a get request with... | <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: Status Codes
Step2: Query Parameters
|
4,024 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
def model_inputs(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: Model Inputs
Step2: Generator network
Step3: Discriminator
Step4: Hyperparameters
Step5: Build network
Step6: Discriminator and Generator L... |
4,025 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_b... | <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: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
4,026 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import scipy.optimize
import scipy.special
import matplotlib.pyplot as plt
import seaborn as sns
import pathlib
import sys
import PaSDqc
%matplotlib inline
sample_mda = PaSDqc.PSDTools.SamplePSD.load_from_file("../data/intro_PSDs/example_MDA.spec", ... | <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 using PaSDqc API
Step2: Load normalization file included in PaSDqc package
Step3: Fit the amplicon distribution curves using the erf... |
4,027 | <ASSISTANT_TASK:>
Python Code:
def loadContributions(file, withsexe=False):
contributions = pd.read_json(path_or_buf=file, orient="columns")
rows = [];
rindex = [];
for i in range(0, contributions.shape[0]):
row = {};
row['id'] = contributions['id'][i]
rindex.append(contributions... | <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: Build clustering model
Step2: Build the optimal model and apply it
Step3: Cluster Profiles
|
4,028 | <ASSISTANT_TASK:>
Python Code:
import pg8000
conn = pg8000.connect(user='postgres', password='password', database="homework2_radhika")
conn.rollback()
conn.rollback()
cursor = conn.cursor()
statement = "SELECT movie_title, release_date from uitem WHERE horror=1 AND scifi=1 ORDER BY release_date DESC;"
cursor.execute... | <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: If you get an error stating that database "homework2" does not exist, make sure that you followed the instructions above exactly. If necessary, ... |
4,029 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('../code/functions')
sys.path.append('/home/simpleElastix/build/SimpleITK-build/Wrapping/Python')
import pickle
import cv2
import time
import SimpleITK as sitk
import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
from cluster import Cluster
f... | <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: Visualization Function
Step2: Registration Functions
|
4,030 | <ASSISTANT_TASK:>
Python Code:
import wicked as w
from IPython.display import display, Math, Latex
def latex(expr):
Function to render any object that has a member latex() function
display(Math(expr.latex()))
w.reset_space()
w.add_space("o", "fermion", "occupied", ['i','j','k','l','m','n'])
w.add_space("... | <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: Generating and implementing many-body equations
Step2: Generating equations for fully contracted terms
Step3: First we convert the expression ... |
4,031 | <ASSISTANT_TASK:>
Python Code:
# We'll also import a few standard python libraries
from matplotlib import pyplot
import numpy as np
import time
# These are the droids you are looking for.
from caffe2.python import core, workspace
from caffe2.proto import caffe2_pb2
# Let's show all plots inline.
%matplotlib inline
pri... | <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 might see a warning saying that caffe2 does not have GPU support. That means you are running a CPU-only build. Don't be alarmed - anything C... |
4,032 | <ASSISTANT_TASK:>
Python Code:
# In iPython or the iPython notebook, it's easiest to use the pylab magic, which
# imports matplotlib, numpy, and scipy.
# The inline flag means that images will be shown here in the notebooks, rather
# than in pop-up windows.
%pylab notebook
# If you are using 'regular' Python, however, ... | <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 figures
Step2: In this case, x is now a NumPy array with 300 values ranging from 0 to 2$\pi$ (included). y is the sine (array of 300 v... |
4,033 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def load_pts(csv_name):
data = np.asarray(pd.read_csv(csv_name, header=None))
X = data[:,0:2]
y = data[:,2]
plt.scatter(X[np.argwhere(y==0).flatten(),0], X[np.argwhere(y==0).flatten(... | <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.阅读并绘制数据
Step2: 该函数将帮助我们绘制模型。
Step3: 2. 将我们的数据分为训练和测试集
Step4: 3. 拟合一个决策树模型
Step5: Now let's plot the model, and find the testing f1_score, ... |
4,034 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from PyFin.api import *
from alphamind.api import *
from alphamind.strategy.strategy import Strategy, RunningSetting
from alphamind.portfolio.meanvariancebuilder import target_vol_buil... | <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. Single Day Analysis
Step2: Portfolio Construction
Step8: 2. Porfolio Construction
|
4,035 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <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: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
4,036 | <ASSISTANT_TASK:>
Python Code:
import sys
print(sys.version)
from typing import List, Tuple
Position = int
Interval = Tuple[Position, Position]
import re
def bad_events(pattern: str, string: str) -> List[Interval]:
# m.span(1) = (m.start(1), m.end(1))
return [m.span(1) for m in re.finditer(f"(?=({pattern}))", ... | <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: Introduction
Step2: The solution I came up with
Step3: Let's compute the union of two consecutive intervals, if they are not disjoint
Step4: ... |
4,037 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
from mdtraj.utils import timing
from msmbuilder.example_datasets import load_doublewell
from msmbuilder.cluster import NDGrid
from msmbuilder.msm import BayesianMarkovStateModel, MarkovStateModel
trjs = load_doubl... | <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 some double-well data
Step2: We'll discretize the space using 10 states
Step3: Now lets try using 50 states
|
4,038 | <ASSISTANT_TASK:>
Python Code:
from datetime import datetime
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from metpy.io import get_upper_air_data
from metpy.io.upperair import UseSampleData
from metpy.plots import SkewT
with UseSampleData(): # Only needed to use our local sample data
# Download 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: Create a new figure. The dimensions here give a good aspect ratio
|
4,039 | <ASSISTANT_TASK:>
Python Code:
from xml.etree import ElementTree as ET
document_tree = ET.parse( './data/mondial_database_less.xml' )
# print names of all countries
for child in document_tree.getroot():
print (child.find('name').text)
# print names of all countries and their cities
for element in document_tree.ite... | <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: XML example
Step2: XML exercise
Step3: 10 countries with the lowest infant mortality rates
Step4: 10 cities with the largest population
Step5... |
4,040 | <ASSISTANT_TASK:>
Python Code:
# Make plots inline
%matplotlib inline
# Make inline plots vector graphics instead of raster graphics
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'svg')
# import modules for plotting and data analysis
import matplotlib.pyplot as plt
import numpy as 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: Now, we'll tackle the "function in time" part of this model by learning how to make and use arrays to represent time.
Step2: We can assign time... |
4,041 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from ecell4.prelude import *
sp1 = Species("A")
print(sp1.serial())
sp1.set_attribute("radius", 0.005)
sp1.set_attribute("D", 1)
sp1.set_attribute("location", "cytoplasm")
print(sp1.has_attribute("radius"))
print(sp1.get_attribute("radius"))
print(sp1.get_attribute("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: 2.1. Species
Step2: There are some naming conventions for the name of Species.
Step3: The arguments in set_attribute is the name of attribute ... |
4,042 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import sys
sys.path.append(os.path.join('..', '..'))
from data_models.parameters import arl_path
results_dir = arl_path('test_results')
from matplotlib import pylab
pylab.rcParams['figure.figsize'] = (8.0, 8.0)
pylab.rcParams['image.cmap'] = 'rainbow'
import 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: Construct LOW core configuration
Step2: We create the visibility. This just makes the uvw, time, antenna1, antenna2, weight columns in a table
... |
4,043 | <ASSISTANT_TASK:>
Python Code:
# A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] ... | <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: Implementing a Neural Network
Step2: We will use the class TwoLayerNet in the file cs231n/classifiers/neural_net.py to represent instances of o... |
4,044 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-3', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <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... |
4,045 | <ASSISTANT_TASK:>
Python Code::
import pandas as pd
X = pd.get_dummies(X, columns=['neighbourhood_group','room_type'], drop_first=True)
<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:
|
4,046 | <ASSISTANT_TASK:>
Python Code:
%%file multihello.py
'''hello from another process
'''
from multiprocessing import Process
def f(name):
print 'hello', name
if __name__ == '__main__':
p = Process(target=f, args=('world',))
p.start()
p.join()
# EOF
!python2.7 multihello.py
if __name__ == '__main__':
... | <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 Windows
Step2: Data parallelism versus task parallelism
Step3: Manager and proxies
Step4: See
Step5: Issues
Step6: Queue and Pipe
Step7:... |
4,047 | <ASSISTANT_TASK:>
Python Code:
# Function for rotating the image files.
def Image_Rotate(img, angle):
Rotates a given image the requested angle. Returns the rotated image.
rows,cols = img.shape
M = cv2.getRotationMatrix2D((cols/2,rows/2), angle, 1)
return(cv2.warpAffine(img,M,(cols,rows)))
# F... | <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: Image_Augmentation
Step4: VGG_Prep
Step5: VGG_16 Bottleneck
Step6: Running the model on the Train, Test, and Validation Data
Step7: Train To... |
4,048 | <ASSISTANT_TASK:>
Python Code:
# Import required modules
import pandas as pd
# Create a values as dictionary of lists
raw_data = {'0': ['first_name', 'Molly', 'Tina', 'Jake', 'Amy'],
'1': ['last_name', 'Jacobson', 'Ali', 'Milner', 'Cooze'],
'2': ['age', 52, 36, 24, 73],
'3': ['preTestScore',... | <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 example data
Step2: Replace the header value with the first row's values
|
4,049 | <ASSISTANT_TASK:>
Python Code:
# General imports
%matplotlib inline
import logging
import numpy as np
import pylab as plt
from scipy import stats
from scipy import integrate
from scipy.integrate import simps,trapz,quad,nquad
from scipy.interpolate import interp1d
from scipy.misc import factorial
# Constants
MMIN,MMAX ... | <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: Constants and Defaults
Step6: Subhalo Mass Function
Step10: Substructure Likelihood Function
Step12: Mass Probability
Step14: Likelihood Fun... |
4,050 | <ASSISTANT_TASK:>
Python Code:
from SimPEG import Mesh, EM, Utils, Maps
from matplotlib.colors import LogNorm
%pylab inline
import numpy as np
from scipy.constants import mu_0
from ipywidgets import interact, IntSlider
import cPickle as pickle
url = "https://storage.googleapis.com/simpeg/kevitsa_synthetic/"
files = ['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: Model
Step2: Question
Step3: Next, we put the model on the mesh
Step4: Forward Simulation
Step5: Compute Predicted Data
Step6: Question
Ste... |
4,051 | <ASSISTANT_TASK:>
Python Code:
G = nx.Graph() #create a graph
G.add_nodes_from([0,1,2,3]) #add some nodes
G.add_edges_from([(0,1),(1,2),(2,3),(3,0)]) #add some edges
pos = {0:[1,1],1:[1,2],2:[2,3],3:[3,2]} #dictionary of positions
nx.draw_networkx(G,pos) ... | <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 layouts
Step2: Draw only specific nodes
Step3: Colors
Step4: Plotting with node/edge attributes
|
4,052 | <ASSISTANT_TASK:>
Python Code:
## import system module
import json
import rethinkdb as r
import time
import datetime as dt
import asyncio
from shapely.geometry import Point, Polygon
import random
import pandas as pd
import os
import matplotlib.pyplot as plt
## import custom module
from streettraffic.server import Traff... | <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: Generate Random Routes
Step2: Now we simply copy the text above and go to https
Step4: Use the web UI
Step5: Be Bold and try 100 routes
|
4,053 | <ASSISTANT_TASK:>
Python Code:
try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet cirq
import cirq
print("installed cirq.")
# Standard imports
import numpy as np
from cirq.contrib.svg import SVGCircuit
exponents = np.linspace(0, 7/4, 8)
exponents
import itertools... | <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: Cross Entropy Benchmarking Theory
Step2: The action of random circuits with noise
Step3: Random circuit
Step4: Estimating fidelity
Step5: Ex... |
4,054 | <ASSISTANT_TASK:>
Python Code:
print("Hello, world!")
x = 5
type(x)
y = 5.5
type(y)
x = 5 * 5
type(x)
y = 5 / 5
type(y)
x = 5 / 5
type(x)
y = int(x)
type(y)
z = str(y)
type(z)
some_list = [1, 2, 'something', 6.2, ["another", "list!"], 7371]
print(some_list[3])
type(some_list)
some_tuple = (1, 2, 'something', 6.2... | <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: Yep, that's all that's needed!
Step2: It's important to note
Step3: What's the type for x?
Step4: What's the type for y?
Step5: There are fu... |
4,055 | <ASSISTANT_TASK:>
Python Code:
print(__doc__)
# Authors: Gael Varoquaux
# Jaques Grobler
# Kevin Hughes
# License: BSD 3 clause
from sklearn.decomposition import PCA
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
e = np.exp(1)
np.ran... | <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 the data
Step2: Plot the figures
|
4,056 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('sDG5tPtsbSA', width=800, height=450)
from os.path import join
image_dir = '../input/dog-breed-identification/train/'
img_paths = [join(image_dir, filename) for filename in
['0c8fe33bd89646b678f6b2891df8a1c... | <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: Sample Code
Step2: Function to Read and Prep Images for Modeling
Step3: Create Model with Pre-Trained Weights File. Make Predictions
Step4: V... |
4,057 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import structcol as sc
from structcol import refractive_index as ri
from structcol import montecarlo as mc
from structcol import detector as det
import pymie as pm
from pymie import size_parameter, index_ratio
import seaborn as sns
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: Run Monte Carlo model and calculate reflectance and polarization for trajectories
Step2: initialize and run trajectories
Step3: calculate refl... |
4,058 | <ASSISTANT_TASK:>
Python Code:
#Original data source
#http:§§//www.content.digital.nhs.uk/catalogue/PUB23139
#Get the datafile
!wget -P data http://www.content.digital.nhs.uk/catalogue/PUB23139/gp-reg-patients-LSOA-alt-tall.csv
#Import best ever data handling package
import pandas as pd
#Load downloaded CSV file
df=pd.... | <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: Previously, I have created a simple sqlite3 database containing administrative open data from NHS Digital (database generator script).
Step2: L... |
4,059 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
# Initialize the random generator seed to compare results
np.random.seed(0)
# Load Iris data set
iris = datasets.load_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: Part 1
Step2: Next code, let you plot the evolution of above computed train and test accuracies.
Step3: This figure points out the necessity o... |
4,060 | <ASSISTANT_TASK:>
Python Code:
import ipyparallel as ipp
c = ipp.Client(profile='mpi')
%%px --group-outputs=engine
from mpi4py import MPI
print(f"Hi, I'm rank %d." % MPI.COMM_WORLD.rank)
%%px
from devito import configuration
configuration['mpi'] = True
%%px
# Keep generated code as simple as possible
configuration['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: In this tutorial, to run commands in parallel over the engines, we will use the %px line magic.
Step2: Overview of MPI in Devito
Step3: An Ope... |
4,061 | <ASSISTANT_TASK:>
Python Code:
import time
import random
#import sys
#a = int(sys.argv[1])
#b = int(sys.argv[2])
def wait(x):
time.sleep(x)
def time_cron(a,b):
time_interval = random.uniform(a,b)
# while(1):
# measure process time
t0 = time.clock()
wait(time_interval)
print time.clock() - t0... | <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) Scrieti doua functii de verificare daca un numar este prim, si verificati care dintre ele este mai eficienta din punct de vedere al timpului.... |
4,062 | <ASSISTANT_TASK:>
Python Code:
from pymldb import Connection
mldb = Connection("http://localhost")
mldb.get("/v1/types")
#keyword arguments to get() are appended to the GET query string
mldb.get("/v1/types", x="y")
#dictionaries arguments to put() and post() are sent as JSON via PUT or POST
mldb.put("/v1/datasets/samp... | <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: Accessing the REST API
Step2: Here we create a dataset and insert two rows of two columns into it
Step3: SQL Queries
|
4,063 | <ASSISTANT_TASK:>
Python Code:
%%bash
echo "Pip Version Info: " && python2 --version && python2 -m pip --version && echo
echo "Google Cloud SDK Info: " && gcloud --version && echo
echo "Ksonnet Version Info: " && ks version && echo
echo "Kubectl Version Info: " && kubectl version
! python2 -m pip install -U pip
# Code... | <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 Pip Packages
Step2: Configure Variables
Step3: Setup Authorization
Step4: Additionally, to interact with the underlying cluster, we c... |
4,064 | <ASSISTANT_TASK:>
Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as 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: Linear regression using the Normal Equation
Step2: The figure in the book actually corresponds to the following code, with a legend and axis la... |
4,065 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from emo_utils import *
import emoji
import matplotlib.pyplot as plt
%matplotlib inline
X_train, Y_train = read_csv('data/train_emoji.csv')
X_test, Y_test = read_csv('data/tesss.csv')
maxLen = len(max(X_train, key=len).split())
index = 1
print(X_train[index], label_to... | <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 - Baseline model
Step2: Run the following cell to print sentences from X_train and corresponding labels from Y_train. Change index to see dif... |
4,066 | <ASSISTANT_TASK:>
Python Code::
import matplotlib.pyplot as plt
plt.scatter(x, y)
plt.show()
<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:
|
4,067 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import pandas
import os
def getPathFor(file_path):
current_directory = %pwd
path = os.path.join(current_directory, file_path)
print("About to open file: {}\n".format(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: Next class is responsible for filtering out lanes detection area
Step2: Here is the result of getColorMask function, that turns all white and y... |
4,068 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from random import random
import math
import numpy as np
import copy
from scipy import stats
import matplotlib.pyplot as plt
import pickle as pkl
from scipy.spatial import distance
import seaborn as sns
sns.set_style('darkgrid')
def loadMovieLens(path='./data/movielens... | <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 data
Step2: Content example
Step3: Splitting data between train/test
Step4: split used for convenience on the average by movie ba... |
4,069 | <ASSISTANT_TASK:>
Python Code:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import numpy as np
np.set_printoptions(threshold=np.nan)
import tensorflow as tf
import time
import pandas as pd
import matplotlib.pyplot as plt
import progressbar
data_path = 'https://raw.githubusercontent.com/michaelneuder/image_quality_a... | <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: everything looks good with c,s, cxs. now to check the down sampled images as well as luminance.
|
4,070 | <ASSISTANT_TASK:>
Python Code:
mylist = [1, 4, -5, 10, -7, 2, 3, -1]
[n for n in mylist if n > 0]
[n for n in mylist if n < 0]
pos = (n for n in mylist if n > 0)
pos
for x in pos:
print(x)
values = ['1', '2', '-3', '-', '4', 'N/A', '5']
def is_int(val):
try:
x = int(val)
return True
except... | <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: 使用列表推导的一个潜在缺陷就是如果输入非常大的时候会产生一个非常大的结果集，占用大量内存。 如果你对内存比较敏感，那么你可以使用生成器表达式迭代产生过滤的元素。比如：
Step2: 有时候，过滤规则比较复杂，不能简单的在列表推导或者生成器表达式中表达出来。 比如，假设过滤的时候需要处理... |
4,071 | <ASSISTANT_TASK:>
Python Code:
# https://github.com/dfm/corner.py
import corner
import hydropy as hp
mpl.rcParams['font.size'] = 16
mpl.rcParams['axes.labelsize'] = 18
mpl.rcParams['xtick.labelsize'] = 16
mpl.rcParams['ytick.labelsize'] = 16
import pylab as p
p.rc('mathtext', default='it')
from biointense.model 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: Respirometer model in pyIDEAS
Step2: We define the model equations and set up the model
Step3: Reading in the observations
Step4: Decubber th... |
4,072 | <ASSISTANT_TASK:>
Python Code:
from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
from pynq.lib import Pmod_PWM
pwm = Pmod_PWM(base.PMODA,0)
import time
# Generate a 10 us clocks with 50% duty cycle
period=10
duty=50
pwm.generate(period,duty)
# Sleep for 4 seconds and stop the timer
time.sleep(... | <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. Connect Scope
Step2: 3. Generate a clock of $50\%$ duty cycle and $10\,\mu$s period
Step3: 4. Generate a clock of $25\%$ duty cycle and $20... |
4,073 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from pathlib import Path
import tensorflow as tf
%matplotlib notebook
#%matplotlib inline
models_data_folder = Path.home() / "Documents/models/"
# create and add up two constants
a = tf.const... | <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: Core (Low Level APIs)
Step2: Eager Execution
Step3: Dataset API
Step4: Save and Restore Variables
Step5: Save and Restore a Model
Step6: Se... |
4,074 | <ASSISTANT_TASK:>
Python Code:
# importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
# Function for Generating 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:
Step2: Function for Random Walks
Step3: Showing a Bunch of Realizations for a Random Walk
Step4: Determining ACF of the Random Walk
|
4,075 | <ASSISTANT_TASK:>
Python Code:
import o2sclpy
import matplotlib.pyplot as plot
import sys
import math
import numpy
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel
plots=True
if 'pytest' in sys.modules:
plots=False
link=o2sclpy.linker()
... | <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: Link the o2scl library
Step2: Create a sample function to interpolate
Step3: Create sample data from our function
Step4: Compute the mean and... |
4,076 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
from IPython.display import display
import pandas as pd
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import humanize
from sqlitedict import SqliteDict
db = SqliteDict('./pet_friendly.sqlite')
pd.set_option('float_format', '{:.2f}'.... | <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 skip the data gathering step. I used a CSV file with the list of countries and regions to parse Airbnb.com website and saved the data as a ... |
4,077 | <ASSISTANT_TASK:>
Python Code:
import caffe
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
import matplotlib as mpl
import numpy as np
import os
import struct
%matplotlib inline
# Function adapted from https://gist.github.com/akesling/5358964.
def load_mnist_test_data(path = "."):
fname_img =... | <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: Since the digits of the MNIST were stored in a special format, we need to load them
Step2: Now we can visualize one by one as follows (<span st... |
4,078 | <ASSISTANT_TASK:>
Python Code:
from predictor import evaluation as ev
from predictor.dummy_mean_predictor import DummyPredictor
predictor = DummyPredictor()
y_train_true_df, y_train_pred_df, y_val_true_df, y_val_pred_df = ev.run_single_val(x, y, ahead_days, predictor)
print(y_train_true_df.shape)
print(y_train_pred_df.... | <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: Done. Let's test the reshape_by_symbol function
Step2: So, the reshape_by_symbol function seems to work with run_single_val. It could be added ... |
4,079 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
rng = np.random.RandomState(1)
X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T
plt.scatter(X[:, 0], X[:, 1])
plt.axis('equal');
from sklearn.decomposition import PCA
pca = PCA(n_compon... | <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: Intuition of PCA
Step2: By eye, what can we say about this dataset?
Step3: PCA learns what the components are an how variance is explained by ... |
4,080 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy
import matplotlib
from matplotlib.patches import Circle, Wedge, Polygon
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
import matplotlib.path as mpat... | <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: Ship test
Step2: Island Test
|
4,081 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/biothings/biothings_explorer#egg=biothings_explorer
from biothings_explorer.hint import Hint
from biothings_explorer.user_query_dispatcher import FindConnection
import nest_asyncio
nest_asyncio.apply()
ht = Hint()
anisindione = ht.query("Anisindione")... | <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: The df object contains the full output from BioThings Explorer. Each row... |
4,082 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
import math
track_params = pd.read_csv('../TRAIN/track_parms.csv')
track_params.tail()
# Create binary labels
track_params['phi_bool'] = track_params.phi.apply(lambda x: "+" if... | <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: Get angle values and cast to boolean
Step2: Create our simple classification targets
Step3: Look at the distributions to see if we have any im... |
4,083 | <ASSISTANT_TASK:>
Python Code:
import yaml
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, Slider
from bokeh.plotting import figure
from bokeh.themes import Theme
from bokeh.io import show, output_notebook
from bokeh.sampledata.sea_surface_temperature import sea_surface_temperature
output_no... | <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: There are various application handlers that can be used to build up Bokeh documents. For example, there is a ScriptHandler that uses the code fr... |
4,084 | <ASSISTANT_TASK:>
Python Code:
query_dict = {'expansions__vectors__rep': 0,
'expansions__k':3,
'labelled':'amazon_grouped-tagged',
'expansions__use_similarity': 0,
'expansions__neighbour_strategy':'linear',
'expansions__vectors__dimensionality': 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: Overall, precision and recall are balanced and roughly equal. Better models are better in both P and R.
Step2: Find the smallest cluster and pr... |
4,085 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pyqg
# create the model object
m = pyqg.BTModel(L=2.*np.pi, nx=256,
beta=0., H=1., rek=0., rd=None,
tmax=40, dt=0.001, taveint=1,
ntd=4)
# in this example we u... | <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: McWilliams performed freely-evolving 2D turbulence ($R_d = \infty$, $\beta =0$) experiments on a $2\pi\times 2\pi$ periodic box.
Step2: Initial... |
4,086 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
!cd toy_datasets; wget -O magic04.data -nc https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.data
import numpy, pandas
from rep.utils import train_test_split
from sklearn.metrics import roc_auc_score
columns = ['fLength', 'fWidth', 'fSize', 'fConc', 'f... | <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 data
Step2: Variables used in training
Step3: Metric definition
Step4: Compute threshold vs metric quality
Step5: The best quality
S... |
4,087 | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
with open('anna.txt', 'r') as f:
text=f.read()
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
encoded = np.array([vocab_to_int[c] 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: First we'll load the text file and convert it into integers for our network to use. Here I'm creating a couple dictionaries to convert the chara... |
4,088 | <ASSISTANT_TASK:>
Python Code:
from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
%matplotlib inline
#import the ususal libraries + the pynoddy UncertaintyAnalysis class
import sys, os
# determine path of repository to set paths corretly below
repo_path = os.path.realpath('... | <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 Gippsland Basin Model
Step2: While we could hard-code parameter variations here, it is much easier to store our statistical information in ... |
4,089 | <ASSISTANT_TASK:>
Python Code:
!pip install -q amplpy ampltools
MODULES=['ampl', 'coin']
from ampltools import cloud_platform_name, ampl_notebook
from amplpy import AMPL, register_magics
if cloud_platform_name() is None:
ampl = AMPL() # Use local installation of AMPL
else:
ampl = ampl_notebook(modules=MODULES)... | <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: Google Colab & Kaggle interagration
Step2: Use %%ampl_eval to evaluate AMPL commands
Step3: Use %%writeifile to create files
Step4: Use %%amp... |
4,090 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
sys.path.append(os.environ["SPARK_HOME"] + "/python/lib/py4j-0.10.4-src.zip")
sys.path.append(os.environ["SPARK_HOME"] + "/python/lib/pyspark.zip")
from pyspark import SparkConf, SparkContext
sconf = SparkConf()
sconf.setAppName("ES-Spark Integration")
sconf.setMaster... | <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: Goal
Step3: Configure ES parameters
Step4: ES returns key-value RDD where key is ID of the document, and value is content of _source field
... |
4,091 | <ASSISTANT_TASK:>
Python Code:
import mne
mne.set_log_level('WARNING')
mne.set_log_level('INFO')
mne.set_config('MNE_LOGGING_LEVEL', 'WARNING', set_env=True)
mne.get_config_path()
from mne.datasets import sample # noqa
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw... | <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: If you'd like to turn information status messages off
Step2: But it's generally a good idea to leave them on
Step3: You can set the default le... |
4,092 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import numpy.polynomial.polynomial as npp
from scipy.stats import norm
from scipy.special import comb
import matplotlib.pyplot as plt
def Plw(n,l,w,delta):
return np.sum([comb(w,l-r)*comb(n-w,r)*(delta**(w-l+2*r))*((1-delta)**(n-w+l-2*r)) for r in range(l+1)])
# 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: Implement the helper function which computes the probability $P_\ell^w$ that a received word $\boldsymbol{y}$ is exactly at Hamming distance $\e... |
4,093 | <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 wr... | <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: 画像分類のための敵対的正則化
Step2: ライブラリをインポートします。neural_structured_learning を nsl と略します。
Step3: ハイパーパラメータ
Step4: MNIST データセット
Step5: モデルを数値的に安定させるには、nor... |
4,094 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-1', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contribut... | <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... |
4,095 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def random_line(m, b, sigma, size=10):
Create a line y = m*x + b + N(0,sigma**2) between x=[-1.0,1.0]
Param... | <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: Line with Gaussian noise
Step5: Write a function named plot_random_line that takes the same arguments as random_line and creates a random line ... |
4,096 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
df = pd.read_csv(url,names=['sepal_length',
'sepal_width',
'petal_length',
'petal_width',
... | <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_html
Step2: Plotting
Step3: It would be nice to encode by color and plot all combinations of values, but this isn't easy with matplotlib.... |
4,097 | <ASSISTANT_TASK:>
Python Code:
with open('tmp/pymotw.txt', 'wt') as f:
f.write('contents go here')
class Context:
def __init__(self):
print('__init__()')
def __enter__(self):
print('__enter__()')
return self
def __exit__(self, exc_type, exc_val, exc_tb):
print('__exit__(... | <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 context manager is enabled by the with statement, and the API involves two methods. The __enter__() method is run when execution flow enters t... |
4,098 | <ASSISTANT_TASK:>
Python Code:
r = requests.get("https://www.topuniversities.com/sites/default/files/qs-rankings-data/357051.txt")
json_dict = r.json()
json_dict = json_dict['data']
json_dict = json_dict[:199]
for e in json_dict:
#Delete useless informations
del e['nid']
del e['logo']
del e['core_id']
... | <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: Per university ratio
Step2: As we can see except for the first few universites which clearly have a higher ratio than the others, the ratio ten... |
4,099 | <ASSISTANT_TASK:>
Python Code:
print('Original bounds: ', t[0], t[-1])
t_obs = t[D['T_flag'] != -1]
D = D[t_obs[0]:t_obs[-1]] # Truncate dataframe so it is sandwiched between observed values
t = D.index
T = D['T']
print('New bounds: ', t[0], t[-1])
t_obs = D.index[D['T_flag'] != -1]
t_interp = D.index[D['T_flag'] == -... | <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: Red dots are interpolated values.
Step2: We want to obtain a stationary "feature" from the data, firt differences are an easy place to start.
S... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.