From 7ffe2fb97b334d3e5a1e70751b0d5177e54ae729 Mon Sep 17 00:00:00 2001 From: aaristov Date: Wed, 9 Jan 2019 17:39:59 +0100 Subject: [PATCH 01/10] index.html --- .gitignore | 9 + LICENSE | 21 + README.md | 118 +++++ bfdc/__init__.py | 2 + bfdc/__main__.py | 11 + bfdc/batch.py | 170 +++++++ bfdc/drift.py | 473 +++++++++++++++++++ bfdc/feature.py | 183 ++++++++ bfdc/gaussfit.py | 295 ++++++++++++ bfdc/iotools.py | 520 +++++++++++++++++++++ bfdc/picassoio.py | 330 +++++++++++++ bfdc/registration.py | 86 ++++ bfdc/xcorr.py | 364 +++++++++++++++ img/BFCC_table.csv_2zero.png | Bin 0 -> 37415 bytes img/bead_track_color.png | Bin 0 -> 2556 bytes img/bead_track_color_BFDC.png | Bin 0 -> 544 bytes img/bead_track_color_BFDC.tif | Bin 0 -> 37615 bytes img/bf_Substack (1-16384-1000)_crop32l.gif | Bin 0 -> 95277 bytes img/bf_Substack (1-16384-1000)_l.gif | Bin 0 -> 1493329 bytes img/dict_crop32.gif | Bin 0 -> 42075 bytes img/dict_sr_crop.gif | Bin 0 -> 2012489 bytes img/sr_Substack (1-16384-1000)_l.gif | Bin 0 -> 1106974 bytes index.html | 1 + requirements.txt | 6 + setup.py | 21 + tests/__init__.py | 0 tests/test_check_limits.py | 40 ++ tests/test_gauss_fit.py | 54 +++ tests/test_get_xy_boundaries.py | 20 + tests/test_iotools.py | 72 +++ tests/test_main.py | 41 ++ tests/test_move_drift_to_zero.py | 26 ++ tests/test_poly_fit.py | 54 +++ tests/test_registration.py | 42 ++ tests/test_xcorr.py | 18 + 35 files changed, 2977 insertions(+) create mode 100755 .gitignore create mode 100755 LICENSE create mode 100755 README.md create mode 100755 bfdc/__init__.py create mode 100755 bfdc/__main__.py create mode 100755 bfdc/batch.py create mode 100755 bfdc/drift.py create mode 100755 bfdc/feature.py create mode 100755 bfdc/gaussfit.py create mode 100755 bfdc/iotools.py create mode 100755 bfdc/picassoio.py create mode 100755 bfdc/registration.py create mode 100755 bfdc/xcorr.py create mode 100755 img/BFCC_table.csv_2zero.png create mode 100755 img/bead_track_color.png create mode 100755 img/bead_track_color_BFDC.png create mode 100755 img/bead_track_color_BFDC.tif create mode 100755 img/bf_Substack (1-16384-1000)_crop32l.gif create mode 100755 img/bf_Substack (1-16384-1000)_l.gif create mode 100755 img/dict_crop32.gif create mode 100755 img/dict_sr_crop.gif create mode 100755 img/sr_Substack (1-16384-1000)_l.gif create mode 100755 index.html create mode 100755 requirements.txt create mode 100755 setup.py create mode 100755 tests/__init__.py create mode 100755 tests/test_check_limits.py create mode 100755 tests/test_gauss_fit.py create mode 100755 tests/test_get_xy_boundaries.py create mode 100755 tests/test_iotools.py create mode 100755 tests/test_main.py create mode 100755 tests/test_move_drift_to_zero.py create mode 100755 tests/test_poly_fit.py create mode 100755 tests/test_registration.py create mode 100755 tests/test_xcorr.py diff --git a/.gitignore b/.gitignore new file mode 100755 index 0000000..a234bee --- /dev/null +++ b/.gitignore @@ -0,0 +1,9 @@ +data +.idea +.ipynb* +*.ipynb +build/* +*.pyc +*.egg +bfdc.egg-info/* +bfdc/__pycache__ diff --git a/LICENSE b/LICENSE new file mode 100755 index 0000000..ab60297 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100755 index 0000000..a933988 --- /dev/null +++ b/README.md @@ -0,0 +1,118 @@ +# Bright Field Drift Correction + +Use bright field signal to trace the drift in 3D with nanometer precision. + +## Requirements +* python 3.6+ +* numpy +* scipy +* scikit-image + +## Installation + +With conda create a new environment with python 3.6: + +`conda create -n pydrift python=3.6` + +Then activate it and run installation: + +`source activate pydrift` + +`pip install git+https://github.com/imodpasteur/Brightfield_Drift_Tracking_3D.git@dev` + +## Usage + +In order to trace the drift in 3D a calibration stack needed (dict.tif). + +Acquire bright field images with the same exposure (movie.tif) in order to trace the drift with nanometer precision. + +You'll need to select a contrast region say 32x32 picels from the dict.tif and save as crop.roi (ImageJ ROI format) + +### Tracing drift: +`python -m bfdc trace dict.tif crop.roi movie.tif` + +### Applying drift to ZOLA table: +`python -m bfdc apply ZOLA_localization_table.csv BFCC_table.csv --smooth 10` + +### Tracing and applying the drift in batch: +`python -m bfdc batch data_folder ` + +Expected file structure: + + +* data_folder + * **FOV**1 + * **dict**_LED_100nm + * dict_LED_100nm_Pos0.**ome.tif** + * 32x32.**roi** + * **sr**_ast_642_LEDskip9 + * **sr**_ast_642_LEDskip9.**Pos0.ome.tif** + * **sr**_ast_642_LEDskip9.Pos0_1.ome.tif + * ZOLA_localization_table.csv + * FOV2 + * ... + +The possible arguments include: +* Recursively parsing folders with fields of views (`--fov_prefix=FOV`) +* Looking for the folder with dictionary stack (`--dict_folder_prefix=dict`) +* Finding ROI file (`--ROI_suffix=roi`) +* Opening dictionary stack next to ROI (`--dict_suffix=ome.tif`) +* Opening STORM movie with bright field frames (`--sr_folder prefix=sr`, `--sr_movie_suffix=Pos0.ome.tif`) +* Automatically selects brightfield frames with background more than (`--filter_bg=100`) + + +### Help and possible arguments: + +`python -m bfdc -h` + +`python -m bfdc trace -h` + +`python -m bfdc apply -h` + +`python -m bfdc batch -h` + +# Example of use + + Super resolution imaging of EDU Alexa 647 in the nucleus: beads photobleaching. + +![input](img/sr_Substack%20(1-16384-1000)_l.gif) + + Luckily, we have recorded the bright field movie along with the fluorescence by alternating illumination between illuimination and LED! + +![input](img/bf_Substack%20(1-16384-1000)_l.gif) + + How can we use it to trace the drift in 3D? + + Using the stack, recorded prior to imaging with the same exposure and same illumination intensity, we can reveal the drift from bright field only in 3D! + +![input](img/dict_sr_crop.gif) + +32x32 px crop form the dictionary and 64x64 crom form the movie tracks the drift with the precision higher than using beads and better sampling than redundant cross-corellation. + +Stack crop ![input](img/dict_crop32.gif) +Movie crop ![input](img/bf_Substack%20(1-16384-1000)_crop32l.gif) + + +Note, how Nikon's Perfect Focus System struggles to maintain the focus! + +![input](img/BFCC_table.csv_2zero.png) + +Fluorescent bead track before and after BFDC. + +![input](img/bead_track_color.png) -> ![input](img/bead_track_color_BFDC.png) + +# Change log + +V0.2.0 Processing tif file sets + +V0.1.4 Batch processing + +V0.1.3 Avoid plots ovelaps from diffrent datasets + +v0.1.2 Removing localizations from the ZOLA table if they come from bright field. + +v0.1.1 Automatically detecting BF frames based on intensity. No more need of skipping parameters to know. + + + + diff --git a/bfdc/__init__.py b/bfdc/__init__.py new file mode 100755 index 0000000..9d016a5 --- /dev/null +++ b/bfdc/__init__.py @@ -0,0 +1,2 @@ +#import bfdc.iotools +#import bfdc.Drift diff --git a/bfdc/__main__.py b/bfdc/__main__.py new file mode 100755 index 0000000..b4acec9 --- /dev/null +++ b/bfdc/__main__.py @@ -0,0 +1,11 @@ +import matplotlib as mpl +mpl.use('TkAgg') +#mpl.use('PS') +from bfdc.drift import main +import logging +logging.basicConfig(level=logging.WARNING) +logger = logging.getLogger(__name__) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/bfdc/batch.py b/bfdc/batch.py new file mode 100755 index 0000000..d792362 --- /dev/null +++ b/bfdc/batch.py @@ -0,0 +1,170 @@ +from bfdc import drift +import tkinter as tk +from tkinter import filedialog +import os +import logging +from glob import glob + +logger = logging.getLogger(__name__) + +sep = os.path.sep +base = lambda path: os.path.basename(os.path.normpath(path)) +relative = lambda path, parent: path[len(parent):] +parent = os.path.dirname + + +class BatchDrift: + + def __init__(self, batch_path, + fov_prefix='FOV', + dict_folder_prefix='dict_', + roi_suffix='roi', + dict_suffix='ome.tif', + sr_folder_prefix='sr', + sr_movie_suffix='Pos0.ome.tif', + zola_dc_filename="ZOLA*BFDC*.csv", + dc_table_filename="BFCC*.csv", + zola_raw_filename="ZOLA_localization_table.csv", + zola_lock_filename="ZOLA_.lock", + smooth=50, + filter_bg=100, + callback=None, + main=None, + **kwargs): + self.batch_path = batch_path + self.fov_prefix = fov_prefix + self.dict_folder_prefix = dict_folder_prefix + self.roi_suffix = roi_suffix + self.dict_suffix = dict_suffix + self.sr_folder_prefix = sr_folder_prefix + self.sr_movie_suffix = sr_movie_suffix + self.zola_dc_filename = zola_dc_filename + self.dc_table_filename = dc_table_filename + self.zola_raw_filename = zola_raw_filename + self.zola_lock_filename = zola_lock_filename + self.smooth = smooth + self.filter_bg = filter_bg + self.callback = callback + self.main = main + + self.do_batch() + + def parse_fovs(self,path): + fov_list = glob(pathname=path + sep + self.fov_prefix + "*" + sep) + self.log(f'Found {len(fov_list)} folders starting with FOV') + for i,f in enumerate(sorted(fov_list)): + self.log(f'{i} -- {relative(f,path)}') + #self.flush() + return fov_list + + def find_file(self, path, name, pattern, expected_number=None): + try: + flist = glob(path + sep + pattern) + if expected_number: + assert len(flist) == expected_number, f'Found {len(flist)} {name}s, while expected {expected_number}' + if flist: + for f in flist: + self.log(f'{base(path)}: Found {name}: {relative(f,path)}') + + else: + self.log(f'No {name} was found using pattern \"{pattern}\"') + + return flist + except IndexError: + self.log(f'{base(path)}: No {name}') + return None + + def find_dict(self, roi_path): + return self.find_file(path=parent(roi_path), + name='dict', + pattern=self.dict_folder_prefix + "*" + self.dict_suffix, + expected_number=1) + + def find_roi(self, fov_path): + return self.find_file(path=fov_path, + name='ROI', + pattern=self.dict_folder_prefix + "*" + sep + "*" + self.roi_suffix, + expected_number=None) + + def find_movies(self, fov_path): + return self.find_file(path=fov_path, + name='movie', + pattern=self.sr_folder_prefix + "*" + sep + "*" + self.sr_movie_suffix) + + def batch_trace_drift(self, bfdict, roi, movie): + bfout = glob(parent(movie) + sep + self.dc_table_filename) + if not bfout: + self.log("start BF tracking") + + args = ["trace", bfdict, roi, movie, '--lock', '1'] + self.main(argsv=args, callback=self.callback) + + else: + self.log('Found BFDC table --- skipping') + + def batch_apply_drift(self, movie:str): + zola_dc_table = glob(parent(movie) + sep + self.zola_dc_filename) + if not zola_dc_table: + drift_tables = glob(parent(movie) + sep + self.dc_table_filename) + for drift_table in drift_tables: + logger.info(f"Found {base(drift_table)}") + zola_table = glob(parent(drift_table) + sep + self.zola_raw_filename) + z_lock = glob(parent(drift_table) + sep + self.zola_lock_filename) + if zola_table and not z_lock: + self.log(f'Found {base(zola_table[0])}') + self.log("Apply drift") + args = f"apply {zola_table[0]} {drift_table} \ + --smooth={str(self.smooth)} --maxbg={str(self.filter_bg)}" + self.main(argsv=args.split(), callback=self.callback) + elif len(z_lock) == 1: + self.log(f'Found {base(z_lock[0])} --- skipping') + + else: + self.log('No ZOLA table found') + + else: + self.log('Folder already processed') + + + + def do_batch(self): + self.log('Start batch process') + self.log(self.batch_path) + fov_list = self.parse_fovs(self.batch_path) + msg = f'Found {len(fov_list)} folders starting with FOV' + self.log(msg) + + for fov in sorted(fov_list): + self.log(f'Processing {relative(fov,self.batch_path)}') + + roi = self.find_roi(fov) + if roi: + bfdict = self.find_dict(roi[0])[0] + movies = self.find_movies(fov) + if movies: + for i,movie in enumerate(movies): + self.log(f'Processing movie {i+1}/{len(movies)}: {relative(movie,self.batch_path)}') + + self.batch_trace_drift(bfdict, roi[0], movie) + self.batch_apply_drift(movie) + else: + self.log(f'No movies found with \"{self.sr_folder_prefix}\" prefix') + + else: + self.log('No ROI') + self.log(f'Finished processing {len(fov_list)}') + return 0 + + def log(self, msg=None, level='info'): + if self.callback: + self.callback({'Message': msg}) + print(msg) + logger.__getattribute__(level)(msg) + + + +if __name__ == '__main__': + root = tk.Tk() + root.withdraw() + path = filedialog.askdirectory() + BatchDrift(path) diff --git a/bfdc/drift.py b/bfdc/drift.py new file mode 100755 index 0000000..b444229 --- /dev/null +++ b/bfdc/drift.py @@ -0,0 +1,473 @@ +import traceback +import logging +import numpy as np +import os, sys +import bfdc.batch as batch +import bfdc.xcorr as xcorr +import bfdc.feature as ft +import bfdc.iotools as iot +import matplotlib as mpl +mpl.use('TkAgg') +import matplotlib.pyplot as plt + +logger = logging.getLogger(__name__) +logger.setLevel(logging.WARNING) +#logger.setLevel(logging.DEBUG) + + +class WrongCrop(Exception): + pass + + +class BadGaussFit(Exception): + pass + + +class DriftFitter: + """ + Automatic Drift Fitter + Crops the stack and tracks the drift + """ + + def __init__(self, cal_stack, roi, radius_xy=3, radius_z=10, smooth_dict=1): + self.roi = roi + self.boundaries = ft.roi_to_boundaries(roi) + self.dict = ft.crop_using_xy_boundaries(cal_stack, boundaries=self.boundaries) + if smooth_dict: self.dict = ft.gf(self.dict, smooth_dict) + logger.debug(f'smooth dict with {smooth_dict}') + self.z_crop = (0, None) + self.zCenter = len(self.dict) // 2 + self.radius_xy = radius_xy + self.radius_z = radius_z + self.z_last = None + + def do_trace(self, + movie, + frame_list, + extend_xy=5, + min_xcorr=0.5, + z_zoom=20, + min_signal=100, + smooth_movie=0, + debug=False, + callback=None): + logging.info(f"doTrace: got the movie with shape {movie.shape}, using {len(frame_list)} frames for tracing") + # for i,frame in enumerate(movie): + # crop frame with extended by 5px boundaries + # run xcorr drift loc + # extract central z frame + # use +5-5 frames out of z stack + # extract xy position + # change boundaries if needed + b = self.boundaries + xc = np.mean([b["xmax"], -b["xmin"]]) + yc = np.mean([b["ymax"], -b["ymin"]]) + x_, y_, z_ = 0, 0, 0 + #z_px = 0 + frame_num = 0 + self.x_correction = 0 + self.y_correction = 0 + self.fit_params = None + + data_save = [frame_num, x_, y_, z_, self.x_correction, self.y_correction] + + out = np.empty((0, len(data_save))) + total = len(frame_list) + problems = [] + i = 0 + + try: + for i, f in enumerate(frame_list): + frame = movie[f] + good = False + z = 0 + logger.debug(f'\nOpen frame {f+1}') + frame_mean = frame.mean() + if frame_mean < min_signal: + logger.debug('Skip frame due to low mean signal') + else: + crop_frame = ft.crop_using_xy_boundaries(mask=frame, boundaries=b, extend=extend_xy) + if smooth_movie: crop_frame = ft.gf(input=crop_frame, sigma=smooth_movie) + logger.debug(f'Cropping frame {crop_frame.shape}') + if min(crop_frame.shape) == 0: + raise (WrongCrop(f"doTrace: problem with boundaries: crop size hits 0 {crop_frame.shape}")) + crop_dict = self.crop_dict() + cc = xcorr.cc_stack(crop_frame, crop_dict) + if cc.max() < min_xcorr: + logger.warning(f'xcorr.max is lower than threshold {min_xcorr}') + #self.z_crop = (0, None) + logger.info('Expanding z limits') + crop_dict = self.crop_dict() + logger.debug('run cc') + cc = xcorr.cc_stack(crop_frame, crop_dict) + + if cc.max() < min_xcorr: + logger.warning(f'xcorr value is still lower than {min_xcorr}, skipping the frame') + problems.append(f + 1) + else: + logger.debug('Fit 3D') + result = xcorr.FitResult() + try: + if self.z_last: + z_init = np.floor(self.z_last) - self.z_crop[0] + else: + z_init = None + result = xcorr.fit_gauss_3d(cc, + radius_xy=self.radius_xy, + radius_z=self.radius_z, + z_zoom=z_zoom, + z_init=z_init, + fit_init=self.fit_params, + debug=debug) + + except ValueError as e: + logger.warning(f'ValueError in gaussian fit: {e}') + raise (ValueError(f'unable to unpack fit_gauss_3d output {result}')) + + except (xcorr.LowXCorr, BadGaussFit): + logger.warning(f'Low cross correlation value for the frame {i+1}. Skip frame') + # if len(out): + # out = np.append(out, np.array([i + 1, x_, y_, z_]).reshape((1, 4)), axis=0) + problems.append(f + 1) + + + """ + except Exception as e: + print(f'Unhandled exception {e}') + traceback.print_stack() + problems.append(f + 1) + """ + try: + x, y, z, good, self.fit_params, _ = result + + except ValueError as e: + logging.error(f'Unable to unpack fit result {e}') + traceback.print_exc() + return + + if not good: + logger.warning(f'Bad fit in frame {f+1}') + problems.append(f + 1) + self.boundaries = ft.roi_to_boundaries(self.roi) + #self.z_crop = (0, -1) + #self.x_correction = 0 + #self.y_correction = 0 + + else: + #z_ = z + self.z_crop[0] - self.zCenter + z_abs = z + self.z_crop[0] + z_ = z_abs - self.zCenter + logger.debug(f'Found z_abs {np.round(z_abs,2)}') + + + self.z_last = z_abs + + x_ = x + self.x_correction - xc - self.radius_xy + y_ = y + self.y_correction - yc - self.radius_xy + logger.debug(f"Saving x_px = {x_}, y_px = {y_}, z_px = {z_}, x_correction = {self.x_correction}, y_correction = {self.y_correction}") + + out = np.append(out, np.array([f + 1, x_, y_, z_, self.x_correction, self.y_correction]).reshape((1, len(data_save))), axis=0) + + self.update_z_crop(z_abs) + logger.debug(f'Updating self.z_crop with {self.z_crop}') + + if debug: + #plt.add_subplot(155) + try: + iot.plot_drift(move_drift_to_zero(out)) + plt.show() + except Exception as e: + logger.error(f'Error in debug plot: {e}') + traceback.print_exc() + return + #self.update_z_crop(z) + self.update_xy_boundaries(x, y, extend_xy) + + print(f'\rProcessed {i+1}/{total} frames (frame {f+1}), found {len(out)} BF frames', end=' ') + sys.stdout.flush() + + if callback and i%10 == 0: + callback({"Progress": {"processed" : i+1, + "total" : total, + "found" : len(out)} + }) + except WrongCrop as e: + print(e) + logging.error('Crop hits the border of the image, abort') + problems.append(f + 1) + + except Exception as e: + logging.error(e) + problems.append(f + 1) + + finally: + n = len(problems) + print(f'\nDone tracing with {n} problem frames') + if callback: callback({'Message': f'Done tracing with {n} problem frames'}) + if n: + print(problems) + + return np.array(out) + + def update_z_crop(self, z): + z1 = np.max([0, z - self.radius_z]).astype(int) + z2 = np.min([z + self.radius_z, len(self.dict) - 1]).astype(int) + logger.debug(f'update_z_crop:z boundaries {z1},{z2}') + self.z_crop = (z1, z2) + + def crop_dict(self): + return self.dict[self.z_crop[0]:self.z_crop[1]] + + def update_xy_boundaries(self, x, y, extend): + logger = logging.getLogger(__name__) + logger.debug('updating xy boundaries') + b = self.boundaries + logger.debug(f'Current boundaries: {b}') + e = extend + x0 = (b['xmax'] - b['xmin']) / 2 + e + y0 = (b['ymax'] - b['ymin']) / 2 + e + logger.debug(f'Computed center xy {(x0,y0)}') + xdif_ = x - x0 + ydif_ = y - y0 + logger.debug(f'Difference xy: {(xdif_,ydif_)}') + if abs(xdif_) > 0.8 or abs(ydif_) > 0.8: + xdif = int(np.round(xdif_, decimals=0)) + ydif = int(np.round(ydif_, decimals=0)) + logger.debug(f'moving xy boundary by {xdif},{ydif}') + b['xmin'] += xdif + b['xmax'] += xdif + self.x_correction += xdif + b['ymin'] += ydif + b['ymax'] += ydif + self.y_correction += ydif + else: + logger.debug('Ignore') + +def trace_drift_auto(args, cal_stack, movie, roi, debug=False, callback=None): + """ + Computes 3D drift on the movie vs cal_stack with auto crop + :param debug: plot data and fit if True + :param args: dict[args.xypixel, args.zstep, args.minsignal] + :param cal_stack: 3d z-stack + :param movie: time series 3D stack + :param roi: readout of IJ roi file + :return: table[frame,x,y,z] in nm + """ + print("Tracing drift using ROI") + if callback: callback({"Message": "Tracing drift using ROI"}) + skip = args.skip + start = args.start + max_frames = args.nframes + min_signal = args.minsignal + movie_path = args.movie + px = [args.xypixel, args.xypixel, args.zstep, args.xypixel, args.xypixel] + + print(f'Pixel size xyz: {px}') + drift_px = np.zeros((1, 4)) + + + try: + n_frames = movie.n_frames + except AttributeError: + n_frames = iot.virtual_stack_shape(movie_path) + + frame_list = iot.skip_stack(n_frames, start=start, skip=skip, maxframes=max_frames) + + try: + fitter = DriftFitter(cal_stack, roi, radius_xy=8, radius_z=12) + drift_px = fitter.do_trace(movie, + frame_list=frame_list, + min_signal=min_signal, + debug=debug, + callback=callback) + except KeyboardInterrupt as e: + print(e) + + drift_nm = drift_px.copy() + try: + drift_nm[:, 1:] = drift_px[:, 1:] * px + except ValueError: + dif = len(drift_px[0,1:]) - len(px) + [px.append(1) for _ in range(dif)] + logger.error(f'trace_drift_auto: Unable to apply pixel size: error in brodcasting, used 1 for the last {dif} columns') + drift_nm[:, 1:] = drift_px[:, 1:] * px + #drift_nm = iot.update_frame_number(drift_nm, start, skip) + return drift_nm + + +def move_drift_to_zero(drift_nm, ref_average=10): + """ + moves to zero the start of the table + :param drift_nm: fxyz table + :param ref_average: how many frames to average to get zero + :return: shifted table + """ + assert drift_nm.ndim == 2 + assert drift_nm.shape[0] > 0 + drift_ref = drift_nm[0:ref_average, :].mean(axis=0) + drift_ref[0] = 0 # frame number should be 0 for reference + drift_ = drift_nm - drift_ref.reshape((1, drift_nm.shape[1])) + return drift_ + + +def apply_drift(zola_table, bf_table, start=None, skip=None, smooth=0, maxbg=0, zinvert=0): + # TODO: save smoothed drift plot with interpolated frame numbers + # TODO: extrapolate to all frame numbers in the ZOLA table + """ + Applies drifto to ZOLA table including interpolation and smoothing + :param zola_table: numpy array + :param bf_table: numpy array + :param start: the first BF frame with respect to the fluorescence signal + :param skip: if BF was acquired with skipping, indicate it, so to intepolate properly + :param smooth: gaussian kernel sigma to the drift before interpolation + :param maxbg: when reconstruction single molecules, some frames will contain BF data with high bg. + Localiations with bg higher than max_bg will be rejected from the localization table. + :return: drift corrected zola table, interpolated and smoothed drift table + """ + bf_table = iot.interpolate_drift_table(bf_table, start=start, skip=skip, smooth=smooth) + + if zinvert: + print('z invert') + bf_table[:, 3] = -1 * bf_table[:, 3] # flip z + + zola_frame_num = int(np.max(zola_table[:, 1])) + + bf_frame_num = int(np.max(bf_table[:, 0])) + + print(f'Frame number for zola/bf_DC : {zola_frame_num}/{bf_frame_num}') + + if bf_frame_num < zola_frame_num: + logger.info(f'Truncating ZOLA table to {bf_frame_num} frames') + zola_table = zola_table[zola_table[:, 1] < bf_frame_num] + # fnum = int(np.max(zola_table[:, 1])) + # print(f'New frame number: {fnum}') + + frame_nums = np.array(zola_table[:, 1], dtype='int') + bf_drift_framed = bf_table[frame_nums - 1] + + zola_table_dc = zola_table.copy() + zola_table_dc[:, [2, 3, 4]] = zola_table_dc[:, [2, 3, 4]] - bf_drift_framed[:, [1, 2, 3]] + zola_table_dc[:, [11, 12, 13]] = bf_drift_framed[:, [1, 2, 3]] + zola_dc_wo_bf = zola_table_dc + if maxbg > 0: + print(f'Filter background < {maxbg}') + zola_dc_wo_bf = zola_table_dc[zola_table_dc[:, 6] < maxbg] + print(f'Filter out {len(zola_table_dc) - len(zola_dc_wo_bf)}') + return zola_dc_wo_bf, bf_table + + +def main(argsv=None, callback=None): + + def log(msg=None, level='info'): + if callback: + callback({'Message': msg}) + print(msg) + logger.__getattribute__(level)(msg) + + log('<==== Start BFDC Python module ====>') + + parser = iot.parse_input() + try: + if argsv is None: + args = parser.parse_args() + else: + args = parser.parse_args(argsv) + except TypeError: + log(f'Wrong args while parsing: {argsv}', level='error') + exit(1) + if args.command == 'trace': + assert isinstance(args.xypixel, int) + assert isinstance(args.zstep, int) + + cal_path = iot.get_abs_path(args.dict) + log(f'Opening calibration {args.dict}') + cal_stack = iot.open_stack(cal_path) + if args.zdirection == 'approach': + cal_stack = cal_stack[::-1] + log(f'Due to calibration direction {args.zdirection}, calibration stack was inverted') + log(f'Imported dictionary {cal_stack.shape}') + + roi_path = iot.get_abs_path(args.roi) + log(f'Opening roi {args.roi}') + roi = ft.read_roi(roi_path) + + movie_path = iot.get_abs_path(args.movie) + if args.lock: + lock = iot.put_trace_lock(os.path.dirname(movie_path)) + log(f'Opening movie {args.movie}') + movie = iot.TiffStackOpener(movie_path) + try: + log(f'Imported movie {movie.shape}') + size_check = iot.check_stacks_size_equals(cal_stack, movie) + except AttributeError: + log(f'Imported movie from the set of tif files') + size_check = True + + if size_check: + log('Stack and movie of equal sizes') + drift_ = trace_drift_auto(args=args, + cal_stack=cal_stack, + movie=movie, + roi=roi, + debug=False, + callback=callback) + else: + log('Stack and movie of different sizes, exiting') + return + + if drift_.shape[0] > 0: + movie_folder = iot.get_parent_path(movie_path) + save_path = os.path.join(movie_folder, args.driftFileName) + iot.save_drift_table(drift_, save_path) + iot.save_drift_plot(move_drift_to_zero(drift_, 10), save_path + "_2zero" + ".png",callback=callback) + + log('Drift table saved, exiting') + else: + log('Drift table empty, exiting') + + if args.lock: + iot.remove_trace_lock(lock) + + elif args.command == 'apply': + logger.debug(args) + zola_path = iot.get_abs_path(args.zola_table) + bf_path = iot.get_abs_path(args.drift_table) + + log(f'Opening localization table') + zola_table = iot.open_csv_table(zola_path) + log(f'Zola table contains {len(zola_table)} localizations from {len(np.unique(zola_table[:,1]))} frames') + + bf_table = iot.open_csv_table(bf_path) + + if args.smooth > 0: + log(f'Apply gaussian filter to the drift with sigma = {args.smooth}') + + bf_table[:, 1:4] = iot.gf1(bf_table[:, 1:4], + sigma=args.smooth, + axis=0) + + log(f'Applying drift') + + zola_table_dc, bf_table_int = apply_drift(bf_table=bf_table, + zola_table=zola_table, + smooth=args.smooth, + start=args.start, + skip=args.skip, + maxbg=args.maxbg, + zinvert=args.zinvert) + + path = os.path.splitext(zola_path)[0] + f'_BFDC_smooth_{args.smooth}.csv' + log(f'saving results to {path}') + iot.save_zola_table(zola_table_dc, path) + iot.save_drift_plot(move_drift_to_zero(bf_table_int), os.path.splitext(path)[0] + '.png', callback=callback) + + elif args.command == 'batch': + batch.BatchDrift(callback=callback, main=main, **vars(args)) + else: + parser.print_help() + + return 0 + + +if __name__ == "__main__": + main() diff --git a/bfdc/feature.py b/bfdc/feature.py new file mode 100755 index 0000000..febb3e7 --- /dev/null +++ b/bfdc/feature.py @@ -0,0 +1,183 @@ +import numpy as np +import scipy.ndimage as ndi +from scipy.ndimage import gaussian_filter as gf +from skimage import filters +import matplotlib as mpl +mpl.use('TkAgg') +#mpl.use('PS') +import matplotlib.pyplot as plt +from bfdc.xcorr import get_abs_max +from read_roi import read_roi_file + +import logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.WARNING) + + +class FeatureExtractor: + """ + Finds a feature in calibration stack to use for drift correction + """ + + def __init__(self, cal_stack, extend = 0, invert=False): + self.cal_stack = np.array(cal_stack,dtype='f')[:] + self.featuremap = highlight_feature(self.cal_stack) + self.mask = get_mask_otsu(self.featuremap) + self.labeled_mask = label_mask(self.mask) + self.peak = get_abs_max(self.featuremap) + self.segment = get_isolated_segment(self.labeled_mask, self.peak) + self.boundaries = get_xy_boundaries(self.segment,extend=extend) + self.crop_stack = crop_using_xy_boundaries(self.cal_stack, self.boundaries,extend=0) + self.invert=invert + + def get_crop(self): + return self.crop_stack + + def plot(self): + plt.figure(figsize=(9, 4)) + plt.subplot(131) + plt.imshow(self.featuremap, cmap='gray', interpolation='nearest') + plt.axis('off') + plt.subplot(132) + plt.imshow(self.labeled_mask) + plt.colorbar() + plt.axis('off') + + plt.tight_layout() + plt.show() + + plt.imshow(self.segment) + plt.show() + + b = self.boundaries + plt.imshow(self.segment[b['ymin']:b['ymax'], b['xmin']:b['xmax']]) + plt.show() + + for i in range(8, 12): + plt.imshow(self.crop_stack[i]) + plt.show() + + +def get_mask_otsu(image): + val = filters.threshold_otsu(image) + + mask = image > val + return mask + + +def label_mask(mask): + """ + see scipy.ndimage.label + """ + labeled_mask, _ = ndi.label(mask) + return labeled_mask + + +def get_isolated_segment(labeled_mask, peak): + """ + provide pixel coodinates of a peak (normally get_abs_max(featuremap)), + the segment among labeled_mask containing this peak will be isolated + :return: mask_ with only one segment + """ + seg_index = labeled_mask[peak] + # print(seg_index) + mask_ = labeled_mask == seg_index + return mask_ + + +def check_limits(img,coordinate): + """ + Checks if the value is inside img borders + :param img: 2d array + :param coordinate: coordinate: int or tuple + :return: coordinate within img.shape + """ + if isinstance(coordinate,tuple): + assert np.ndim(img) == len(coordinate) + shape = img.shape + out = np.array(coordinate) + for i,c in enumerate(coordinate): + c = max([0,c]) + out[i] = min([shape[i]-1,c]) + return tuple(out) + + elif isinstance(coordinate,int): + assert np.ndim(img) == 1 + shape = len(img) + c=coordinate + c = max([0, c]) + out = min([shape - 1, c]) + return out + + +def get_xy_boundaries(segment, extend = 0): + """ + Detects the xy baoundaries of a binary mask (segment) + :segment: binary 2D dataset + :return: dict{xmin,xmax,ymin,ymax} + """ + assert np.ndim(segment) == 2, print("please provide 2D dataset") + qy, qx = np.indices(segment.shape) + e = extend + xmin = qx[segment].min() - e + xmax = qx[segment].max() + e + ymin = qy[segment].min() - e + ymax = qy[segment].max() + e + xmin,ymin = check_limits(segment,(xmin,ymin)) + xmax,ymax = check_limits(segment,(xmax,ymax)) + return dict(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax) + + +def crop_using_xy_boundaries(mask:np.ndarray, boundaries:dict, extend:int=0): + """ + :mask: any 2D or 3D dataset + :boundaries: dict{xmin,xmax,ymin,ymax} + :return: cropped mask + """ + b = boundaries + logger.debug(f'Boundaries: {b}') + logger.debug(f'Image shape {mask.shape}') + e = extend + x_max = mask.shape[-1] + y_max = mask.shape[-2] + x1 = max(0, b['xmin'] - e) + x2 = min(x_max, b['xmax'] + e) + y1 = max(0, b['ymin'] - e) + y2 = min(y_max, b['ymax'] + e) + logger.debug(f'Boundaries with limits and extension ({e}): xmin:xmax, ymin:ymax {x1}:{x2}, {y1},{y2}') + if np.ndim(mask) == 3: + return mask[:, y1:y2, x1:x2] + elif np.ndim(mask) == 2: + return mask[y1:y2, x1:x2] + else: + raise (TypeError("Please use numpy array with 2 or 3 dimensions")) + + +def highlight_feature(cal_stack): + diff = [] + grad = [] + for i in range(len(cal_stack) - 1): + dd = cal_stack[i] - cal_stack[i + 1] + diff.append(dd) + dd = get_grad(cal_stack[i]) + grad.append(dd) + + diff = np.array(diff).mean(axis=0) + grad = np.array(grad).mean(axis=0) + featuremap = gf(diff, 5) * gf(grad, 5) + return featuremap + + +def get_grad(varray): + vgrad = np.gradient(varray) + fulgrad = np.sqrt(vgrad[0] ** 2 + vgrad[1] ** 2) + return fulgrad + + +def read_roi(path): + return read_roi_file(path) + + +def roi_to_boundaries(roi): + roi = roi[list(roi)[0]] + return dict(xmin = roi['left'],xmax = roi['left'] + roi['width'], ymin = roi['top'], ymax = roi['top'] + roi['height']) diff --git a/bfdc/gaussfit.py b/bfdc/gaussfit.py new file mode 100755 index 0000000..9f6cdeb --- /dev/null +++ b/bfdc/gaussfit.py @@ -0,0 +1,295 @@ +#!/usr/bin/env python +""" +Routines for fitting gaussians + +Hazen 4/09 +""" + +import math +import numpy +import scipy +import scipy.optimize +import logging + +logger = logging.getLogger(__name__) + +sigma = 1.0 + + + +def fitAFunctionLS(data, params, fn): + """ + Least Squares fitting. + """ + result = params + errorfunction = lambda p: numpy.ravel(fn(*p)(*numpy.indices(data.shape)) - data) + good = True + [result, cov_x, infodict, mesg, success] = scipy.optimize.leastsq(errorfunction, params, full_output = 1, maxfev = 500) + err = errorfunction(result) + err = scipy.sum(err * err) + if (success < 1) or (success > 4): + logger.warning(f'Fitting problem! {success}, {mesg}') + good = False + return [result, good] + + +def fitAFunctionMLE(data, params, fn): + """ + MLE fitting, following Laurence and Chromy. + """ + result = params + def errorFunction(p): + fit = fn(*p)(*numpy.indices(data.shape)) + t1 = 2.0 * numpy.sum(fit - data) + t2 = 2.0 * numpy.sum(data * numpy.log(fit/data)) + return t1 - t2 + good = True + try: + [result, fopt, iter, funcalls, warnflag] = scipy.optimize.fmin(errorFunction, params, full_output = 1, maxiter = 500, disp = False) + except: + warnflag = 1 + if (warnflag != 0): + logger.warning(f'Fitting problem!') + good = False + return [result, good] + + +# +# Fitting a gaussian, this is almost a verbatim copy of: +# +# http://www.scipy.org/Cookbook/FittingData#head-11870c917b101bb0d4b34900a0da1b7deb613bf7 +# + +def fixedSymmetricGaussian(background, height, center_x, center_y): + global sigma + width = 2.0 * sigma + return lambda x,y: background + height*numpy.exp(-(((center_x-x)/width)**2 + ((center_y-y)/width)**2) * 2) + +def symmetricGaussian1D(background, height, center_x, width): + return lambda x: background + height*numpy.exp(-(((center_x-x)/width)**2) * 2) + +def symmetricGaussian1DonRamp(background, height, center_x, width, ramp): + return lambda x: background + ramp * x + height*numpy.exp(-(((center_x-x)/width)**2) * 2) + +def symmetricGaussian(background, height, center_x, center_y, width): + return lambda x,y: background + height*numpy.exp(-(((center_x-x)/width)**2 + ((center_y-y)/width)**2) * 2) + +def fixedEllipticalGaussian(background, height, center_x, center_y, width_x, width_y): + return lambda x,y: background + height*numpy.exp(-(((center_x-x)/width_x)**2 + ((center_y-y)/width_y)**2) * 2) + +def ellipticalGaussian(background, height, center_x, center_y, a, b, c): + return lambda x,y: background + height*numpy.exp(-(a*(center_x-x)**2 + b*(center_x-x)*(center_y-y) + c*(center_y-y)**2)) + +def ellipticalGaussianOnRamp(background, height, center_x, center_y, a, b, c, ramp_x, ramp_y): + bg = lambda x, y: background + ramp_x * x + ramp_y * y + return lambda x,y: bg(x,y) + height*numpy.exp(-(a*(center_x-x)**2 + b*(center_x-x)*(center_y-y) + c*(center_y-y)**2)) + +def ellipticalGaussian3dOnRamp(background, height, center_z, center_y, center_x, el_z, el_y, el_x, an_xy, an_yz, an_xz, ramp_x, ramp_y, ramp_z): + return lambda z, y, x: background + \ + ramp_x * x + ramp_y * y + ramp_z * z + \ + height * numpy.exp(-(el_x * (center_x - x) ** 2 + \ + el_y * (center_y - y) ** 2 + \ + el_z * (center_z - z) ** 2 + \ + an_xy * (center_x - x) * (center_y - y) + \ + an_xz * (center_x - x) * (center_z - z) + \ + an_yz * (center_y - y) * (center_z - z))) \ + +def fitEllipticalGaussian3D(data:numpy.ndarray, init:list=None): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + """ + assert data.ndim == 3 + if init is None or len(init) != 14: + params = [data.min(), + data.max(), + 0.5 * data.shape[0], + 0.5 * data.shape[1], + 0.5 * data.shape[2], + 4.0 / data.shape[0], + 4.0 / data.shape[1], + 4.0 / data.shape[2], + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0] + else: params = init + return fitAFunctionLS(data, params, ellipticalGaussian3dOnRamp) + +def fitFixedSymmetricGaussian(data, a_sigma): + """ + Data is assumed centered on a gaussian of a fixed width. + """ + global sigma + sigma = a_sigma + params = [numpy.min(data), + numpy.max(data)-numpy.min(data), + 0.5 * data.shape[0], + 0.5 * data.shape[1]] + return fitAFunctionLS(data, params, fixedSymmetricGaussian) + +def fitFixedSymmetricGaussianMLE(data, a_sigma): + """ + Data is assumed centered on a gaussian of a fixed width. + """ + global sigma + sigma = a_sigma + params = [numpy.min(data), + numpy.max(data)-numpy.min(data), + 0.5 * data.shape[0], + 0.5 * data.shape[1]] + return fitAFunctionMLE(data, params, fixedSymmetricGaussian) + +def fitSymmetricGaussian1D(data, width = 0.25): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + Outputs [(min,max,x,width),good] + """ + params = [numpy.min(data), + numpy.max(data)-numpy.min(data), + numpy.argmax(data), + width * data.shape[0]] + return fitAFunctionLS(data, params, symmetricGaussian1D) + +def fitSymmetricGaussian1DonRamp(data, width = 0.25): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + Outputs [(min,max,x,width),good] + """ + params = [numpy.min(data), + numpy.max(data)-numpy.min(data), + numpy.argmax(data), + width * data.shape[0], + 0] + return fitAFunctionLS(data, params, symmetricGaussian1DonRamp) + +def fitSymmetricGaussian(data, sigma): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + Outputs [(min, max, y, x, sigma), good] + """ + params = [numpy.min(data), + numpy.max(data), + 0.5 * data.shape[0], + 0.5 * data.shape[1], + 2.0 * sigma] + return fitAFunctionLS(data, params, symmetricGaussian) + +def fitSymmetricGaussianMLE(data, sigma): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + """ + params = [numpy.min(data), + numpy.max(data), + 0.5 * data.shape[0], + 0.5 * data.shape[1], + 2.0 * sigma] + return fitAFunctionMLE(data, params, symmetricGaussian) + +def fitFixedEllipticalGaussian(data, sigma): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + """ + params = [numpy.min(data), + numpy.max(data), + 0.5 * data.shape[0], + 0.5 * data.shape[1], + 2.0 * sigma, + 2.0 * sigma] + return fitAFunctionLS(data, params, fixedEllipticalGaussian) + +def fitFixedEllipticalGaussianOnRamp(data): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + """ + params = [numpy.min(data), + numpy.max(data), + 0.5 * data.shape[0], + 0.5 * data.shape[1], + 0.3 * data.shape[0], + 0, + 0.3 * data.shape[1], + 0, + 0] + return fitAFunctionLS(data, params, ellipticalGaussianOnRamp) + +def fitFixedEllipticalGaussianMLE(data, sigma): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + """ + params = [numpy.min(data), + numpy.max(data), + 0.5 * data.shape[0], + 0.5 * data.shape[1], + 2.0 * sigma, + 2.0 * sigma] + return fitAFunctionMLE(data, params, fixedEllipticalGaussian) + +def fitEllipticalGaussian(data): + """ + Data is assumed centered on the gaussian and of size roughly 2x the width. + """ + params = [numpy.min(data), + numpy.max(data), + 0.5 * data.shape[0], + 0.5 * data.shape[1], + 4.0 / data.shape[0], + 0.0, + 4.0 / data.shape[1]] + return fitAFunctionLS(data, params, ellipticalGaussian) + + +# +# Fitting multiple gaussians +# +def twoSymmetricGaussian1D(height1, center_x1, width1, height2, center_x2, width2): + return lambda x: height1*numpy.exp(-(((center_x1-x)/width1)**2) * 2) + height2*numpy.exp(-(((center_x2-x)/width2)**2) * 2) + +def fitTwoSymmetricGaussian1D(data, height1, center_x1, width1, height2, center_x2, width2): + params = [height1, + center_x1, + width1, + height2, + center_x2, + width2] + return fitAFunctionLS(data, params, twoSymmetricGaussian1D) + + +# +# Fitting a Lorentzian +# + +def lorentzian(offset, height, center, width): + return lambda x: offset + height/3.14159 * (width/((x-center)**2 + width**2)) + +def fitLorentzian(data): + params = [numpy.min(data), + numpy.max(data) - numpy.min(data), + numpy.argmax(data), + 1.2] + return fitAFunctionLS(data, params, lorentzian) + + +# The MIT License +# +# Copyright (c) 2012 Zhuang Lab, Harvard University +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. +# diff --git a/bfdc/iotools.py b/bfdc/iotools.py new file mode 100755 index 0000000..7c063df --- /dev/null +++ b/bfdc/iotools.py @@ -0,0 +1,520 @@ +import argparse + +import numpy as np +import os +import matplotlib as mpl +mpl.use('TkAgg') +#mpl.use('PS') +import matplotlib.pyplot as plt +from scipy import interpolate +from scipy.ndimage import gaussian_filter1d as gf1 +from skimage import io +import bfdc.picassoio as pio +import subprocess + +import logging + +logger = logging.getLogger(__name__) +logger.setLevel(logging.WARNING) + + +def open_stack(path): + if path.endswith('ome.tif'): + return io.imread(path) + elif os.path.isdir(path): + stack = TiffStackOpener(path, virtual=False) + return stack.get_stack() + else: + logger.error('Please provide either ome.tif or a folder with .tif files') + raise TypeError + +def open_virtual_stack_picasso(path): + movie, [_] = pio.load_movie(path) + return movie + + +def open_virtual_stack(path:str): + ''' + Opens virtual stack, returns iterable + :param path: string + :return: iterable + ''' + if os.path.isdir(path): + stack = TiffStackOpener(path,virtual=True) + return stack.picasso_ome + elif path.endswith('ome.tif'): + return open_virtual_stack_picasso(path=path) + else: + logger.error('Please provide either ome.tif or a folder with .tif files') + raise TypeError + + +def virtual_stack_shape(folder_path, suffix='.tif'): + fff = [] + flist = os.scandir(folder_path) + for f in flist: + if f.name.endswith(suffix): + fff.append(f) + shape = io.imread(fff[-1].path).shape + depth = len(fff) + return (depth,) + shape + + +class TiffStackOpener: + """ + Check the content of the folder, reads both ome.tif stacks in virtual or real mode and sets of .tif files. + """ + + def __init__(self, path, virtual=True): + self.path = path + self.movie_template = 'img_{:0>9d}_Default_000.tif' + self.stack_template = 'img_000000000_Default_{:0>3d}.tif' + self.picasso_ome = None + self.stack = None + self.tif_set = None + self.file_list = [] + if os.path.isdir(path): + logger.info('Opening virtual tif set') + self.open_tif_set() + self.get_file_list() + # self.tif_type_selector() + elif path.endswith('ome.tif'): + if virtual: + logger.info('Opening virtual ome.tif stack') + self.open_ome_tif_virtual() + self.type = 'ome_virt' + else: + logger.info('Opening ome.tif stack to the memory') + self.open_ome_tif_memory() + self.type = 'ome_mem' + + def open_ome_tif_virtual(self): + # Launch picasso tiff reader + self.picasso_ome = open_virtual_stack_picasso(self.path) + + def open_ome_tif_memory(self): + # Use skimage.io.imread + self.stack = io.imread(self.path) + return self.stack + + def open_tif_set(self): + # scan thorugh the folder + self.tif_set = self.tif_type_selector() + + def tif_movie_opener(self, limit=100000, template='img_{:0>9d}_Default_000.tif'): + for i in range(limit): + fname = template.format(i) + path = os.path.join(self.path, fname) + try: + f = io.imread(path) + yield f + except IOError: + logger.debug(f"file {os.path.join(path, fname)} doesn't exist, break") + break + + def tif_zstack_opener(self): + return self.tif_movie_opener(template='img_000000000_Default_{:0>3d}.tif') + + def tif_type_selector(self): + if os.path.exists(os.path.join(self.path, self.movie_template.format(1))): + logger.info('Tif movie detected') + return self.tif_movie_opener() + elif os.path.exists(os.path.join(self.path, self.stack_template.format(1))): + logger.info('Tif stack detected') + return self.tif_zstack_opener() + else: + logging.error(f'''File name pattern not recognized.\n + Tried path: {os.path.join(self.path, self.stack_template.format(0))}''') + return -1 + + def get_stack(self): + if self.tif_set: + stack = [] + try: + for i, f in enumerate(self.tif_set): + stack.append(f) + print(f'\rReading {i}-th frame', end='') + except KeyboardInterrupt: + logging.error('\nUser interrupt...') + finally: + self.stack = np.array(stack) + logging.info(f'\nStack shape {self.stack.shape}') + return self.stack + elif self.picasso_ome: + stack = [] + try: + for i, f in enumerate(self.picasso_ome): + stack.append(f) + print(f'\rReading {i}-th frame', end='') + except KeyboardInterrupt: + logging.error('\nUser interrupt...') + finally: + self.stack = np.array(stack) + logging.info(f'\nStack shape {self.stack.shape}') + return self.stack + + def get_file_list(self): + for f in os.scandir(self.path): + if f.name.endswith('.tif'): + self.file_list.append(f.name) + self.file_list = sorted(self.file_list) + + @property + def n_frames(self): + if self.picasso_ome: + return self.picasso_ome.n_frames + + elif self.stack: + return self.stack.shape[0] + + elif self.tif_set: + return len(self.file_list) + + else: + logger.error('Stack type not defined') + raise AttributeError + + def __len__(self): + return self.n_frames + + def __iter__(self): + for i in self.file_list: + yield io.imread(os.path.join(self.path,i)) + + @property + def shape(self): + if self.picasso_ome: + return self.picasso_ome.shape + + elif self.stack: + return self.stack.shape + + elif self.tif_set: + depth = len(self.file_list) + img = io.imread(os.path.join(self.path, self.file_list[0])) + shape = img.shape + return (depth,) + shape + + else: + logger.error('Stack type not defined to invoke the shape') + raise AttributeError('Unable to get stack shape as stack is not properly loaded') + + def __getitem__(self, i): + # return n-th item + if self.picasso_ome: + return self.picasso_ome[i] + elif self.stack: + return self.stack[i] + elif self.tif_set: + return io.imread(os.path.join(self.path, self.file_list[i])) + else: + logger.error('Stack type not defined') + raise AttributeError('Unable to get item as stack is not properly loaded') + + + +def save_drift_table(table: np.ndarray, path): + save_table(table, path, fmt='drift_table') + + +def save_table(table, path: str, fmt: str = 'drift_table'): + if fmt == 'drift_table': + try: + logger.info(f'Saving results to {path}') + np.savetxt(path + ".csv", table, fmt='%.1f', delimiter=',', comments='', newline='\r\n', + header='"frame","x [nm]","y [nm]","z [nm]"') + print('') + except IOError as e: + logger.error('Problem saving drift table: ') + logger.error(e.strerror) + else: + logger.error('Please specify valid format: ["drift_table"]') + logger.error('File NOT saved!') + + +def get_abs_path(path): + dir_path = os.path.abspath(path) + return dir_path + + +def get_parent_path(path): + return os.path.abspath(os.path.join(path, os.pardir)) + + +def open_csv_table(path, show_header=False): + """ + Loads thunderstorm compatible csv table into numpy array + First line is omitted as contains header + :param path: path to the table.csv + :param show_header: bool, shows 'head path' + :return: numpy array + """ + if show_header: + subprocess.check_output(['head', '-n', '1', path]) + return np.loadtxt(path, delimiter=',', skiprows=1) + + +def save_zola_table(table, path): + header = 'id,frame,x [nm],y [nm],z [nm],intensity,background,chi2,crlbX,crlbY,crlbZ,driftX,driftY,driftZ,' \ + 'occurrenceMerging ' + np.savetxt(path, table[:, :15], fmt='%.2f', delimiter=',', comments='', newline='\r\n', header=header) + + +def plot_drift(table): + plt.figure(figsize=(6,4), dpi=150) + plt.plot(table[:, 0], table[:, 1:]) + plt.xlabel('frame') + plt.ylabel('Drift, nm') + plt.legend(['x', 'y', 'z', 'x_shift', 'y_shift']) + plt.title('Drift BF, nm') + plt.grid() + + +def save_drift_plot(table, path, callback=None): + plot_drift(table) + plt.savefig(path) + plt.close() + if callback: callback({"Plot":path}) + logger.info(f"Saved drift plot to {path}") + + +def interpolate_drift_table(table, start=0, skip=0, smooth=10): + """ + Smooth and interpolate a table + :param table: fxyz (nm) array + :param start: in case of renumbering needed : first frame + :param skip: how many frame were skipped + :param smooth: gaussian smoothing sigma + :return: interpolated table + """ + w = table.shape[1] + if smooth > 0: + table = smooth_drift_table(table, sigma=smooth) + + table = update_frame_number(table, start=start, skip=skip) + + time = table[:, 0] + # print(time.shape) + time_new = np.arange(1, max(time) + 1) + new_table = np.zeros((len(time_new), w)) + new_table[:, 0] = time_new + for col in range(1, w): + y = table[:, col] + # print(y.shape) + f = interpolate.interp1d(time, y, fill_value='extrapolate') + ynew = f(time_new) + new_table[:, col] = ynew + logger.info(f'interpolating from {len(time)} to {len(ynew)} frames') + return new_table + + +def smooth_drift_table(table, sigma): + drift = gf1(table[:, 1:], sigma=sigma, axis=0) + table_smooth = table.copy() + table_smooth[:, 1:] = drift + return table_smooth + + +def check_stacks_size_equals(cal_stack, movie): + logger.info(f'check_stacks_size_equals: Input shapes {cal_stack.shape,movie.shape}') + if len(cal_stack.shape) == len(movie.shape) == 3: + x1, x2 = cal_stack.shape[1], cal_stack.shape[2] + y1, y2 = movie.shape[1], movie.shape[2] + return x1 == y1 and x2 == y2 + else: + raise (ValueError('cal_stack.shape: wrong shapes!')) + + +def check_multi_channel(movie, channel=2, channel_position=1): + """ + Checks if stack contains channels and returns single channel stack + :param movie: numpy array zxy or zcxy + :param channel: 1 - first channel, 2 - second channel, etc + :param channel_position: 1 - for zcxy, 0 - for czxy + :return: numpy array zxy + """ + logger.info(f'check_multi_channel: Input shape {movie.shape}') + ndim = len(movie.shape) + if ndim == 3: + logger.info(f'check_multi_channel: Returning shape {movie.shape}') + return movie + elif ndim == 4: + if channel_position == 1: + logger.info(f'check_multi_channel: Returning shape {movie[:,channel-1].shape}') + return movie[:, channel - 1] + elif channel_position == 0: + logger.info(f'check_multi_channel: Returning shape {movie[channel-1].shape}') + return movie[channel - 1] + else: + raise (TypeError(f'check_multi_channel: channel order not understood, movie shape {movie.shape}')) + + +def skip_stack(n_frames: int, start: int, skip: int, maxframes: int): + """ + Now works with virtual stack + :param n_frames: total frame number + :param start: in case of skipping: first frame to pick up (starts form 1) + :param skip: number of frames skipped to get the right frame + (for example, ch2 with alternating illumination refers to start=2,skip=1) + :param maxframes: maximum number of frames in case of cropped dataset + :return: index list + """ + # logger.info('skip_stack: starting frame skipping routines') + + index_list = np.arange(n_frames) + if start > 0: + start = start - 1 + if skip == 0: + skip = None + if maxframes == 0: + maxframes = None + index_list = index_list[start:maxframes:skip] + logger.info(f'skip_stack: returning frame list with {len(index_list)} frames') + return index_list + + +def update_frame_number(table, start, skip): + """ + updates frame number int the table using skip/start frames indices + :param table: fxyz array + :param start: first frame of selection + :param skip: every skip-th frame from selection + :return: table with updated frame column + """ + if skip > 0 or start > 0: + if table[0, 0] == 1: + table[:, 0] -= 1 + elif table[0, 0] == 0: + pass + else: + raise (ValueError("update_frame_number: Wrong table. Expected frame numbers starting with 0 or 1")) + table[:, 0] *= skip + table[:, 0] += start - 1 + logger.info("update_frame_number: Updated frame numbers successfully") + return table + + +def put_trace_lock(path, name="BFDC_.lock"): + f = open(path + os.sep + name, mode='w') + f.close() + logger.info('Setting lock') + return path + os.sep + name + + +def remove_trace_lock(path): + try: + os.remove(path) + logger.info('Removing lock') + return 0 + except IOError: + logger.error('Problem removing lock') + return 1 + + +def parse_input(): + # Main parser + parser = argparse.ArgumentParser('BFDC') + subparsers = parser.add_subparsers(dest='command') + + for command in ['trace', 'apply', 'batch']: + subparsers.add_parser(command) + + # trace + trace_parser = subparsers.add_parser('trace', help='identify drift in 3D') + trace_parser.add_argument('dict', type=str, default='data/LED_stack_full_100nm.tif', + help='calibration stack file') + trace_parser.add_argument('roi', type=str, default='', + help='calibration file roi from ImageJ') + trace_parser.add_argument('movie', type=str, default='data/sr_2_LED_movie.tif', + help='movie stack file') + trace_parser.add_argument('-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') + trace_parser.add_argument('--zdirection', type=str, default='approach', help='Choose approach/retract for the direction of calibration. Default: approach') + trace_parser.add_argument('-xypx', '--xypixel', type=int, default=110, help='xy pixel size in nm. Default: 110') + trace_parser.add_argument('--nframes', type=int, default=None, + help='now many frames to analyse from the movie. Default: None') + trace_parser.add_argument('--driftFileName', type=str, default='BFCC_table', + help='filename for the drift table. Default: "BFCC_table.csv"') + trace_parser.add_argument('--minsignal', type=int, default=0, + help='Minimal mean intensity to treat the image as brightfield. Default: 0') + trace_parser.add_argument('--skip', type=int, default=0, + help='how many frames to skip form the movie. Default: 0') + trace_parser.add_argument('--start', type=int, default=0, + help='how many frames to skip in the beginning of the movie. Default: 0') + trace_parser.add_argument('--channel', type=int, default=2, + help='channel index (starts with 1) for the movie. Default: 2') + trace_parser.add_argument('--channel_position', type=int, default=1, + help='channel position (starts with 0) for the movie. Default: 1') + trace_parser.add_argument('--lock', type=int, default=0, + help='if 1, will create BFDC_.lock file in the movie folder. Default: 0') + trace_parser.add_argument('--verbose', type=int, default=0, + help='If 1, will output debug info. Default: 0') + + # apply + apply_parser = subparsers.add_parser('apply', help='apply drift 3D to ZOLA table') + apply_parser.add_argument('zola_table', type=str, default='', + help='ZOLA localization table, format ifxyz.......dxdydz') + apply_parser.add_argument('drift_table', type=str, default='BFCC_table.csv', + help='3D drift table, format fxyz') + apply_parser.add_argument('--skip', type=int, default=0, + help='how many frames to skip form the movie. Default: 0') + apply_parser.add_argument('--start', type=int, default=0, + help='how many frames to skip in the beginning of the movie. Default: 0') + + apply_parser.add_argument('--smooth', type=int, default=0, help='gaussian smoothing for the drift. Default: 0') + apply_parser.add_argument('--maxbg', type=int, default=0, + help='reject localizations with high background. Default: 0') + apply_parser.add_argument('--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') + + # batch + batch_parser = subparsers.add_parser('batch', help='''Batch trace and apply drift 3D to ZOLA table''') + batch_parser.add_argument('batch_path', type=str, default='', + help='data path') + batch_parser.add_argument('-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') + batch_parser.add_argument('--zdirection', type=str, default='approach', + help='Choose approach/retract for the direction of calibration. Default: approach') + batch_parser.add_argument('-xypx', '--xypixel', type=int, default=110, help='xy pixel size in nm. Default: 110') + batch_parser.add_argument('--skip', type=int, default=0, + help='how many frames to skip form the movie. Default: 0') + batch_parser.add_argument('--start', type=int, default=0, + help='how many frames to skip in the beginning of the movie. Default: 0') + + batch_parser.add_argument('--fov_prefix', type=str, default='FOV', + help='Prefix of a folder with a single field of view. Default: FOV ') + + batch_parser.add_argument('--dict_folder_prefix', type=str, default='dict', + help='Prefix of the dictionary folder') + + batch_parser.add_argument('--ROI_suffix', type=str, default='roi', + help='Extension of roi file. Default: roi') + + batch_parser.add_argument('--dict_suffix', type=str, default='ome.tif', + help='Extension of dictionary stack file. Default: ome.tif') + + batch_parser.add_argument('--sr_folder_prefix', type=str, default='sr', + help='Prefix of the super-resolution movie folder. Default: sr') + + batch_parser.add_argument('--sr_movie_suffix', type=str, default='Pos0.ome.tif', + help='Extension of super-resolution movie stack file. Default: Pos0.ome.tif') + + batch_parser.add_argument('--zola_dc_filename', type=str, default='ZOLA*BFDC*.csv', + help='ZOLA DC table name. Default: ZOLA*BFDC*.csv') + + batch_parser.add_argument('--dc_table_filename', type=str, default='BFCC*.csv', + help='Drift track table name. Default: BFCC*.csv') + + batch_parser.add_argument('--zola_raw_filename', type=str, default='ZOLA_localization_table.csv', + help='ZOLA localization table name. Default: ZOLA_localization_table.csv') + + batch_parser.add_argument('--zola_lock_filename', type=str, default='ZOLA_.lock', + help='ZOLA DC table name. Default: ZOLA_.lock') + + batch_parser.add_argument('--smooth', type=int, default=50, help='gaussian smoothing for the drift. Default: 50') + batch_parser.add_argument('--filter_bg', type=int, default=100, + help='''Use this value to detect BF frames. + Frames with the mean value more than this number are counted as bright field. + In the reconstuction table, background more than this number will be filtered out. + Default: 100 + ''') + batch_parser.add_argument('--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') + + return parser diff --git a/bfdc/picassoio.py b/bfdc/picassoio.py new file mode 100755 index 0000000..f5d3617 --- /dev/null +++ b/bfdc/picassoio.py @@ -0,0 +1,330 @@ +""" + picasso.io + ~~~~~~~~~~ + + General purpose library for handling input and output of files + + :author: Joerg Schnitzbauer, 2015 + :copyright: Copyright (c) 2015 Jungmann Lab, Max Planck Institute of Biochemistry +""" +import os.path as _ospath +import numpy as _np +#import yaml as _yaml +import re as _re +import struct as _struct +import json as _json +import os as _os +import threading as _threading + + +class NoMetadataFileError(FileNotFoundError): + pass + + +def multiple_filenames(path: str, index: object) -> object: + base, ext = _ospath.splitext(path) + filename = base + '_' + str(index) + ext + return filename + + +def load_tif(path): + movie = TiffMultiMap(path, memmap_frames=False) + info = movie.info() + return movie, [info] + + +def load_movie(path, prompt_info=None): + base, ext = _ospath.splitext(path) + ext = ext.lower() + if ext == '.tif': + return load_tif(path) + + +def load_info(path, qt_parent=None): + path_base, path_extension = _ospath.splitext(path) + filename = path_base + '.yaml' + try: + with open(filename, 'r') as info_file: + info = list(_yaml.load_all(info_file)) + except FileNotFoundError as e: + print('\nAn error occured. Could not find metadata file:\n{}'.format(filename)) + # if qt_parent is not None: + # _QMessageBox.critical(qt_parent, 'An error occured', 'Could not find metadata file:\n{}'.format(filename)) + raise NoMetadataFileError(e) + return info + + +class TiffMap: + TIFF_TYPES = {1: 'B', 2: 'c', 3: 'H', 4: 'L', 5: 'RATIONAL'} + TYPE_SIZES = {'c': 1, 'B': 1, 'h': 2, 'H': 2, 'i': 4, 'I': 4, 'L': 4, 'RATIONAL': 8} + + def __init__(self, path, verbose=False): + if verbose: + print('Reading info from {}'.format(path)) + self.path = _ospath.abspath(path) + self.file = open(self.path, 'rb') + self._tif_byte_order = {b'II': '<', b'MM': '>'}[self.file.read(2)] + self.file.seek(4) + self.first_ifd_offset = self.read('L') + + # Read info from first IFD + self.file.seek(self.first_ifd_offset) + n_entries = self.read('H') + for i in range(n_entries): + self.file.seek(self.first_ifd_offset + 2 + i * 12) + tag = self.read('H') + type = self.TIFF_TYPES[self.read('H')] + count = self.read('L') + if tag == 256: + self.width = self.read(type, count) + elif tag == 257: + self.height = self.read(type, count) + elif tag == 258: + bits_per_sample = self.read(type, count) + dtype_str = 'u' + str(int(bits_per_sample / 8)) + # Picasso uses internatlly exclusively little endian byte order... + self.dtype = _np.dtype(dtype_str) + # ... the tif byte order might be different, so we also store the file dtype + self._tif_dtype = _np.dtype(self._tif_byte_order + dtype_str) + self.frame_shape = (self.height, self.width) + self.frame_size = self.height * self.width + + # Collect image offsets + self.image_offsets = [] + offset = self.first_ifd_offset + while offset != 0: + self.file.seek(offset) + n_entries = self.read('H') + if n_entries is None: + # Some MM files have trailing nonsense bytes + break + for i in range(n_entries): + self.file.seek(offset + 2 + i * 12) + tag = self.read('H') + if tag == 273: + type = self.TIFF_TYPES[self.read('H')] + count = self.read('L') + self.image_offsets.append(self.read(type, count)) + break + self.file.seek(offset + 2 + n_entries * 12) + offset = self.read('L') + self.n_frames = len(self.image_offsets) + + self.lock = _threading.Lock() + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.close() + + def __getitem__(self, it): + with self.lock: # Otherwise we get messed up when reading frames from multiple threads + if isinstance(it, tuple): + if isinstance(it, int) or _np.issubdtype(it[0], _np.integer): + return self[it[0]][it[1:]] + elif isinstance(it[0], slice): + indices = range(*it[0].indices(self.n_frames)) + stack = _np.array([self.get_frame(_) for _ in indices]) + if len(indices) == 0: + return stack + else: + if len(it) == 2: + return stack[:, it[1]] + elif len(it) == 3: + return stack[:, it[1], it[2]] + else: + raise IndexError + elif it[0] == Ellipsis: + stack = self[it[0]] + if len(it) == 2: + return stack[:, it[1]] + elif len(it) == 3: + return stack[:, it[1], it[2]] + else: + raise IndexError + elif isinstance(it, slice): + indices = range(*it.indices(self.n_frames)) + return _np.array([self.get_frame(_) for _ in indices]) + elif it == Ellipsis: + return _np.array([self.get_frame(_) for _ in range(self.n_frames)]) + elif isinstance(it, int) or _np.issubdtype(it, _np.integer): + return self.get_frame(it) + raise TypeError + + def __iter__(self): + for i in range(self.n_frames): + yield self[i] + + def __len__(self): + return self.n_frames + + def info(self): + info = {'Byte Order': self._tif_byte_order, 'File': self.path, 'Height': self.height, + 'Width': self.width, 'Data Type': self.dtype.name, 'Frames': self.n_frames} + # The following block is MM-specific + self.file.seek(self.first_ifd_offset) + n_entries = self.read('H') + for i in range(n_entries): + self.file.seek(self.first_ifd_offset + 2 + i * 12) + tag = self.read('H') + type = self.TIFF_TYPES[self.read('H')] + count = self.read('L') + if count * self.TYPE_SIZES[type] > 4: + self.file.seek(self.read('L')) + if tag == 51123: + # This is the Micro-Manager tag. We generate an info dict that contains any info we need. + readout = self.read(type, count).strip(b'\0') # Strip null bytes which MM 1.4.22 adds + mm_info = _json.loads(readout.decode()) + info['Micro-Manager Metadata'] = mm_info + info['Camera'] = mm_info['Camera'] + return info + + def get_frame(self, index, array=None): + self.file.seek(self.image_offsets[index]) + frame = _np.reshape(_np.fromfile(self.file, dtype=self._tif_dtype, count=self.frame_size), self.frame_shape) + # We only want to deal with little endian byte order downstream: + if self._tif_byte_order == '>': + frame.byteswap(True) + frame = frame.newbyteorder('<') + return frame + + def read(self, type, count=1): + if type == 'c': + return self.file.read(count) + elif type == 'RATIONAL': + return self.read_numbers('L') / self.read_numbers('L') + else: + return self.read_numbers(type, count) + + def read_numbers(self, type, count=1): + size = self.TYPE_SIZES[type] + fmt = self._tif_byte_order + count * type + try: + return _struct.unpack(fmt, self.file.read(count * size))[0] + except _struct.error: + return None + + def close(self): + self.file.close() + + def tofile(self, file_handle, byte_order=None): + do_byteswap = (byte_order != self.byte_order) + for image in self: + if do_byteswap: + image = image.byteswap() + image.tofile(file_handle) + + +class TiffMultiMap: + + def __init__(self, path, memmap_frames=False, verbose=False): + self.path = _ospath.abspath(path) + self.dir = _ospath.dirname(self.path) + base, ext = _ospath.splitext(_ospath.splitext(self.path)[0]) # split two extensions as in .ome.tif + base = _re.escape(base) + pattern = _re.compile(base + '_(\d*).ome.tif') # This matches the basename + an appendix of the file number + entries = [_.path for _ in _os.scandir(self.dir) if _.is_file()] + matches = [_re.match(pattern, _) for _ in entries] + matches = [_ for _ in matches if _ is not None] + paths_indices = [(int(_.group(1)), _.group(0)) for _ in matches] + self.paths = [self.path] + [path for index, path in sorted(paths_indices)] + self.maps = [TiffMap(path, verbose=verbose) for path in self.paths] + self.n_maps = len(self.maps) + self.n_frames_per_map = [_.n_frames for _ in self.maps] + self.n_frames = sum(self.n_frames_per_map) + self.cum_n_frames = _np.insert(_np.cumsum(self.n_frames_per_map), 0, 0) + self.dtype = self.maps[0].dtype + self.height = self.maps[0].height + self.width = self.maps[0].width + self.shape = (self.n_frames, self.height, self.width) + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.close() + + def __getitem__(self, it): + if isinstance(it, tuple): + if it[0] == Ellipsis: + stack = self[it[0]] + if len(it) == 2: + return stack[:, it[1]] + elif len(it) == 3: + return stack[:, it[1], it[2]] + else: + raise IndexError + elif isinstance(it[0], slice): + indices = range(*it[0].indices(self.n_frames)) + stack = _np.array([self.get_frame(_) for _ in indices]) + if len(indices) == 0: + return stack + else: + if len(it) == 2: + return stack[:, it[1]] + elif len(it) == 3: + return stack[:, it[1], it[2]] + else: + raise IndexError + if isinstance(it[0], int) or _np.issubdtype(it[0], _np.integer): + return self[it[0]][it[1:]] + elif isinstance(it, slice): + indices = range(*it.indices(self.n_frames)) + return _np.array([self.get_frame(_) for _ in indices]) + elif it == Ellipsis: + return _np.array([self.get_frame(_) for _ in range(self.n_frames)]) + elif isinstance(it, int) or _np.issubdtype(it, _np.integer): + return self.get_frame(it) + raise TypeError + + def __iter__(self): + for i in range(self.n_frames): + yield self[i] + + def __len__(self): + return self.n_frames + + def close(self): + for map in self.maps: + map.close() + + def get_frame(self, index): + # TODO deal with negative numbers + for i in range(self.n_maps): + if self.cum_n_frames[i] <= index < self.cum_n_frames[i + 1]: + break + else: + raise IndexError + return self.maps[i][index - self.cum_n_frames[i]] + + def info(self): + info = self.maps[0].info() + info['Frames'] = self.n_frames + return info + + def to_file(self, file_handle, byte_order=None): + for map in self.maps: + map.tofile(file_handle, byte_order) + + +def get_movie_groups(paths): + groups = {} + if len(paths) > 0: + pattern = _re.compile( + '(.*?)(_(\d*))?.ome.tif') # This matches the basename + an optional appendix of the file number + matches = [_re.match(pattern, path) for path in paths] + match_infos = [{'path': _.group(), 'base': _.group(1), 'index': _.group(3)} for _ in matches] + for match_info in match_infos: + if match_info['index'] is None: + match_info['index'] = 0 + else: + match_info['index'] = int(match_info['index']) + basenames = set([_['base'] for _ in match_infos]) + for basename in basenames: + match_infos_group = [_ for _ in match_infos if _['base'] == basename] + group = [_['path'] for _ in match_infos_group] + indices = [_['index'] for _ in match_infos_group] + group = [path for (index, path) in sorted(zip(indices, group))] + groups[basename] = group + return groups diff --git a/bfdc/registration.py b/bfdc/registration.py new file mode 100755 index 0000000..df08d5a --- /dev/null +++ b/bfdc/registration.py @@ -0,0 +1,86 @@ +import imreg_dft as ird +import numpy as np +import logging +from skimage import io +import sys +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class RegArray: + def __init__(self, zoom, rotation, shift_x, shift_y): + self.zoom = zoom + self.rotation = rotation + self.shift_x = shift_x + self.shift_y = shift_y + + +def check_size(img:np.ndarray, template:np.ndarray) -> bool: + """ + Check the shape for the arrays. + :param img: numpy array + :param template: numpy array + :return: True if equal, False if different, raises AssertionError if different dimensions. + """ + dims = (img.ndim, template.ndim) + assert dims == (2, 2) , logger.error(f'Expected 2D arrays for both images, got {dims}') + shape1, shape2 = img.shape, template.shape + if shape1 == shape2: + return True + else: + return False + + +def pad_inputs(img:np.ndarray, template:np.ndarray, mode='median') -> (np.ndarray, np.ndarray): + """ + Pads two arrays to get the same size using np.pad function + :param img: numpy array + :param template: numpy array + :param mode: same as numpy.pad, 'median' by default. + :return: tuple with padded (img, template) + """ + if not check_size(img,template): + shape1, shape2 = img.shape, template.shape + shape_out = (max(shape1[0],shape2[0]), max(shape1[1],shape2[1])) + if shape1 RegArray: + if check_size(main_cam_frame, drift_cam_frame): + i1,i2 = main_cam_frame, drift_cam_frame + else: + i1, i2 = pad_inputs(main_cam_frame, drift_cam_frame) + result = ird.similarity(i1,i2) + out = RegArray(zoom=result['scale'], rotation=result['angle'], shift_x=result['tvec'][0], shift_y=result['tvec'][1]) + return out + + +def register_files(path1:str, path2:str): + f1 = io.imread(path1) + f2 = io.imread(path2) + arr = register_imgs(f1, f2) + return arr + + +if __name__ == '__main__': + args = ['/Volumes/Imod-grenier/Andrey/Phase retrieve/drift-correction/BFDC/bfdc/registration.py', '/Volumes/Imod-grenier/Mickael/yeast_imaging_EdU/andor_cam_transformmatrix/Pos0/img_000000000_Default0_000.tif', '/Volumes/Imod-grenier/Mickael/yeast_imaging_EdU/thorlabs_cam_bigbeads_transformmatrix/Pos0/img_000000000_Default0_000.tif'] + + try: + p1, p2 = args[1], args[2] + except IndexError: + print(f'Missing arguments path1, path2: {args}') + + arr = register_files(p1,p2) + exit(0) \ No newline at end of file diff --git a/bfdc/xcorr.py b/bfdc/xcorr.py new file mode 100755 index 0000000..9740627 --- /dev/null +++ b/bfdc/xcorr.py @@ -0,0 +1,364 @@ +import numpy as np +import matplotlib as mpl + +mpl.use('TkAgg') +import matplotlib.pyplot as plt +from skimage.feature import match_template +import logging + +logger = logging.getLogger(__name__) +# logger.setLevel(logging.WARNING) +logger.setLevel(logging.DEBUG) + + +class LowXCorr(Exception): + pass + + +class FitResult(): + + def __init__(self, + x=None, + y=None, + z=None, + good=False, + result_fit=None, + z_px=None): + self.x = x + self.y = y + self.z = z + self.good = good + self.result_fit = result_fit + self.z_px = z_px + + def __iter__(self): + for a in (self.x, self.y, self.z, self.good, self.result_fit, self.z_px): + yield a + + def __repr__(self): + return 'x={}, y={}, z={}, good={}, result_fit={}, z_px={}'.format(self.x, + self.y, self.z, self.good, self.result_fit, + self.z_px) + + +def get_abs_max(data): + """ + peaks up absolute maximum position in the stack/ 2D image + First dimension comes first + :param data: np.array + :return: abs max coordinates in data.shape format + """ + return np.unravel_index(np.argmax(data), data.shape) + + +def fit_gauss_3d(stack: np.ndarray, + radius_xy: int = 4, + radius_z: int = 8, + z_zoom: int = 20, + min_xcorr: np.float = 0.5, + z_init: np.float = None, + fit_init: list = None, + debug=False) -> FitResult: + """ + Detects maximum on the stack in 3D + Fitting 2D gaussian in xy, 4-order polynomial in z + Ouputs [x,y,z] in pixels + """ + + from bfdc import gaussfit + + logger.debug(f'Start fit_gauss_3d with the stack shape zyx {stack.shape}') + logger.debug(f'radius_xy={radius_xy}, radius_z={radius_z}, z_zoom={z_zoom}') + assert np.ndim(stack) == 3, logger.error( + f'fit_gauss_3d: input stack shape is wrong, expected 3 dim, got {stack.shape}') + + if False: + plt.imshow(stack.max(axis=0)) + plt.title('Max xy projection of cc-stack') + plt.show() + + z_px, y_px, x_px = get_abs_max(stack) + logger.debug(f'Got absolute maximum xyz (px) {(x_px, y_px, z_px )}') + cc_value = np.max(stack) + if cc_value < min_xcorr: + # raise(LowXCorr("fit_gauss_3d: Cross corellation value os too low!")) + logger.error("fit_gauss_3d: Cross corellation value os too low!") + return FitResult() + else: + logger.debug(f'cc peak value={cc_value}') + + r, rz = radius_xy, radius_z + if z_init is None: + z_start = max(z_px - rz, 0) + z_stop = min(z_px + rz, len(stack)) + z_crop = (z_start, z_stop) + logger.debug(f'Computing z boundaries from peak: z_start={z_start}, z_stop={z_stop}') + else: + z_init = int(z_init) + z_start = max(z_init - rz, 0) + z_stop = min(z_init + rz, len(stack)) + z_crop = (z_start, z_stop) + logger.debug(f'Using z boundaries: z_start={z_start}, z_stop={z_stop}') + + _, y_max, x_max = stack.shape + y1 = max(0, y_px - r) + y2 = min(y_max, y_px + r) + x1 = max(0, x_px - r) + x2 = min(x_max, x_px + r) + cut_stack = stack[z_start:z_stop, y1:y2, x1:x2] + logger.debug(f'After cutting x,y,z, we got cut_stack shape {cut_stack.shape}') + if cut_stack.shape != (z_stop - z_start, 2 * r, 2 * r): + logger.error( + f'Wrong cut_stack shape: expected {(z_stop - z_start, 2 * r + 1, 2 * r +1 )}, got {cut_stack.shape}') + return FitResult() + + xy_proj = cut_stack.max(axis=0) + zx_proj = cut_stack.max(axis=1) + zy_proj = cut_stack.max(axis=2) + z_proj = cut_stack.max(axis=(1, 2)) + # z_proj = cut_stack[:,r].max(axis=1) #ignore y + # z_proj = cut_stack[:,r,r] #ignore xy + # z_proj = cut_stack[:,r,r] + + # [(_min, _max, y, x, sig), good] = gaussfit.fitSymmetricGaussian(xy_proj,sigma=1) + logger.debug('Fit gauss xy') + try: + [(_min, _max, y, x, _, _, _), good] = gaussfit.fitEllipticalGaussian(xy_proj) + #[result_fit, good] = gaussfit.fitEllipticalGaussian3D(cut_stack, init=fit_init) + #background, height, z, y, x, el_x, el_y, el_z, an_xy, an_yz, an_xz, ramp_x, ramp_y, ramp_z = result_fit + + except Exception as e: + logger.error(f'Error in gaussian fit: {e}') + #logger.error(f'result: {result_fit}') + return FitResult() + + zfitter = FitPoly1D(z_proj, zoom=20, radius=8) + z = zfitter(plot=debug) + logger.debug(f'raw xyz {np.round((x, y, z),2)}') + + x_found = x - r + x_px + y_found = y - r + y_px + logger.debug(f'xy found: {np.round((x_found, y_found),2)}') + + z_found = z + z_start + + if debug: + # fitted_ellipsoid = gaussfit.ellipticalGaussian3dOnRamp(*result_fit)(*np.indices(cut_stack.shape)) + + # fit_residue = cut_stack - fitted_ellipsoid + + fig = plt.figure(dpi=72, figsize=(8, 3)) + + fig.add_subplot(131) + plt.imshow(xy_proj) + plt.plot(x,y,'r+') + plt.title('xy') + plt.colorbar() + + fig.add_subplot(132) + plt.imshow(zx_proj) + plt.plot(x,z,'r+') + plt.title('zx') + plt.colorbar() + + fig.add_subplot(133) + plt.imshow(zy_proj) + plt.plot(y,z,'r+') + plt.title('zy') + plt.colorbar() + ''' + fig.add_subplot(334) + plt.imshow(fitted_ellipsoid.max(axis=0)) + plt.title('fit xy') + plt.colorbar() + + fig.add_subplot(335) + plt.imshow(fitted_ellipsoid.max(axis=1)) + plt.title('fit zx') + plt.colorbar() + + fig.add_subplot(336) + plt.imshow(fitted_ellipsoid.max(axis=2)) + plt.title('fit zy') + plt.colorbar() + + fig.add_subplot(337) + plt.imshow(fit_residue.max(axis=0)) + plt.title('residue xy') + plt.colorbar() + + fig.add_subplot(338) + plt.imshow(fit_residue.max(axis=1)) + plt.title('residue zx') + plt.colorbar() + + fig.add_subplot(339) + plt.imshow(fit_residue.max(axis=2)) + plt.title('residue zy') + plt.colorbar() + + + fig.add_subplot(144) + plt.plot(z_proj, 'o', label='cc curve') + plt.plot(gaussfit.symmetricGaussian1DonRamp(*params)(np.arange(len(z_proj))), '--', label='gauss fit') + plt.legend() + + fig.add_subplot(144) + try: + plt.plot(zfitter.x + z_start, z_proj, 'o', label='cc curve') + plt.plot(zfitter.x_new + z_start, zfitter.ext_curve, '--', label='poly fit') + plt.plot(zfitter.x_new[np.argmax(zfitter.ext_curve)]+ z_start, max(zfitter.ext_curve), 'o', label='z found') + plt.legend() + except ValueError as e: + logger.error(f'Error in subplot 144: {e}') + ''' + plt.tight_layout() + plt.show() + + return FitResult(x_found, y_found, z_found, good, None, z_px) + + +class FitPoly1D: + + def __init__(self, curve, zoom=10, order=4, radius=5, peak='max'): + """ + Fits polynomial of given order to 1D curve and returns descreet absolute max/min after subsampling the fit. + :curve: 1D ndarraay + :zoom: upsampling + :order: polynomial order to fit + :peak: 'max' or 'min' + :return: z coordinate (first dimension with the best match) + """ + logger.debug(f'Start FitPoly1D init with a curve on length {len(curve)}') + self.curve_full = curve + self.curve = curve + self.zoom = zoom + self.order = order + self.crop = 0 + + if peak == 'max': + self.peak = np.argmax + elif peak == 'min': + self.peak = np.argmin + else: + raise (ValueError(f'peak value \'{self.peak}\' not recognized. Expected \'max\' or \'min\'')) + + if radius: self.do_crop(radius) + self.interpolate() + self.fit_poly() + self.detect_peak() + + def do_crop(self, radius): + logger.debug(f'FitPoly1D:do_crop') + index = self.peak(self.curve) + a = max(0, index - radius) + b = min(len(self.curve), index + radius) + self.curve = self.curve[a:b] + self.crop = a + + def interpolate(self): + logger.debug(f'FitPoly1D:interpolate') + self.x_full = np.arange(len(self.curve_full)) + self.x = np.arange(self.crop, self.crop + len(self.curve)) + self.x_new = np.linspace(self.crop, + len(self.curve) + self.crop, + num=self.zoom * len(self.curve), + endpoint=False, + dtype='f') + + def fit_poly(self): + logger.debug(f'FitPoly1D:fit_poly') + fit = np.polyfit(self.x, self.curve, deg=self.order) + poly = np.poly1d(fit) + self.ext_curve = poly(self.x_new) + + def detect_peak(self): + logger.debug(f'FitPoly1D:detect_peak') + self.subpx_fit = self.x_new[self.peak(self.ext_curve)] + logger.debug(f'FitPoly1D:detect_peak found {self.subpx_fit}') + + def plot(self): + logger.debug(f'FitPoly1D:plot') + plt.plot(self.x_full, self.curve_full, 'o', label='raw curve') + plt.plot(self.x_new, self.ext_curve, '--', label='poly fit') + plt.plot(self.subpx_fit, self.ext_curve[self.peak(self.ext_curve)], 'o', label=f'z found{self.subpx_fit}') + plt.legend() + plt.title('FitPoly1D plot') + plt.show() + + def __call__(self, plot=False): + if plot: self.plot() + + return self.subpx_fit + + +def fit_z_MSE(frame: np.ndarray, template: np.ndarray, zoom: int, order: int = 4, plot: bool = False) -> float: + """ + Fits MSE between frame 2D and template 3D to find z minimum + :frame: 2D ndarraay + :template: 3D array + :zoom: intepropation for the first dimension + :order: polynomial order to fit + :plot: bool, will show the curve and fit + :return: z coordinate (first dimension with the best match) + """ + z_px = 0.0 + assert np.ndim(frame) == 2, 'wrong frame shape' + assert np.ndim(template) == 3 + mse = (template - frame) ** 2 / len(template) + curve = mse.mean(axis=(1, 2)) + fitter = FitPoly1D(curve, zoom=20, order=4, peak='min') + z_px = fitter(plot=plot) + logger.debug(f'found z {z_px}') + return z_px + + +def cc_template(image, template, plot=False): + try: + cc = match_template(image, template, pad_input=True) + if plot: + plt.imshow(image) + plt.title('image') + plt.show() + plt.imshow(template) + plt.title('template') + plt.show() + plt.imshow(cc) + plt.title('cc') + plt.colorbar() + plt.show() + return cc + except ValueError: + logging.error(f"Error in cc_template. Image shape {image.shape}, template shape {template.shape }") + + +def cc_stack(image, stack, plot=False): + image = np.array(image - np.mean(image), dtype='f') + stack = np.array(stack - np.mean(stack), dtype='f') + out = [] + for t in stack: + out.append(cc_template(image, t)) + out = np.array(out) + if plot: + fig = plt.figure(figsize=(6, 2)) + + fig.add_subplot(131) + plt.imshow(out.max(axis=0)) + plt.title('max xy') + plt.colorbar() + + fig.add_subplot(132) + plt.imshow(out.max(axis=1)) + plt.title('max zx') + plt.colorbar() + + fig.add_subplot(133) + plt.imshow(out.max(axis=2)) + plt.title('max zy') + plt.colorbar() + plt.show() + return out + + +def cc_max(cc_out): + return np.unravel_index(np.argmax(cc_out), cc_out.shape) diff --git a/img/BFCC_table.csv_2zero.png b/img/BFCC_table.csv_2zero.png new file mode 100755 index 0000000000000000000000000000000000000000..7791ded2dc1fe8696c7f322b8b88236aa32c2f09 GIT binary patch literal 37415 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version='0.4.2', + packages=['bfdc'], + install_requires=[ + 'scikit-image', + 'pillow>5.1', + 'numpy', + 'scipy', + 'matplotlib', + 'read-roi', + 'imreg_dft' + ], + url='https://github.com/imodpasteur/Brightfield_Drift_Tracking_3D.git', + license='MIT', + author='Andrey Aristov', + author_email='aaristov@pasteur.fr', + description='Acquire bright field images along with the super resolution data and use it to track drift in 3D with nanometer precision!' +) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/tests/test_check_limits.py b/tests/test_check_limits.py new file mode 100755 index 0000000..3e872ea --- /dev/null +++ b/tests/test_check_limits.py @@ -0,0 +1,40 @@ +from unittest import TestCase +import numpy as np +from bfdc.feature import check_limits + + +class TestCheckLimits(TestCase): + def test_check_limits_2d(self): + img = np.zeros((512,512)) + peak = (-1,550) + expected = (0,511) + out = check_limits(img,peak) + self.assertEqual(out,expected) + + def test_check_limits_linear(self): + img = np.zeros(12) + peak = 5 + expected = 5 + out = check_limits(img,peak) + self.assertEqual(out,expected) + + def test_check_limits_linear0(self): + img = np.zeros(12) + peak = 0 + expected = 0 + out = check_limits(img,peak) + self.assertEqual(out,expected) + + def test_check_limits_linear_neg(self): + img = np.zeros(12) + peak = -1 + expected = 0 + out = check_limits(img,peak) + self.assertEqual(out,expected) + + def test_check_limits_linear_pos(self): + img = np.zeros(12) + peak = 15 + expected = 11 + out = check_limits(img,peak) + self.assertEqual(out,expected) diff --git a/tests/test_gauss_fit.py b/tests/test_gauss_fit.py new file mode 100755 index 0000000..9d19916 --- /dev/null +++ b/tests/test_gauss_fit.py @@ -0,0 +1,54 @@ +#!/Users/andrey/anaconda3/envs/pydrift-test/python3 + +from unittest import TestCase +import numpy as np +import bfdc.gaussfit as gf +#import matplotlib.pyplot as plt + +class TestFitPoly1D(TestCase): + + def test_1D(self): + background = 0.2 + height = 0.8 + center_x = 8 + width = 6 + ramp = 0.01 + expected = [background, height, center_x, width, ramp] + x = np.arange(16, dtype='f') + print(x) + gauss1d = gf.symmetricGaussian1DonRamp(*expected)(x) + #plt.plot(gauss1d, '.-') + #plt.show() + result, good = gf.fitSymmetricGaussian1DonRamp(gauss1d, 0.25) + for i,j in zip(result, expected): + self.assertAlmostEqual(first=i, second=j, delta=0.01*abs(i), msg=f'result = {result}, expected {expected}') + + def test_2D(self): + background = 0.2 + height = 0.8 + center_x = 6 + center_y = 3 + c = .3 + b = .1 + a = .2 + ramp_x = 0.01 + ramp_y = -0.02 + expected = [background, height, center_x, center_y, a, b, c, ramp_x, ramp_y] + x = np.indices((16,6), dtype='f') + #print(x) + gauss2d = gf.ellipticalGaussianOnRamp(*expected)(*x) + #plt.imshow(gauss2d) + #plt.show() + result, good = gf.fitFixedEllipticalGaussianOnRamp(gauss2d) + for i,j in zip(result, expected): + self.assertAlmostEqual(first=i, second=j, delta=0.01*abs(i), msg=f'result = {result}, expected {expected}') + + def test_ramp_3D(self): + background = 1 + ramp = np.array((1,2,3)) + shape = np.array([16,8,8]) + indices = np.indices(shape) + bg_fn = lambda indices: background + (ramp.reshape((indices.shape[0],1,1,1)) * indices).sum(axis=0) + z, y, x = indices + bg = bg_fn(indices) + self.assertEqual(bg.ndim, len(shape)) diff --git a/tests/test_get_xy_boundaries.py b/tests/test_get_xy_boundaries.py new file mode 100755 index 0000000..bf47cbd --- /dev/null +++ b/tests/test_get_xy_boundaries.py @@ -0,0 +1,20 @@ +from unittest import TestCase +import numpy as np +from bfdc.feature import get_xy_boundaries + + +class TestGet_xy_boundaries(TestCase): + def test_get_xy_boundaries(self): + segment = np.zeros((10,10),dtype='bool') + segment[3:6,3:6] = True + out = get_xy_boundaries(segment) + expected = dict(xmin=3,xmax=5,ymin=3,ymax=5) + self.assertEqual(out,expected) + + def test_get_xy_boundaries_ext(self): + segment = np.zeros((10,10),dtype='bool') + segment[3:6,3:6] = True + out = get_xy_boundaries(segment,extend=5) + expected = dict(xmin=0,xmax=9,ymin=0,ymax=9) + self.assertEqual(out,expected) + diff --git a/tests/test_iotools.py b/tests/test_iotools.py new file mode 100755 index 0000000..67547b2 --- /dev/null +++ b/tests/test_iotools.py @@ -0,0 +1,72 @@ +from unittest import TestCase +import numpy as np +from bfdc.iotools import check_stacks_size_equals, check_multi_channel,skip_stack +import logging +import bfdc.picassoio as pio + +logger = logging.getLogger(__name__) + + +class Test_check_multi_channel(TestCase): + + def test_single_channel(self): + stack = np.zeros((10, 16, 16)) + out = check_multi_channel(stack).shape + expected = (10, 16, 16) + self.assertEqual(out, expected) + + def test_dual_channel(self): + stack = np.zeros((10, 2, 16, 16)) + stack[:, 1] = 1 + out = check_multi_channel(stack) + out_shape = check_multi_channel(stack).shape + expected = np.ones((10, 16, 16)) + expected_shape = (10, 16, 16) + res1 = np.unique(np.equal(out, expected)) + #logger.info(f"{__name__}: res1 {res1}") + self.assertEqual(res1, [True]) + self.assertTupleEqual(out_shape, expected_shape) + + +class TestCheck_stacks_size_equals(TestCase): + + def test_check_stacks_size_equals(self): + stack1 = np.zeros((10, 15, 15)) + stack2 = np.zeros((15, 15, 15)) + self.assertTrue(check_stacks_size_equals(stack1, stack2)) + + def test_check_stacks_size_noequals(self): + stack1 = np.zeros((10, 13, 13)) + stack2 = np.zeros((15, 15, 15)) + self.assertFalse(check_stacks_size_equals(stack1, stack2)) + + +class TestSkip_stack(TestCase): + + def test_skip_stack_default(self): + + #stack = np.zeros((100,15,15)) + stack,[_] = pio.load_movie('./data/full_frame/sr_642_redLED_start_10_skip_10_SP_100nm_1_MMStack_Pos0-100f.ome.tif') + input = stack.shape + n_frames = stack.n_frames + expected_index_list = list(np.arange(n_frames)) + print(f'input shape {input}') + start = 0 + skip = 0 + nframes = 0 + index_list = skip_stack(n_frames=len(stack), start=start, skip=skip, maxframes=nframes) + self.assertListEqual(expected_index_list,list(index_list),msg=f"test_skip_stack_default: {input}, {len(index_list)}") + + def test_skip_stack_skipping10(self): + stack, [_] = pio.load_movie('./data/crop_frame/sr_2_LED_movie.tif') + input = stack.shape + start = 10 + skip = 10 + nframes = None + n_frames = stack.n_frames + expected_index_list = list(np.arange(start-1,n_frames,skip)) + print(f'input shape {input}') + index_list = skip_stack(n_frames=len(stack), start=start, skip=skip, maxframes=nframes) + self.assertListEqual(expected_index_list, list(index_list), + msg=f"test_skip_stack_default: {input}, {len(index_list)}") + diff --git a/tests/test_main.py b/tests/test_main.py new file mode 100755 index 0000000..205d0d1 --- /dev/null +++ b/tests/test_main.py @@ -0,0 +1,41 @@ +from unittest import TestCase,skip +import numpy as np +from bfdc.iotools import check_stacks_size_equals +from bfdc.drift import main +import sys, os +import logging +logger = logging.getLogger(__name__) + + +class TestMain(TestCase): + @skip + def test_main(self): + par = dict(command='trace', + dict='data/LED_stack_full_100nm.tif', + driftFileName='BFCC_table.csv', + movie='data/sr_2_LED_movie.tif', + nframes=10, + skip=0, + start=0, + xypixel=110, + zstep=100) + sys.argv[1:] = ['trace', + f'{par["dict"]}', + f'{par["movie"]}', + f'--nframes {par["nframes"]}'] + + main(par) + + +class TestCheck_stacks_size_equals(TestCase): + + def test_check_stacks_size_equals(self): + stack1 = np.zeros((10,15,15)) + stack2 = np.zeros((15,15,15)) + self.assertTrue(check_stacks_size_equals(stack1,stack2)) + + def test_check_stacks_size_noequals(self): + stack1 = np.zeros((10, 13, 13)) + stack2 = np.zeros((15, 15, 15)) + self.assertFalse(check_stacks_size_equals(stack1, stack2)) + diff --git a/tests/test_move_drift_to_zero.py b/tests/test_move_drift_to_zero.py new file mode 100755 index 0000000..e02a6b6 --- /dev/null +++ b/tests/test_move_drift_to_zero.py @@ -0,0 +1,26 @@ +from unittest import TestCase +import numpy as np +from bfdc.drift import move_drift_to_zero + +class TestMove_drift_to_zero(TestCase): + def test_move_drift_to_zero(self): + table = np.ones((20,4)) + table[:,0] = np.arange(20) + expected = table.copy() + expected[:,1:] = 0 + result = move_drift_to_zero(table) + self.assertTrue(np.array_equiv(result,expected)) + + def test_move_drift_to_zero1(self): + table = np.ones((20,4)) + table[:,0] = np.arange(1,21) + expected = table.copy() + expected[:,1:] = 0 + result = move_drift_to_zero(table) + self.assertTrue(np.array_equiv(result,expected)) + + def test_move_drift_to_zero_wrong_shape1(self): + table = np.ones((0, 5)) + with self.assertRaises(AssertionError): + result = move_drift_to_zero(table) + diff --git a/tests/test_poly_fit.py b/tests/test_poly_fit.py new file mode 100755 index 0000000..bdce7f9 --- /dev/null +++ b/tests/test_poly_fit.py @@ -0,0 +1,54 @@ +#!/Users/andrey/anaconda3/envs/pydrift-test/python3 + +from unittest import TestCase, skip +import numpy as np +from bfdc.xcorr import FitPoly1D + + +class TestFitPoly1D(TestCase): + + def test_parabola_max(self): + indices = np.arange(20) + expected = 10 + zoom = 100 + curve = 5 - (indices-expected) ** 2 + fit = FitPoly1D(curve=curve, zoom=zoom, order=2, peak='max') + result = fit() + self.assertAlmostEqual(result, expected, delta=1/zoom, msg=f'result = {result}, expected {expected}') + + def test_parabola_min(self): + indices = np.arange(20) + expected = 10 + zoom = 100 + curve = 5 + (indices - expected) ** 2 + fit = FitPoly1D(curve=curve, zoom=zoom, order=2, peak='min') + result = fit() + self.assertAlmostEqual(result, expected, delta=1 / zoom, msg=f'result = {result}, expected {expected}') + + def test_parabola_min_4order(self): + indices = np.arange(20) + expected = 10 + zoom = 100 + curve = 5 + (indices - expected) ** 2 + fit = FitPoly1D(curve=curve, zoom=zoom, order=4, peak='min') + result = fit() + self.assertAlmostEqual(result, expected, delta=1/zoom, msg=f'result = {result}, expected {expected}') + + @skip + def test_parabola_min_plot(self): + indices = np.arange(20) + expected = 10 + zoom = 100 + curve = 5 + (indices - expected) ** 2 + fit = FitPoly1D(curve=curve, zoom=zoom, order=2, peak='min') + result = fit(plot=True) + self.assertAlmostEqual(result, expected, delta=1 / zoom, msg=f'result = {result}, expected {expected}') + + def test_parabola_min_plot_big_rad(self): + indices = np.arange(20) + expected = 10 + zoom = 100 + curve = 5 + (indices - expected) ** 2 + fit = FitPoly1D(curve=curve, zoom=zoom, order=2, peak='min', radius=20) + result = fit(plot=False) + self.assertAlmostEqual(result, expected, delta=1 / zoom, msg=f'result = {result}, expected {expected}') \ No newline at end of file diff --git a/tests/test_registration.py b/tests/test_registration.py new file mode 100755 index 0000000..a57a333 --- /dev/null +++ b/tests/test_registration.py @@ -0,0 +1,42 @@ +from bfdc.registration import check_size, pad_inputs +import unittest as ut +import numpy as np + + +class test_check_size(ut.TestCase): + def test_wrong_dim(self): + img = np.ones((10, 15)) + tmp = np.ones((5, 6, 7)) + with self.assertRaises(AssertionError): + check_size(img, tmp) + + def test_wrong_size(self): + img = np.ones((10, 15)) + tmp = np.ones((5, 20)) + out = check_size(img, tmp) + self.assertFalse(out) + + def test_good_size(self): + img = np.ones((10, 15)) + tmp = np.ones((10, 15)) + out = check_size(img, tmp) + self.assertTrue(out) + + + +class test_pad_inputs(ut.TestCase): + def test_even_dim(self): + img = np.ones((10, 16)) + tmp = np.ones((18, 14)) + new_img, new_tmp = pad_inputs(img,tmp) + self.assertTupleEqual(new_img.shape, new_tmp.shape) + + def test_odd_dim(self): + img = np.ones((10, 15)) + tmp = np.ones((17, 14)) + new_img, new_tmp = pad_inputs(img,tmp) + self.assertTupleEqual(new_img.shape, new_tmp.shape) + + +if __name__ == '__main__': + ut.main() \ No newline at end of file diff --git a/tests/test_xcorr.py b/tests/test_xcorr.py new file mode 100755 index 0000000..2aa1dc7 --- /dev/null +++ b/tests/test_xcorr.py @@ -0,0 +1,18 @@ + +from unittest import TestCase, skip +import numpy as np +from bfdc.xcorr import FitResult + + +class TestFitResult(TestCase): + + def test_init(self): + a = FitResult() + x,y,z,good,result_fit,z_px = a + for a, b in zip((x,y,z,good,result_fit,z_px), (None, None, None, False, None, None)): + self.assertEqual(a,b) + def test_repr(self): + a = FitResult() + b = repr(a) + self.assertEqual(b,'x={}, y={}, z={}, good={}, result_fit={}, z_px={}'.format(None, + None, None, False, None, None)) From 4fb78b2f304feeb8331cb0376ed337b4643f0e75 Mon Sep 17 00:00:00 2001 From: aaristov Date: Tue, 19 Feb 2019 10:32:45 +0100 Subject: [PATCH 02/10] add pandas opener --- bfdc/iotools.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/bfdc/iotools.py b/bfdc/iotools.py index 7c063df..d8822ba 100755 --- a/bfdc/iotools.py +++ b/bfdc/iotools.py @@ -11,7 +11,7 @@ from skimage import io import bfdc.picassoio as pio import subprocess - +import pandas as pd import logging logger = logging.getLogger(__name__) @@ -253,6 +253,23 @@ def open_csv_table(path, show_header=False): subprocess.check_output(['head', '-n', '1', path]) return np.loadtxt(path, delimiter=',', skiprows=1) +supported_ext = {'csv': + {'load': pd.read_csv, 'save': pd.to_csv}, + 'hdf5': + {'load': pd.read_hdf, 'save': pd.to_hdf} +} + +def open_localization_table(path): + ''' + Generalized table openeneer based on pandas + ''' + ext = get_ext(path) + try: + file_handler = supported_ext(ext) + except IndexError: + logger.error(f'Unsupported file format. Expected {list(supported_ext)}') + + def save_zola_table(table, path): header = 'id,frame,x [nm],y [nm],z [nm],intensity,background,chi2,crlbX,crlbY,crlbZ,driftX,driftY,driftZ,' \ From a396a7ef70cad6439422440f59c47392a6880230 Mon Sep 17 00:00:00 2001 From: aaristov Date: Tue, 19 Feb 2019 11:38:00 +0100 Subject: [PATCH 03/10] add pandas opener with tests --- bfdc/iotools.py | 124 +++++++++++++++++++++++++++--------------- tests/test_iotools.py | 52 +++++++++++++----- 2 files changed, 119 insertions(+), 57 deletions(-) diff --git a/bfdc/iotools.py b/bfdc/iotools.py index d8822ba..70819e4 100755 --- a/bfdc/iotools.py +++ b/bfdc/iotools.py @@ -1,10 +1,5 @@ import argparse -import numpy as np -import os -import matplotlib as mpl -mpl.use('TkAgg') -#mpl.use('PS') import matplotlib.pyplot as plt from scipy import interpolate from scipy.ndimage import gaussian_filter1d as gf1 @@ -13,6 +8,11 @@ import subprocess import pandas as pd import logging +import numpy as np +import os +import matplotlib as mpl +mpl.use('TkAgg') +# mpl.use('PS') logger = logging.getLogger(__name__) logger.setLevel(logging.WARNING) @@ -25,27 +25,30 @@ def open_stack(path): stack = TiffStackOpener(path, virtual=False) return stack.get_stack() else: - logger.error('Please provide either ome.tif or a folder with .tif files') + logger.error( + 'Please provide either ome.tif or a folder with .tif files') raise TypeError + def open_virtual_stack_picasso(path): movie, [_] = pio.load_movie(path) return movie -def open_virtual_stack(path:str): +def open_virtual_stack(path: str): ''' Opens virtual stack, returns iterable :param path: string :return: iterable ''' if os.path.isdir(path): - stack = TiffStackOpener(path,virtual=True) + stack = TiffStackOpener(path, virtual=True) return stack.picasso_ome elif path.endswith('ome.tif'): return open_virtual_stack_picasso(path=path) else: - logger.error('Please provide either ome.tif or a folder with .tif files') + logger.error( + 'Please provide either ome.tif or a folder with .tif files') raise TypeError @@ -109,7 +112,8 @@ def tif_movie_opener(self, limit=100000, template='img_{:0>9d}_Default_000.tif') f = io.imread(path) yield f except IOError: - logger.debug(f"file {os.path.join(path, fname)} doesn't exist, break") + logger.debug( + f"file {os.path.join(path, fname)} doesn't exist, break") break def tif_zstack_opener(self): @@ -179,7 +183,7 @@ def __len__(self): def __iter__(self): for i in self.file_list: - yield io.imread(os.path.join(self.path,i)) + yield io.imread(os.path.join(self.path, i)) @property def shape(self): @@ -197,7 +201,8 @@ def shape(self): else: logger.error('Stack type not defined to invoke the shape') - raise AttributeError('Unable to get stack shape as stack is not properly loaded') + raise AttributeError( + 'Unable to get stack shape as stack is not properly loaded') def __getitem__(self, i): # return n-th item @@ -209,8 +214,8 @@ def __getitem__(self, i): return io.imread(os.path.join(self.path, self.file_list[i])) else: logger.error('Stack type not defined') - raise AttributeError('Unable to get item as stack is not properly loaded') - + raise AttributeError( + 'Unable to get item as stack is not properly loaded') def save_drift_table(table: np.ndarray, path): @@ -253,32 +258,47 @@ def open_csv_table(path, show_header=False): subprocess.check_output(['head', '-n', '1', path]) return np.loadtxt(path, delimiter=',', skiprows=1) -supported_ext = {'csv': - {'load': pd.read_csv, 'save': pd.to_csv}, - 'hdf5': - {'load': pd.read_hdf, 'save': pd.to_hdf} -} - + +supported_ext = {'.csv': + {'load': pd.read_csv, + 'save': pd.DataFrame.to_csv}, + '.hdf5': + {'load': pd.read_hdf, 'save': pd.DataFrame.to_hdf} + } + + +def get_ext(path): + """ + Get File extension + """ + return os.path.splitext(path)[1] + + def open_localization_table(path): ''' Generalized table openeneer based on pandas ''' ext = get_ext(path) try: - file_handler = supported_ext(ext) - except IndexError: - logger.error(f'Unsupported file format. Expected {list(supported_ext)}') + file_handler = supported_ext[ext] + return file_handler['load'](path) + except KeyError: + logger.error( + f'Unsupported file format. Expected {list(supported_ext)}') + raise TypeError( + f'Unsupported file format. Expected {list(supported_ext)}') def save_zola_table(table, path): header = 'id,frame,x [nm],y [nm],z [nm],intensity,background,chi2,crlbX,crlbY,crlbZ,driftX,driftY,driftZ,' \ 'occurrenceMerging ' - np.savetxt(path, table[:, :15], fmt='%.2f', delimiter=',', comments='', newline='\r\n', header=header) + np.savetxt(path, table[:, :15], fmt='%.2f', delimiter=',', + comments='', newline='\r\n', header=header) def plot_drift(table): - plt.figure(figsize=(6,4), dpi=150) + plt.figure(figsize=(6, 4), dpi=150) plt.plot(table[:, 0], table[:, 1:]) plt.xlabel('frame') plt.ylabel('Drift, nm') @@ -291,7 +311,8 @@ def save_drift_plot(table, path, callback=None): plot_drift(table) plt.savefig(path) plt.close() - if callback: callback({"Plot":path}) + if callback: + callback({"Plot": path}) logger.info(f"Saved drift plot to {path}") @@ -333,7 +354,8 @@ def smooth_drift_table(table, sigma): def check_stacks_size_equals(cal_stack, movie): - logger.info(f'check_stacks_size_equals: Input shapes {cal_stack.shape,movie.shape}') + logger.info( + f'check_stacks_size_equals: Input shapes {cal_stack.shape,movie.shape}') if len(cal_stack.shape) == len(movie.shape) == 3: x1, x2 = cal_stack.shape[1], cal_stack.shape[2] y1, y2 = movie.shape[1], movie.shape[2] @@ -357,13 +379,16 @@ def check_multi_channel(movie, channel=2, channel_position=1): return movie elif ndim == 4: if channel_position == 1: - logger.info(f'check_multi_channel: Returning shape {movie[:,channel-1].shape}') + logger.info( + f'check_multi_channel: Returning shape {movie[:,channel-1].shape}') return movie[:, channel - 1] elif channel_position == 0: - logger.info(f'check_multi_channel: Returning shape {movie[channel-1].shape}') + logger.info( + f'check_multi_channel: Returning shape {movie[channel-1].shape}') return movie[channel - 1] else: - raise (TypeError(f'check_multi_channel: channel order not understood, movie shape {movie.shape}')) + raise (TypeError( + f'check_multi_channel: channel order not understood, movie shape {movie.shape}')) def skip_stack(n_frames: int, start: int, skip: int, maxframes: int): @@ -386,7 +411,8 @@ def skip_stack(n_frames: int, start: int, skip: int, maxframes: int): if maxframes == 0: maxframes = None index_list = index_list[start:maxframes:skip] - logger.info(f'skip_stack: returning frame list with {len(index_list)} frames') + logger.info( + f'skip_stack: returning frame list with {len(index_list)} frames') return index_list @@ -404,7 +430,8 @@ def update_frame_number(table, start, skip): elif table[0, 0] == 0: pass else: - raise (ValueError("update_frame_number: Wrong table. Expected frame numbers starting with 0 or 1")) + raise (ValueError( + "update_frame_number: Wrong table. Expected frame numbers starting with 0 or 1")) table[:, 0] *= skip table[:, 0] += start - 1 logger.info("update_frame_number: Updated frame numbers successfully") @@ -444,9 +471,12 @@ def parse_input(): help='calibration file roi from ImageJ') trace_parser.add_argument('movie', type=str, default='data/sr_2_LED_movie.tif', help='movie stack file') - trace_parser.add_argument('-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') - trace_parser.add_argument('--zdirection', type=str, default='approach', help='Choose approach/retract for the direction of calibration. Default: approach') - trace_parser.add_argument('-xypx', '--xypixel', type=int, default=110, help='xy pixel size in nm. Default: 110') + trace_parser.add_argument( + '-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') + trace_parser.add_argument('--zdirection', type=str, default='approach', + help='Choose approach/retract for the direction of calibration. Default: approach') + trace_parser.add_argument('-xypx', '--xypixel', type=int, + default=110, help='xy pixel size in nm. Default: 110') trace_parser.add_argument('--nframes', type=int, default=None, help='now many frames to analyse from the movie. Default: None') trace_parser.add_argument('--driftFileName', type=str, default='BFCC_table', @@ -467,7 +497,8 @@ def parse_input(): help='If 1, will output debug info. Default: 0') # apply - apply_parser = subparsers.add_parser('apply', help='apply drift 3D to ZOLA table') + apply_parser = subparsers.add_parser( + 'apply', help='apply drift 3D to ZOLA table') apply_parser.add_argument('zola_table', type=str, default='', help='ZOLA localization table, format ifxyz.......dxdydz') apply_parser.add_argument('drift_table', type=str, default='BFCC_table.csv', @@ -477,19 +508,24 @@ def parse_input(): apply_parser.add_argument('--start', type=int, default=0, help='how many frames to skip in the beginning of the movie. Default: 0') - apply_parser.add_argument('--smooth', type=int, default=0, help='gaussian smoothing for the drift. Default: 0') + apply_parser.add_argument('--smooth', type=int, default=0, + help='gaussian smoothing for the drift. Default: 0') apply_parser.add_argument('--maxbg', type=int, default=0, help='reject localizations with high background. Default: 0') - apply_parser.add_argument('--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') + apply_parser.add_argument( + '--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') # batch - batch_parser = subparsers.add_parser('batch', help='''Batch trace and apply drift 3D to ZOLA table''') + batch_parser = subparsers.add_parser( + 'batch', help='''Batch trace and apply drift 3D to ZOLA table''') batch_parser.add_argument('batch_path', type=str, default='', help='data path') - batch_parser.add_argument('-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') + batch_parser.add_argument( + '-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') batch_parser.add_argument('--zdirection', type=str, default='approach', help='Choose approach/retract for the direction of calibration. Default: approach') - batch_parser.add_argument('-xypx', '--xypixel', type=int, default=110, help='xy pixel size in nm. Default: 110') + batch_parser.add_argument('-xypx', '--xypixel', type=int, + default=110, help='xy pixel size in nm. Default: 110') batch_parser.add_argument('--skip', type=int, default=0, help='how many frames to skip form the movie. Default: 0') batch_parser.add_argument('--start', type=int, default=0, @@ -525,13 +561,15 @@ def parse_input(): batch_parser.add_argument('--zola_lock_filename', type=str, default='ZOLA_.lock', help='ZOLA DC table name. Default: ZOLA_.lock') - batch_parser.add_argument('--smooth', type=int, default=50, help='gaussian smoothing for the drift. Default: 50') + batch_parser.add_argument('--smooth', type=int, default=50, + help='gaussian smoothing for the drift. Default: 50') batch_parser.add_argument('--filter_bg', type=int, default=100, help='''Use this value to detect BF frames. Frames with the mean value more than this number are counted as bright field. In the reconstuction table, background more than this number will be filtered out. Default: 100 ''') - batch_parser.add_argument('--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') + batch_parser.add_argument( + '--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') return parser diff --git a/tests/test_iotools.py b/tests/test_iotools.py index 67547b2..e3134cf 100755 --- a/tests/test_iotools.py +++ b/tests/test_iotools.py @@ -1,29 +1,48 @@ from unittest import TestCase import numpy as np -from bfdc.iotools import check_stacks_size_equals, check_multi_channel,skip_stack +import bfdc.iotools as iot import logging import bfdc.picassoio as pio +import pandas as pd logger = logging.getLogger(__name__) +class Test_pd_io(TestCase): + + def test_get_ext(self): + path = 'safdsa/dsfadf.csv' + ext = iot.get_ext(path) + self.assertEqual(ext, '.csv') + + def test_csv_open(self): + path = "data/NUP-20180612-FOV1/results/BFCC_table.csv" + df = iot.open_localization_table(path) + self.assertIsInstance(df, pd.DataFrame) + + def test_txt_open(self): + path = "data/NUP-20180612-FOV1/results/ZOLA_localization_table_ZolaProtocol.txt" + with self.assertRaises(TypeError): + df = iot.open_localization_table(path) + + class Test_check_multi_channel(TestCase): def test_single_channel(self): stack = np.zeros((10, 16, 16)) - out = check_multi_channel(stack).shape + out = iot.check_multi_channel(stack).shape expected = (10, 16, 16) self.assertEqual(out, expected) def test_dual_channel(self): stack = np.zeros((10, 2, 16, 16)) stack[:, 1] = 1 - out = check_multi_channel(stack) - out_shape = check_multi_channel(stack).shape + out = iot.check_multi_channel(stack) + out_shape = iot.check_multi_channel(stack).shape expected = np.ones((10, 16, 16)) expected_shape = (10, 16, 16) res1 = np.unique(np.equal(out, expected)) - #logger.info(f"{__name__}: res1 {res1}") + # logger.info(f"{__name__}: res1 {res1}") self.assertEqual(res1, [True]) self.assertTupleEqual(out_shape, expected_shape) @@ -33,20 +52,21 @@ class TestCheck_stacks_size_equals(TestCase): def test_check_stacks_size_equals(self): stack1 = np.zeros((10, 15, 15)) stack2 = np.zeros((15, 15, 15)) - self.assertTrue(check_stacks_size_equals(stack1, stack2)) + self.assertTrue(iot.check_stacks_size_equals(stack1, stack2)) def test_check_stacks_size_noequals(self): stack1 = np.zeros((10, 13, 13)) stack2 = np.zeros((15, 15, 15)) - self.assertFalse(check_stacks_size_equals(stack1, stack2)) + self.assertFalse(iot.check_stacks_size_equals(stack1, stack2)) class TestSkip_stack(TestCase): def test_skip_stack_default(self): - #stack = np.zeros((100,15,15)) - stack,[_] = pio.load_movie('./data/full_frame/sr_642_redLED_start_10_skip_10_SP_100nm_1_MMStack_Pos0-100f.ome.tif') + # stack = np.zeros((100,15,15)) + stack, [_] = pio.load_movie( + './data/full_frame/sr_642_redLED_start_10_skip_10_SP_100nm_1_MMStack_Pos0-100f.ome.tif') input = stack.shape n_frames = stack.n_frames expected_index_list = list(np.arange(n_frames)) @@ -54,8 +74,12 @@ def test_skip_stack_default(self): start = 0 skip = 0 nframes = 0 - index_list = skip_stack(n_frames=len(stack), start=start, skip=skip, maxframes=nframes) - self.assertListEqual(expected_index_list,list(index_list),msg=f"test_skip_stack_default: {input}, {len(index_list)}") + index_list = iot.skip_stack(n_frames=len(stack), + start=start, + skip=skip, + maxframes=nframes) + self.assertListEqual(expected_index_list, list( + index_list), msg=f"test_skip_stack_default: {input}, {len(index_list)}") def test_skip_stack_skipping10(self): stack, [_] = pio.load_movie('./data/crop_frame/sr_2_LED_movie.tif') @@ -64,9 +88,9 @@ def test_skip_stack_skipping10(self): skip = 10 nframes = None n_frames = stack.n_frames - expected_index_list = list(np.arange(start-1,n_frames,skip)) + expected_index_list = list(np.arange(start-1, n_frames, skip)) print(f'input shape {input}') - index_list = skip_stack(n_frames=len(stack), start=start, skip=skip, maxframes=nframes) + index_list = iot.skip_stack(n_frames=len( + stack), start=start, skip=skip, maxframes=nframes) self.assertListEqual(expected_index_list, list(index_list), msg=f"test_skip_stack_default: {input}, {len(index_list)}") - From 99cb074fb272f6eb67d9caf992a441d04a76062e Mon Sep 17 00:00:00 2001 From: aaristov Date: Tue, 19 Feb 2019 16:18:31 +0100 Subject: [PATCH 04/10] rewrite frame interpolation --- bfdc/iotools.py | 24 +++++------------------- 1 file changed, 5 insertions(+), 19 deletions(-) diff --git a/bfdc/iotools.py b/bfdc/iotools.py index 70819e4..5cba277 100755 --- a/bfdc/iotools.py +++ b/bfdc/iotools.py @@ -316,33 +316,19 @@ def save_drift_plot(table, path, callback=None): logger.info(f"Saved drift plot to {path}") -def interpolate_drift_table(table, start=0, skip=0, smooth=10): - """ - Smooth and interpolate a table - :param table: fxyz (nm) array - :param start: in case of renumbering needed : first frame - :param skip: how many frame were skipped - :param smooth: gaussian smoothing sigma - :return: interpolated table - """ - w = table.shape[1] - if smooth > 0: - table = smooth_drift_table(table, sigma=smooth) - - table = update_frame_number(table, start=start, skip=skip) - - time = table[:, 0] +def interpolate_frames(loc_table: pd.DataFrame, bf_table): + w = bf_table.shape[1] + time = bf_table[:, 0] # print(time.shape) - time_new = np.arange(1, max(time) + 1) + time_new = loc_table['frame'].values new_table = np.zeros((len(time_new), w)) new_table[:, 0] = time_new for col in range(1, w): - y = table[:, col] + y = bf_table[:, col] # print(y.shape) f = interpolate.interp1d(time, y, fill_value='extrapolate') ynew = f(time_new) new_table[:, col] = ynew - logger.info(f'interpolating from {len(time)} to {len(ynew)} frames') return new_table From a6e9e459f13eb1ed6d5c75d4ca938d34ab61a25e Mon Sep 17 00:00:00 2001 From: aaristov Date: Tue, 19 Feb 2019 16:19:25 +0100 Subject: [PATCH 05/10] use pandas for localization table handling --- README.md | 2 + bfdc/drift.py | 109 ++++++++++++++++++++++++++---------------- bfdc/xcorr.py | 15 ++++-- setup.py | 5 +- tests/test_iotools.py | 16 +++++++ 5 files changed, 100 insertions(+), 47 deletions(-) diff --git a/README.md b/README.md index a933988..b8a6089 100755 --- a/README.md +++ b/README.md @@ -103,6 +103,8 @@ Fluorescent bead track before and after BFDC. # Change log +V0.5.0 Using pandas for table handling + V0.2.0 Processing tif file sets V0.1.4 Batch processing diff --git a/bfdc/drift.py b/bfdc/drift.py index b444229..1483fe4 100755 --- a/bfdc/drift.py +++ b/bfdc/drift.py @@ -1,7 +1,9 @@ import traceback import logging import numpy as np -import os, sys +import os +import sys +import pandas as pd import bfdc.batch as batch import bfdc.xcorr as xcorr import bfdc.feature as ft @@ -41,16 +43,16 @@ def __init__(self, cal_stack, roi, radius_xy=3, radius_z=10, smooth_dict=1): self.radius_z = radius_z self.z_last = None - def do_trace(self, - movie, - frame_list, - extend_xy=5, - min_xcorr=0.5, - z_zoom=20, - min_signal=100, - smooth_movie=0, - debug=False, - callback=None): + def do_trace(self, + movie, + frame_list, + extend_xy=5, + min_xcorr=0.5, + z_zoom=20, + min_signal=100, + smooth_movie=0, + debug=False, + callback=None): logging.info(f"doTrace: got the movie with shape {movie.shape}, using {len(frame_list)} frames for tracing") # for i,frame in enumerate(movie): # crop frame with extended by 5px boundaries @@ -63,7 +65,7 @@ def do_trace(self, xc = np.mean([b["xmax"], -b["xmin"]]) yc = np.mean([b["ymax"], -b["ymin"]]) x_, y_, z_ = 0, 0, 0 - #z_px = 0 + # z_px = 0 frame_num = 0 self.x_correction = 0 self.y_correction = 0 @@ -263,7 +265,7 @@ def trace_drift_auto(args, cal_stack, movie, roi, debug=False, callback=None): px = [args.xypixel, args.xypixel, args.zstep, args.xypixel, args.xypixel] print(f'Pixel size xyz: {px}') - drift_px = np.zeros((1, 4)) + drift_px = np.zeros((1, 6)) try: @@ -276,10 +278,10 @@ def trace_drift_auto(args, cal_stack, movie, roi, debug=False, callback=None): try: fitter = DriftFitter(cal_stack, roi, radius_xy=8, radius_z=12) drift_px = fitter.do_trace(movie, - frame_list=frame_list, - min_signal=min_signal, - debug=debug, - callback=callback) + frame_list=frame_list, + min_signal=min_signal, + debug=debug, + callback=callback) except KeyboardInterrupt as e: print(e) @@ -287,7 +289,9 @@ def trace_drift_auto(args, cal_stack, movie, roi, debug=False, callback=None): try: drift_nm[:, 1:] = drift_px[:, 1:] * px except ValueError: + logger.error(f'Unable to apply px size. px = {px}, drift_nm.shape = {drift_nm.shape}') dif = len(drift_px[0,1:]) - len(px) + logger.error(f'dif in shape drift_px[0,1:] - len(px) = {dif}') [px.append(1) for _ in range(dif)] logger.error(f'trace_drift_auto: Unable to apply pixel size: error in brodcasting, used 1 for the last {dif} columns') drift_nm[:, 1:] = drift_px[:, 1:] * px @@ -310,7 +314,13 @@ def move_drift_to_zero(drift_nm, ref_average=10): return drift_ -def apply_drift(zola_table, bf_table, start=None, skip=None, smooth=0, maxbg=0, zinvert=0): +def apply_drift(zola_table: pd.DataFrame, + bf_table: np.ndarray, + start=None, + skip=None, + smooth=0, + maxbg=0, + zinvert=0): # TODO: save smoothed drift plot with interpolated frame numbers # TODO: extrapolate to all frame numbers in the ZOLA table """ @@ -324,36 +334,51 @@ def apply_drift(zola_table, bf_table, start=None, skip=None, smooth=0, maxbg=0, Localiations with bg higher than max_bg will be rejected from the localization table. :return: drift corrected zola table, interpolated and smoothed drift table """ - bf_table = iot.interpolate_drift_table(bf_table, start=start, skip=skip, smooth=smooth) + logger.info(f'Smooth table with sigma={smooth}') + bf_table_sm = iot.smooth_drift_table(bf_table, sigma=smooth) + logger.info(f'interpolation the drift table') + bf_table_int = iot.interpolate_frames(zola_table, bf_table_sm) + logger.info(f'table size change from {bf_table_sm.shape} to {bf_table_int.shape}') if zinvert: print('z invert') - bf_table[:, 3] = -1 * bf_table[:, 3] # flip z + bf_table_int[:, 3] = -1 * bf_table_int[:, 3] # flip z - zola_frame_num = int(np.max(zola_table[:, 1])) - - bf_frame_num = int(np.max(bf_table[:, 0])) + zola_table_dc = zola_table.copy() - print(f'Frame number for zola/bf_DC : {zola_frame_num}/{bf_frame_num}') + zola_frame_num = int(np.max(zola_table_dc['frame'])) - if bf_frame_num < zola_frame_num: - logger.info(f'Truncating ZOLA table to {bf_frame_num} frames') - zola_table = zola_table[zola_table[:, 1] < bf_frame_num] - # fnum = int(np.max(zola_table[:, 1])) - # print(f'New frame number: {fnum}') + bf_frame_num = int(np.max(bf_table_int[:, 0])) - frame_nums = np.array(zola_table[:, 1], dtype='int') - bf_drift_framed = bf_table[frame_nums - 1] + print(f'Frame number for zola/bf_DC : {zola_frame_num}/{bf_frame_num}') - zola_table_dc = zola_table.copy() - zola_table_dc[:, [2, 3, 4]] = zola_table_dc[:, [2, 3, 4]] - bf_drift_framed[:, [1, 2, 3]] - zola_table_dc[:, [11, 12, 13]] = bf_drift_framed[:, [1, 2, 3]] - zola_dc_wo_bf = zola_table_dc + frame_nums = np.array(zola_table['frame'], dtype='int') + + for i, col in enumerate(['x [nm]', 'y [nm]', 'z [nm]']): + try: + zola_table_dc[col] = zola_table_dc[col].to_numpy() - bf_table_int[:, i + 1] + logger.debug(f'Apply drift for column {col}') + except KeyError: + logger.warning(f'{col} is missing in localization table, skipping') + + for i, col in enumerate(['driftX', 'driftY', 'driftZ']): + try: + zola_table_dc[col] = bf_table_int[:, i + 1] + logger.debug(f'Save drift into column {col}') + except KeyError: + logger.warning(f'No drift column {col} found, skipping') + + zola_dc_wo_bf = zola_table_dc.copy() if maxbg > 0: - print(f'Filter background < {maxbg}') - zola_dc_wo_bf = zola_table_dc[zola_table_dc[:, 6] < maxbg] - print(f'Filter out {len(zola_table_dc) - len(zola_dc_wo_bf)}') - return zola_dc_wo_bf, bf_table + logger.info(f'Filter background < {maxbg}') + for col in ['offset [photon]', 'background']: + try: + zola_dc_wo_bf = zola_table_dc[zola_table_dc['background'] < maxbg] + logger.debug(f'Filter column {col}') + except KeyError: + pass + logger.info(f'Filter out {len(zola_table_dc) - len(zola_dc_wo_bf)}') + return zola_dc_wo_bf, bf_table_int def main(argsv=None, callback=None): @@ -434,8 +459,8 @@ def log(msg=None, level='info'): bf_path = iot.get_abs_path(args.drift_table) log(f'Opening localization table') - zola_table = iot.open_csv_table(zola_path) - log(f'Zola table contains {len(zola_table)} localizations from {len(np.unique(zola_table[:,1]))} frames') + zola_table = iot.open_localization_table(zola_path) + log(f'Zola table contains {len(zola_table)} localizations from {len(np.unique(zola_table["frame"]))} frames') bf_table = iot.open_csv_table(bf_path) @@ -458,7 +483,7 @@ def log(msg=None, level='info'): path = os.path.splitext(zola_path)[0] + f'_BFDC_smooth_{args.smooth}.csv' log(f'saving results to {path}') - iot.save_zola_table(zola_table_dc, path) + zola_table_dc.to_csv(path, index=False) iot.save_drift_plot(move_drift_to_zero(bf_table_int), os.path.splitext(path)[0] + '.png', callback=callback) elif args.command == 'batch': diff --git a/bfdc/xcorr.py b/bfdc/xcorr.py index 9740627..a8fa78f 100755 --- a/bfdc/xcorr.py +++ b/bfdc/xcorr.py @@ -7,8 +7,8 @@ import logging logger = logging.getLogger(__name__) -# logger.setLevel(logging.WARNING) -logger.setLevel(logging.DEBUG) +logger.setLevel(logging.WARNING) +#logger.setLevel(logging.DEBUG) class LowXCorr(Exception): @@ -116,6 +116,15 @@ def fit_gauss_3d(stack: np.ndarray, zx_proj = cut_stack.max(axis=1) zy_proj = cut_stack.max(axis=2) z_proj = cut_stack.max(axis=(1, 2)) + ''' + _, _x, _y = get_abs_max(cut_stack) + logger.debug(f'Looking for xy peak {(_x, _y)}') + + z_crop = cut_stack[:, _y-1:_y+2, _x-1:_x+2] + logger.debug(f'Crop {z_crop.shape}') + + z_proj = z_crop.mean(axis=(1, 2)) + ''' # z_proj = cut_stack[:,r].max(axis=1) #ignore y # z_proj = cut_stack[:,r,r] #ignore xy # z_proj = cut_stack[:,r,r] @@ -132,7 +141,7 @@ def fit_gauss_3d(stack: np.ndarray, #logger.error(f'result: {result_fit}') return FitResult() - zfitter = FitPoly1D(z_proj, zoom=20, radius=8) + zfitter = FitPoly1D(z_proj, zoom=20, radius=5) z = zfitter(plot=debug) logger.debug(f'raw xyz {np.round((x, y, z),2)}') diff --git a/setup.py b/setup.py index 8e78401..ea0995d 100755 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name='bfdc', - version='0.4.2', + version='0.5.0', packages=['bfdc'], install_requires=[ 'scikit-image', @@ -11,7 +11,8 @@ 'scipy', 'matplotlib', 'read-roi', - 'imreg_dft' + 'imreg_dft', + 'pandas' ], url='https://github.com/imodpasteur/Brightfield_Drift_Tracking_3D.git', license='MIT', diff --git a/tests/test_iotools.py b/tests/test_iotools.py index e3134cf..808a822 100755 --- a/tests/test_iotools.py +++ b/tests/test_iotools.py @@ -19,6 +19,10 @@ def test_csv_open(self): path = "data/NUP-20180612-FOV1/results/BFCC_table.csv" df = iot.open_localization_table(path) self.assertIsInstance(df, pd.DataFrame) + self.assertEqual(df.shape[1], 4) + print(df.columns) + with self.assertRaises(KeyError): + df['yyy'] def test_txt_open(self): path = "data/NUP-20180612-FOV1/results/ZOLA_localization_table_ZolaProtocol.txt" @@ -26,6 +30,18 @@ def test_txt_open(self): df = iot.open_localization_table(path) +class Test_interpolate_frames(TestCase): + + def test_frame_interpolation(self): + bf_path = "data/NUP-20180612-FOV1/results/BFCC_table.csv" + loc_path = "data/NUP-20180612-FOV1/results/ZOLA_localization_table.csv" + bf_table = iot.open_csv_table(bf_path) + loc_table = iot.open_localization_table(loc_path) + new_bf_table = iot.interpolate_frames(loc_table, bf_table) + self.assertEqual(len(new_bf_table), len(loc_table)) + print(loc_table.columns) + + class Test_check_multi_channel(TestCase): def test_single_channel(self): From a945df23dd1bf6a6f3f700cdfc4b628335f2a7ce Mon Sep 17 00:00:00 2001 From: aaristov Date: Wed, 9 Jan 2019 17:39:59 +0100 Subject: [PATCH 06/10] index.html --- index.html | 1 + 1 file changed, 1 insertion(+) create mode 100755 index.html diff --git a/index.html b/index.html new file mode 100755 index 0000000..ce01362 --- /dev/null +++ b/index.html @@ -0,0 +1 @@ +hello From 067e7a4a3d400ac5bb1f26d4242831e8a73881af Mon Sep 17 00:00:00 2001 From: aaristov Date: Tue, 19 Feb 2019 10:32:45 +0100 Subject: [PATCH 07/10] add pandas opener --- bfdc/iotools.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/bfdc/iotools.py b/bfdc/iotools.py index 7c063df..d8822ba 100755 --- a/bfdc/iotools.py +++ b/bfdc/iotools.py @@ -11,7 +11,7 @@ from skimage import io import bfdc.picassoio as pio import subprocess - +import pandas as pd import logging logger = logging.getLogger(__name__) @@ -253,6 +253,23 @@ def open_csv_table(path, show_header=False): subprocess.check_output(['head', '-n', '1', path]) return np.loadtxt(path, delimiter=',', skiprows=1) +supported_ext = {'csv': + {'load': pd.read_csv, 'save': pd.to_csv}, + 'hdf5': + {'load': pd.read_hdf, 'save': pd.to_hdf} +} + +def open_localization_table(path): + ''' + Generalized table openeneer based on pandas + ''' + ext = get_ext(path) + try: + file_handler = supported_ext(ext) + except IndexError: + logger.error(f'Unsupported file format. Expected {list(supported_ext)}') + + def save_zola_table(table, path): header = 'id,frame,x [nm],y [nm],z [nm],intensity,background,chi2,crlbX,crlbY,crlbZ,driftX,driftY,driftZ,' \ From 3cab99748e7a49e651780cb61396abe0720d085a Mon Sep 17 00:00:00 2001 From: aaristov Date: Tue, 19 Feb 2019 11:38:00 +0100 Subject: [PATCH 08/10] add pandas opener with tests --- bfdc/iotools.py | 124 +++++++++++++++++++++++++++--------------- tests/test_iotools.py | 52 +++++++++++++----- 2 files changed, 119 insertions(+), 57 deletions(-) diff --git a/bfdc/iotools.py b/bfdc/iotools.py index d8822ba..70819e4 100755 --- a/bfdc/iotools.py +++ b/bfdc/iotools.py @@ -1,10 +1,5 @@ import argparse -import numpy as np -import os -import matplotlib as mpl -mpl.use('TkAgg') -#mpl.use('PS') import matplotlib.pyplot as plt from scipy import interpolate from scipy.ndimage import gaussian_filter1d as gf1 @@ -13,6 +8,11 @@ import subprocess import pandas as pd import logging +import numpy as np +import os +import matplotlib as mpl +mpl.use('TkAgg') +# mpl.use('PS') logger = logging.getLogger(__name__) logger.setLevel(logging.WARNING) @@ -25,27 +25,30 @@ def open_stack(path): stack = TiffStackOpener(path, virtual=False) return stack.get_stack() else: - logger.error('Please provide either ome.tif or a folder with .tif files') + logger.error( + 'Please provide either ome.tif or a folder with .tif files') raise TypeError + def open_virtual_stack_picasso(path): movie, [_] = pio.load_movie(path) return movie -def open_virtual_stack(path:str): +def open_virtual_stack(path: str): ''' Opens virtual stack, returns iterable :param path: string :return: iterable ''' if os.path.isdir(path): - stack = TiffStackOpener(path,virtual=True) + stack = TiffStackOpener(path, virtual=True) return stack.picasso_ome elif path.endswith('ome.tif'): return open_virtual_stack_picasso(path=path) else: - logger.error('Please provide either ome.tif or a folder with .tif files') + logger.error( + 'Please provide either ome.tif or a folder with .tif files') raise TypeError @@ -109,7 +112,8 @@ def tif_movie_opener(self, limit=100000, template='img_{:0>9d}_Default_000.tif') f = io.imread(path) yield f except IOError: - logger.debug(f"file {os.path.join(path, fname)} doesn't exist, break") + logger.debug( + f"file {os.path.join(path, fname)} doesn't exist, break") break def tif_zstack_opener(self): @@ -179,7 +183,7 @@ def __len__(self): def __iter__(self): for i in self.file_list: - yield io.imread(os.path.join(self.path,i)) + yield io.imread(os.path.join(self.path, i)) @property def shape(self): @@ -197,7 +201,8 @@ def shape(self): else: logger.error('Stack type not defined to invoke the shape') - raise AttributeError('Unable to get stack shape as stack is not properly loaded') + raise AttributeError( + 'Unable to get stack shape as stack is not properly loaded') def __getitem__(self, i): # return n-th item @@ -209,8 +214,8 @@ def __getitem__(self, i): return io.imread(os.path.join(self.path, self.file_list[i])) else: logger.error('Stack type not defined') - raise AttributeError('Unable to get item as stack is not properly loaded') - + raise AttributeError( + 'Unable to get item as stack is not properly loaded') def save_drift_table(table: np.ndarray, path): @@ -253,32 +258,47 @@ def open_csv_table(path, show_header=False): subprocess.check_output(['head', '-n', '1', path]) return np.loadtxt(path, delimiter=',', skiprows=1) -supported_ext = {'csv': - {'load': pd.read_csv, 'save': pd.to_csv}, - 'hdf5': - {'load': pd.read_hdf, 'save': pd.to_hdf} -} - + +supported_ext = {'.csv': + {'load': pd.read_csv, + 'save': pd.DataFrame.to_csv}, + '.hdf5': + {'load': pd.read_hdf, 'save': pd.DataFrame.to_hdf} + } + + +def get_ext(path): + """ + Get File extension + """ + return os.path.splitext(path)[1] + + def open_localization_table(path): ''' Generalized table openeneer based on pandas ''' ext = get_ext(path) try: - file_handler = supported_ext(ext) - except IndexError: - logger.error(f'Unsupported file format. Expected {list(supported_ext)}') + file_handler = supported_ext[ext] + return file_handler['load'](path) + except KeyError: + logger.error( + f'Unsupported file format. Expected {list(supported_ext)}') + raise TypeError( + f'Unsupported file format. Expected {list(supported_ext)}') def save_zola_table(table, path): header = 'id,frame,x [nm],y [nm],z [nm],intensity,background,chi2,crlbX,crlbY,crlbZ,driftX,driftY,driftZ,' \ 'occurrenceMerging ' - np.savetxt(path, table[:, :15], fmt='%.2f', delimiter=',', comments='', newline='\r\n', header=header) + np.savetxt(path, table[:, :15], fmt='%.2f', delimiter=',', + comments='', newline='\r\n', header=header) def plot_drift(table): - plt.figure(figsize=(6,4), dpi=150) + plt.figure(figsize=(6, 4), dpi=150) plt.plot(table[:, 0], table[:, 1:]) plt.xlabel('frame') plt.ylabel('Drift, nm') @@ -291,7 +311,8 @@ def save_drift_plot(table, path, callback=None): plot_drift(table) plt.savefig(path) plt.close() - if callback: callback({"Plot":path}) + if callback: + callback({"Plot": path}) logger.info(f"Saved drift plot to {path}") @@ -333,7 +354,8 @@ def smooth_drift_table(table, sigma): def check_stacks_size_equals(cal_stack, movie): - logger.info(f'check_stacks_size_equals: Input shapes {cal_stack.shape,movie.shape}') + logger.info( + f'check_stacks_size_equals: Input shapes {cal_stack.shape,movie.shape}') if len(cal_stack.shape) == len(movie.shape) == 3: x1, x2 = cal_stack.shape[1], cal_stack.shape[2] y1, y2 = movie.shape[1], movie.shape[2] @@ -357,13 +379,16 @@ def check_multi_channel(movie, channel=2, channel_position=1): return movie elif ndim == 4: if channel_position == 1: - logger.info(f'check_multi_channel: Returning shape {movie[:,channel-1].shape}') + logger.info( + f'check_multi_channel: Returning shape {movie[:,channel-1].shape}') return movie[:, channel - 1] elif channel_position == 0: - logger.info(f'check_multi_channel: Returning shape {movie[channel-1].shape}') + logger.info( + f'check_multi_channel: Returning shape {movie[channel-1].shape}') return movie[channel - 1] else: - raise (TypeError(f'check_multi_channel: channel order not understood, movie shape {movie.shape}')) + raise (TypeError( + f'check_multi_channel: channel order not understood, movie shape {movie.shape}')) def skip_stack(n_frames: int, start: int, skip: int, maxframes: int): @@ -386,7 +411,8 @@ def skip_stack(n_frames: int, start: int, skip: int, maxframes: int): if maxframes == 0: maxframes = None index_list = index_list[start:maxframes:skip] - logger.info(f'skip_stack: returning frame list with {len(index_list)} frames') + logger.info( + f'skip_stack: returning frame list with {len(index_list)} frames') return index_list @@ -404,7 +430,8 @@ def update_frame_number(table, start, skip): elif table[0, 0] == 0: pass else: - raise (ValueError("update_frame_number: Wrong table. Expected frame numbers starting with 0 or 1")) + raise (ValueError( + "update_frame_number: Wrong table. Expected frame numbers starting with 0 or 1")) table[:, 0] *= skip table[:, 0] += start - 1 logger.info("update_frame_number: Updated frame numbers successfully") @@ -444,9 +471,12 @@ def parse_input(): help='calibration file roi from ImageJ') trace_parser.add_argument('movie', type=str, default='data/sr_2_LED_movie.tif', help='movie stack file') - trace_parser.add_argument('-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') - trace_parser.add_argument('--zdirection', type=str, default='approach', help='Choose approach/retract for the direction of calibration. Default: approach') - trace_parser.add_argument('-xypx', '--xypixel', type=int, default=110, help='xy pixel size in nm. Default: 110') + trace_parser.add_argument( + '-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') + trace_parser.add_argument('--zdirection', type=str, default='approach', + help='Choose approach/retract for the direction of calibration. Default: approach') + trace_parser.add_argument('-xypx', '--xypixel', type=int, + default=110, help='xy pixel size in nm. Default: 110') trace_parser.add_argument('--nframes', type=int, default=None, help='now many frames to analyse from the movie. Default: None') trace_parser.add_argument('--driftFileName', type=str, default='BFCC_table', @@ -467,7 +497,8 @@ def parse_input(): help='If 1, will output debug info. Default: 0') # apply - apply_parser = subparsers.add_parser('apply', help='apply drift 3D to ZOLA table') + apply_parser = subparsers.add_parser( + 'apply', help='apply drift 3D to ZOLA table') apply_parser.add_argument('zola_table', type=str, default='', help='ZOLA localization table, format ifxyz.......dxdydz') apply_parser.add_argument('drift_table', type=str, default='BFCC_table.csv', @@ -477,19 +508,24 @@ def parse_input(): apply_parser.add_argument('--start', type=int, default=0, help='how many frames to skip in the beginning of the movie. Default: 0') - apply_parser.add_argument('--smooth', type=int, default=0, help='gaussian smoothing for the drift. Default: 0') + apply_parser.add_argument('--smooth', type=int, default=0, + help='gaussian smoothing for the drift. Default: 0') apply_parser.add_argument('--maxbg', type=int, default=0, help='reject localizations with high background. Default: 0') - apply_parser.add_argument('--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') + apply_parser.add_argument( + '--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') # batch - batch_parser = subparsers.add_parser('batch', help='''Batch trace and apply drift 3D to ZOLA table''') + batch_parser = subparsers.add_parser( + 'batch', help='''Batch trace and apply drift 3D to ZOLA table''') batch_parser.add_argument('batch_path', type=str, default='', help='data path') - batch_parser.add_argument('-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') + batch_parser.add_argument( + '-z', '--zstep', type=int, default=100, help='z-step in nm. Default: 100') batch_parser.add_argument('--zdirection', type=str, default='approach', help='Choose approach/retract for the direction of calibration. Default: approach') - batch_parser.add_argument('-xypx', '--xypixel', type=int, default=110, help='xy pixel size in nm. Default: 110') + batch_parser.add_argument('-xypx', '--xypixel', type=int, + default=110, help='xy pixel size in nm. Default: 110') batch_parser.add_argument('--skip', type=int, default=0, help='how many frames to skip form the movie. Default: 0') batch_parser.add_argument('--start', type=int, default=0, @@ -525,13 +561,15 @@ def parse_input(): batch_parser.add_argument('--zola_lock_filename', type=str, default='ZOLA_.lock', help='ZOLA DC table name. Default: ZOLA_.lock') - batch_parser.add_argument('--smooth', type=int, default=50, help='gaussian smoothing for the drift. Default: 50') + batch_parser.add_argument('--smooth', type=int, default=50, + help='gaussian smoothing for the drift. Default: 50') batch_parser.add_argument('--filter_bg', type=int, default=100, help='''Use this value to detect BF frames. Frames with the mean value more than this number are counted as bright field. In the reconstuction table, background more than this number will be filtered out. Default: 100 ''') - batch_parser.add_argument('--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') + batch_parser.add_argument( + '--zinvert', type=int, default=0, help='invert z axis for drift. Default: 0') return parser diff --git a/tests/test_iotools.py b/tests/test_iotools.py index 67547b2..e3134cf 100755 --- a/tests/test_iotools.py +++ b/tests/test_iotools.py @@ -1,29 +1,48 @@ from unittest import TestCase import numpy as np -from bfdc.iotools import check_stacks_size_equals, check_multi_channel,skip_stack +import bfdc.iotools as iot import logging import bfdc.picassoio as pio +import pandas as pd logger = logging.getLogger(__name__) +class Test_pd_io(TestCase): + + def test_get_ext(self): + path = 'safdsa/dsfadf.csv' + ext = iot.get_ext(path) + self.assertEqual(ext, '.csv') + + def test_csv_open(self): + path = "data/NUP-20180612-FOV1/results/BFCC_table.csv" + df = iot.open_localization_table(path) + self.assertIsInstance(df, pd.DataFrame) + + def test_txt_open(self): + path = "data/NUP-20180612-FOV1/results/ZOLA_localization_table_ZolaProtocol.txt" + with self.assertRaises(TypeError): + df = iot.open_localization_table(path) + + class Test_check_multi_channel(TestCase): def test_single_channel(self): stack = np.zeros((10, 16, 16)) - out = check_multi_channel(stack).shape + out = iot.check_multi_channel(stack).shape expected = (10, 16, 16) self.assertEqual(out, expected) def test_dual_channel(self): stack = np.zeros((10, 2, 16, 16)) stack[:, 1] = 1 - out = check_multi_channel(stack) - out_shape = check_multi_channel(stack).shape + out = iot.check_multi_channel(stack) + out_shape = iot.check_multi_channel(stack).shape expected = np.ones((10, 16, 16)) expected_shape = (10, 16, 16) res1 = np.unique(np.equal(out, expected)) - #logger.info(f"{__name__}: res1 {res1}") + # logger.info(f"{__name__}: res1 {res1}") self.assertEqual(res1, [True]) self.assertTupleEqual(out_shape, expected_shape) @@ -33,20 +52,21 @@ class TestCheck_stacks_size_equals(TestCase): def test_check_stacks_size_equals(self): stack1 = np.zeros((10, 15, 15)) stack2 = np.zeros((15, 15, 15)) - self.assertTrue(check_stacks_size_equals(stack1, stack2)) + self.assertTrue(iot.check_stacks_size_equals(stack1, stack2)) def test_check_stacks_size_noequals(self): stack1 = np.zeros((10, 13, 13)) stack2 = np.zeros((15, 15, 15)) - self.assertFalse(check_stacks_size_equals(stack1, stack2)) + self.assertFalse(iot.check_stacks_size_equals(stack1, stack2)) class TestSkip_stack(TestCase): def test_skip_stack_default(self): - #stack = np.zeros((100,15,15)) - stack,[_] = pio.load_movie('./data/full_frame/sr_642_redLED_start_10_skip_10_SP_100nm_1_MMStack_Pos0-100f.ome.tif') + # stack = np.zeros((100,15,15)) + stack, [_] = pio.load_movie( + './data/full_frame/sr_642_redLED_start_10_skip_10_SP_100nm_1_MMStack_Pos0-100f.ome.tif') input = stack.shape n_frames = stack.n_frames expected_index_list = list(np.arange(n_frames)) @@ -54,8 +74,12 @@ def test_skip_stack_default(self): start = 0 skip = 0 nframes = 0 - index_list = skip_stack(n_frames=len(stack), start=start, skip=skip, maxframes=nframes) - self.assertListEqual(expected_index_list,list(index_list),msg=f"test_skip_stack_default: {input}, {len(index_list)}") + index_list = iot.skip_stack(n_frames=len(stack), + start=start, + skip=skip, + maxframes=nframes) + self.assertListEqual(expected_index_list, list( + index_list), msg=f"test_skip_stack_default: {input}, {len(index_list)}") def test_skip_stack_skipping10(self): stack, [_] = pio.load_movie('./data/crop_frame/sr_2_LED_movie.tif') @@ -64,9 +88,9 @@ def test_skip_stack_skipping10(self): skip = 10 nframes = None n_frames = stack.n_frames - expected_index_list = list(np.arange(start-1,n_frames,skip)) + expected_index_list = list(np.arange(start-1, n_frames, skip)) print(f'input shape {input}') - index_list = skip_stack(n_frames=len(stack), start=start, skip=skip, maxframes=nframes) + index_list = iot.skip_stack(n_frames=len( + stack), start=start, skip=skip, maxframes=nframes) self.assertListEqual(expected_index_list, list(index_list), msg=f"test_skip_stack_default: {input}, {len(index_list)}") - From 1e16a877c85de38360fc74a3c506e8ebda79a121 Mon Sep 17 00:00:00 2001 From: aaristov Date: Tue, 19 Feb 2019 16:18:31 +0100 Subject: [PATCH 09/10] rewrite frame interpolation --- bfdc/iotools.py | 24 +++++------------------- 1 file changed, 5 insertions(+), 19 deletions(-) diff --git a/bfdc/iotools.py b/bfdc/iotools.py index 70819e4..5cba277 100755 --- a/bfdc/iotools.py +++ b/bfdc/iotools.py @@ -316,33 +316,19 @@ def save_drift_plot(table, path, callback=None): logger.info(f"Saved drift plot to {path}") -def interpolate_drift_table(table, start=0, skip=0, smooth=10): - """ - Smooth and interpolate a table - :param table: fxyz (nm) array - :param start: in case of renumbering needed : first frame - :param skip: how many frame were skipped - :param smooth: gaussian smoothing sigma - :return: interpolated table - """ - w = table.shape[1] - if smooth > 0: - table = smooth_drift_table(table, sigma=smooth) - - table = update_frame_number(table, start=start, skip=skip) - - time = table[:, 0] +def interpolate_frames(loc_table: pd.DataFrame, bf_table): + w = bf_table.shape[1] + time = bf_table[:, 0] # print(time.shape) - time_new = np.arange(1, max(time) + 1) + time_new = loc_table['frame'].values new_table = np.zeros((len(time_new), w)) new_table[:, 0] = time_new for col in range(1, w): - y = table[:, col] + y = bf_table[:, col] # print(y.shape) f = interpolate.interp1d(time, y, fill_value='extrapolate') ynew = f(time_new) new_table[:, col] = ynew - logger.info(f'interpolating from {len(time)} to {len(ynew)} frames') return new_table From 3fe4e1fdd250da8c3463f8d08c59d6d190bf4dc6 Mon Sep 17 00:00:00 2001 From: aaristov Date: Tue, 19 Feb 2019 16:19:25 +0100 Subject: [PATCH 10/10] use pandas for localization table handling --- README.md | 2 + bfdc/drift.py | 109 ++++++++++++++++++++++++++---------------- bfdc/xcorr.py | 15 ++++-- setup.py | 5 +- tests/test_iotools.py | 16 +++++++ 5 files changed, 100 insertions(+), 47 deletions(-) diff --git a/README.md b/README.md index a933988..b8a6089 100755 --- a/README.md +++ b/README.md @@ -103,6 +103,8 @@ Fluorescent bead track before and after BFDC. # Change log +V0.5.0 Using pandas for table handling + V0.2.0 Processing tif file sets V0.1.4 Batch processing diff --git a/bfdc/drift.py b/bfdc/drift.py index b444229..1483fe4 100755 --- a/bfdc/drift.py +++ b/bfdc/drift.py @@ -1,7 +1,9 @@ import traceback import logging import numpy as np -import os, sys +import os +import sys +import pandas as pd import bfdc.batch as batch import bfdc.xcorr as xcorr import bfdc.feature as ft @@ -41,16 +43,16 @@ def __init__(self, cal_stack, roi, radius_xy=3, radius_z=10, smooth_dict=1): self.radius_z = radius_z self.z_last = None - def do_trace(self, - movie, - frame_list, - extend_xy=5, - min_xcorr=0.5, - z_zoom=20, - min_signal=100, - smooth_movie=0, - debug=False, - callback=None): + def do_trace(self, + movie, + frame_list, + extend_xy=5, + min_xcorr=0.5, + z_zoom=20, + min_signal=100, + smooth_movie=0, + debug=False, + callback=None): logging.info(f"doTrace: got the movie with shape {movie.shape}, using {len(frame_list)} frames for tracing") # for i,frame in enumerate(movie): # crop frame with extended by 5px boundaries @@ -63,7 +65,7 @@ def do_trace(self, xc = np.mean([b["xmax"], -b["xmin"]]) yc = np.mean([b["ymax"], -b["ymin"]]) x_, y_, z_ = 0, 0, 0 - #z_px = 0 + # z_px = 0 frame_num = 0 self.x_correction = 0 self.y_correction = 0 @@ -263,7 +265,7 @@ def trace_drift_auto(args, cal_stack, movie, roi, debug=False, callback=None): px = [args.xypixel, args.xypixel, args.zstep, args.xypixel, args.xypixel] print(f'Pixel size xyz: {px}') - drift_px = np.zeros((1, 4)) + drift_px = np.zeros((1, 6)) try: @@ -276,10 +278,10 @@ def trace_drift_auto(args, cal_stack, movie, roi, debug=False, callback=None): try: fitter = DriftFitter(cal_stack, roi, radius_xy=8, radius_z=12) drift_px = fitter.do_trace(movie, - frame_list=frame_list, - min_signal=min_signal, - debug=debug, - callback=callback) + frame_list=frame_list, + min_signal=min_signal, + debug=debug, + callback=callback) except KeyboardInterrupt as e: print(e) @@ -287,7 +289,9 @@ def trace_drift_auto(args, cal_stack, movie, roi, debug=False, callback=None): try: drift_nm[:, 1:] = drift_px[:, 1:] * px except ValueError: + logger.error(f'Unable to apply px size. px = {px}, drift_nm.shape = {drift_nm.shape}') dif = len(drift_px[0,1:]) - len(px) + logger.error(f'dif in shape drift_px[0,1:] - len(px) = {dif}') [px.append(1) for _ in range(dif)] logger.error(f'trace_drift_auto: Unable to apply pixel size: error in brodcasting, used 1 for the last {dif} columns') drift_nm[:, 1:] = drift_px[:, 1:] * px @@ -310,7 +314,13 @@ def move_drift_to_zero(drift_nm, ref_average=10): return drift_ -def apply_drift(zola_table, bf_table, start=None, skip=None, smooth=0, maxbg=0, zinvert=0): +def apply_drift(zola_table: pd.DataFrame, + bf_table: np.ndarray, + start=None, + skip=None, + smooth=0, + maxbg=0, + zinvert=0): # TODO: save smoothed drift plot with interpolated frame numbers # TODO: extrapolate to all frame numbers in the ZOLA table """ @@ -324,36 +334,51 @@ def apply_drift(zola_table, bf_table, start=None, skip=None, smooth=0, maxbg=0, Localiations with bg higher than max_bg will be rejected from the localization table. :return: drift corrected zola table, interpolated and smoothed drift table """ - bf_table = iot.interpolate_drift_table(bf_table, start=start, skip=skip, smooth=smooth) + logger.info(f'Smooth table with sigma={smooth}') + bf_table_sm = iot.smooth_drift_table(bf_table, sigma=smooth) + logger.info(f'interpolation the drift table') + bf_table_int = iot.interpolate_frames(zola_table, bf_table_sm) + logger.info(f'table size change from {bf_table_sm.shape} to {bf_table_int.shape}') if zinvert: print('z invert') - bf_table[:, 3] = -1 * bf_table[:, 3] # flip z + bf_table_int[:, 3] = -1 * bf_table_int[:, 3] # flip z - zola_frame_num = int(np.max(zola_table[:, 1])) - - bf_frame_num = int(np.max(bf_table[:, 0])) + zola_table_dc = zola_table.copy() - print(f'Frame number for zola/bf_DC : {zola_frame_num}/{bf_frame_num}') + zola_frame_num = int(np.max(zola_table_dc['frame'])) - if bf_frame_num < zola_frame_num: - logger.info(f'Truncating ZOLA table to {bf_frame_num} frames') - zola_table = zola_table[zola_table[:, 1] < bf_frame_num] - # fnum = int(np.max(zola_table[:, 1])) - # print(f'New frame number: {fnum}') + bf_frame_num = int(np.max(bf_table_int[:, 0])) - frame_nums = np.array(zola_table[:, 1], dtype='int') - bf_drift_framed = bf_table[frame_nums - 1] + print(f'Frame number for zola/bf_DC : {zola_frame_num}/{bf_frame_num}') - zola_table_dc = zola_table.copy() - zola_table_dc[:, [2, 3, 4]] = zola_table_dc[:, [2, 3, 4]] - bf_drift_framed[:, [1, 2, 3]] - zola_table_dc[:, [11, 12, 13]] = bf_drift_framed[:, [1, 2, 3]] - zola_dc_wo_bf = zola_table_dc + frame_nums = np.array(zola_table['frame'], dtype='int') + + for i, col in enumerate(['x [nm]', 'y [nm]', 'z [nm]']): + try: + zola_table_dc[col] = zola_table_dc[col].to_numpy() - bf_table_int[:, i + 1] + logger.debug(f'Apply drift for column {col}') + except KeyError: + logger.warning(f'{col} is missing in localization table, skipping') + + for i, col in enumerate(['driftX', 'driftY', 'driftZ']): + try: + zola_table_dc[col] = bf_table_int[:, i + 1] + logger.debug(f'Save drift into column {col}') + except KeyError: + logger.warning(f'No drift column {col} found, skipping') + + zola_dc_wo_bf = zola_table_dc.copy() if maxbg > 0: - print(f'Filter background < {maxbg}') - zola_dc_wo_bf = zola_table_dc[zola_table_dc[:, 6] < maxbg] - print(f'Filter out {len(zola_table_dc) - len(zola_dc_wo_bf)}') - return zola_dc_wo_bf, bf_table + logger.info(f'Filter background < {maxbg}') + for col in ['offset [photon]', 'background']: + try: + zola_dc_wo_bf = zola_table_dc[zola_table_dc['background'] < maxbg] + logger.debug(f'Filter column {col}') + except KeyError: + pass + logger.info(f'Filter out {len(zola_table_dc) - len(zola_dc_wo_bf)}') + return zola_dc_wo_bf, bf_table_int def main(argsv=None, callback=None): @@ -434,8 +459,8 @@ def log(msg=None, level='info'): bf_path = iot.get_abs_path(args.drift_table) log(f'Opening localization table') - zola_table = iot.open_csv_table(zola_path) - log(f'Zola table contains {len(zola_table)} localizations from {len(np.unique(zola_table[:,1]))} frames') + zola_table = iot.open_localization_table(zola_path) + log(f'Zola table contains {len(zola_table)} localizations from {len(np.unique(zola_table["frame"]))} frames') bf_table = iot.open_csv_table(bf_path) @@ -458,7 +483,7 @@ def log(msg=None, level='info'): path = os.path.splitext(zola_path)[0] + f'_BFDC_smooth_{args.smooth}.csv' log(f'saving results to {path}') - iot.save_zola_table(zola_table_dc, path) + zola_table_dc.to_csv(path, index=False) iot.save_drift_plot(move_drift_to_zero(bf_table_int), os.path.splitext(path)[0] + '.png', callback=callback) elif args.command == 'batch': diff --git a/bfdc/xcorr.py b/bfdc/xcorr.py index 9740627..a8fa78f 100755 --- a/bfdc/xcorr.py +++ b/bfdc/xcorr.py @@ -7,8 +7,8 @@ import logging logger = logging.getLogger(__name__) -# logger.setLevel(logging.WARNING) -logger.setLevel(logging.DEBUG) +logger.setLevel(logging.WARNING) +#logger.setLevel(logging.DEBUG) class LowXCorr(Exception): @@ -116,6 +116,15 @@ def fit_gauss_3d(stack: np.ndarray, zx_proj = cut_stack.max(axis=1) zy_proj = cut_stack.max(axis=2) z_proj = cut_stack.max(axis=(1, 2)) + ''' + _, _x, _y = get_abs_max(cut_stack) + logger.debug(f'Looking for xy peak {(_x, _y)}') + + z_crop = cut_stack[:, _y-1:_y+2, _x-1:_x+2] + logger.debug(f'Crop {z_crop.shape}') + + z_proj = z_crop.mean(axis=(1, 2)) + ''' # z_proj = cut_stack[:,r].max(axis=1) #ignore y # z_proj = cut_stack[:,r,r] #ignore xy # z_proj = cut_stack[:,r,r] @@ -132,7 +141,7 @@ def fit_gauss_3d(stack: np.ndarray, #logger.error(f'result: {result_fit}') return FitResult() - zfitter = FitPoly1D(z_proj, zoom=20, radius=8) + zfitter = FitPoly1D(z_proj, zoom=20, radius=5) z = zfitter(plot=debug) logger.debug(f'raw xyz {np.round((x, y, z),2)}') diff --git a/setup.py b/setup.py index 8e78401..ea0995d 100755 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name='bfdc', - version='0.4.2', + version='0.5.0', packages=['bfdc'], install_requires=[ 'scikit-image', @@ -11,7 +11,8 @@ 'scipy', 'matplotlib', 'read-roi', - 'imreg_dft' + 'imreg_dft', + 'pandas' ], url='https://github.com/imodpasteur/Brightfield_Drift_Tracking_3D.git', license='MIT', diff --git a/tests/test_iotools.py b/tests/test_iotools.py index e3134cf..808a822 100755 --- a/tests/test_iotools.py +++ b/tests/test_iotools.py @@ -19,6 +19,10 @@ def test_csv_open(self): path = "data/NUP-20180612-FOV1/results/BFCC_table.csv" df = iot.open_localization_table(path) self.assertIsInstance(df, pd.DataFrame) + self.assertEqual(df.shape[1], 4) + print(df.columns) + with self.assertRaises(KeyError): + df['yyy'] def test_txt_open(self): path = "data/NUP-20180612-FOV1/results/ZOLA_localization_table_ZolaProtocol.txt" @@ -26,6 +30,18 @@ def test_txt_open(self): df = iot.open_localization_table(path) +class Test_interpolate_frames(TestCase): + + def test_frame_interpolation(self): + bf_path = "data/NUP-20180612-FOV1/results/BFCC_table.csv" + loc_path = "data/NUP-20180612-FOV1/results/ZOLA_localization_table.csv" + bf_table = iot.open_csv_table(bf_path) + loc_table = iot.open_localization_table(loc_path) + new_bf_table = iot.interpolate_frames(loc_table, bf_table) + self.assertEqual(len(new_bf_table), len(loc_table)) + print(loc_table.columns) + + class Test_check_multi_channel(TestCase): def test_single_channel(self):

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