def __profilePrep(self, input_dir):
Io.deleteDirectory(self.path_provider.output_tmp_path)
Io.makeDirectories(self.path_provider.output_tmp_path)
Io.copyFiles(src=input_dir,
dest=self.path_provider.output_tmp_path,
file_names=self.file_names,
file_name_regex="*.tif")
print('Profiling')
Io.deleteDirectory(self.path_provider.output_data_path)
Io.makeDirectories(self.path_provider.output_data_path)
Tr.profileToProfile(input_data=self.path_provider.output_tmp_path,
out_path=self.path_provider.output_data_path)
Io.copyFiles(src=input_dir,
dest=self.path_provider.output_data_path,
file_names=self.file_names,
file_name_regex="*.TFW")
# print('Translating into one file...')
# Io.makeDirectories(self.path_provider.output_data_path)
# Tr.translateIntoOneFile(input_data=self.path_provider.output_tmp2_path,
# out_path=self.path_provider.output_data_path)
print('Cleaning temp dirs...')
Io.deleteDirectory(self.path_provider.output_tmp_path)
def translate(self, chainpos, angle, axis):
"""Eigentlich eine transrotate funktion
Zuerst wird translatiert dann rotiert
"""
if chainpos == 1:
RotMat = tr.rotation_matrix(angle, axis)
oldori = self.orients[0]
newori = np.dot(RotMat,oldori)
self.orients[0] = newori
return self
if chainpos > 1:
point = self.roots[chainpos-1] # hier ändern
RotMat = tr.rotation_matrix(angle, axis, point)
oldroot = self.roots[chainpos-1].toggle_trans()
newroot = np.dot(RotMat,oldroot)[:3,:1] # mal länge
oldori = self.orients[chainpos-1]()
newori = np.dot(tr.unit_vector(RotMat),oldori)
self.roots[chainpos-1] = newroot
self.orients[chainpos] = newori
return self
def profileMerge(self, input_dir):
self.__profilePrep(input_dir)
print('Merging...')
Io.deleteFile(self.path_provider.merged_file)
Io.makeDirectories(self.path_provider.output_merged_path)
Tr.gdalMerge(input_data=self.path_provider.output_data_path,
out_file=self.path_provider.merged_file)
def generate_datasets(self):
self.ip_data_folder = IP_Folder.get()
self.op_data_folder = OP_Folder.get()
#
#op_mvavg_data_folder = OP_Folder.get()+'\\MovingAverage'
if self.dataset_radio_button == 2:
self.op_sqrt_data_folder = self.op_data_folder + '\\SquareRoot'
self.create_folder(self.op_sqrt_data_folder)
algos.square_root(self.ip_data_folder, self.op_sqrt_data_folder)
def basicMerge(self, input_dir):
Io.deleteDirectory(self.path_provider.output_data_path)
Io.makeDirectories(self.path_provider.output_data_path)
Io.copyFiles(src=input_dir,
dest=self.path_provider.output_data_path,
file_names=self.file_names)
print('Merging...')
Io.deleteFile(self.path_provider.merged_file)
Io.makeDirectories(self.path_provider.output_merged_path)
Tr.gdalMerge(input_data=self.path_provider.output_data_path,
out_file=self.path_provider.merged_file,
is_pct=True)
def process_wine(filename):
red_wine = numpy.genfromtxt(filename, delimiter=";", skip_header=True)
# deletes rows containing NaN values
red_wine = red_wine[~numpy.isnan(red_wine).any(axis=1)]
# classifies wines into 1 and 0
for row in red_wine:
if row[-1] > 5:
row[-1] = 1
else:
row[-1] = 0
# removes outliers from selected columns
list_del = {3, 4, 9}
for i in list_del:
li = Transformations.find_outliers(red_wine[:, i], 1.8)
for j in li:
red_wine = numpy.delete(red_wine, j, axis=0)
# adds features
# y = red_wine[:, -1]
# x = red_wine[:, 0:-1]
# x = Transformations.add_feature(x, [1, 4])
# red_wine = numpy.concatenate((x, y.reshape(y.shape[0], 1)), axis=1)
# selects features
# red_wine = Transformations.select_feature(red_wine, [8, 3])
# normalizes each column
for i in range(red_wine.shape[1]):
red_wine[:, i] = normalize(red_wine[:, i])
return red_wine
def commandLinePrompt(current_steps, current_ingredients):
user_input = float(commandLineIntro(current_steps, current_ingredients))
#Ingredients
if user_input == 0:
Ingredients.main(current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
elif user_input == 1:
Steps.tools(current_steps, current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
elif user_input == 2:
Steps.methods(current_steps, current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
elif user_input == 3:
Steps.steps(current_steps, current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
elif user_input == 4:
current_ingredients = ServingSizeTransform.main(current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
# Healthier
elif user_input == 5:
current_ingredients, current_steps = Transformations.main(
3, current_steps, current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
# Less Healthy
elif user_input == 6:
current_ingredients, current_steps = Transformations.main(
4, current_steps, current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
# Vegetarian
elif user_input == 7:
current_ingredients, current_steps = Transformations.main(
1, current_steps, current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
# Non-Vegetarian
elif user_input == 8:
current_ingredients, current_steps = Transformations.main(
2, current_steps, current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
elif user_input == 9:
return
elif user_input == 10:
current_ingredients, current_steps = Transformations.main(
5, current_steps, current_ingredients)
user_input = commandLinePrompt(current_steps, current_ingredients)
def _shift_sys(md_sys, radius, radius_buffer=1):
"""
"""
# enlarge radius so atoms do not clash
radius += radius_buffer
rn_pos = agm.points_on_sphere(npoints=1, ndim=3, radius=radius)[0]
mx_trans = cgt.translation_matrix(rn_pos)
sys_add_all_atms = list(range(len(md_sys.atoms)))
md_sys.mm_atm_coords(-1, mx_trans, False, *sys_add_all_atms)
def transform(self): # Tranforms ECI to obs
self.obs = np.copy(
self.X) # Copies the values of X just to have the same dimensions
for i in range(0, len(self.sats)): # Goes through each sat
for i2 in range(0, np.ma.size(self.X, axis=1)): # For all time
self.obs[i][i2] = tr.ECI2obs(
self.X[i][i2][0], self.X[i][i2][1], self.X[i][i2][2],
self.gs_llh[0], self.gs_llh[1], self.gs_llh[2],
self.time[i2]) # This function is in Transformations.py
def main():
img = cv2.imread('Lenna.jpg', 3)
print(img.shape)
img = tr.RGB2YCBCR(img)
Y, Cr, Cb = cv2.split(img)
pathlib.Path('./SecondAttempt').mkdir(parents=True, exist_ok=True)
(Sl, (Sh1, Sv1, Sd1), (Sh2, Sv2, Sd2)) = pywt.wavedec2(Y, 'db1', level=2)
ReducedCb = downscale_local_mean(Cb, (2, 2))
ReducedCr = downscale_local_mean(Cr, (2, 2))
CbPlus = ReducedCb
CbMinus = ReducedCb
for i in range(0, ReducedCb.shape[0]):
for j in range(0, ReducedCb.shape[1]):
if ReducedCb[i, j] < 0:
CbPlus[i, j] = 0
elif ReducedCb[i, j] > 0:
CbMinus[i, j] = 0
CrPlus = ReducedCr
CrMinus = ReducedCr
for i in range(0, ReducedCr.shape[0]):
for j in range(0, ReducedCr.shape[1]):
if ReducedCr[i, j] < 0:
CrPlus[i, j] = 0
elif ReducedCb[i, j] > 0:
CrMinus[i, j] = 0
ReducedCbMinus = downscale_local_mean(CbMinus, (2, 2))
# Sd1 = ReducedCbMinus
# Sh2 = CrPlus
# Sv2 = CbPlus
# Sd2 = CrMinus
NewYSecondTry = pywt.waverec2(
(Sl, (Sh1, Sv1, ReducedCbMinus), (CrPlus, CbPlus, CrMinus)), 'db1')
cv2.imwrite("./SecondAttempt/NewYSecondTry.jpg", NewYSecondTry)
cv2.waitKey(0)
cv2.destroyAllWindows()
h = hf.Halftone('./SecondAttempt/NewYSecondTry.jpg')
h.make(angles=[0, 15, 30, 45],
antialias=True,
percentage=10,
sample=1,
scale=2,
style='grayscale')
def arb_rot_matrix(vector):
"""
Rotate a vector so it aligns with a randomly generated vector. Return the
rotation matrix.
"""
vt_u = agv.get_unit_vt(vector) # scale vector to length of 1
# generate random vector of length 1
rand_vt = points_on_sphere(1, ndim=3, radius=1)[0]
# get angle phi between vt_u and rand_vt
phi = agv.get_ang(vt_u, rand_vt)
rot_axis = np.cross(vt_u, rand_vt)
Mr = cgt.rotation_matrix(phi, rot_axis)
return Mr
def get_dihedral(ptI, ptJ, ptK, ptL, return_cross=False):
"""
Define a dihedral/improper between the points I, J, K and L. Defines the planes
IJK and JKL (if improper is intended to be used, consider right order of atoms).
Sources: http://cbio.bmt.tue.nl/pumma/index.php/Theory/Potentials
http://www.vitutor.com/geometry/distance/line_plane.html
http://kitchingroup.cheme.cmu.edu/blog/2015/01/18/Equation-of-a-plane-through-three-points/
"""
if not isinstance(ptI, np.ndarray):
ptI = np.array(ptI)
if not isinstance(ptJ, np.ndarray):
ptJ = np.array(ptJ)
if not isinstance(ptK, np.ndarray):
ptK = np.array(ptK)
if not isinstance(ptL, np.ndarray):
ptL = np.array(ptL)
# plane IJK
v1 = ptI - ptJ
v2 = ptJ - ptK
# the cross product v1xv2 is a vector normal to both vectors (i.e. to the plane)
cp1 = np.cross(v1, v2)
# plane JKL
v1 = ptJ - ptK
v2 = ptJ - ptL
cp2 = np.cross(v1, v2)
# angle between two planes is the same as the two vectors which are orthogonal
# to each plane
if return_cross is True:
return (cgt.angle_between_vectors(cp1, cp2), cp1, cp2)
else:
return cgt.angle_between_vectors(cp1, cp2)
def transforms(self):
composed = transforms.Compose([
tfs.Rescale(out_size=self.rescale_shape),
tfs.ReCrop(out_size = (self.nn_input_image_shape)),
tfs.Rotate(rotations = self.rotation_angles,rotation_probabilities = self.rotation_angle_probabilities),
tfs.Flip(flip_probabilites = self.flip_hor_ver_probabilities),
tfs.Apply_Gaussian_filter(probabity=self.gaussian_filter_probability),
tfs.Apply_Unsharpmask_filter(probabity=self.unsharpmask_filter_probability)
])
return composed
def get_angle(ptI, ptJ, ptK):
"""
Bla.
Get angle between three points I, J and K.
"""
if not isinstance(ptI, np.ndarray):
ptI = np.array(ptI)
if not isinstance(ptJ, np.ndarray):
ptJ = np.array(ptJ)
if not isinstance(ptK, np.ndarray):
ptK = np.array(ptK)
v1 = ptI - ptJ
v2 = ptK - ptJ
return cgt.angle_between_vectors(v1, v2)
def stateVectorUpdate(stateVec,del_t):
transition_quat = getTransitionQuat(stateVec[10:13],del_t)
result_quat = trnsfrm.quaternion_multiply(stateVec[3:7],transition_quat)
return np.array([ [stateVec[0] + (stateVec[7]*del_t)], #0
[stateVec[1] + (stateVec[8]*del_t)], #1
[stateVec[2] + (stateVec[9]*del_t)], #2
[result_quat[0]], #3
[result_quat[1]], #4
[result_quat[2]], #5
[result_quat[3]], #6
[stateVec[7]], #7
[stateVec[8]], #8
[stateVec[9]], #9
[stateVec[10]], #10
[stateVec[11]], #11
[stateVec[12]] ]) #12
def stateVectorUpdate(stateVec, del_t):
transition_quat = getTransitionQuat(stateVec[10:13], del_t)
result_quat = trnsfrm.quaternion_multiply(stateVec[3:7], transition_quat)
return np.array([
[stateVec[0] + (stateVec[7] * del_t)], #0
[stateVec[1] + (stateVec[8] * del_t)], #1
[stateVec[2] + (stateVec[9] * del_t)], #2
[result_quat[0]], #3
[result_quat[1]], #4
[result_quat[2]], #5
[result_quat[3]], #6
[stateVec[7]], #7
[stateVec[8]], #8
[stateVec[9]], #9
[stateVec[10]], #10
[stateVec[11]], #11
[stateVec[12]]
]) #12
def find_passes(self, grav, minel):
i = 0
first = 1
counter = 0
for i in range(0, len(self.sats)):
tcomp = datetime(2000, 1, 1, 0, 0, 0, 0)
for i2 in range(0, np.ma.size(self.X, axis=1)):
if self.time[i2] - tcomp > timedelta(0, 20 * 60):
if self.obs[i][i2][1] > 0:
counter += 1
if i2:
i2 -= 1
temp = self.time[i2]
start = 1
passx = np.array(
[i, datetime(2000, 1, 1, 0, 0, 0, 0), 0, 0, 0])
el = 1
while el > 0 or start:
r = pr.sgp4Prop_fine(self.line1[i], self.line2[i],
temp, grav)
azi, el, ran = tr.ECI2obs(r[0], r[1], r[2],
self.gs_llh[0],
self.gs_llh[1],
self.gs_llh[2], temp)
passx = np.vstack(
(passx, (i, self.time[i2], azi, el, ran)))
if start:
if el > 0:
start = 0
temp += timedelta(0, fine)
maxel = np.amax(passx, axis=0)[3]
if maxel > minel:
if first:
self.passes = np.copy(passx)
first = 0
else:
self.passes = np.vstack((self.passes, passx))
del passx
tcomp = temp
return counter
def basicTile(self, is_pct, zoom):
print('Translating')
Io.deleteFile(self.path_provider.translated_file)
Tr.gdalTranslate(input_file=self.path_provider.merged_file,
out_file=self.path_provider.translated_file,
is_pct=is_pct)
print('Warping')
Io.deleteFile(self.path_provider.warped_file)
Tr.gdalWarp(in_file=self.path_provider.translated_file,
out_file=self.path_provider.warped_file)
print('Tiling')
Io.deleteDirectory(self.path_provider.output_tiles_path)
Io.makeDirectories(self.path_provider.output_tiles_path)
Tr.gdal2Tiles(in_file=self.path_provider.warped_file,
out_dir=self.path_provider.output_tiles_path,
zoom=zoom)
def __init__(self, transformation_parameters):
self.nn_input_image_shape = transformation_parameters['nn_input_image_shape']
crop_pixel_count = transformation_parameters['crop_pixel']
self.rescale_shape = [x+y for x,y in zip(self.nn_input_image_shape,crop_pixel_count)]
self.rotation_angles = transformation_parameters['rotation_angles']
self.rotation_angle_probabilities = transformation_parameters['rotaion_angle_probabilities']
self.flip_hor_ver_probabilities = transformation_parameters['flip_hor_ver_probabilities']
self.gaussian_filter_probability = transformation_parameters['gaussian_filter_probability']
self.unsharpmask_filter_probability = transformation_parameters['unsharp_mask_filter_probability']
self.Rescaler = tfs.Rescale(out_size=self.rescale_shape)
self.ReCropper = tfs.ReCrop(out_size = (self.nn_input_image_shape))
self.Rotator = tfs.Rotate(rotations = self.rotation_angles,
rotation_probabilities = self.rotation_angle_probabilities)
self.Flipper = tfs.Flip(flip_probabilites = self.flip_hor_ver_probabilities)
self.Gaussian_filter = tfs.Apply_Gaussian_filter(probabity=self.gaussian_filter_probability)
self.Unsharpmask_filter = tfs.Apply_Unsharpmask_filter(probabity=self.unsharpmask_filter_probability)
def main():
state = pd.read_spss('/Users/kellenbullock/Desktop/Geographic Analysis II/Data/5303_EX_A.sav')
Counties = state.query("Scale == 'Counties'")
Schools = state.query("Scale == 'Schools'")
Tracts = state.query("Scale == 'Tracts'")
assigned_var_c = Counties[['Pct_Black', 'Pct_Two_Plus', 'Pct_SNAP', 'Pct_FIRE_I', 'Pct_Poverty', 'Pct_Unemp', 'Med_HomeValue', 'Pct_White', 'Pct_BlueCollar_O', 'Pct_Hispanic']]
assigned_var_s = Schools[['Pct_Black', 'Pct_Two_Plus', 'Pct_SNAP', 'Pct_FIRE_I', 'Pct_Poverty', 'Pct_Unemp', 'Med_HomeValue', 'Pct_White', 'Pct_BlueCollar_O', 'Pct_Hispanic']]
assigned_var_t = Tracts[['Pct_Black', 'Pct_Two_Plus', 'Pct_SNAP', 'Pct_FIRE_I', 'Pct_Poverty', 'Pct_Unemp', 'Med_HomeValue', 'Pct_White', 'Pct_BlueCollar_O', 'Pct_Hispanic']]
assigned_var_t = assigned_var_t.reset_index()
assigned_var_s = assigned_var_s.reset_index()
assigned_var_s = assigned_var_s.drop(columns=['index'])
assigned_var_t = assigned_var_t.drop(columns=['index'])
# New Variables
var_c = Counties[['Pct_Unemp', 'Med_HomeValue', 'Pct_White', 'Pct_BlueCollar_O', 'Pct_Hispanic']]
var_s = Schools[['Pct_Unemp', 'Med_HomeValue', 'Pct_White', 'Pct_BlueCollar_O', 'Pct_Hispanic']]
var_t = Tracts[['Pct_Unemp', 'Med_HomeValue', 'Pct_White', 'Pct_BlueCollar_O', 'Pct_Hispanic']]
var_t = var_t.reset_index()
var_s = var_s.reset_index()
var_t = var_t.drop(columns=['index'])
var_s = var_s.drop(columns=['index'])
# 1.
#EDA.figures(var_c, 'Counties')
#EDA.figures(var_t, 'Tracts')
#EDA.figures(var_s, 'Schools')
print('=====County=======')
#EDA.Descrptives(Counties, var_c)
print('======== Tracts =========')
#EDA.Descrptives(Tracts, var_t)
print('====== School Districts =======')
#EDA.Descrptives(Schools, var_s)
# 1 part b:
#test_trans(assigned_var_c, 'Pct_Black')
print('************')
#test_trans(assigned_var_c, 'Pct_Hispanic')
# 2,
# This will make a pearson's r correlation matrix at the County scale
# These do not work in the spyder IDE. Please see the jupyter notebook for outputs.
corr = assigned_var_c.corr()
corr.style.background_gradient(cmap='coolwarm').set_precision(3)
EDA.df_to_pdf(corr, 'Matrix_1')
''' Easy way to order Correlations but without signs:
correlations = assigned_var_c.corr().abs()
stack = correlations.unstack()
stack_order = s.sort_values(kind='quicksort')
'''
# 2. a. 8 strongest Person's correaltions. There was no easy way to do this I had to pull everythong by hand.
corr_table = {
'Variables': ['Pct_Poverty / Pct_SNAP', 'Pct_Unemp / Pct_SNAP', 'Pct_White / Pct_Unemp', 'Pct_White / Pct_SNAP', 'Pct_Unemp / Pct_Poverty', 'Pct_White / Pct_Two_Plus', 'Pct_White / Pct_Poverty', 'Pct_Fire_I / Pct_BlueCollar_O'],
'Correlation': [0.757, 0.722, - 0.672, - 0.645, 0.605, - 0.599, - 0.593, - 0.574]
}
all_scale = {
'Variables': ['Pct_Poverty / Pct_SNAP', 'Pct_Unemp / Pct_SNAP', 'Pct_White / Pct_Unemp', 'Pct_White / Pct_SNAP', 'Pct_Unemp / Pct_Poverty', 'Pct_White / Pct_Two_Plus', 'Pct_White / Pct_Poverty', 'Pct_Fire_I / Pct_BlueCollar_O'],
'County': [0.757, 0.722, - 0.672, - 0.645, 0.605, - 0.599, - 0.593, - 0.574],
'Tract': [0.773, 0.679, -0.474, -0.564, 0.628, -0.296, -0.495, -0.584],
'School District': [0.732, 0.590, -0.440, -0.515, 0.494, -0.569, -0.442, -0.380]
}
pearson_r_table = pd.DataFrame(corr_table)
pearson_r_table.to_excel('County Correlation.xlsx')
EDA.df_to_pdf(pearson_r_table, 'Decending_corr')
all_scale = pd.DataFrame(all_scale)
all_scale.to_excel('Scale_Correlation.xlsx')
EDA.df_to_pdf(all_scale, 'Scale_Correlation')
# 3.
assigned_c_repub = Counties[['Pct_Repub', 'Pct_Black', 'Pct_Two_Plus', 'Pct_SNAP', 'Pct_FIRE_I', 'Pct_Poverty', 'Pct_Unemp', 'Med_HomeValue', 'Pct_White', 'Pct_BlueCollar_O', 'Pct_Hispanic']]
assigned_c_repub = assigned_c_repub.reset_index()
assigned_c_repub = assigned_c_repub.drop(columns=['index'])
assigned_c_repub['Pct_Black'] = Transformations.log_trans(assigned_c_repub, 'Pct_Black')
assigned_c_repub['Pct_Hispanic'] = Transformations.log_trans(assigned_c_repub, 'Pct_Hispanic')
corr2 = assigned_c_repub.corr()
corr2.style.background_gradient(cmap='coolwarm').set_precision(3)
EDA.df_to_pdf(corr2, 'Matrix_2')
# c.
chosen = corr2[['Pct_Repub', 'Pct_Poverty', 'Pct_Unemp', 'Pct_White', 'Pct_SNAP']]
x = ['Pct_Repub', 'Pct_Poverty', 'Pct_Unemp', 'Pct_White', 'Pct_SNAP']
y = 'Pct_Repub'
corr_scatter(chosen, x, y)
# c. 4 scatter plots with y axis being pct_republican
chosen = assigned_c_repub[['Pct_Repub', 'Pct_Poverty', 'Pct_Unemp', 'Pct_White', 'Pct_SNAP']]
# Running partial correlation 4 times:
Partial_Corr.partial_corr(chosen[['Pct_Repub', 'Pct_Poverty']])
Partial_Corr.partial_corr(chosen[['Pct_Repub', 'Pct_White']])
Partial_Corr.partial_corr(chosen[['Pct_Repub', 'Pct_SNAP']])
Partial_Corr.partial_corr(chosen[['Pct_Repub', 'Pct_Unemp']])
# 5.
bivar_regres(chosen['Pct_Repub'], chosen['Pct_Poverty']) # Model 1
bivar_regres(chosen['Pct_Repub'], chosen['Pct_SNAP']) # Model 2
bivar_regres(chosen['Pct_Repub'], chosen['Pct_White']) # Model 3
bivar_regres(chosen['Pct_Repub'], chosen['Pct_Unemp']) # Model 4
#===============================================================================
sys_cutfrom = ag_lammps.read_lmpdat(args.lmpdat, dcd=args.dcd, frame_idx_start=args.f, frame_idx_stop=args.f)
# replicate cell if desired
if args.rep is not None:
# read last frame since the frame selection was done with read_lmpdat already
sys_cutfrom.replicate_cell(n_start=args.rep[0], n_stop=args.rep[1], direction="a", frame_id=-1, adjust_box=True)
sys_cutfrom.replicate_cell(n_start=args.rep[2], n_stop=args.rep[3], direction="b", frame_id=-1, adjust_box=True)
sys_cutfrom.replicate_cell(n_start=args.rep[4], n_stop=args.rep[5], direction="c", frame_id=-1, adjust_box=True)
sys_cutfrom.fetch_molecules_by_bonds()
sys_cutfrom.mols_to_grps()
# shift sys_cutfrom by given vector
if args.shft is not None:
args.shft = np.array(args.shft)
M_shft = cgt.translation_matrix(args.shft)
atm_idxs = list(range(len(sys_cutfrom.atoms)))
sys_cutfrom.mm_atm_coords(-1, M_shft, False, *atm_idxs)
del (atm_idxs, M_shft)
#===============================================================================
# PREPARE THE CUTTING SHAPE SYSTEM
#===============================================================================
if args.lmpdat_cutshape is not None or args.dcd_cutshape is not None:
sys_cutshape = ag_lammps.read_lmpdat(args.lmpdat_cutshape, dcd=args.dcd_cutshape, frame_idx_start=args.f_cutshape, frame_idx_stop=args.f_cutshape)
# replicate cell if desired
if args.rep_cutshape is not None:
sys_cutshape.replicate_cell(n_start=args.rep_cutshape[0], n_stop=args.rep_cutshape[1], direction="a", frame_id=-1, adjust_box=True)
sys_cutshape.replicate_cell(n_start=args.rep_cutshape[2], n_stop=args.rep_cutshape[3], direction="b", frame_id=-1, adjust_box=True)
sys_cutshape.replicate_cell(n_start=args.rep_cutshape[4], n_stop=args.rep_cutshape[5], direction="c", frame_id=-1, adjust_box=True)
def get_rotational_matrix(molid,
atom_idx1,
atom_idx2,
axis_start,
axis_end,
frame_id=-1):
"""
Get the matrix that rotates the bond between atom 1 and atom 2 onto axis.
All atoms of selection are rotated on the axis defined by axis_start and
axis_end. The rotational matrix is then defined by the axis formed of
atom_idx1 and atom_idx2 (it is rotated so it lies on the former axis).
Parameters
----------
atom_idx1 : int
index of first atom
atom_idx2 : int
index of second atom
axis_start : numpy-array
starting point of the axis (atom_idx1 will be translated onto that point)
axis_stop : numpy-array
ending point of the axis; start and end form the axis and therefor
determine the rotation
Returns
-------
trans_matrix : list {float float ...}
translational matrix to move atom_idx1 to axis_start
rot_matrix : list {float float ...}
rotational matrix to rotate given axis to the desired one
"""
# get relevant coordinates
atom_coords1 = vmd_coords_numpy_arrays(
molid, frame_id, selection="index {}".format(atom_idx1))
atom_coords2 = vmd_coords_numpy_arrays(
molid, frame_id, selection="index {}".format(atom_idx2))
# define both axis
axis1 = atom_coords2 - atom_coords1
axis1 /= np.linalg.norm(axis1)
axis2 = axis_end - axis_start
try:
axis2 /= np.linalg.norm(axis2)
except TypeError:
# vector of length 1 already
pass
# angle alpha between axis1 and axis2 (so we can rotate about it)
alpha = np.arccos(
np.dot(axis1, axis2) / (np.linalg.norm(axis1) * np.linalg.norm(axis2)))
# rotational axis is crossproduct of axis1 and axis2
rotaxis = np.cross(axis1, axis2)
# create rotational matrix
matrix1 = cgt.rotation_matrix(alpha, rotaxis)
return list(matrix1.flatten())
output_name = "{}_{}".format(args.o, i)
if args.restart:
subdir = "CBZ_Dimer_anti_2H_flexible_scan_B97D3_cc-pVDZ_{}".format(i)
dimeric_sys.oldchk = "CBZ_Dimer_anti_2H_flexible_scan_B97D3_cc-pVDZ_{}.chk".format(
i)
else:
subdir = output_name
os.mkdir(subdir)
# calculate the current shift, multiply by -1 to make the shift towards
# each other (x-coordinate is negative and we are moving according
# the x-axis)
if args.restart is False:
current_shift = unit_vt_shift * (i * 0.1 * -1)
cur_Tm = cgt.translation_matrix(current_shift)
# translate the coordinates but let the coordinates from frame 0 stay
# the same
coords = dimeric_sys.mm_atm_coords(
0, cur_Tm, True, *list(range(30, len(dimeric_sys.atoms))))
dimeric_sys.ts_coords.append(coords)
dimeric_sys.chk = "{}.chk".format(output_name)
dimeric_sys.write_gau(
"{}.gau".format(output_name),
-1,
True,
title="CBZ anti-dimer h-bonds scan - interatomic distance: {}".format(
i))
#if args.restart is False:
# dimeric_sys.write_lmpdat("{}.lmpdat".format(output_name), -1,
from ImageClient import ImPI
from SQLUtils import SQLUtils
import Transformations as trans
import pandas as pd
## Download the new images
images = ImPI().fetch_images_by_tag("cats", "/home/pi/c_drive/test_dl")
## Create raw and thumb files
image_out_data = pd.DataFrame()
for link in images:
image_data = trans.expand_dir_and_transform(images[link], link)
image_out_data = image_out_data.append(image_data, ignore_index=True)
print("Finished processing {}".format(images[link]))
image_out_data.to_csv("sample_output.csv", index=False)
## Write data out to SQL
SQLUtils("novartis_dummy_db").write_dataframe_safe(image_out_data,
"pulled_images")
def Ransac(Matches, TYPE, N, Epsilon):
# Random sample consensus(RANSAC) is an iterative method to estimate parameters
# of a mathematical model from a set of observed data that contains outliers.
# By iteratively fitting a model to the data we find the best model by its ability
# to characterize a threshold of inlier points.
# Input: Corresponding Image coordinates in the two images (Matches)
# Threshold number of inlier points (N)
# Threshold value for Sum of Squared differences (Epsilon)
# TYPE determines the model to compute (1=Affine, 2=Homography)
# Outputs: Number of inlier points
# Affine or Homography transformation matrix
# Inlier count
In_count = 0
# Inlier array
Inlier_Data = []
while (In_count < N):
# Randomly pick out points from the matches
r = np.round((len(Matches) - 1)*np.random.random(np.int(np.round(len(Matches)/2)) + 1)).astype(int)
Subsample = Matches[r,:]
# Keep track of the inliers
if (In_count > 0):
Subsample = np.concatenate((Subsample, np.asarray(Inlier_Data)), axis=0)
# Isolate the matches
Image_Coords_1 = np.asarray(Subsample)[:,0:2]
Image_Coords_2 = np.asarray(Subsample)[:,2:4]
# Homography
if (TYPE == 2):
# Compute the homography matrix
H = Transformations.Homography(Image_Coords_1, Image_Coords_2)
# Compute the transformation and the inverse transformation
X2 = Transformations.HomographyTransformer(H, Matches[:, 0:2])
X1 = Transformations.HomographyTransformer(np.linalg.inv(H), Matches[:, 2:4])
# Compute the euclidean distance
ssd = SumEucDistance(X1, Matches[:, 0:2], X2, Matches[:, 2:4])
# Affine
elif (TYPE == 1):
# Compute the affine matrix
A = Transformations.AffineTransformer(Image_Coords_1, Image_Coords_2)
# Compute the transformation coordinates and inverse transformation coordinates
X2 = np.matmul(A, np.transpose(np.hstack((np.asarray(Matches[:,0:2]), np.ones(shape=(len(Matches),1))))))
X1 = np.matmul(np.linalg.inv(A), np.transpose(np.hstack((np.asarray(Matches[:,2:4]), np.ones(shape=(len(Matches),1))))))
# Compute the euclidean distance
ssd = SumEucDistance(X1[0:2].T, Matches[:, 0:2], X2[0:2].T, Matches[:, 2:4])
else:
raise ValueError("Incorrect Type")
# Extract the outliers
for i in range(0, len(ssd)):
if (ssd[i] < Epsilon):
if [Matches[i,0],Matches[i,1],Matches[i,2],Matches[i,3]] not in np.asarray(Inlier_Data).tolist():
Inlier_Data.append(Matches[i,:])
In_count += 1
print(In_count)
# Compute the transformation using the inliers
Inlier_Data = np.asarray(Inlier_Data, dtype=float)
if (TYPE == 1):
return Inlier_Data, Transformations.AffineTransformer(Inlier_Data[:, 0:2], Inlier_Data[:, 2:4])
elif (TYPE == 2):
return Inlier_Data, Transformations.Homography(Inlier_Data[:, 0:2], Inlier_Data[:, 2:4])
def fill_up(self,
num_bins,
iterations=10,
fill_up_plots=False,
point_plots=False,
RO=True,
t=1):
# consider every label seperately
label_confidence = []
for label in self.D.labels:
label_idx = self.D.labels.index(label)
'''collect training data'''
data = self.D.X_b_train[self.D.Y_b_train == label]
'''remove outliers, rotate data'''
if RO:
data = Transformations.remove_outliers_lof(data)
trafo = self.trafo()
data = trafo.transform(data)
cdfs_scaled = np.empty((len(data[0]), num_bins))
fitted_cdf = np.empty((len(data[0]), num_bins))
fitted_ = np.empty((len(data[0]), num_bins))
num_fill_up = 0
data_range = []
DE_list = []
if fill_up_plots:
f, ax = plt.subplots(nrows=1,
ncols=len(data[0]),
figsize=(6, 2.5))
# consider every dimension
for line in range(len(data[0])):
'''project onto line, determine borders'''
d = data[:, line]
d_min = min(d)
d_max = max(d)
data_range.append([d_min, d_max])
'''define Density Estimator here!'''
DE_list.append(self.DE(num_bins))
DE_list[line].estimate(d, d_min, d_max)
'''estimate distribution'''
fitted = self.density_func.fit(DE_list[line].mids,
DE_list[line].values, d)
fitted_[line] = copy.deepcopy(fitted)
fitted_cdf[line] = np.cumsum(fitted)
fitted_cdf[line] = fitted_cdf[line] / fitted_cdf[line][-1]
'''to be filled up: the differences between the distribution curve and the histogram'''
diff = fitted - DE_list[line].values
'''number of points to add'''
num_points_line = (len(d) /
sum(DE_list[line].values)) * sum(diff)
num_fill_up = max(num_fill_up, num_points_line)
'''probability distribution for the fill-up'''
if sum(diff) == 0:
cdfs_scaled[line] = [0] * num_bins
else:
diff = diff / sum(diff)
diff = [max(diff[i], 0) for i in range(len(diff))]
cdfs_scaled[line] = np.cumsum(diff)
cdfs_scaled[line] = (
cdfs_scaled[line] /
cdfs_scaled[line][-1]) * num_points_line
if fill_up_plots:
barWidth = DE_list[line].mids[1] - DE_list[line].mids[0]
fill = fitted_[line] - DE_list[line].values
ax[line].bar(DE_list[line].mids,
DE_list[line].values,
label='data',
color='teal',
width=barWidth)
ax[line].bar(DE_list[line].mids,
[max(fill[i], 0) for i in range(len(fill))],
bottom=DE_list[line].values,
label='fill up',
color='goldenrod',
width=barWidth,
hatch="...",
edgecolor="white")
ax[line].plot(DE_list[line].mids,
fitted_[line],
label='fitted',
c='mediumvioletred',
linewidth=2)
ax[line].get_xaxis().set_ticks([])
ax[line].get_yaxis().set_ticks([])
if fill_up_plots:
ax[-1].legend()
plt.show()
# f.savefig('Results/Example_cluster_distr.pdf', format='pdf', dpi=1200, bbox_inches='tight')
# determine the number of added points in total: max over dimensions
num_fill_up = int(num_fill_up)
if num_fill_up == 0:
label_confidence.append(0)
continue
# best out of 10: go for the result with the highest confidence
best_conf = 0
leftover_points = []
# kNN_rnd_dist, kNN_rnd_std = confidence_kNN_rnd_coeff(data_range, num_fill_up)
kNN_rnd_dist, kNN_rnd_std = Confidence.confidence_kNN_train_sized_coeff(
data, num_fill_up)
for it in range(iterations):
points = np.empty((num_fill_up, 0))
# generate points
for line in range(len(data[0])):
'''adjust cdf (in case there have to be more points added because of other lines)'''
distr_scaled = fitted_cdf[line] * max(
(num_fill_up - cdfs_scaled[line][-1]), 0)
cdf = cdfs_scaled[line] + distr_scaled
cdf = cdf / cdf[-1] # normalize
'''generate random values according to the cdf'''
values = np.random.rand(num_fill_up)
value_bins = np.searchsorted(cdf, values)
coords = np.array([
random.uniform(DE_list[line].grid[value_bins[i]],
DE_list[line].grid[value_bins[i] + 1])
for i in range(num_fill_up)
]).reshape(num_fill_up, 1)
points = np.concatenate((points, coords), axis=1)
'''compute the confidence of the result'''
if len(points) < 20:
conf_b, conf_a, l_p = (0, 0, [[]])
else:
conf_b, conf_a, l_p = Confidence.confidence_kNN_rnd(
points, kNN_rnd_dist, t * kNN_rnd_std)
# add the points to the data set
if conf_a > best_conf:
best_conf = conf_a
leftover_points = copy.deepcopy(l_p)
# leftover_points = points
if point_plots:
plt.figure(it)
plt.scatter(data[:, 0],
data[:, 1],
c=self.colors[label_idx],
alpha=0.2,
s=3)
plt.scatter(points[:, 0],
points[:, 1],
c='red',
alpha=0.8,
s=8)
if len(l_p) > 0 and len(l_p[0]) > 0:
plt.scatter(l_p[:, 0],
l_p[:, 1],
c=self.colors[label_idx],
alpha=0.8,
s=8)
plt.show()
if len(data[0]) > 2:
plt.figure(it * 100)
plt.scatter(data[:, 0],
data[:, 2],
c=self.colors[label_idx],
alpha=0.2,
s=3)
plt.scatter(points[:, 0],
points[:, 2],
c='red',
alpha=0.8,
s=8)
if len(l_p) > 0 and len(l_p[0]) > 0:
plt.scatter(l_p[:, 0],
l_p[:, 2],
c=self.colors[label_idx],
alpha=0.8,
s=8)
plt.show()
'''remove the points with low confidence, discard the result entirely
if the confidence is too low. Transform back the leftover points'''
if len(leftover_points
) > 0: # and 1 / best_conf <= kNN_rnd_dist + t*kNN_rnd_std:
add_me = trafo.transform_back(leftover_points)
self.added_points = np.concatenate((self.added_points, add_me))
self.added_labels = np.append(self.added_labels,
[label] * len(add_me))
label_confidence.append(best_conf)
if point_plots:
plt.show()
return label_confidence
args = parser.parse_args()
cbz = agum.Unification()
cbz.read_lmpdat(args.lmpdat)
cbz.import_dcd(args.dcd)
cbz.read_frames(frame=args.start)
with open(args.out, "w") as f_out:
f_out.write("{:>9}{:>17}{:>17}{:>17}{:>17}\n".format(
"Step", "ang_C13_N8_C5", "ang_C13_N8_C9", "ang_C9_N8_C5",
"ang_b1_N8_b2"))
for n, frame in enumerate(cbz.ts_coords[args.start:]):
# omega-1/-2/-3 - angles between carbon and nitrogen
ang_C13_N8_C5 = cgt.angle_between_vectors(frame[24] - frame[15],
frame[24] - frame[7])
ang_C13_N8_C9 = cgt.angle_between_vectors(frame[24] - frame[15],
frame[24] - frame[25])
ang_C9_N8_C5 = cgt.angle_between_vectors(frame[24] - frame[25],
frame[24] - frame[7])
# phi - angle between benzenes' center of masses
com_b1 = agg.get_com(
[frame[14], frame[16], frame[18], frame[20], frame[22], frame[15]],
[
cbz.atm_types[cbz.atoms[14].atm_key].weigh,
cbz.atm_types[cbz.atoms[16].atm_key].weigh,
cbz.atm_types[cbz.atoms[18].atm_key].weigh,
cbz.atm_types[cbz.atoms[20].atm_key].weigh,
cbz.atm_types[cbz.atoms[22].atm_key].weigh,
cbz.atm_types[cbz.atoms[15].atm_key].weigh
batch_size=args.batch_size)
def TrainData():
return images_train, labels_train
pre_transform_images, labels = tf.case({
tf.equal(data2use, 'stats'): StatsData,
tf.equal(data2use, 'train'): TrainData,
tf.equal(data2use, 'val'): ValData
})
# Applying our transformations on the images (and handling their corresponding labels as well):
# Transformations should be applied on the raw images, before applying any standartization, whitening etc'.
transformer = Transformations.Transformer(transformations=TRANSFORMATIONS_LIST)
images, labels = transformer.TransformImages_TF_OP(pre_transform_images,
labels)
# The example classifer was trained on standartized images, so applying standartization AFTER the transformations were applied:
post_processed_images = tf.map_fn(
lambda im: tf.image.per_image_standardization(im), images)
train_batches_per_epoch = int(
np.ceil(num_of_samples * args.train_portion / args.batch_size))
val_batches_per_epoch = int(
np.ceil(num_of_samples * (1 - args.train_portion) / args.batch_size))
# Example classifier model:
classifier = cifar10.inference(post_processed_images)
logits = classifier.inference_logits()
if target_turn > control_turn:
control_turn = min( target_turn, control_turn + 0.1 )
elif target_turn < control_turn:
control_turn = max( target_turn, control_turn - 0.1 )
else:
control_turn = target_turn
twist = TwistStamped();
for i in range(6):
dXnp[i] = dX[i]
if(changeframe!=1): # Change the frame of the veloccity
R=my.quaternion_matrix([rot[3],rot[0],rot[1],rot[2]])
A=np.concatenate((R[0:3][:,0:3],np.zeros(shape=(3,3))),1)
B=np.concatenate((np.zeros(shape=(3,3)),R[0:3][:,0:3]),1)
Screw=np.vstack((A,B))
dXnp=np.dot(Screw,dXnp)
twist.twist.linear.x = control_speed*dXnp[0]* direction;
twist.twist.linear.y = control_speed*dXnp[1]* direction;
twist.twist.linear.z = control_speed*dXnp[2]* direction;
twist.twist.angular.x = control_turn*dXnp[3]* direction;
twist.twist.angular.y = control_turn*dXnp[4]* direction;
twist.twist.angular.z = control_turn*dXnp[5]* direction;
pub.publish(twist)
#print("loop: {0}".format(count))
def myfun_cutout(x):
transformed_image = Transformations.cutout(x, level)
return transformed_image
import Ransac
import Transformations
import numpy as np
import matplotlib.pyplot as plt
I1 = plt.imread('library1.jpg')
I2 = plt.imread('library2.jpg')
matches = np.loadtxt('library_matches.txt')
Input_coord1 = matches[:,0:2]
Input_coord2 = matches[:,2:4]
### LS Affine transformation ###
Affine_T = Transformations.AffineTransformer(Input_coord1, Input_coord2)
# Generate a pseudo z-axis
PS_Input_coord1 = np.hstack((Input_coord1, np.ones(shape=(len(Input_coord1), 1))))
PS_Input_coord2 = np.hstack((Input_coord2, np.ones(shape=(len(Input_coord2), 1))))
# Transformed coordinates
Tr_coord1 = np.matmul(np.linalg.inv(Affine_T),np.transpose(PS_Input_coord2))
Tr_coord2 = np.matmul(Affine_T,np.transpose(PS_Input_coord1))
# Residuals and mean error
Residuals_IC1 = Tr_coord2.T-PS_Input_coord2
Residuals_IC2 = Tr_coord1.T-PS_Input_coord1
Mean_Res_IC1 = np.mean(Residuals_IC1)
Mean_Res_IC2 = np.mean(Residuals_IC2)
def main():
img = cv2.imread('Lenna.jpg', 3)
img = tr.RGB2YCBCR(img)
Y, Cr, Cb = cv2.split(img)
print(Y.shape)
pathlib.Path('./FirstAttempt').mkdir(parents=True, exist_ok=True)
cv2.imwrite("./FirstAttempt/OldY.jpg", Y)
cv2.waitKey(0)
cv2.destroyAllWindows()
(Sl, (Sh, Sv, Sd)) = pywt.dwt2(Y, 'haar')
ReducedCb = downscale_local_mean(Cb, (2, 2))
ReducedCr = downscale_local_mean(Cr, (2, 2))
# Sh = ReducedCb
# Sv = ReducedCr
NewY = pywt.idwt2((Sl, (ReducedCb, ReducedCr, Sd)), 'haar')
cv2.imwrite("./FirstAttempt/NewYFirstTry.jpg", NewY)
cv2.waitKey(0)
cv2.destroyAllWindows()
h = hf.Halftone('./FirstAttempt/NewYFirstTry.jpg')
h.make(angles=[0, 15, 30, 45],
antialias=True,
percentage=10,
sample=1,
scale=2,
style='grayscale')