def comparingBoxPlots(dictObj, plottedData="R0", saveName="default"):
osName = platform.system()
files = "images\\" if osName.lower() == "windows" else "images/"
osExtension = "win" if osName.lower() == "windows" else "Linux"
if plottedData == "R0":
if len(dictObj) > 0:
und_labels = []
und_R0data = []
R0AnalyzedData = []
reg_labels = []
reg_R0data = []
for key, value in dictObj.items():
if "NC_" in key or "VC_" in key or "SC_" in key:
und_labels.append(key)
und_R0data.append(value[0])
else:
reg_labels.append(key)
reg_R0data.append(value[0])
R0AnalyzedData.append(value[1])
flr.saveObjUsingPickle(flr.fullPath("R0"+osExtension+ saveName, "picklefile")+".pkl", dictObj)
boxplot(und_R0data,oneD=False, pltTitle="R0 Comparison (box)", xlabel="Model Name",
ylabel="Infected Agents (R0)", labels=und_labels, savePlt=True, saveName=osExtension+"9R0_box_"+saveName)
#statfile.barChart(und_R0data, oneD=False, pltTitle="R0 Comparison (bar)", xlabel="Model Name",
# ylabel="Infected Agents (R0)", labels=und_labels, savePlt=True, saveName=osExtension+"9R0_bar_"+saveName)
boxplot(reg_R0data,oneD=False, pltTitle="R0 Comparison (box)", xlabel="Model Name",
ylabel="Infected people (R0)", labels=reg_labels, savePlt=True, saveName=osExtension+"restR0_box_"+saveName)
#statfile.barChart(reg_R0data, oneD=False, pltTitle="R0 Comparison (bar)", xlabel="Model Name",
# ylabel="Infected Agents (R0)", labels=reg_labels, savePlt=True, saveName=osExtension+"restR0_bar_"+saveName)
elif plottedData == "inf":
if len(dictObj) > 0 or True:
labels = []
infectedCounts = []
labels1 = []
infectedCounts1 = []
for key, value in dictObj.items():#InfectedCountDict.items():
if "NC_" in key or "VC_" in key or "SC_" in key:
labels.append(key)
infectedCounts.append(value)
else:
labels1.append(key)
infectedCounts1.append(value)
print(labels, labels1)
flr.saveObjUsingPickle(flr.fullPath("infectedCount"+osExtension+saveName, "picklefile")+".pkl", dictObj)
boxplot(infectedCounts,oneD=False, pltTitle="Infection Comparison", xlabel="Model Name",
ylabel="Total # of Infected Agents", labels=labels, savePlt=True, saveName=osExtension+"9infe_box_"+saveName)
#statfile.barChart(infectedCounts, oneD=False, pltTitle="Infection Comparison (bar)", xlabel="Model Name",
# ylabel="Total Infected Agents", labels=labels, savePlt=True, saveName=osExtension+"9infe_bar_"+saveName)
boxplot(infectedCounts1,oneD=False, pltTitle="Infection Comparison", xlabel="Model Name",
ylabel="Total # of Infected Agents", labels=labels1, savePlt=True, saveName=osExtension+"rest_infe_box_"+saveName)
def barChart(data, oneD=False, pltTitle="Some Title", xlabel="Default X", ylabel="Default Y", labels=[], showPlt=True, savePlt=False, saveName="defaultimage.png"):
fig1, ax1 = plt.subplots()
ax1.set_title(pltTitle)
if oneD:
mean, _, _, standardDev = analyzeData(data)
barLoc = [1]
width=0.05
else:
# each entry looks like (mean, median, mode, stdDev)
dataList = [analyzeData(simData) for simData in data]
mean = [int(a[0]*10)/10 for a in dataList]
standardDev = [a[3] for a in dataList]
barLoc = np.arange( len(data))
width = 0.4
barObject = ax1.bar(barLoc, mean, width, yerr=standardDev)
#ax1.yaxis.grid(True)
ax1.set_xticks(barLoc)
ax1.set_xticklabels(labels, rotation=45, ha="right")
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
for bar in barObject:
height = bar.get_height()
ax1.annotate('{}'.format(height), xy=(bar.get_x() + bar.get_width() / 2, height), xytext=(0, 3), # 3 points vertical offset
textcoords="offset points", ha='center', va='bottom')
plt.tight_layout()
if savePlt:
if not saveName.endswith(".png"):
saveName+=".png"
plt.savefig(flr.fullPath(saveName, "outputs"))
else:
plt.show()
plt.close()
def boxplot(data,
oneD=False,
pltTitle="Some Title",
xlabel="Default X",
ylabel="Default Y",
labels=[],
showPlt=True,
savePlt=False,
saveName="defaultimage.png",
outputDir="outputs"):
"""
Parameters:
- data: the data to plot, can be a one or two dimentional list, if a 2D list is passed, each row is going to be a data for a separate box plot
- oneD: bool to tell if "data" is one dimentional or not
- pltTitle: the title of the plot
- xlabel: label for the x axis
- ylabel: label for the y axis
- labels: labels for each box plot, pass a list with one entry if there's one B&W, and a list filled with entries for multiple B&W
- showPlt: boolean, show the plot or not
- savePlt: boolean, save the plot with the given filename or not
- saveName: string, ends with .png o some file format, save the plot with this name
"""
# nice example of boxplots:
# https://matplotlib.org/2.0.1/examples/statistics/boxplot_color_demo.html
fig1, ax1 = plt.subplots()
ax1.set_title(pltTitle)
ax1.boxplot(data, vert=True)
ax1.yaxis.grid(True)
if oneD:
xticks = [1]
else:
xticks = [a + 1 for a in range(len(data))]
ax1.set_xticks(xticks)
ax1.set_xticklabels(labels)
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
#ax.spines['bottom'].set_visible(False)
#ax.spines['left'].set_visible(False)
plt.setp(ax1.get_xticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
#if labels != [] and len(labels) == len(data):
# #plt.setp(ax1, xticks=xticks, xticklabels=labels)
# #ax1.set_xticklabels(labels, rotation=45, ha="right")
plt.tight_layout()
if savePlt:
if not saveName.endswith(".png"):
saveName += ".png"
print("image saved as", saveName)
plt.savefig(flr.fullPath(saveName, outputDir))
else:
plt.show()
plt.close()
def generateVisualByLoading(fileNames, plottedData="inf", saveName='default'):
# get all csv in filenames and create the visuals for it
nameList = list(fileNames.keys())
fileList = [name+".csv" for name in nameList]
dataDict = dict()
for name, fileName in zip(nameList, fileList):
val = flr.openCsv(flr.fullPath(fileName, folder="outputs"))
print(val)
break
dataDict[name] = val
comparingBoxPlots(dataDict, plottedData=plottedData, saveName=saveName)
], ('(npMean, stdev, rangeVal, median)', (0.486, 0.21448543074064494,
1.1, 0.5)))),
('SA_SP', ([
0.1, 0.2, 0.0, 0.2, 0.0, 0.1, 0.1, 0.1, 0.0, 0.2, 0.0, 0.0, 0.1,
0.1, 0.0, 0.1, 0.1, 0.1, 0.0, 0.1, 0.0, 0.2, 0.1, 0.2, 0.1, 0.1,
0.1, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.0, 0.1, 0.0, 0.1,
0.1, 0.1, 0.3, 0.1, 0.0, 0.2, 0.1, 0.0, 0.0, 0.1, 0.2, 0.2, 0.0,
0.0, 0.1, 0.2, 0.2, 0.3, 0.1, 0.0, 0.2, 0.1, 0.3, 0.0, 0.0, 0.0,
0.2, 0.2, 0.1, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.4, 0.1, 0.1, 0.0,
0.0, 0.1, 0.0, 0.0, 0.4, 0.1, 0.0, 0.0, 0.1, 0.0, 0.0, 0.1, 0.2,
0.2, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.2, 0.1
], ('(npMean, stdev, rangeVal, median)',
(0.09399999999999999, 0.0925418824100742, 0.4, 0.1))))
])
for k, v in dataDict.items():
dataDict[k] = v[0]
import fileRelated as flr
import pandas as pd
modelName = "R0export"
dfobj = pd.DataFrame.from_dict(dataDict, orient="index")
flr.save_df_to_csv(flr.fullPath(modelName + ".csv", "outputs"), dfobj)
if __name__ == '__main__':
pass
stuff = {'a': [1, 1, 1], "b": [2, 2, 2]}
df = pd.DataFrame(stuff)
print(df)
#profile.runctx('model_framework.main()', globals(), {})
def main():
"""intialize and run the model, for indepth detail about the config or how to run the code, go to the github page for this code"""
# you can control for multiple interventions by adding a case:
# [(modified attr1, newVal), (modified attr2, newVal), ...]
# simulation name --> simulation controlled variable(s)
# dont use . or - in the simulation name because the names are used to save images, or any symbols below
modelConfig = main_config.modelConfig
R0_controls = {
"World": [
("DynamicCapacity", False),
],
"Infection": [
("baseP", 1.25),
("SeedNumber", 100),
],
"HybridClass": [
("ChangedSeedNumber", 10),
],
}
# this overrides the previous experiments, since base_p is being chnaged
R0_controls = {
"World": [
("DynamicCapacity", False),
],
"HybridClass": [
("ChangedSeedNumber", 10),
],
}
def cross_scenarios(scenario1, scenario2):
experiments = {}
for keyname, experiment1 in scenario1.items():
for screenname, screen in scenario2.items():
experiment_name = screenname + "_" + keyname
experiments[experiment_name] = screen.copy()
for key, value in experiment1.items():
#print(key, value)
experiments[experiment_name][key] = value.copy()
return copy.deepcopy(experiments)
def print_nicely(experiment_scenarios):
for ex_name, ex_config in experiment_scenarios.items():
print("\n", "*" * 20, "\n", ex_name)
for ex_config_name, ex_config_list in ex_config.items():
print(ex_config_name, ":", ex_config_list)
#experiment2 = cross_scenarios(experiment.vaccine3, experiment.low_med)
#experiment3 =cross_scenarios(experiment.vaccine4, experiment.facemask3)
experiment1 = experiment.original_3x3
experiment2 = cross_scenarios(experiment.different_base_p_jump_025,
experiment.medium_student_vary_policy)
experiment3 = cross_scenarios(experiment.medium_student_vary_policy,
experiment.off_campus_multiplier)
#print(len(experiment3))
#print_nicely(experiment3)
print(len(experiment1))
basemodel = {"basemodel": {}}
multi_experiments = {
"request_1": experiment1,
"request_2": experiment2,
"request_3": experiment3,
}
for (request_name, modelConfigs) in multi_experiments.items():
R0Dict = dict()
InfectedCountDict = dict()
output_dir = fileRelated.fullPath(request_name, "outputs")
Path(output_dir).mkdir(parents=False, exist_ok=True)
output_folder = "outputs/" + request_name
for index, (modelName,
modelControl) in enumerate(modelConfigs.items()):
print("finished", index)
configCopy = copy.deepcopy(modelConfig)
#print("*"*20)
#print(configCopy["Agents"].keys())
#print("*"*20)
#print(f"started working on initializing the simualtion for {modelName}")
for categoryKey, listOfControls in modelControl.items():
#print(listOfControls)
for (specificKey, specificValue) in listOfControls:
configCopy[categoryKey][specificKey] = specificValue
R0Count, multiCounts = 1, 100
#print(configCopy)
if index > -1:
#model_framework.simpleCheck(configCopy, days=10, visuals=True, debug=True, modelName=modelName)
InfectedCountDict[modelName] = model_framework.multiSimulation(
multiCounts,
configCopy,
days=100,
debug=False,
modelName=modelName,
outputDir=output_folder)
R0Dict[modelName] = model_framework.R0_simulation(
configCopy,
R0_controls,
R0Count,
debug=False,
timeSeriesVisual=False,
R0Visuals=True,
modelName=modelName,
outputDir=output_folder)
# the value of the dictionary is ([multiple R0 values], (descriptors, (tuple of useful data like mean and stdev))
print(InfectedCountDict.items())
print(R0Dict.items())
if True:
simulationGeneration = "0"
saveName = "comparingModels_" + simulationGeneration
statfile.comparingBoxPlots(R0Dict,
plottedData="R0",
saveName=saveName,
outputDir=output_folder)
statfile.comparingBoxPlots(InfectedCountDict,
plottedData="inf",
saveName=saveName,
outputDir=output_folder)
for key, value in R0Dict.items():
if R0Dict[key][1] == "(npMean, stdev, rangeVal, median)":
R0Dict[key] = value[0]
# else do nothing
#print(key, value)
print(R0Dict)
# check if dict is not empty
R0_df = pd.DataFrame(R0Dict)
fileRelated.save_df_to_csv(
fileRelated.fullPath("R0_data.csv", output_folder), R0_df)
else: # never ran after jan 30
#statfile.generateVisualByLoading(ControlledExperiment, plottedData="inf", saveName=saveName)
model_framework.createFilledPlot(modelConfig,
modelName="baseModel",
simulationN=3,
outputDir=output_folder)
break
