Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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7,100 | <ASSISTANT_TASK:>
Python Code:
import os
import re
import psycopg2
import getpass
from collections import OrderedDict
# database config
sqluser=getpass.getuser()
# keep sqlpass blank if using peer authentication
sqlpass=''
# database
sqldb='mimic'
sqlschema='public,mimiciii'
query_schema = 'set search_path to ' + sqlsc... | <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: Benchmark all concepts in make-concepts.sql
Step2: Benchmark all concepts in make-concepts.sql
Step3: Compare parallel with no parallel
|
7,101 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import sys,os
ia898path = os.path.abspath('/etc/jupyterhub/ia898_1s2017/')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
nb = ia.nbshow(2)
f = mpimg... | <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: Plotamos seu histograma e calculamos a transformação de contraste que equaliza o histograma baseado
Step2: A aplicação da transformação T em f
... |
7,102 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
def checkerboard(size):
Return a 2d checkboard of 0.0 and 1.0 as a NumPy array
a = np.zeros((size,size))
a[::2,::2] = 1.0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Checkerboard
Step3: Use vizarray to visualize a checkerboard of size=20 with a block size of 10px.
Step4: Use vizarray to visualize a checkerb... |
7,103 | <ASSISTANT_TASK:>
Python Code:
import re
from pyknow import *
class Producto(Fact):
Producto que ha comprado un cliente.
>>> Producto(nombre="pepsi", tipo="refresco de cola", cantidad=1)
pass
class Cupon(Fact):
Cupón a generar para la próxima compra del cliente.
>>> Cupon(tipo="2x1",... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step5: Hechos
Step8: Objetivo 1
Step9: Pruebas
Step12: Objetivo 2
Step13: Pruebas
Step14: Si compramos ambos productos de un pack no se nos debe g... |
7,104 | <ASSISTANT_TASK:>
Python Code:
print(9876543)
name = 12345
number = 100000
print("ID before: " + str(id(number)))
number = 123456
print("ID after: " + str(id(number)))
number = 100000
print("ID before: " + str(id(number)))
number = number + 1
print("ID after: " + str(id(number)))
print(f"ID of number ({number}): " ... | <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: <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: ... |
7,105 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)... | <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 tutorial tut-events-vs-annotations describes in detail the
Step2: Reading and writing events from/to a file
Step3: When writing event arra... |
7,106 | <ASSISTANT_TASK:>
Python Code:
from nbloader import Notebook
loaded_notebook = Notebook('test.ipynb')
loaded_notebook.run_all()
loaded_notebook.ns['a']
loaded_notebook.ns['b']
loaded_notebook.run_tag('add_one')
print(loaded_notebook.ns['a'])
loaded_notebook.run_tag('add_one')
print(loaded_notebook.ns['a'])
loaded_... | <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 above commad loades a notebook as an object. This can be done inside a jupyter notebook or a regular python script.
Step2: After loaded_not... |
7,107 | <ASSISTANT_TASK:>
Python Code:
import mysql.connector
import pandas as pd
df= pd.read_csv('C:/Users/Alex/Documents/eafit/semestres/X semestre/programacion/taller2.tsv', sep = '\t')
df[:1]
CREATE TABLE enfermedad
(
id_enfermedad int PRIMARY KEY,
nombre varchar(255)
);
create table plataforma
(
id_plataforma int primar... | <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: La idea de este taller es manipular archivos (leerlos, parsearlos y escribirlos) y hacer lo mismo con bases de datos estructuradas.
Step2: Qué ... |
7,108 | <ASSISTANT_TASK:>
Python Code:
import mariadb
import json
with open('../credentials.json', 'r') as crd_json_fd:
json_text = crd_json_fd.read()
json_obj = json.loads(json_text)
credentials = json_obj["Credentials"]
username = credentials["username"]
password = credentials["password"]
table_name = "publications"
... | <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: 2. Counting publications.
Step2: 3. Distinct Affiliations
Step3: 3. TF-IDF and K-Means?
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7,109 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sqlite3
%matplotlib inline
# Connect to the MIMIC database
conn = sqlite3.connect('data/mimicdata.sqlite')
# Create our test query
test_query =
SELECT subject_id, hadm_id, admittime, dischtime, admission_type,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Connect to the database
Step4: Load the chartevents data
Step5: Review the patient's heart rate
Step6: In a similar way, we can select rows f... |
7,110 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
figsize(8, 6)
import sys
sys.path.insert(0, "../")
import pandas
import numpy
from folding_group import FoldingGroupClassifier
from rep.data import LabeledDataStorage
from rep.report import ClassificationReport
from rep.report.metrics import RocAuc
from sklearn.metrics 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: Import
Step2: Reading initial data
Step3: Data preprocessing
Step4: Define mask for non-B events
Step5: Define features
Step6: Test that B-... |
7,111 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
df = pd.read_csv('preparation.csv',delimiter=',')
df.shape
!ls
59400*0.8
dftrain = pd.read_csv('preparation.csv',delimiter=',',nrows=47520)
dfpayment = pd.read_csv('trainingset.csv',delimiter=',',nrows=47520)
dftrain.tail()
dftrain['terrain'] = 'dat... | <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: take 80% as train dataframe, and leave 20% as a validator/to check the accuracy of our prediction before applying the model to the real test dat... |
7,112 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import Hodograph, SkewT
from metpy.units import units
col_names = ['pressure', 'height', 'te... | <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: Getting Data
Step2: Thermodynamic Calculations
Step3: Basic Skew-T Plotting
Step4: Advanced Skew-T Plotting
Step5: Adding a Hodograph
|
7,113 | <ASSISTANT_TASK:>
Python Code:
print("The answer should be three: " + str(1+2))
!nvidia-smi
#imports
import h5py
import pandas as pd
import numpy as np
import pprint as pp
import tensorflow as tf
from tensorflow.contrib import rnn
import math
import matplotlib.pyplot as plt
import warnings
import prepareData as pr... | <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: Let's execute the cell below to display information about the GPUs running on the server.
Step2: 2. Lab Overview
Step3: Data Preparation
Step4... |
7,114 | <ASSISTANT_TASK:>
Python Code:
# Loads the training and test data sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
first_image = X_train[0, :, :]
# To interpret the values as a 28x28 image, we need to reshape
# the numpy array, which is one dimensional.
plt.imshow(first_image, cmap=plt.cm.Greys);
num_class... | <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: Multilayer Perceptron
Step2: Different Ways to Summarize Model
Step3: Train Classifier
Step4: Model Evaluation
Step5: Predicting a Couple of... |
7,115 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X, y = iris.data, iris.target
classifier = KNeighborsClassifier()
y
from sklearn.model_selection import train_test_split
train_X, test_X, train_y, test_y = train_test_split(X, y, ... | <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: Si pensamos la forma en que normalmente se aplica el aprendizaje automático, la idea de una partición de entrenamiento y test tiene sentido. Los... |
7,116 | <ASSISTANT_TASK:>
Python Code:
from chemspipy import ChemSpider
# Tip: Store your security token as an environment variable to reduce the chance of accidentally sharing it
import os
mytoken = os.environ['CHEMSPIDER_SECURITY_TOKEN']
cs = ChemSpider(security_token=mytoken)
comp = cs.get_compound(2157)
comp
print(comp.... | <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: Then connect to ChemSpider by creating a ChemSpider instance using your security token
Step2: All your interaction with the ChemSpider database... |
7,117 | <ASSISTANT_TASK:>
Python Code:
from google.cloud import aiplatform
REGION = "us-central1"
PROJECT = !(gcloud config get-value project)
PROJECT = PROJECT[0]
# Set `PATH` to include the directory containing KFP CLI
PATH = %env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
%%writefile ./pipeline_vertex/pipeline_vertex_a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Understanding the pipeline design
Step3: Compile the pipeline
Step4: Let us make sure that the ARTIFACT_STORE has been created, and let us cre... |
7,118 | <ASSISTANT_TASK:>
Python Code:
def maxProfit(a , b , n ) :
maxP = - 1
for i in range(0 , n + 1 ) :
sumA = sum(a[: i ] )
sumB = sum(b[i : ] )
maxP = max(maxP , sumA + sumB )
return maxP
if __name__== "__main __":
a =[2 , 3 , 2 ]
b =[10 , 30 , 40 ]
print(maxProfit(a , b , 4 ) )
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
7,119 | <ASSISTANT_TASK:>
Python Code:
from obspy import UTCDateTime
from obspy.clients.fdsn import Client as FDSN_Client
from obspy import read_inventory
client = FDSN_Client("GEONET")
inventory = client.get_stations(latitude=-42.693,longitude=173.022,maxradius=0.5, starttime = "2016-11-13 11:05:00.000",endtime = "2016-11-1... | <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 GeoNet FDSN client
Step2: Accessing Station Metadata
Step3: The following examples dive into retrieving different information from the ... |
7,120 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dat... | <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: 2 - Overview of the Problem set
Step2: We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. ... |
7,121 | <ASSISTANT_TASK:>
Python Code:
r = Symbol('r',positive=True)
V = 1/r
F = - diff(V, r)
F
epos = np.array([[0.0, 1.0, 0.0],
[0.2, 0.3, 0.0]])
npos = np.array([[0.0, 0.0, .0]])
def compute_bare_force(npos, epos, F, r):
forces = np.zeros_like(npos)
for ion_idx,ion_pos in enumerate(npos):
... | <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: One solution is to smooth the contribution inside some cutoff radius $R_c$.
Step2: The goal is to fit a constant function with a polynomial tha... |
7,122 | <ASSISTANT_TASK:>
Python Code:
ph_sel_name = "DexDem"
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' ... | <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 software and filenames definitions
Step2: Data folder
Step3: List of data files
Step4: Data load
Step5: Laser alternation selection
Ste... |
7,123 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import csv
import io
import urllib.request
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
url = 'http://radwatch.berkeley.edu/sites/default/files/dosenet/etch_roof.csv'
response = urllib.request.urlopen(url)
reader = csv.r... | <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: For this example, I will bin DoseNet data from our device on the Etcheverry Roof and average the data. Afterwards, I will plot the data to show... |
7,124 | <ASSISTANT_TASK:>
Python Code:
dx = 0.3
x = np.arange(0, 10, dx) # returns [0, dx, 2dx, 3dx, 4dx, 5dx, ...]
print(x)
f1 = np.sin(x)
f2 = x**2/100
f3 = np.log(1+x)-1
fs = [f1, f2, f3]
for i in range(3): plt.plot(x, fs[i])
df1 = np.cos(x)
df2 = x/50
df3 = 1/(1+x)
dfs = [df1, df2, df3]
def derivative(f, dx):
return (... | <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: Now let us see if we can calculate these derivatives numerically.
Step2: That worked pretty well, but we can do even better by using central di... |
7,125 | <ASSISTANT_TASK:>
Python Code:
class Item(object):
def __init__(self, name, description, location):
self.name = name
self.description = description
self.location = location
def update_location(self, new_location):
pass
class Equipment(Item):
pass
c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step5: Composition
Step6: This has the basic functionality implemented but there are some improvements we can make.
Step7: The API documentation for... |
7,126 | <ASSISTANT_TASK:>
Python Code:
%reload_ext watermark
%watermark -p networkx
import networkx as nx
from networkx.algorithms.community import k_clique_communities, girvan_newman
import matplotlib.pyplot as plt
%matplotlib inline
GA = nx.read_gexf('../data/ga_graph.gexf')
gn_comm = girvan_newman(GA)
first_iteration_comm ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Girvan Newman Algorithm
Step3: K-Clique Communities
Step4: Karate Club Time
Step5: Validation
|
7,127 | <ASSISTANT_TASK:>
Python Code:
!pip install --user apache-beam[gcp]
import os
import googleapiclient.discovery
import shutil
from google.cloud import bigquery
from matplotlib import pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.callbacks import TensorBoard
... | <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: Restart the kernel before proceeding further (On the Notebook menu - Kernel - Restart Kernel).
Step2: Re-train our model with trips_last_5min f... |
7,128 | <ASSISTANT_TASK:>
Python Code:
import gzip
import cPickle as pickle
with gzip.open("../data/train.pklz", "rb") as train_file:
train_set = pickle.load(train_file)
with gzip.open("../data/test.pklz", "rb") as test_file:
test_set = pickle.load(test_file)
with gzip.open("../data/questions.pklz", "rb") as questions_... | <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: Make training set
Step2: It means that user 0 tried to solve question number 1 which has 77 tokens for question and he or she answered at 61st ... |
7,129 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_induced_power
print(__doc__)
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: Set parameters
|
7,130 | <ASSISTANT_TASK:>
Python Code:
import kfp
import kfp.gcp as gcp
import kfp.dsl as dsl
import kfp.compiler as compiler
import kfp.components as comp
import datetime
import kubernetes as k8s
# Required Parameters
PROJECT_ID='<ADD GCP PROJECT HERE>'
GCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'
# Optional Parameters, but... | <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 client
Step2: Writing the program code
Step3: Create a Docker container
Step4: Build docker image
Step5: If you want to use docker to... |
7,131 | <ASSISTANT_TASK:>
Python Code:
test_data_df.head()
train_data_df.Sentiment.value_counts()
import numpy as np
np.mean([len(s.split(" ")) for s in train_data_df.Text])
import re, nltk
from sklearn.feature_extraction.text import CountVectorizer
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer... | <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: Let's count how many labels do we have for each sentiment class.
Step2: Finally, let's calculate the average number of words per sentence. We c... |
7,132 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
def char_probs(s):
Find the probabilities of the unique characters in the string s.
Parameters
----------
s : str
A string of characters.
... | <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: Character counting and entropy
Step4: The entropy is a quantiative measure of the disorder of a probability distribution. It is used extensivel... |
7,133 | <ASSISTANT_TASK:>
Python Code:
from siphon.simplewebservice.ndbc import NDBC
data_types = NDBC.buoy_data_types('46042')
print(data_types)
df = NDBC.realtime_observations('46042')
df.tail()
df = df.dropna(axis='columns', how='all')
df.head()
# Your code goes here
# supl_obs =
# %load solutions/get_obs.py
import pan... | <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: In this case, we'll just stick with the standard meteorological data. The "realtime" data from NDBC contains approximately 45 days of data from ... |
7,134 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import sys
sys.path.append('../..')
from matplotlib import pylab
pylab.rcParams['figure.figsize'] = 16, 10
import functools
import numpy
import scipy
import scipy.special
import time
from crocodile.clean import *
from crocodile.synthesis import *
from crocodile.simulate... | <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: Generate baseline coordinates for an observation with the VLA over 6 hours, with a visibility recorded every 10 minutes. The phase center is fix... |
7,135 | <ASSISTANT_TASK:>
Python Code:
import QuantLib as ql
import matplotlib.pyplot as plt
%matplotlib inline
ql.__version__
# option data
maturity_date = ql.Date(15, 1, 2016)
spot_price = 127.62
strike_price = 130
volatility = 0.20 # the historical vols or implied vols
dividend_rate = 0.0163
option_type = ql.Option.Call
... | <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: Let us consider a European and an American call option for AAPL with a strike price of \$130 maturing on 15th Jan, 2016. Let the spot price be \... |
7,136 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
# only need this line for Python 2.7 ... by importing print() we also get support for unpacking within print
# * for unpacking is not recognized in this context in Python 2.7 normally
# arguments on print and behavior of print in this exam... | <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 a point of curiosity though ... how would we do it using math instead of relying on print tricks which probably convert the numbers to string... |
7,137 | <ASSISTANT_TASK:>
Python Code:
from osgeo import gdal
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
# %load ../neon_aop_python_functions/raster2array.py
# raster2array.py reads in the first band of geotif file and returns an array and associated... | <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 also need to import the following functions created in previous lessons
Step2: Calculate Hillshade
Step3: Now that we have a function to ge... |
7,138 | <ASSISTANT_TASK:>
Python Code:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.io import *
from fastai.conv_learner import *
from fastai.column_data import *
# PATH = Path('data/nietzsche/')
PATH = 'data/nietzsche/'
get_data("https://s3.amazonaws.com/text-datasets/nietzsche.txt", f'{PATH}nietzsche.... | <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. Setup
Step2: Sometimes it's useful to have a zero value in the dataset, eg
Step3: Map from chars to indices and back again
Step4: idx will... |
7,139 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.2,<2.3"
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
times1 = np.linspace(0,1,201)
times2 = np.linspace(90,91,201)
b.add_dataset('lc', time... | <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. See Building a System for more details.
Step2: Now we'll create empty lc... |
7,140 | <ASSISTANT_TASK:>
Python Code:
import ee
from IPython import display
import math
from matplotlib import pyplot
import numpy
from osgeo import gdal
import tempfile
import tensorflow as tf
import urllib
import zipfile
ee.Initialize()
input_image = ee.Image('LANDSAT/LT5_L1T_TOA_FMASK/LT50100551998003CPE00')
def print_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: Initialize the Earth Engine client. This assumes that you have already configured Earth Engine credentials in this Datalab instance. If not, see... |
7,141 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import sygma
import omega
import stellab
#loading the observational data module STELLAB
stellab = stellab.stellab()
# OMEGA parameters for MW
mass_loading = 1 # How much mass is ejected from the galaxy per stellar mass formed
nb_1a_per_m = 3.0e-3 # Nu... | <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: Simulation of the Milky Way
Step2: Comparison of chemical evolution prediction with observation
Step3: Tracing back to simple stellar populati... |
7,142 | <ASSISTANT_TASK:>
Python Code:
a = 5
b = a + 3.1415
c = a / b
print(a, b, c)
s = 'Ice cream' # A string
f = [1, 2, 3, 4] # A list
d = 3.1415928 # A floating point number
i = 5 # An integer
b = True # A boolean value
type(s)
isinstance(s, str) ... | <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: Note, we did not need to declare variable types (like in fortran), we could just assign anything to a variable and it works. This is the power o... |
7,143 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pymc3 import *
import numpy as np
import matplotlib.pyplot as plt
size = 200
true_intercept = 1
true_slope = 2
x = np.linspace(0, 1, size)
# y = a + b*x
true_regression_line = true_intercept + true_slope * x
# add noise
y = true_regression_line + np.random.normal... | <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: Generating data
Step2: Estimating the model
Step3: This should be fairly readable for people who know probabilistic programming. However, woul... |
7,144 | <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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Description:
Step1: <table class="tfo-notebook-buttons" align="left">
Step2: Some imports we will need for the tutorial. We will use tensorflow_federated, the open... |
7,145 | <ASSISTANT_TASK:>
Python Code:
from flexx.webruntime import launch
rt = launch('http://flexx.rtfd.org', 'xul', title='Test title')
from flexx.pyscript import py2js
print(py2js('square = lambda x: x**2'))
def foo(n):
res = []
for i in range(n):
res.append(i**2)
return res
print(py2js(foo))
def foo(n... | <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: flexx.pyscript
Step2: flexx.react
Step3: A signal can have multiple upstream signals.
Step4: Dynamism provides great flexibility
Step5: flex... |
7,146 | <ASSISTANT_TASK:>
Python Code:
from google.colab import drive
drive.mount('/content/gdrive')
! mkdir gdrive/MyDrive/rf_keras
%cd gdrive/MyDrive/rf_keras
! ls
! git clone https://github.com/google-research/receptive_field.git
! ls
%cd receptive_field/
! ls
! pip install .
! pip install tensorflow
import tensorflow.comp... | <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: Computing receptive field parameters of tf.keras.applications models.
Step2: Bonus stuff
|
7,147 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pyplot as plt
import pandas as pd
print(pd.__version__)
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
# let's see what compute devices we have av... | <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 and validating our model
Step2: Descison Boundaries for 2 Dimensions
Step3: Converting our Keras Model to TensorFlow.js
Step4: Use th... |
7,148 | <ASSISTANT_TASK:>
Python Code:
import nltk
from tethne.readers import zotero
import matplotlib.pyplot as plt
from nltk.corpus import stopwords
import gensim
import networkx as nx
import pandas as pd
from collections import defaultdict, Counter
wordnet = nltk.WordNetLemmatizer()
stemmer = nltk.SnowballStemmer('english')... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: 1.7. Finding concepts in texts - Latent Dirichlet Allocation
Step3: We will represent our documents as a list of lists. Each sub-list contains ... |
7,149 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
import os
from collections import Counter
from nltk.tokenize import TweetTokenizer
import codecs
from random import randint
tf.__version__
with codecs.open(os.path.join('../data', 'sent.csv'), 'r', encoding='utf-8') as f:
corpus_line_by_line ... | <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: Vanilla Generative Adversarial <img src="http
Step3: State of art weight Initialization strategy
Step4: Discriminator
Step5: Generator
Step6:... |
7,150 | <ASSISTANT_TASK:>
Python Code:
print("A", "B", "A|B", "A&B", "not A")
for A in [False, True]:
for B in [False, True]:
print(A, B, A or B, A and B, not A)
number = 987
rbase = 16
result = ""
while number > 0:
remainder = number % rbase
result = str(remainder) + result
number = number // rbase
pr... | <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: Opdracht 2
Step2: Opdracht 3
Step3: Opdracht 4
|
7,151 | <ASSISTANT_TASK:>
Python Code:
from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
%%microblaze base.PMODA
#include "xparameters.h"
#include "xtmrctr.h"
#include "gpio.h"
#include "timer.h"
#include <pmod_grove.h>
#define TCSR0 0x00
#define TLR0 0x04
#define TCR0 0x08
#define TCSR1 0x10
#define T... | <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. Use Microblaze to control the ultrasonic ranger
Step2: 2. Do one-time distance measurement
|
7,152 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('../')
import numpy as np
from anemoi import MiniZephyr25D, SimpleSource, AnalyticalHelmholtz
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_matplotlib_format... | <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: Error plots for MiniZephyr vs. the AnalyticalHelmholtz response
Step2: Relative error of the MiniZephyr solution (in %)
|
7,153 | <ASSISTANT_TASK:>
Python Code:
for i in range(10):
print(i)
for i in range(300, 306):
print(i)
for i in range(15, 26, 3):
print(i)
numbers = list(range(10))
print(numbers[3:])
words = "Be yourself; everyone else is already taken".split()
for i in range(len(words)):
print(words[i])
for word in word... | <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, range() will return a number of integers, starting from zero, up to (but not including) the number which we pass as an argument to the fun... |
7,154 | <ASSISTANT_TASK:>
Python Code:
%run "../Functions/1. Google form analysis.ipynb"
%run "../Functions/4. User comparison.ipynb"
#getAllResponders()
setAnswerTemporalities(gform)
# small sample
#allData = getAllUserVectorData( getAllUsers( rmdf1522 )[:10] )
# complete set
#allData = getAllUserVectorData( getAllUsers( rm... | <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 vectors of users
Step2: getAllUserVectorData
Step3: Correlation Matrix
Step4: List of users and their sessions
Step5: List of sessions ... |
7,155 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-cm2-hr4', 'land')
# 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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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
7,156 | <ASSISTANT_TASK:>
Python Code:
# Toy Features Dictionary
features = {"sq_footage": [ 1000, 2000, 3000, 4000, 5000],
"house_type": ["house", "house", "apt", "apt", "townhouse"]}
feat_cols = [
tf.feature_column.numeric_column('sq_footage'),
tf.feature_column.indicator_column(
tf.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:
Step1: Feature Column Definition
Step2: Inspect Transformed Data
Step3: Excercise 1
Step4: Excercise 2
Step5: Excercise 3
|
7,157 | <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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Description:
Step1: 不毛の高原
Step2: TensorFlow Quantum をインストールします。
Step3: 次に、TensorFlow とモジュールの依存関係をインポートします。
Step5: 1. 概要
Step7: ここでは、1 つのパラメータ $\theta_{1,1}$ の勾配... |
7,158 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
if not os.getenv("IS_TESTING... | <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: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Step3: Before you begin
Step4: Region
Step5:... |
7,159 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from datetime import datetime
s = pd.Series([0.13, 0.21, 0.15, 'NaN', 0.29, 0.09, 0.24, -10], dtype='f',
index = [datetime(2015,11,16,15,41,23), datetime(2015,11,16,15,42,22), datetime(2015,11,16,15,43,25), datetime(2015,11,16,15,44,20), datetime(2015... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Series
Step2: As you can see, it's dealt with our missing value nicely - this is one of the nice things about Pandas.
Step3: Note this also go... |
7,160 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
from statsmodels.graphics.tsaplots import plot_predict
from statsmodels.tsa.arima_process import arma_generate_sample
from statsmodels.tsa.arima.model import ARIMA
np.random.seed(12345)
arparams = np.array([0.75, -0.25])
maparams ... | <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: Generate some data from an ARMA process
Step2: The conventions of the arma_generate function require that we specify a 1 for the zero-lag of th... |
7,161 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from pandas import date_range
import bqplot.pyplot as plt
from bqplot import *
security_1 = np.cumsum(np.random.randn(150)) + 100.
security_2 = np.cumsum(np.random.randn(150)) + 100.
fig = plt.figure(title='Security 1')
axes_options = {'x': {'label': 'Index'}, 'y': {'l... | <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: Basic Line Chart
Step2: We can explore the different attributes by changing each of them for the plot above
Step3: In a similar way, we can al... |
7,162 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
def blight_model():
# Your code here
return # Your answer here
df_train = pd.read_csv('train.csv', encoding = "ISO-8859-1")
df_test = pd.read_csv('test.csv', encoding = "ISO-8859-1")
df_train.columns
list_to_remove = ['balance_due',... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1:
Step2: Train, keep, test split
Step3: Train a NeuralNet and see the performance
|
7,163 | <ASSISTANT_TASK:>
Python Code:
reviews_test = pd.read_csv('data/reviews_test.csv', header=0, encoding='utf-8')
reviews_train = pd.read_csv('data/reviews_train.csv', header=0, encoding='utf-8')
X_train_raw = reviews_train.comment
y_train_raw = reviews_train.reting
X_test_raw = reviews_test.comment
y_test_raw = reviews_t... | <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: Загрузка модели word2vec
Step2: Подготовка данных
Step3: Обучение модели
Step4: Результаты
|
7,164 | <ASSISTANT_TASK:>
Python Code:
# Share functions used in multiple notebooks
%run Shared-Functions.ipynb
# Load up the packages to investigate the data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
%matplotlib inline
import seaborn as sns
import os
# OS-independent wa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: ACKNOWLEDGEMENT
Step2: This means that the dataset has 97 rows and 2 columns. Let's see what the data looks like.
Step3: Step 1
Step4: The co... |
7,165 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install tensorflow==1.15.3
import numpy as np
import seaborn as sns
import pandas as pd
import tensorflow as tf
SEQ_LEN = 10
def create_time_series():
freq = (np.random.random() * 0.5) + 0.1 # 0.1 to 0.6
ampl = 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: <h2> RNN </h2>
Step2: <h3> Input Fn to read CSV </h3>
Step3: Reading data using the Estimator API in tf.estimator requires an input_fn. This i... |
7,166 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
reviews = pd.read_csv('reviews.txt', header=None)
labels = pd.read_csv('labels.txt', header=None)
reviews.head()
labels.head()
from collections import Counter
tota... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Preparing the data
Step2: Counting word frequency
Step3: Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in... |
7,167 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
%matplotlib notebook
import matplotlib
import matplotlib.pyplot as plt
import codecs
import os
import collections
from six.moves import cPickle
from six import text_type
import time
from __future__ import print_function
class Args():
def _... | <SYSTEM_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 needed for Jupiter
Step2: Imports needed for utilities
Step4: Args, to define all parameters
Step5: Load the data
Step6: Let's see ho... |
7,168 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Keras imports
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
# Build the model with keras
model = Sequential()
model.add( Dense( 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: Defining the model with keras
Step2: Training the network
Step3: and define a function to look at the predictions of the model (which for the ... |
7,169 | <ASSISTANT_TASK:>
Python Code:
# Install required package (Katib SDK).
!pip install kubeflow-katib==0.13.0
from kubeflow.katib import KatibClient
from kubernetes.client import V1ObjectMeta
from kubeflow.katib import V1beta1Experiment
from kubeflow.katib import V1beta1AlgorithmSpec
from kubeflow.katib import V1beta1Alg... | <SYSTEM_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 required packages
Step2: Define your Experiment
Step3: You can print the Experiment's info to verify it before submission.
Step4: Crea... |
7,170 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi']=150
# Gravity Recovery and Climate Experiment (GRACE) Data
# Source: http://grace.jpl.nasa.gov/
# Current surface mass change data, measuring equivalent water thickness in cm, versus time
# This data fetcher use... | <SYSTEM_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 scale factor
Step2: Plot EWD $\times$ scale factor
|
7,171 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function # Python 2/3 compatibility
import numpy as np
import pandas as pd
from IPython.display import Image
## Your Turn
## Your Turn
## Choosing an Estimator
# http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
Image("http://sci... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load Data
Step2: Vectorize
Step3: Model
Step4: Model Tuning
Step5: Feeling Good? - Let's Update Kaggle Submission
Steps
|
7,172 | <ASSISTANT_TASK:>
Python Code:
problem_name = "librispeech_clean"
asr_problem = problems.problem(problem_name)
encoders = asr_problem.feature_encoders(None)
model_name = "transformer"
hparams_set = "transformer_librispeech_tpu"
hparams = trainer_lib.create_hparams(hparams_set,data_dir=data_dir, problem_name=problem_nam... | <SYSTEM_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 checkpoint
Step2: Define transcribe function
Step3: Decoding prerecorded examples
Step5: Recording your own examples
|
7,173 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15.0, 8.0)
# First, we need to know what's in the data file.
!head R11ceph.dat
class Cepheids(object):
def __init__(self,filename):
# Read in the data and store it in... | <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 Look at Each Host Galaxy's Cepheids
Step2: OK, now we are all set up! Let's plot some data.
|
7,174 | <ASSISTANT_TASK:>
Python Code:
# 2001 census area units
path = hp.DATA_DIR/'collected'/'Geographical Table.csv'
f = pd.read_csv(path, dtype={'SAU': str})
f = f.rename(columns={
'SAU': 'au2001',
'SAU.Desc': 'au_name',
'TA': 'territory',
'Region': 'region',
})
del f['Water']
f.head()
# rental area units... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Process area units and rental areas into GeoJSON
Step2: Create geodata for rental areas
Step3: Choose representative points for rental areas u... |
7,175 | <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 *
model = Model()
NUM_LAYERS=2
INPUT_DIM=50
HIDDEN_DIM=10
builder = LSTMBuilder(NUM_LAYERS, INPUT_DIM, HIDDEN_DIM, mo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: An LSTM/RNN overview
Step2: Note that when we create the builder, it adds the internal RNN parameters to the model.
Step3: If our LSTM/RNN was... |
7,176 | <ASSISTANT_TASK:>
Python Code:
#graphistry
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more options, see https://github.com/graphistry/pygraphistry#configure
#splunk
SPLUNK = {
'host': 'SPLUNK.MYSI... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Imports
Step2: Helpers
Step3: Splunk
Step4: Bro/Zeek
Step5: Graphistry
Step7: Notebook intro
Step9: 1. Hunting through encrypted traffic
S... |
7,177 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
fname = "../data/periods.csv"
df = pd.read_csv(fname)
df
df[df.name=="Permian"].start
df.loc[df.name=='Cretaceous', 'start'] = 145.0
df.loc[df.name=='Cretaceous', 'start']
df.to_csv("../data/pdout.csv")
import csv
with open(fname) as f:
reader = csv.reader(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: We can get the start of the Permian like this
Step2: Let's fix the start of the Cretaceous
Step3: After you have changed or added to a DataFra... |
7,178 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
# plot the predicted values and actual values (for the test data)
def plot_result(test_df, pred_df, dt_col="timestamp", value_col="value", past_seq_len=1):
# target column of dataframe is "value"
# default past sequence length is 1
pred_valu... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 0. Helper function definations
Step2: 1. load data
Step3: Now we download the dataset and load it into a pandas dataframe.
Step4: Below are s... |
7,179 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import sympy as sp
from devito import *
#NBVAL_IGNORE_OUTPUT
from examples.seismic import Model, plot_velocity
shape = (301, 501) # Number of grid point (nx, ny, nz)
spacing = (10., 10) # Grid spacing in m. The domain size is now 3km by 5km
origin = (0., 0) # What 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: We will create a simple velocity model here by hand for demonstration purposes. This model essentially consists of three layers, each with a dif... |
7,180 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import rc
rc('text', usetex=True)
from bigmali.grid import Grid
from bigmali.prior import TinkerPrior
from bigmali.hyperparameter import get
import numpy as np
from scipy.stats import lognorm
from numpy.random import norma... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step2: Probability Functions
Step3: Results
Step4: Turning into Probabilistic Catalogue
|
7,181 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
from pyquickhelper.helpgen import NbImage
NbImage("images/dicho.png")
def recherche_dichotomique(element, liste_triee):
a = 0
b = len(liste_triee)-1
m = (a+b)//2
while a < b :
if liste_triee[m] == el... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Lorsqu'on décrit n'importe quel algorithme, on évoque toujours son coût, souvent une formule de ce style
Step2: Version itérative
Step3: Vers... |
7,182 | <ASSISTANT_TASK:>
Python Code:
simplify(diff(x**n,x))
from sympy import *
init_printing()
x,n = symbols('x n')
funkcije = [1,x**n,sin(x),cos(x), exp(x),log(x)]
tabela = [[f,diff(f,x)] for f in funkcije]
tabela
from pandas import DataFrame
DataFrame(tabela,columns={"$f(x)$","$f'(x)$"})
# za lepši izpis uporabimo funkci... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Lepši izpis tabele dobimo, če uporabimo knjižnico za delo s tabelami in podatki Pandas.
Step2: Pravila za odvajanje
Step3: Naloga
|
7,183 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
def trapz(f, a, b, N):
Integrate the function f(x) over the range [a,b] with N points.
x = np.linspace(a,b,N+1)
h = np.diff(x)[1]
y = f(x)
m = .5 * (y[1:(len(y)-1)] + y[2... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Trapezoidal rule
Step3: Now use scipy.integrate.quad to integrate the f and g functions and see how the result compares with your trapz functio... |
7,184 | <ASSISTANT_TASK:>
Python Code:
# Authors: Tal Linzen <linzen@nyu.edu>
# Denis A. Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD (3-clause)
import pandas as pd
import mne
from mne.stats import linear_regression, fdr_correction
from mne.viz import 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: Psycholinguistically relevant word characteristics are continuous. I.e.,
Step2: We observe that there appears to be a monotonic dependence of E... |
7,185 | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (3, 6)
layer_0 = Input(shape=data_in_shape)
layer_1 = TimeDistributed(Dense(4))(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
np.random.seed(4000 +... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: [wrappers.TimeDistributed.1] wrap a Conv2D layer with 6 3x3 filters (input
Step2: export for Keras.js tests
|
7,186 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import numpy.ma as ma
from scipy.integrate import odeint
mag = lambda r: np.sqrt(np.sum(np.power(r, 2)))
def g(y, t, q, m, n,d, k):
n: the number of particles
d: the number of dimensions
(for fun's sake I want this
to work for k-dimensional sy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Point Charge Dynamics
Step2: Let's define our time intervals, so that odeint knows which time stamps to iterate over.
Step3: Some other consta... |
7,187 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# Import the necessary libraries
import csv, os
from shapely.geometry import Point, mapping
import fiona, shapely
from fiona import Collection
import numpy as np
print "fiona version: {}".format(fiona.__version__)
print "shapely version: {}".format(shapely.__version__)
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Reading csv and printing as dictionary
Step2: Use shapely to make points
Step3: Geopandas reading a geopackage
Step4: Write geopandas datafra... |
7,188 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage.filters as scnf
import sys
# Add a new path with needed .py files.
sys.path.insert(0, 'C:\Users\Dominik\Documents\GitRep\kt-2015-DSPHandsOn\MedianFilter\Python')
import gitInformation
gitInformation.printInformation()... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: I am trying to remove white noise from the original wave with different filters.
Step2: Smooth the signal with a moving averege filter.
Step3: ... |
7,189 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('../input')
from flight_revenue_simulator import simulate_revenue, score_me
def pricing_function(days_left, tickets_left, demand_level):
Sample pricing function
price = demand_level - 10
return price
simulate_revenue(days_left=7, tickets_left=50, p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: In case you want to check your understanding of the simulator logic, here is a simplified version of some of the key logic (leaving out the code... |
7,190 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame([[1,2,3,4,5],[6,7,8,9,10],[11,12,13,14,15]],columns=['A','B','C','D','E'])
def g(df):
df.index += 1
df_out = df.stack()
df.index -= 1
df_out.index = df_out.index.map('{0[1]}_{0[0]}'.format)
return df_out.to_frame().T
df = g(df.copy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
7,191 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2016 Google Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# !pip install --upgrade google-api-python-client
import io, os, subprocess, sys, time, datetime, requests, itchat
from itchat.content import *
from googleapiclient.discovery import build
# H... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 导入需要用到的一些功能程序库：
Step2: Using Google Cloud Platform's Machine Learning APIs
Step3: 图片二进制base64码转换 (Define image pre-processing functions)
Step4... |
7,192 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
sys.path.append(os.environ["SPARK_HOME"] + "/python/lib/py4j-0.9-src.zip")
sys.path.append(os.environ["SPARK_HOME"] + "/python/lib/pyspark.zip")
from pyspark import SparkConf, SparkContext
from pyspark import SparkFiles
from pyspark import StorageLevel
from pyspark im... | <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: Spark does lazy evaluation. If we have a chain of transformations, Spark won't execute them untill an action is invoked.
Step3: Actions
S... |
7,193 | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
%matplotlib inline
np.random.seed(1)
y_hat = tf.constant(36, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now that you have imported the library, we will walk you through its different applications. You will start with an example, where we compute fo... |
7,194 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
7,195 | <ASSISTANT_TASK:>
Python Code:
node1 = tf.constant(3.0, tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)
sess = tf.Session()
print(sess.run([node1, node2]))
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b # + provides a shortcut for tf.add(a, b)
add... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: New functionalities
|
7,196 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import IPython.html.widgets as widgets
import IPython.display as display
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import pandas as pd
pd.set_option('display.float_format', lambda x: '%.4f' % x)
pylab.rcParams['figure.figsize'] = 14, 8
pd.set_opti... | <SYSTEM_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 see how the dataset is structured.
Step2: Now let's plot the price, CAPE, and 1YPE data. We'll normalize the price data, adjusting for 20... |
7,197 | <ASSISTANT_TASK:>
Python Code:
# Necessary imports
import os
import time
from nbminer.notebook_miner import NotebookMiner
from nbminer.cells.cells import Cell
from nbminer.features.features import Features
from nbminer.stats.summary import Summary
from nbminer.stats.multiple_summary import MultipleSummary
from nbminer... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Combined Clustering
Step2: Prediction of group
|
7,198 | <ASSISTANT_TASK:>
Python Code:
import pandas
import numpy as np
import matplotlib.pyplot as plt
df_lit = pandas.read_csv("../Data/childrens_lit.csv.bz2", sep='\t', index_col=0, encoding = 'utf-8', compression='bz2')
#drop rows where the text is missing.
df_lit = df_lit.dropna(subset=['text'])
#view the dataframe
df_lit... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <a id='fit'></a>
Step2: <a id='dtd'></a>
Step3: Merge back in with the original dataframe.
Step4: Now we can sort the dataframe for the topic... |
7,199 | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (5, 5, 5, 2)
conv = Conv3D(4, (3,3,3), strides=(1,1,1), padding='valid',
data_format='channels_last', dilation_rate=(1,1,1),
activation='linear', use_bias=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=la... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: [convolutional.Conv3D.1] 2 3x3x3 filters on 4x4x4x2 input, strides=(1,1,1), padding='valid', data_format='channels_last', dilation_rate=(1,1,1),... |
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