Tutorial Download Python
Janita Locklin
This tutorial introduces the reader informally to the basic concepts andfeatures of the Python language and system. It helps to have a Pythoninterpreter handy for hands-on experience, but all examples are self-contained,so the tutorial can be read off-line as well.
The tutorial will take you through the understanding of the Python programming language, help you deeply learn the concepts, and show you how to apply practical programming techniques to your specific challenges.
This Python Tutorial is very well suited for Beginners, and also for experienced programmers with other programming languages like C++ and Java. This specially designed free Python tutorial will help you learn Python Programming Language in the most efficient way, with topics from basics to advanced (like Web-scraping, Django, Deep-Learning, etc.) with examples.
Prerequisites This tutorial assumes RabbitMQ is installed and running on localhost on the standard port (5672). In case you use a different host, port or credentials, connections settings would require adjusting.
In this part of the tutorial we'll write two small programs in Python; aproducer (sender) that sends a single message, and a consumer (receiver) that receivesmessages and prints them out. It's a "Hello World" of messaging.
RabbitMQ speaks multiple protocols. This tutorial uses AMQP 0-9-1, which is an open,general-purpose protocol for messaging. There are a number of clients for RabbitMQin many different languages. In this tutorialseries we're going to use Pika 1.0.0,which is the Python client recommendedby the RabbitMQ team. To install it you can use thepip package management tool:
Please keep in mind that this and other tutorials are, well, tutorials. They demonstrate one new concept at a time and may intentionally oversimplify some things and leave out others. For example topics such as connection management, error handling, connection recovery, concurrency and metric collection are largely omitted for the sake of brevity. Such simplified code should not be considered production ready.
Using dynos and databases to complete this tutorial counts towards your usage. We recommend using our low-cost plans to complete this tutorial. Eligible students can apply for platform credits through our new Heroku for GitHub Students program.
The sample app has an additional Procfile for local development on Microsoft Windows, located in the file Procfile.windows. Later tutorial steps use this instead to start a different web server compatible with Windows.
This website contains a set of tutorials about how to use the ECCO Central Production Version 4 (ECCO v4) global ocean and sea-ice state estimate. The tutorials were written in Python and make use of the ecco_v4_py Python library, a library written specifically for loading, plotting, and analyzing ECCO v4 state estimate fields.
Python web support in Visual Studio includes several project templates, such as web applications in the Bottle, Flask, and Django frameworks. When installing Python with the Visual Studio Installer, check "Python Web Support" under optional to install these templates. For this tutorial, start with an empty project.
If you are interested in learning more about NetworkX, graph theory and network analysisthen you should check out nx-guides. There you can find tutorials,real-world applications and in-depth examinations of graphs and network algorithms.All the material is official and was developed and curated by the NetworkX community.
OT-2 users should review the robot setup and pipette information on the Get Started page. Specifically, see attaching pipettes and initial calibration. You can use either a single-channel or 8-channel pipette for this tutorial. Most OT-2 code examples will use a P300 Single-Channel GEN2 pipette.
The Flex and OT-2 use similar labware for serial dilution. The tutorial code will use the labware listed in the table below, but as long as you have labware of each type you can modify the code to run with your labware.
Every protocol needs to have a metadata dictionary with information about the protocol. At minimum, you need to specify what version of the API the protocol requires. The scripts for this tutorial were validated against API version 2.16, so specify:
To summarise the discussion, here is your first Selenium test on Python. You may save it in the file selenium_test.py and run python selenium_test.py to run the test.
NASA's Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes HLS products in the LP DAAC Cumulus cloud archive as Cloud Optimized GeoTIFFs (COG). This tutorial will demonstrate how to query and subset HLS data using the NASA Common Metadata Repository (CMR) SpatioTemporal Asset Catalog (STAC) application programming interface (API). Because these data are stored as COGs, this tutorial will teach users how to load subsets of individual files into memory for just the bands you are interested in--a paradigm shift from the more common workflow where you would need to download a .zip/HDF file containing every band over the entire scene/tile. This tutorial covers how to process HLS data (quality filtering and EVI calculation), visualize, and "stack" the scenes over a region of interest into an xarray data array, calculate statistics for an EVI time series, and export as a comma-separated values (CSV) file--providing you with all of the information you need for your area of interest without having to download the source data file. The Enhanced Vegetation Index (EVI), is a vegetation index similar to NDVI that has been found to be more sensitive to ground cover below the vegetated canopy and saturates less over areas of dense green vegetation.
This tutorial was developed using an example use case for crop monitoring over a single large farm field in northern California. The goal of the project is to observe HLS-derived mean EVI over a farm field in northern California without downloading the entirety of the HLS source data.
This tutorial will show how to use the CMR-STAC API to investigate the HLS collections available in the cloud and search for and subset to the specific time period, bands (layers), and region of interest for our use case, load subsets of the desired COGs into a Jupyter Notebook directly from the cloud, quality filter and calculate EVI, stack the time series, visualize the time series, and export a CSV of statistics on the EVI of the single farm field.
Congrats! You have pulled your first HLS asset from the cloud using STAC!Below, we will remove variables that were set in sections 1-2 that we will no longer use for the duration of the tutorial. This is good practice in order to keep a clean environment and to save space in memory.
Above, we have added the limit as a parameter to the dictionary that we will post to the search endpoint to submit our request for data. As we keep moving forward in the tutorial, we will continue adding parameters to the params dictionary.
NOTE: if the cell above has fails, make sure that you have downloaded the Field_Boundary.geojson file from the HLS-Tutorial Repository. You will need to make sure that the file is saved in the same directory as the directory that you are running the tutorial in. If you are still encountering issues, you can add the entire filepath to the file (ex: field = gp.read_file('C:/Username/HLS-Tutorial/Field_Boundary.geojson') and try again.
This tutorial will walk you through creating a basic blog applicationcalled Flaskr. Users will be able to register, log in, create posts,and edit or delete their own posts. You will be able to package andinstall the application on other computers.
In this tutorial, we'll work on a couple of basic examples to get you started, but there is much more documentation about Python scripting available on this wiki. If you are totally new to Python and want to understand how it works, we also have a basic introduction to Python.
Now our box appeared. Many of the buttons that add objects in FreeCAD actually do two things: add the object, and recompute. If you turned on the Show script commands in python console option above, try adding a sphere with the GUI button; you'll see the two lines of Python code being executed one after the other.
The true power of FreeCAD lies in its faithful modules, with their respective workbenches. The FreeCAD base application is more or less an empty container. Without its modules it can do little more than create new, empty documents. Each module not only adds new workbenches to the interface, but also new Python commands and new object types. As a result several different, and even totally incompatible, object types can coexist in the same document. The most important modules in FreeCAD that we'll look at in this tutorial are: Part, Mesh, Sketcher and Draft.
This is a tutorial node. It exercises the feature of providing hardcoded token values in the database after a node type has been initialized. It sets output booleans to the truth value of whether corresponding inputs appear in the hardcoded token list.
Congratulations! You have finished the Cantera Python tutorial. You should nowbe ready to begin using Cantera on your own. Please see the Next Stepssection on the Getting Started page, for assistance withintermediate and advanced Cantera functionality. Good luck!
This tutorial is an introduction to using Python with Oracle Database. It contains beginner and advanced material. Sections can be done in any order. Choose the content that interests you and your skill level. The tutorial has scripts to run and modify, and has suggested solutions.
If you need to create a new user, review the grants created in samples/tutorial/sql/create_user.sql. Then open a terminal window, change to the samples/tutorial/sql directory, and run the create_user.sql script as the SYSTEM user, for example:
Edit db_config.py and change the default values to match the connection information for your environment. Alternatively you can set the given envionment variables in your terminal window. For example, the default username is "pythonhol" unless the envionment variable "PYTHON_USER" contains a different username. The default connection string is for the 'orclpdb1' database service on the same machine as Python. (In Python Database API terminology, the connection string parameter is called the "data source name", or "dsn".) Using envionment variables is convenient because you will not be asked to re-enter the password when you run scripts:
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