Skip to main content

Python: How do I read the data in this multipage TIFF file to produce the desired image?

I am working with TIFF files that represent the readings of detectors in electron microscopy, and I know how this particular image should look, but I'm unsure how to get that result from the raw data in question. The TIFF files in question have several pages corresponding to frames on which data was taken, but when I look at each individual frame, they seem more or less like white noise, so there must be some other way to massage the data to look how it's meant to. I've tried reading each frame into a numpy array and taking the sum over all frames to produce a new image, and it seemed like this almost worked for some of the images in question, though not all. Preferably, I'd like to produce a numpy array representing the new image that looks as it is meant to.

The actual TIFF image itself is too large to attach here, so I'll link to where it can be downloaded on the EMPIAR database. It is /data/ds1_tifs/20180309_Vn_ribosome_0001.tif on this page, the first image listed under ds1_tifs. You'll want to unselect everything else and download this image alone, since the full dataset is obviously absurdly large. The result image should look like this.

My posts on cryo-em discussion boards haven't gained much traction, so any help would be appreciated.



source https://stackoverflow.com/questions/72999180/python-how-do-i-read-the-data-in-this-multipage-tiff-file-to-produce-the-desire

Comments

Popular posts from this blog

How to show number of registered users in Laravel based on usertype?

i'm trying to display data from the database in the admin dashboard i used this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count(); echo $users; ?> and i have successfully get the correct data from the database but what if i want to display a specific data for example in this user table there is "usertype" that specify if the user is normal user or admin i want to user the same code above but to display a specific usertype i tried this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count()->WHERE usertype =admin; echo $users; ?> but it didn't work, what am i doing wrong? source https://stackoverflow.com/questions/68199726/how-to-show-number-of-registered-users-in-laravel-based-on-usertype

Why is my reports service not connecting?

I am trying to pull some data from a Postgres database using Node.js and node-postures but I can't figure out why my service isn't connecting. my routes/index.js file: const express = require('express'); const router = express.Router(); const ordersCountController = require('../controllers/ordersCountController'); const ordersController = require('../controllers/ordersController'); const weeklyReportsController = require('../controllers/weeklyReportsController'); router.get('/orders_count', ordersCountController); router.get('/orders', ordersController); router.get('/weekly_reports', weeklyReportsController); module.exports = router; My controllers/weeklyReportsController.js file: const weeklyReportsService = require('../services/weeklyReportsService'); const weeklyReportsController = async (req, res) => { try { const data = await weeklyReportsService; res.json({data}) console...

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...