def test_plotting(self):
"""
Testing the fit function
and the image data when data are available
"""
plot = ScatterPlot(1)
plot.set_data([0, 1], [0, 1])
img_data = plot.get_image_data()
assert plot.get_fit_function()
assert img_data
def plot():
"""
This page displays a scatter plot of all gathered tweets
The x-axis is the sentiment value.
The y-axis is the weather value.
The closer points are to the 'identity' line,
the closer they fit our hypothesis
"""
scatter_plot = ScatterPlot(1)
scatter_plot.load_data()
img_data = scatter_plot.get_image_data()
refresh = request.args.get('refresh', 0, type=int)
if refresh:
return img_data
return render_template('plot.html', data=img_data)
def plot():
"""
This page displays a scatter plot of all gathered tweets
The x-axis is the sentiment value.
The y-axis is the weather value.
The closer points are to the 'identity' line,
the closer they fit our hypothesis
"""
scatter_plot = ScatterPlot(1)
scatter_plot.load_data()
img_data = scatter_plot.get_image_data()
refresh = request.args.get('refresh', 0, type=int)
if refresh:
return img_data
return render_template('plot.html', data=img_data)
def test_plotting(self):
"""
Testing the fit function
and the image data when data are available
"""
plot = ScatterPlot(1)
plot.set_data([0, 1], [0, 1])
img_data = plot.get_image_data()
assert plot.get_fit_function()
assert img_data
def test_plotting_empty(self):
"""
Testing the fit function
and the image data when no data is available
or when weather data are random
and thus not among the allowed discrete values
"""
plot = ScatterPlot(1)
img_data = plot.get_image_data()
assert not (plot.get_fit_function())
plot.set_data([random.random(), random.random()],
[random.random(), random.random()])
assert not (plot.get_fit_function())
assert img_data
def test_plotting_empty(self):
"""
Testing the fit function
and the image data when no data is available
or when weather data are random
and thus not among the allowed discrete values
"""
plot = ScatterPlot(1)
img_data = plot.get_image_data()
assert not(plot.get_fit_function())
plot.set_data([random.random(), random.random()], [random.random(), random.random()])
assert not(plot.get_fit_function())
assert img_data
def plotNetworkPerformance(bestPopulation, topology, fileName, networkSize):
if(topology == Topology.Random):
# Connectivity on x-axis and error on y-axis
xAxis = []
yAxis = []
for item in bestPopulation:
connectivity = item[0][0,0]
error = item[1]
xAxis.append(connectivity)
yAxis.append(error)
xAxis = np.array(xAxis)
yAxis = np.array(yAxis)
seriesName = "Network Size "+str(networkSize)
# Plot the results
scatter = plot.ScatterPlot(fileName, "Random Graph Connectivity Optimization using GA", "Connectivity vs Performance", "Connectivity", "MSE")
scatter.setSeries(seriesName, xAxis, yAxis)
scatter.createOutput()
averageDiameterList = np.array(averageDiameterList).reshape(
len(averageDiameterList), 1)[:, 0]
averageClusteringCoefficientList = np.array(
averageClusteringCoefficientList).reshape(
len(averageClusteringCoefficientList), 1)[:, 0]
# # Plot 1 - Network parameters vs error
# fileName = "/Attachment_Vs_Error.html"
# scatter = plot.ScatterPlot(folderName+fileName, "Scale Free Networks Optimization using GA", "Attachment vs Performance", "Attachment", "Mean Square Error")
# scatter.setSeries("Network Performance", attachmentList, errorList)
# scatter.createOutput()
# Plot 2 - Average degree vs error
fileName = "/AverageDegree_Vs_Error.html"
scatter = plot.ScatterPlot(folderName + fileName,
"Small World Graphs Optimization using GA",
"Average Degree vs Performance", "Average Degree",
"Mean Square Error")
scatter.setSeries("Network Performance", averageDegreeList, errorList)
scatter.createOutput()
# Plot 3 - Average path length vs error
fileName = "/AveragePathLength_Vs_Error.html"
scatter = plot.ScatterPlot(folderName + fileName,
"Small World Graphs Optimization using GA",
"Average Path Length vs Performance",
"Average Path Length", "Mean Square Error")
scatter.setSeries("Network Performance", averagePathLengthList, errorList)
scatter.createOutput()
# Plot 4 - Diameter vs error
fileName = "/Diameter_Vs_Error.html"
averagePathLengthList.append(averagePathLength)
averageDiameterList.append(averageDiameter)
averageClusteringCoefficientList.append(averageClusteringCoefficient)
attachmentList = np.array(attachmentList).reshape(len(attachmentList),1)[:,0]
errorList = np.array(errorList).reshape(len(errorList),1)[:,0]
averageDegreeList = np.array(averageDegreeList).reshape(len(averageDegreeList),1)[:,0]
averagePathLengthList = np.array(averagePathLengthList).reshape(len(averagePathLengthList),1)[:,0]
averageDiameterList = np.array(averageDiameterList).reshape(len(averageDiameterList),1)[:,0]
averageClusteringCoefficientList = np.array(averageClusteringCoefficientList).reshape(len(averageClusteringCoefficientList),1)[:,0]
# Plot 1 - Network parameters vs error
fileName = "/Attachment_Vs_Error.html"
scatter = plot.ScatterPlot(folderName+fileName, "Scale Free Networks Optimization using GA", "Attachment vs Performance", "Attachment", "Mean Square Error")
scatter.setSeries("Network Performance", attachmentList, errorList)
scatter.createOutput()
# Plot 2 - Average degree vs error
fileName = "/AverageDegree_Vs_Error.html"
scatter = plot.ScatterPlot(folderName+fileName, "Scale Free Networks Optimization using GA", "Average Degree vs Performance", "Average Degree", "Mean Square Error")
scatter.setSeries("Network Performance", averageDegreeList, errorList)
scatter.createOutput()
# Plot 3 - Average path length vs error
fileName = "/AveragePathLength_Vs_Error.html"
scatter = plot.ScatterPlot(folderName+fileName, "Scale Free Networks Optimization using GA", "Average Path Length vs Performance", "Average Path Length", "Mean Square Error")
scatter.setSeries("Network Performance", averagePathLengthList, errorList)
0]
errorList = np.array(errorList).reshape(len(errorList), 1)[:, 0]
averageDegreeList = np.array(averageDegreeList).reshape(
len(averageDegreeList), 1)[:, 0]
averagePathLengthList = np.array(averagePathLengthList).reshape(
len(averagePathLengthList), 1)[:, 0]
averageDiameterList = np.array(averageDiameterList).reshape(
len(averageDiameterList), 1)[:, 0]
averageClusteringCoefficientList = np.array(
averageClusteringCoefficientList).reshape(
len(averageClusteringCoefficientList), 1)[:, 0]
# Plot 1 - Network parameters vs error
fileName = "/Probability_Vs_Error.html"
scatter = plot.ScatterPlot(folderName + fileName,
"Erdos Renyi Networks Optimization using GA",
"Probability vs Performance", "Probability",
"Mean Square Error")
scatter.setSeries("Network Performance", attachmentList, errorList)
scatter.createOutput()
# Plot 2 - Average degree vs error
fileName = "/AverageDegree_Vs_Error.html"
scatter = plot.ScatterPlot(folderName + fileName,
"Erdos Renyi Networks Optimization using GA",
"Average Degree vs Performance", "Average Degree",
"Mean Square Error")
scatter.setSeries("Network Performance", averageDegreeList, errorList)
scatter.createOutput()
# Plot 3 - Average path length vs error
fileName = "/AveragePathLength_Vs_Error.html"