def main(open_path):
while True:
files = easygui.fileopenbox('Please locate csv files',
'Specify File',
default=open_path,
filetypes='*.csv',
multiple=True)
if files:
open_path = "{0:s}\*.csv".format(os.path.dirname(files[0]))
csv_files = {}
for file in files:
file_data = get_data(file)
if is_valid_log(file_data):
try:
name = datetime.strptime(os.path.basename(file),
"%Y-%m-%d %H-%M-%S.csv")
except ValueError:
name = os.path.basename(file).split(".")[0]
csv_files[name] = file_data
else:
easygui.msgbox(
"The file {0:s} is not a valid file it will be removed."
.format(os.path.basename(file)))
plot_graphs(csv_files)
else:
return open_path
def train_model(open_path):
"""
:param open_path:
:return:
"""
# TODO x = motor power, y velocity, z time
while True:
file = easygui.fileopenbox('Please locate csv file',
'Specify File',
default=open_path,
filetypes='*.csv')
if file:
open_path = "{0:s}\*.csv".format(os.path.dirname(file))
file_data = get_data(file)
# TODO make this loop thought the steps as many times as they are number of paths
if is_valid_log(file_data, visualize.LEGACY_COLUMNS):
x, _ = get_features(file_data)
outlier = OutlierAndScalingSelection(file_data, x)
outlier.show()
del outlier
else:
break
def find_constants(open_path):
"""
:param open_path:
:return:
"""
if not os.path.exists(MODEL_FILE):
easygui.msgbox("There are no models to use to classify the data. Please train algorithm first.")
return
clf = joblib.load(MODEL_FILE)
if is_empty_model(clf):
easygui.msgbox("The model has not been fitted yet. Please add training data to the model.")
return
fig = plt.figure("Scaled 3d data")
ax3d = Axes3D(fig)
fig, ax2d = plt.subplots(1, 1, num="Fitted data")
# plt.ion()
while True:
file = easygui.fileopenbox('Please locate csv file', 'Specify File', default=open_path, filetypes='*.csv')
if file:
open_path = "{0:s}\*.csv".format(os.path.dirname(file))
file_data = get_data(file)
if is_valid_log(file_data):
ax2d.cla()
ax3d.cla()
plot_hyperplane(clf, ax3d)
k_v, k_k, k_acc = find_gain(clf, file_data, is_data=True, ax3d=ax3d, ax2d=ax2d)
# TODO ask user to give the max acceleration of the current spline
# TODO scale k_acc / ()
plt.show()
easygui.msgbox("""
The kV of this log is {0:f}.
The kK of this log is {1:f}.
The kAcc of this log is {2:f}.""".format(k_v, k_k, k_acc))
else:
easygui.msgbox(
"The file {0:s} is not a valid file.".format(os.path.basename(file)))
else:
break
plt.ioff()
plt.close("all")
return open_path
def find_constants(open_path):
"""
This is the main loop which runs until the user no selects any file. Retrieves the saved model for separating the data.
:param open_path: the default location to start your search
:return: the ending location the folder search was looking at
"""
if not os.path.exists(MODEL_FILE):
easygui.msgbox(
"There are no models to use to classify the data. Please train algorithm first."
)
return
clf = joblib.load(MODEL_FILE)
if is_empty_model(clf):
easygui.msgbox(
"The model has not been fitted yet. Please fit data to the model.")
return
while True:
file = easygui.fileopenbox('Please locate csv file',
'Specify File',
default=open_path,
filetypes='*.csv')
if file:
open_path = "{0:s}\*.csv".format(os.path.dirname(file))
file_data = get_data(file)
legacy_log = is_valid_log(file_data, visualize.LEGACY_COLUMNS)
current_log = is_valid_log(file_data)
if legacy_log or current_log:
if legacy_log and not current_log:
easygui.msgbox(
"Because this log is missing information that makes it optimal "
"for manipulating the data efficiently results may be inaccurate"
)
# TODO make it so that when closing the figure using the GUI it reopens normally
plot = ConstantViewer(clf)
plot.graph(file_data)
plot.show()
else:
easygui.msgbox("The file {0:s} is not a valid file.".format(
os.path.basename(file)))
else:
break
plt.close("all")
return open_path
def train_model(open_path):
"""
:param open_path:
:return:
"""
# TODO x = motor power, y velocity, z time
while True:
files = easygui.fileopenbox('Please locate csv file',
'Specify File',
default=open_path,
filetypes='*.csv',
multiple=True)
if files:
open_path = "{0:s}\*.csv".format(os.path.dirname(files[0]))
all_features = None
all_data = None
for file in files:
file_data = get_data(file)
if is_valid_log(file_data, LEGACY_COLUMNS):
features, _ = get_features(file_data)
time = MinMaxScaler().fit_transform(features[:, 2].reshape(
-1, 1))
features[:, 2] = time.reshape(1, -1)
if all_features is None:
all_features = features
all_data = file_data
else:
all_features = np.concatenate((all_features, features))
file_data['pathNumber'] += (
all_data["pathNumber"].max() + 1)
all_data = np.concatenate((all_data, file_data))
else:
easygui.msgbox(
"The file {0:s} is not a valid file, it will not be plotted."
.format(os.path.basename(file)))
go_through_process(all_features, all_data)
else:
break
def main(open_path):
"""
This is the main loop which runs until the user no selects any file.
:param open_path: the default location to start your search
:return: the ending location the folder search was looking at
"""
while True:
files = easygui.fileopenbox('Please locate csv files',
'Specify File',
default=open_path,
filetypes='*.csv',
multiple=True)
if files:
open_path = "{0:s}\*.csv".format(os.path.dirname(files[0]))
csv_files = {}
for file in files:
file_data = get_data(file)
if is_valid_log(file_data, NEEDED_KEYS):
try:
name = datetime.strptime(os.path.basename(file),
"%Y-%m-%d %H-%M-%S.csv")
except ValueError:
name = os.path.basename(file).split(".")[0]
csv_files[name] = file_data
else:
easygui.msgbox(
"The file {0:s} is not a valid file, it will not be plotted."
.format(os.path.basename(file)))
plots = Plot(csv_files)
plots.show()
plots.close_all()
else:
return open_path
import matplotlib.pyplot as plt
from visualize.helper import get_data, get_velocity, get_acceleration
if __name__ == '__main__':
data = get_data(r"C:\Users\mariu\Documents\Logs\2018-03-21 08-29-04.csv")
time = data["Time"]
velocity = get_velocity(time, data, actual=False)
acceleration = get_acceleration(time, velocity)
velocities = plt.subplot(211)
accelerations = plt.subplot(212)
velocities.plot(time, velocity)
accelerations.scatter(time, acceleration)
import numpy as np
X = np.linspace(time.min(), time.max(), velocity.shape[0])
coefficients = np.polyfit(time, np.log(velocity), 10) # Use log(x) as the input to polyfit.
velocities.plot(time, np.polyval(coefficients, time), "--", label="fit")
velocity = get_velocity(time, data, actual=True)
acceleration = get_acceleration(time, velocity)
velocities.plot(time, velocity)
accelerations.scatter(time, acceleration)
plt.show()
return new_features, features
def reverse_scalling(features, scalers, outlier_prediction):
features = np.copy(features)
for scaler, index in zip(scalers.keys(), scalers.values()):
index = index[outlier_prediction == 1]
features[index] = scaler.inverse_transform(features[index])
return features
data = helper.get_data(r"..\example_data\2018-03-21 08-29-04.csv")
features, col = get_features(data)
filters = data['motionState'] == 'MOVING'
data = data[filters]
features = features[filters]
# f = helper.get_features(data)
XTE = data['XTE']
lagE = data['lagE']
angleE = data['angleE']
targetX = data["xTarget"]
targetY = data["yTarget"]
targetAngle = data["angleTarget"]
def train_model(open_path):
"""
:param open_path:
:return:
"""
# TODO add lasso selection of points for data that was not classified manually.
# TODO Should be able to select outliers and what side is positive or not
# TODO create 2d plots for every dimension and use lasso selection from there
fig = plt.figure("Complete classifier")
ax3d = Axes3D(fig)
ax3d.set_xlabel('Average motor power')
ax3d.set_ylabel('Velocity')
ax3d.set_zlabel('Time')
total_data = {}
already_used_files = set()
changed_anything = False
hyperplane = None
plt.ion()
if os.path.exists(MODEL_FILE):
answer = easygui.boolbox("A model already exists do you wish to use it?")
if answer is None:
return
elif answer:
clf = joblib.load(MODEL_FILE)
hyperplane = plot_hyperplane(clf, ax3d)
data = np.load(MODEL_DATA_FILE)
total_data["features"] = data["features"]
total_data["labels"] = data["labels"]
accelerating = total_data["features"][total_data["labels"] == 0]
decelerating = total_data["features"][total_data["labels"] == 1]
ax3d.scatter(accelerating[:, 0], accelerating[:, 1], accelerating[:, 2], c="red",
label="acceleration")
ax3d.scatter(decelerating[:, 0], decelerating[:, 1], decelerating[:, 2], c="blue",
label="deceleration")
already_used_files.add(*data["files"])
plt.show()
else:
clf = create_blank_classifier()
changed_anything = True
else:
clf = create_blank_classifier()
while True:
file = easygui.fileopenbox('Please locate csv file', 'Specify File', default=open_path, filetypes='*.csv')
if file:
open_path = "{0:s}\*.csv".format(os.path.dirname(file))
file_data = get_data(file)
# TODO make this loop thought the steps as many times as they are number of paths
if is_valid_log(file_data, visualize.LEGACY_COLUMNS):
x, _ = get_features(file_data)
y = get_labels(file_data)
# x = x[file_data["motionState"] == 'MOVING']
# y = y[file_data["motionState"] == 'MOVING']
outlier = IsolationForest(n_jobs=-1, random_state=0)
temp_y = y[y != OUTLIER] = 1
outlier.fit(x, temp_y)
prediction = outlier.predict(x)
# outlier = LocalOutlierFactor(n_jobs=-1, )
# outlier = EllipticEnvelope(random_state=0)
# prediction = outlier.fit_predict(x)
y[prediction == OUTLIER] = OUTLIER
outliers = x[y == OUTLIER]
accelerating = x[y == ACCELERATING]
decelerating = x[y == DECELERATING]
outlier_power, outlier_velocity, outlier_time = separate_feature(outliers)
accelerating_power, accelerating_velocity, accelerating_time = separate_feature(accelerating)
decelerating_power, decelerating_velocity, decelerating_time = separate_feature(decelerating)
temp_fig = plt.figure(os.path.basename(file).split(".")[0])
temp_ax = Axes3D(temp_fig)
temp_ax.set_xlabel('Average motor power')
temp_ax.set_ylabel('Velocity')
temp_ax.set_zlabel('Time')
outlier_line = temp_ax.scatter(outlier_power, outlier_velocity, outlier_time, c="black",
label="outliers")
acceleration_line = temp_ax.scatter(accelerating_power, accelerating_velocity, accelerating_time,
c="red",
label="accelerating")
deceleration_line = temp_ax.scatter(decelerating_power, decelerating_velocity, decelerating_time,
c="blue",
label="decelerating")
plt.show()
easygui.msgbox("Next without outliers and rescaled")
x = x[prediction != OUTLIER]
y = y[prediction != OUTLIER]
x = MinMaxScaler().fit_transform(x)
outlier_line.remove()
acceleration_line.remove()
deceleration_line.remove()
accelerating = x[y == ACCELERATING]
decelerating = x[y == DECELERATING]
accelerating_power, accelerating_velocity, accelerating_time = separate_feature(accelerating)
decelerating_power, decelerating_velocity, decelerating_time = separate_feature(decelerating)
acceleration_line = temp_ax.scatter(accelerating_power, accelerating_velocity, accelerating_time,
c="red",
label="accelerating")
deceleration_line = temp_ax.scatter(decelerating_power, decelerating_velocity, decelerating_time,
c="blue",
label="decelerating")
# train, test, train_L, test_L = train_test_split(x, y, train_size=.8, test_size=.2, random_state=0,
# shuffle=True)
# clf.fit(train, train_L)
clf.fit(x, y)
plot_hyperplane(clf, temp_ax)
if len(total_data) == 0:
total_data = {"features": x, "labels": y}
changed_anything = True
elif file not in already_used_files:
new_x = np.concatenate((total_data["features"], x))
new_y = np.concatenate((total_data["labels"], y))
temp_x = np.hstack((new_x, new_y.reshape((-1, 1))))
temp_x = np.unique(temp_x, axis=0)
new_x = temp_x[:, :-1]
new_y = temp_x[:, -1]
total_data["features"] = new_x
total_data["labels"] = new_y.ravel()
clf.fit(total_data["features"], total_data["labels"])
changed_anything = True
if file not in already_used_files: # FIXME can this just be in a single if statement?
ax3d.scatter(accelerating[:, 0], accelerating[:, 1], accelerating[:, 2], c="red",
label="positive")
ax3d.scatter(decelerating[:, 0], decelerating[:, 1], decelerating[:, 2], c="blue",
label="negative")
if hyperplane is not None:
hyperplane.remove()
hyperplane = plot_hyperplane(clf, ax3d)
already_used_files.append(file)
else:
easygui.msgbox(
"The file {0:s} is not a valid file.".format(os.path.basename(file)))
else:
break
if changed_anything and not is_empty_model(clf):
joblib.dump(clf, MODEL_FILE)
np.savez(MODEL_DATA_FILE, features=total_data["features"], labels=total_data["labels"],
files=already_used_files)
easygui.msgbox("Model saved.")
plt.close("all")
return open_path
