text_prompt stringlengths 168 30.3k | code_prompt stringlengths 67 124k |
|---|---|
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Una vez importadas las funciones de nuestra librería, podemos empezar declarando variables (o constantes) conocidas para nosotros.
Step2: Nota ... | <ASSISTANT_TASK:>
Python Code:
# Esta libreria tiene las funciones principales que utilizaremos
from sympy import var, Matrix, Function, sin, cos, pi, trigsimp
# Esta libreria contiene una funcion que la va a dar un formato "bonito" a nuestras ecuaciones
from sympy.physics.mechanics import mechanics_printing
mechanics_... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <p style="text-align
Step2: <p style="text-align
Step3: <p style="text-align
Step4: <p style="text-align
Step5: <p style="text-align
Step6: ... | <ASSISTANT_TASK:>
Python Code:
current_number = 2
while current_number <= 16:
twice_number = current_number + current_number
print(f"{current_number} and {current_number} are {twice_number}")
current_number = twice_number
current_number = 2
while current_number <= 16:
twice_number = current_number + ... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Title
Step2: Resources
Step3: Run the model on a single batch of data, and inspect the output
Step4: Compile the model for training
| <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: Plugin setup
Step2: Model setup
Step3: Spectral fitting
Step4: It seems that the effective area between GBM and BAT do not agree!
Step5: Let... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%matplotlib notebook
from threeML import *
import os
gbm_dir = "gbm"
bat_dir = "bat"
bat = OGIPLike('BAT',
observation=os.path.join(bat_dir,'gbm_bat_joint_BAT.pha'),
response=os.path.join(bat_dir,'gbm_bat_joint_BAT.rsp'))
bat.set_active_me... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Did you try it, and get an error saying Problem loading page or Unable to connect? So did I. It’s because we forgot to spin up the dev server fi... | <ASSISTANT_TASK:>
Python Code:
%cd ../examples/superlists/
!python3 functional_tests.py
%%writefile functional_tests.py
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import unittest
class NewVisitorTest(unittest.TestCase):
def setUp(self):
self.browser = webdriver.Fire... |
<SYSTEM_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 in Classifications
Step2: Count sources that were missed in PS1 PSC v1
Step3: Loop over reason some stars are missing
Step4: What's goin... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.ticker import MultipleLocator
from matplotlib import rcParams
from matplotlib.legend import Legend
import seaborn as sns
rcParams["font.family"] = "sans-serif"
rcPar... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Pipe Fitting App
Step2: Slab Fitting App
| <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from geoscilabs.gpr.GPRlab1 import downloadRadargramImage, PipeWidget, WallWidget
from SimPEG.utils import download
URL = "http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png"
radargramImage = downloadRadargramImage(URL)
PipeWidget(radargramImag... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 텐서플로 2.0 시작하기
Step2: MNIST 데이터셋을 로드하여 준비합니다. 샘플 값을 정수에서 부동소수로 변환합니다
Step3: 층을 차례대로 쌓아 tf.keras.Sequential 모델을 만듭니다. 훈련에 사용할 옵티마이저(optimizer)와 ... | <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: Validating Models
Step2: Let's fit a K-neighbors classifier
Step3: Now we'll use this classifier to predict labels for the data
Step4: Finall... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn')
from sklearn.datasets import load_digits
digits = load_digits()
X = digits.data
y = digits.target
from sklearn.neighbors import KNeighborsClassi... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 利用 Keras 来训练多工作器（worker）
Step2: 准备数据集
Step3: 构建 Keras 模型
Step4: 让我们首先尝试用少量的 epoch 来训练模型，并在单个工作器（worker）中观察结果，以确保一切正常。 随着训练的迭代，您应该会看到损失（loss）下... | <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: Introdução à Programação em Python
Step2: Para resolver o primeiro item, utilizamos o comando while que significa enquanto
Step3: Reparem que ... | <ASSISTANT_TASK:>
Python Code:
def DivResto(num, base):
Retorna o quociente e resto da divisão de num por base
return num//base, num%base
dec = 14
bin = "" # string vazia
div, resto = DivResto(dec,2)
dec = div
bin = str(resto) + bin
print(bin)
# repete mais uma vez, pois temos outro dígito
div, rest... |
<SYSTEM_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 models and API of astropy.modeling.models is explained in the astropy documentation in more detail.
Step2: Likelihoods and Posteriors
Step3... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
# ignore warnings to make notebook easier to see online
# COMMENT OUT THESE LINES FOR ACTUAL ANALYSIS
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
import matplotlib.pyplot as plt
try:
import seaborn as sns
sns.set_palette(... |
<SYSTEM_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 there's a bit of variance year-to-year and especially difference in 2020 with Hawkeye, grab a month from each year
Step2: Calculate the f... | <ASSISTANT_TASK:>
Python Code:
from pybaseball import statcast, utils
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pybaseball.plotting import plot_bb_profile
# Grab 1 month per year
dfs = []
for year in range(2015, 2021):
print(f"Starting year {year}")
dfs.append(statcast(start_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: mapValues
Step2: When using mapValues(), the x in the above lambda function refers to the element value, not including the element key.
Step3: ... | <ASSISTANT_TASK:>
Python Code:
# create an example RDD
map_exp_rdd = sc.textFile('../../data/mtcars.csv')
map_exp_rdd.take(4)
# split auto model from other feature values
map_exp_rdd_1 = map_exp_rdd.map(lambda x: x.split(',')).map(lambda x: (x[0], x[1:]))
map_exp_rdd_1.take(4)
# remove the header row
header = map_exp_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: Then it's time to load some data to estimate segregation. We use the data of 2000 Census Tract Data for the metropolitan area of Sacramento, CA,... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import geopandas as gpd
from pysal.explore import segregation
import pysal.lib
s_map = gpd.read_file(pysal.lib.examples.get_path("sacramentot2.shp"))
s_map.columns
gdf = s_map[['geometry', 'HISP_', 'TOT_POP']]
gdf['composition'] = gdf['HISP_'] / gdf['TOT_POP']
gdf.pl... |
<SYSTEM_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: Splitting data between train/test
Step3: split used for convenience on the average by movie baseline
Step4: cleaning
... | <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: Create Data
Step2: Select Based On The Result Of A Select
| <ASSISTANT_TASK:>
Python Code:
# Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
%%sql
-- Create a table of criminals
CREATE TABLE criminals (pid, name, age, sex, city, minor);
INSERT INTO criminals VALUES (412, 'James Smith', 15, 'M', 'Santa Rosa', 1);
INSERT INTO criminals VALUES (234, 'Bill Ja... |
<SYSTEM_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 knee is located by passing x and y values to knee_locator.
Step2: There are plotting functions to visualize the knee point on the raw data ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from kneed.data_generator import DataGenerator as dg
from kneed.knee_locator import KneeLocator
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
x = [3.07, 3.38, 3.55, 3.68, 3.78, 3.81, 3.85, 3.88, 3.9, 3.93]
y = [0.0, 0.3, 0.4... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Date/Time data handling
Step2: In addition to datetime there are simpler objects for date and time information only, respectively.
Step3: Havi... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Set some Pandas options
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', 20)
pd.set_option('display.max_rows', 25)
from datetime import datetime
now = dateti... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Start with initializing a euclidean N-dimensional algebra and assign our pseudoscalar to $I$, pretty standard.
Step2: Anti-symmetric
Step3: Wh... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
def func2Mat(f,I):
'''
Convert a function acting on a vector into a matrix, given
the space defined by psuedoscalar I
'''
A = I.basis()
B = [f(a) for a in A]
M = [float(b | a) for a in A for b in B]
return np.array(M).reshape(len(B), le... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Toy example
Step2: Plotting parameters
Step3: We run a an optimization loop with standard settings
Step4: We see that some minima is found an... | <ASSISTANT_TASK:>
Python Code:
print(__doc__)
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
from skopt.learning import ExtraTreesRegressor
from skopt import Optimizer
from skopt.plots import plot_gaussian_process
noise_level = 0.1
# Our 1D toy problem, this is the function we are trying 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: Functions in the module accept the following arguments.
Step2: We can also plot the posterior distribution.
Step3: We will load the classifica... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
Acc_nbc = np.loadtxt('Data/nbc_anneal.csv', delimiter=',', skiprows=1)
Acc_aode = np.loadtxt('Data/aode_anneal.csv', delimiter=',', skiprows=1)
names = ("AODE", "NBC")
x=np.zeros((len(Acc_nbc),2),'float')
x[:,0]=Acc_aode/100
x[:,1]=Acc_nbc/100
#we consider the differe... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Search individuals method
Step2: Note
Step3: In this case, the Family ID can be exchanged through the protocol, although the named field is no... | <ASSISTANT_TASK:>
Python Code:
from ga4gh.client import client
c = client.HttpClient("http://1kgenomes.ga4gh.org")
#Obtain dataSet id REF: -> `1kg_metadata_service`
dataset = c.search_datasets().next()
counter = 0
for individual in c.search_individuals(dataset_id=dataset.id):
if counter > 5:
break
co... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Read input tables
Step2: Block tables to get candidate set
Step3: Debug blocking output
Step4: Match tuple pairs in candidate set
Step5: Sel... | <ASSISTANT_TASK:>
Python Code:
import py_entitymatching as em
import profiler
import pandas as pd
## Read input tables
A = em.read_csv_metadata('dblp_demo.csv', key='id')
B = em.read_csv_metadata('acm_demo.csv', key='id')
len(A), len(B), len(A) * len(B)
A.head(2)
B.head(2)
# If the tables are large we can downsample t... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
| <ASSISTANT_TASK:>
Python Code:
from collections import defaultdict
def gcd(a , b ) :
if(b == 0 ) :
return a
return gcd(b , a % b )
def splitArray(arr , N ) :
mp = defaultdict(int )
for i in range(N ) :
mp[arr[i ] ] += 1
G = 0
for i in mp :
G = gcd(G , mp[i ] )
if(G > 1 ) :
print(... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
| <ASSISTANT_TASK:>
Python Code:
import pandas as pd
data = {'spike-2': [1,2,3], 'hey spke': [4,5,6], 'spiked-in': [7,8,9], 'no': [10,11,12]}
df = pd.DataFrame(data)
s = 'spike'
def g(df, s):
spike_cols = [col for col in df.columns if s in col and col != s]
return spike_cols
result = g(df.copy(),s)
<END_TASK> |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-3', 'landice')
# 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: 对于所有Matplotlib图，我们首先创建一个图形和一个轴。以最简单的形式，可以如下创建图形和轴：
Step2: 在Matplotlib中，图形（类plt.Figure的一个实例）可以被认为是一个包含所有代表轴，图形，文本和标签的对象的容器。轴（类plt.Axes的实例）就是我们在上... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
#使用seaborn-whitegrid风格
plt.style.use('seaborn-whitegrid')
import numpy as np
fig = plt.figure()
ax = plt.axes()
fig = plt.figure()
ax = plt.axes()
x = np.linspace(0, 10, 1000)
ax.plot(x, np.sin(x));
plt.plot(x, np.sin(x));
plt.plot(x,... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Running Compliance Checker on the Scripps Pier shore station data
| <ASSISTANT_TASK:>
Python Code:
import compliance_checker
print(compliance_checker.__version__)
# First import the compliance checker and test that it is installed properly.
from compliance_checker.runner import CheckSuite, ComplianceChecker
# Load all available checker classes.
check_suite = CheckSuite()
check_suite.lo... |
<SYSTEM_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 datasets
Step2: The GMQLDataset
Step3: Filtering the dataset regions based on a predicate
Step4: From this operation we can learn sev... | <ASSISTANT_TASK:>
Python Code:
import gmql as gl
dataset1 = gl.get_example_dataset("Example_Dataset_1")
dataset2 = gl.get_example_dataset("Example_Dataset_2")
dataset1.schema
dataset2.schema
filtered_dataset1 = dataset1.reg_select((dataset1.chr == 'chr3') & (dataset1.start >= 30000))
filtered_dataset_2 = dataset2[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: Load the files
Step2: First visualize the cluster
| <ASSISTANT_TASK:>
Python Code:
import numpy as np
import h5py
import matplotlib.pyplot as plt
%matplotlib inline
# Now nexa modules|
import sys
sys.path.append("../")
from visualization.data_clustering import visualize_data_cluster_text_to_image
# First we load the file
file_location = '../results_database/text_wall_... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step 1
Step1: Step 2
Step2: Step 3
Step3: 2. Applying Sobel filters on HMDI input with Python
Step 1
Step4: Step 2
Step5: Step 3
Step7: 3. Applyin... | <ASSISTANT_TASK:>
Python Code:
from pynq import Overlay
Overlay("vbx.bit").download()
from pynq.drivers import HDMI
from pynq.drivers.video import VMODE_1920x1080,VMODE_1280x720
vmode=VMODE_1280x720
#vmode=VMODE_1920x1080
hdmi_out = HDMI('out',video_mode=vmode)
hdmi_in = HDMI('in', video_mode=vmode,frame_list=hdmi_ou... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Learning Curves
Step2: They all come from the same underlying process. But if you were asked to make a prediction, you would be more likely to ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.svm import SVR
from sklearn import cross_validation
rng = np.random.RandomState(42)
n_samples = 200
kernels = ['linear', 'poly', 'rbf']
true_fun = lambda X: X ** 3
X = np.sort(5 * (rng.ra... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: What is a SparkSession?
Step2: Check the SparkSession variable
Step3: What is a Dataframe?
Step4: Create another Dataframe
Step5: Check the ... | <ASSISTANT_TASK:>
Python Code:
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Python Spark SQL basic example") \
.master("spark://helk-spark-master:7077") \
.enableHiveSupport() \
.getOrCreate()
spark
first_df = spark.range(10).toDF("numbers")
first_df.show()
dog_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: The diffusion follows Fick's law of diffusion
Step2: We will solve the partial differential equation (PDE) using method of lines. We discretize... | <ASSISTANT_TASK:>
Python Code:
reactions = [
('k', {'A': 1}, {'B': 1, 'A': -1}),
]
names, params = 'A B'.split(), ['k']
D = [8e-9, 8e-9] # He diffusion constant in water at room temperature
import sympy as sym
x, h = sym.symbols('x h')
d2fdx2 = sym.Function('f')(x).diff(x, 2)
d2fdx2.as_finite_difference([x-h, x,... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. Constructing API GET Request
Step2: You often want to send some sort of data in the URL’s query string. This data tells the API what informa... | <ASSISTANT_TASK:>
Python Code:
# Import required libraries
import requests
import json
from __future__ import division
import math
import csv
import matplotlib.pyplot as plt
# set key
key="be8992a420bfd16cf65e8757f77a5403:8:44644296"
# set base url
base_url="http://api.nytimes.com/svc/search/v2/articlesearch"
# set 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: Your Turn
Step2: Notes
Step3: Then, given the gradient of MSE wrt to w and b, we can define how we update the parameters via SGD
Step4: The w... | <ASSISTANT_TASK:>
Python Code:
import keras.backend as K
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from kaggle_data import load_data, preprocess_data, preprocess_labels
X_train, labels = load_data('../data/kaggle_ottogroup/train.csv', train=True)
X_train, scaler = preprocess_data(X_train)
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: Beside the usual model in previous sections, let’s create a model that run a Backend instance to simulate and obtain results.
Step4: Let’s defi... | <ASSISTANT_TASK:>
Python Code:
!pip install -q sciunit
import sciunit, random
from sciunit import Test
from sciunit.capabilities import Runnable
from sciunit.scores import BooleanScore
from sciunit.models import RunnableModel
from sciunit.models.backends import register_backends, Backend
class RandomNumBackend(Backen... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Init
Step2: Downloading genomes
Step3: Indexing genomes
Step4: Simulating a gradient community
Step6: Simulating isotope incorporation
Step7... | <ASSISTANT_TASK:>
Python Code:
workDir = "/home/nick/notebook/SIPSim/t/M.bark_M.ext/"
import os
import sys
%load_ext rpy2.ipython
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
!cd $workDir; \
seqDB_tools accession-GI2fasta < M.barkeri_refseq.txt > M.barkeri.fna
!cd $workDir; \
seqDB_tools accession-GI2fa... |
<SYSTEM_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 is a code sample showing how to read the data and draw a colored plot.
| <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
%matplotlib notebook
# Load the csv with pandas
df = pd.read_csv('hipgalv.LSR.csv', index_col=0)
#print(df)
# Use matplotlib's default "Reds" colormap. More colormaps and information 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: If you evaluate a Python expression that returns a value, that value is displayed as output of the code cell. This only happens, however, for t... | <ASSISTANT_TASK:>
Python Code:
print('hello, world.')
# Would show 9 if this were the last line, but it is not, so shows nothing
4 + 5
# I hope we see 11.
5 + 6
a = 5 + 6
a
import numpy as np
import scipy.integrate
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_formats = {'svg',}
#... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <div class="alert alert-info"><h4>Note</h4><p>Before applying SSP (or any artifact repair strategy), be sure to observe
Step2: The example data... | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import (create_eog_epochs, create_ecg_epochs,
compute_proj_ecg, compute_proj_eog)
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.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: View the spark context, the main entry point to the Spark API
Step2: DataFrames
Step3: Create a new DataFrame that contains “young users” only... | <ASSISTANT_TASK:>
Python Code:
!pyspark
sc
users = context.load("s3n://path/to/users.json", "json")
young = users.filter(users.age<21)
young = users[users.age<21]
young.select(young.name, young.age+1)
young.groupBy("gender").count()
young.join(logs, logs.userId == users.userId, "left_outer")
young.registerTempT... |
<SYSTEM_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 - Problem Statement
Step2: The model is stored in a python dictionary where each variable name is the key and the corresponding value is a te... | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import scipy.io
import scipy.misc
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from PIL import Image
from nst_utils import *
import numpy as np
import tensorflow as tf
%matplotlib inline
model = load_vgg_model("pretrained-model/imagenet-vgg-ve... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Instantiate AnyPath for different addresses
Step2: Next, let's see how what files are present in each location.
Step3: We capture all contents... | <ASSISTANT_TASK:>
Python Code:
from paralleldomain.utilities.any_path import AnyPath
absolute_path = "/home/nisseknudsen/Data/testset_dgp"
absolute_anypath = AnyPath(path=absolute_path)
relative_path = "testset_dgp"
relative_anypath = AnyPath(path=relative_path)
s3_path = "s3://pd-sdk-c6b4d2ea-0301-46c9-8b63-ef20c0d01... |
<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: Why do we need to improve the traing method?
Step4: Now let's train the data set the way before, to validate our new class.
Step5: Now a sine ... | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
from random import random
from IPython.display import FileLink, FileLinks
def σ(z):
return 1/(1 + np.e**(-z))
def σ_prime(z):
return np.e**(z) / (np.e**z + 1)**2
def Plot(fn, *args, **kwargs):
argL... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Matplotlib magic
Step2: There are many ways to import matplotlib, but the most common way is
Step3: Q1
Step4: Let's look at the first few row... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import matplotlib.pyplot as plt
df = pd.read_csv('imdb.csv', delimiter='\t')
df.head()
df.head(2)
df['Year'].head(3)
df[['Year','Rating']].head(3)
df[:10]
df[['Year','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:
Step2: Note
Step3: Note
| <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import datetime
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
df = pd.read_csv('data/pm25.csv')
print(df.shape)
df.head()
df.isnull().sum()*100/df.shape[0]
df.dropna(subset=['pm2.5'], axis=0... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Download the HI-mass data of Westmeier et al. 2017
Step2: There are 31 galaxies in this sample, hence the array has 31 rows. This data can be r... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pydftools as df
from pydftools.plotting import mfplot
import numpy as np
from urllib.request import Request, urlopen # For getting the data online
from IPython.display import display, Math, Latex, Markdown, TextDisplayObject
req = Request('http://quantumholism.c... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import your saliency model into pysaliency
Step2: If you have an LSUN submission file prepared, you can load it with pysaliency.SaliencyMapMode... | <ASSISTANT_TASK:>
Python Code:
# TODO: Add ModelFromDirectory for log densities
# TODO: Change defaults for saliency map convertor (at least in LSUN subclass)
# TODO: Write fit functions optimize_for_information_gain(model, stimuli, fixations)
my_model = pysaliency.SaliencyMapModelFromDirectory(stimuli_salicon_train, ... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Closed Form Matting Energy
Step2: Now that TensorFlow Graphics is installed, let's import everything needed to run the demos contained in this ... | <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: The object new_data has been reprojected to Alberts and a linear model have been fitted with residuals stored as residuals
Step2: The empirical... | <ASSISTANT_TASK:>
Python Code:
from external_plugins.spystats import tools
%run ../HEC_runs/fit_fia_logbiomass_logspp_GLS.py
from external_plugins.spystats import tools
hx = np.linspace(0,800000,100)
new_data.residuals[:10]
gvg.plot(refresh=False,legend=False,percentage_trunked=20)
plt.title("Semivariogram of residua... |
<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: Import SystemML API
Step3: Import numpy, sklearn, and define some helper functions
Step5: Example 1
Step6: Examine execution plans, and incre... | <ASSISTANT_TASK:>
Python Code:
!pip show systemml
from systemml import MLContext, dml, dmlFromResource
ml = MLContext(sc)
print ("Spark Version:" + sc.version)
print ("SystemML Version:" + ml.version())
print ("SystemML Built-Time:"+ ml.buildTime())
ml.execute(dml(s = 'Hello World!').output("s")).get("s")
import sys,... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Functions for convolution, pooling, droput, etc.
Step2: 2> Dropout Layer
Step3: 3> Pooling Layer
Step4: 4> Normalization Layer
Step5: 5> Con... | <ASSISTANT_TASK:>
Python Code:
# Import files
import os
import sys
import numpy as np
import matplotlib as plt
import tensorflow as tf
import time
import random
import math
import pandas as pd
import sklearn
from scipy import misc
import glob
import pickle
%matplotlib inline
plt.pyplot.style.use('ggplot')
# RELU GLORO... |
<SYSTEM_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 API Key on Kaggle
| <ASSISTANT_TASK:>
Python Code:
!pip install --user --upgrade kaggle
import IPython
IPython.Application.instance().kernel.do_shutdown(True) #automatically restarts kernel
!ls ./kaggle.json
import os
current_dir=!pwd
current_dir=current_dir[0]
os.environ['KAGGLE_CONFIG_DIR']=current_dir
!${HOME}/.local/bin/kaggle datase... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Diabetes dataset
Step2: Our goal is to fit a linear model using Elastic Net regularisation, which predicts the disease progression for a given ... | <ASSISTANT_TASK:>
Python Code:
from prox_elasticnet import ElasticNet, ElasticNetCV
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(319159)
from sklearn.datasets import load_diabetes
diabetes = load_diabetes()
X = diabetes.data
y = diabetes.target
prop_train = 0.8
n_pts = len(y)
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: Focusing on one of the periapse tables for now
| <ASSISTANT_TASK:>
Python Code:
df = df[df.BIN_PATTERN_INDEX == 'LINEAR linear_0006']
# now can drop that column
df = df.drop('BIN_PATTERN_INDEX', axis=1)
bin_tables = df.BIN_TBL.value_counts()
bin_tables
for ind in bin_tables.index:
print(ind)
print(df[df.BIN_TBL==ind].orbit_segment.value_counts())
df = df[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: We have to 'preconfigure' the pipeline, because we need to build up the list of targets so that we correctly set up the later stages of the pipe... | <ASSISTANT_TASK:>
Python Code:
pipe = Pipeline(linkname = 'dSphs')
configfile = 'config/master_dSphs.yaml'
pipe.preconfigure(configfile)
pipe.update_args(dict(config=configfile))
pipe.linknames
pipe['data']
pipe['data'].linknames
pipe.print_status()
pipe.print_status(recurse=True)
pipe['data']['analyze-roi']
pipe[... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
| <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.arange(12).reshape(3, 4)
a = np.delete(a, 2, axis = 0)
<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:
Step2: Creating your first neural network with TF-Slim
Step3: Let's create the model and examine its structure.
Step4: Let's create some 1d regressio... | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import math
import numpy as np
import tensorflow as tf
import time
from datasets import dataset_utils
# Main sl... |
<SYSTEM_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 k-means algorithm is one of the most popular clustering algorithms and very simple with respect to the implementation. Clustering has the go... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib as mpl
mpl.use('TkAgg')
import matplotlib.pyplot as plt
import sklearn.datasets as sk
from matplotlib import animation
from matplotlib.animation import PillowWriter # Disable if you don't want to save any GIFs.
%matplotlib inline
data_selection = 1 # ... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Compute and display the Mandelbrot set using Spark SQL using plain old-fashioned ASCII graphics for the output
Step4: Mandelbrot in SQL, displa... | <ASSISTANT_TASK:>
Python Code:
# the function definition
def mandelbrot(cR, cI, maxIterations):
zR = cR
zI = cI
i = 1
# Iterative formula for Mandelbrot set: z => z^2 + c
# Escape point: |z|^2 >= 4. Note: z nd c are complex numbers
while (zR*zR + zI*zI < 4.0 and i < maxIterations):
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: Vamos agora fazer um teste, calculando o histograma usando a função np.histogram e depois calculando as estatísticas da imagem
Step2: Os valore... | <ASSISTANT_TASK:>
Python Code:
def h2stats(h):
import numpy as np
#import ia898.src as ia
hn = 1.0*h/h.sum() # compute the normalized image histogram
v = np.zeros(6) # number of statistics
# compute statistics
n = len(h) # number of gray values
v[0] = np.sum((np.arange(n)*hn)) # mean
v[... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
| <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.arange(1,11)
accmap = np.array([0,1,0,0,0,-1,-1,2,2,1])
add = np.max(accmap)
mask = accmap < 0
accmap[mask] += add+1
result = np.bincount(accmap, weights = a)
<END_TASK> |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Classification with linear discrimant analysis
Step2: Look at performance over time
| <ASSISTANT_TASK:>
Python Code:
# Authors: Martin Billinger <martin.billinger@tugraz.at>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import ShuffleSpl... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Eulerian Cycles
Step2: Differences between network types
Step3: Some context on these networks is given below, first for MIT8. It stems from a... | <ASSISTANT_TASK:>
Python Code:
from networkit import *
%matplotlib inline
cd ~/workspace/NetworKit
G = readGraph("input/PGPgiantcompo.graph", Format.METIS)
# 2-2) and 2-3) Decide whether graph is Eulerian or not
# Load/generate 3 graphs of different types
mit8 = readGraph("input/MIT8.edgelist", Format.EdgeListTabZero... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Pytorch Introduction
Step2: Here we start defining the linear regression model, recall that in linear regression, we are optimizing for the squ... | <ASSISTANT_TASK:>
Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(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: Question
Step2: Classification accuracy
Step3: Null accuracy
Step4: Comparing the true and predicted response values
Step5: Conclusion
Step6... | <ASSISTANT_TASK:>
Python Code:
# read the data into a Pandas DataFrame
import pandas as pd
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data'
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
pima = pd.read_csv... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Trap 1
Step2: Now, let's see what happens if we move the import of random outside the scope of get_random_array
| <ASSISTANT_TASK:>
Python Code:
# Import Node and Function module
from nipype import Node, Function
# Create a small example function
def add_two(x_input):
return x_input + 2
# Create Node
addtwo = Node(Function(input_names=["x_input"],
output_names=["val_output"],
funct... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: MPI and cluster computing
Step2: Executes with mpiexec
Step3: Coding for multiple "personalities" (nodes, actually)
Step4: Collective communi... | <ASSISTANT_TASK:>
Python Code:
%%file hellompi.py
Parallel Hello World
from mpi4py import MPI
import sys
size = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
name = MPI.Get_processor_name()
sys.stdout.write(
"Hello, World! I am process %d of %d on %s.\n"
% (rank, size, name))
!mpiexec -n 4 python... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Python for STEM Teachers<br/>Oregon Curriculum Network
Step3: <div align="center">graphic by Kenneth Snelson</div>
| <ASSISTANT_TASK:>
Python Code:
import json
series_types = ["Don't Know", "Other nonmetal", "Alkali metal",
"Alkaline earth metal", "Nobel gas", "Metalloid",
"Halogen", "Transition metal", "Post-transition metal",
"Lanthanoid", "Actinoid"]
class Element... |
<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: Noisy Likelihoods
Step4: We'll again define our prior (via prior_transform) to be uniform in each dimension from -10 to 10 and 0 everywhere els... | <ASSISTANT_TASK:>
Python Code:
# system functions that are always useful to have
import time, sys, os
# basic numeric setup
import numpy as np
from numpy import linalg
# inline plotting
%matplotlib inline
# plotting
import matplotlib
from matplotlib import pyplot as plt
# seed the random number generator
rstate = np.ra... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nuist', 'sandbox-2', 'seaice')
# 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:
| <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
np.random.seed(123)
birds = np.random.choice(['African Swallow', 'Dead Parrot', 'Exploding Penguin'], size=int(5e4))
someTuple = np.unique(birds, return_counts=True)
def g(someTuple):
return pd.DataFrame(np.column_stack(someTuple),columns=['birdT... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Tabular data
Step2: Normalization
Step3: Categorical data
Step5: Exercises
Step6: Text
| <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
from sklearn import linear_model
x = np.array([[0, 0], [1, 1], [2, 2]])
y = np.array([0, 1, 2])
print(x,y)
clf = linear_model.LinearRegression()
clf.fit(x, y)
print(clf.coef_)
x_missing = np.array([... |
<SYSTEM_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 start with the temperature, T = 300 °C.
Step2: Next we'll create variables for $Q_n = 37,600 \frac{J}{mol}$ and the universal gas con... | <ASSISTANT_TASK:>
Python Code:
import pint
from math import exp, sqrt
u = pint.UnitRegistry()
Q_ = u.Quantity
T = Q_(300, u.degC)
print('T = {}'.format(T))
T.ito('degK')
print('T = {}'.format(T))
T = T.magnitude * u.kelvin
print(T)
Qn = 37600 * u.J/u.mol
R = 8.31 * u.J/(u.mol*u.kelvin)
PN1 = 0.10
PN2 = 5.0
CN1 = (4... |
<SYSTEM_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 example notebook, we will walk through the creation of logsums from
Step2: We'll also load the saved model from the mode choice estimat... | <ASSISTANT_TASK:>
Python Code:
import larch, numpy, pandas, os
from larch import P, X
larch.__version__
hh, pp, tour, skims = larch.example(200, ['hh', 'pp', 'tour', 'skims'])
exampville_mode_choice_file = larch.example(201, output_file='exampville_mode_choice.html')
m = larch.read_metadata(exampville_mode_choice_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: Block Paradigms
Step2: df contains about 5 minutes of data recorded at 1000Hz. There are 4 channels, EDA, ECG, RSP and the Photosensor used to ... | <ASSISTANT_TASK:>
Python Code:
# Import packages
import neurokit as nk
import pandas as pd
import numpy as np
import matplotlib
import seaborn as sns
# Plotting preferences
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = [14.0, 10.0] # Bigger figures
sns.set_style("whitegrid") # White background
sns.set_pa... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Tf-idf and document similarity
Step2: Lets cluster!
Step3: But what did we get?
| <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('../data/wiki/wiki.csv.gz', encoding='utf8', index_col=None)
df['text'] = df.text.str[:3000]
totalvocab_stemmed = []
totalvocab_tokenized = []
for doc_text in df.text:
allwords_stemmed = tokenize_and_stem(doc_text) #for each item in 'synopses', tokenize/stem
total... |
<SYSTEM_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 also list and download other datasets interactively just typing
Step2: The fileids method provided by all the datasets in nltk.corpus g... | <ASSISTANT_TASK:>
Python Code:
import nltk
nltk.download("movie_reviews")
from nltk.corpus import movie_reviews
len(movie_reviews.fileids())
movie_reviews.fileids()[:5]
movie_reviews.fileids()[-5:]
negative_fileids = movie_reviews.fileids('neg')
positive_fileids = movie_reviews.fileids('pos')
len(negative_fileids), ... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: select interested category to compare using DS-FDR
Step2: output the list of differential abundant taxa (True indicates statistical significanc... | <ASSISTANT_TASK:>
Python Code:
!qiime tools import \
--input-path ../data/deblur-feature-table.biom \
--type 'FeatureTable[Frequency]' \
--source-format BIOMV210Format \
--output-path ../data/dblr_haddad.qza
!qiime dsfdr permutation-fdr \
--i-table ../data/dblr_haddad.qza \
--m-metadata-file ../data/metadata_rare2k.tx... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Enable inline plotting in the Jupyter Notebook
Step2: Intro to H2O Data Munging
Step3: View the top of the H2O frame.
Step4: View the bottom ... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy
from numpy.random import choice
from sklearn.datasets import load_boston
from h2o.estimators.random_forest import H2ORandomForestEstimator
import h2o
h2o.init()
# transfer the boston data from pandas to H2O
boston_data = load_boston()
X = pd.DataFrame(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: Generate Data
Step2: It's easier if we select the correct X and Y axis. Usually, The Y axis would be the value we want to predict and X would b... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
from scipy import stats
import collections
import time
from sklearn.linear_model import SGDRegressor
total_bills = np.random.randint(100, size=1000)
tips = total_bills * 0.10
x = pd.Series(tips, name='tips')
y = pd.Series(total_... |
<SYSTEM_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, let's load the data. Write the path to your iris.csv file (i.e. the one from Lab 02) in the cell below
Step2: Execute the cell below to l... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
path_to_csv = "data/iris.csv"
df = pd.read_csv(path_to_csv, index_col=['species', 'sample_number'])
df.head()
df.plot(kind='hist');
versicolor = df.loc['versicolor']
versicolor.plot(kind='hist');
versicolor.plot(kind='hist', subplots=True, layou... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Lerne Lineare Regression auf Daten
Step2: <span style="color
Step3: <span style="color
Step4: <span style="color
| <ASSISTANT_TASK:>
Python Code:
#Importiere Python Libraries
%matplotlib inline
import pylab as pl
import seaborn as sns
sns.set(font_scale=1.7)
from plotly.offline import init_notebook_mode, iplot
from plotly.graph_objs import *
import plotly.tools as tls
#Set to True
init_notebook_mode(connected=True)
import scipy as ... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Steps
Step4: Craigslist data table columns
Step5: Local data files
Step16: Create FIPS look-up tables
Step17: Get data for a single region
Step18: ... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import psycopg2
import paramiko
import os
import numpy as np
import json
import zipfile
DATA_DIR=os.path.join('..','data')
Path to local data directory
#read postgres connection parameters
with open('postgres_settings.json') as settings_file:
settings = json.... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Then, we define a function that will be mapped onto each job. This function takes the initial
Step2: Then, we load the ChemKED file and generat... | <ASSISTANT_TASK:>
Python Code:
import cantera as ct
import numpy as np
from multiprocessing import Pool
from pyked import ChemKED
# Suppress warnings from loading the mechanism file
ct.suppress_thermo_warnings()
def run_simulation(T, P, X):
gas = ct.Solution('LLNL_sarathy_butanol.cti')
gas.TPX = T, P, X
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: A resonator object takes the path of the data file as an argument (mandatory). The path can be retrieved by using the file UUID and qkit's file ... | <ASSISTANT_TASK:>
Python Code:
## start qkit and import the necessary classes; here we assume a already configured qkit environment
import qkit
qkit.start()
from qkit.analysis.resonator import Resonator
r = Resonator(qkit.fid.measure_db['XXXXXX'])
r.fit_lorentzian(f_min = 5.0e9) ## set lower frequency boundary
r.fit... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Rabbit Redux
Step3: Now update run_simulation with the following changes
Step4: Test your changes in run_simulation
Step6: Next, update plot_... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from modsim import *
system = System(t0 = 0,
t_end = 10,
adult_pop0 = 10,
birth_rate = 0.9,
death_rate = 0.5)
system
def run_simulation(system):
Runs a proportional growth model.
Adds TimeSe... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Looking at the vectors
Step2: Here's the first 25 "words" in glove.
Step3: This is how you can look up a word vector.
Step4: Just for fun, le... | <ASSISTANT_TASK:>
Python Code:
def get_glove(name):
with open(path+ 'glove.' + name + '.txt', 'r') as f: lines = [line.split() for line in f]
words = [d[0] for d in lines]
vecs = np.stack(np.array(d[1:], dtype=np.float32) for d in lines)
wordidx = {o:i for i,o in enumerate(words)}
save_array(res_pat... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: parameters to set
Step2: generate data
Step3: plot timeseries. Can take a minute to appear due to plot size and complexity.
Step4: functions ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rc('xtick', labelsize=14)
matplotlib.rc('ytick', labelsize=14)
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: df_raw['2yf1y'] = df_raw[['1y','3y']].apply(lambda x
| <ASSISTANT_TASK:>
Python Code:
df_raw.tail()
def getForward(v,t1=1,t2=2):
return (np.power(np.power(1+v[1]/100,t2)/np.power(1+v[0]/100,t1),1/(t2-t1))-1)*100
ind1 = 0
ind2 = 1
v2 = df_raw.iloc[-1,ind2]
v1 = df_raw.iloc[-1,ind1]
t1 = int(df_raw.columns[ind1].strip('y'))
t2 = int(df_raw.columns[ind2].strip('y'))
print... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We can see that simulating the data as an AR(1) model is not effective in giving us anything similar the aquired data. This is due to the fact ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
from sklearn import datasets, linear_model
%matplotlib inline
def set_data(p, x):
temp = x.flatten()
n = len(temp[p:])
x_T = temp[p:].reshape((n, 1))
X_p = np.ones((n, p + 1))
for i in range(1, 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: Basic rich display
Step3: Use the HTML object to display HTML in the notebook that reproduces the table of Quarks on this page. This will requi... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
from IPython.display import HTML
from IPython.display import display
assert True # leave this to grade the import statements
Image(url='http://upload.wikimedia.org/wikipedia/commons/4/43/The_Earth_seen_from_Apollo_17_with_transparent_background.png')
ass... |
<SYSTEM_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 get different sections of a string, we can use groups via parenthesis
Step2: If parenthesis are actually part of the pattern, they need to b... | <ASSISTANT_TASK:>
Python Code:
import re
phoneNumRegex = re.compile(r'\d\d\d-\d\d\d-\d\d\d\d')
phoneNumRegex.search('My number is 415-555-4242') # returns a match object
mo = phoneNumRegex.search('My number is 415-555-4242') # store match object
mo.group() # print matched strings in match object
phoneNumRegex = re.com... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Simple Multiplication
Step2: Of course, trivial identities are applied.
Step3: In the case of word labels, adjacent words are not fused
Step4:... | <ASSISTANT_TASK:>
Python Code:
import vcsn
ctx = vcsn.context('law_char, q')
def exp(e):
return ctx.expression(e)
exp('a*b') * exp('ab*')
exp('<2>a') * exp('<3>\e')
exp('<2>a') * exp('\z')
exp('a') * exp('b') # Two one-letter words
exp('ab') # One two-letter word
exp('(a)(b)') # Two one-letter words
exp('ab') *... |
<SYSTEM_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... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if ... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Host and user information
Step2: List of file prefixes for micrograph images and XML metadata
Step3: For each name in the list
| <ASSISTANT_TASK:>
Python Code:
import mdcs
user='admin'
pswd='admin'
host='http://127.0.0.1:8000'
template_name='TEM-Tutorial'
name_list=[
"Al6061_DF_343C",
"Al6061_SAED_343C",
"Al6061-BF-343C",
"CoAl-W-900-1000h-SAED",
"CoAlW-900-1000h",
"TRIP700-750C-carbide-SAED",
"TRIP700-carbide-750C-... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (10, 20)
DON'T MODIFY 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: What is our independent variable? What is our dependent variable?
Step2: Median reaction time, congruent condition
Step3: Notice that not a si... | <ASSISTANT_TASK:>
Python Code:
# Imports
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from scipy.stats import ttest_rel, norm
# Read in data
df = pd.read_csv('stroopdata.csv')
IQR_congruent = df['Congruent'].quantile(0.75) - df['Congruent'].quantile(0.25)
IQR_incong... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.