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#!/usr/bin/env python | |
import sys | |
import struct | |
with open(sys.argv[1], "rb") as f: | |
[nx, ny, nz] = [x[0] for x in struct.iter_unpack('i', f.read(12))] | |
# don't care about mode, start | |
_ = f.read(16) |
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#!/usr/bin/env python | |
from cryosparc.tools import CryoSPARC | |
import json | |
import argparse | |
import numpy as np | |
# you would need to enter your information for CryoSPARC here | |
with open('/u/rposert/instance-info.json', 'r') as f: | |
instance_info = json.load(f) |
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from cryosparc.tools import CryoSPARC | |
import numpy as np | |
cs = CryoSPARC( | |
# you'll need env variables here, or just set stuff directly | |
) | |
cs.test_connection() | |
# we will filter fullsize_particles so that only the intersection with | |
# downsampled particles are retained. ctf, alignments, etc. will all |
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return { | |
Span = function (elem) | |
if quarto.doc.is_format('docx') then | |
if elem.classes:includes('aside') then | |
return "" | |
else | |
return elem | |
end | |
end | |
end |
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#!/usr/bin/env python | |
import numpy as np | |
import pandas as pd | |
import umap | |
import sys | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
from sklearn.preprocessing import StandardScaler | |
from cryosparc.dataset import Dataset |
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