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BAORadio
AnaPAON4
Commits
7cbed580
Commit
7cbed580
authored
Jun 25, 2019
by
perdereau
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Plain Diff
typos + add plotting tracks with selection of good track in datacard files
parent
9313f687
Changes
3
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3 changed files
with
139 additions
and
8 deletions
+139
-8
Python/README
Python/README
+1
-1
Python/cmp_simddat.py
Python/cmp_simddat.py
+136
-5
Python/viewtfmap.py
Python/viewtfmap.py
+2
-2
No files found.
Python/README
View file @
7cbed580
scripts in this folder :
viewtfmap.py (+mynormalize) : reading and
f\
plotting from t-f map
viewtfmap.py (+mynormalize) : reading and plotting from t-f map
(or t-ra) fits files written by visiavg
cmp_simddat.py : to plot fitted and input tracks (visi vs time at some freq) used in the fitting of pointing & phase with satellites (and sources)
...
...
Python/cmp_simddat.py
View file @
7cbed580
...
...
@@ -7,7 +7,7 @@ from matplotlib import pyplot as plt
# data and fitted results from trkacxfit
# this will also plot the difference (in gray, scale on the right)
# unused tracks are alsor plotted
#numero_antenne =
0,
1,2,
3
pour 1H, 2H,...
#numero_antenne = 1,2,
..
pour 1H, 2H,...
# make_fig_scan_xc(filename,cross,nombre_de_traces)
# plots of all cross-cor visi comparision between
...
...
@@ -15,10 +15,11 @@ from matplotlib import pyplot as plt
# this will also plot the difference (in gray, scale on the right)
# for the real part
# unused tracks are alsor plotted
# cross = 0,1,...,5 pour 1Hx2H, 1Hx3H, ...,3Hx4H
# cross = 1,...,6 pour 1Hx2H, 1Hx3H, ...,3Hx4H
names_ac
=
[
'1H'
,
'2H'
,
'3H'
,
'4H'
]
names_xc
=
[
'1Hx2H'
,
'1Hx3H'
,
'1Hx4H'
,
'2Hx3H'
,
'2Hx4H'
,
'3Hx4H'
]
#cx_1_check_gal.fits
keynam
=
'HIERARCH NOM_OBJET'
...
...
@@ -114,7 +115,7 @@ def make_fig_scan(ffnam,ant,ntot):
#plt.figure(figsize=(20,17))
f
,
axarr
=
plt
.
subplots
(
nrows
,
ncols
,
figsize
=
(
ncols
*
4
,
nrows
*
4
))
axarf
=
axarr
.
flatten
()
f
.
suptitle
(
'A
ntenna '
+
str
(
ant
)
)
f
.
suptitle
(
'A
uto '
+
names_ac
[
ant
-
1
]
)
for
ip
in
np
.
arange
(
ntot
):
i
=
get_num
(
ant
,
ip
+
1
,
ntot
)
...
...
@@ -147,7 +148,8 @@ def make_fig_scan_xc(ffnam,cross,ntot):
f
,
axarr
=
plt
.
subplots
(
nrows
,
ncols
,
figsize
=
(
ncols
*
4
,
nrows
*
4
))
axarf
=
axarr
.
flatten
()
f
.
suptitle
(
'CrossCor '
+
str
(
cross
))
f
.
suptitle
(
'CrossCor '
+
names_xc
[
cross
-
1
])
for
ip
in
np
.
arange
(
ntot
):
i
=
get_num_cx
(
cross
,
ip
+
1
,
ntot
)
...
...
@@ -179,4 +181,133 @@ def make_fig_scan_xc(ffnam,cross,ntot):
plt
.
tight_layout
()
plt
.
subplots_adjust
(
top
=
0.9
)
def
get_good_xctrks
(
datac
):
flist
=
open
(
datac
,
"r"
)
ntrk
=
0
ngtrk
=
0
goods
=
[]
for
line
in
flist
:
iok
=
0
if
"@trk"
in
line
:
print
'found track '
ntrk
=
ntrk
+
1
if
"NOCX"
in
line
:
goods
.
append
(
iok
)
continue
if
"NOACX"
in
line
:
goods
.
append
(
iok
)
continue
ngtrk
=
ngtrk
+
1
goods
.
append
(
1
)
return
ntrk
,
ngtrk
,
goods
def
get_good_actrks
(
datac
):
flist
=
open
(
datac
,
"r"
)
ntrk
=
0
ngtrk
=
0
goods
=
[]
for
line
in
flist
:
iok
=
0
if
"@trk"
in
line
:
print
'found track '
ntrk
=
ntrk
+
1
if
"NOAC"
in
line
:
goods
.
append
(
iok
)
continue
if
"NOACX"
in
line
:
goods
.
append
(
iok
)
continue
ngtrk
=
ngtrk
+
1
goods
.
append
(
1
)
return
ntrk
,
ngtrk
,
goods
def
make_fig_scan_xc_sel
(
ffnam
,
cross
,
datac
):
hda
=
fits
.
open
(
ffnam
)
ntot
,
nok
,
goods
=
get_good_xctrks
(
datac
)
print
ntot
,
nok
,
goods
nrows
=
np
.
int
(
np
.
sqrt
(
nok
))
ncols
=
nok
/
nrows
ncols
=
ncols
+
nok
-
ncols
*
nrows
print
str
(
nrows
)
+
" "
+
str
(
ncols
)
f
,
axarr
=
plt
.
subplots
(
nrows
,
ncols
,
figsize
=
(
ncols
*
4
,
nrows
*
4
))
axarf
=
axarr
.
flatten
()
f
.
suptitle
(
'CrossCor '
+
names_xc
[
cross
-
1
])
ipl
=
0
for
ip
in
np
.
arange
(
ntot
):
if
goods
[
ip
]
==
0
:
continue
i
=
get_num_cx
(
cross
,
ip
+
1
,
ntot
)
print
hda
[
i
].
header
[
keynam
]
print
hda
[
i
+
1
].
header
[
keynam
]
#axarf[ip ].plot(hda[i].data,label='Signal')
#axarf[ip ].plot(hda[i+1].data,label='Simu')
#axarf[ip ].legend()
axarf
[
ipl
].
plot
(
hda
[
i
].
data
,
label
=
'Real'
)
axarf
[
ipl
].
plot
(
hda
[
i
+
1
].
data
,
label
=
'Imag'
)
axarf
[
ipl
].
plot
(
hda
[
i
+
2
].
data
,
label
=
'simu(real)'
,
linestyle
=
":"
)
axarf
[
ipl
].
plot
(
hda
[
i
+
3
].
data
,
label
=
'simu(imag)'
,
linestyle
=
":"
)
axarf
[
ipl
].
legend
()
vmaxr
=
np
.
max
(
hda
[
i
].
data
)
*
1.2
vminr
=
np
.
min
(
hda
[
i
].
data
)
*
1.2
vmaxi
=
np
.
max
(
hda
[
i
+
1
].
data
)
*
1.2
vmini
=
np
.
min
(
hda
[
i
+
1
].
data
)
*
1.2
vmax
=
np
.
max
((
vmaxr
,
vmaxi
))
vmin
=
np
.
min
((
vminr
,
vmini
))
axarf
[
ipl
].
set_ylim
((
vmin
,
vmax
))
axarf
[
ipl
].
set_title
(
'Track '
+
str
(
ip
))
axarf
[
ipl
].
set
(
xlabel
=
'time (mn)'
,
ylabel
=
'signal (AU)'
)
ax2
=
axarf
[
ipl
].
twinx
()
ax2
.
plot
((
hda
[
i
].
data
-
hda
[
i
+
2
].
data
)
/
(
vmax
-
vmin
),
label
=
'Data-Simu (real)'
,
color
=
'gray'
)
ax2
.
set_ylim
((
-
1
,
1
))
ax2
.
legend
(
loc
=
3
)
ipl
=
ipl
+
1
plt
.
tight_layout
()
plt
.
subplots_adjust
(
top
=
0.9
)
def
make_fig_scan_sel
(
ffnam
,
ant
,
datac
):
ntot
,
nok
,
goods
=
get_good_actrks
(
datac
)
print
ntot
,
nok
,
goods
hda
=
fits
.
open
(
ffnam
)
nrows
=
np
.
int
(
np
.
sqrt
(
nok
))
ncols
=
nok
/
nrows
ncols
=
ncols
+
nok
-
ncols
*
nrows
print
str
(
nrows
)
+
" "
+
str
(
ncols
)
#plt.figure(figsize=(20,17))
f
,
axarr
=
plt
.
subplots
(
nrows
,
ncols
,
figsize
=
(
ncols
*
4
,
nrows
*
4
))
axarf
=
axarr
.
flatten
()
f
.
suptitle
(
'Auto '
+
names_ac
[
ant
-
1
])
ipl
=
0
for
ip
in
np
.
arange
(
ntot
):
if
goods
[
ip
]
==
0
:
continue
i
=
get_num
(
ant
,
ip
+
1
,
ntot
)
print
hda
[
i
].
header
[
keynam
]
print
hda
[
i
+
1
].
header
[
keynam
]
axarf
[
ipl
].
plot
(
hda
[
i
].
data
,
label
=
'Signal'
)
axarf
[
ipl
].
plot
(
hda
[
i
+
1
].
data
,
label
=
'Simu'
)
axarf
[
ipl
].
legend
()
vscale
=
np
.
max
(
hda
[
i
].
data
)
-
1.
vmin
=
1
-
0.2
*
vscale
vmax
=
(
1.
+
vscale
)
*
1.1
axarf
[
ipl
].
set_ylim
((
vmin
,
vmax
))
axarf
[
ipl
].
set_title
(
'Track '
+
str
(
ip
))
axarf
[
ipl
].
set
(
xlabel
=
'time (mn)'
,
ylabel
=
'signal (AU)'
)
ax2
=
axarf
[
ipl
].
twinx
()
ax2
.
plot
(
hda
[
i
].
data
-
hda
[
i
+
1
].
data
,
label
=
'Data-Simu'
,
color
=
'gray'
)
ax2
.
set_ylim
((
-
1
,
9
))
ax2
.
legend
(
loc
=
2
)
ipl
=
ipl
+
1
plt
.
tight_layout
()
plt
.
subplots_adjust
(
top
=
0.9
)
Python/viewtfmap.py
View file @
7cbed580
...
...
@@ -29,8 +29,8 @@ names = ['1H','2H','3H','4H','1V','2V','3V','4V','1Hx2H','1Hx3H','1Hx4H','2Hx3H'
# the for the cross-correlations - the TF amplitudes are complex : in each case one HDU with real part, one with imaginary part averages
# and two for their respective variances
# NB the variance computation is optional
# at the end of the fits files, acddition HDUs contain 1D data (in reverse order) : times (center of bins
)
,
frequencies and RA
#
(
omitted in the case o
d
RA-freq maps
# at the end of the fits files, acddition HDUs contain 1D data (in reverse order) : times (center of bins,
in seconds),
#
frequencies (MHz) and RA (hours,
omitted in the case o
f
RA-freq maps
#
# --------------------------------
...
...
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