Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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12,500 | <ASSISTANT_TASK:>
Python Code:
!pip install dicttoxml
import json
from dicttoxml import dicttoxml
PETS = {}
PET_STATUS_INDEX = {}
TAG_INDEX = {}
ORDERS = {}
ORDER_STATUS_INDEX = {}
JSON = 'application/json'
XML = 'application/xml'
content_type = JSON
class MissingField(Exception):
def __init__(self, type_name, fie... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: POST /pet
Step2: PUT /pet
Step3: GET /pet/findByStatus
Step4: GET /pet/findByTags
Step5: GET /pet/
Step6: POST /pet/
Step7: DELETE /pet/
S... |
12,501 | <ASSISTANT_TASK:>
Python Code:
# Imports
import os
import sys
import pandas as pd
import seaborn as sb
# Custom Imports
sys.path.insert(0, '../../')
import stats_toolbox as st
from stats_toolbox.utils.data_loaders import load_fem_preg_2002
# Graphics setup
%pylab inline --no-import-all
sb.set_context('notebook', font_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load Data
Step2: Constructing Histograms
Step3: Getting frequencies for values
Step4: or equivilantly
Step5: Visualising
Step6: By default ... |
12,502 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
VS = 230.0 # Secondary voltage (V)
amps = arange(0, 65.2, 6.52) # Current values (A)
Req = 0.0445 # Equivalent R (ohms)
Xeq = 0.0645 # Equivalent X (ohms)
I = amps * array ([[0.8 - 0.6j], # Lagging
[1... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Define all the parameters
Step2: Calculate the current values for the three power factors.
Step3: Calculate VP/a
Step4: Calculate voltage reg... |
12,503 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
'''step 1'''
# Load all necessary modules here, for clearness
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# from torchvision.datasets import MNIST
import torchvision
from torchvision ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1.1 Commonly required module
Step2: 1.2 Random seed setting for reproducibility
Step3: 2. Data split and Cross Validatioin
Step5: 2.2 Calcula... |
12,504 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
!wget --no-check-certificate https://openaq-data.s3.amazonaws.com/2018-04-06.csv -P /Users/nipun/Downloads/
import pandas as pd
df = pd.read_csv("/Users/nipun/Downloads/2018-04-06.csv")
df = df[(df.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Downloading data from OpenAQ for 2018-04-06
Step2: Downloading World GeoJson file
Step3: Creating india.json correspdonding to Indian data
|
12,505 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import sqlite3
import pandas as pd
import seaborn as sns
sns.set_style("white")
conn = sqlite3.connect('../data/output/database.sqlite')
c = conn.cursor()
def execute(sql):
'''
Executes a SQL command on the 'c' cursor and returns ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: Current visualization ideas
Step6: Odd outliers throughout needs to be explored and cleaned more
|
12,506 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
pd.__version__
import matplotlib.pyplot as plt
import matplotlib
matplotlib.__version__
pump_df = pd.read_csv('https://raw.githubusercontent.com/yy/dviz-course/master/data/pumps.csv')
pump_df.head()
# TODO: write your code here
# Your code here
len(pump_df)
p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: You can check the version of the library. Because pandas is fast-evolving library, you want to make sure that you have the up-to-date version of... |
12,507 | <ASSISTANT_TASK:>
Python Code:
##importing python module
import os
import pandas
import numpy
import gseapy
import mygene
import ipywidgets
import qgrid
import urllib2
qgrid.nbinstall(overwrite=True)
qgrid.set_defaults(remote_js=True, precision=4)
from IPython.display import IFrame
import matplotlib.image as mpimg
impo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Read in differential expression results as a Pandas data frame to get differentially expressed gene list
Step2: Translate Ensembl IDs to Gene S... |
12,508 | <ASSISTANT_TASK:>
Python Code:
from datetime import datetime
datetime(year=2015, month=7, day=4)
from dateutil import parser
date = parser.parse("4th of July, 2015")
date
date.strftime('%A')
import numpy as np
date = np.array('2015-07-04', dtype=np.datetime64)
date
date + np.arange(12)
np.datetime64('2015-07-04')
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Or, using the dateutil module, you can parse dates from a variety of string formats
Step2: Once you have a datetime object, you can do things l... |
12,509 | <ASSISTANT_TASK:>
Python Code:
import epoxpy
from epoxpy.lib import A
a = A()
a.visualize(show_ports=True)
from epoxpy.lib import C10
c10 = C10()
c10.visualize(show_ports=True)
from epoxpy.lib import Epoxy_A_10_B_20_C10_2_Blend
import mbuild as mb
import random
random.seed(1024)
blend = Epoxy_A_10_B_20_C10_2_Blend()
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Particle C10
Step2: Epoxy blend (10 A's, 20 B's and 2 C10's)
|
12,510 | <ASSISTANT_TASK:>
Python Code:
import rebound
import numpy as np
sim = rebound.Simulation()
OMEGA = 0.00013143527 # [1/s]
sim.ri_sei.OMEGA = OMEGA
surface_density = 400. # kg/m^2
particle_density = 400. # kg/m^3
sim.G = 6.67428e-11 # N m^2 / kg^2
sim.dt = 1e-3*2.*np.pi/OMEGA
sim.softening = 0.2 ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Next up, setting up several constants. We will be simulating a shearing sheet, a box with shear-periodic boundary conditions. This is a local ap... |
12,511 | <ASSISTANT_TASK:>
Python Code:
from datetime import datetime,timedelta, time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from data_helper_functions import *
from IPython.display import display
pd.options.display.max_columns = 999
%matplotlib inline
desired_channel = 'BAND_01'
desired_date = d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Build up sensor to pvoutput model
Step2: ...finally ready to model!
Step3: Linear model
Step4: When only keeping the photometer data, random ... |
12,512 | <ASSISTANT_TASK:>
Python Code:
def do_something(arg1, arg2):
A short sentence describing what this function does.
More description
Parameters
----------
arg1 : type1
Description of the parameter ``arg1``
arg2 : type2
Description of the parameter ``arg2``
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Documenting Code
Step2: 1b
Step3: 1c
Step6: 1d
Step7: 1e
Step8: Problem 2
Step9: 2b
Step10: 2c
Step11: You should see various files have... |
12,513 | <ASSISTANT_TASK:>
Python Code:
import iris
import numpy as np
import holoviews as hv
import holocube as hc
from cartopy import crs
from cartopy import feature as cf
hv.notebook_extension()
%%output size=400
feats = [cf.LAND, cf.OCEAN, cf.RIVERS, cf.LAKES, cf.BORDERS, cf.COASTLINE]
features = hv.Overlay([hc.GeoFeature(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Plotting with projections
Step2: Below is the full list of cartopy projections that can be displayed using matplotlib.
Step3: We can test the ... |
12,514 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
import pandexo.engine.justdoit as jdi
import numpy as np
import os
exo_dict = jdi.load_exo_dict('HD 189733 b')
exo_dict['observation']['sat_level'] = 80 #saturation level in percent of full well
exo_dict['observation']['sat_unit'] =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load Exo Dict for Specific Planet
Step2: Edit exoplanet observation inputs
Step3: Edit exoplanet inputs using one of three options
Step4: 2)... |
12,515 | <ASSISTANT_TASK:>
Python Code:
import torch
@torch.jit.script
def activation_cell(cx):
return torch.tanh(cx)
# note takes non-parameter jit.script functions activation_cell from context at definition time! (Probably will want to do this in a factory style function even if it's not 100% Pythonic)
@torch.jit.script
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Functional Interfacefor a Cell
|
12,516 | <ASSISTANT_TASK:>
Python Code:
#import our modules
from __future__ import print_function
import fwdpy as fp
import numpy as np
import datetime
import time
#set up our sim
rng = fp.GSLrng(101)
nregions = [fp.Region(0,1,1),fp.Region(2,3,1)]
sregions = [fp.ExpS(1,2,1,-0.1),fp.ExpS(1,2,0.1,0.001)]
rregions = [fp.Region(0,3... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Calculating sliding windows
Step2: Using pylibseq
|
12,517 | <ASSISTANT_TASK:>
Python Code:
# Import modules you might use
import numpy as np
# Some data, in a list
my_data = [12, 5, 17, 8, 9, 11, 21]
# Function for calulating the mean of some data
def mean(data):
# Initialize sum to zero
sum_x = 0.0
# Loop over data
for x in data:
# Add to sum
su... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Notice that, rather than using parentheses or brackets to enclose units of code (such as loops or conditional statements), python simply uses in... |
12,518 | <ASSISTANT_TASK:>
Python Code:
!rm mnist_data.mat && wget https://github.com/KordingLab/lab_teaching_2015/raw/master/session_2/mnist_data.mat
import numpy as np
from scipy.io import loadmat
mnist = loadmat('mnist_data.mat')
X = mnist['X']
y = mnist['y']
print("Size of X and y: ", X.shape, y.shape)
def sigmoid(z):
g... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: Back propagation algorithm
Step4: Work flow for computing final $\Theta_1, \Theta_2$
Step5: Analyzing weights
|
12,519 | <ASSISTANT_TASK:>
Python Code:
def multiplek(y_test, topk):
multik = []
for i, score in enumerate(y_test):
if y_test[i] in topk[i]:
multik.append(y_test[i])
else:
multik.append(topk[i][0])
return multik
pred = grid.predict(X_test.ravel()) # predicts a category
p_pro... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Predicting top 3 categories
Step2: The function below brings all the steps above so it can be applied to the X_test set.
Step3: The function b... |
12,520 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
# seed the random number generator so we all get the same results
numpy.random.seed(18)
# som... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Part One
Step2: Here's what that distribution looks like
Step3: make_sample draws a random sample from this distribution. The result is a Num... |
12,521 | <ASSISTANT_TASK:>
Python Code:
%%bash
git --help
%%bash
git clone https://github.com/cosmoscalibur/herramientas_computacionales.git herramientas
%%bash
cd herramientas
ls -oha
%%bash
cd herramientas
git remote -v
%%bash
cd herramientas
git init
%%bash
cd herramientas
git remote add pruebas https://github.com/cosmo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Configuración
Step2: De esta forma crearemos una copia del repositorio git herramientas_computacionales en el directorio ./herramientas. Si no ... |
12,522 | <ASSISTANT_TASK:>
Python Code::
import cv2
%matplotlib notebook
%matplotlib inline
from matplotlib import pyplot as plt
img = cv2.imread("hsv_ball.jpg",cv2.IMREAD_GRAYSCALE)
_,mask = cv2.threshold(img, 220,255,cv2.THRESH_BINARY_INV)
titles = ['images',"mask"]
images = [img,mask]
for i in range(2):
plt.subplot(1,2,i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
12,523 | <ASSISTANT_TASK:>
Python Code:
import math
import time
import diffrax
import equinox as eqx # https://github.com/patrick-kidger/equinox
import jax
import jax.nn as jnn
import jax.numpy as jnp
import jax.random as jrandom
import jax.scipy as jsp
import matplotlib
import matplotlib.pyplot as plt
import optax # https://... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First let's define the vector field for the CDE.
Step2: Now wrap up the whole CDE solve into a model.
Step3: Toy dataset of spirals.
Step4: T... |
12,524 | <ASSISTANT_TASK:>
Python Code:
import sys
import pandas as pd # check out Modin https://towardsdatascience.com/get-faster-pandas-with-modin-even-on-your-laptops-b527a2eeda74
import numpy as np
import json
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import datetime
# Add path to APS m... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Check if there are missing values.
Step2: Fill missing values where necessary.
Step3: Feature engineering
Step4: Add historical values, e.g. ... |
12,525 | <ASSISTANT_TASK:>
Python Code:
import logging
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
root = logging.getLogger()
root.addHandler(logging.StreamHandler())
%matplotlib inline
# download from Google Drive: https://drive.google.com/open?id=0B9cazFzBtPuCOFNiUHYwcVFVODQ
# Representative exampl... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. Choose a representative species for a case study
Step2: 2. Rasterize the species, to get a matrix of pixels
Step3: 2.1 Plot to get an idea
... |
12,526 | <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
!python -m pip install iree-compiler iree-runtime iree-tools-tf -f https://github.com/google/iree/rel... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Image edge detection module
Step2: High Level Compilation With IREE
Step3: Low-Level Compilation
|
12,527 | <ASSISTANT_TASK:>
Python Code:
import featuretools as ft
es = ft.demo.load_mock_customer(return_entityset=True)
feature_defs = ft.dfs(entityset=es,
target_dataframe_name="customers",
agg_primitives=["mean", "sum", "mode", "n_most_common"],
trans_primitiv... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: By default, describe_feature uses the existing column and DataFrame names and the default primitive description templates to generate feature de... |
12,528 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib as mpl
import plotnine as p9
import matplotlib.pyplot as plt
import itertools
import warnings
warnings.simplefilter("ignore")
from sklearn import neighbors, preprocessing, impute, metrics, model_selection, linear_model, svm, feature... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: Classification 1
Step4: Evaluating a classifier
Step5: Confusion matrix and metrics
|
12,529 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
from operator import itemgetter
from collections import Counter
import scipy.stats as stat
from gensim.models import Word2Vec
from nltk import corpus
import FastGaussianLDA2
wvmodel = Word2Vec.load_word2vec_format(
"/Users/michael/Documents/Gaussian_LDA-m... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading the word_vector model with GenSim
Step2: Sets of vocab to filter on
Step3: Document cleaning
|
12,530 | <ASSISTANT_TASK:>
Python Code:
class Pessoa(object):
def __init__(self, nome, idade):
self.nome = nome
self.idade = idade
joao = Pessoa()
joao = Pessoa('João', 20)
print(joao, '\n')
print(joao.nome)
print(joao.idade)
joao.nome = 'João Pedro'
print(joao.nome)
maria = Pessoa('Maria', 20)
print(maria)
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Encapsulamento
Step2: Desta forma não conseguimos recuperar e nem alterar o valor dessa variável privada. Para isso precisamos construir dois m... |
12,531 | <ASSISTANT_TASK:>
Python Code:
# I import useful libraries (with functions) so I can visualize my data
# I use Pandas because this dataset has word/string column titles and I like the readability features of commands and finish visual products that Pandas offers
import pandas as pd
import matplotlib.pyplot as plt
impor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Uploaded data into Python¶
Step2: Next
Step3: These data points can be plotted on top of a calculated pitch line
Step4: I now have an input (... |
12,532 | <ASSISTANT_TASK:>
Python Code:
# NBVAL_IGNORE_OUTPUT
import numpy as np
import matplotlib.pyplot as plot
import math as mt
import matplotlib.ticker as mticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import cm
# NBVAL_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: From Devito's library of examples, we import some specific functions, such as
Step2: Previously we used the expression configuration ['log-leve... |
12,533 | <ASSISTANT_TASK:>
Python Code:
import word2vec
word2vec.word2phrase('./text8', './text8-phrases', verbose=True)
word2vec.word2vec('./text8-phrases', './text8.bin', size=100, verbose=True)
word2vec.word2clusters('./text8', './text8-clusters.txt', 100, verbose=True)
import word2vec
model = word2vec.load('./text8.bin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Run word2phrase to group up similar words "Los Angeles" to "Los_Angeles"
Step2: This will create a text8-phrases that we can use as a better in... |
12,534 | <ASSISTANT_TASK:>
Python Code:
names = ['foo','bar','rf']
dates = pd.date_range(start='2015-01-01',end='2018-12-31', freq=pd.tseries.offsets.BDay())
n = len(dates)
rdf = pd.DataFrame(
np.zeros((n, len(names))),
index = dates,
columns = names
)
np.random.seed(1)
rdf['foo'] = np.random.normal(loc = 0.1/252,sc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Build and run Target Strategy
Step2: Now use the PTE rebalance algo to trigger a rebalance whenever predicted tracking error is greater than 1%... |
12,535 | <ASSISTANT_TASK:>
Python Code:
x=5
print(x)
import numpy as np
def sin_signal(t, omega=0.1, t0=0.):
A sinusoidal signal
signal = np.sin(omega * (t-t0))
return signal
# let's try
t = np.linspace(0., 100., 10)
# The output of the last entry in a cell gets printed
sin_signal(t)
# Sometimes, the output is too... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Hi this is normal text
Step3: We can define functions and call them later
Step4: The output can be suppressed adding a ";" at the end of the c... |
12,536 | <ASSISTANT_TASK:>
Python Code:
import graphlab as gl
import re
import matplotlib.pyplot as plt
gl.canvas.set_target('ipynb')
%matplotlib inline
amazon = gl.SFrame.read_csv('Amazon.csv', verbose=False)
google = gl.SFrame.read_csv('GoogleProducts.csv', verbose=False)
truth = gl.SFrame.read_csv('Amzon_GoogleProducts_perfe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <a id="eval"></a> Evaluation functions
Step2: <a id="model"></a> Record Linker model
Step3: <a id="feature"></a> Feature Engineering
Step4: <... |
12,537 | <ASSISTANT_TASK:>
Python Code:
from pymldb import Connection
mldb = Connection("http://localhost/")
print mldb.put('/v1/procedures/import_rcp', {
"type": "import.text",
"params": {
"headers": ["user_id", "recipe_id"],
"dataFileUrl": "file://mldb/mldb_test_data/favorites.csv.gz",
"output... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The sequence of procedures below is based on the one explained in the Mapping Reddit demo notebook.
Step2: We then train an SVD decomposition a... |
12,538 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', '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
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
12,539 | <ASSISTANT_TASK:>
Python Code:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.
Step2: Visualize Data
Step3: Preprocess Data
Step4: Setup TensorFlow
Step5:... |
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Python Code:
import logging
logging.basicConfig(level = logging.INFO)
import smurff
import numpy as np
import scipy.sparse as sp
def gen_matrix(shape, num_latent, density = 1.0 ):
Generate a matrix by multipling two factors.
Sparsify if asked.
X = np.random.normal(size=(shape[0]... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Out-of-matrix prediction using synthetic data
Step2: Train the model
Step3: Make a PredictSession
Step4: Out-of-matrix prediction using side-... |
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Python Code:
!ls -lh ../data/reads
!gunzip -c ../data/reads/mutant1_OIST-2015-03-28.fq.gz | head -8
!fastqc ../data/reads/mutant1_OIST-2015-03-28.fq.gz
from IPython.display import IFrame
IFrame('../data/reads/mutant1_OIST-2015-03-28_fastqc.html', width=1000, height=1000)
import gzip
from Bio import... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We see that there are five files four of these are mutants, and and one reference original sample.
Step2: Each read in the fastq file format ha... |
12,542 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat
from shogun import features, MulticlassLabels, Math
# load the dataset
dataset = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = datas... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Creating the autoencoder
Step2: Pre-training
Step3: Fine-tuning
Step4: Evaluation
Step5: The figure shows the corrupted examples and their r... |
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Python Code:
import pandas as pd
df_red = pd.read_csv("data/winequality-red.csv",sep=";")
df_white = pd.read_csv("data/winequality-white.csv",sep=";")
# Add the type column
df_red['type'] = 1
df_white['type'] = 0
df = pd.concat([df_red,df_white], axis=0)
df.describe()
import matplotlib.pyplot as plt
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Las variables de entrada utilizadas se basan en pruebas fisioquímicas de los vinos y corresponden a
Step2: Se puede notar la existencia de una ... |
12,544 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.zeros(9).reshape(3,3) # 3 x 3 matrix filled with zeros
print(a)
print(a.dtype)
a1 = np.zeros((3, 3)) # same matrix, note the tuple parameter
b = np.random.rand(6) # random numbers between 0-1
print(b)
a1 = np.full((3, 4), 8)
print(a1)
i = np.eye(4)... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Creating and filling arrays
Step2: Vector filled with random number.
Step3: Matrix filled with constant.
Step4: Identity matrix.
Step5: Crea... |
12,545 | <ASSISTANT_TASK:>
Python Code:
import os
from pathlib import Path
testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'Tutorial_11')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
print ("Your simulation will be stored in %s" % testfolder)
from bifacial_radiance import... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: If desired, you can view the Oct file at this point
Step2: And adjust the view parameters, you should see this image.
Step3: View the geometry... |
12,546 | <ASSISTANT_TASK:>
Python Code:
import sympy
from sympy import *
from sympy.abc import x, n, z, t, k
init_printing() # for nice printing, a-la' TeX
%run "sums.py"
# duplicated code, put it into "sums.py"
def expand_sum_in_eq(eq_term):
lhs, rhs = eq_term.lhs, eq_term.rhs
return Eq(lhs, expand_Sum(rhs))
f = I... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Since coefficient in the triangle on the rhs are a part of Pascal triangle, namely A104712, the following is a generalization
Step2: again, fib... |
12,547 | <ASSISTANT_TASK:>
Python Code:
x = [1,2,3]
lambda x: max(x)
a = range(-5,5)
## with builtins
b = map(abs,a)
c = [abs(x) for x in a]
print b==c,b
## with your own function
b = [x**2 for x in a]
print b==c,b
import types
## filter
a = ['', 'fee', '', '', '', 'fi', '', '', '', '', 'foo', '', '', '', '', '', 'fum']
b = f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Simple list comprehensions
Step2: Filtering list comprehensions
Step3: Nested list comprehensions
Step4: Having fun with Zip
|
12,548 | <ASSISTANT_TASK:>
Python Code:
def greetings(f):
print(f)
def wrapper(*args, **kwargs):
print("dekorator foo mówi: ", "Hello world!")
return f(*args, **kwargs)
return wrapper
@greetings
def foo(a, b):
print(a, b)
print("Foo function")
def bar():
pass
foo(4... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h1><center>?</center></h1>
Step3: moduł functools
Step4: singledispatch
Step5: lru_cache (least recently used)
Step6: partial
|
12,549 | <ASSISTANT_TASK:>
Python Code:
print('Hello, world')
# This is a code cell
my_variable = 5
print(my_variable)
import hail as hl
from bokeh.io import output_notebook, show
hl.init()
output_notebook()
hl.utils.get_1kg('data/')
! ls -1 data/
hl.import_vcf('data/1kg.vcf.bgz').write('data/1kg.mt', overwrite=True)
mt... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Modes
Step2: This is a markdown cell, so even if something looks like code (as below), it won't get executed!
Step3: Common gotcha
Step4: Now... |
12,550 | <ASSISTANT_TASK:>
Python Code:
# python standard library
from fractions import Fraction
spam = 'offer is secret, click secret link, secret sports link'.split(',')
print(len(spam))
ham = 'play sports today, went play sports, secret sports event, sports is today, sports costs money'.split(',')
print(len(ham))
class Mail... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step12: The terms have to be changed to be either all plural or all singular. In this case I changed 'sport' to 'sports' where needed.
Step16: SpamDet... |
12,551 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.array([1, 2, 3, 4])
print 'Вектор:\n', a
b = np.array([1, 2, 3, 4, 5], dtype=float)
print 'Вещественный вектор:\n', b
c = np.array([True, False, True], dtype=bool)
print 'Булевский вектор:\n', c
print 'Тип булевского вектора:\n', c.dtype
d = np.arange(start=10... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. Создание векторов
Step2: Тип значений вектора можно узнать с помощью numpy.ndarray.dtype
Step3: Другим способом задания вектора является фу... |
12,552 | <ASSISTANT_TASK:>
Python Code:
# @title Install dependencies
# @markdown Only execute if not already installed and running a cloud runtime
!pip install -q timesketch_api_client
# @title Import libraries
# @markdown This cell will import all the libraries needed for the running of this colab.
import altair as alt # For ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Fill in your event data
Step2: Attributes / Tags (optional)
|
12,553 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
data = pd.read_csv('../input/fifa-2018-match-statistics/FIFA 2018 Statistics.csv')
y = (data['Man of the Match'] == "Yes") # Convert from string... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Here is how to calculate and show importances with the eli5 library
|
12,554 | <ASSISTANT_TASK:>
Python Code:
# In order to run this code, you need an already trianed model (see the accompanying notebook)
import graphlab as gl
model = gl.load_model('pattern_mining_model.gl')
model
def predict(x):
# Construct an SFrame
sf = gl.SFrame(x)
# Add your own business logic here
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We can expose the trained model as a REST endpoint. This will allow other applications to consume the predictions from the model.
Step2: 2. C... |
12,555 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'ukesm1-0-mmh', 'aerosol')
# 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
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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
12,556 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.5
from google.cloud import bigquery
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
CSV_COLUMNS = ['fa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h2> Input </h2>
Step2: <h2> Create features out of input data </h2>
Step3: <h2> Serving input function </h2>
Step4: <h2> tf.estimator.train_... |
12,557 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Automatic differentiation and gradient tape
Step2: Derivatives of a function
Step3: Higher-order gradients
Step4: Gradient tapes
Step5: At t... |
12,558 | <ASSISTANT_TASK:>
Python Code:
# we assume that we have the dynet module in your path.
# OUTDATED: we also assume that LD_LIBRARY_PATH includes a pointer to where libcnn_shared.so is.
from dynet import *
pc = ParameterCollection()
NUM_LAYERS=2
INPUT_DIM=50
HIDDEN_DIM=10
builder = LSTMBuilder(NUM_LAYERS, INPUT_DIM, HID... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: An LSTM/RNN overview
Step2: Note that when we create the builder, it adds the internal RNN parameters to the ParameterCollection.
Step3: If ou... |
12,559 | <ASSISTANT_TASK:>
Python Code:
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Eager execution
Step2: 2. A NumPy-like library for numerical computation and machine learning
Step3: Tensors behave similarly to NumPy arrays,... |
12,560 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
masses = np.arange(0.1, 0.96, 0.05) # list of masses
from scipy.interpolate import interp1d
ages = np.arange(5.0e6, 3.1e7, 1.0e6) # ages requested
# open output file objects
output_files = [open('files/dmestar_{:0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Magnetic Mass Tracks
Step2: Magnetic Isochrones
Step3: Dartmouth & MARCS; Solar abundance
Step4: Interpolate isochrones onto a finer mass gri... |
12,561 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mh', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <SYSTEM_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... |
12,562 | <ASSISTANT_TASK:>
Python Code:
import ovation.lab.constants as constants
import ovation.lab.results as results
import ovation.download as dowload
from ovation.lab.session import connect
s = connect(input("Email: "), api=constants.LAB_STAGING_HOST) # use constants.LAB_PRODUCTION_HOST for production
batch = input("Batc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: API Key
Step2: Find fastq results for samples in batch
Step3: results.get_sample_results pulls all WorkflowSampleResults for the given batch a... |
12,563 | <ASSISTANT_TASK:>
Python Code:
%%R
library(dplyr)
playoffs <- data_frame(
holiday = 'playoff',
ds = as.Date(c('2008-01-13', '2009-01-03', '2010-01-16',
'2010-01-24', '2010-02-07', '2011-01-08',
'2013-01-12', '2014-01-12', '2014-01-19',
'2014-02-02', '2015-01-11', '... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Above we have included the superbowl days as both playoff games and superbowl games. This means that the superbowl effect will be an additional ... |
12,564 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Quantum Chess REST API
Step2: It is possible to play the game in interactive mode, by applying moves to the board. Split the knight on b1 to a3... |
12,565 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pylab as plt
X = [10, 20, 30, 40, 50, 60, 70]
y = [40, 45, 50, 65, 70, 70, 80]
plt.figure(figsize=(4, 2))
plt.scatter(X, y)
plt.xlabel("training time"); plt.ylabel("productivity")
plt.show()
plt.figure(figsize=(6, 3))
plt.scatter(X, y)
plt.xlabel("training time")
plt.yl... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: scatter plot 을 보면 훈련시간이 늘어남에 따라 생산성도 높아지고 있음을 쉽게 볼 수 있다. 따라서 훈련시간과 생산성 사이에 밀접한 관계가 있음을 알 수 있다.
Step2: 가장 좋은 표본회귀식은 전체적으로 추정오차, 즉 잔차를 가장 작게 해 줄 ... |
12,566 | <ASSISTANT_TASK:>
Python Code:
ph_sel_name = "Dex"
data_id = "12d"
# ph_sel_name = "all-ph"
# data_id = "7d"
from fretbursts import *
init_notebook()
from IPython.display import display
data_dir = './data/singlespot/'
import os
data_dir = os.path.abspath(data_dir) + '/'
assert os.path.exists(data_dir), "Path '%s' doe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Load software and filenames definitions
Step2: Data folder
Step3: List of data files
Step4: Data load
Step5: Laser alternation selection
Ste... |
12,567 | <ASSISTANT_TASK:>
Python Code:
def environmentScoreNoRounding(speciesData, nodeConfig, biomassData):
numTimesteps = len(biomassData[nodeConfig[0]['nodeId']])
scores = np.empty(numTimesteps)
for timestep in range(numTimesteps):
# Calculate the Ecosystem Score for this timestep
biomass = 0
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step2: Using cube root instead of log
Step3: Shannon index, based on number of individuals
Step4: Shannon index, based on biomass
Step5: Biomass-bas... |
12,568 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# Load the iris dataset
iris = datasets.load_iris()
# Create X from the features
X = i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Create The Data
Step2: View The Data
Step3: Split The Data Into Training And Test Sets
Step4: Standardize Features
Step5: Run Logistic Regre... |
12,569 | <ASSISTANT_TASK:>
Python Code:
def ci_pendulo_doble(x, y):
# tome en cuenta que las longitudes de los eslabones son 2 y 2
l1, l2 = 2, 2
from numpy import arccos, arctan2, sqrt
# YOUR CODE HERE
raise NotImplementedError()
return q1, q2
from numpy.testing import assert_allclose
assert_allclose(ci_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Obtenga las posiciones en el espacio articular, $q_1$ y $q_2$, necesarias para que el punto final del pendulo doble llegue a las coordenadas $p_... |
12,570 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
import phoebe
from phoebe import u # units
logger = phoebe.logger()
b = phoebe.default_binary(contact_binary=True)
b.add_dataset('lc', times=phoebe.linspace(0,0.5,101))
b.run_compute(irrad_method='none', model='no_spot')
b.add_feature('spot', compone... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: As always, let's do imports and initialize a logger and a new bundle.
Step2: Model without Spots
Step3: Adding Spots
Step4: Comparing Light C... |
12,571 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import lifelines
import matplotlib.pylab as plt
%matplotlib inline
data = lifelines.datasets.load_dd()
data.head()
data.tail()
from lifelines import KaplanMeierFitter
kmf = KaplanMeierFitter()
# kaplan-meier
# KaplanMeierFitter.fit(event_times, event_observed=None,
... | <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: political leaders
|
12,572 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'sandbox-2', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("na... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
12,573 | <ASSISTANT_TASK:>
Python Code:
# some random heights of the family
height = [1.75, 1.65, 1.71, 1.89, 1.79]
# some random weights of the family
weight = [65.4, 59.2, 63.6, 88.4, 68.7]
# Now if we go to calculate BMI
weight / height ** 2
import numpy as np # selective import
# Convet the followoing list to numpy arrays
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Solution
Step2: Note
Step4: Numpy
Step6: Numpy Subsetting
Step8: Exercise
Step10: 2. Baseball player's height
Step12: 3. Baseball playe... |
12,574 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import statsmodels.api as sm
import numpy as np
import pandas as pd
data = sm.datasets.sunspots.load()
from datetime import datetime
dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog))
endog = pd.Series(data.endog, index=dates... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Getting started
Step2: Right now an annual date series must be datetimes at the end of the year.
Step3: Using Pandas
Step4: Instantiate the m... |
12,575 | <ASSISTANT_TASK:>
Python Code:
if boolean-expression:
statements-when-true
else:
statemrnts-when-false
x = 15
y = 20
z = 2
x > y
z*x <= y
y >= x-z
z*10 == x
raining = False
snowing = True
age = 45
age < 18 and raining
age >= 18 and not snowing
not snowing or not raining
age == 45 and not snowing
if boolean-e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Python’s Relational Operators
Step2: A. 4
Step3: A. 4
Step4: elif versus a series of if statements
Step5: Check Yourself
Step6: A. One
Step... |
12,576 | <ASSISTANT_TASK:>
Python Code:
[10, 20, 30, 40]
['dog', 'fish', 'bird']
['bob', 3.14, 42, ['sam', 55]]
cheeses = ['Chedder', 'Pepper Jack', 'Queso Fresca']
grades = [99, 84, 91]
empty = []
cheeses = ['Chedder', 'Pepper Jack', 'Queso Fresca']
cheeses[0] = 'Gouda'
print( cheeses )
for cheese in cheeses:
print( ch... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The elements of a list don't have to be the same data type in Python (in other languages they do)
Step2: As you can see, lists can be nested
St... |
12,577 | <ASSISTANT_TASK:>
Python Code:
!pip install git+git://github.com/lindermanlab/ssm-jax-refactor.git
import ssm
import jax.random as jr
import jax.numpy as np
import matplotlib.pyplot as plt
from tensorflow_probability.substrates import jax as tfp
from ssm.hmm import BernoulliHMM
from ssm.plots import gradient_cmap
fro... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Imports and Plotting Functions
Step2: Bernoulli HMM
Step3: From the true model, we can sample synthetic data
Step4: Let's view the synthetic ... |
12,578 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from datetime import datetime
import trulia.stats
import geocoder
import json
from datetime import timedelta
from collections import defaultdict
import time
import requests
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: Below is a time series of the weather in Chapel Hill, NC every morning over a few years. You can clearly see an annual cyclic pattern, which sh... |
12,579 | <ASSISTANT_TASK:>
Python Code:
%tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(version='2.3.0')
import deepchem as dc
tasks, datasets, transformers = dc.molnet.load_muv... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Let's start by loading the data. We will use the MUV dataset. It includes 74,501 molecules in the training set, and 9313 molecules in the vali... |
12,580 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer, StandardScaler
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
stats = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Data preparation
Step2: The file Seasons_Stats.csv contains the statics of all players since 1950. First, we drop a couple of blank columns, an... |
12,581 | <ASSISTANT_TASK:>
Python Code:
import requests
import json
import pandas as pd
SERVER = 'http://data.neonscience.org/api/v0/'
SITECODE = 'WOOD'
PRODUCTCODE = 'DP1.00041.001'
#Get availability
site_request = requests.get(SERVER+'sites/'+SITECODE)
site_json = site_request.json()
for product in site_json['data']['dataPro... | <SYSTEM_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 get soil temperature data from NEON's Woodworth site. Soil temperature data is measured and recorded automatically by soil temeprature pro... |
12,582 | <ASSISTANT_TASK:>
Python Code:
REGION = 'us-central1'
PROJECT_ID = !(gcloud config get-value core/project)
PROJECT_ID = PROJECT_ID[0]
BUCKET = 'gs://' + PROJECT_ID
!bq --location=US mk census
%%bigquery
CREATE OR REPLACE TABLE census.data AS
SELECT age, workclass, education_num, occupation, hours_per_week,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: First, create a BigQuery dataset. We will then query a public BigQuery dataset to populate a table in this dataset. This is census data. We will... |
12,583 | <ASSISTANT_TASK:>
Python Code:
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import urllib2
def check_condition(row):
if row[-1] == 0:
return False
return True
url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'
'/data/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: What is the total number of synapses in our data set?
Step2: What is the maximum number of synapses at a given point in our data set?
Step3: W... |
12,584 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
ad_data = pd.read_csv('advertising.csv')
ad_data.head()
ad_data.info()
ad_data.describe()
sns.distplot(ad_data['Age'],kde=False,bins=30,color='blue')
sns.jointplot(data=ad_... | <SYSTEM_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 the Data
Step2: Check the head of ad_data
Step3: Use info and describe() on ad_data
Step4: Exploratory Data Analysis
Step5: Create a joi... |
12,585 | <ASSISTANT_TASK:>
Python Code:
# Load the needed packages
import os
import matplotlib.pyplot as plt
import numpy as np
from netCDF4 import Dataset
import awot
from awot.graph import FlightLevel, RadarVerticalPlot, MicrophysicalVerticalPlot
%matplotlib inline
file1 = "WCR.OWLES13.20131215.225944_234806.up-down.nc"
#fil... | <SYSTEM_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>Supply input data and plotting characteristics. In this case we'll use a file from the OWLeS project and corrected field of velocity data usi... |
12,586 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
%matplotlib inline
path = "data/dogscats/"
#path = "data/dogscats/sample/"
import os, json
from glob import glob
import numpy as np
np.set_printoptions(precision=4, linewidth=100)
from matplotlib import pyplot as plt
# check that ~/.keras... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Define path to data
Step2: A few basic libraries that we'll need for the initial exercises
Step3: We have created a file most imaginatively ca... |
12,587 | <ASSISTANT_TASK:>
Python Code:
import vcsn
c = vcsn.context('lat<lan_char(abc), lan_char(bce)>, nmin')
l = c.levenshtein()
l
a1 = vcsn.context('lan_char(abc), b').expression("bac+cab").derived_term().strip().partial_identity()
a1
a2 = vcsn.context('lan_char(bce), b').expression("bec+bebe").automaton().cominimize().str... | <SYSTEM_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 Levenshtein automaton only has one state, but has $N*M + N + M$ transitions, for alphabets of size $N$ and $M$.
Step2: The automaton can be... |
12,588 | <ASSISTANT_TASK:>
Python Code:
fruits = ['Apple', 'Mango', 'Grapes', 'Jackfruit',
'Apple', 'Banana', 'Grapes', [1, "Orange"]]
# processing the entire list
for fruit in fruits:
print(fruit, end=", ")
#
print("*"*30)
fruits.insert(0, "kiwi")
print( fruits)
# help(fruits.insert)
# Including
ft1 = list(fru... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: NOTE
Step2: Removing
Step3: Appending
Step4: Ordering
Step5: Inverting
Step6: The function enumerate() returns a tuple of two elements in e... |
12,589 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.linspace(0,10,11)
# your code here
#start by defining the length of the array
arrayLength = 10
#let's set the array to currently be an array of 0s
myArray = np.zeros(arrayLength) #make a numpy array of 10 zeros
# Let's define the first element of the array
myArray[... | <SYSTEM_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 do a lot with the numpy module. Below is an example to jog your memory
Step2: Do you remember loops? Let's use a while loop to make an ... |
12,590 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def fun_log(z):
return 1/(1+np.exp(-z))
z = np.linspace(-5, 5)
plt.figure(figsize = (8,6))
plt.plot(z, fun_log(z), lw = 2)
plt.xlabel('$z$')
plt.ylabel('$\sigma(z)$')
plt.grid()
plt.show()
def reg_log(B,Xa):
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: Notamos que
Step2: 3. Funcional de costo
Step3: Diseñar un clasificador binario con regresión logística.
Step4: Los parámetros del clasificad... |
12,591 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import theano.tensor as T
x = np.random.randn(100)
with pm.Model() as model:
mu = pm.Normal('mu', mu=0, sd=1)
sd = pm.Normal('sd', mu=0, sd=1)
obs... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Hm, looks like something has gone wrong, but what? Let's look at the values getting proposed using the Print operator
Step2: Looks like sd is a... |
12,592 | <ASSISTANT_TASK:>
Python Code:
import re
three_repeating_characters = re.compile(r'(.)\1{2}')
with open('../inputs/day14.txt', 'r') as f:
salt = f.readline().strip()
# TEST DATA
# salt = 'abc'
print(salt)
import hashlib
hash_index= {}
def get_hash_string(key):
if key in hash_index:
return hash_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Hash index
Step2: Part Two
|
12,593 | <ASSISTANT_TASK:>
Python Code:
spam = ["eggs", 7.12345] # This is a list, a comma-separated sequence of values between square brackets
print spam
print type(spam)
eggs = [spam,
1.2345,
"fooo"] # No problem with multi-line declaration
print eggs
spam = [] # And this is an empty list
print sp... | <SYSTEM_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 mix all kind of types inside a list
Step2: What about tuples?
Step3: What about both together?
Step4: Let's go back to lists
Step5: ... |
12,594 | <ASSISTANT_TASK:>
Python Code:
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os, json, math, shutil
import datetime
import numpy as np
import logging
# SET TF ERROR LOG VERBOSITY
logging.getLogger("tensorflow").setLevel(loggin... | <SYSTEM_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 Google Cloud Storage Bucket for ML model exports
Step2: Setup parameters for notebook scheduling
Step3: Locating the CSV files
St... |
12,595 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
capitais = pd.read_csv('capitais.csv', sep=',')
capitais.head() # Imprime os 5 primeiros elementos do dataframe
capitais = pd.read_csv('capitais.csv', sep=',', index_col='municipio')
capitais.head()
len(capitais.index)
capitais.index
capitais.columns
capitais["popul... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Note que o índice que foi gerado é o padrão e não o default. Para defirmos a coluna municipio como sendo o índice, precisamos passar o parâmetro... |
12,596 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-esm2m', 'land')
# 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... |
12,597 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'inm-cm5-h', 'toplevel')
# 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: 2... |
12,598 | <ASSISTANT_TASK:>
Python Code:
import pickle
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
data_dict = pickle.load(open("../final_project/final_project_dataset.pkl", "r") )
### first element is our labels, any added elements are predictor
### features. Keep this 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: Create a decision tree classifier (just use the default parameters), train it on all the data. Print out the accuracy.
Step2: Now you’ll add i... |
12,599 | <ASSISTANT_TASK:>
Python Code:
# -*- coding: UTF-8 -*-
import pandas as pd
import numpy as np
import re
from bs4 import BeautifulSoup
def review_to_wordlist(review):
'''
把IMDB的评论转成词序列
参考：http://blog.csdn.net/longxinchen_ml/article/details/50629613
'''
# 去掉HTML标签，拿到内容
review_text = BeautifulSoup... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 载入数据集
Step2: 预处理数据
Step3: 特征处理
Step4: 朴素贝叶斯训练
Step5: 提交最终的结果到kaggle，AUC为：0.85728，排名300左右，50%的水平
Step6: 提交最终的结果到kaggle，AUC为：0.88956，排名260左... |
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