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# CHEM 1000 - Spring 2022
Prof. Geoffrey Hutchison, University of Pittsburgh
## 5 Scalar and Vector Operators
Chapter 5 in [*Mathematical Methods for Chemists*](http://sites.bu.edu/straub/mathematical-methods-for-molecular-science/)
By the end of this session, you should be able to:
- Understand the concept of vecto... | github_jupyter |
### Overview
This notebook is tested using SageMaker `Studio SparkMagic - PySpark Kernel`. Please ensure that you see `PySpark (SparkMagic)` in the top right on your notebook.
This notebook does the following:
* Demonstrates how you can visually connect Amazon SageMaker Studio Sparkmagic kernel to an EMR cluster
* E... | github_jupyter |
# 1. Transforming Data with dplyr
Learn verbs you can use to transform your data, including select, filter, arrange, and mutate. You'll use these functions to modify the counties dataset to view particular observations and answer questions about the data
## The counties dataset
This particular dataset is from the 2015... | github_jupyter |
# Train a VAE on L1000 Data
```
import sys
import pathlib
import numpy as np
import pandas as pd
sys.path.insert(0, "../../scripts")
from utils import load_data, infer_L1000_features
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from sklearn.decomposition import PCA
from tensorflow import keras... | github_jupyter |
# Pandas Tips & Tricks & More
### Hello Kaggler!
### <span style="color:PURPLE">Objective of this kernal is to demonstrate most commonly used</span> <span style="color:red">Pandas Tips & Tricks and More</span> .
# Contents
Note : Please use below links to navigate the note book
1. [Check Package Version](#CheckPack... | github_jupyter |
# Model Specification for 1st-Level fMRI Analysis
Nipype provides also an interfaces to create a first level Model for an fMRI analysis. Such a model is needed to specify the analysis-specific information, such as **condition**, their **onsets**, and **durations**. For more information, make sure to check out [nipype.... | github_jupyter |
# Introduction
Oftentimes data will come to us with column names, index names, or other naming conventions that we are not satisfied with. In that case, you'll learn how to use pandas functions to change the names of the offending entries to something better.
You'll also explore how to combine data from multiple Data... | github_jupyter |
# The surface energy balance
____________
<a id='section1'></a>
## 1. Energy exchange mechanisms at the Earth's surface
____________
The surface of the Earth is the boundary between the atmosphere and the land, ocean, or ice. Understanding the energy fluxes across the surface are very important for three main reason... | github_jupyter |
If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets as well as other dependencies. Right now this requires the current master branch of both. Uncomment the following cell and run it.
```
#! pip install git+https://github.com/huggingface/transformers.git
#! pip in... | github_jupyter |
# BOSS Calibration Tutorial
The purpose of this tutorial is to reconstruct and document the calibration steps from detected electrons to calibrated flux, as described [here](https://trac.sdss3.org/wiki/BOSS/pipeline/FluxToPhotons) (requires SDSS3 login).
```
%pylab inline
import astropy.io.fits as fits
import bossdat... | github_jupyter |
# Current SARS-CoV-2 Viral Diversity Supports Transmission Rule-Out by Genomic Sequencing
When community transmission levels are high, there will be many coincidences in which individuals in the same workplace, classroom, nursing home, or other institution test positive for SARS-CoV-2 purely by chance. Genomic sequenc... | github_jupyter |
# Import requried libraries
```
import pandas as pd # for manipulating data
import numpy as np # Manipulating arrays
import keras # High level neural network API
import tensorflow as tf # Framework use for dataflow
from sklearn.model_selection import train_test_split # To split the data into train and validation
from ... | github_jupyter |
<h1 align="center">ML For Defect Analysis</h1>
## 1. Building the Model
```
import warnings
warnings.filterwarnings('ignore')
import os
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
#Split the data into train, validation & ... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import seaborn as sns
palette = 'muted'
sns.set_palette(palette)
sns.set_color_codes(palette)
# 让Mac下图片的显示更清晰些
%config InlineBackend.figure_format = 'retina'
mu_params = [-1, 0, 1]
sd_params = [0.5, 1, 1.5]
x = np.linspace... | github_jupyter |
```
## tensorflow-gpu==2.3.0rc1 bug to load_weight after call inference
!pip install tensorflow-gpu==2.2.0
import yaml
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow_tts.processor.ljspeech import LJSpeechProcessor
from tensorflow_tts.processor.ljspeech import symbols, _symb... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
# getting the utils file here
import os, sys
import xbos_services_getter as xsg
import datetime
import calendar
import pytz
import numpy as np
import pandas as pd
import itertools
import time
from pathlib import Path
import pickle
import yaml
pd.set_option('display.max_columns', N... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
#export
from exp.nb_02_callbacks import *
```
# Initial Setup
```
x_train, y_train, x_valid, y_valid = get_data(url=MNIST_URL)
train_ds = Dataset(x=x_train, y=y_train)
valid_ds = Dataset(x=x_valid, y=y_valid)
nh = 50
bs = 16
c = y_train.max().item() + 1
loss_... | github_jupyter |
TSG061 - Get tail of all container logs for pods in BDC namespace
=================================================================
Steps
-----
### Parameters
```
since_seconds = 60 * 60 * 1 # the last hour
coalesce_duplicates = True
```
### Instantiate Kubernetes client
```
# Instantiate the Python Kubernetes cli... | github_jupyter |
Write a function to draw a circular smiley face with eyes, a nose, and a mouth. One argument should set the overall size of the face (the circle radius). Optional arguments should allow the user to specify the `(x, y)` position of the face, whether the face is smiling or frowning, and the color of the lines. The defaul... | github_jupyter |
# SageMaker/DeepAR demo on electricity dataset
This notebook complements the [DeepAR introduction notebook](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/deepar_synthetic/deepar_synthetic.ipynb).
Here, we will consider a real use case and show how to use DeepAR on... | github_jupyter |
# Taller 1. Introducción a Python con Numpy
Bienvenido al primer taller. Contiene ejercicios para una breve introducción a Python. Si ya ha utilizado Python antes, este taller le ayudará a familiarizarse con las funciones que necesitamos.
**Instrucciones:**
- Se utilizará Python 3.
- Evite utilizar bucles-for y b... | github_jupyter |
## 1. Bitcoin and Cryptocurrencies: Full dataset, filtering, and reproducibility
<p>Since the <a href="https://newfronttest.bitcoin.com/bitcoin.pdf">launch of Bitcoin in 2008</a>, hundreds of similar projects based on the blockchain technology have emerged. We call these cryptocurrencies (also coins or cryptos in the I... | github_jupyter |
```
import pvl
import struct
import matplotlib.pyplot as plt
import numpy as np
import datetime
import os.path
import binascii
chan_file = '/home/arsanders/testData/chandrayaan/forwardDescending/input/M3G20081129T171431_V03_L1B.LBL'
image_file = chan_file
header = pvl.load(chan_file)
# chan1m32isis requires 4 different... | github_jupyter |
# Scalar uniform quantisation of random variables
This tutorial considers scalar quantisation implemented using a uniform quantiser and applied over random variables with different Probability Mass Functions (PMFs). In particular we will consider uniform- and Gaussian-distributed random variables so to comment on the o... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
# Setting Boundary Conditions on the Perimeter of a Raster.
<hr>
<small>For more Landlab tutorials, click here: <a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html">https://landlab.readthedocs.io/en... | github_jupyter |
## Part 3 - Deploy the model
In the second notebook we created a basic model and exported it to a file. In this notebook we'll use that same model file to create a REST API with Microsoft ML Server. The Ubuntu DSVM has an installation of ML Server for testing deployments. We'll create a REST API with our model and tes... | github_jupyter |
# Download and process the Bay Area's walkable network
```
import time
import os, zipfile, requests, pandas as pd, geopandas as gpd, osmnx as ox, networkx as nx
ox.config(use_cache=True, log_console=True)
print('ox {}\nnx {}'.format(ox.__version__, nx.__version__))
start_time = time.time()
# point to the shapefile fo... | github_jupyter |
### Python 开发命令行工具
Python 作为一种脚本语言，可以非常方便地用于系统（尤其是\*nix系统）命令行工具的开发。Python 自身也集成了一些标准库，专门用于处理命令行相关的问题。
#### 命令行工具的一般结构
**1. 标准输入输出**
\*nix 系统中，一切皆为文件，因此标准输入、输出可以完全可以看做是对文件的操作。标准化输入可以通过管道（pipe）或重定向（redirect）的方式传递：
```
# script reverse.py
#!/usr/bin/env python
... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><span><a href="#Name" data-toc-modified-id="Name-1"><span class="toc-item-num">1 </span>Name</a></span></li><li><span><a href="#Search" data-toc-modified-id="Search-2"><span class="toc-i... | github_jupyter |
# An introduction to geocoding
Geocoders are tools to which you pass in an address / place of interest and it gives back the coordinates of that place.
The **`arcgis.geocoding`** module provides types and functions for geocoding, batch geocoding and reverse geocoding.
```
from arcgis.gis import GIS
from arcgis impor... | github_jupyter |
This is a simple NLP project which predicts the sentiment of the movie reviews from IMDB dataset.
```
import numpy as np
import pandas as pd
import os
import glob
import csv
import random
```
Gathering the Datasets and converting them to a single csv file
```
# Since all the reviews and sentiments are in txt file I ... | github_jupyter |
<p style="font-family: Arial; font-size:2.75em;color:purple; font-style:bold"><br>
## Regresssion with scikit-learn
using Soccer Dataset
<br></p>
We will again be using the open dataset from the popular site <a href="https://www.kaggle.com">Kaggle</a> that we used in Week 1 for our example.
Recall that this <a href... | github_jupyter |
# Explainable fraud detection model
In this example we develop a small fraud detection model for credit card transactions based on XGBoost, export it to TorchScript using Hummingbird (https://github.com/microsoft/hummingbird) and run Shapley Value Sampling explanations (see https://captum.ai/api/shapley_value_sampling... | github_jupyter |
```
import numpy as np
import importlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from cp2k_spm_tools import cp2k_grid_orbitals, cp2k_ftsts, qe_utils
lat_param = 4.37 # angstrom
wfn_file = "./examples/polyphenylene_cp2k_scf/PROJ-RESTART.wfn"
xyz_file = "./examples/polyphenylene_cp2k_scf/... | github_jupyter |
```
# from utils_torsion_dataset_generator import *
from util_2nd_round_generator import *
%%capture cap1 --no-stderr
# Create force field object
forcefield = ForceField('param_valence.offxml', allow_cosmetic_attributes=True)
# Create dictionaries storing molecules and attributes
molecules_list_dict, molecule_attribu... | github_jupyter |
# HiddenLayer Training Demo - PyTorch
```
import os
import time
import random
import numpy as np
import torch
import torchvision.models
import torch.nn as nn
from torchvision import datasets, transforms
import hiddenlayer as hl
```
## Basic Use Case
To track your training, you need to use two Classes: History to sto... | github_jupyter |
```
%matplotlib notebook
from pydub import AudioSegment
import tqdm
import json
import os
import statistics
import argparse
from utils import get_msecs, video_to_wav, extract_features, read_audio_file, get_wav
from models import model_torch
import librosa
import torch
import numpy as np
import sed_vis
import dcase_uti... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import torch
print(torch.__version__)
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
GDRIVE = '/content/drive/MyDrive/2516'
models = [
'RDN_50epoch_Baseline_Bicubic',
'RDN_50epoch_AblateCMLRLGFF_Bicubic',
... | github_jupyter |
This notebook is written by pythonash.
I was meant to find the proper parameter containing learning rate, dropout rate, and so on.
This notebook will be modified until either I finally get optimal structure or this competition is ended with my indifference due to my work.
```
import pandas as pd
import seaborn as sn... | github_jupyter |
### import libraries
```
! pip install netCDF4
import netCDF4 # python API to work with netcdf (.nc) files
import os
import datetime
from osgeo import gdal, ogr, osr
import numpy as np # library to work with matrixes and computations in general
import matplotlib.pyplot as plt # plotting library
from auxiliary_classes ... | github_jupyter |
# Implementation of a Devito self adjoint variable density visco- acoustic isotropic modeling operator <br>-- Nonlinear Ops --
## This operator is contributed by Chevron Energy Technology Company (2020)
This operator is based on simplfications of the systems presented in:
<br>**Self-adjoint, energy-conserving second-... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#export
from fastai.data.all import *
from fastai.text.core import *
#hide
from nbdev.showdoc import *
#default_exp text.models.awdlstm
#default_cls_lvl 3
```
# AWD-LSTM
> AWD LSTM from [Smerity et al.](https://arxiv.org/pdf/1708.... | github_jupyter |
# gerekli kütüphaneler
```
# uyarı ayarı
import warnings
warnings.filterwarnings("ignore")
# veri işleme
import pandas as pd
import numpy as np
# istatistik
import scipy as sc
import hypothetical
import pingouin
import statsmodels as sm
# veri görselleştirme
import matplotlib.pyplot as plt
%matplotlib inline
import... | github_jupyter |
# In-Class Coding Lab: Web Services and APIs
### Overview
The web has long evolved from user-consumption to device consumption. In the early days of the web when you wanted to check the weather, you opened up your browser and visited a website. Nowadays your smart watch / smart phone retrieves the weather for you and... | github_jupyter |
# Preface
The locations requiring configuration for your experiment are commented in capital text.
# Setup
**Installations**
```
!pip install apricot-select
!pip install sphinxcontrib-napoleon
!pip install sphinxcontrib-bibtex
!git clone https://github.com/decile-team/distil.git
!git clone https://github.com/circu... | github_jupyter |
# Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can u... | github_jupyter |
# Solving Linear Equations
```
import numpy as np
import scipy.linalg as la
```
## Linear Equations
Consider a set of $m$ linear equations in $n$ unknowns:
\begin{align*}
a_{11} x_1 + &a_{12} x_2& +& ... + &a_{1n} x_n &=& b_1\\
\vdots && &&\vdots &= &\vdots\\
a_{m1} x_1 + &a_{m2} x_2& +& ... + &a_{mn} x_n &=&b_m ... | github_jupyter |
# Combining features and adsorption energies into one dataframe
---
### Import Modules
```
import os
print(os.getcwd())
import sys
import time; ti = time.time()
import pickle
import copy
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
pd.set_option("display.max_columns", No... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sys
from scipy import interpolate
import math
from ipywidgets import *
def firstQuad(data):
colnames = data.columns.values
colcount = len(colnames)
rowcount = len(data[colnames[0]])
# do a little clean... | github_jupyter |
<a href="https://colab.research.google.com/github/KwonDoRyoung/AdvancedBasicEducationProgram/blob/main/Challenge01.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from google.colab import drive
drive.mount('/content/drive')
import os
import csv... | github_jupyter |
```
import os
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['mathtext.fontset'] = 'stix'
```
Load 200ns Aib9 trajectory
```
infile = '../../DATA/Train/AIB9/sum_phi_200ns.npy'
input_x = np.load(infile)
bins=np.arange(-15., 17, 1)
num_bins=len(bins)
idx_200ns=np.digitize(input_x, bins)
di=1
N_mean=n... | github_jupyter |
# Continuous Control
---
Congratulations for completing the second project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program! In this notebook, you will learn how to control an agent in a more challenging environment, where the goal ... | github_jupyter |
# Workshop 4: cartopy and best practices
# Part II: Best Practices
Here I dump my entire accumulated wisdom upon you, not so much hoping that you know it all by the end, but that you know of the concepts and know what to search for. I realize that many lessons will be learned the hard way.
## 1. Technical tips
### ... | github_jupyter |
# Step 7: Fit a nonrigid transformation
```
import os
import numpy as np
from functools import partial
from skimage.external import tifffile
from phathom.registration import registration as reg
from phathom import plotting
from phathom import io
from phathom.utils import pickle_save, pickle_load, read_voxel_size
worki... | github_jupyter |
# DoWhy-The Causal Story Behind Hotel Booking Cancellations
We consider the problem of estimating what impact does assigning a room different to what a customer had reserved has on the booking cancellation.
The... | github_jupyter |
# Carvana Image Masking Challenge
https://www.kaggle.com/c/carvana-image-masking-challenge
```
IMG_ROWS = 480
IMG_COLS = 320
TEST_IMG_ROWS = 1918
TEST_IMG_COLS = 1280
```
## Загружаем исходные изображения
```
import cv2
import numpy as np
from scipy import ndimage
from glob import glob
SAMPLE = 5000
train_img_pa... | github_jupyter |
# Classificação
```
import os
import numpy as np
from sklearn.datasets import make_moons, make_circles, make_classification
import itertools
import numpy as np
import matplotlib.pyplot as plt
# make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matpl... | github_jupyter |
# GLM: Robust Linear Regression
Author: [Thomas Wiecki](https://twitter.com/twiecki)
This tutorial first appeard as a post in small series on Bayesian GLMs on my blog:
1. [The Inference Button: Bayesian GLMs made easy with PyMC3](http://twiecki.github.com/blog/2013/08/12/bayesian-glms-1/)
2. [This world is far f... | github_jupyter |
```
import copy
import logging
import argparse
import sys, os
import numpy as np.nfrom math import pi
import matplotlib.pyplot as plt
from os.path import dirname, abspath, join
sys.path.append("../")
import matplotlib
%matplotlib inline
import matplotlib as mpl
mpl.use('Qt5Agg')
import matplotlib.pyplot as plt
impor... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Solution Notebook
## Problem: Implement an algorithm to have a robot move from the upper left corner to the bottom right corner of a gri... | github_jupyter |
Convolutional Dictionary Learning
=================================
This example demonstrates the use of [cbpdndl.ConvBPDNDictLearn](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.cbpdndl.html#sporco.dictlrn.cbpdndl.ConvBPDNDictLearn) for learning a convolutional dictionary from a set of colour training image... | github_jupyter |
```
"""
Snowflake + DataRobot Prediction API example code.
1. Data extracted via Snowflake python connector
2. Python scoring http request sent
3. Data written back to Snowflake via connector as raw json and flattened in Snowflake
4. Data flattened in python
5. Batch Scoring Script scoring
*******
NOTE:
Write back o... | github_jupyter |
```
!unzip ./archive\ \(67\).zip
import pandas as pd
data = pd.read_csv('./names-by-nationality.csv')
data.head()
len(data)
data.isna().sum()
import json
def object_to_int(data,coloum):
info_dict = {}
all_info = []
index = -1
for info in data[coloum]:
if info not in info_dict:
index ... | github_jupyter |
TSG094 - Grafana logs
=====================
Steps
-----
### Parameters
```
import re
tail_lines = 2000
pod = None # All
container = "grafana"
log_files = [ "/var/log/supervisor/log/grafana*.log" ]
expressions_to_analyze = []
```
### Instantiate Kubernetes client
```
# Instantiate the Python Kubernetes client in... | github_jupyter |
# Strings
A string is a sequence of characters.
Computers do not deal with characters, they deal with numbers (binary). Even though you may see characters on your screen, internally it is stored and manipulated as a combination of 0's and 1's.
This conversion of character to a number is called encoding, ... | github_jupyter |
# Pommerman Demo.
This notebook demonstrates how to train Pommerman agents. Please let us know at support@pommerman.com if you run into any issues.
```
import os
import sys
import numpy as np
from pommerman.agents import SimpleAgent, RandomAgent, PlayerAgent, BaseAgent
from pommerman.configs import ffa_v0_env
from p... | github_jupyter |
## Do why example on ihdp(Infant Health and Development Program) dataset
```
# importing required libraries
import os, sys
sys.path.append(os.path.abspath("../../"))
import dowhy
from dowhy.do_why import CausalModel
import pandas as pd
import numpy as np
```
#### Loading Data
```
data= pd.read_csv("https://raw.githu... | github_jupyter |
# Notebook for testing performance of Visual Recognition Custom Classifiers
[Watson Developer Cloud](https://www.ibm.com/watsondevelopercloud) is a platform of cognitive services that leverage machine learning techniques to help partners and clients solve a variety of business problems. Furthermore, several of the WDC ... | github_jupyter |
### Show distribution of heights and Z time form the most recent ephys round of animals
```
import math
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
from numpy import median
from scipy.stats import... | github_jupyter |
```
from pathflowai.utils import load_sql_df
import torch
import pickle
import os
import sys, os
import umap, numba
from sklearn.preprocessing import LabelEncoder
from torch_cluster import knn_graph
from torch_geometric.data import Data
import numpy as np
from torch_geometric.utils import train_test_split_edges
impor... | github_jupyter |
```
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all' # default is ‘last_expr’
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('/data/home/marmot/camtrap/PyCharm/CameraTraps-benchmark')
sys.path.append('/data/home/marmot/camtrap/PyCharm/CameraTrap... | github_jupyter |
<a href="https://colab.research.google.com/github/kareem1925/Ismailia-school-of-AI/blob/master/quantum_mnist_classification/Classifying_mnist_data_using_quantum_features.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
We will first install Qulacs pl... | github_jupyter |
# Module Efficiency History and Projections
```
import numpy as np
import pandas as pd
import os,sys
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 22})
plt.rcParams['figure.figsize'] = (12, 8)
```
This journal covers the development of a historical baseline and baseline future projection of averag... | github_jupyter |
```
#Creating a colletion called movies_bulk and rewriting previous updates to the collection
import pymongo
from pymongo import MongoClient, UpdateOne
from datetime import datetime
import pprint
import re
from IPython.display import clear_output
# Replace XXXX with your connection URI from the Atlas UI
client = Mongo... | github_jupyter |
Minando datos de 8ch.net con python
====================
[](https://anaconda.org/bc-privsec-devel/chanscrape)
**Esta es la libreta #2 de 2 en esta serie. **
El codigo fuente, asi como instrucciones para instalar y ejecutar e... | github_jupyter |
<img src="https://github.com/pmservice/ai-openscale-tutorials/raw/master/notebooks/images/banner.png" align="left" alt="banner">
# Working with Watson Machine Learning
The notebook will train, create and deploy a Credit Risk model. It will then configure OpenScale to monitor drift in data and accuracy by injecting sa... | github_jupyter |
```
import tensorflow as tf
print(tf.__version__)
!pip install keras-tuner
import kerastuner
from kerastuner.tuners import RandomSearch, Hyperband, BayesianOptimization
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from ... | github_jupyter |
# Generates images from text prompts with a CLIP conditioned Decision Transformer.
By Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings).
```
# @title Licensed under the MIT License
# Copyright (c) 2021 Katherine Crowson
# Permission is hereby granted, free of charge, to any perso... | github_jupyter |
## Reinterpreting by patching an existing HistFactory pdf spec
An important pattern in High-Energy physics in the reinterpretation of analyses with respect to new signal models.
The main idea is that a given phase space selection (an "analysis") designed for some original BSM physics signal may not only be efficient... | github_jupyter |
# Run Ad-Hoc Model Bias Analysis
## Run Bias Analysis In The Notebook using `smclarify`
https://github.com/aws/amazon-sagemaker-clarify
```
!pip install -q smclarify==0.1
from smclarify.bias.report import *
from smclarify.util.dataset import Datasets, german_lending_readable_values
from typing import Dict
from collec... | github_jupyter |
```
import re
import os
import random
import itertools
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
from urllib.parse import urlparse
from sklearn import metrics
from tensorflow import keras
from sklearn.ensemble import RandomForestClassifier
from tensorflow.keras import backend ... | github_jupyter |
```
import requests as req
import sentinelhub as sh
import matplotlib.pyplot as plt
import numpy as np
import instance_id as inid
import mimetypes
import sentinelhub.constants
from spectral import *
%matplotlib notebook
#%matplotlib inline
from PIL import Image
im = Image.open('rukban.tif')
import numpy as np
imarray ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_openml
from future import standard_library
standard_library.install_aliases()
from builtins import range
from builtins import object
import os
import pickle as pickle
from IPython.display import clear_output
VAL_RATIO = 0.1
TEST_R... | github_jupyter |
<img src="Images/slide_1_clustering.png" width="700" height="700">
<img src="Images/slide_2_clustering.png" width="700" height="700">
## Text Vectorization
Question: What is text vectorization?
Answer: The process to transform text data to numerical vectors
## Options for Text Vectorization
- Count the number of ... | github_jupyter |
# Derive Extended Interval Algebra
<b>NOTE:</b> From a derivation point-of-view, what distinquishes this algebra from Allen's algebra it the definition of <b>less than</b> used to define intervals. In particular, this derivation uses '=|<' rather than '<', which allows intervals to be degenerate (i.e., equal a point)... | github_jupyter |
# 第1章: 準備運動
## 00. 文字列の逆順
***
文字列”stressed”の文字を逆に（末尾から先頭に向かって）並べた文字列を得よ．
```
str = 'stressed'
ans = str[::-1]
print(ans)
```
## 01. 「パタトクカシーー」
***
「パタトクカシーー」という文字列の1,3,5,7文字目を取り出して連結した文字列を得よ．
```
str = 'パタトクカシーー'
ans = str[::2]
print(ans)
```
## 02. 「パトカー」＋「タクシー」＝「パタトクカシーー」
***
「パトカー」＋「タクシー」の文字を先頭から交互に連結して文字列「パタ... | github_jupyter |
## Facial Filters
Using your trained facial keypoint detector, you can now do things like add filters to a person's face, automatically. In this optional notebook, you can play around with adding sunglasses to detected face's in an image by using the keypoints detected around a person's eyes. Checkout the `images/` di... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
import os
import glob
import sys
sys.path.insert(0, '../scripts/')
from football_field import create_football_field
from plots import plot_play
import math
%matplotlib inline
pd.options.display.max_columns = 100
%load_ext ... | github_jupyter |
```
%%html
<link href="http://mathbook.pugetsound.edu/beta/mathbook-content.css" rel="stylesheet" type="text/css" />
<link href="https://aimath.org/mathbook/mathbook-add-on.css" rel="stylesheet" type="text/css" />
<style>.subtitle {font-size:medium; display:block}</style>
<link href="https://fonts.googleapis.com/css?fa... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
%matplotlib inline
torch.manual_seed(777) # reproducibility
# Hyper parameters
num_epochs = 30
num_classes = 10
batch_size = 100
learning_rate = 0.001
... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%run plot.py
```
### Function for the random step
$DX$ is the standard deviation, $bias$ is the constant average of the step
```
# random seed for reproducibility
np.random.seed(12345)
# function for the random step, using lambda construction
# int() for cleaner... | github_jupyter |
# Recurrent Neural Networks with ``gluon``
With gluon, now we can train the recurrent neural networks (RNNs) more neatly, such as the long short-term memory (LSTM) and the gated recurrent unit (GRU). To demonstrate the end-to-end RNN training and prediction pipeline, we take a classic problem in language modeling as ... | github_jupyter |
## This notebook constructs the GRAND dam network using the Free-Flow Rivers Dataset (Grill et al., 2019)
```
import os
import numpy as np
import pandas as pd
import geopandas as gpd
import rasterio
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
from scipy import stats
im... | github_jupyter |
# Module 6
## Video 29: Working with Aggregated Cargo Movements Data
**Python for the Energy Industry**
In this lesson, we will be working with the data from the previous lesson. We will practice visualising this data.
[Cargo Movements documentation](https://vortechsa.github.io/python-sdk/endpoints/cargo_movements/... | github_jupyter |
```
import csv
import datetime
import h5py
import itertools
import keras
import numpy as np
import os
import pandas as pd
import pescador
import random
import sys
import tensorflow as tf
import time
sys.path.append("../src")
import localmodule
# Define constants.
dataset_name = localmodule.get_dataset_name()
folds = l... | github_jupyter |
# Characterization of Discrete Systems in the Spectral Domain
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Communications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sasch... | github_jupyter |
```
import cv2
import numpy as np
import string
import random
import glob
import imgaug.augmenters as iaa
import matplotlib.pyplot as plt
%matplotlib inline
bg_pattern = glob.glob(r"pattern/*.*")
random.shuffle(bg_pattern)
```
# Augmentation settings for Anchor, Positive, Negative
```
sometimes = lambda aug: iaa.... | github_jupyter |
# Multiple Regression Analysis: Further Issues
## Effects of Data Scaling on OLS Statistics
By analysing an example, we have when the data for **dependent variable** are scaled to $k$ times as before,
- the OLS coefficient estimates are scaled to $\DeclareMathOperator*{\argmin}{argmin}
\DeclareMathOperator*{\argmax}{... | github_jupyter |
```
# The URL of the MISP instance to connect to
misp_url = 'http://127.0.0.1:8080'
# Can be found in the MISP web interface under ||
# http://+MISP_URL+/users/view/me -> Authkey
misp_key = 'LBelWqKY9SQyG0huZzAMqiEBl6FODxpgRRXMsZFu'
# Should PyMISP verify the MISP certificate
misp_verifycert = False
```
# Getting the ... | github_jupyter |
```
name = '2016-06-10-arcgis-intro'
title = 'Introduction to ArcGIS and its Python interface'
tags = 'gis, maps, basics'
author = 'Melanie Froude'
from nb_tools import connect_notebook_to_post
from IPython.core.display import HTML
html = connect_notebook_to_post(name, title, tags, author)
```
Today Melanie lead the ... | github_jupyter |
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