kandi X-RAY | ABIF Summary
kandi X-RAY | ABIF Summary
Handle ABIF (Applied Biosystems Genetic Analysis Data File Format) FSA, AB1 and HID files.
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- Unpack data .
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ABIF Examples and Code Snippets
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Trending Discussions on ABIF
QUESTION
I am very confused. I need to read binary files (.fsa extension by Applied Biotechnology aka ABIF, FASTA files) and I ran into a problem reading signed integers. I am doing everything according to this manual https://drive.google.com/file/d/1zL-r6eoTzFIeYDwH5L8nux2lIlsRx3CK/view?usp=sharing So, for example, let's look at the fDataSize field in the header of a file https://drive.google.com/file/d/1rrL01B_gzgBw28knvFit6hUIA5jcCDry/view?usp=sharing
I know that it is supposed to be 2688 (according to the manual, it is a signed integer of 32 bits), which is 00000000 00000000 00001010 10000000 in binary form. Indeed, when I read these 32 bits as an array of 4 bytes, I get [0, 0, 10, -128], which is exactly the same in binary form.
However, if I read it as Integer, it results in 16809994, which is 00000001 00000000 10000000 00001010 in bits.
As I understood from multiple forums, they use Swap and htonl functions to convert integers from little-endian order to big-endian. They also recommend using BSWAP EAX instruction for 32bit integers. But in this case they work in a kind of wrong way, specifically: Swap, applied to 16809994, returns 16779904 or 00000001 00000000 00001010 10000000, and BSWAP instruction converts 16809994 to 176160769, i.e. 00001010 10000000 00000000 00000001
As we can see, built-in functions do something different from what I need. Swap is likely to return the correct result, but, for some reason, reading these bits as an Integer changes the left-most byte. So, what is wrong and what do I do?
Upd. 1 For storing the header data I use the following record:
...ANSWER
Answered 2020-Nov-26 at 07:35Here is a implementation example using pure pascal:
QUESTION
We have Net Asset Values (NAV) over several years for over 100 mutual funds where the mutual funds start and end at different dates.
The NAVs are monthly and we also made a column marking them with a number for each month (1-12) and the year. We also have monthly risk free return, where we did the same by making a column and marking them with a number for each month (1-12) and the year.
We now need to subtract the right monthly risk free asset to the dedicated NAV. The data for the risk free return and the NAV are in different data frames. We have pulled 50 lines from each data frames if this helps illustrate the problem/create a solution.
Here is a selection of the data frame for the NAV (dput(dfNAV[1:50,]
):
ANSWER
Answered 2020-Mar-31 at 18:01Sound like join dfNAV
and dfRF
using Month
and Year
as identifiers. Take all columns from dfNAV
and Rf
from dfRF
.
base R:
QUESTION
We have daily returns from well over 100 mutual funds that we wish to convert into monthly returns. The monthly return should not be the average from each month, but the funds' return at the end of each month. The funds start and end at different points in time, and they need to remain by themself (not adding all mutual fund returns into 1 each month).
As of the picture one may see the dataframe (df) and parts of its content. The dates are in the first column and it needs to be sorted. enter image description here We would love some help to solve this problem.
I believe that it contains sufficient information be able to write the code.
DATA,dput(df5[1:50,])
- "Date" "Name" "Nav"
- 2012-01-02 Aktiva 10 121.738
- 2012-01-03 Aktiva 10 121.87
- 2012-01-04 Aktiva 10 121.906
- 2012-01-05 Aktiva 10 121.89
- 2012-01-06 Aktiva 10 121.949
- 2012-01-09 Aktiva 10 122.024
- 2012-01-10 Aktiva 10 122.205
- 2012-01-11 Aktiva 10 122.219
- 2012-01-12 Aktiva 10 122.324
- 2012-01-13 Aktiva 10 122.309
- 2012-01-16 Aktiva 10 122.45
- 2012-01-17 Aktiva 10 122.433
- 2012-01-18 Aktiva 10 122.483
- 2012-01-19 Aktiva 10 122.596
- 2012-01-20 Aktiva 10 122.548
- 2012-01-23 Aktiva 10 122.653
- 2012-01-24 Aktiva 10 122.507
- 2012-01-25 Aktiva 10 122.582
- 2012-01-26 Aktiva 10 122.783
- 2012-01-27 Aktiva 10 122.804
- 2012-01-30 Aktiva 10 122.749
- 2012-01-31 Aktiva 10 122.865
- 2012-02-01 Aktiva 10 123.044
- 2012-02-02 Aktiva 10 123.184
- 2012-02-03 Aktiva 10 123.32
- 2012-02-06 Aktiva 10 123.402
- 2012-02-07 Aktiva 10 123.322
- 2012-02-08 Aktiva 10 123.342
- 2012-02-09 Aktiva 10 123.421
- 2012-02-10 Aktiva 10 123.368
- 2012-02-13 Aktiva 10 123.418
- 2012-02-14 Aktiva 10 123.389
- 2012-02-15 Aktiva 10 123.558
- 2012-02-16 Aktiva 10 123.735
- 2012-02-17 Aktiva 10 123.636
- 2012-02-20 Aktiva 10 123.68
- 2012-02-21 Aktiva 10 123.701
- 2012-02-22 Aktiva 10 123.705
- 2012-02-23 Aktiva 10 123.663
- 2012-02-24 Aktiva 10 123.723
- 2012-02-27 Aktiva 10 123.77
- 2012-02-28 Aktiva 10 123.9
- 2012-02-29 Aktiva 10 123.91
- 2012-03-01 Aktiva 10 123.95
- 2012-03-02 Aktiva 10 124.02
- 2012-03-05 Aktiva 10 123.98
- 2012-03-06 Aktiva 10 123.74
- 2012-03-07 Aktiva 10 123.79
- 2012-03-08 Aktiva 10 123.92
- 2012-03-09 Aktiva 10 124.05
dput(df[1:50,]) data looks like this:
...ANSWER
Answered 2020-Mar-31 at 11:59With tidyverse
and lubridate
you can do the following.
You can group_by
month and then filter
to show only the last row of data for each month. arrange
is used to sort by Date
just in case.
Edit: Also group_by
year(Date)
in this example. Results updated with new data provided.
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