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#SHARPpy

######Sounding/Hodograph Analysis and Research Program in Python

SHARPpy is a collection of open source sounding and hodograph analysis routines, a sounding plotting package, and an interactive application for analyzing real-time soundings all written in Python. It was developed to provide the atmospheric science community a free and consistent source of sounding analysis routines. SHARPpy is constantly updated and vetted by professional meteorologists and climatologists within the scientific community to help maintain a standard source of sounding routines.

======================================================================= #####Developer Requests:

1.) Many people have put an immeasurable amount of time into developing this software package. If SHARPpy is used to develop a weather product or contributes to research that leads to a scientific publication, please acknowledge the SHARPpy project by citing the code. You can use this ready-made citation entry or provide a link back to this website:

Halbert, K. T., W. G. Blumberg, and P. T. Marsh, 2015: "SHARPpy: Fueling the Python Cult". 
Preprints, 5th Symposium on Advances in Modeling and Analysis Using Python, Phoenix AZ.

http://sharppy.github.io/SHARPpy/index.html

https://github.com/sharppy/SHARPpy

Additionally, Jeff Whitaker created the Basemap package, from which we have borrowed data and code to develop the SHARPpy data selector GUI.

2.) Also, please send an email letting us know where SHARPpy is being used or has helped your work at this address so we may track the success of the project: [email protected].

3.) All bug reports and feature requests should be submitted through the Github issues page in order to assist the developers in tracking the issues noted by the users. Before you open a new issue, please check to see if your issue (or a similar one) has already been opened. If your issue already exists, please add a comment to the issue comment thread explaining your bug report or feature request with as much detail as possible. More detail will help the developers fix the issue (in the case of a bug report). The issues page for the SHARPpy project can be found here:

https://github.com/sharppy/SHARPpy/issues

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Required Packages:

• NumPy

• PySide

SHARPpy is primarily tested and used with the Anaconda Python Distribution from Continuum Analytics. We recommend you use Python 2.7 instead of Python 3 as SHARPpy is not Python 3 compatable yet. Anaconda can be downloaded here: https://store.continuum.io/cshop/anaconda/

Since SHARPpy requires the PySide package, you will need to install it. If you choose to use the Anaconda distribution, PySide can be installed through the Anaconda package manager by typing:

conda install PySide

After installing all the required Python packages for SHARPpy, you now can install the SHARPpy package to your computer. You'll need to download it to your computer first and open up a command line prompt. You can download it as a ZIP file (link on the right) or clone the Git respository (you will need the git program) into a directory on your computer by typing this into your command line:

git clone https://github.com/sharppy/SHARPpy.git

If you follow the route of cloning SHARPpy, you can update to the most recent SHARPpy package by typing the following within the folder you downloaded SHARPpy to:

git pull origin master

Once the package has been downloaded to your computer, use your command line to navigate into the SHARPpy directory and type this command in to install SHARPpy:

python setup.py install

After installing the package, you can run the SHARPpy GUI and interact with the SHARPpy libraries through Python scripts.

A video tutorial for installing on Windows: https://dl.dropboxusercontent.com/u/6375163/SHARPpy.mp4

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To run the SHARPpy GUI and interact with real-time observed and forecast soundings, copy the runsharp folder to the location at which you wish to run the program. Navigate to that folder in your terminal and run the following command:

python full_gui.py

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To learn more about interacting with the SHARPpy libraries using the Python programming language, see the tutorial listed in tutorials/ and check out the link:

http://nbviewer.ipython.org/github/sharppy/SHARPpy/blob/master/tutorials/SHARPpy_basics.ipynb

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To open a sounding, select a sounding source (observed, GFS, HRRR, etc.), a cycle time, and then select profile time(s) to view in the GUI. Next, click on your desired location on the point and click map. Once all of these are selected, click "Generate Profiles".

After all profiles have been generated, a window should show up with your desired data. Below are things you can do:

1. Advance through the profiles (if more than one is selected) using the left and right arrow keys.
2. Change the hodograph cursor or point the hodograph window is centered on by right clicking on the hodograph.
3. Modify the right 2 insets by right clicking on either one. Different insets are available to help the user interrogate the data.
4. Zoom in/out the Skew-T or hodograph by using the scroll wheel function on your mouse or trackpad.
5. Graphically modify the Skew-T and hodograph by clicking and dragging the points of the temperature/dewpoint/hodograph lines. Recalculations of all indices will take place when this is done. (Added 2/19/2015 by Tim Supinie.)
6. View different parcels that can be lifted and lift custom parcels.
7. Compare the profiles and hodograph from severe weather sounding analogs retrieved by SARS by clicking on any of the analogs displayed.
8. Save an image of the sounding you are viewing (Control+S; Windows/Linux, Command+S; OS X)
9. Open up a text file that contains observed sounding data you wish to view. While in the sounding picker, use the keys Control+O for Windows/Linux, Command+O for OS X. Text files must be in a tabular format. See the OAX file in the tutorials folder for an example.

1. SARS - Sounding Analog Retrieval System provides matching of the current sounding to past severe weather events.
2. STP STATS - Information on the significant tornado parameter with CIN (STPC) associated with the sounding.
3. SHIP - Distribution of expected hail sizes associated with the significant hail parameter (SHIP).
4. STP COND - Conditional probablities for different tornado strengths based on STPC.
5. WINTER - Information on precipitation type, melting and freezing in the profile, and the dendritic growth zone.
6. FIRE - Fire weather information such as wind speed and humidity in the boundary layer.
7. VROT - Conditional probabilities for different tornado strengths based on the 0.5 degree rotational velocity. (Double click inside the inset to input a VROT value.)

The GUI uses color to highlight the features a forecaster ought to look at. Most indices have a color ranking and thresholds using these colors (1, very high values to 6, very low values):

1. MAGENTA
2. RED
3. YELLOW
4. WHITE
5. LIGHT BROWN
6. DARK BROWN

By default, soundings opened up in the GUI show these 4 parcels in the lower left inset window:

1.) Surface-based Parcel

2.) 100 mb Mixed-layer Parcel

3.) Forecasted Surface Parcel

4.) Most-Unstable Parcel

Double clicking on this inset will allow you to swap out these parcels for two others:

1.) Effective Inflow Layer Mean Parcel

2.) User Defined Parcel

The user defined parcel can be set by right clicking on the Skew-T and selecting a custom parcel to lift. The location of the cursor (or readout cursor) selects the level (or bottom of the layer) you are lifting.

Clicking on any of the 4 parcels in the inset will change the a) the parcel trace drawn on the Skew-T and b) change the parcel used in the parcel trajectory calculation (aka Storm Slinky.)

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Known Issues:

• Text can sometimes overlap. (Windows)
• The program’s menu bar does not display (minimal issue since there are very few menu bar functions) (Windows)
• SHARPpy will not work with QT 4.8.6.0 on Linux. There is a bug in the QT package affects the ability of the GUI to render. See this issue for a fix: sharppy#51 (Linux)
• Some observed soundings will be unable to be loaded into the program due to data quality issues. This is a preventative measure taken by the program that checks the sounding data for a.) incorrect ordering of the data such as in the height or pressure arrays or b.) unrealistic data values. (All OSes)

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SHARPpy is currently managed by the following co-developers (in no particular order):

• Patrick Marsh (SPC)
• Kelton Halbert (OU School of Meteorology)
• Greg Blumberg (OU/CIMMS)
• Tim Supinie (OU School of Meteorology)

Questions and concerns not related to bug reports or feature requests should be may be directed to the team through this email: [email protected]