def main():
# First, get the dossier
infofile = raw_input(str("Specify the path to the json file with animal information: "))
infofile = os.path.abspath(infofile)
if not os.path.isfile(infofile):
print("Invalid info file! Try again?")
exit(1)
batch_mode = raw_input(str("Use all entries in the info file? [y/n] ")).lower()
if batch_mode == 'n':
name_list = raw_input(str("Specify the names of the animals, space separated. (e.g., 'NSS_01 SS_01') ")).split()
elif batch_mode == 'y':
name_list = None
else:
print("Invalid answer.")
exit(1)
variable_names = raw_input(str("Which variables do you want to use? (e.g., 'Y Velocity Curvature') ")).split()
num_exps = input(str("Specify the number of comparisons you want to run for each individual: "))
start_time = input(str("Specify the start time of the overall segment in which you want to run the comparisons: "))
end_time = input(str("Specify the end time of the overall segment in which you want to run the comparisons: "))
norm_mode = raw_input(str("Which time segment do you want to normalize over: the predetermined baseline or the specific segment [b/s]: "))
if norm_mode == 'b': norm_mode = 'baseline'
elif norm_mode == 's': norm_mode = 'spec'
else:
print("Invalid norm mode")
exit(1)
intervals = raw_input(str("Do you want to specify a set of interval lengths to use? [y/n] "))
if intervals == 'y':
interval_lengths = raw_input(str("Which interval lengths would you like to use? (e.g., '0.5 1.0 1.25 2.4') ")).split()
interval_lengths = [float(l) for l in interval_lengths]
elif intervals == 'n':
interval_lengths = None
else:
print("That wasn't quite one of the options...")
exit(1)
output = raw_input(str("Do you want to write the results into a file? [y/n] "))
if output == 'y':
outdir = raw_input(str("Specify the output directory: "))
outdir = os.path.abspath(outdir)
outfilename = raw_input(str("Specify the output file name: "))
elif output != 'n':
print("That wasn't quite one of the options...")
exit(1)
animals = locomotion.getAnimalObjs(infofile,name_list)
for a in animals:
locomotion.trajectory.getCurveData(a)
if output == 'y':
locomotion.trajectory.runIndividualVariabilityTests(animals, variable_names, norm_mode, num_exps, interval_lengths, outdir, outfilename, start_time, end_time)
else:
locomotion.trajectory.runIndividualVariabilityTests(animals, variable_names, norm_mode, num_exps, interval_lengths, None, None, start_time, end_time)
def runRobustnessTest(test_key, variables, norm_mode, start_min, end_min):
NUM_TESTS = len(testData[test_key]["framerates"]) * len(
testData[test_key]["densities"])
results = np.zeros([NUM_CURVES, NUM_TESTS])
for curve_no in range(NUM_CURVES):
curve_str = str(curve_no).zfill(ZFILL_LEN)
json_path = PATH_TO_RES_DIRECTORY + "/{}/CRV_{}.json".format(
test_key, curve_str)
# Load all animals
animals = locomotion.getAnimalObjs(json_path)
for a in animals:
locomotion.trajectory.getCurveData(a)
# Run BDD against control animal (index 0)
control = animals[0]
for a_no, a in enumerate(animals[1:]):
bdd = locomotion.trajectory.computeOneBDD(a, control, variables,
start_min, end_min,
start_min, end_min,
norm_mode)
results[curve_no][a_no] = bdd
output = PATH_TO_RES_DIRECTORY + "/{}/Results_BDD.csv".format(test_key)
pd.DataFrame(results).to_csv(output, index=False)
def main():
# First, get the dossier
infofile = raw_input(str("Specify the path to the json file with animal information: "))
infofile = os.path.abspath(infofile)
if not os.path.isfile(infofile):
print("Invalid info file! Try again?")
exit(1)
batch_mode = raw_input(str("Use all entries in the info file? [y/n] ")).lower()
if batch_mode == 'n':
name_list = raw_input(str("Specify the names of the animals, space separated. (e.g., 'NSS_01 SS_01') ")).split()
elif batch_mode == 'y':
name_list = None
else:
print("Invalid answer.")
exit(1)
variable_names = raw_input(str("Which variables do you want to use? (e.g., 'Y Velocity Curvature') ")).split()
start_time = input(str("Specify the start time of the segment you want to compare: "))
end_time = input(str("Specify the end time of the segment you want to compare: "))
norm_mode = raw_input(str("Which time segment do you want to normalize over: the predetermined baseline or the segment specified above [b/s]: "))
if norm_mode == 'b': norm_mode = 'baseline'
elif norm_mode == 's': norm_mode = 'spec'
else:
print("Invalid norm mode")
exit(1)
output = raw_input(str("Do you want to write the results into a file? [y/n] "))
if output == 'y':
outdir = raw_input(str("Specify the output directory: "))
outdir = os.path.abspath(outdir)
outfilename = raw_input(str("Specify the output file name: "))
sort_table = raw_input(str("Do you want to sort the output? [y/n] "))
if sort_table == 'y':
sort_table = True
elif sort_table == 'n':
sort_table = False
else:
print("Invalid input.")
exit(1)
square_table = raw_input(str("Do you want the distance table to be square instead of upper triangular? [y/n] "))
if square_table == 'y':
square_table = True
elif square_table == 'n':
square_table = False
else:
print("Invalid input.")
exit(1)
color_min = input(str("What should be the minimum value for the distance table color scale? "))
color_max = input(str("What should be the maximum value for the distance table color scale? "))
#check 0<=0min<max<=1
elif output != 'n':
print("That wasn't quite one of the options...")
exit(1)
animals = locomotion.getAnimalObjs(infofile,name_list)
for a in animals:
locomotion.trajectory.getCurveData(a)
D = locomotion.trajectory.computeAllBDD(animals, variable_names, start_time, end_time, norm_mode)
if output == 'y':
locomotion.write.postProcess(animals, D, outdir, outfilename, sort_table, square_table, color_min, color_max)
def main():
# First, get the dossier
infofile = raw_input(
str("Specify the path to the json file with animal information: "))
infofile = os.path.abspath(infofile)
if not os.path.isfile(infofile):
print("Invalid info file! Try again?")
exit(1)
batch_mode = raw_input(
str("Use all entries in the info file? [y/n] ")).lower()
if batch_mode == 'n':
name_list = raw_input(
str("Specify the names of the animals, space separated. (e.g., 'NSS_01 SS_01') "
)).split()
elif batch_mode == 'y':
name_list = None
else:
print("Invalid answer.")
exit(1)
start_time = input(
str("Specify the start time of the segment you want to compare: "))
end_time = input(
str("Specify the end time of the segment you want to compare: "))
grid_size = input(
str("Specify the grid size for the heatmap (in the same units as the x- and y- dimensions): "
))
output = raw_input(
str("Do you want to write the results into a file? [y/n] "))
if output == 'y':
outdir = raw_input(str("Specify the output directory: "))
outdir = os.path.abspath(outdir)
outfilename = raw_input(str("Specify the output file name: "))
sort_table = raw_input(str("Do you want to sort the output? [y/n] "))
if sort_table == 'y':
sort_table = True
elif sort_table == 'n':
sort_table = False
else:
print("Invalid input.")
exit(1)
square_table = raw_input(
str("Do you want the distance table to be square instead of upper triangular? [y/n] "
))
if square_table == 'y':
square_table = True
elif square_table == 'n':
square_table = False
else:
print("Invalid input.")
exit(1)
color_min = input(
str("What should be the minimum value for the distance table color scale?"
))
color_max = input(
str("What should be the maximum value for the distance table color scale?"
))
elif output != 'n':
print("That wasn't quite one of the options...")
exit(1)
animals = locomotion.getAnimalObjs(infofile, name_list)
for a in animals:
x_dim, y_dim = a.getDims()
if not float(x_dim / grid_size).is_integer() or not float(
y_dim / grid_size).is_integer():
print("The grid size must divide each of the dimensions evenly.")
exit(1)
for a in animals:
locomotion.heatmap.getSurfaceData(a, grid_size, start_time, end_time)
D = locomotion.heatmap.computeAllCSD(animals)
if output == 'y':
locomotion.write.postProcess(animals, D, outdir, outfilename,
sort_table, square_table, color_min,
color_max)