def make_individul_plots(ippath, oppath, fileinfo, columns, x_max_factor=1):
#print("1:" + oppath)
# extract file list
imglist = [x for x in os.listdir(ippath) if not x.startswith('.')]
for key, value in columns.items():
# get range
data_range = FindRange(ippath, variable=key)
# make histogram for individual dataset
for img in imglist:
#print("2:" + oppath)
df_segments_s = pd.read_csv(
os.path.join(ippath, img, 'segments_s.csv'))
# histogram for average length
plt.figure(figsize=(5, 5))
ax = IndividualHisto(df_segments_s,
column=key,
bin_range=data_range,
x_max_factor=x_max_factor)
ax.set_xlabel(value['x_label'])
ax.set_ylabel('Frequency (%)')
DirCheck(os.path.join(oppath, 'histo', value['file_label']))
opfilename = os.path.join(oppath, 'histo', value['file_label'],
img + '.png')
plt.savefig(opfilename)
plt.close()
return
def make_individul_plots_all(ippath,
oppath,
fileinfo,
filestat,
columns,
x_max_factor=1):
#print("1:" + oppath)
# extract file list
labels, uniques = pd.factorize(filestat['Group'])
for i in uniques:
# level: Group
tmp_df = filestat[filestat['Group'] == i]
data_merge = []
filenames = tmp_df['Names']
for key, value in columns.items():
# level: thickness, length
# get range
data_range = FindRange(ippath, variable=key)
plt.figure(figsize=(5, 5))
for idx, filename in enumerate(filenames):
df_segments_s = pd.read_csv(
os.path.join(ippath, filename, 'segments_s.csv'))
# IndividualHisto(df_segments_s, column = key, bin_range = data_range, x_max_factor = x_max_factor)
binsize = 100
bins = np.linspace(data_range[0], data_range[1] * x_max_factor,
binsize)
data = df_segments_s[key]
ax = plt.subplot(len(filenames), 1, idx + 1)
ax.hist(data,
bins=bins,
weights=(np.zeros_like(data) + 1. / data.size) * 100)
ax.set_xlabel(value['x_label'])
ax.set_ylabel('Frequency (%)')
DirCheck(os.path.join(oppath, 'histo_summary'))
opfilename = os.path.join(
oppath, 'histo_summary',
'comp_' + key + '_' + str(i) + "_" + str(i) + '.png')
plt.savefig(opfilename)
plt.close()
return
def convert_xml_csv(ippath, oppath):
filelist, fileabslist = ListFiles(ippath, extension='.xml')
for idx, f in enumerate(filelist):
filename = f.replace('.xml', '')
ip = os.path.join(ippath, f)
op = os.path.join(oppath, filename)
print(ip)
print(op)
# create path
if filename not in os.listdir(oppath):
DirCheck(op)
# convert *.xml to *.csv
csv_all = MSXmlReader(ip)
# save each spreadsheet into individual *.csv file
for key, value in csv_all.items():
oppath_tmp = os.path.join(op, key + '.csv')
value.to_csv(oppath_tmp, index=False)
nchannel = int(sys.argv[5]) crop_region = int(sys.argv[6]) op_dir = str(sys.argv[7]) ip_path = os.path.join(path, analysis_dir, analysis_subdir, csvdata_dir) # create output path dir_for_check = [] # op_dir = 'csvdata_crop' op_path = os.path.join(path, analysis_dir, analysis_subdir, op_dir) dir_for_check.append(op_path) for i in range(nchannel): dir_tmp = os.path.join(op_path, str(i+1)) dir_for_check.append(dir_tmp) print(dir_for_check) DirCheck(dir_for_check) # %% # load crop data dir_par = 'par' path_cropdata = os.path.join(path, analysis_dir, dir_par, 'cropsize.csv') df_cropdata = pd.read_csv(path_cropdata, header = 0) # display(df_cropdata) # %% # load image stat path_imgstat = os.path.join(path, analysis_dir, 'preprocessing', 'imginfo', 'imgstat.csv') df_imgstat = pd.read_csv(path_imgstat, header = 0) # display(df_imgstat)
oppath_tmp = os.path.join(op, key + '.csv')
value.to_csv(oppath_tmp, index=False)
#%% [markdown]
# ### Execution
# To run the code, please change `path` to the directory hosts the raw data.
#%%
path = 'data'
ipdir = 'raw'
opdir = 'csv'
ippath = os.path.join(path, ipdir)
oppath = os.path.join(path, opdir)
# make dir
DirCheck(oppath)
# convert files in batch
convert_xml_csv(ippath, oppath)
#%% [markdown]
# ## Part 2:
# Calulating the length and average thickness of each segment.
# * Inputs: nodes.csv, points.csv, segments.csv
# * Outputs: segments_s.csv
#
# `SegStats` extracts euclidean coordinates and thickness of each point, then calculate the total length and average thickness.
#%% [markdown]
# ### Functions
#%%
nchannel = int(sys.argv[7])
crop_region = int(sys.argv[8])
pad_pixel = int(sys.argv[9])
pixelsize = float(sys.argv[10])
nnd_plot_dir = str(sys.argv[11])
# input path
nnd_data_path = os.path.join(path, analysis_dir, st_dir, nnd_dir, nnd_data_dist_dir)
print(nnd_data_path)
# output folder
nnd_plot_path = os.path.join(path, analysis_dir, st_dir, nnd_dir, nnd_plot_dir)
dir_check = []
dir_check.append(nnd_plot_path)
DirCheck(dir_check)
# %%
# create grouped filename and file path
group = {
'wildtype': 'w',
'knockout': 'k'
}
ippath = {
'nnddata':{
'dir': nnd_data_path,
'ext': '.csv',
}
}
oppath = {
# functions ============================================
# ========================================================
#%%
path = '/Volumes/LaCie_DataStorage/Asensio_Lab'
raw_dir = 'alignment_opencv_01'
sub_dir = 'KO_2_reordered_rename'
ippath = os.path.join(path, raw_dir, sub_dir)
print(ippath)
op_dir = 'alignment_opencv_01_std'
oppath = os.path.join(path, op_dir, sub_dir)
DirCheck(oppath)
#%%
filename, filepath = ListFiles(ippath, '.tif')
print(filepath)
#%%
inten_min = 0.0
inten_max = 0.0
for i in range(len(filename)):
ipfilepath = os.path.join(ippath, filename[i] + '.tif')
opfilepath = os.path.join(oppath, filename[i] + '.tif')
ROI = [4352, 3088, 4096, 4096]
im = Image.open(ipfilepath)
im_std = Standardize(im, ROI)
from core.img import openimage, AlignImages, AlignImagesStack, AlignImages_Affine, AlignImagesStack_Affine # functions ============================================ # ======================================================== #%% path = '/Volumes/LaCie_DataStorage/Asensio_Lab' raw_dir = 'output' sub_dir = 'KO_2_reordered_rename' ippath = os.path.join(path, raw_dir, sub_dir) print(ippath) dircheck = [] op_dir = 'alignment_opencv_01' opmatch_dir = 'alignment_match_opencv_01' oppath = os.path.join(path, op_dir, sub_dir) oppath_match = os.path.join(path, opmatch_dir, sub_dir) dircheck.append(oppath) dircheck.append(oppath_match) DirCheck(dircheck) #%% factor = 1/16 center = 50 AlignImagesStack_Affine(ippath, oppath, oppath_match, factor, centerimg = center, ext = '.tif')
path = '/Volumes/LaCie_DataStorage/Asensio_Lab'
project_dir = 'TEM_stitching'
ip_dir = 'data'
op_dir = 'data_renamed'
ippath = os.path.join(path, project_dir, ip_dir)
print(ippath)
oppath = os.path.join(path, project_dir, op_dir)
print(oppath)
#%%
foldernames = ListFolders(ippath)
dirchecklist = []
for i in foldernames:
dirchecklist.append(os.path.join(oppath, i))
DirCheck(dirchecklist)
#%%
par_path = os.path.join(path, project_dir, 'par', 'tile_dimension.csv')
par_data= pd.read_csv(par_path)
display(par_data)
#%%
filelist, fileabslist = ListFiles(ippath, extension = '.mrc')
# print(filelist)
org_filename = par_data['cellname']
class imgfile:
def __init__(self, filename, filepath, foldername, folderpath):
self.filename = filename
self.filepath = filepath
from shutil import copyfile
from core.fileop import DirCheck, ListFiles, ListFolders, SplitAll, SortByFolder
from core.imgstitching import IdxConverter
#%%
path = '/Volumes/LaCie_DataStorage/Asensio_Lab'
wd_dir = 'TEM_stitching'
ip_dir = 'output'
ippath = os.path.join(path, wd_dir, ip_dir)
print(ippath)
dircheck = []
op_dir = 'output_reordered'
oppath = os.path.join(path, wd_dir, op_dir)
dircheck.append(oppath)
DirCheck(dircheck)
#%%
# load par
par_dir = 'par'
par_path = os.path.join(path, wd_dir, par_dir, 'tile_dimension.csv')
DimData = pd.read_csv(par_path)
display(DimData)
DimData['filename'] = DimData['filename'].astype(str)
DimData.dtypes
#%%
for i in ListFolders(ippath):
i = 'rescue'
ippath_tmp = os.path.join(ippath, i)
oppath_tmp = os.path.join(oppath, i)
DirCheck(oppath_tmp)