def create_plot(filename, file, sampling='easy'):
data = pd.read_csv(filename)
print filename
rel_col = ['Start X', 'Start Y', 'Start Z', 'End X', 'End Y', 'End Z']
data = data[rel_col]
data_1 = data[['Start X', 'Start Y', 'Start Z']]
data_2 = data[['End X', 'End Y', 'End Z']]
data_2.columns = ['Start X', 'Start Y', 'Start Z']
dat_new = pd.concat((data_1, data_2), axis=0)
dat_new = dat_new.dropna(axis=0)
dat_new = (dat_new - dat_new.mean()) / np.linalg.norm(dat_new.values)
#set sampling rate
sam_dict = {'easy': 0.7, 'medium': 0.1, 'hard': 0.01}
sam_rate = sam_dict[sampling]
#sample using sampling rate
dat_new.reset_index(drop=True, inplace=True)
ind = np.random.choice(dat_new.index,
size=dat_new.shape[0] * sam_rate,
replace=False)
dat_new = dat_new.iloc[ind, :]
# find highest, lowest standard deviation vector and orient the model accordingly
highest = np.where(dat_new.std() == dat_new.std().max())[0][0]
lowest = np.where(dat_new.std() == dat_new.std().min())[0][0]
if highest == 1:
dat_new = np.dot(dat_new, rotateZMatrix(math.pi / 2))
elif highest == 2:
dat_new = np.dot(dat_new.values, rotateYMatrix(math.pi / 2))
data_plot = pd.DataFrame(dat_new,
columns=['Start X', 'Start Y', 'Start Z'])
#Plot
fig = plt.figure()
data_plot = pd.DataFrame(data_plot,
columns=['Start X', 'Start Y', 'Start Z'])
ax = plt.axes(projection='3d')
x = data_plot['Start X']
y = data_plot['Start Y']
z = data_plot['Start Z']
#name the plot files
name_front = 'temp/' + file[:-4] + '_front'
name_side = 'temp/' + file[:-4] + '_side'
name_top = 'temp/' + file[:-4] + '_top'
ax.scatter(x, y, z)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.view_init(azim=0)
plt.savefig('../static/' + name_front + sampling)
#side view
ax.view_init(azim=90)
plt.savefig('../static/' + name_side + sampling)
#top view
ax.view_init(elev=90, azim=0)
plt.savefig('../static/' + name_top + sampling)
def create_plot(filename, file, sampling = 'easy'):
data= pd.read_csv(filename)
print filename
rel_col = ['Start X','Start Y','Start Z','End X','End Y','End Z']
data = data[rel_col]
data_1 = data[['Start X','Start Y','Start Z']]
data_2 = data[['End X','End Y','End Z']]
data_2.columns = ['Start X','Start Y','Start Z']
dat_new = pd.concat((data_1,data_2),axis=0)
dat_new = dat_new.dropna(axis=0)
dat_new = (dat_new-dat_new.mean())/np.linalg.norm(dat_new.values)
#set sampling rate
sam_dict = {'easy':0.7,'medium':0.1,'hard':0.01}
sam_rate = sam_dict[sampling]
#sample using sampling rate
dat_new.reset_index(drop=True,inplace=True)
ind = np.random.choice(dat_new.index, size=dat_new.shape[0]*sam_rate, replace=False)
dat_new = dat_new.iloc[ind,:]
# find highest, lowest standard deviation vector and orient the model accordingly
highest = np.where(dat_new.std()==dat_new.std().max())[0][0]
lowest = np.where(dat_new.std()==dat_new.std().min())[0][0]
if highest == 1:
dat_new = np.dot(dat_new,rotateZMatrix(math.pi/2))
elif highest == 2:
dat_new = np.dot(dat_new.values,rotateYMatrix(math.pi/2))
data_plot = pd.DataFrame(dat_new, columns = ['Start X','Start Y','Start Z'])
#Plot
fig = plt.figure()
data_plot = pd.DataFrame(data_plot, columns = ['Start X','Start Y','Start Z'])
ax = plt.axes(projection='3d')
x = data_plot['Start X']
y = data_plot['Start Y']
z = data_plot['Start Z']
#name the plot files
name_front = 'temp/' + file[:-4] + '_front'
name_side = 'temp/' + file[:-4] + '_side'
name_top = 'temp/' + file[:-4] + '_top'
ax.scatter(x, y, z)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.view_init(azim=0)
plt.savefig('../static/'+ name_front + sampling)
#side view
ax.view_init(azim=90)
plt.savefig('../static/'+ name_side + sampling)
#top view
ax.view_init(elev=90,azim=0)
plt.savefig('../static/'+ name_top + sampling)
#There are 6 relevant columns and 3 of them repeat so concat them on top of each other
dat_1 = load_dat[['Start X','Start Y','Start Z']]
dat_2 = load_dat[['End X','End Y','End Z']]
dat_2.columns = ['Start X','Start Y','Start Z']
dat_new = pd.concat((dat_1,dat_2),axis=0)
dat_new = dat_new.dropna(axis=0)
columns = dat_new.columns
#normalize using the norm
dat_new = (dat_new -dat_new.mean())/np.linalg.norm(dat_new.values)
# find highest, lowest standard deviation vector and orient the model accordingly
highest = np.where(dat_new.std()==dat_new.std().max())[0][0]
lowest = np.where(dat_new.std()==dat_new.std().min())[0][0]
if highest == 1:
dat_new = np.dot(dat_new,rotateZMatrix(math.pi/2))
elif highest == 2:
dat_new = np.dot(dat_new.values,rotateYMatrix(math.pi/2))
dat_new = pd.DataFrame(dat_new, columns = ['Start X','Start Y','Start Z'])
dat_new['number']= np.ones((dat_new.shape[0],1))*i
#max and min xyz after scale
max_x = dat_new['Start X'].max()
max_y = dat_new['Start Y'].max()
max_z = dat_new['Start Z'].max()
min_x = dat_new['Start X'].min()
min_y = dat_new['Start Y'].min()
min_z = dat_new['Start Z'].min()
#calculate width height length
width = max_y-min_y
length = max_x-min_x
height = max_z-min_z
#ratio of length and width
ratio_xy = float(dat_new['Start Y'].std())/(