fapyc 0.2.0

A Python wrapper for the FAPEC data compressor.

FaPyc

A Python wrapper for the FAPEC data compressor. (C) DAPCOM Data Services S.L. - https://www.dapcom.es

The free decompression-only library is included, which has some limitations such as the maximum number of threads and the recovery of corrupted files. Only a 'dummy' compression library is provided: You can get free evaluation licenses at https://www.dapcom.es/get-fapec/ to test the compressor. For full licenses, please contact us at [e-mail]

Usage

There are 3 main execution modes:

• File: When invoking Fapyc or Unfapyc on a filename, it will (de)compress it directly into another file.
• Buffer: You can load the whole file to (de)compress on e.g. a byte array, and then invoke Fapyc/Unfapyc which will leave the result in the output buffer. Obviously, you should be careful with large/huge files!
• File-to-buffer decompression: You can directly decompress a file (without having to load it beforehand) and leave its decompressed output in a buffer, which you can use afterwards.
• Chunk: FAPEC internally works in 'chunks' of data, of up to 384MB each, which allows to progressively (de)compress a huge file while keeping memory usage under control. For now, this feature is only available in the FAPEC CLI and C API, not in Fapyc/Unfapyc yet.

Examples

Compress and decompress a file

In this example we use the kmall option of FAPEC, suitable for this kind of geomaritime data files from Kongsberg Maritime:

from fapyc import Fapyc, Unfapyc

filename = input("Path to KMALL file: ")

print("Preparing to compress %s" % (filename))
# Here we invoke FAPEC to directly run on files,
# so the memory usage will be much smaller (just 5MB or so)
# although it won't allow us to directly access the
# (de)compressed buffers.
f = Fapyc(filename, chunksize = 2048576, blen = 512)
f.compress_kmall()

print("Preparing to decompress %s" % (filename + ".fapec"))
uf = Unfapyc(filename + ".fapec")
uf.decompress(output=filename+".dec")

Compress and decompress a buffer

In this example we use the tab option of FAPEC, which typically outperforms gzip and bzip2 on tabulated text data:

from fapyc import Fapyc, Unfapyc

filename = input("Path to file: ")
file = open(filename, "rb")
# Beware - Load the whole file to memory
data = file.read()
f = Fapyc(buffer = data)
# Invoke our tabulated-text compression algorithm
# indicating a comma separator
f.compress_tabtxt(sep1=',')
print("Ratio =", round(float(len(data))/len(f.outputBuffer), 4))

# Now we decompress the buffer
uf = Unfapyc(buffer = f.outputBuffer)
uf.decompress()

Decompress a file into a buffer, and do some operations on it

Here we provide a quite specific use case, based on the Gaia (E)DR3 bulk catalogue (which is publicly available as FAPEC-compressed CSVs). In this example, we decompress one of the files, get its CSV-formatted contents with Pandas, apply some filtering conditions, and generate a histogram.

from fapyc import Unfapyc
from io import BytesIO
import pandas as pd
import matplotlib.pyplot as plt

# Read the FAPEC file into a buffer
filename = input("Path to CSV-FAPEC file: ")
file = open(filename, "rb")
# Beware - we load the whole file to memory
data = file.read()

# Decompress the buffer
uf = Unfapyc(buffer = data)
uf.decompress()

# Regenerate the CSV from the bytes buffer
df = pd.read_csv(BytesIO(uf.outputBuffer), comment="#")
print("Info from the full CSV:")
print(df.info())
# Prepare some nice histograms for all data
plt.subplot(2,2,1)
plt.title("Full CSV: skymap (%d sources)" % df.shape[0])
plt.xlabel("RA")
plt.ylabel("DEC")
plt.hist2d(df.ra, df.dec, bins=(100, 100), cmap=plt.cm.jet)
plt.colorbar()
plt.subplot(2,2,2)
plt.title("Full CSV: G dist")
plt.xlabel("G magnitude")
plt.ylabel("Counts")
plt.yscale("log")
plt.hist(df.phot_g_mean_mag, bins=(50))

# Now let's repeat, but doing the histogram from only the values that fulfil
# some conditions on some of the CSV fields
iter_csv = pd.read_csv(BytesIO(uf.outputBuffer), comment="#", iterator=True, chunksize=1000)
df = pd.concat((x.query("ra_error < 0.1 & dec_error < 0.1 & ruwe > 0 & ruwe < 5") for x in iter_csv))
print("Info from the filtered CSV:")
print(df.info())
plt.subplot(2,2,3)
plt.title("Filtered CSV: skymap (%d sources)" % df.shape[0])
plt.xlabel("RA")
plt.ylabel("DEC")
plt.hist2d(df.ra, df.dec, bins=(100, 100), cmap=plt.cm.jet)
plt.colorbar()
plt.subplot(2,2,4)
plt.title("Filtered CSV: G dist")
plt.xlabel("G magnitude")
plt.ylabel("Counts")
plt.yscale("log")
plt.hist(df.phot_g_mean_mag, bins=(50))

plt.show()