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# Radial Velocity Orbit-fitting with RadVel
## Week 6, Intro-to-Astro 2021
### Written by Ruben Santana & Sarah Blunt, 2018
#### Updated by Joey Murphy, June 2020
#### Updated by Corey Beard, July 2021
## Background information
Radial velocity measurements tell us how the velocity of a star changes along the directi... | github_jupyter |
```
import math
import string
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import logit
from IPython.display import display
import tensorflow as tf
from tensorflow.keras.layers import (Input, Dense, Lambda, Flatten, Reshape, BatchNormalization, Layer,
... | github_jupyter |
```
import pandas as pd
import geopandas
import glob
import matplotlib.pyplot as plt
import numpy as np
import seaborn
import shapefile as shp
from paths import *
from refuelplot import *
setup()
wpNZ = pd.read_csv(data_path + "/NZ/windparks_NZ.csv", delimiter=';')
wpBRA = pd.read_csv(data_path + '/BRA/turbine_data.csv... | github_jupyter |
# dwtls: Discrete Wavelet Transform LayerS
This library provides downsampling (DS) layers using discrete wavelet transforms (DWTs), which we call DWT layers.
Conventional DS layers lack either antialiasing filters and the perfect reconstruction property, so downsampled features are aliased and entire information of inp... | github_jupyter |
<a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/sprinkler_pgm.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Directed graphical models
We illustrate some basic properties of DGMs.
```
!pip install causal... | github_jupyter |
```
import json
from datetime import datetime, timedelta
import matplotlib.pylab as plot
import matplotlib.pyplot as plt
from matplotlib import dates
import pandas as pd
import numpy as np
import matplotlib
matplotlib.style.use('ggplot')
%matplotlib inline
# Read data from http bro logs
with open("http.log",'r') as in... | github_jupyter |
# Method4 DCT based DOST + Huffman encoding
## Import Libraries
```
import mne
import numpy as np
from scipy.fft import fft,fftshift
import matplotlib.pyplot as plt
from scipy.signal import butter, lfilter
from scipy.signal import freqz
from scipy import signal
from scipy.fftpack import fft, dct, idct
from itertools ... | github_jupyter |
# Quickstart
In this tutorial, we explain how to quickly use ``LEGWORK`` to calculate the detectability of a collection of sources.
```
%matplotlib inline
```
Let's start by importing the source and visualisation modules of `LEGWORK` and some other common packages.
```
import legwork.source as source
import legwork.... | github_jupyter |
```
from __future__ import division, print_function
import os
import torch
import pandas
import numpy as np
from torch.utils.data import DataLoader,Dataset
from torchvision import utils, transforms
from skimage import io, transform
import matplotlib.pyplot as plt
import warnings
#ignore warnings
warnings.filterwarning... | github_jupyter |
```
%matplotlib inline
import gym
import matplotlib
import numpy as np
import sys
from collections import defaultdict
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.blackjack import BlackjackEnv
from lib import plotting
matplotlib.style.use('ggplot')
env = BlackjackEnv()
def mc_prediction(policy,... | github_jupyter |
```
#Fill the paths below
PATH_FRC = "" # git repo directory path
PATH_ZENODO = "" # Data and models are available here: https://zenodo.org/record/5831014#.YdnW_VjMLeo
DATA_FLAT = PATH_ZENODO+'/data/goi_1000/flat_1000/*.png'
DATA_NORMAL = PATH_ZENODO+'/data/goi_1000/standard_1000/*.jpg'
GAUSS_L2_MODEL = PATH_ZENODO+'... | github_jupyter |
```
import datetime
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import casadi as cas
import pickle
import copy as cp
# from ..</src> import car_plotting
# from .import src.car_plotting
PROJECT_PATH = '/home/nbuckman/Dropbox (MIT)/DRL/2020_01_cooperative_mpc/mpc-multiple-vehicles/'
sys.path.appe... | github_jupyter |
```
import ast
from glob import glob
import sys
import os
from copy import deepcopy
import networkx as nx
from stdlib_list import stdlib_list
STDLIB = set(stdlib_list())
CONVERSIONS = {
'attr': 'attrs',
'PIL': 'Pillow',
'Image': 'Pillow',
'mpl_toolkits': 'matplotlib',
'dateutil': 'python-dateutil... | github_jupyter |
```
import pandas as pd
import numpy as np
%matplotlib inline
import joblib
import json
import tqdm
import glob
import numba
import dask
import xgboost
from dask.diagnostics import ProgressBar
import re
ProgressBar().register()
fold1, fold2 = joblib.load("./valid/fold1.pkl.z"), joblib.load("./valid/fold2.pkl.z")
tra... | github_jupyter |
```
from imports import *
import pickle
# device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
device = torch.device("cuda:0")
2048*6*10
def get_encoder(model_name):
if model_name == 'mobile_net':
md = torchvision.models.mobilenet_v2(pretrained=True)
encoder = nn.Seq... | github_jupyter |
# Write custom inference script and requirements to local folder
```
! mkdir inference_code
%%writefile inference_code/inference.py
# This is the script that will be used in the inference container
import os
import json
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
def model_fn(model_d... | github_jupyter |
# Ejercicio: Spectral clustering para documentos
El clustering espectral es una técnica de agrupamiento basada en la topología de gráficas. Es especialmente útil cuando los datos no son convexos o cuando se trabaja, directamente, con estructuras de grafos.
##Preparación d elos documentos
Trabajaremos con documentos ... | github_jupyter |
```
# Copyright 2020 IITK EE604A Image Processing. All Rights Reserved.
#
# Licensed under the MIT License. Use and/or modification of this code outside of EE604 must reference:
#
# © IITK EE604A Image Processing
# https://github.com/ee604/ee604_assignments
#
# Author: Shashi Kant Gupta, Chiranjeev Prachand and Prof ... | github_jupyter |
```
# super comms script
import serial
from time import sleep
import math
from tqdm import *
import json
def set_target(motor, location, ser, output=True):
if ser.is_open:
if motor =='A':
ser.write(b'A')
else:
ser.write(b'B')
target_bytes = location.to_bytes... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.... | github_jupyter |
```
import json
import os
from pprint import *
from tqdm import *
from utils.definitions import ROOT_DIR
path_load = "mpd.v1/data/" #json folder
path_save = ROOT_DIR + "/data/original/" #where to save csv
playlist_fields = ['pid','name', 'collaborative', 'modified_at', 'num_albums', 'num_tracks', 'num_followers',
'num_... | github_jupyter |
# This Jupyter Notebook contains the full code needed to write the ColumnTransformer blog
## Import Necessary Packages
```
import pandas as pd
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import train_test... | github_jupyter |
<img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="35%" align="right" border="0"><br>
# Python for Finance
**Analyze Big Financial Data**
O'Reilly (2014)
Yves Hilpisch
<img style="border:0px solid grey;" src="http://hilpisch.com/python_for_finance.png" alt="Python for Finance" width="30%" a... | github_jupyter |
# 第６章 スモール言語を作る
```
# !pip install pegtree
import pegtree as pg
from pegtree.colab import peg, pegtree, example
%%peg
Program = { // 開式非終端記号 Expression*
#Program
} EOF
EOF = !. // ファイル終端
Expression =
/ FuncDecl // 関数定義
/ VarDecl // 変数定義
/ IfExpr // if 式
/ Binary // 二項演算
```
import pegtree as ... | github_jupyter |
## Dataset
https://data.wprdc.org/dataset/allegheny-county-restaurant-food-facility-inspection-violations/resource/112a3821-334d-4f3f-ab40-4de1220b1a0a
This data set is a set of all of the restaurants in Allegheny County with geographic locations including zip code, size, description of use, and a "status" ranging fro... | github_jupyter |
# Dimensionality Reduction Example
Using the IMDB data, feature matrix and apply dimensionality reduction to this matrix via PCA and SVD.
```
%matplotlib inline
import json
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.sparse import lil_matrix
from sklearn.neighbor... | github_jupyter |
## Image segmentation with CamVid
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai import *
from fastai.vision import *
from fastai.callbacks.hooks import *
```
The One Hundred Layer Tiramisu paper used a modified version of Camvid, with smaller images and few classes. You can get it from the C... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@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 ... | github_jupyter |
# Equilibrium analysis Chemical reaction
Number (code) of assignment: 2N4
Description of activity:
Report on behalf of:
name : Pieter van Halem
student number : 4597591
name : Dennis Dane
student number :4592239
Data of student taking the role of contact person:
name :
email address :
```
import numpy as n... | github_jupyter |
# Creating a simple PDE model
In the [previous notebook](./1-an-ode-model.ipynb) we show how to create, discretise and solve an ODE model in pybamm. In this notebook we show how to create and solve a PDE problem, which will require meshing of the spatial domain.
As an example, we consider the problem of linear diffus... | github_jupyter |
# Writing Functions
This lecture discusses the mechanics of writing functions and how to encapsulate scripts as functions.
```
# Example: We're going to use Pandas dataframes to create a gradebook for this course
import pandas as pd
# Student Rosters:
students = ['Hao', 'Jennifer', 'Alex']
# Gradebook columns:
col... | github_jupyter |
# 线性回归
:label:`sec_linear_regression`
*回归*（regression）是能为一个或多个自变量与因变量之间关系建模的一类方法。
在自然科学和社会科学领域，回归经常用来表示输入和输出之间的关系。
在机器学习领域中的大多数任务通常都与*预测*（prediction）有关。
当我们想预测一个数值时，就会涉及到回归问题。
常见的例子包括：预测价格（房屋、股票等）、预测住院时间（针对住院病人等）、
预测需求（零售销量等）。
但不是所有的*预测*都是回归问题。
在后面的章节中，我们将介绍分类问题。分类问题的目标是预测数据属于一组类别中的哪一个。
## 线性回归的基本元素
*线性回归*（linear r... | github_jupyter |
## Load Model, plain 2D Conv
```
import os
os.chdir("../..")
os.getcwd()
import numpy as np
import torch
import json
from distributed.model_util import choose_model, choose_old_model, load_model, extend_model_config
from distributed.util import q_value_index_to_action
import matplotlib.pyplot as plt
model_name = "conv... | github_jupyter |
```
ls ../test-data/
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tables as tb
import h5py
import dask.dataframe as dd
import dask.bag as db
import blaze
fname = '../test-data/EQY_US_ALL_BBO_201402/EQY_US_ALL_BBO_20140206.h5'
max_sym = '/SPY/no_suffix'
fname = '../tes... | github_jupyter |
**Copyright 2021 The TensorFlow Authors.**
```
#@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 ag... | github_jupyter |
# Testing cnn for classifying universes
Nov 10, 2020
```
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torchsummary import summary
from torch.utils.data import DataLoade... | github_jupyter |
#### Abstract Classes: contains abstract methods
Abstract methods are those which are only declared but they've no implementation
**All methods need to be implemented (mandatory)
Module -- abc
|
|
|---> ABC (Class)
|
|---> Abstract method ... | github_jupyter |
```
# This mounts your Google Drive to the Colab VM.
from google.colab import drive
drive.mount('/content/drive')
# TODO: Enter the foldername in your Drive where you have saved the unzipped
# assignment folder, e.g. 'cs231n/assignments/assignment1/'
FOLDERNAME = None
assert FOLDERNAME is not None, "[!] Enter the fold... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Libraries-and-functions" data-toc-modified-id="Libraries-and-functions-1"><span class="toc-item-num">1 </span>Libraries and functions</a></span><ul class="toc-item"><li><span><a href="#Import-lib... | github_jupyter |
# Segmentation
Image segmentation is another early as well as an important image processing task. Segmentation is the process of breaking an image into groups, based on similarities of the pixels. Pixels can be similar to each other in multiple ways like brightness, color, or texture. The segmentation algorithms are t... | github_jupyter |
# Results Analysis
This notebook analyzes results produced by the _anti-entropy reinforcement learning_ experiments. The practical purpose of this notebook is to create graphs that can be used to display anti-entropy topologies, but also to extract information relevant to each experimental run.
```
%matplotlib noteb... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU (this may not be needed on your computer)
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=1
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
if len(gpu_devices)>0:
tf.config.experim... | github_jupyter |
# **Space X Falcon 9 First Stage Landing Prediction**
## Web scraping Falcon 9 and Falcon Heavy Launches Records from Wikipedia
We will be performing web scraping to collect Falcon 9 historical launch records from a Wikipedia page titled `List of Falcon 9 and Falcon Heavy launches`
[https://en.wikipedia.org/wiki/Li... | github_jupyter |
```
import pandas as pd
import numpy as np
import sys
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeClassifier
from sklearn import preprocessing
col_names = ["duration","protocol_type","service","flag","src_bytes",
"dst_by... | github_jupyter |
Branching GP Regression on hematopoietic data
--
*Alexis Boukouvalas, 2017*
**Note:** this notebook is automatically generated by [Jupytext](https://jupytext.readthedocs.io/en/latest/index.html), see the README for instructions on working with it.
test change
Branching GP regression with Gaussian noise on the hemat... | github_jupyter |
```
%matplotlib inline
import itertools
import os
os.environ['CUDA_VISIBLE_DEVICES']=""
import numpy as np
import gpflow
import gpflow.training.monitor as mon
import numbers
import matplotlib.pyplot as plt
import tensorflow as tf
```
# Demo: `gpflow.training.monitor`
In this notebook we'll demo how to use `gpflow.trai... | github_jupyter |
<img width="10%" alt="Naas" src="https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160"/>
# IUCN - Extinct species
<a href="https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/IUCN/IUCN_Extinct_species.ipynb" target="_parent"><img src=... | github_jupyter |
# ThaiNER (Bi-LSTM CRF)
using pytorch
By Mr.Wannaphong Phatthiyaphaibun
Bachelor of Science Program in Computer and Information Science, Nong Khai Campus, Khon Kaen University
https://iam.wannaphong.com/
E-mail : wannaphong@kkumail.com
Thank you Faculty of Applied Science and Engineering, Nong Khai Campus, Khon K... | github_jupyter |
# Flight Price Prediction
---
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
pip list
```
## Importing dataset
1. Check whether any null values are there or not. if it is present then following can be done,
1. Imputing data using Imputation method in s... | github_jupyter |
<h1><center>Solving Linear Equations with Quantum Circuits</center></h1>
<h2><center>Ax = b</center></h2>
<h4><center> Attempt to replicate the following paper </center></h4>
<h3><center>Algorithm for a simpler 2 x 2 example</center></h3>
![imag... | github_jupyter |
```
from mocpy import MOC
import numpy as np
from astropy import units as u
from astropy.coordinates import SkyCoord
%matplotlib inline
# Plot the polygon vertices on a matplotlib axis
def plot_graph(vertices):
import matplotlib.pyplot as plt
from matplotlib import path, patches
fig = plt.figure()
... | github_jupyter |
# MCMC sampling using the emcee package
## Introduction
The goal of Markov Chain Monte Carlo (MCMC) algorithms is to approximate the posterior distribution of your model parameters by random sampling in a probabilistic space. For most readers this sentence was probably not very helpful so here we'll start straight wi... | github_jupyter |
#Stock Price Predictor
This is a Jupyter notebook that you can use to get prediction of adjusted close stock price per the specified day range after the last day from the training data set. The prediction is made by training the machine learning model with historical trade of the stock data. This is the result of stud... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$ \newcommand{\bra}[1]{\langle #1|} $
$ \newcommand{\ket}[1]{|#1\rangle} $
$ \newcommand{\braket}[2]{\langle #1|#2\rangle} $
$ \newcommand{\dot}[2]{ #1 \cdot #2} $
$ \newcommand{\biginner}[2]{\left\langle... | github_jupyter |
```
import os
import struct
import pandas as pd
import numpy as np
import talib as tdx
def readTdxLdayFile(fname="data/sh000001.day"):
dataSet=[]
with open(fname,'rb') as fl:
buffer=fl.read() #读取数据到缓存
size=len(buffer)
rowSize=32 #通信达day数据，每32个字节一组数据
code=os.path.basename(fname).replace('.day','')
... | github_jupyter |
```
import argparse
import torch.distributed as dist
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import test # import test.py to get mAP after each epoch
from models import *
from utils.datasets import *
from utils.utils import *
from mymodel import *
# Hyperparameters (results68: 5... | github_jupyter |
```
#hide
%load_ext autoreload
%autoreload 2
# default_exp analysis
```
# Analysis
> The analysis functions help a modeler quickly run a full time series analysis.
An analysis consists of:
1. Initializing a DGLM, using `define_dglm`.
2. Updating the model coefficients at each time step, using `dglm.update`.
3. Fore... | github_jupyter |
# Support Vector Machines
Support Vector Machines (SVM) are an extension of the linear methods that attempt to separate classes with hyperplans.
These extensions come in three steps:
1. When classes are linearly separable, maximize the margin between the two classes
2. When classes are not linearly separable, maximiz... | github_jupyter |
```
import caffe
import numpy as np
import matplotlib.pyplot as plt
import os
from keras.datasets import mnist
from caffe.proto import caffe_pb2
import google.protobuf.text_format
plt.rcParams['image.cmap'] = 'gray'
%matplotlib inline
```
Loading the model
```
model_def = 'example_caffe_mnist_model.prototxt'
model_we... | github_jupyter |
# Transpose convolution: Upsampling
In section 10.5.3, we discussed how transpose convolutions are can be used to upsample a lower resolution input into a higher resolution output. This notebook contains fully functional PyTorch code for the same.
```
import matplotlib.pyplot as plt
import torch
import math
```
Firs... | github_jupyter |
```
import tensorflow as tf
print(tf.__version__)
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
def plot_series(time, series, format="-", start=0, end=None):
plt.plot(time[start:end], series[start:end], format)
plt.xlabel("Time")
plt.ylabel("Value")... | github_jupyter |
# Genentech Cervical Cancer - Feature Selection
https://www.kaggle.com/c/cervical-cancer-screening/
```
# imports
import sys # for stderr
import numpy as np
import pandas as pd
import sklearn as skl
from sklearn import metrics
import matplotlib.pyplot as plt
%matplotlib inline
# settings
%logstop
%logstart -o 'cc_f... | github_jupyter |
```
import nltk
import difflib
import time
import gc
import itertools
import multiprocessing
import pandas as pd
import numpy as np
import xgboost as xgb
import lightgbm as lgb
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.metri... | github_jupyter |
# Homework 1
The maximum score of this homework is 100+10 points. Grading is listed in this table:
| Grade | Score range |
| --- | --- |
| 5 | 85+ |
| 4 | 70-84 |
| 3 | 55-69 |
| 2 | 40-54 |
| 1 | 0-39 |
Most exercises include tests which should pass if your solution is correct.
However successful test do not guaran... | github_jupyter |
# Optimiztion with `mystic`
```
%matplotlib notebook
```
`mystic`: approximates that `scipy.optimize` interface
```
"""
Example:
- Minimize Rosenbrock's Function with Nelder-Mead.
- Plot of parameter convergence to function minimum.
Demonstrates:
- standard models
- minimal solver interface
- pa... | github_jupyter |
```
# Load dependencies
import numpy as np
import pandas as pd
from uncertainties import ufloat
from uncertainties import unumpy
```
# Biomass C content estimation
Biomass is presented in the paper on a dry-weight basis. As part of the biomass calculation, we converted biomass in carbon-weight basis to dry-weight ba... | github_jupyter |
# Introduction
<div class="alert alert-info">
**Code not tidied, but should work OK**
</div>
<img src="../Udacity_DL_Nanodegree/031%20RNN%20Super%20Basics/SimpleRNN01.png" align="left"/>
# Neural Network
```
import numpy as np
import matplotlib.pyplot as plt
import pdb
```
**Sigmoid**
```
def sigmoid(x):
re... | github_jupyter |
<a id="title_ID"></a>
# JWST Pipeline Validation Notebook: calwebb_detector1, dark_current unit tests
<span style="color:red"> **Instruments Affected**</span>: NIRCam, NIRISS, NIRSpec, MIRI, FGS
### Table of Contents
<div style="text-align: left">
<br> [Introduction](#intro)
<br> [JWST Unit Tests](#unit)
<br> ... | github_jupyter |
## Dự án 01: Xây dựng Raspberry PI thành máy tính cho Data Scientist (PIDS)
## Bài 01. Cài đặt TensorFlow và các thư viện cần thiết
##### Người soạn: Dương Trần Hà Phương
##### Website: [Mechasolution Việt Nam](https://mechasolution.vn)
##### Email: mechasolutionvietnam@gmail.com
---
## 1. Mở đầu
Nếu bạn muốn chạy mộ... | github_jupyter |
# House Price Prediction With TensorFlow
[![open_in_colab][colab_badge]][colab_notebook_link]
[![open_in_binder][binder_badge]][binder_notebook_link]
[colab_badge]: https://colab.research.google.com/assets/colab-badge.svg
[colab_notebook_link]: https://colab.research.google.com/github/UnfoldedInc/examples/blob/master... | github_jupyter |
# Semantic Image Clustering
**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)<br>
**Date created:** 2021/02/28<br>
**Last modified:** 2021/02/28<br>
**Description:** Semantic Clustering by Adopting Nearest neighbors (SCAN) algorithm.
## Introduction
This example demonstrates how to app... | github_jupyter |
```
import json
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras
import matplotlib.pyplot as plt
import random
import librosa
import math
# path to json
data_path = "C:\\Users\\Saad\\Desktop\\Project\\MGC\\Data\\data.json"
def load_data(data_path):
with ope... | github_jupyter |
# Notebook 3 - Advanced Data Structures
So far, we have seen numbers, strings, and lists. In this notebook, we will learn three more data structures, which allow us to organize data. The data structures are `tuple`, `set`, and `dict` (dictionary).
## Tuples
A tuple is like a list, but is immutable, meaning that it ca... | github_jupyter |
### Mount Google Drive (Works only on Google Colab)
```
from google.colab import drive
drive.mount('/content/gdrive')
```
# Import Packages
```
import os
import numpy as np
import pandas as pd
from zipfile import ZipFile
from PIL import Image
from tqdm.autonotebook import tqdm
from IPython.display import display
fr... | github_jupyter |
<a id='pd'></a>
<div id="qe-notebook-header" align="right" style="text-align:right;">
<a href="https://quantecon.org/" title="quantecon.org">
<img style="width:250px;display:inline;" width="250px" src="https://assets.quantecon.org/img/qe-menubar-logo.svg" alt="QuantEcon">
</a>
</div>
#... | github_jupyter |
# In this note book the following steps are taken:
1. Remove highly correlated attributes
2. Find the best hyper parameters for estimator
3. Find the most important features by tunned random forest
4. Find f1 score of the tunned full model
5. Find best hyper parameter of model with selected features
6. Find f1 score of... | github_jupyter |
# Anailís ghramadaí trí [deplacy](https://koichiyasuoka.github.io/deplacy/)
## le [Stanza](https://stanfordnlp.github.io/stanza)
```
!pip install deplacy stanza
import stanza
stanza.download("ga")
nlp=stanza.Pipeline("ga")
doc=nlp("Táimid faoi dhraíocht ag ceol na farraige.")
import deplacy
deplacy.render(doc)
deplac... | github_jupyter |
<a href="https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/tutorials/MMClassification_python.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# MMClassification Python API tutorial on Colab
In this tutorial, we wi... | github_jupyter |
```
```
# **Deep Convolutional Generative Adversarial Network (DC-GAN):**
DC-GAN is a foundational adversarial framework developed in 2015.
It had a major contribution in streamlining the process of designing adversarial frameworks and visualizing intermediate representations, thus, making GANs more accessible to b... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
from functools import reduce
import seaborn as sns; sns.set(rc={'figure.figsize':(15,15)})
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from sklearn.preprocessing import MinMaxScaler
engine = create_engine('postgresql://postgres:mimi... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | github_jupyter |
```
from pykat import finesse
from pykat.commands import *
import numpy as np
import matplotlib.pyplot as plt
import scipy
from IPython import display
pykat.init_pykat_plotting(dpi=200)
base1 = """
l L0 10 0 n0 #input laser
... | github_jupyter |
# Web crawling exercise
```
from selenium import webdriver
```
## Quiz 1
- 아래 URL의 NBA 데이터를 크롤링하여 판다스 데이터 프레임으로 나타내세요.
- http://stats.nba.com/teams/traditional/?sort=GP&dir=-1
### 1.1 webdriver를 실행하고 사이트에 접속하기
```
driver = webdriver.Chrome()
url = "http://stats.nba.com/teams/traditional/?sort=GP&dir=-1"
driver.get(... | github_jupyter |
# Custom Building Recurrent Neural Network
**Notation**:
- Superscript $[l]$ denotes an object associated with the $l^{th}$ layer.
- Superscript $(i)$ denotes an object associated with the $i^{th}$ example.
- Superscript $\langle t \rangle$ denotes an object at the $t^{th}$ time-step.
- **Sub**script $i$ den... | github_jupyter |
# Data description:
I'm going to solve the International Airline Passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. The data ranges from January 1949 to December 1960 or 12 years, with 144 observation... | github_jupyter |
# Comparing soundings from NCEP Reanalysis and various models
We are going to plot the global, annual mean sounding (vertical temperature profile) from observations.
Read in the necessary NCEP reanalysis data from the online server.
The catalog is here: <https://psl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanaly... | github_jupyter |
# `numpy`
မင်္ဂလာပါ၊ welcome to the week 07 of Data Science Using Python.
We will go into details of `numpy` this week (as well as do some linear algebra stuffs).
## `numpy` အကြောင်း သိပြီးသမျှ
* `numpy` ဟာ array library ဖြစ်တယ်၊
* efficient ဖြစ်တယ်၊
* vector နဲ့ matrix တွေကို လွယ်လွယ်ကူကူ ကိုင်တွယ်နိုင်တယ... | github_jupyter |
```
import pandas as pd
import datetime
from finquant.portfolio import build_portfolio
from finquant.moving_average import compute_ma, ema
from finquant.moving_average import plot_bollinger_band
from finquant.efficient_frontier import EfficientFrontier
### DOES OUR OPTIMIZATION ACTUALLY WORK?
# COMPARING AN OPTIMIZED ... | github_jupyter |
# Isolation Forest (IF) outlier detector deployment
Wrap a scikit-learn Isolation Forest python model for use as a prediction microservice in seldon-core and deploy on seldon-core running on minikube or a Kubernetes cluster using GCP.
## Dependencies
- [helm](https://github.com/helm/helm)
- [minikube](https://github... | github_jupyter |
```
from IPython.display import Latex
# Latex(r"""\begin{eqnarray} \large
# Z_{n+1} = Z_{n}^(-e^(Z_{n}^p)^(e^(Z_{n}^p)^(-e^(Z_{n}^p)^(e^(Z_{n}^p)^(-e^(Z_{n}^p))))))
# \end{eqnarray}""")
```
# Parameterized machine learning algo:
## tanh(Z) = (a exp(Z) - b exp(-Z)) / (c exp(Z) + d exp(-Z))
### with parameters a,b,c,... | github_jupyter |
# Hashtags
```
from nltk.tokenize import TweetTokenizer
import os
import pandas as pd
import re
import sys
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from IPython.display import clear_output
def squeal(text=None):
clear_output(wait=True)
... | github_jupyter |
# Graph
> in progress
- toc: true
- badges: true
- comments: true
- categories: [self-taught]
- image: images/bone.jpeg
- hide: true
https://towardsdatascience.com/using-graph-convolutional-neural-networks-on-structured-documents-for-information-extraction-c1088dcd2b8f
CNNs effectively capture patterns in data in Eu... | github_jupyter |
# 08 - Common problems & bad data situations
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons Licence" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" title='This work is licensed under a Creative Commons Attribution 4.0 International License.... | github_jupyter |
```
import tensorflow as tf
import tensorflow as tf
from tensorflow.python.keras.applications.vgg19 import VGG19
model=VGG19(
include_top=False,
weights='imagenet'
)
model.trainable=False
model.summary()
from tensorflow.python.keras.preprocessing.image import load_img, img_to_array
from tensorflow.python.keras.... | github_jupyter |
```
%reload_ext watermark
%matplotlib inline
from os.path import exists
from metapool.metapool import *
from metapool import (validate_plate_metadata, assign_emp_index, make_sample_sheet, KLSampleSheet, parse_prep, validate_and_scrub_sample_sheet, generate_qiita_prep_file)
%watermark -i -v -iv -m -h -p metapool,sample... | github_jupyter |
<img src="../../images/banners/python-basics.png" width="600"/>
# <img src="../../images/logos/python.png" width="23"/> Conda Environments
## <img src="../../images/logos/toc.png" width="20"/> Table of Contents
* [Understanding Conda Environments](#understanding_conda_environments)
* [Understanding Basic Package Man... | github_jupyter |
# Configuraciones para el Grupo de Estudio
<img src="./img/f_mail.png" style="width: 700px;"/>
## Contenidos
- ¿Por qué jupyter notebooks?
- Bash
- ¿Que es un *kernel*?
- Instalación
- Deberes
## Python y proyecto Jupyter
<img src="./img/py.jpg" style="width: 500px;"/>
<img src="./img/jp.png" style="width: 100px;"/... | github_jupyter |
# MHKiT Quality Control Module
The following example runs a simple quality control analysis on wave elevation data using the [MHKiT QC module](https://mhkit-software.github.io/MHKiT/mhkit-python/api.qc.html). The data file used in this example is stored in the [\\\\MHKiT\\\\examples\\\\data](https://github.com/MHKiT-S... | github_jupyter |
```
import tushare as ts
import sina_data
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
from datetime import datetime, timedelta
from dateutil.parser import parse
import time
import common_util
import os
def get_time(date=False, utc=False, msl=3):
if date:
time_fmt = "%Y-%m-%d ... | github_jupyter |
```
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (10, 6)
import numpy as np
from numpy.lib import stride_tricks
import cv2
from matplotlib.colors import hsv_to_rgb
import matplotlib.pyplot as plt
import numpy as np
np.set_printoptions(precision=3)
class PatchMatch(object):
def __init__(self, ... | github_jupyter |
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