def run():
# get all containers
containers = gwy.gwy_app_data_browser_get_containers()
# save each container as gwy file with file name
for c in containers:
# get datafield 0 for each container for func to operate on
gwy.gwy_app_data_browser_select_data_field(c, 0)
gwy.gwy_process_func_run("arc_revolve", c, gwy.RUN_IMMEDIATE)
def heightediting(data, k):
gwy.gwy_app_data_browser_select_data_field(data, k)
# Flatten the data
gwy.gwy_process_func_run('flatten_base', data, gwy.RUN_IMMEDIATE)
datafield = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
mask = gwy.DataField.new_alike(datafield, False)
datafield.grains_mark_height(mask, 30, False)
# Re-do polynomial correction with masked height
s["/module/polylevel/masking"] = 1
gwy.gwy_process_func_run('polylevel', data, gwy.RUN_IMMEDIATE)
# Re-do align rows with masked heights
s["/module/linematch/masking"] = 1
gwy.gwy_process_func_run('align_rows', data, gwy.RUN_IMMEDIATE)
# # Remove scars
# gwy.gwy_process_func_run('scars_remove', data, gwy.RUN_IMMEDIATE)
# Gaussian filter to remove noise
current_data = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
filter_width = 5 * dx * 1e9
current_data.filter_gaussian(filter_width)
# Set zero to mean value
gwy.gwy_process_func_run('zero_mean', data, gwy.RUN_IMMEDIATE)
return data
def load_data(self ):
self.channel_id = self.combobox_channels.get_active()
model = self.combobox_channels.get_model()
active = self.combobox_channels.get_active()
if active >= 0:
self.channel_str = model[self.channel_id][0]
#print self.channel_str
self.direction_id = self.combobox_directions.get_active()
#if self.gwydata.param['full_path'][-6:]=='Z_mtrx':
# data_id = self.channel_id
#else:
data_id = self.channel_id * 2 + self.direction_id
#print self.channel_id,self.direction_id, data_id
self.data_id_str = '/'+str(data_id)+'/'
print data_id,self.data_id_str
self.d_origin = self.c[self.data_id_str + 'data']
#self.d = self.d_origin.duplicate()
if self.d_origin:
self.c.set_object_by_name(self.data_id_str + 'data',self.d_origin)
else:
self.d_origin = self.c[self.data_id_str + 'data']
gwy.gwy_app_data_browser_select_data_field(self.c,data_id)
self.get_active_process()
print self.process_id
if self.process_id == 1:
gwy.gwy_app_data_browser_select_data_field(self.c, data_id)
gwy.gwy_process_func_run("level", self.c, gwy.RUN_IMMEDIATE)
elif self.process_id == 2:
gwy.gwy_process_func_run("align_rows", self.c, gwy.RUN_IMMEDIATE)
elif self.process_id == 3:
gwy.gwy_process_func_run("level", self.c, gwy.RUN_IMMEDIATE)
gwy.gwy_process_func_run("align_rows", self.c, gwy.RUN_IMMEDIATE)
self.d = self.c[self.data_id_str + 'data']
d_process = self.d.duplicate()
self.data_min = self.d.get_min()
self.data_max = self.d.get_max()
self.data_dif = self.data_max - self.data_min
self.scale_min_current = self.scale_min.get_value()
self.scale_max_current = self.scale_max.get_value()
bottom = self.data_min + self.scale_min_current/100*self.data_dif
top = self.data_min + self.scale_max_current/100*self.data_dif
#print bottom,top
if bottom <= top:
d_process.clamp(bottom, top)
#self.d.clamp(bottom, top)
self.d = d_process
else:
pass
def process_topo(self, data_ch_id):
"""Processes the data with Gwyddion Python module.
Gwyddion topography processing functions: see config file.
Before saving the image, the colorrange needs adjustment. The
colorrange settings are stored in the container for each spm
file. (see online gwyfile-format)
Args:
data_ch_id (int): Channel of the container should be processed.
"""
for ch in data_ch_id:
gwy.gwy_app_data_browser_select_data_field(self.container, ch)
self.run_gwy_func = {
gwy.RUN_IMMEDIATE: config.run_gwy_immediate_func
}
for k, funcs in self.run_gwy_func.iteritems():
for func in funcs:
gwy.gwy_process_func_run(func, self.container, k)
self.match_ch_topo = "/" + str(ch) + "/base/range-type"
self.container[self.match_ch_topo] = 2
def gwybas(filename):
#add the gwyddion folders to the path, making sure they can be found
import sys
sys.path.append('C:\Program Files (x86)\Gwyddion\\bin')
sys.path.append('C:\Program Files (x86)\Gwyddion\share\gwyddion\pygwy')
#import gwyddion
import gwy
import gwyutils
#load the file and add to data browser
c = gwy.gwy_file_load(filename, gwy.RUN_IMMEDIATE)
gwy.gwy_app_data_browser_add(c)
#Set the right settings for the align_rows command
settings = gwy.gwy_app_settings_get()
settings['/module/linematch/direction'] = int(gwy.ORIENTATION_HORIZONTAL)
settings['/module/linematch/do_extract'] = False
settings['/module/linematch/do_plot'] = False
settings[
'/module/linematch/method'] = 0 # 0: poly, 1: median, 2: median of diff,3: modus,4: matching, 5: trimemd mean, 6: trimmed mean of diff
settings['/module/linematch/masking'] = 2
settings['/module/linematch/max_degree'] = 3 #Order of polynominal
settings['/module/linematch/trim_fraction'] = 0.05
#print the datafield ID's corresponding to the different channels of the AFM, such as height, phase and error, usually the first one (0) is the height
# print gwy.gwy_app_data_browser_get_data_ids(c)
#itterate over the different datafield ID's/AFM channels, and do processing on them
for datafield_id in gwy.gwy_app_data_browser_get_data_ids(c):
# datafield = c['/%d/data' % datafield_id]
#set the color range to automatic with tials cut off (corresponding to number 2)
c['/%d/base/range-type' % datafield_id] = 2
#select the datafield_ID/AFM channel to process
gwy.gwy_app_data_browser_select_data_field(c, datafield_id)
#level the plane
gwy.gwy_process_func_run("level", c, gwy.RUN_IMMEDIATE)
#align the rows, with settings chosen above line 19-25
gwy.gwy_process_func_run("align_rows", c, gwy.RUN_IMMEDIATE)
#remove scars a couple of times (button bashing)
gwy.gwy_process_func_run("scars_remove", c, gwy.RUN_IMMEDIATE)
gwy.gwy_process_func_run("scars_remove", c, gwy.RUN_IMMEDIATE)
gwy.gwy_process_func_run("scars_remove", c, gwy.RUN_IMMEDIATE)
gwy.gwy_process_func_run("scars_remove", c, gwy.RUN_IMMEDIATE)
gwy.gwy_process_func_run("scars_remove", c, gwy.RUN_IMMEDIATE)
#fix lowest point to zero
gwy.gwy_process_func_run('fix_zero', c, gwy.RUN_IMMEDIATE)
#define new file names, I chose to simply add the wanted extention to the original filename
newname = filename + '.gwy'
newname2 = filename + '.jpg'
#find the ID/Channel corresponding to the height, of this one a JPG will be created, .gwy will contain all channels
ids = gwy.gwy_app_data_browser_find_data_by_title(c, 'Height')
gwy.gwy_app_data_browser_select_data_field(c, ids[0])
gwy.gwy_file_save(c, newname, gwy.RUN_NONINTERACTIVE)
gwy.gwy_file_save(c, newname2, gwy.RUN_NONINTERACTIVE)
#remove the current container, makes room for the next file
gwy.gwy_app_data_browser_remove(c)
def otsuthresholdgrainfinding(data, k):
# 'align_rows' function
# s["/module/linematch/direction"] = 0
# s["/module/linematch/do_extract"] = False
# s["/module/linematch/do_plot"] = False
# s["/module/linematch/masking"] = 2
# s["/module/linematch/max_degree"] = 0
# s["/module/linematch/method"] = 0 # uses median
# s["/module/linematch/trim_fraction"] = float(0.05)
# Select channel 'k' of the file
gwy.gwy_app_data_browser_select_data_field(data, k)
datafield = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
mask = gwy.DataField.new_alike(datafield, False)
# Apply a 1.5 pixel gaussian filter
data_field = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
data_field.filter_gaussian(1.5)
# # Mask data that are above thresh*sigma from average height.
# # Sigma denotes root-mean square deviation of heights.
# # This criterium corresponds to the usual Gaussian distribution outliers detection if thresh is 3.
# datafield.mask_outliers(mask, 1)
# # Shift contrast - equivalent to 'fix zero' - essential for next step to work
datafield.add(-datafield.get_min())
# Calculate min, max and range of data to allow calculation of relative value for grain thresholding
min_datarange = datafield.get_min()
max_datarange = datafield.get_max()
datarange = max_datarange + min_datarange
# Calculate Otsu threshold for data
o_threshold = datafield.otsu_threshold()
o_threshold = o_threshold + min_datarange
# Calculate relative threshold for grain determination
rel_threshold = 100 * (o_threshold / datarange)
# Mask grains using either relative threshold of the data, i.e. a percentage
# this can be set manually i.e. 35 or
# as the otsu threshold expressed as a percentage which is rel_threshold
# this will fail unless the min_datarange is negative
datafield.grains_mark_height(mask, rel_threshold, False)
# # Invert mask to maks things below the membrane
mask.grains_invert()
gwy.gwy_process_func_run("align_rows", data, gwy.RUN_IMMEDIATE)
# # gwy.gwy_process_func_run("level", data, gwy.RUN_IMMEDIATE)
# gwy.gwy_process_func_run('flatten_base', data, gwy.RUN_IMMEDIATE)
# Select channel 'k' of the file
gwy.gwy_app_data_browser_select_data_field(data, k)
datafield = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
mask = gwy.DataField.new_alike(datafield, False)
# # Mask data that are above thresh*sigma from average height.
# # Sigma denotes root-mean square deviation of heights.
# # This criterium corresponds to the usual Gaussian distribution outliers detection if thresh is 3.
# datafield.mask_outliers(mask, 1)
# # Shift contrast - equivalent to 'fix zero' - essential for next step to work
datafield.add(-datafield.get_min())
# Calculate min, max and range of data to allow calculation of relative value for grain thresholding
min_datarange = datafield.get_min()
max_datarange = datafield.get_max()
datarange = max_datarange + min_datarange
# Calculate Otsu threshold for data
o_threshold = datafield.otsu_threshold()
o_threshold = o_threshold + min_datarange
# Calculate relative threshold for grain determination
rel_threshold = 100 * (o_threshold / datarange)
# Mask grains using either relative threshold of the data, i.e. a percentage
# this can be set manually i.e. 35 or
# as the otsu threshold expressed as a percentage which is rel_threshold
# this will fail unless the min_datarange is negative
datafield.grains_mark_height(mask, rel_threshold, False)
gwy.gwy_process_func_run('zero_mean', data, gwy.RUN_IMMEDIATE)
# # Invert mask to make things below the membrane
mask.grains_invert()
# Calculate pixel width in nm
dx = datafield.get_dx()
# Calculate minimum feature size in pixels (integer)
minsize = int(2000e-9 / dx)
# Remove grains smaller than (size) in integer pixels
mask.grains_remove_by_size(minsize)
mask.grains_invert()
gwy.gwy_process_func_run('zero_mean', data, gwy.RUN_IMMEDIATE)
mask.grains_invert()
# Numbering grains for grain analysis
grains = mask.number_grains()
print max(grains)
# Update data to show mask, comment out to remove mask
s['/module/pixmap/draw_mask'] = True
data['/0/mask/red'] = 0.1234
#excluding mask, zero mean
stats = datafield.area_get_stats_mask(mask, gwy.MASK_EXCLUDE, 0, 0,
datafield.get_xres(),
datafield.get_yres())
datafield.add(-stats[0])
return data, mask, datafield, grains
def editfile(data, k):
# select each channel of the file in turn
# this is run within the for k in chosen_ids loop so k refers to the index of each chosen channel to analyse
# NONINTERACTIVE is only for file modules
gwy.gwy_app_data_browser_select_data_field(data, k)
# align rows (b)
gwy.gwy_process_func_run("align_rows", data, gwy.RUN_IMMEDIATE)
# flatten the data (c)
gwy.gwy_process_func_run("level", data, gwy.RUN_IMMEDIATE)
# align rows (d)
gwy.gwy_process_func_run("align_rows", data, gwy.RUN_IMMEDIATE)
datafield = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
mask = gwy.DataField.new_alike(datafield, False)
datafield.grains_mark_height(mask, 10, False)
# Re-do polynomial correction with masked height (e)
s["/module/polylevel/masking"] = 0
gwy.gwy_process_func_run('polylevel', data, gwy.RUN_IMMEDIATE)
# Re-do align rows with masked heights (f)
s["/module/linematch/masking"] = 0
gwy.gwy_process_func_run('align_rows', data, gwy.RUN_IMMEDIATE)
# Re-do level with masked heights (g)
s["/module/polylevel/masking"] = 1
gwy.gwy_process_func_run('polylevel', data, gwy.RUN_IMMEDIATE)
# flatten base (h)
gwy.gwy_process_func_run('flatten_base', data, gwy.RUN_IMMEDIATE)
# remove scars (i)
gwy.gwy_process_func_run('scars_remove', data, gwy.RUN_IMMEDIATE)
# Fix zero (j)
gwy.gwy_process_func_run('zero_mean', data, gwy.RUN_IMMEDIATE)
# Apply a 1.5 pixel gaussian filter (k)
data_field = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
data_field.filter_gaussian(1)
# # Shift contrast - equivalent to 'fix zero'
# datafield.add(-data_field.get_min())
return data
import gwy
import gwyutils
import sys
filename = sys.argv[1]
# '/Users/pabloherrero/sabat/stm_data/november19_experiments/QE1/2019-11-12-QE1_001.sxm'
f = open(filename, "r")
container = gwy.gwy_app_file_load(filename)
ids = gwy.gwy_app_data_browser_get_data_ids(container)
cons = gwy.gwy_app_data_browser_get_containers()
for c in cons:
dfields = gwyutils.get_data_fields_dir(c)
for key in dfields.keys():
datafield = dfields[key]
print(datafield.get_xreal())
gwy.gwy_app_data_browser_select_data_field(container, ids[0])
container['/%u/base/palette' % ids[0]] = 'Gold'
gwy.gwy_process_func_run("level", container, gwy.RUN_INTERACTIVE)
gwy.gwy_process_func_run("scars_remove", container, gwy.RUN_INTERACTIVE)
gwy.gwy_file_save(container, '%s-%02u.png' % (basename, 0),
gwy.RUN_NONINTERACTIVE)
def runbatch(root, cwd, pdr, pngexp, ratio):
# Export PNG with scalebar
s = gwy.gwy_app_settings_get()
s['/module/pixmap/title_type'] = 0
s['/module/pixmap/ztype'] = 0
s['/module/pixmap/xytype'] = 0
s['/module/pixmap/draw_maskkey'] = False
# ... (*lots* of possible settings, see ~/.gwyddion/settings)
Files_To_Open = [ f for f in listdir(cwd) if isfile(join(cwd,f)) ]
try:
mkdir(join(cwd,'Processed'))
except Exception as sym:
print ('Already Exist')
Tobe_Saved = join(cwd, 'Processed')
filename_save = cwd.split('/')[-1]
print (Files_To_Open)
#Load first file to use as Merged file
for filename in Files_To_Open:
print(filename)
try:
Temp = gwy.gwy_file_load(join(cwd,filename), RUN_NONINTERACTIVE)
print(type(Temp))
if type(Temp) == gwy.Container :
print('right type')
Cont_Dest = Temp
Files_To_Open.remove(filename)
break
Files_To_Open.remove(filename)
print('loadedbutnot')
except Exception as sym:
print('except')
print ("not proper file"+str(sym)+"\n")
continue
#Add into current browser and Make Visible on display
gwy.gwy_app_data_browser_add(Cont_Dest)
Cont_Dest.set_boolean_by_name('/0/data/visible', 1)
print (Files_To_Open)
#File Merge
#First Container
DataFields = gwyutils.get_data_fields_dir(Cont_Dest)
for key in DataFields.keys():
title = Cont_Dest.get_string_by_name(key+"/title")
if (title == 'Amplitude') : Cont_Dest.remove_by_prefix('/'+key.split('/')[1]+'/')
Cont_Dest.set_string_by_name(key+'/title', title+'.'+Files_To_Open[0])
#Rest of Containers
for filename in Files_To_Open :
#print (orgfile, join(cwd,filename))
try:
Temp_Source = gwy.gwy_file_load(join(cwd,filename), RUN_NONINTERACTIVE)
if type(Temp_Source) == gwy.Container:
Cont_Source = Temp_Source
pass
else:
continue
except Exception as sym:
print ("not proper file"+sym+"\n")
continue
DataFields = gwyutils.get_data_fields_dir(Cont_Source)
for key in DataFields.keys():
ID = key.split('/')[1]
title = Cont_Source.get_string_by_name(key+"/title")
if (title == 'Height') :
Cont_Source.set_string_by_name(key+'/title', title+'.'+filename)
gwy.gwy_app_data_browser_copy_channel(Cont_Source, int(ID), Cont_Dest)
print (key, title)
gwy_app_data_browser_remove(Cont_Source)
del Cont_Source
print (gc.collect())
#Change Palette, Flatten, Correct line, Remove Scars, Change Scale
DataFields = gwyutils.get_data_fields_dir(Cont_Dest)
for key in DataFields.keys():
ID = key.split('/')[1]
title = Cont_Dest.get_string_by_name(key+"/title")
print (title+'\n')
# Subtract polynomial background
coeffs = DataFields[key].fit_polynom(3, 3)
DataFields[key].subtract_polynom(3, 3, coeffs)
DataFields[key].data_changed()
#Get X Y scale
si = {'x' : 'um' , 'y' : 'um'}
size_x = DataFields[key].get_xreal()*1000000
if (size_x < 1.0):
size_x = size_x * 1000
si['x'] = 'nm'
size_y = DataFields[key].get_yreal()*1000000
if (size_y < 1.0):
size_y = size_y * 1000
si['y'] = 'nm'
scale = str(size_x)+si['x']+'by'+str(size_y)+si['y']
title = title + '_'+ scale
# Line and scar correction (run module functions)
gwy.gwy_app_data_browser_select_data_field(Cont_Dest, int(ID))
gwy.gwy_process_func_run("line_correct_median", Cont_Dest, gwy.RUN_IMMEDIATE)
gwy.gwy_process_func_run("scars_remove", Cont_Dest, gwy.RUN_IMMEDIATE)
gwy.gwy_process_func_run("fix_zero", Cont_Dest, gwy.RUN_IMMEDIATE)
#Get Color Type
colorr = Cont_Dest.get_int32_by_name('/'+ID+'/base/range-type')
#Change_Color Palette
Cont_Dest.set_string_by_name('/'+ID+'/base/palette', 'Gold')
#Get Height Distribution and get Percentile color set range
#Get CDH
histogram = gwy.DataLine(1, 1, False)
DataFields[key].cdh(histogram, 512)
data = histogram.get_data()
#Get Percentile Range
Data_Range = DataFields[key].get_min_max()
Histogram_pct = [(float(index))/512 for index, value in enumerate(data) if (data[index] >= ratio[1] and data[index-1] <= ratio[1]) or (data[index] <= ratio[0] and data[index+1] >= ratio[0])]
Range = Data_Range[1]-Data_Range[0]
Color_Range = {'min': Data_Range[0]+Range*Histogram_pct[0], 'max':Data_Range[0]+Range*Histogram_pct[1]}
Cont_Dest.set_int32_by_name('/0/base/range-type' , 1)
Cont_Dest.set_double_by_name('/0/base/min', Color_Range['min'])
Cont_Dest.set_double_by_name('/0/base/max', Color_Range['max'])
#Change Color Range into (Full:0, Manual:1, Auto:2, Adaptive:3)
Cont_Dest.set_int32_by_name('/'+ID+'/base/range-type', 2)
print (title)
gwy.gwy_file_save(Cont_Dest, Tobe_Saved+'/'+str(title)+'%d.png' % int(ID), gwy.RUN_NONINTERACTIVE)
Cont_Dest.set_boolean_by_name('/'+ID+'/data/visible', 0)
gwy.gwy_file_save(Cont_Dest,Tobe_Saved+'/'+filename_save+'.gwy', gwy.RUN_NONINTERACTIVE)
gwy_app_data_browser_remove(Cont_Dest)
configureGwySettings(s)
#Process both trace and retrace
for i in height_ids:
###
### CORRECTING THE DATA WITH GWYDDION FUNCTIONS
### HAVE INCREDIBLY UNPLEASANT LOOKING EDITING OF SETTINGS TO DO FOR EACH FUNCTION
### PROBABLY A NEATER WAY TO DO THIS BUT CBA TO LOOK FOR IT NOW
### COPIED THE SETTINGS FROM THE ~/.gwyddion/settings file
###
gwy.gwy_app_data_browser_select_data_field(gwy_rawdata, i)
#level the data
gwy.gwy_process_func_run('level', gwy_rawdata, gwy.RUN_IMMEDIATE)
#flatten the data
gwy.gwy_process_func_run('flatten_base', gwy_rawdata,
gwy.RUN_IMMEDIATE)
#remove scars
gwy.gwy_process_func_run('scars_remove', gwy_rawdata,
gwy.RUN_IMMEDIATE)
#execute 'align_rows' function
#s["/module/linematch/method"] = 2
#gwy.gwy_process_func_run('align_rows', gwy_rawdata, gwy.RUN_IMMEDIATE)
#Gaussian filter to remove noise
current_data = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
