/tags/python/ Recent content in python on Eduardo González Solares Hugo -- gohugo.io en-us © 2018 Eduardo González Solares Mon, 26 Mar 2018 14:33:02 +0200 /post/dask-distributed/ Mon, 26 Mar 2018 14:33:02 +0200 /post/dask-distributed/ Very brief introduction to Dask Dask is a flexible parallel computing library for analytic computing. Image processing using Dask I am going to show how to perform the processing of images using a distributed system in Dask. The processing of one image follows the next steps: Load image from disk. Each image contains four subimages or extensions and those can be processed in parallel up to certain point. Each image extension is first preprocessed. /post/image-registration-skimage/ Thu, 15 Feb 2018 11:22:52 +0100 /post/image-registration-skimage/ Toggle code In this post I am going to show a very basic example of image registration. I am using medical instead of astronomical images just for fun. Data The initial image corresponds to Human HT29 cells which are by nature fairly smooth and elliptical. Since I do not have two independent obtained images from the same sample, I am going to use just the one image. Let image1 be the original image and construct image2 applying a translation of 20 pixels in both axis, rotation of 10 degrees, and scale of 1. /post/jupyter-kernel/ Sun, 28 Jan 2018 08:49:04 +0100 /post/jupyter-kernel/ When creating Jupyter notebooks we find ourselves in most occasions repeating a lot of setup code at the start: loading numpy, scipy, matplotlib, setting figures size and style, etc. I describe here how to create a custom kernel with customizations that load the required packages at startup up and make them available by default in our Jupyter notebooks. Create a new Jupyter Kernel The first thing is to make sure we have the ipykernel module installed. /post/esoarchive/ Sat, 27 Jan 2018 08:08:53 +0100 /post/esoarchive/ The ESO Raw Data Archive is a reference archive for data obtained with any of the ESO telescopes, including VISTA, VST, etc. Currently there is no API to query the archive and retrieve data although scripts to access the archive programmatically are available from the archive FAQ. The official solution relies on shell scripts to query the different archive URLs and submit and retrieve forms using wget. Another option is using Python with urllib or requests to do the same, i. /post/tf-timeseries-i/ Mon, 18 Dec 2017 09:45:28 +0100 /post/tf-timeseries-i/ This is a quick example on using TensorFlow to learn about a time series and perform forecasting. This can be used as an entry point for detecting anomalies. Module import import numpy as np import tensorflow as tf from tensorflow.contrib.timeseries.python.timeseries import NumpyReader tf.logging.set_verbosity(tf.logging.WARN) tf.__version__ 1.4.0 Plots customisation %matplotlib inline import matplotlib import matplotlib.pyplot as plt import seaborn as sns sns.set() sns.set_context("notebook", font_scale=1.2, rc={"lines.linewidth": 1.5}) Create time series In this section we create the time series that we are going to use for training, fitting and prediction.