def genStackPlotvsTimeGraph(filename, numTimeStep, filterList=None):
"""Generates a stack plot of prey states over time step for Intention and
non intention modes. NOTE: Will probably need to include cautiousness
as another graph later.
NOTE: Requires the last dataframe in dataframes to be the one that makes prey die the fastest
Inputs:
filename: the name of the csv file to read from
numTimeStep: the time for which the prey can go without eating before
dying of hunger.
filterList: a filter list to filter the runs by. Most likely use if
we need to filter to only get the standard seed from a csv with
varied parameters. eg. [["predSightDistance", 10]]"""
modes, dfs = getPerceptionTierDataframes(filename)
plt.rc('font', family='serif')
fig, axes = plt.subplots(1, 3)
fig.tight_layout(w_pad=1, h_pad=1, rect=[0, 0, .9, .9])
fig.suptitle("Prey Status over Time", fontsize=16)
axs = axes.flat
plt.style.use('ggplot')
colorList = ['#4FADAC', '#5386A6', '#2F5373']
maxX = 0 # initialize. This will be set later and will make all x axes the same.
graphInfo = []
# loop through dataframes. We will want one stackplot per mode.
for perceptionNum in range(len(dfs)):
perceptionDf = dfs[perceptionNum]
# filter
if filterList != None:
perceptionDf = ds.filterDataFrame(perceptionDf, filterList)
aliveOverTime, eatenOverTime, starvedOverTime = helpStackPlot(
perceptionDf, numTimeStep)
graphInfo.append([aliveOverTime, starvedOverTime, eatenOverTime])
maxX = calcMaxX(graphInfo)
for perceptionNum in range(len(dfs)):
# x-list is timestep
x_list = range(maxX)
axs[perceptionNum].set_title(modes[perceptionNum], fontsize=12)
axs[perceptionNum].set_ylabel("Average Number of Prey", fontsize=10)
axs[perceptionNum].set_xlabel("Timestep of Simulation", fontsize=10)
axs[perceptionNum].tick_params(axis='both',
which='major',
labelsize=9,
direction='in')
axs[perceptionNum].set(ylim=(0, 20), xlim=(0, maxX))
plt.sca(axs[perceptionNum])
yList1 = extendList(graphInfo[perceptionNum][0][:maxX], maxX)
yList2 = extendList(graphInfo[perceptionNum][1][:maxX], maxX)
yList3 = extendList(graphInfo[perceptionNum][2][:maxX], maxX)
plt.stackplot(x_list, yList1, yList2, yList3, colors=colorList)
fig.legend(labels=["Alive", "Starved", "Eaten"])
plt.rc('text', usetex=True)
plt.show()
def getPerceptionTierDataframes(filename):
"""Takes in a csv file and returns data frames according to perception tiers
Inputs:
filename: String, the name of the csv file to read from"""
data = pd.read_csv(filename)
df0 = ds.filterDataFrame(
data, [["targetedAware", True], ["proximityAware", True]])
df1 = ds.filterDataFrame(
data, [["targetedAware", False], ["proximityAware", True]])
df2 = ds.filterDataFrame(
data, [["targetedAware", False], ["proximityAware", False]])
dfs = [df0, df1, df2]
modes = [
r"Proximity and Attention Awareness", r"Only Proximity Awareness",
r"No Awareness"
]
return modes, dfs
def getPerceptionDataframesCaution(filename, cfilename):
"""New version of getPerceptionTierDataframes() that includes the caution mode.
Inputs:
filename: the filename to read from"""
data = pd.read_csv(filename)
df0 = ds.filterDataFrame(
data, [["targetedAware", True], ["proximityAware", True]])
df1 = ds.filterDataFrame(
data, [["targetedAware", False], ["proximityAware", True]])
df2 = ds.filterDataFrame(
data, [["targetedAware", False], ["proximityAware", False]])
df3 = pd.read_csv(cfilename)
dfs = [df0, df1, df2, df3]
modes = [
r"Proximity + Attention", r"Proximity Only", r"Unaware", r"Cautious"
]
return modes, dfs
def getCautiousSeedData(filename, newfilename, param):
data = pd.read_csv(filename)
df = hd.filterDataFrame(data, [["targetedAware", True], ["proximityAware", True]])
paramDict = {}
for val, group in df.groupby(param):
groupProbs = []
groupLengths = []
for _, run in group.iterrows():
probs, lengths = ast.literal_eval(run["targetInfo"])
groupProbs += probs
groupLengths += lengths
avg, _, _ = hd.listStats(groupProbs)
paramDict[str(val)] = [avg, groupLengths]
with open(newfilename, 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(["keys", "values"])
for key, value in paramDict.items():
writer.writerow([key, value])
def hungerGraph(filename, cautiousFile=None):
labelsize = 18
legendsize = 14
titlesize = 20
ticksize = 16
data = pd.read_csv(filename)
plt.style.use('ggplot')
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
df0 = hd.filterDataFrame(data, [["targetedAware", True], ["proximityAware", True], ["predSightAngle", 90]])
df1 = hd.filterDataFrame(data, [["targetedAware", False], ["proximityAware", True], ["predSightAngle", 90]])
df2 = hd.filterDataFrame(data, [["targetedAware", False], ["proximityAware", False], ["predSightAngle", 90]])
dfs = [df0, df1, df2]
modes = [r"Proximity + Attention", r"Proximity Only", r"Unaware"]
colorIter = iter(['#4FADAC', '#5386A6', '#2F5373', '#C59CE6'])
intention_list = []
proximity_list = []
unaware_list = []
all_lists = [intention_list, proximity_list, unaware_list]
if cautiousFile:
dfs.append(pd.read_csv(cautiousFile))
modes.append(r"Cautious")
cautious_list = []
all_lists.append(cautious_list)
fig = plt.figure(figsize=(5,5))
x = [step for step in range(1, 10000+2, 500)]
for n_steps in x:
print("step:",n_steps)
for i in range(3 + (cautiousFile != None)):
df = dfs[i]
groupLifeTimes = []
for _, run in df.iterrows():
counts = run["preyCountOverTime"]
foodPerPrey = run["foodPerPrey"]
preyPerPred = run["preyPerPred"]
revisedCounts, _ = h.getNewPreyCountOverTimeList(foodPerPrey, counts, preyPerPred, n_steps)
lifetimes = hd.lifeTimes(revisedCounts)
groupLifeTimes += lifetimes
avg, _, ci = hd.listStats(groupLifeTimes)
all_lists[i].append([avg, ci[0], ci[1]])
for i in range(len(all_lists)):
tier = all_lists[i]
val = []
l_ci = []
u_ci = []
for point in tier:
a, lc, uc = point
val.append(a)
l_ci.append(lc)
u_ci.append(uc)
color = next(colorIter)
fig.gca().plot(x, val, label=modes[i], color=color, linewidth=2)
fig.gca().fill_between(x, l_ci, u_ci, color=color, alpha=.15)
ax = fig.gca()
ax.set(ylim=(0, 10000), xlim=(0,10000-1))
ax.set_ylabel(r"prey lifespan (time steps)", fontsize=labelsize, fontweight='bold')
ax.set_xlabel(r"maximum fasting interval (time steps)", fontsize=labelsize, fontweight='bold')
ax.tick_params(axis='both', which='major', labelsize=ticksize, direction='in')
plt.legend(prop={"size":legendsize})
plt.title(r"Maximum Fasting Interval", fontsize=titlesize, fontweight='bold')
fig.savefig("hunger.pdf", bbox_inches='tight', pad_inches=0)
plt.close('all')
def linearRunGraph(filename, param, n_steps, cautiousFile=None):
labelsize = 18
legendsize = 14
titlesize = 20
ticksize = 16
data = pd.read_csv(filename)
plt.style.use('ggplot')
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
df0 = hd.filterDataFrame(data, [["targetedAware", True], ["proximityAware", True]])
df1 = hd.filterDataFrame(data, [["targetedAware", False], ["proximityAware", True]])
df2 = hd.filterDataFrame(data, [["targetedAware", False], ["proximityAware", False]])
dfs = [df0, df1, df2]
modes = [r"Proximity + Attention", r"Proximity Only", r"Unaware"]
if cautiousFile:
df3 = pd.read_csv(cautiousFile)
dfs.append(df3)
modes.append(r"Cautious")
colorIter = iter(['#4FADAC', '#5386A6', '#2F5373', '#C59CE6'])
fig = plt.figure(figsize=(5,5))
for i in range(3 + (cautiousFile != None)):
df = dfs[i]
paramValues = []
survival = []
up_ci = []
low_ci = []
for val, group in df.groupby(param):
groupLifeTimes = []
for _, run in group.iterrows():
counts = run["preyCountOverTime"]
foodPerPrey = run["foodPerPrey"]
preyPerPred = run["preyPerPred"]
revisedCounts, _ = h.getNewPreyCountOverTimeList(foodPerPrey, counts, preyPerPred, n_steps)
lifetimes = hd.lifeTimes(revisedCounts)
groupLifeTimes += lifetimes
avg, _, ci = hd.listStats(groupLifeTimes)
paramValues.append(val)
survival.append(avg)
low_ci.append(ci[0])
up_ci.append(ci[1])
color = next(colorIter)
plt.plot(paramValues, survival, label=modes[i], color=color, linewidth=2)
plt.fill_between(paramValues, low_ci, up_ci, color=color, alpha=.15)
global labels
ax = fig.gca()
ax.set(ylim=(0, 10000))
ax.set_ylabel(r"prey lifespan (time steps)", fontsize=labelsize, fontweight='bold')
ax.set_xlabel(r""+ labels[param][1], fontsize=labelsize, fontweight='bold')
ax.tick_params(axis='both', which='major', labelsize=ticksize, direction='in')
plt.legend(prop={"size":legendsize})
plt.title(r"" + labels[param][0], fontsize=titlesize, fontweight='bold')
fig.tight_layout()
fig.savefig(param + '.pdf', bbox_inches='tight', pad_inches=0)
plt.close('all')
def genNewStackPlotvsTimeGraph(filename,
numTimeStep,
filterList=None,
cfilename=None):
"""Generates separate stack plots of prey states over time step for intention and
non intention modes. Includes an option to make a stackplot for the caution mode.
Inputs:
filename: String, the name of the csv file to read from
numTimeStep: Int, the time for which the prey can go without eating before
dying of hunger.
filterList: List, a filter list to filter the runs by. Most likely use if
we need to filter to only get the standard seed from a csv with
varied parameters. eg. [["predSightDistance", 10]]"""
labelsize = 18
legendsize = 14
titlesize = 20
ticksize = 16
# Determine whether the "Cautious" stackplot should be made.
if cfilename:
modes, dfs = getPerceptionDataframesCaution(filename, cfilename)
else:
modes, dfs = getPerceptionTierDataframes(filename)
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.style.use('ggplot')
colorList = ['#4FADAC', '#5386A6', '#2F5373']
maxX = 0 # Initialize. This will be set later and will make all x axes the same.
graphInfo = []
# loop through dataframes. We will want one stackplot per mode.
for perceptionNum in range(len(dfs)):
perceptionDf = dfs[perceptionNum]
# filter
if filterList != None:
perceptionDf = ds.filterDataFrame(perceptionDf, filterList)
aliveOverTime, eatenOverTime, starvedOverTime = helpStackPlot(
perceptionDf, numTimeStep)
graphInfo.append([aliveOverTime, starvedOverTime, eatenOverTime])
maxX = calcMaxX(graphInfo)
for perceptionNum in range(len(dfs)):
fig = plt.figure(figsize=[5, 5])
axs = fig.gca()
# x-list is timestep
x_list = range(maxX)
axs.set_title("" + modes[perceptionNum],
fontsize=titlesize,
fontweight='bold')
axs.set_ylabel("" + "Average Number of Prey",
fontsize=labelsize,
fontweight='bold')
axs.set_xlabel("" + "Timestep of Simulation",
fontsize=labelsize,
fontweight='bold')
axs.tick_params(axis='both',
which='major',
labelsize=ticksize,
direction='in')
axs.set(ylim=(0, 20), xlim=(0, maxX))
yList1 = extendList(graphInfo[perceptionNum][0][:maxX], maxX)
yList2 = extendList(graphInfo[perceptionNum][1][:maxX], maxX)
yList3 = extendList(graphInfo[perceptionNum][2][:maxX], maxX)
plt.stackplot(x_list, yList1, yList2, yList3, colors=colorList)
plt.legend(labels=["Alive", "Starved", "Eaten"],
prop={"size": legendsize})
fig.tight_layout()
fig.savefig("hungerstack" + str(perceptionNum) + '.pdf',
bbox_inches='tight',
pad_inches=0)
plt.close('all')