def run():
c = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
filename = c['/filename']
dir_path,basename = os.path.split(filename)
basename = basename[0:-4]
#print dir_path,base_name
d = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
data_field_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
meta_field = '/' + str(data_field_id) + '/meta'
title = '/' + str(data_field_id) + '/data/title'
channel = c[title]
#print channel
gwy.gwy_app_data_browser_select_data_field(c,data_field_id)
#meta = c[meta_field]
#print meta.keys_by_name()
#bias = meta['Bias']
#bias, bu = bias.split(' ')
#current = meta['Z controller Setpoint']
#current, cu = current.split(' ')
#current = float(current) * 1e12
#cu = 'pA'
#current_bias = 'pm'
#xd = d.get_xreal() * 1e9
#yd = d.get_yreal() * 1e9
#xyu = 'nm'
output_path = dir_path
output_name = basename + '.png'
output = os.path.join(output_path,output_name)
gwy.gwy_file_save(c, output, gwy.RUN_NONINTERACTIVE)
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 run():
c = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
# key = "/0/select/line"
selections = [
key for key in c.keys_by_name() if "/{}/select".format(_id) in key
]
# print(selections)
# create new combo box
combo = gtk.combo_box_new_text()
# add in valid selection options
for selection in selections:
combo.append_text(selection)
# set default value as first in selections list, otherwise -1: None
combo.set_active(0 if len(selections) else -1)
# set up generic dialogue window
dialogue = gtk.Dialog(
title="Choose Selection",
flags=gtk.DIALOG_MODAL,
buttons=(
gtk.STOCK_CANCEL,
gtk.RESPONSE_REJECT,
gtk.STOCK_OK,
gtk.RESPONSE_ACCEPT,
),
)
# add combobox widget to dialogue
dialogue.get_content_area().pack_start(combo)
# main loop and wait for user response
dialogue.show_all()
response = dialogue.run()
# heavy lifting done below here...
# is user pressed OK
if response == gtk.RESPONSE_ACCEPT:
key = selections[combo.get_active()]
# for each datafield in container
for _id0 in gwy.gwy_app_data_browser_get_data_ids(c):
# copy line
line = c[key].duplicate()
# add to new datafield with new key
c.set_object(
gwy.gwy_key_from_name(key.replace(str(_id), str(_id0))), line)
# kill dialogue
dialogue.destroy()
def get_current_datafield():
"""
Short version of function L{gwy.gwy_app_data_browser_get_current}(gwy.APP_DATA_FIELD)
@return: current datafield
"""
return gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
def get_current_container():
"""
Short version of function L{gwy.gwy_app_data_browser_get_current}(gwy.APP_CONTAINER)
@return: current container
"""
return gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
def get_current_datafield():
"""
Short version of function L{gwy.gwy_app_data_browser_get_current}(gwy.APP_DATA_FIELD)
@return: current datafield
"""
return gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
def get_current_container():
"""
Short version of function L{gwy.gwy_app_data_browser_get_current}(gwy.APP_CONTAINER)
@return: current container
"""
return gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
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
def run():
# get current image data
_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
container = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
# analyse_particles_otsu_median(container, datafield_id=datafield_id)
# get datafield and compute threshold
datafield = container.get_object_by_name(get_data_key(_id))
threshold = threshold_otsu_median(datafield)
# create grain mask
grains = create_mask(datafield, threshold)
# add mask field to container
container.set_object_by_name(get_mask_key(_id), grains)
# set mask colour and opacity
set_mask_colour(container, _id)
def run():
# get current data and correct container for file
_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
container = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
# create undo point
gwy.gwy_undo_qcheckpoint(container, container.keys())
# get datafield and compute threshold
datafield = container.get_object(gwy.gwy_app_get_data_key_for_id(_id))
threshold = threshold_otsu_median(datafield)
# create grain mask
grains = create_mask(datafield, threshold)
# add mask field to container
container.set_object(gwy.gwy_app_get_mask_key_for_id(_id), grains)
# set mask colour and opacity
set_mask_colour(container, _id)
def run():
import sys
import os
home = os.getenv('HOME')
sys.path.append(home + '/.gwyddion/pygwy/')
from SpeckView.BE.Spektrum import Spektrum
Spektrum(home + '/.gwyddion/pygwy/SpeckView/BE/',
gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER))
def run():
import sys
import os
home = os.getenv('HOME')
sys.path.append(home + '/.gwyddion/pygwy/')
from SpeckView.BE.Spektrum import Spektrum
Spektrum(
home + '/.gwyddion/pygwy/SpeckView/BE/',
gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
)
def imagedetails(data):
# select the channel file of chosen_ids
gwy.gwy_app_data_browser_select_data_field(data, k)
datafield = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
xres = datafield.get_xres()
yres = datafield.get_yres()
xreal = datafield.get_xreal()
yreal = datafield.get_yreal()
dx = xreal / xres
dy = yreal / yres
return xres, yres, xreal, yreal, dx, dy
def grainfinding(data, minarea, k, thresholdingcriteria, gaussian, dx):
# 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)
# Gaussiansize = 0.25e-9 / dx
# datafield.filter_gaussian(Gaussiansize)
mask = gwy.DataField.new_alike(datafield, False)
Gaussiansize = gaussian / dx
datafield.filter_gaussian(Gaussiansize)
# Mask data that are above thresh*sigma from average height.
# Sigma denotes root-mean square deviation of heights.
# This criterion corresponds to the usual Gaussian distribution outliers detection if thresh is 3.
datafield.mask_outliers2(mask, 5, thresholdingcriteria)
# 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])
# Set the image display to fixed range and the colour scale for the images
maximum_disp_value = data.set_int32_by_name(
"/" + str(k) + "/base/range-type", int(1))
minimum_disp_value = data.set_double_by_name("/" + str(k) + "/base/min",
float(minheightscale))
maximum_disp_value = data.set_double_by_name("/" + str(k) + "/base/max",
float(maxheightscale))
### Editing grain mask
# Remove grains touching the edge of the mask
mask.grains_remove_touching_border()
# Calculate pixel width in nm
# dx = datafield.get_dx()
# Calculate minimum feature size in pixels (integer) from a real size specified in the main
minsize = int(minarea / dx)
# Remove grains smaller than the minimum size in integer pixels
mask.grains_remove_by_size(minsize)
# Numbering grains for grain analysis
grains = mask.number_grains()
# Update data to show mask, comment out to remove mask
# data['/%d/mask' % k] = mask
return data, mask, datafield, grains
def run(container, mode):
container = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
filename = container['/filename']
path, _ = os.path.splitext(filename)
output_path = path + OUTPUT_SUFFIX
if not os.path.exists(output_path):
os.mkdir(output_path)
for i, id_ in enumerate(gwy.gwy_app_data_browser_get_data_ids(container)):
title = container['/{}/data/title'.format(id_)]
gwy.gwy_app_data_browser_select_data_field(container, id_)
output_filename = '{:03d}_{}.{}'.format(id_, title, OUTPUT_FILESUFFIX)
output_filename = os.path.join(output_path, output_filename)
mode = gwy.RUN_INTERACTIVE if i == 0 else gwy.RUN_NONINTERACTIVE
print 'Exporting {} ...'.format(output_filename)
gwy.gwy_file_save(container, output_filename, mode)
def run(container, mode):
data_field_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
data_field = container[gwy.gwy_app_get_data_key_for_id(data_field_id)]
new_container = gwy.Container()
gwy.gwy_app_data_browser_add(new_container)
# Need to populate container with dummy data, otherwise
# gwy_app_data_browser_copy_channel crashes.
dummy_id = gwy.gwy_app_data_browser_add_data_field(
data_field, new_container, False
)
new_id = gwy.gwy_app_data_browser_copy_channel(
container, data_field_id, new_container
)
gwy.gwy_app_data_browser_select_data_field(new_container, new_id)
# Get rid of dummy reference.
new_container.remove_by_prefix('/{}/'.format(dummy_id))
def run():
### Create undo point
#key=gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_KEY)
#gwy.gwy_app_undo_checkpoint(gwy.data, [key])
### get Data Field
originalDField = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
dummyDF = originalDField.new_alike()
sixtyDegreeField = originalDField.new_rotated(dummyDF, 60. * (PI / 180.),
gwy.INTERPOLATION_LINEAR,
gwy.ROTATE_RESIZE_SAME_SIZE)
onetwentyDegreeField = originalDField.new_rotated(
dummyDF, 120. * (PI / 180.), gwy.INTERPOLATION_LINEAR,
gwy.ROTATE_RESIZE_SAME_SIZE)
originalDField.sum_fields(originalDField, sixtyDegreeField)
originalDField.sum_fields(originalDField, onetwentyDegreeField)
originalDField.multiply(1. / 3.)
originalDField.data_changed()
def run():
# get current image data
datafield_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
container = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
laplacian_of_gaussian(container, datafield_id=datafield_id)
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 run():
# get open container
container = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
# run algorithm
low_signal_threshold(container)
def run():
# get current image data
datafield_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
container = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
analyse_particles_otsu(container, datafield_id=datafield_id)
import gwy
import gwyutils
import re
import os
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw
import numpy as np
from skimage import io, exposure, img_as_uint, img_as_float
# get the current container and datafield
c = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
d = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD)
data_field_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
gwy.gwy_app_data_browser_select_data_field(c,data_field_id)
filename = c['/filename']
basename = filename[0:-4]
print filename.split('/')[-1][:-4]
meta_field = '/' + str(data_field_id) + '/meta'
title = '/' + str(data_field_id) + '/data/title'
channel = c[title]
print channel
meta = c[meta_field]
print meta.keys_by_name()
bias = meta['Bias']
bias, bu = bias.split(' ')
current = meta['Z controller Setpoint']
current, cu = current.split(' ')
current = float(current) * 1e12
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)
current_data.filter_gaussian(0.75)
current_data.data_changed() #not sure this line is needed
makeBinaryMask(gwy_rawdata, i)
#polynomial correction with masked height
s["/module/polylevel/masking"] = 0
gwy.gwy_process_func_run('polylevel', gwy_rawdata, gwy.RUN_IMMEDIATE)
#remove scars
#gwy.gwy_process_func_run('scars_remove', gwy_rawdata, gwy.RUN_IMMEDIATE)
#Re-do align rows with masked heights
s["/module/linematch/masking"] = 0
s["/module/linematch/method"] = 0
def run():
# get current data and correct container for file
datafield_id = gwy.gwy_app_data_browser_get_current(gwy.APP_DATA_FIELD_ID)
container = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
save_gwy_grain_mask_to_JSON(container, datafield_id=datafield_id)
def boundbox(cropwidth, datafield, grains, dx, dy, xreal, yreal, xres, yres):
# Function to return the coordinates of the bounding box for all grains.
# contains 4 coordinates per image
bbox = datafield.get_grain_bounding_boxes(grains)
# Remove all data up to index 4 (i.e. the 0th, 1st, 2nd, 3rd).
# These are the bboxes for grain zero which is the background and should be ignored
del bbox[:4]
# Find the center of each grain in x
center_x = datafield.grains_get_values(grains, gwy.GRAIN_VALUE_CENTER_X)
# Delete the background grain center
del center_x[0]
# Find the center of each grain in y
center_y = datafield.grains_get_values(grains, gwy.GRAIN_VALUE_CENTER_Y)
# Delete the background grain center
del center_y[0]
# Find the centre of the grain in pixels
# get active container
data = gwy.gwy_app_data_browser_get_current(gwy.APP_CONTAINER)
# get current number of files
orig_ids = gwy.gwy_app_data_browser_get_data_ids(data)
# Define the width of the image to crop to
cropwidth = int((cropwidth / xreal) * xres)
# make 2d array for grains
multidim_grain_array = np.reshape(np.array(grains), (xres, yres))
for i in range(len(center_x)):
px_center_x = int((center_x[i] / xreal) * xres)
px_center_y = int((center_y[i] / yreal) * yres)
# ULcol = px_center_x - cropwidth
xmin = px_center_x - cropwidth
# ULrow = px_center_y - cropwidth
ymin = px_center_y - cropwidth
# BRcol = px_center_x + cropwidth
xmax = px_center_x + cropwidth
# BRrow = px_center_y + cropwidth
ymax = px_center_y + cropwidth
# making sure cropping boxes dont run outside the image dimensions
if xmin < 0:
xmin = 0
xmax = 2 * cropwidth
if ymin < 0:
ymin = 0
ymax = 2 * cropwidth
if xmax > xres:
xmax = xres
xmin = xres - 2 * cropwidth
if ymax > yres:
ymax = yres
ymin = yres - 2 * cropwidth
# print ULcol, ULrow, BRcol, BRrow
# crop the data
crop_datafield_i = datafield.duplicate()
crop_datafield_i.resize(xmin, ymin, xmax, ymax)
# add cropped datafield to active container
gwy.gwy_app_data_browser_add_data_field(crop_datafield_i, data,
i + (len(orig_ids)))
# cropping the grain array:
grain_num = i + 1
cropped_np_grain = multidim_grain_array[ymin:ymax, xmin:xmax]
cropped_grain = [
1 if i == grain_num else 0 for i in cropped_np_grain.flatten()
]
# make a list containing each of the cropped grains
try:
cropped_grains.append(cropped_grain)
except NameError:
cropped_grains = [cropped_grain]
# Generate list of datafields including cropped fields
crop_ids = gwy.gwy_app_data_browser_get_data_ids(data)
return bbox, orig_ids, crop_ids, data, cropped_grains, cropwidth
