def ccl(data, rad, loc, npskyrad, mask=None):
data_copy = data.copy()
if type(mask) == type(None):
mask = np.ones(np.shape(data_copy), dtype=bool)
else:
mask = mask.astype(bool)
sky_ann = disk.disk(npskyrad[1], (loc[1],loc[0]), data_copy.shape) ^ \
disk.disk(npskyrad[0], (loc[1],loc[0]), data_copy.shape)
sky_mask = sky_ann * mask * np.isfinite(data_copy)
sky = np.median(data_copy[np.where(sky_mask)])
data_copy -= sky
x_data = data_copy.shape[1]
y_data = data_copy.shape[0]
x_rad = (x_data - 1) / 2
y_rad = (y_data - 1) / 2
weights = np.ones(data_copy.shape, dtype=float)
ind = np.indices(weights.shape)
x_ind = ind[1] - x_rad
y_ind = ind[0] - y_rad
weights[np.where(np.sqrt(x_ind**2 + y_ind**2) > rad)] = 0
return ctr.col(data_copy, weights=weights)
def calcnoisepix(im, y, x, npskyrad, mask=None):
'''
Implementation of noise pixel calculation as described in the IRAC
handbook. The box_centroider.pro routine was used as reference.
Formula is N = sum(image)**2/sum(image**2).
Inputs
------
im: array-like
Intended to be a 2D image, although any array should work.
y: float
Y position of the target
x: float
X position of the target
npskyrad: 2-tuple of floats
Radii for sky annulus (sky should be subtracted before
calculating noise pixels in case it varies significantly,
as is the case for spitzer subarrays)
mask: boolean array
Mask for the image. Same shape as im.
Returns
-------
N: float
Noise pixels
Revisions
---------
rchallen Initial Implementation [email protected]
'''
# Create a mask if not supplied. Otherwise, make sure mask is
# boolean
if type(mask) == type(None):
mask = np.ones(np.shape(im), dtype=bool)
else:
mask = mask.astype(bool)
skymsk = disk.disk(npskyrad[1], (x,y), im.shape) ^ \
disk.disk(npskyrad[0], (x,y), im.shape)
sky = np.average(im[mask * skymsk])
flux = np.sum(im[mask] - sky)
N = flux**2 / np.sum((im[mask] - sky)**2)
return N
def guessDisk():
for i in ["a","b","c", "d", "e", "f"]:
for j in ["/dev/sd", "/dev/hd"]:
path = j+i
if os.path.exists(path):
return disk(path)
return None
def shrinkLinux(our_disk, our_part):
print "\nTEDD will now shrink your Linux partition to make room for the encrypted overlay."
shrink_prompt = raw_input("May we proceed? [Y/n]: ")
if not shrink_prompt.lower().startswith("n"):
print "This may take a while depending on your file system and partition size. Please be patient."
our_part.resize()
our_disk = disk(our_disk.path)
our_part = our_disk.partitions[our_part.path]
def getDisk():
our_disk = guessDisk()
disk_prompt = raw_input("\nWhat disk contains your Linux Installation? [%s]: " % our_disk.path)
while disk_prompt != "" and not os.path.exists(disk_prompt):
print "Disk not found."
disk_prompt = raw_input("\nWhat disk contains your Linux Installation? [%s]: " % our_disk.path)
if disk_prompt != "":
our_disk = disk(disk_prompt)
return our_disk
def col(data, loc, npskyrad, mask=None):
data_copy = data.copy()
if type(mask) == type(None):
mask = np.ones(np.shape(data_copy), dtype=bool)
else:
mask = mask.astype(bool)
sky_ann = disk.disk(npskyrad[1], (loc[1],loc[0]), data_copy.shape) ^ \
disk.disk(npskyrad[0], (loc[1],loc[0]), data_copy.shape)
sky_mask = sky_ann * mask * np.isfinite(data_copy)
sky = np.median(data_copy[np.where(sky_mask)])
data_copy -= sky
return ctr.col(data_copy)
def getDiskFromDB(self):
try:
showDisk = self.cur.execute("SELECT * from disk").fetchall()
disks = []
i = 0
for t in showDisk:
disks.append(disk(t[0], t[1], t[2]))
i += 1
if len(disks) > 0:
return disks
print("Nada que mostrar")
return 1
except:
print("Error en consulta de playlist")
return 1
def scan_gpt_table2(path, mode, flags=2):
if os.name == 'nt' and len(path) == 2 and path[1] == ':':
path = '\\\\.\\' + path
d = disk.disk(path, mode)
d.seek(512)
blk = d.read(512)
gpt = GPT(blk)
d.seek(gpt.u64PartitionEntryLBA * 512)
blk = d.read(gpt.dwNumberOfPartitionEntries *
gpt.dwNumberOfPartitionEntries)
gpt.parse(blk)
for i in range(gpt.dwNumberOfPartitionEntries):
if not gpt.partitions[i].u64StartingLBA: break
blocks = gpt.partitions[i].u64EndingLBA - gpt.partitions[
i].u64StartingLBA + 1
print "Found a %s GPT partition:\n- Name: %s\n- Type: %s\n- Offset: 0x%08X" % (
print_size(blocks * 512), gpt.partitions[i].name(),
partition_uuids[gpt.partitions[i].gettype()],
gpt.partitions[i].u64StartingLBA)
# Bit 0: system partition; bit 63: no auto mount (MS)
print "- Attributes: 0x%X" % gpt.partitions[i].u64Attributes
print "Save it? [y=Yes q=QUIT] ",
C = raw_input().lower()
if C == 'q':
sys.exit(1)
if C == 'y':
first = gpt.partitions[i].u64StartingLBA
last = gpt.partitions[i].u64EndingLBA
#~ src = disk.partition(d, first*512, blocks*512)
#~ src.seek(0)
if lz4f and (flags & 1): # compress with lz4 if available
dst = lz4file('Partition@%08Xh.bin' % first, 'wb')
else:
dst = open(
'Partition@%08Xh.%s' % (first,
('bin', 'vhd')[flags & 2 != 0]),
'wb')
print "Saving partition of %d sectors (%s) at %X" % (
last - first + 1, print_size((last - first + 1) * 512), first)
copy_sectors(d, dst, first, last)
if flags & 2: # append a fixed VHD footer
dst.write(mk_vhd_fixed_footer((last - first + 1) * 512))
print ""
def resultToDiscObj(self, result):
artis = None
dis = None
co = None
disks = []
for y in result:
for x in y['release-list']:
dis = x['artist-credit'][0]['name']
artis = x['title']
try:
co = x['country']
except:
co = "EMPTY"
dis = disk(dis, artis, co)
disks.append(dis)
return disks
slot_1[0] = bb_sig
snd = sound(debug)
if options.scc_rom:
parts = options.scc_rom.split(':')
scc_obj = scc(parts[1], snd, debug)
scc_sig = scc_obj.get_signature()
scc_slot = int(parts[0])
slot_1[scc_slot] = scc_sig
slot_2[scc_slot] = scc_sig
if options.disk_rom:
parts = options.disk_rom.split(':')
disk_slot = int(parts[0])
disk_obj = disk(parts[1], debug, parts[2])
slot_1[disk_slot] = disk_obj.get_signature()
if options.rom:
parts = options.rom.split(':')
rom_slot = int(parts[0])
rom_obj = gen_rom(parts[1], debug)
rom_sig = rom_obj.get_signature()
slot_1[rom_slot] = rom_sig
if len(rom_sig[0]) >= 32768:
slot_2[rom_slot] = rom_sig
subpage = 0x00
slots = (slot_0, slot_1, slot_2, slot_3)
dest="cluster_size",
help=
"force a specified cluster size between 512, 1024, 2048, 4096, 8192, 16384, 32768 (since MS-DOS) or 65536 bytes (Windows NT+) for FAT. exFAT permits up to 32M. Default: based on medium size. Accepts 'k' and 'm' postfix for Kibibytes and Mebibytes.",
metavar="CLUSTER")
opts, args = par.parse_args()
if not args:
print "mkfat error: you must specify a target volume to apply a FAT12/16/32 or exFAT file system!"
par.print_help()
sys.exit(1)
if os.name == 'nt' and len(args[0]) == 2 and args[0][1] == ':':
disk_name = '\\\\.\\' + args[0]
else:
disk_name = args[0]
dsk = disk.disk(disk_name, 'r+b')
params = {}
if opts.fs_type:
t = opts.fs_type.lower()
if t == 'fat12':
format = fat12_mkfs
elif t == 'fat16':
format = fat16_mkfs
elif t == 'fat32':
format = fat32_mkfs
params['fat32_allows_few_clusters'] = 1
elif t == 'exfat':
format = exfat_mkfs
else:
from disk import disk
import json
my_disk = disk(json.load(open("input.json")))
my_disk.convert_binary_to_grid_squares()
my_disk.create_groups()
print(my_disk.get_group_count())
def fitgaussian(y, x=None, bgpars=None, fitbg=0, guess=None,
mask=None, weights=None, maskg=False, yxguess=None):
"""
Fits an N-dimensional Gaussian to (value, coordinate) data.
Parameters
----------
y : ndarray
Array giving the values of the function.
x : ndarray
(optional) Array (any shape) giving the abcissas of y (if
missing, uses np.indices(y). The highest dimension must be
equal to the number of other dimensions (i.e., if x has 6
dimensions, the highest dimension must have length 5). The
rest of the dimensions must have the same shape as y. Must be
sorted ascending (which is not checked), if guess is not
given.
bgpars : ndarray or tuple, 3-elements
Background parameters, the elements determine a X- and Y-linearly
dependant level, of the form:
f = Y*bgparam[0] + X*bgparam[1] + bgparam[2]
(Not tested for 1D yet).
fitbg : Integer
This flag indicates the level of background fitting:
fitbg=0: No fitting, estimate the bg as median(data).
fitbg=1: Fit a constant to the bg (bg = c).
fitbg=2: Fit a plane as bg (bg = a*x + b*y + c).
guess : tuple, (width, center, height)
Tuple giving an initial guess of the Gaussian parameters for
the optimizer. If supplied, x and y can be any shape and need
not be sorted. See gaussian() for meaning and format of this
tuple.
mask : ndarray
Same shape as y. Values where its corresponding mask value is
0 are disregarded for the minimization. Only values where the
mask value is 1 are considered.
weights : ndarray
Same shape as y. This array defines weights for the
minimization, for scientific data the weights should be
1/sqrt(variance).
Returns
-------
params : ndarray
This array contains the best fitting values parameters: width,
center, height, and if requested, bgpars. with:
width : The fitted Gaussian widths in each dimension.
center : The fitted Gaussian center coordinate in each dimension.
height : The fitted height.
err : ndarray
An array containing the concatenated uncertainties
corresponding to the values of params. For example, 2D input
gives np.array([widthyerr, widthxerr, centeryerr, centerxerr,
heighterr]).
Notes
-----
If the input does not look anything like a Gaussian, the result
might not even be the best fit to that.
Method: First guess the parameters (if no guess is provided), then
call a Levenberg-Marquardt optimizer to finish the job.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> import gaussian as g
>>> # parameters for X
>>> lx = -3. # low end of range
>>> hx = 5. # high end of range
>>> dx = 0.05 # step
>>> # parameters of the noise
>>> nc = 0.0 # noice center
>>> ns = 1.0 # noise width
>>> na = 0.2 # noise amplitude
>>> # 1D Example
>>> # parameters of the underlying Gaussian
>>> wd = 1.1 # width
>>> ct = 1.2 # center
>>> ht = 2.2 # height
>>> # x and y data to fit
>>> x = np.arange(lx, hx + dx / 2., dx)
>>> x += na * np.random.normal(nc, ns, x.size)
>>> y = g.gaussian(x, wd, ct, ht) + na * np.random.normal(nc, ns, x.size)
>>> s = x.argsort() # sort, in case noise violated order
>>> xs = x[s]
>>> ys = y[s]
>>> # calculate guess and fit
>>> (width, center, height) = g.gaussianguess(ys, xs)
>>> (fw, fc, fh, err) = g.fitgaussian(ys, xs)
>>> # plot results
>>> plt.clf()
>>> plt.plot(xs, ys)
>>> plt.plot(xs, g.gaussian(xs, wd, ct, ht))
>>> plt.plot(xs, g.gaussian(xs, width, center, height))
>>> plt.plot(xs, g.gaussian(xs, fw, fc, fh))
>>> plt.title('Gaussian Data, Guess, and Fit')
>>> plt.xlabel('Abcissa')
>>> plt.ylabel('Ordinate')
>>> # plot residuals
>>> plt.clf()
>>> plt.plot(xs, ys - g.gaussian(xs, fw, fc, fh))
>>> plt.title('Gaussian Fit Residuals')
>>> plt.xlabel('Abcissa')
>>> plt.ylabel('Ordinate')
>>> # 2D Example
>>> # parameters of the underlying Gaussian
>>> wd = (1.1, 3.2) # width
>>> ct = (1.2, 3.1) # center
>>> ht = 2.2 # height
>>> # x and y data to fit
>>> nx = (hx - lx) / dx + 1
>>> x = np.indices((nx, nx)) * dx + lx
>>> y = g.gaussian(x, wd, ct, ht) + na * np.random.normal(nc, ns, x.shape[1:])
>>> # calculate guess and fit
>>> #(width, center, height) = g.gaussianguess(y, x) # not in 2D yet...
>>> (fw, fc, fh, err) = g.fitgaussian(y, x, (wd, ct, ht))
>>> # plot results
>>> plt.clf()
>>> plt.title('2D Gaussian Given')
>>> plt.xlabel('X')
>>> plt.ylabel('Y')
>>> plt.imshow( g.gaussian(x, wd, ct, ht))
>>> plt.clf()
>>> plt.title('2D Gaussian With Noise')
>>> plt.xlabel('X')
>>> plt.ylabel('Y')
>>> plt.imshow(y)
>>> #plt.imshow( g.gaussian(x, width, center, height)) # not in 2D yet...
>>> plt.clf()
>>> plt.title('2D Gaussian Fit')
>>> plt.xlabel('X')
>>> plt.ylabel('Y')
>>> plt.imshow( g.gaussian(x, fw, fc, fh))
>>> plt.clf()
>>> plt.title('2D Gaussian Fit Residuals')
>>> plt.xlabel('X')
>>> plt.ylabel('Y')
>>> plt.imshow(y - g.gaussian(x, fw, fc, fh))
>>> # All cases benefit from...
>>> # show difference between fit and underlying Gaussian
>>> # Random data, your answers WILL VARY.
>>> np.array(fw) - np.array(wd)
array([ 0.00210398, -0.00937687])
>>> np.array(fc) - np.array(ct)
array([-0.00260803, 0.00555011])
>>> np.array(fh) - np.array(ht)
0.0030143371034774269
>>> Last Example:
>>> x = np.indices((30,30))
>>> g1 = g.gaussian(x, width=(1.2, 1.15), center=(13.2,15.75), height=1e4,
>>> bgpars=[0.0, 0.0, 100.0])
>>> error = np.sqrt(g1) * np.random.randn(30,30)
>>> y = g1 + error
>>> var = g1
>>>
>>> plt.figure(1)
>>> plt.clf()
>>> plt.imshow(y, origin='lower_left', interpolation='nearest')
>>> plt.colorbar()
>>> plt.title('2D Gaussian')
>>> plt.xlabel('X')
>>> plt.ylabel('Y')
>>>
>>> guess = ((1.2,1.2),(13,16.),1e4)
>>> reload(g)
>>> fit = g.fitgaussian(y, x, bgpars=[0.0, 0.0, 110.], fitbg=1, guess=guess,
>>> mask=None, weights=1/np.sqrt(var))
>>> print(fit[0])
Revisions
---------
2007-09-17 Joe Initial version, portions adapted from
http://www.scipy.org/Cookbook/FittingData.
[email protected]
2007-11-13 Joe Made N-dimensional.
2008-12-02 Nate Included error calculation, and return Fixed a bug
in which if the initial guess was None, and incorrect
shape array was generated. This caused gaussian guess
to fail.
[email protected]
2009-10-25 Converted to standard doc header, fixed examples to
return 4 parameters.
2011-05-03 patricio Added mask, weights, and background-fitting options.
[email protected]
"""
if x == None:
x = np.indices(np.shape(y))
else:
if ( ((x.ndim == 1) and (x.shape != y.shape))
or ((x.ndim > 1) and (x.shape[1:] != y.shape))):
raise ValueError, "x must give coordinates of points in y."
# Default mask: all good
if mask == None:
mask = np.ones(np.shape(y))
# Default weights: no weighting
if weights == None:
weights = np.ones(np.shape(y))
# Mask the gaussian if requested:
medmask = np.copy(mask)
if maskg and (yxguess != None or guess != None):
if yxguess != None:
center = yxguess
elif guess != None:
center = guess[1]
medmask *= (1 - d.disk(3, center, np.shape(y)))
# Estimate the median of the image:
medbg = np.median(y[np.where(medmask)])
if bgpars == None:
bgpars = [0.0, 0.0, medbg]
# get a guess if not provided
if guess == None:
if yxguess == None:
guess = gaussianguess(y-medbg, mask=mask)
else:
guess = gaussianguess(y-medbg, mask=mask, yxguess=yxguess)
# "ravel" the guess
gparams = np.append(guess[0], guess[1])
gparams = np.append(gparams, guess[2])
# Background params to fit:
if fitbg == 0:
bgparams = []
elif fitbg == 1:
bgparams = bgpars[2]
elif fitbg == 2:
bgparams = bgpars
# Concatenate sets of parameters we want to fit:
params = np.append(gparams, bgparams)
# Rest of parameters needed by residuals:
args = (x, y, mask, weights, bgpars, fitbg)
# The fit:
p, cov, info, mesg, success = so.leastsq(residuals, params, args,
full_output=True)
try:
err = np.sqrt(np.diagonal(cov))
except:
err = None
return p, err
def apphot(image,
ctr,
photap,
skyin,
skyout,
betahw,
targpos,
mask=None,
imerr=None,
skyfrac=0.0,
med=False,
nochecks=False,
expand=1,
order=1,
aperr=False,
nappix=False,
skylev=False,
skyerr=False,
nskypix=False,
nskyideal=False,
status=False,
isbeta=False,
betaper=False):
"""
Perform aperture photometry on the input image.
Parameters
----------
image : 2D ndimage
Float array containing object to measure.
ctr : 2 elements tuple
x,y location of object's center.
photap : Scalar
Size of photometry apperture in pixels.
skyin : Scalar
Inner sky annulus edge, in pixels.
skyout : Scalar
Outer sky annulus edge, in pixels.
betahw : Scalar
Half-width of box size around centroid for beta calculation.
targpos : 2 elements tuple
x,y location of object's center calculated from mean image.
mask : 2D ndimage
Byte array giving status of corresponding pixel in
Image: bad pixel=0, good pixel=1. Default: all pixels
are good. Same shape as image.
imerr : 2D ndimage
Error estimate for each pixel in the image. Suggest
sqrt(image/flat/gain+rdnoise^2), with proper adjustment
if a sky, dark, or bias frame has been removed. Same
shape as image.
skyfrac : Scalar
Minimum fraction of sky pixels required to be good.
Must be in range 0 < skyfrac < 1.
med : Boolean
If True use median rather than mean in sky level estimation.
nochecks : Boolean
Set to True to skip checks of input sanity.
expand : Integer scalar
Positive integer factor by which to blow up image, for
more accurate aperture arithmetic. If expand=5, each
pixel becomes a 5x5 block of pixels. If the pixel is
on the edge of an aperture or annulus radius, some of
the 5x5 block will be counted and some will not.
order : Integer scalar
Set order to 0 to do nearest-neighbor interpolation if expand.
Default: 1, bilinear interpolation.
aperr : Boolean
Set to True to return flux error.
nappix : Boolean
Set to True to return number of total pixels in aperture.
skylev : Boolean
Set to True to return the sky level.
skyerr : Boolean
Set to True to return the error in the sky level.
nskypix : boolean
Set to True to return the number of good pixels in sky annulus.
nskyideal: Boolean
Set to True to return the number of pixels that should
be in sky annulus.
status : Boolean
Set to True to return a status flag.
If status = 0, result is good. Bits:
0 = there are NaN(s) in the photometry aperture
1 = there are masked pixel(s) in the photometry aperture
2 = the aperture is off the edge of the image
3 = a fraction less than skyfrac of the sky annulus pixels
is in the image and not masked
isbeta : boolean
If True photometric extraction aperture scales with noise pixel
parameter (beta).
betaper : Scalar
Returns aperture size used for beta.
Returns
-------
This function returns the flux within Photap pixels of
[Cx,Cy], after subtracting the average per-pixel flux in the
sky annulus. See POCEDURE for details of the calculation.
Notes
-----
The sky level is the mean, error-weighted mean (if errors are
present), or median (if /med is set) of the non-masked Image
pixels in the sky annulus. NaN values and values whose errors
are zero (for the error-weighted mean) are not included in the
average. No flagging is done if these values are found in the
sky annulus. SKYERR is the error in the mean, even for the
median calculation. Errors in the median can only be estimated
using the compute-intensive bootstrap Monte-Carlo method.
The sky value is subtracted from the Image. The photometric
flux is then the total of Image pixels in the aperture, whether
in the Mask or not. NaN values are not included in the
calculation, and set the STATUS flag. It would be much better
for the user to pass an interpolated image instead.
For expansion, it is recommended to use bilinear
interpolation, which is flux-conserving:
sz = [50, 50]
expand = 4
a = dblarr(sz)
a[25, 25] = 1
b = rebin(a, expand * sz) / expand^2
print, total(b)
1.0000000
a[25, 26] = 1
b = rebin(a, expand * sz) / expand^2
print, total(b)
2.0000000
a[26, 26] = 3
b = rebin(a, expand * sz) / expand^2
print, total(b)
5.0000000
Of course, pixels on the high-indexed edge will not follow that.
Neither will integer-arithmetic images, particularly at low
integer values (such as masks).
If either the entire sky annulus or the entire aperture is bad
or filled with NaNs, the function sets a flag and returns NaN
for all incalculable values.
Examples
--------
This being one of my most important routines, and being also
complex, the following examples are also its test suite. Any
changes should produce exactly the same numerical results.
These tests should also be available in the accompanying file
apphottest.pro.
import sys
sys.path.append('/home/esp01/code/python/photpipe/lib/')
import apphot as ap
import myasym as asy
test = 0
ntest = 11
testout = np.zeros((5, ntest))
testright = np.zeros((5, ntest))
sz = [50, 50]
sig = 3.0, 3.0
ctr = 25.8, 25.2
photap = 12
skyin = 12
skyout = 15
ampl = 1000.0
sky = 100.0
h = ampl/(2*np.pi*sig[0]*sig[1]) # height that make integral equal to ampl
f = asy.gaussian(h, ctr[0], ctr[1], sig[0], sig[1])
r,l = np.indices(sz)
image = f(r,l) + sky
plt.figure(1,(9,7))
plt.clf()
plt.title('Gaussian')
plt.xlabel('X coordinate')
plt.ylabel('Y coordinate')
plt.pcolor(r, l, image, cmap=plt.cm.gray)
plt.axis([0, sz[1]-1, 0, sz[0]-1])
plt.colorbar()
plt.show()
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout,
aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, aperr, skylev, skyerr, status]
test += 1
# A little of the Gaussian leaks from aperture to sky, rest is right.
mask = np.ones(sz, byte)
mask[24,24] = 0
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout, mask = mask,
aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, 0, skylev, 0, status]
test += 1
# We use the bad value since it's in the aperture, but we flag it.
image[25,24] = np.nan
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout, mask = mask,
aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, 0, skylev, 0, status]
test += 1
# We can't use a NaN! Flagged, and value changes. Bad value still flagged.
ctr2 = [48.8, 48.2]
f = asy.gaussian(h, ctr2[0], ctr2[1], sig[0], sig[1])
image2 = f(r,l) + sky
plt.figure(2,(9,7))
plt.clf()
plt.title('Gaussian')
plt.xlabel('X coordinate')
plt.ylabel('Y coordinate')
plt.pcolor(r, l, image2, cmap=plt.cm.gray)
plt.axis([0, sz[1]-1, 0, sz[0]-1])
plt.colorbar()
plt.show()
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image2, ctr2, photap, skyin, skyout, mask = mask,
aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, 0, skylev, 0, status]
test += 1
# Flagged that we're off the image.
skyfrac = 0.5
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image2, ctr2, photap, skyin, skyout,
mask=mask, skyfrac=skyfrac,
aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, 0, skylev, 0, status]
test += 1
# Flagged that we are off the image and have insufficient sky.
# Same numbers.
f = asy.gaussian(h, ctr[0], ctr[1], sig[0], sig[1])
image = f(r,l) + sky
imerr = np.sqrt(image)
mask = np.ones(sz, byte)
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout, mask=mask,
imerr=imerr, aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, aperr, skylev, skyerr, status]
test += 1
# Estimates for errors above. Basic numbers don't change.
imerr[25, 38] = 0
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout, mask=mask,
imerr=imerr, aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, aperr, skylev, skyerr, status]
test += 1
# The zero-error pixel is ignored in the sky average. Small changes result.
imerr[25, 38] = np.nan
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout, mask=mask,
imerr=imerr, aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, aperr, skylev, skyerr, status]
test += 1
# The NaN in the sky error is ignored, with the same result.
image[25, 38] = np.nan
imerr = sqrt(image)
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout, mask=mask,
imerr=imerr, aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, aperr, skylev, skyerr, status]
test += 1
# The NaN in the sky data is ignored, with the same result.
# FINDME: my aplev is changing
##
f = asy.gaussian(h, ctr[0], ctr[1], sig[0], sig[1])
image = f(r,l) + sky
imerr = sqrt(image)
expand = 5
order = 0 # sample = 1
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout, expand = expand,
mask = mask, imerr = imerr, order=order,
aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, aperr, skylev, skyerr, status]
test += 1
# Slight changes.
expand = 5
order = 1 # IDL sample = 0
aplev, aperr, skylev, skyerr, \
status = ap.apphot(image, ctr, photap, skyin, skyout, expand = expand,
mask = mask, imerr = imerr, order=order,
aperr=True, skylev=True, skyerr=True, status=True)
print(aplev, aperr, skylev, skyerr, status)
testout [:, test] = [aplev, aperr, skylev, skyerr, status]
test += 1
# Slight changes. Why the flag?
skyerrest = np.sqrt(sky/(np.pi * (skyout**2 - skyin**2))) # skyerr estimate
# 0.62687732
print( 'Correct:' )
print( [ampl,
np.sqrt(ampl + np.pi * photap**2 * (sky+skyerrest**2)), # aperr estim.
# Note that background flux of 100 contributes a lot!
# 215.44538
sky,
skyerrest,
0] )
print( 'Test results:')
print( testout)
print( 'Correct results:')
print( testright)
print( 'Differences:')
print( testout - testright)
Revisions:
---------
Written by: Joseph Harrington, Cornell. 27-02-2004
[email protected]
18-03-2004 jh Added nochecks keyword.
19-03-2004 jh Added error calculation.
13-01-2005 jh Fixed header comment. Added NAN keyword.
14-10-2005 jh Found and fixed major bug in sky mask
calculation (needed parens around
subtraction next to mask multiplication).
Added skyfrac.
07-11-2005 shl35 Added STATUS keyword, error-weighted sky mean.
16-11-2005 jh Rewrote, using meanerr. Fixed major bug in
error calc. Added scaling and test cases.
24-11-2005 jh Return NAPPIX, NSKYPIX, NSKYIDEAL (all renamed).
30-11-2005 jh Changed NAPPIX, NSKYPIX, NSKYIDEAL to give
fractional, unexpanded pixels.
21-07-2010 patricio Converted to python.
"""
# tini = time.time()
# Returned values (include aperture size)
retidx = [
True, aperr, nappix, skylev, skyerr, nskypix, nskyideal, status,
betaper
]
# indexes
# consider return the whole list always
#FINDME: use iaplev etc ... or use a dicctionary
aplev, aperr, nappix, skylev, \
skyerr, nskypix, nskyideal, stat, betaper = np.arange(9)
ret = np.zeros(9) #changed from 8 to 9
ret[:] = np.nan
# set error status to 'good' = 0
ret[stat] = 0
status = 0
# bit flag definitions
statnan = 2**0
statbad = 2**1
statap = 2**2
statsky = 2**3
# internal skyfrac, so we don't set it in caller
iskyfrac = skyfrac
# Check inputs
sz = np.shape(image)
if not nochecks:
if np.ndim(image) != 2:
# FINDME: raise exceptions instead
print('image must be a 2D array')
# break
return ret[np.where(retidx)]
if mask is None:
mask = np.ones(sz, dtype=np.byte)
if np.ndim(mask) != 2:
print('mask must be a 2D array')
return ret[np.where(retidx)]
if (np.shape(image) != np.shape(mask)):
print('image and mask sizes differ')
return ret[np.where(retidx)]
if imerr is not None:
if (np.shape(image) != np.shape(imerr)):
print('image and imerr sizes differ')
return ret[np.where(retidx)]
if (iskyfrac < 0.0) or (iskyfrac > 1.0):
print('skyfrac must be in range [0,1]')
return ret[np.where(retidx)]
if expand != np.long(expand) or expand < 1:
print('invalid expand')
return ret[np.where(retidx)]
# Expand
iexpand = int(expand)
isz = np.array(sz,
dtype=int) + (np.array(sz, dtype=int) - 1) * (iexpand - 1)
ictr = iexpand * np.array(ctr)
iphotap = iexpand * photap
iskyin = iexpand * skyin
iskyout = iexpand * skyout
y, x = np.arange(sz[0]), np.arange(sz[1])
yi, xi = np.linspace(0, sz[0] - 1,
isz[0]), np.linspace(0, sz[1] - 1, isz[1])
iimage = i2d.interp2d(image, expand=iexpand, y=y, x=x, yi=yi, xi=xi)
imask = i2d.interp2d(mask, expand=iexpand, y=y, x=x, yi=yi, xi=xi)
imask = imask == True
if imerr is not None:
iimerr = i2d.interp2d(imerr, expand=iexpand, y=y, x=x, yi=yi, xi=xi)
# SKY
# make sky annulus mask
skyann = np.bitwise_xor(di.disk(iskyout, ictr, isz),
di.disk(iskyin, ictr, isz))
skymask = skyann * imask * np.isfinite(iimage) # flag NaNs to eliminate
# from nskypix
# Check for skyfrac violation
# FINDME: include NaNs and zero errors
ret[nskypix] = np.sum(skymask) / iexpand**2.0
szsky = (int(np.ceil(iskyout)) * 2 + 3) * np.array([1, 1], dtype=int)
ctrsky = (ictr % 1.0) + np.ceil(iskyout) + 1.0
# nskyideal = all pixels in sky
ret[nskyideal] = np.sum(
np.bitwise_xor(di.disk(iskyout, ctrsky, szsky),
di.disk(iskyin, ctrsky, szsky))) / iexpand**2.0
if ret[nskypix] < iskyfrac * ret[nskyideal]:
status |= statsky
if ret[nskypix] == 0: # no good pixels in sky?
status |= statsky
ret[stat] = status
print('no good pixels in sky')
return ret[np.where(retidx)]
# Calculate the sky and sky error values:
# Ignore the status flag from meanerr, it will skip bad values intelligently.
if med: # Do median sky
ret[skylev] = np.median(iimage[np.where(skymask)])
if imerr is not None:
# FINDME: We compute the standard deviation of the mean, not the
# median. The standard deviation of the median is complicated and can
# only be estimated statistically using the bootstrap method. It's
# also very computationally expensive. We could alternatively use the
# maximum of the standard deviation of the mean and the mean
# separation of values in the middle of the distribution.
dummy, ret[skyerr] = me.meanerr(iimage,
iimerr,
mask=skymask,
err=True)
ret[skyerr] *= iexpand # Expand correction. Since repeats are correlated,
# error in mean was improved by sqrt(iexpand^2).
else: # Do mean
if imerr is not None:
ret[skylev], ret[skyerr] = me.meanerr(iimage,
iimerr,
mask=skymask,
err=True)
ret[skyerr] *= iexpand # Expand correction.
else:
ret[skylev] = np.mean(iimage[np.where(skymask)])
#Calculate Beta values. If True photometric extraction aperture scales with
#noise pixel parameter (beta).
if isbeta == True and betahw > 0:
#Using target position from mean image
ctr_y = int(targpos[1])
ctr_x = int(targpos[0])
betahw = int(betahw)
# Create a box of width and length (betahw) around the target position
betabox = image[ctr_y - betahw:ctr_y + betahw + 1,
ctr_x - betahw:ctr_x + betahw + 1]
# Subtract the background
betabox -= ret[skylev]
#beta = sum(I(i))^2 / sum(I(i)^2) see details below describing the noise pixels
#https://irachpp.spitzer.caltech.edu/page/noisepix
beta = np.sum(betabox)**2 / np.sum(betabox**2)
iphotap += iexpand * (np.sqrt(beta) + photap)
#Return aperture size used for beta.
ret[betaper] = iphotap
elif betahw == 0:
raise ValueError(
"Could not evalaute beta. Please update POET photom.pcf to betahw > 0."
)
# APERTURE
# make aperture mask, extract data and mask
apmask, dstatus = di.disk(iphotap, ictr, isz, status=True)
if dstatus: # is the aperture fully on the image?
status |= statap
aploc = np.where(apmask) # report number of pixels in aperture
ret[nappix] = np.sum(
apmask[aploc]) / iexpand**2.0 # make it unexpended pixels
if ret[nappix] == 0: # is there *any* good aperture?
status |= statbad
ret[stat] = status
return ret[np.where(retidx)]
if np.all(np.isfinite(iimage[aploc]) == 0): # all aperture pixels are NaN?
status |= statnan
ret[stat] = status
return ret[np.where(retidx)]
# subtract sky here to get a flag if it's NaN
apdat = iimage[aploc] - ret[skylev]
apmsk = imask[aploc]
# flag NaNs and bad pixels
goodies = np.isfinite(apdat)
if np.any(goodies == 0):
status |= statnan
if np.any(apmsk == 0):
status |= statbad
# PHOTOMETRY
# Do NOT multiply by bad pixel mask! We need to use the interpolated
# pixels here, unfortunately.
ret[aplev] = np.sum(apdat[goodies])
# Expand correction. We overcount by iexpand^2.
ret[aplev] /= iexpand**2.0
# if we have uncertainties...
if imerr is not None:
# Flag NaNs. Zeros are ok, if weird.
apunc = iimerr[aploc]
apuncloc = np.isfinite(apunc)
if not np.all(apuncloc):
status |= statnan
# Multiply by mask for the aperture error calc. The error on a
# replaced pixel is unknown. In one sense, it's infinite. In
# another, it's zero, or should be close. So, ignore those points.
# Sky error still contributes.
apunc[np.where(apuncloc == False)] = 0
ret[aperr] = np.sqrt(
np.sum(apmsk * apunc**2.0) + np.size(aploc) * ret[skyerr]**2.0)
# Expand correction. We overcount by iexpand^2, but that's inside sqrt:
# sqrt(sum(iexpand^2 * (indep errs))), so div. by iexpand
ret[aperr] /= iexpand
ret[stat] = status
# ttotal = time.time() - tini
return ret[np.where(retidx)]
sizes = {0:'B', 10:'KiB',20:'MiB',30:'GiB',40:'TiB',50:'EiB'}
k = 0
for k in sorted(sizes):
if (fsinfo['required_size'] / (1<<k)) < 1024: break
free_clusters = fsinfo['clusters'] - 1
print "Successfully applied FAT32 to a %d %s volume.\n%d clusters of %.1f KB.\n%d %s free in %d clusters." % (fsinfo['required_size']/(1<<k), sizes[k], fsinfo['clusters'], fsinfo['cluster_size']/1024.0, free_clusters*boot.cluster/(1<<k), sizes[k], free_clusters)
print "\nFAT #1 @0x%X, Data Region @0x%X, Root (cluster #%d) @0x%X" % (boot.fatoffs, boot.cl2offset(2), 2, boot.cl2offset(2))
return 0
if __name__ == '__main__':
if len(sys.argv) < 2:
print "mkfat error: you must specify a target volume to apply a FAT12/16/32 file system!"
sys.exit(1)
if os.name == 'nt' and len(sys.argv[1])==2 and sys.argv[1][1]==':':
disk_name = '\\\\.\\'+sys.argv[1]
else:
disk_name = sys.argv[1]
dsk = disk.disk(disk_name, 'r+b')
params = {'reserved_size':32, 'backup_sectors':6}
if len(sys.argv)==3:
params['wanted_cluster'] = int(sys.argv[2])
fat32_mkfs(dsk, dsk.size, params=params)
# -*- coding: mbcs -*-
import os, sys, optparse
import logging
logging.basicConfig(level=logging.DEBUG, filename='PYTEST.LOG', filemode='w')
from datetime import datetime
from ctypes import *
from utils import FSguess
from FAT import *
import disk
disk = disk.disk('\\\\.\\G:', 'r+b')
#~ disk = disk.disk('FLOPPY.IMA', 'r+b')
#~ disk = disk.disk('G.ima', 'r+b')
fstyp = FSguess(boot_fat16(disk))
disk.seek(0)
if fstyp in ('FAT12', 'FAT16'):
boot = boot_fat16(disk)
elif fstyp == 'FAT32':
boot = boot_fat32(disk)
elif fstyp == 'EXFAT':
boot = boot_exfat(disk)
elif fstyp == 'NTFS':
print fstyp, "file system not supported. Aborted."
sys.exit(1)
else:
print "File system not recognized. Aborted."
sys.exit(1)
if options.scc_rom:
for o in options.scc_rom:
parts = o.split(':')
scc_obj = scc(parts[2], snd, debug)
scc_slot = int(parts[0])
scc_subslot = int(parts[1])
put_page(scc_slot, scc_subslot, 1, scc_obj)
put_page(scc_slot, scc_subslot, 2, scc_obj)
if options.disk_rom:
for o in options.disk_rom:
parts = o.split(':')
disk_slot = int(parts[0])
disk_subslot = int(parts[1])
disk_obj = disk(parts[2], debug, parts[3])
put_page(disk_slot, disk_subslot, 1, disk_obj)
if options.rom:
for o in options.rom:
parts = o.split(':')
rom_slot = int(parts[0])
rom_subslot = int(parts[1])
offset = 0x4000
if len(parts) == 4:
offset = int(parts[3], 16)
rom_obj = gen_rom(parts[2], debug, offset=offset)
page_offset = offset // 0x4000
for p in range(page_offset, page_offset + rom_obj.get_n_pages()):
put_page(rom_slot, rom_subslot, p, rom_obj)
def rotfitgaussian(y,
x=None,
bgpars=None,
fitbg=0,
guess=None,
mask=None,
weights=None,
maskg=False,
yxguess=None):
if type(x) == type(None):
x = np.indices(np.shape(y))
else:
if (((x.ndim == 1) and (x.shape != y.shape))
or ((x.ndim > 1) and (x.shape[1:] != y.shape))):
raise ValueError("x must give coordinates of points in y.")
# Default mask: all good
if type(mask) == type(None):
mask = np.ones(np.shape(y))
# Default weights: no weighting
if type(weights) == type(None):
weights = np.ones(np.shape(y))
# Mask the gaussian if requested:
medmask = np.copy(mask)
if maskg and (type(yxguess) != type(None) or type(guess) != type(None)):
if type(yxguess) != type(None):
center = yxguess
elif type(guess) != type(None):
center = guess[1]
medmask *= (1 - d.disk(3, center, np.shape(y)))
# Estimate the median of the image:
medbg = np.median(y[np.where(medmask)])
if type(bgpars) == type(None):
bgpars = [0.0, 0.0, medbg]
# Get a guess if not provided. Note: the guess for rotation is set to 0 rad
if type(guess) == type(None):
if type(yxguess) == type(None):
guess = gaussianguess(y - medbg, mask=mask)
else:
guess = gaussianguess(y - medbg, mask=mask, yxguess=yxguess)
theta = 0
guess = np.append(guess, theta)
# "ravel" the guess
gparams = np.append(guess[0], guess[1])
gparams = np.append(gparams, guess[2])
gparams = np.append(gparams, guess[3])
# Background params to fit:
if fitbg == 0:
bgparams = []
elif fitbg == 1:
bgparams = bgpars[2]
elif fitbg == 2:
bgparams = bgpars
# Concatenate sets of parameters we want to fit:
params = np.append(gparams, bgparams)
# Set up bounds (only for theta)
lbounds = np.zeros(params.shape) - np.inf
ubounds = np.zeros(params.shape) + np.inf
if fitbg == 0:
lbounds[-1] = 0.
ubounds[-1] = np.pi / 2.
elif fitbg == 1:
lbounds[-2] = 0.
ubounds[-2] = np.pi / 2
elif fitbg == 2:
lbounds[-3] = 0.
ubounds[-3] = np.pi / 2
# Rest of parameters needed by residuals:
args = (x, y, mask, weights, bgpars, fitbg)
# The fit:
# These commented lines force a bounded fit. It runs much slower
# and essentially the same thing can be achieved my modding the
# fitted rotation.
# res = so.least_squares(rotresiduals, params, bounds=(lbounds, ubounds),
# args=args)
# p = res['x'] # Best-fit parameters
# jac = res['jac'] # Jacobian at p
# status = res['status'] # Status message
# mesg = res['message'] # Interpretation of status
# succ = res['success'] # Success boolean
# Get covariance from the jacobian
# cov = np.linalg.inv(np.dot(jac.T, jac))
# err = np.sqrt(np.diagonal(cov))
# The fit:
p, cov, info, mesg, success = so.leastsq(rotresiduals,
params,
args,
full_output=True)
err = np.sqrt(np.diagonal(cov))
return p, err
def plu(self, events, UP):
if self.principal and UP.my_player == self.NAME:
dev = hackers.detectar(self, UP)
if dev != None:
return [dev]
tim = time.time()
if tim > self.LM + self.Tdaniado:
self.daniable = True
self.first_time = True
self.permitido_act = True
self.Tdaniado = self.Tdaniado_init
self.escudo_activado = False
else:
self.titilar()
if self.PERDIO:
return ["destroyme"]
key = events.get_keyboard()
something = False
self.programador = False
if key[pygame.K_F5] and key[pygame.K_F8]:
self.programador = True
if self.programador:
self.speed = 7
else:
self.analizar_backgrounds(UP)
self.speed = self.speed_base
if (self.ID == 0):
condA = key[pygame.K_UP]
condB = key[pygame.K_DOWN]
condC = key[pygame.K_LEFT]
condD = key[pygame.K_RIGHT]
condE = key[pygame.K_SPACE]
else:
print self.ID
print events.get_m()
print events.fv(self.ID)
condA = events.get_m()[events.fv(self.ID)][1]
condB = events.get_m()[events.fv(self.ID)][2]
condC = events.get_m()[events.fv(self.ID)][3]
condD = events.get_m()[events.fv(self.ID)][4]
condE = events.get_m()[events.fv(self.ID)][5]
if condA:
if not (ev(self.n_allowed, "arriba")) or self.programador:
self._y -= self.speed
if self.pact != "ad":
self.lpact = self.pact
self.pact = "ad"
something = True
if condB:
if not (ev(self.n_allowed, "abajo")) or self.programador:
self._y += self.speed
if self.pact != "at":
self.lpact = self.pact
self.pact = "at"
something = True
if condC:
if not (ev(self.n_allowed, "izquierda")) or self.programador:
self._x -= self.speed
if self.pact != "iz":
self.lpact = self.pact
self.pact = "iz"
something = True
if condD:
if not (ev(self.n_allowed, "derecha")) or self.programador:
self._x += self.speed
if self.pact != "de":
self.lpact = self.pact
self.pact = "de"
something = True
if (something == False):
self.surface = ei.tux.adelante.positions[0]
self.pact = "no"
if self.pact == "ad":
if self.lpact != self.pact:
self.ciclo += 1
if self.ciclo / 8 > 2:
self.ciclo = 0
self.surface = ei.tux.atras.positions[self.ciclo / 8]
if self.pact == "at":
if self.lpact != self.pact:
self.ciclo += 1
if self.ciclo / 8 > 2:
self.ciclo = 0
self.surface = ei.tux.adelante.positions[self.ciclo / 8]
if self.pact == "de":
if self.lpact != self.pact:
self.ciclo += 1
if self.ciclo / 4 > 5:
self.ciclo = 0
self.surface = ei.tux.derecha.positions[self.ciclo / 4]
if self.pact == "iz":
if self.lpact != self.pact:
self.ciclo += 1
if self.ciclo / 4 > 5:
self.ciclo = 0
self.surface = ei.tux.izquierda.positions[self.ciclo / 4]
self.colicion_detection(UP)
if condE:
if not self.disparando_discos:
self.disparando_discos = True
if self.discos > 0:
if self.pact != "no":
pact = self.pact
else:
pact = "at"
UP.objetos.append(
disk.disk(self._x, self._y, pact, len(UP.objetos),
"Disco"))
self.discos -= 1
N = False
for q in UP.objetos:
if q.TYPE == "Disco":
N = True
if N == False:
self.disparando_discos = False
def vopen(path, mode='rb', what='auto'):
"""Opens a disk, partition or volume according to 'what' parameter: 'auto'
selects the volume in the first partition or disk; 'disk' selects the raw disk;
'partitionN' tries to open partition number N; 'volume' tries to open a file
system. """
if DEBUG & 2: log("Volume.open in '%s' mode", what)
# Tries to open a raw disk or disk image (plain or VHD)
if os.name == 'nt' and len(path) == 2 and path[1] == ':':
path = '\\\\.\\' + path
if path.lower().endswith('.vhd'): # VHD image
d = vhdutils.Image(path, mode)
else:
d = disk.disk(path, mode) # disk or disk image
if DEBUG & 2: log("Opened disk: %s", d)
d.seek(0)
if what == 'disk':
return d
# Tries to access a partition
mbr = partutils.MBR(d.read(512), disksize=d.size)
if DEBUG & 2: log("Opened MBR: %s", mbr)
valid_mbr = 1
n = mbr.partitions[0].size()
if mbr.wBootSignature != 0xAA55:
if DEBUG & 2: log("Invalid Master Boot Record")
valid_mbr = 0
elif mbr.partitions[0].bType != 0xEE and (not n or n > d.size):
if DEBUG & 2: log("Invalid Primary partition size in MBR")
valid_mbr = 0
elif mbr.partitions[0].bStatus not in (0, 0x80):
if DEBUG & 2: log("Invalid Primary partition status in MBR")
valid_mbr = 0
if not valid_mbr:
if DEBUG & 2: log("Invalid Master Boot Record")
if what in ('auto', 'volume'):
if DEBUG & 2:
log("Trying to open a File system (Volume) in plain disk")
d.seek(0)
d.mbr = None
v = openvolume(d)
if v != 'EINV': return v
if DEBUG & 2: log("No known file system found, returning RAW disk")
d.seek(0)
return d
else: # partition mode
return 'EINVMBR'
# Tries to open MBR or GPT partition
if DEBUG & 2: log("Ok, valid MBR")
partition = 0
if what.startswith('partition'):
partition = int(re.match('partition(\d+)', what).group(1))
if DEBUG & 2: log("Trying to open partition #%d", partition)
part = None
if mbr.partitions[0].bType == 0xEE: # GPT
d.seek(512)
gpt = partutils.GPT(d.read(512), 512)
if DEBUG & 2: log("Opened GPT Header: %s", gpt)
d.seek(gpt.u64PartitionEntryLBA * 512)
blk = d.read(gpt.dwNumberOfPartitionEntries *
gpt.dwNumberOfPartitionEntries)
gpt.parse(blk)
blocks = gpt.partitions[partition].u64EndingLBA - gpt.partitions[
partition].u64StartingLBA + 1
if DEBUG & 2:
log("Opening Partition #%d: %s", partition,
gpt.partitions[partition])
part = disk.partition(d,
gpt.partitions[partition].u64StartingLBA * 512,
blocks * 512)
part.seek(0)
part.mbr = mbr
part.gpt = gpt
# TODO: protect against invalid partition entries!
else:
index = 0
if partition > 0:
index = 1 # opens Extended Partition
part = disk.partition(d, mbr.partitions[index].offset(),
mbr.partitions[index].size())
if DEBUG & 2:
log("Opened %s partition @%016x (LBA %016x) %s",
('Primary', 'Extended')[index],
mbr.partitions[index].chsoffset(),
mbr.partitions[index].lbaoffset(),
partutils.raw2chs(mbr.partitions[index].sFirstSectorCHS))
if partition > 0:
wanted = 1
extpart = part
while wanted <= partition:
bs = extpart.read(512)
ebr = partutils.MBR(
bs, disksize=d.size) # reads Extended Boot Record
if DEBUG & 2: log("Opened EBR: %s", ebr)
if ebr.wBootSignature != 0xAA55:
if DEBUG & 2: log("Invalid Extended Boot Record")
if what == 'auto':
return d
else:
return 'EINV'
if DEBUG & 2:
log("Got partition @%016x (@%016x rel.) %s",
ebr.partitions[0].chsoffset(),
ebr.partitions[0].lbaoffset(),
partutils.raw2chs(ebr.partitions[0].sFirstSectorCHS))
if DEBUG & 2:
log("Next logical partition @%016x (@%016x rel.) %s",
ebr.partitions[1].chsoffset(),
ebr.partitions[1].lbaoffset(),
partutils.raw2chs(ebr.partitions[1].sFirstSectorCHS))
if wanted == partition:
if DEBUG & 2:
log(
"Opening Logical Partition #%d @%016x %s",
partition, ebr.partitions[0].offset(),
partutils.raw2chs(
ebr.partitions[0].sFirstSectorCHS))
part = disk.partition(d, ebr.partitions[0].offset(),
ebr.partitions[0].size())
part.seek(0)
break
if ebr.partitions[1].dwFirstSectorLBA and ebr.partitions[
1].dwTotalSectors:
if DEBUG & 2:
log(
"Scanning next Logical Partition @%016x %s size %.02f MiB",
ebr.partitions[1].offset(),
partutils.raw2chs(
ebr.partitions[1].sFirstSectorCHS),
ebr.partitions[1].size() // (1 << 20))
extpart = disk.partition(d, ebr.partitions[1].offset(),
ebr.partitions[1].size())
else:
break
wanted += 1
part.mbr = mbr
def open(x):
return openvolume(x)
disk.partition.open = open # adds an open member to partition object
if what in ('volume', 'auto'):
v = part.open()
if DEBUG & 2: log("Returning opened Volume %s", v)
return v
else:
if DEBUG & 2: log("Returning partition object")
return part
