def test_delete(save_location, compare_to):
dat = DataHandler('tests')
dat.delete(save_location=save_location)
exists = dat.check_group_exists(location=save_location, create=False)
assert exists == compare_to
def test_load_no_group():
dat = DataHandler('tests')
save_location = 'not_a_location'
with pytest.raises(NameError):
loaded = dat.load(parameters=parameters,
save_location=save_location)
def load_and_process(db_name,
save_location,
parameters,
interpolated_samples=100):
#TODO: move interpolated samples is None check out of interpolation
# function and add it here, no sense in having it check in the function
# and have it do nothing, should only call interpolate if interpolating
"""
Loads the parameters from the save_location,
returns a dictionary of the interpolated and sampled data
NOTE: if interpolated_samples is set to None, the raw data will be
returned without interpolation and sampling
Parameters
----------
db_name: string
the database where the data is saved
save_location: string
points to the location in the hdf5 database to read from
parameters: list of strings
the parameters to load and interpolate
interpolated_samples: positive int, Optional (Default=100)
the number of samples to take (evenly) from the interpolated data
if set to None, no interpolated or sampling will be done, the raw
data will be returned
"""
# load data from hdf5 database
dat = DataHandler(db_name=db_name)
data = dat.load(parameters=parameters, save_location=save_location)
# If time is not passed in, create a range from 0 to the length of any
# other parameter in the list that is not time. This assumes that any
# data passed in at once will be of the same length
data_len = len(data[list(data.keys())[0]])
if 'time' not in parameters:
data['time'] = np.ones(data_len)
total_time = np.sum(data['time'])
dat = []
# interpolate for even sampling and save to our dictionary
if interpolated_samples is not None:
for key in data:
if key != 'time':
data[key] = interpolate_data(
data=data[key],
time_intervals=data['time'],
interpolated_samples=interpolated_samples)
else:
interpolated_samples = data_len
# since we are interpolating over time, we are not interpolating
# the time data, instead evenly sample interpolated_samples from
# 0 to the sum(time)
data['cumulative_time'] = np.linspace(0, total_time, interpolated_samples)
data['read_location'] = save_location
return data
def test_group_exists(location, create, compare_to):
dat = DataHandler('tests')
exists = dat.check_group_exists(
location=location,
create=create)
assert exists == compare_to
def test_statistical_error(ideal,
save_data,
regen,
save_location=save_location):
dat = DataHandler("test")
if ideal is None:
# this is done in calculate_error, but we do it here so our manual
# comparison is loading the same data
ideal = "ideal_trajectory"
fake_data = dat.load(parameters=[ideal, "ee_xyz"],
save_location=save_location)
manual_error = np.linalg.norm((fake_data["ee_xyz"] - fake_data[ideal]),
axis=1)
traj = TrajectoryError(db_name="test",
time_derivative=0,
interpolated_samples=None)
data = traj.statistical_error(
save_location=save_location.split("/")[0],
ideal=ideal,
sessions=1,
runs=1,
save_data=save_data,
regen=regen,
)
def test_rename_old_save_location_does_not_exist():
old_save_location = 'test_old_does_not_exist'
new_save_location = 'test_new'
with pytest.raises(KeyError):
dat = DataHandler('tests')
# try to read from key that doesn't exist
dat.rename(old_save_location=old_save_location,
new_save_location=new_save_location,
delete_old=False)
def test_load(parameters, compare_to, key):
dat = DataHandler('tests')
save_location = 'test_loading'
test_data = {'test_data': np.ones(3)}
dat.save(
data=test_data,
save_location='test_loading',
overwrite=True)
loaded = dat.load(parameters=parameters,
save_location=save_location)
assert np.all(loaded[key] == compare_to)
def test_calculate_error(ideal, save_location=save_location):
dat = DataHandler('test')
if ideal is None:
ideal = 'ideal_trajectory'
fake_data = dat.load(parameters=[ideal, 'ee_xyz'],
save_location=save_location)
manual_error = np.linalg.norm((fake_data['ee_xyz'] - fake_data[ideal]),
axis=1)
traj = TrajectoryError(db_name='test',
time_derivative=0,
interpolated_samples=None)
data = traj.calculate_error(save_location=save_location, ideal=ideal)
assert np.array_equal(manual_error, data['error'])
def __init__(self, db_name, save_location):
"""
PARAMETERS
----------
db_name: string
the name of the database that holds the intercept scans
save_location: string
the location in the database the data is saved
"""
self.save_location = save_location
self.dat = DataHandler(db_name)
self.fig = Figure() # figsize=(10,12), dpi=100)
self.a = self.fig.add_subplot(111)
self.ideal = self.dat.load(parameters=["ideal"],
save_location=self.save_location)["ideal"]
self.test_que = []
def test_rename(old_save_location, new_save_location, delete_old, compare_to):
dat = DataHandler('tests')
# save data to rename / move
dat.save(data={'float': 3.14},
save_location=old_save_location,
overwrite=True)
# make sure the new entry key is available
dat.delete(save_location=new_save_location)
# rename to new key
dat.rename(old_save_location=old_save_location,
new_save_location=new_save_location,
delete_old=delete_old)
# check if the old location exists
exists = dat.check_group_exists(location=old_save_location, create=False)
assert exists == compare_to
# check if the new location exists
exists = dat.check_group_exists(location=new_save_location, create=False)
assert exists is True
def __init__(self, db_name, time_derivative=0, interpolated_samples=100):
'''
PARAMETERS
----------
db_name: string
the name of the database to load data from
interpolated_samples: positive int, Optional (Default=100)
the number of samples to take (evenly) from the interpolated data
if set to None, no interpolated or sampling will be done, the raw
data will be returned
time_derivative: int, Optional (Default: 0)
0: position
1: velocity
2: acceleration
3: jerk
'''
# instantiate our data processor
self.dat = DataHandler(db_name)
self.db_name = db_name
self.time_derivative = time_derivative
self.interpolated_samples = interpolated_samples
def load_and_process(interpolated_samples, parameters):
dat = DataHandler("tests")
loc = "fake_trajectory"
steps = 147
fake_traj_data = random_trajectories.generate(steps=steps, plot=False)
dat.save(data=fake_traj_data, save_location="fake_trajectory", overwrite=True)
data = proc.load_and_process(
db_name="tests",
save_location=loc,
parameters=parameters,
interpolated_samples=interpolated_samples,
)
if interpolated_samples is None:
interpolated_samples = steps
for key in parameters:
if key == "time":
key = "cumulative_time"
assert len(data[key]) == interpolated_samples
def convert(npz_loc, db_name, save_location, overwrite=False):
"""
accepts a npz file location and saves its data to the database at the
specified save_location
PARAMETERS
----------
npz_loc: string
location and name of the npz file
db_name: string
database to save data to
save_location: string
save location in the database
overwrite: boolean, Optional (Default: False)
True to overwrite previous data
NOTE this will be triggered if data is being saved to the same hdf5
group (folder), not necessarily the same key. In this case you will
need to set it to True. Other data will not be erased, only data with
the same keys will be overwritten
"""
dat = DataHandler(db_name)
npz = np.load(npz_loc)
keys = npz.keys()
new_data = {}
for key in keys:
new_data[key] = npz[key]
dat.save(data=new_data, save_location=save_location, overwrite=overwrite)
keys = dat.get_keys(save_location)
data = dat.load(parameters=keys, save_location=save_location)
def test_rename_dataset():
old_save_location = 'test_saving'
new_save_location = 'test_saving_moved'
dat = DataHandler('tests')
# save data to rename / move
dat.save(data={'float': 3.14},
save_location=old_save_location,
overwrite=True)
# make sure the new entry key is available
dat.delete(save_location=new_save_location)
dat.rename(old_save_location=old_save_location + '/int',
new_save_location=new_save_location + '/int',
delete_old=False)
# check if the old location exists
exists = dat.check_group_exists(location=old_save_location, create=False)
assert exists is True
# check if the new location exists
exists = dat.check_group_exists(location=new_save_location, create=False)
assert exists is True
def test_rename_new_save_location_exists():
old_save_location = 'test_rename'
new_save_location = 'test_already_exists'
with pytest.raises(Exception):
dat = DataHandler('tests')
# save data to rename / move
dat.save(data={'float': 3.14},
save_location=old_save_location)
# create data at new location
dat.save(data={'float': 3.14},
save_location=new_save_location,
overwrite=True)
# try to rename data onto existing key
dat.rename(old_save_location=old_save_location,
new_save_location=new_save_location,
delete_old=False)
def test_get_keys():
dat = DataHandler('tests')
# location exists
keys = dat.get_keys(save_location='test_loading')
# location doesn't exist
dat.delete(save_location='fake_location')
with pytest.raises(KeyError):
keys = dat.get_keys(save_location='fake_location')
def test_calc_cartesion_points():
db = "tests"
dat = DataHandler(db)
class fake_robot_config:
def __init__(self):
self.N_JOINTS = 3
self.N_LINKS = 2
def Tx(self, name, q):
assert len(q) == self.N_JOINTS
return [1, 2, 3]
robot_config = fake_robot_config()
# number of time steps
steps = 10
# passing in the right dimensions of joint angles
q = np.zeros((steps, robot_config.N_JOINTS))
expected_shape = [
[steps, robot_config.N_JOINTS, 3],
[steps, robot_config.N_LINKS, 3],
[steps, 3],
]
data = proc.calc_cartesian_points(robot_config=robot_config, q=q)
# catch error in the assertion of the functions output's shape
for ii in range(0, len(expected_shape)):
for jj in range(0, len(np.array(data[ii]).shape)):
assert (
np.array(data[ii]).shape[jj] == expected_shape[ii][jj],
(
"Expected %i Received %i"
% (expected_shape[ii][jj], np.asarray(data[ii]).shape[jj])
),
)
def review(save_name, ideal_function, num_to_plot=10):
'''
loads the data from save name and gets num_to_plot tests that most
closley match the ideal function that was passed in during the scan
PARAMETERS
----------
save_name: string
the location in the intercepts_scan database to load from
ideal_fuinction: lambda function(n_timesteps)
used as the desired profile to compare against. The review function
will use this to find the closest matching results
num_to_plot: int, Optional (Default: 10)
the number of tests to find that most closley match the ideal
'''
dat = DataHandler('intercepts_scan')
ideal_data = dat.load(parameters=['ideal', 'total_intercepts'],
save_location='%s' % save_name)
ideal = ideal_data['ideal']
num = ideal_data['total_intercepts']
if num_to_plot > num:
print('Only %i runs to plot' % num)
num_to_plot = num
run_data = []
errors = []
n_bins = 30
for ii in range(0, num):
data = dat.load(
parameters=['intercept_bounds', 'intercept_mode',
'y', 'error', 'num_active',
'num_inactive', 'title'],
save_location='%s/%05d' % (save_name, ii))
if data['title'] == 'proportion_time_neurons_active':
y, bins_out = np.histogram(np.squeeze(data['y']),
bins=np.linspace(0, 1, n_bins))
data['x'] = 0.5*(bins_out[1:]+bins_out[:-1])
data['y'] = y
else:
data['x'] = np.cumsum(np.ones(len(data['y'])))
ideal = [ideal_function(x) for x in data['x']]
diff_to_ideal = ideal - data['y']
error = np.sum(np.abs(diff_to_ideal))
run_data.append(data)
errors.append(error)
indices = np.array(errors).argsort()[:num_to_plot]
print('Plotting...')
plt.figure()
for ii in range(0, num_to_plot):
ind = indices[ii]
data = run_data[ind]
if data['title'] == 'proportion_time_neurons_active':
plt.bar(data['x'], data['y'], width=1/(2*n_bins),
edgecolor='white', alpha=0.5,
label=('%i: err:%.2f \n%s: %s' %
(ind, errors[ind], data['intercept_bounds'],
data['intercept_mode'])))
else:
plt.plot(np.squeeze(data['x']), np.squeeze(data['y']),
label=('%i: err:%.2f \n%s: %s' %
(ind, errors[ind], data['intercept_bounds'],
data['intercept_mode'])))
plt.title(data['title'])
plt.plot(np.squeeze(data['x']), ideal, c='k', lw=3, linestyle='--',
label='ideal')
plt.legend()
def review(save_name, ideal_function, num_to_plot=10, db_name="intercepts_scan"):
"""
loads the data from save name and gets num_to_plot tests that most
closley match the ideal function that was passed in during the scan
PARAMETERS
----------
save_name: string
the location in the intercepts_scan database to load from
ideal_fuinction: lambda function(n_timesteps)
used as the desired profile to compare against. The review function
will use this to find the closest matching results
num_to_plot: int, Optional (Default: 10)
the number of tests to find that most closley match the ideal
"""
dat = DataHandler(db_name)
ideal_data = dat.load(
parameters=["ideal", "total_intercepts"], save_location="%s" % save_name
)
ideal = ideal_data["ideal"]
num = ideal_data["total_intercepts"]
if num_to_plot > num:
print("Only %i runs to plot" % num)
num_to_plot = num
run_data = []
errors = []
n_bins = 30
for ii in range(0, num):
data = dat.load(
parameters=[
"intercept_bounds",
"intercept_mode",
"y",
"error",
"num_active",
"num_inactive",
"title",
],
save_location="%s/%05d" % (save_name, ii),
)
if data["title"] == "proportion_time_neurons_active":
y, bins_out = np.histogram(
np.squeeze(data["y"]), bins=np.linspace(0, 1, n_bins)
)
data["x"] = 0.5 * (bins_out[1:] + bins_out[:-1])
data["y"] = y
else:
data["x"] = np.cumsum(np.ones(len(data["y"])))
ideal = [ideal_function(x) for x in data["x"]]
diff_to_ideal = ideal - data["y"]
error = np.sum(np.abs(diff_to_ideal))
run_data.append(data)
errors.append(error)
indices = np.array(errors).argsort()[:num_to_plot]
print("Plotting...")
plt.figure()
for ii in range(0, num_to_plot):
ind = indices[ii]
data = run_data[ind]
if data["title"] == "proportion_time_neurons_active":
plt.bar(
data["x"],
data["y"],
width=1 / (2 * n_bins),
edgecolor="white",
alpha=0.5,
label=(
"%i: err:%.2f \n%s: %s"
% (
ind,
errors[ind],
data["intercept_bounds"],
data["intercept_mode"],
)
),
)
else:
plt.plot(
np.squeeze(data["x"]),
np.squeeze(data["y"]),
label=(
"%i: err:%.2f \n%s: %s"
% (
ind,
errors[ind],
data["intercept_bounds"],
data["intercept_mode"],
)
),
)
plt.title(data["title"])
plt.plot(np.squeeze(data["x"]), ideal, c="k", lw=3, linestyle="--", label="ideal")
plt.legend()
import numpy as np
import pytest
from abr_analyze.plotting import TrajectoryError
from abr_analyze.data_handler import DataHandler
from abr_analyze.utils import random_trajectories
dat = DataHandler('test')
save_location = 'traj_err_test/session000/run000'
# generate a random trajectory and an ideal
data = random_trajectories.generate(steps=100)
# generate another trajectory and ideal so we can test passing in a custom ideal
data_alt = random_trajectories.generate(steps=100)
# save the second ideal to the first data dict
data['alt_traj'] = data_alt['ideal_trajectory']
dat.save(data=data, save_location=save_location, overwrite=True)
@pytest.mark.parametrize('ideal', ((None), ('ideal_trajectory'), ('alt_traj')))
def test_calculate_error(ideal, save_location=save_location):
dat = DataHandler('test')
if ideal is None:
ideal = 'ideal_trajectory'
fake_data = dat.load(parameters=[ideal, 'ee_xyz'],
save_location=save_location)
manual_error = np.linalg.norm((fake_data['ee_xyz'] - fake_data[ideal]),
axis=1)
traj = TrajectoryError(db_name='test',
time_derivative=0,
interpolated_samples=None)
def run(encoders, intercept_vals, input_signal, seed=1,
db_name='intercepts_scan', save_name='example', notes='',
analysis_fncs=None, **kwargs):
'''
runs a scan for the proportion of neurons that are active over time
PARAMETERS
----------
encoders: array of floats (n_neurons x n_inputs)
the values that specify along what vector a neuron will be
sensitive to
intercept_vals: array of floats (n_intercepts to try x 3)
the [left_bound, mode, right_bound] to pass on to the triangluar
intercept function in network_utils
input_signal: array of floats (n_timesteps x n_inputs)
the input signal that we want to check our networks response to
seed: int
the seed used for any randomization in the sim
save_name: string, Optional (Default: proportion_neurons)
the name to save the data under in the intercept_scan database
notes: string, Optional (Default: '')
any additional notes to save with the scan
analysis_fncs: list of network_utils functions to apply to the spike trains
the function must accept network and input signal, and return a list of
data and activity
'''
if not isinstance(analysis_fncs, list):
analysis_fncs = [analysis_fncs]
print('Input Signal Shape: ', np.asarray(input_signal).shape)
loop_time = 0
elapsed_time = 0
for ii, intercept in enumerate(intercept_vals):
start = timeit.default_timer()
elapsed_time += loop_time
print('%i/%i | ' % (ii+1, len(intercept_vals))
+ '%.2f%% Complete | ' % (ii/len(intercept_vals)*100)
+ '%.2f min elapsed | ' % (elapsed_time/60)
+ '%.2f min for last sim | ' % (loop_time/60)
+ '~%.2f min remaining...'
% ((len(intercept_vals)-ii)*loop_time/60),
end='\r')
# create our intercept distribution from the intercepts vals
# Generates intercepts for a d-dimensional ensemble, such that, given a
# random uniform input (from the interior of the d-dimensional ball), the
# probability of a neuron firing has the probability density function given
# by rng.triangular(left, mode, right, size=n)
np.random.seed(seed)
triangular = np.random.triangular(
# intercept_vals = [left, right, mode]
left=intercept[0],
right=intercept[1],
mode=intercept[2],
size=encoders.shape[1],
)
intercepts = nengo.dists.CosineSimilarity(encoders.shape[2] + 2).ppf(1 - triangular)
intercept_list = intercepts.reshape((1, encoders.shape[1]))
print()
print(intercept)
print(intercept_list)
# create a network with the new intercepts
network = signals.DynamicsAdaptation(
n_input=encoders.shape[2],
n_output=1, # number of output is irrelevant
n_neurons=encoders.shape[1],
intercepts=intercept_list,
seed=seed,
encoders=encoders,
**kwargs)
# get the spike trains from the sim
spike_trains = network_utils.get_activities(
network=network, input_signal=input_signal,
synapse=0.005)
# loop through the analysis functions
for func in analysis_fncs:
func_name = func.__name__
y, activity = func(network=network, input_signal=input_signal,
pscs=spike_trains)
# get the number of active and inactive neurons
num_active, num_inactive = (
network_utils.n_neurons_active_and_inactive(activity=activity))
if ii == 0:
dat = DataHandler(db_name)
dat.save(
data={'total_intercepts': len(intercept_vals),
'notes': notes},
save_location='%s/%s' % (save_name, func_name),
overwrite=True)
# not saving activity because takes up a lot of disk space
data = {'intercept_bounds': intercept[:2],
'intercept_mode': intercept[2],
'y': y,
'num_active': num_active,
'num_inactive': num_inactive,
'title': func_name
}
dat.save(data=data, save_location='%s/%s/%05d' %
(save_name, func_name, ii), overwrite=True)
loop_time = timeit.default_timer() - start
class LiveFigure():
'''
Class that handles the live plotting of the figure in the gui
'''
def __init__(self, db_name, save_location):
'''
PARAMETERS
----------
db_name: string
the name of the database that holds the intercept scans
save_location: string
the location in the database the data is saved
'''
self.save_location = save_location
self.dat = DataHandler(db_name)
self.fig = Figure()#figsize=(10,12), dpi=100)
self.a = self.fig.add_subplot(111)
self.ideal = self.dat.load(
parameters=['ideal'],
save_location=self.save_location)['ideal']
self.test_que = []
def plot(self,i):
'''
plots the data specified in the global variables that get updated with
the functions linked to the gui buttons
'''
global save_val
global keep_test_val
global toggle_ideal_val
global clear_plot_val
global legend_loc_val
global update_plot
global plt_col
if update_plot:
update_plot = False
if clear_plot_val:
data = None
self.test_que = []
a = self.fig.add_subplot(111)
a.clear()
clear_plot_val = False
else:
intercept_keys = self.get_intercept_vals_from_buttons()
data = self.load(keys=intercept_keys)
if data is not None:
a = self.fig.add_subplot(111)
a.clear()
label = '(%.1f, %.1f), %.1f\nACTV:%i | INACTV:%i | %.2f'%(
data['intercept_bounds'][0],
data['intercept_bounds'][1],
data['intercept_mode'],
data['num_active'],
data['num_inactive'],
data['y_mean']
)
if data['title'] == 'proportion_time_neurons_active':
a.set_xlim(0, 1)
a.bar(data['x'], data['y'], width=1/(2*self.bins),
label=label, edgecolor='white',
alpha=0.5, color=plt_col[0])
else:
a.set_ylim(0, 1)
a.plot(data['x'], data['y'], label=label, c=plt_col[0])
a.set_title(data['title'])
if keep_test_val:
current_test = {'label': label, 'y': data['y'],
'x': data['x']}
self.test_que.append(current_test)
keep_test_val = False
if self.test_que:
for aa, test in enumerate(self.test_que):
if aa+1 >= len(plt_col):
plt_col.append(None)
if data['title'] == 'proportion_time_neurons_active':
a.bar(test['x'], test['y'],
width=1/(2*self.bins),
label=test['label'],
edgecolor='white', alpha=0.5,
color=plt_col[aa+1])
else:
a.plot(test['x'], test['y'],
label=test['label'], c=plt_col[aa+1])
a.legend(loc=legend_loc_val%4+1)
if save_val:
a.figure.savefig('%s/intercept_scan_%s.png'
% (figures_dir, data['title']))
msg = ('Figure saved to:'
+ ' %s/intercept_scan_%s.png'
% (figures_dir, data['title']))
print(msg)
#TODO: make the font smaller while this is printed out
save_val = False
def get_intercept_vals_from_buttons(self):
'''
updates the intercept values that are linked to a specific test based
on the values in the text boxes
'''
global intercept_vals
intercept_keys = []
for ii, val in enumerate(intercept_vals):
intercept_keys.append(val.get())
return intercept_keys
def load(self, keys):
'''
loads the required data for plotting from the test specified by the
provided keys
PARAMETERS
----------
keys: list of 3 strings
this list is created in the intercept scanner page and it links the
test numbers to their intercept ranges and mode
'''
global key_dict
global valid_val
self.bins = 30
try:
test_num = int(key_dict[keys[0]][keys[1]][keys[2]])
data = self.dat.load(
parameters=['intercept_bounds', 'intercept_mode', 'y',
'title', 'num_active', 'num_inactive'],
save_location='%s/%05d'%(self.save_location, test_num))
# get the mean before converting y to a histogram
data['y_mean'] = np.mean(data['y'])
if data['title'] == 'proportion_time_neurons_active':
y, bins_out = np.histogram(
np.squeeze(data['y']), bins=np.linspace(0, 1, self.bins))
data['x'] = 0.5*(bins_out[1:]+bins_out[:-1])
data['y'] = y
else:
data['x'] = np.cumsum(np.ones(len(data['y'])))
valid_val.set('Valid')
return data
except:
print('Test does not exist')
valid_val.set('Invalid')
return None
class TrajectoryError():
def __init__(self, db_name, time_derivative=0, interpolated_samples=100):
'''
PARAMETERS
----------
db_name: string
the name of the database to load data from
interpolated_samples: positive int, Optional (Default=100)
the number of samples to take (evenly) from the interpolated data
if set to None, no interpolated or sampling will be done, the raw
data will be returned
time_derivative: int, Optional (Default: 0)
0: position
1: velocity
2: acceleration
3: jerk
'''
# instantiate our data processor
self.dat = DataHandler(db_name)
self.db_name = db_name
self.time_derivative = time_derivative
self.interpolated_samples = interpolated_samples
def statistical_error(self,
save_location,
ideal=None,
sessions=1,
runs=1,
save_data=True,
regen=False):
'''
calls the calculate error function to get the trajectory for all runs
and sessions specified at the save location and calculates the mean
error and confidence intervals
PARAMETERS
----------
save_location: string
location of data in database
ideal: string, Optional (Default: None)
This tells the function what trajectory to calculate the error
relative to
None: use the saved filter data at save_location
if string: key of Nx3 data in database to use
sessions: int, Optional (Default: 1)
the number of sessions to calculate error for
runs: int, Optional (Default: 1)
the number of runs in each session
save_data: boolean, Optional (Default: True)
True to save data, this saves the error for each session
regen: boolean, Optional (Default: False)
True to regenerate data
False to load data if it exists
'''
if regen is False:
exists = self.dat.check_group_exists(
'%s/statistical_error_%s' %
(save_location, self.time_derivative))
if exists:
ci_errors = self.dat.load(
parameters=[
'mean', 'upper_bound', 'lower_bound', 'ee_xyz',
'ideal_trajectory', 'time', 'time_derivative',
'read_location', 'error'
],
save_location='%s/statistical_error_%i' %
(save_location, self.time_derivative))
# still using as boolean, just a python cheatcode
exists = len(ci_errors['mean'])
else:
exists = False
if not exists:
errors = []
for session in range(sessions):
session_error = []
for run in range(runs):
print('%.3f processing complete...' %
(100 * ((run + 1) + (session * runs)) /
(sessions * runs)),
end='\r')
loc = ('%s/session%03d/run%03d' %
(save_location, session, run))
data = self.calculate_error(save_location=loc, ideal=ideal)
session_error.append(np.sum(data['error']))
errors.append(session_error)
ci_errors = proc.get_mean_and_ci(raw_data=errors)
ci_errors['time_derivative'] = self.time_derivative
if save_data:
self.dat.save(data=ci_errors,
save_location='%s/statistical_error_%i' %
(save_location, self.time_derivative),
overwrite=True)
return ci_errors
def calculate_error(self, save_location, ideal=None):
'''
loads the ee_xyz data from save_location and compares it to ideal. If
ideal is not passed in, it is assuemed that a filtered path planner is
saved in save_location under the key 'ideal_trajectory' and will be
used as the reference for the error calculation. The data is loaded,
interpolated, and differentiated. The two norm error is returned.
the following dict is returned
data = {
'ee_xyz': list of end-effector positions (n_timesteps, xyz),
'ideal_trajectory': list of path planner positions
shape of (n_timesteps, xyz)
'time': list of timesteps (n_timesteps),
'time_derivative': int, the order of differentiation applied,
'read_location': string, the location the raw data was loaded from,
'error': the two-norm error between the end-effector trajectory and
the path planner followed that run
PARAMETERS
----------
save_location: string
location of data in database
ideal: string, Optional (Default: None)
This tells the function what trajectory to calculate the error
relative to
None: use the saved filter data at save_location
if string: key of Nx3 data in database to use
'''
if ideal is None:
ideal = 'ideal_trajectory'
parameters = ['ee_xyz', 'time', ideal]
# load and interpolate data
data = proc.load_and_process(
db_name=self.db_name,
save_location=save_location,
parameters=parameters,
interpolated_samples=self.interpolated_samples)
if ideal == 'ideal_trajectory':
data['ideal_trajectory'] = data['ideal_trajectory'][:, :3]
dt = np.sum(data['time']) / len(data['time'])
# integrate data
if self.time_derivative > 0:
# set our keys that are able to be differentiated to avoid errors
differentiable_keys = ['ee_xyz', 'ideal_trajectory']
if ideal is not None:
differentiable_keys.append(ideal)
for key in data:
if key in differentiable_keys:
# differentiate the number of times specified by
# time_derivative
for _ in range(0, self.time_derivative):
data[key][:, 0] = np.gradient(data[key][:, 0], dt)
data[key][:, 1] = np.gradient(data[key][:, 1], dt)
data[key][:, 2] = np.gradient(data[key][:, 2], dt)
data['time_derivative'] = self.time_derivative
data['read_location'] = save_location
data['error'] = np.linalg.norm((data['ee_xyz'] - data[ideal]), axis=1)
return data
def plot(self,
ax,
save_location,
step=-1,
c=None,
linestyle='--',
label=None,
loc=1,
title='Trajectory Error to Path Planner'):
data = self.dat.load(parameters=['mean', 'upper_bound', 'lower_bound'],
save_location='%s/statistical_error_%i' %
(save_location, self.time_derivative))
vis.plot_mean_and_ci(ax=ax,
data=data,
c=c,
linestyle=linestyle,
label=label,
loc=loc,
title=title)
def __init__(self, parent, db_name, save_location, *args):
'''
PARAMETERS
----------
db_name: string
the name of the database that holds the intercept scans
save_location: string
the location in the database the data is saved
'''
global key_dict
global intercept_vals
global save_val
global keep_test_val
global toggle_ideal_val
global clear_plot_val
global legend_loc_val
global update_plot
global plt_col
save_val = False
keep_test_val = False
toggle_ideal_val = True
clear_plot_val = False
legend_loc_val = 1
update_plot = True
# set some step boundaries for possible values
mode_step = 0.1
bound_step = 0.1
mode_range = [-0.9, 0.9]
bound_range = [-0.9, 0.9]
plt_col = ['r', 'b', 'y', 'k', 'tab:grey']
# instanitate our item creating class
self.create = GuiItems()
# instantiate our gui parameter class
self.pars = FontsAndColors()
# instantiate our button function class
self.button = ButtonFun()
# instantiate our data loading class and get defaults
self.dat = DataHandler(db_name=db_name)
data = self.dat.load(
parameters=['ideal', 'total_intercepts'],
save_location=save_location)
self.ideal = data['ideal']
runs = data['total_intercepts']
key_dict = {}
for ii in range(0,runs):
data = self.dat.load(
parameters=['intercept_bounds','intercept_mode'],
save_location='%s/%05d'%(save_location,ii))
left_bound = '%.1f'%data['intercept_bounds'][0]
right_bound = '%.1f'%data['intercept_bounds'][1]
mode = '%.1f'%data['intercept_mode']
# if first time with this left bound, save all the data now
if left_bound not in key_dict:
key_dict[left_bound] = {right_bound: {mode: '%05d'%ii}}
# left bound exists, check if the right bound has already been saved
elif right_bound not in key_dict[left_bound]:
key_dict[left_bound][right_bound] = {mode: '%05d'%ii}
# left and right bound combination already exist, check if mode saved
elif mode not in key_dict[left_bound][right_bound]:
key_dict[left_bound][right_bound][mode] = '%05d'%ii
# instantiate our frame
tk.Frame.__init__(self, parent)
self.configure(background=self.pars.background_color)
# instantiate the cells in our main frame
# CELL 1: cell for our plot
frame_plot = tk.Frame(self, parent)
frame_plot.grid(row=0,column=0, padx=10)
frame_plot.configure(background=self.pars.background_color)
# CELL 2: cell for our save / hold / clear buttons
frame_plot_buttons = tk.Frame(self, parent)
frame_plot_buttons.grid(row=0, column=1, padx=10)
frame_plot.configure(background=self.pars.background_color)
# CELL 3: cell for our intercept setting buttons and text boxes
frame_intercept_val = tk.Frame(self, parent)
frame_intercept_val.grid(row=1, column=0, padx=10)
frame_intercept_val.configure(background=self.pars.background_color)
# CELL 4: frame for printouts / notes to user
frame_notes = tk.Frame(self, parent)
frame_notes.grid(row=1, column=1, padx=10)
frame_notes.configure(background=self.pars.background_color)
# create our plotting buttons
keep_button = self.create.button(
frame=frame_plot_buttons,
text='Keep Test',
function=lambda: self.button.keep_test(self),
row=0, col=0)
clear_button = self.create.button(
frame=frame_plot_buttons,
text='Clear Plot',
function=lambda: self.button.clear_plot(self),
row=1, col=0)
# ideal_button = self.create.button(
# frame=frame_plot_buttons,
# text='Toggle Ideal',
# function=lambda: self.button.toggle_ideal(self),
# row=2, col=0)
save_button = self.create.button(
frame=frame_plot_buttons,
text='Save',
function=lambda: self.button.save(self),
row=3, col=0)
legend_loc_button = self.create.button(
frame=frame_plot_buttons,
text='Legend Loc',
function=lambda: self.button.legend_loc(self),
row=4, col=0)
# def callback(*args):
# print('button press')
# create our string variables for intercept values
left_bound_val = tk.StringVar()
#left_bound_val.trace('w', callback)
left_bound_val.set(left_bound)
right_bound_val = tk.StringVar()
#right_bound_val.trace('w', callback)
right_bound_val.set(right_bound)
mode_val = tk.StringVar()
#mode_val.trace('w', callback)
mode_val.set(mode)
# set our intercept_vals to a starting value
intercept_vals = [left_bound_val, right_bound_val, mode_val]
# create our intercept setting buttons
left_bound_up = self.create.button(
frame=frame_intercept_val,
text='/\\',
function=lambda: self.button.val_up(left_bound_val, bound_step),
row=0, col=0)
left_bound_down = self.create.button(
frame=frame_intercept_val,
text='\/',
function=lambda: self.button.val_down(left_bound_val, bound_step),
row=1, col=0)
right_bound_up = self.create.button(
frame=frame_intercept_val,
text='/\\',
function=lambda: self.button.val_up(right_bound_val, bound_step),
row=0, col=4)
right_bound_down = self.create.button(
frame=frame_intercept_val,
text='\/',
function=lambda: self.button.val_down(right_bound_val, bound_step),
row=1, col=4)
mode_up = self.create.button(
frame=frame_intercept_val,
text='/\\',
function=lambda: self.button.val_up(mode_val, mode_step),
row=0, col=2)
mode_down = self.create.button(
frame=frame_intercept_val,
text='\/',
function=lambda: self.button.val_down(mode_val, mode_step),
row=1, col=2)
# create our intercept setting entry box
left_bound_entry = self.create.entry_box(
frame=frame_intercept_val,
text_var=left_bound_val,
row=0, col=1)
right_bound_entry = self.create.entry_box(
frame=frame_intercept_val,
text_var=right_bound_val,
row=0, col=5)
mode_entry = self.create.entry_box(
frame=frame_intercept_val,
text_var=mode_val,
row=0, col=3)
# labels for entry boxes
left_bound_label = self.create.label(
frame=frame_intercept_val,
text='Left',
row=1, col=1)
right_bound_label = self.create.label(
frame=frame_intercept_val,
text='Right',
row=1, col=5)
mode_label = self.create.label(
frame=frame_intercept_val,
text='Mode',
row=1, col=3)
# label that triggers if test with selected values does not exist
global valid_val
#global valid_col
valid_val = tk.StringVar()
#valid_col = tk.StringVar()
valid_val.set('')
#valid_col.set('green')
valid_label = self.create.label(
frame=frame_notes,
textvariable=valid_val,
row=1, col=1,
font=self.pars.XL_FONT)
# Plotting Window
canvas = FigureCanvasTkAgg(live.fig, self)
canvas.draw()
canvas.get_tk_widget().grid(row=0, column=0)
canvas.get_tk_widget().configure(background=self.pars.background_color)
def run(
intercept_vals,
input_signal,
seed=1,
db_name="intercepts_scan",
save_name="example",
notes="",
encoders=None,
analysis_fncs=None,
network_class=None,
network_ens_type=None,
force_params=None,
angle_params=None,
means=None,
variances=None,
**kwargs
):
"""
runs a scan for the proportion of neurons that are active over time
PARAMETERS
----------
intercept_vals: array of floats (n_intercepts to try x 3)
the [left_bound, mode, right_bound] to pass on to the triangluar
intercept function in network_utils
input_signal: array of floats (n_timesteps x n_inputs)
the input signal that we want to check our networks response to
seed: int
the seed used for any randomization in the sim
save_name: string, Optional (Default: proportion_neurons)
the name to save the data under in the intercept_scan database
notes: string, Optional (Default: '')
any additional notes to save with the scan
analysis_fncs: list of network_utils functions to apply to the spike trains
the function must accept network and input signal, and return a list of
data and activity
"""
if network_class is None:
network_class = signals.DynamicsAdaptation
if not isinstance(analysis_fncs, list):
analysis_fncs = [analysis_fncs]
if network_ens_type == "force":
n_neurons = force_params["n_neurons"]
n_ensembles = force_params["n_ensembles"]
n_input = force_params["n_input"]
elif network_ens_type == "angle":
n_neurons = angle_params["n_neurons"]
n_ensembles = angle_params["n_ensembles"]
n_input = angle_params["n_input"]
elif network_ens_type is None:
n_neurons = encoders.shape[1]
n_ensembles = encoders.shape[0]
n_input = encoders.shape[2]
print("Running intercepts scan on %s" % network_class.__name__)
print("Input Signal Shape: ", np.asarray(input_signal).shape)
loop_time = 0
elapsed_time = 0
for ii, intercept in enumerate(intercept_vals):
start = timeit.default_timer()
elapsed_time += loop_time
print(
"%i/%i | " % (ii + 1, len(intercept_vals))
+ "%.2f%% Complete | " % (ii / len(intercept_vals) * 100)
+ "%.2f min elapsed | " % (elapsed_time / 60)
+ "%.2f min for last sim | " % (loop_time / 60)
+ "~%.2f min remaining..." % ((len(intercept_vals) - ii) * loop_time / 60),
end="\r",
)
triangular = np.random.triangular(
left=intercept[0],
right=intercept[1],
mode=intercept[2],
size=n_neurons * n_ensembles,
)
intercepts = nengo.dists.CosineSimilarity(n_input + 2).ppf(1 - triangular)
# intercepts = nengo.dists.CosineSimilarity(1000 + 2).ppf(1 - triangular)
intercepts = intercepts.reshape((n_ensembles, n_neurons))
force_params["intercepts"] = intercepts
# get the spike trains from the sim
network = network_class(
force_params=force_params,
angle_params=angle_params,
means=means,
variances=variances,
seed=seed,
)
if network_ens_type == "force":
network_ens = network.force_ens
synapse = force_params["tau_output"]
elif network_ens_type == "angle":
network_ens = network.angle_ens
synapse = angle_params["tau_output"]
spike_trains = network_utils.get_activities(
network=network,
network_ens=network_ens,
input_signal=input_signal,
synapse=synapse,
)
for func in analysis_fncs:
func_name = func.__name__
y, activity = func(
pscs=spike_trains, n_neurons=n_neurons, n_ensembles=n_ensembles
)
# get the number of active and inactive neurons
num_active, num_inactive = network_utils.n_neurons_active_and_inactive(
activity=activity
)
if ii == 0:
dat = DataHandler(db_name)
dat.save(
data={"total_intercepts": len(intercept_vals), "notes": notes},
save_location="%s/%s" % (save_name, func_name),
overwrite=True,
)
# not saving activity because takes up a lot of disk space
data = {
"intercept_bounds": intercept[:2],
"intercept_mode": intercept[2],
"y": y,
"num_active": num_active,
"num_inactive": num_inactive,
"title": func_name,
}
dat.save(
data=data,
save_location="%s/%s/%05d" % (save_name, func_name, ii),
overwrite=True,
)
loop_time = timeit.default_timer() - start
def test_save(data, overwrite):
save_location = 'test_saving'
dat = DataHandler('tests')
dat.save(data=data,
save_location=save_location,
overwrite=overwrite)
def test_save_error():
dat = DataHandler('tests')
with pytest.raises(Exception):
dat.save(data={'bool': True},
save_location='test_saving',
overwrite=False)
def test_save_type_error():
dat = DataHandler('tests')
with pytest.raises(TypeError):
dat.save(data=np.ones(10),
save_location='test_saving',
overwrite=True)
