mirror of
https://github.com/Unidata/python-awips.git
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jupyter notebook cleanup for 0.9.11
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parent
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commit
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9 changed files with 860 additions and 732 deletions
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@ -53,45 +53,6 @@ several commercial off-the-shelf (COTS) and Free or Open Source software
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(FOSS) products to assist in its operation. The following components,
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(FOSS) products to assist in its operation. The following components,
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working together and communicating, compose the entire AWIPS system.
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working together and communicating, compose the entire AWIPS system.
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AWIPS Python Stack
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------------------
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A number of Python packages are bundled with the AWIPS EDEX and CAVE
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installations.
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====================== ============== ==============================
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Package Version RPM Name
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====================== ============== ==============================
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Python 2.7.11 awips2-python
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**awips** **0.9.10** **awips2-python-awips**
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cartopy 0.15.1 awips2-python-cartopy
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cherrypy 3.8.2 awips2-python-cherrypy
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cycler 0.10.0 awips2-python-cycler
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cython 0.25.2 awips2-python-cython
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dateutil 2.4.2 awips2-python-dateutil
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gfe 16.4.1 awips2-python-gfe
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h5py 1.3.0 awips2-python-h5py
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jep 3.5.3 awips2-python-jep
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matplotlib 1.5.1 awips2-python-matplotlib
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metpy 0.5.0 awips2-python-metpy
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nose 1.3.7 awips2-python-nose
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numpy 1.9.2 awips2-python-numpy
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pint 0.7.2 awips2-python-pint
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pmw 1.3.2 awips2-python-pmw
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pupynere 1.0.15 awips2-python-pupynere
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pyparsing 2.0.3 awips2-python-pyparsing
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qpid 0.32 awips2-python-qpid
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scientific 2.8 awips2-python-scientific
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scipy 0.15.1 awips2-python-scipy
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shapely 1.4.4 awips2-python-shapely
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six 1.10.0 awips2-python-six
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tables 3.3.0 awips2-python-tables
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tpg 3.2.2 awips2-python-tpg
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werkzeug 0.12.1 awips2-python-werkzeug
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====================== ============== ==============================
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EDEX
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EDEX
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----
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----
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@ -65,7 +65,7 @@ author = 'Unidata'
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# built documents.
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# built documents.
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#
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#
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# The short X.Y version.
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# The short X.Y version.
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version = '0.9.10'
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version = '0.9.11'
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# The full version, including alpha/beta/rc tags.
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# The full version, including alpha/beta/rc tags.
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# The language for content autogenerated by Sphinx. Refer to documentation
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# The language for content autogenerated by Sphinx. Refer to documentation
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@ -4,7 +4,7 @@ Python AWIPS Data Access Framework
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`AWIPS <http://unidata.github.io/awips2>`_ is a weather display and analysis package developed by the National Weather Service for operational forecasting. UCAR's `Unidata Program Center <http://www.unidata.ucar.edu/software/awips2/>`_ supports a non-operational open-source release of the AWIPS software (`EDEX <http://unidata.github.io/awips2/#edex>`_, `CAVE <http://unidata.github.io/awips2/#cave>`_, and `python-awips <https://github.com/Unidata/python-awips>`_).
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`AWIPS <http://unidata.github.io/awips2>`_ is a weather display and analysis package developed by the National Weather Service for operational forecasting. UCAR's `Unidata Program Center <http://www.unidata.ucar.edu/software/awips2/>`_ supports a non-operational open-source release of the AWIPS software (`EDEX <http://unidata.github.io/awips2/#edex>`_, `CAVE <http://unidata.github.io/awips2/#cave>`_, and `python-awips <https://github.com/Unidata/python-awips>`_).
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The python-awips package provides a data access framework for requesting grid and geometry datasets from an `EDEX <http://unidata.github.io/awips2>`_ server.
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The python-awips package provides a data access framework for requesting grid and geometry datasets from an `EDEX <http://unidata.github.io/awips2/#edex>`_ server.
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.. _Jupyter Notebook: http://nbviewer.jupyter.org/github/Unidata/python-awips/tree/master/examples/notebooks
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.. _Jupyter Notebook: http://nbviewer.jupyter.org/github/Unidata/python-awips/tree/master/examples/notebooks
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@ -20,8 +20,8 @@ Requirements
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- Shapely 1.4+
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- Shapely 1.4+
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- MetPy and enum34 to run the `Jupyter Notebook`_ examples
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- MetPy and enum34 to run the `Jupyter Notebook`_ examples
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How to Use
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Quick Example
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~~~~~~~~~~
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~~~~~~~~~~~~~
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::
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::
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request = DataAccessLayer.newDataRequest()
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request = DataAccessLayer.newDataRequest()
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dataTypes = DataAccessLayer.getSupportedDatatypes()
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dataTypes = DataAccessLayer.getSupportedDatatypes()
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request.setDatatype("grid")
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request.setDatatype("grid")
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request.addLocationNames("RAP40")
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request.addLocationNames("RAP13")
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request.setParameters("T")
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request.setParameters("T")
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request.setLevels("0.0SFC")
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request.setLevels("0.0SFC")
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cycles = DataAccessLayer.getAvailableTimes(request, True)
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cycles = DataAccessLayer.getAvailableTimes(request, True)
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@ -30,12 +30,11 @@
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"\n",
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"\n",
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"50 dBZ corresponds to the 16-level color *light red* (**FF6060**). Note that `FF6060` is not used in the NWS AWIPS color scale, instead RGB value is given as `255,0,0` (hex code **FF0000**). 60 dBZ is not quite exactly where white starts, but it makes sense that it would. Obviously the AWIPS D2D authors took some liberties with their 256-level rendering, not adhering strictly to \"dark red\" for dBZ values between 60-65 (white was for 70 dBZ and above on the 16-level colormap). For this exercise we will assume 50 dBZ should be red and 60 dBZ white, and 75 dBZ cyan.\n",
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"50 dBZ corresponds to the 16-level color *light red* (**FF6060**). Note that `FF6060` is not used in the NWS AWIPS color scale, instead RGB value is given as `255,0,0` (hex code **FF0000**). 60 dBZ is not quite exactly where white starts, but it makes sense that it would. Obviously the AWIPS D2D authors took some liberties with their 256-level rendering, not adhering strictly to \"dark red\" for dBZ values between 60-65 (white was for 70 dBZ and above on the 16-level colormap). For this exercise we will assume 50 dBZ should be red and 60 dBZ white, and 75 dBZ cyan.\n",
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"\n",
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"\n",
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"\n",
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"**Setup**\n",
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"### Setup\n",
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"\n",
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"\n",
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"> pip install python-awips matplotlib cartopy metpy\n",
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"> pip install python-awips matplotlib cartopy metpy\n",
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"\n",
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"\n",
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"### Python Script\n",
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"**Python Script**\n",
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"\n",
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"\n",
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"Download this script as a [Jupyter Notebook](http://nbviewer.jupyter.org/github/Unidata/python-awips/blob/master/examples/notebooks/NEXRAD_Level_3_Plot_with_Matplotlib.ipynb)."
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"Download this script as a [Jupyter Notebook](http://nbviewer.jupyter.org/github/Unidata/python-awips/blob/master/examples/notebooks/NEXRAD_Level_3_Plot_with_Matplotlib.ipynb)."
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]
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]
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 1,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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"outputs": [
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{
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{
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"name": "stdout",
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"name": "stdout",
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 3,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 4,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"pygments_lexer": "ipython2",
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"version": "2.7.11"
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"version": "2.7.14"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}
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}
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"## Create Request for Data Type warning"
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"## Create Request for Data Type Warning"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 1,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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"outputs": [
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{
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{
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"name": "stdout",
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"name": "stdout",
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"#### Create a dictionary of lists for each parameter"
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"## Create a dictionary of lists for each parameter"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 2,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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"outputs": [
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{
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{
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"name": "stdout",
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"name": "stdout",
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"#### Print out basic info for each warning"
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"**Print out basic info for each warning**"
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]
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 15,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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"outputs": [
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{
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{
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"name": "stdout",
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 6,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 13,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 11,
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"outputs": [
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"outputs": [
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{
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"data": {
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"pygments_lexer": "ipython2",
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"version": "2.7.11"
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"version": "2.7.14"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}
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}
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