View difference between Paste ID: eSaLSW5c and cjAD35Y3

-# -*- coding: utf-8 -*-
+# @author: Brian Keschinger
-"""
+
-Created on Thu Jun  7 21:54:32 2018
+
 import seaborn as sns
-@author: Brian Keschinger
+
-"""
+
 # Start utility functions
-# Imports
+'''-------------------------------------------------------
 Function: ResetPlot
 Description: Clears the figure being used in pyplot and sets up our dimensions, axes, image, and layout.
 ----------------------------------------------------------'''
 def ResetPlot():
     global ax
-##################################
+    global img
     # Clear the current plot so we can reuse it
-##################################
+
 
-''' ---------------------------------------------------------------------------------------------------------------
+
     ax = plt.gca()
     ax.set_autoscale_on(False)
-@param[in] None
+
-@return[out] None
+
- ------------------------------------------------------------------------------------------------------------------ '''
+
     ax.axes.get_yaxis().set_visible(False)
 
-    global img
+
     plt.axis([1, 1920, 1, 1080])
 
     # flipud(): flips an array in the "up/down" direction so it's flipping the image
     # Then we say that the plot origin is lower which also flips the image
     # The result is a correctly shown image with a coordinate system origin of the lower left
     plt.imshow(np.flipud(img), origin='lower')
 
     # We're hiding the axes and their labels anyway so this will auto fit nicely to the figure area
     plt.tight_layout()
 
 
 '''----------------------------------------------------------
 Function: ConvertDataToAdjustedGrid
 Description: Used to convert the image's pixel-based grid to a new grid size
 @param[in] dataFrame: Data Frame that contains a type column that contains kills and deaths
 @param[in] cellSize: int that's the new grid cell size used to convert the 1920x1080 grid
 @return[out] adjustedGrid: a 2D Array consisting of summed integer values that will act as the converted grid
 -------------------------------------------------------------'''
 def ConvertDataToAdjustedGrid(dataFrame, cellSize):
     # Using the gridSize find out how many rows and columns the new array and grid will be
     xAxisSize = int(1920 / cellSize)
     yAxisSize = int(1080 / cellSize)
 
     # Create a fixed-size array that starts with all zeros
-''' ---------------------------------------------------------------------------------------------------------------
+
 
     # Loop through all the rows of the data
     # Convert the raw pixel coordinates to the adjusted grid
     # Add 1 for any kill and subtract 1 for deaths
     for index, row in dataFrame.iterrows():
- ------------------------------------------------------------------------------------------------------------------ '''
+
         yCell = (yAxisSize-1) - int(np.floor(row.y / cellSize)) # 0,0 for a 2D array will be the top left so we want to flip the yAxis
         adjustedGrid[yCell][xCell] += 1 if (row.type == 'k') else -1
 
     return adjustedGrid
 
 '''----------------------------------------------------------
 Function: CreateAndExportHeatmap
 Description: Used to create and save to disk a heatmap from a dataset
 @param[in] fileName: string that will be used to save the image to disc and name the graph
 @param[in] dataFrame: Data Frame that contains a type column that contains kills and deaths
 @param[in] cellSize: int that's the new grid cell size used to convert the 1920x1080 grid
 -------------------------------------------------------------'''
 def CreateAndExportHeatmap(fileName, dataFrame, cellSize):
     global ax
     ResetPlot()
     newAdjustedGrid = ConvertDataToAdjustedGrid(dataFrame, cellSize)
 
     # Find the min and max value in the array
     minVal = np.amin(newAdjustedGrid)
-''' ---------------------------------------------------------------------------------------------------------------
+
 
     # Between the min and max find our new bounds
     upperBound = max(abs(minVal), abs(maxVal))
 
     # Using the upperBound we found, set the color bounds from -upperBound to +upperBound so that white sits at 0 properly
- ------------------------------------------------------------------------------------------------------------------ '''
+
 
     # Set the plot title from the fileName but replace dashes with spaces
     ax.set_title(fileName.replace('-',' '))
-    ResetPlot()
+
     # Save the image to disk (directory must be created)
     # We're using 500 dpi because the original figure size is small and 500 will push the actual graph to be larger than 1920x1080
     # We'll pull this into PhotoShop and overlay it onto our map with some nice opacities, etc.
     fig = sns_heatmap.get_figure()
     fig.savefig('Exported_Graphs/' + fileName +'.png', dpi=500)
 # End utility functions
 
 # Read the CSVs from disk
 CTF_data = pd.read_csv("Playtest_Data/6-7_Playtest_CTF.csv")
 BMB_data = pd.read_csv("Playtest_Data/6-7_Playtest_Bomb.csv")
 
 # data[key] == something returns a boolean value
 # by insterting that value into data.loc it returns all the rows where that is true
 CTF_deathPositions = CTF_data.loc[CTF_data['type']=='d']
 CTF_killPositions = CTF_data.loc[CTF_data['type']=='k']
 
-    # Save the image to disk
+
 img = plt.imread("Source_Images/Map_Overhead.png")
 
-    # @TO-DO protect against the directory not being created
+
 # 1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 24, 30, 40, 60, 120
 newGridCellSize = 120
 
-##################################
+
 CreateAndExportHeatmap('BMB-All-Kills-and-Deaths-Heatmap', BMB_data, newGridCellSize)