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Efficient way to create/unpack large bitfields in C?


Efficient way to create/unpack large bitfields in C?

By : Christopher Lee
Date : November 21 2020, 07:01 PM
it should still fix some issue Use bit 0 and 31 to determine endianness and pack 3 10-bit values in the middle. One easy way to test matching endianness is to set bit 0 to 0 and bit 31 to 1. On the receiving end, if bit 0 is 1, assert that bit 31 is 0 and swap endianness. Otherwise, if bit 0 is 0, assert that bit 31 is 1 and extract the 3 values.
code :


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Efficient way to create a large number of SharePoint folders

Efficient way to create a large number of SharePoint folders


By : etb
Date : March 29 2020, 07:55 AM
To fix this issue You could use Batch Updating to create the folders and items. This is much faster as creating item by item.
To read the source folder structure you could increase performance with a CAML query.
Most efficient way to create a large file (> 1GB)

Most efficient way to create a large file (> 1GB)


By : Moiz Ismail
Date : March 29 2020, 07:55 AM
hop of those help? Create a sparse file. That is, open a file, seek to a position above 1GB and write some bytes.
Relevant: Create file with given size in Java
What is the most efficient way to create large PDFs with TCPDF?

What is the most efficient way to create large PDFs with TCPDF?


By : Einav
Date : March 29 2020, 07:55 AM
around this issue Answer to question 1:
Well, this is another one caused by my inexperience with PHP. The blank PDF was resulting from the default max_execution_time for PHP set to 30 sec. I used ini_set to set the value to 300 for the PDF generation script, and my report was generated in full.
Efficient way to create a large matrix in python from a table of interactions

Efficient way to create a large matrix in python from a table of interactions


By : Јцѕт Мэ
Date : March 29 2020, 07:55 AM
wish of those help I have a csv file in the following structure, indicating "interactions". , Look at using pd.crosstab:
code :
pd.crosstab(df['node1'],df['node2'],df['interaction'],aggfunc='first').fillna(0)
node2  ABA   ABC   TAH
node1                 
ABC    0.0  0.00  0.42
XYZ    0.3  0.74  0.24
Python: efficient way to create new csv from large dataset

Python: efficient way to create new csv from large dataset


By : user1950854
Date : March 29 2020, 07:55 AM
help you fix your problem Stage 1
If you necessarily want to iterate in the for loop then besides using pd.to_csv which likely to improve performance you can do the following:
code :
...
SetBadElem = set(BadElemList)
...
for i,Elem_Loc in enumerate(MasterList):
    if Elem_Loc not in SetBadElem:
        line ='\n%s,%.25f'%(j,MasterListStrain1[Elem_Loc])
        outfile.write(line)  
...
SetBadElem = set(BadElemList)
...
for Elem in Master:
    if Elem not in SetBadElem:
        line ='\n%s,%.25f'%(Elem['elem'], Elem['Max_Principal_Strain'])
        outfile.write(line)  
formatter = lambda row: '\n%s,%.25f'%(row['elem'], row['Max_Principal_Strain'])

Master['Formatted'] = Master.apply(formatter)
import numpy as np
formatter = lambda row: '\n%s,%.25f'%(row['elem'], row['Max_Principal_Strain']) if row not in SetBadElem else np.nan
Master.dropna()  
Master.to_csv(filename)
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