Tibeuleu/FOC_Reduction
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fix depreciation warning matplotlib

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This commit is contained in:
Thibault Barnouin
2024-11-26 16:58:37 +01:00
parent 3f97202a03
commit 83e58d6a26
2 changed files with 64 additions and 53 deletions
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12
package/FOC_reduction.py
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@@ -40,9 +40,9 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
display_crop = False display_crop = False
# Background estimation # Background estimation
error_sub_type = "freedman-diaconis" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51)) error_sub_type = "scott" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
subtract_error = 1.0 subtract_error = 2.0
display_bkg = False display_bkg = True
# Data binning # Data binning
pxsize = 0.05 pxsize = 0.05
@@ -66,10 +66,10 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
rotate_North = True rotate_North = True
# Polarization map output # Polarization map output
P_cut = 0.999 # if >=1.0 cut on the signal-to-noise else cut on the confidence level in Q, U P_cut = 5 # if >=1.0 cut on the signal-to-noise else cut on the confidence level in Q, U
SNRi_cut = 3.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%. SNRi_cut = 5.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
flux_lim = None # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None flux_lim = None # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
scale_vec = 2 scale_vec = 3
step_vec = 1 # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length step_vec = 1 # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length
# Pipeline start # Pipeline start

105
package/lib/plots.py
View File

@@ -2192,8 +2192,7 @@ class image_lasso_selector(object):
self.mask = np.zeros(self.img.shape[:2], dtype=bool) self.mask = np.zeros(self.img.shape[:2], dtype=bool)
self.mask[self.indices] = True self.mask[self.indices] = True
if hasattr(self, "cont"): if hasattr(self, "cont"):
for coll in self.cont.collections: self.cont.remove()
coll.remove()
self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors="white", linewidths=1) self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors="white", linewidths=1)
if not self.embedded: if not self.embedded:
self.displayed.set_data(array) self.displayed.set_data(array)
@@ -2305,11 +2304,7 @@ class slit(object):
for p in self.pix: for p in self.pix:
self.mask[tuple(p)] = (np.abs(np.dot(rot2D(-self.angle), p - self.rect.get_center()[::-1])) < (self.height / 2.0, self.width / 2.0)).all() self.mask[tuple(p)] = (np.abs(np.dot(rot2D(-self.angle), p - self.rect.get_center()[::-1])) < (self.height / 2.0, self.width / 2.0)).all()
if hasattr(self, "cont"): if hasattr(self, "cont"):
for coll in self.cont.collections: self.cont.remove()
try:
coll.remove()
except AttributeError:
return
self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors="white", linewidths=1) self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors="white", linewidths=1)
if not self.embedded: if not self.embedded:
self.displayed.set_data(array) self.displayed.set_data(array)
@@ -2408,11 +2403,7 @@ class aperture(object):
x0, y0 = self.circ.center x0, y0 = self.circ.center
self.mask = np.sqrt((xx - x0) ** 2 + (yy - y0) ** 2) < self.radius self.mask = np.sqrt((xx - x0) ** 2 + (yy - y0) ** 2) < self.radius
if hasattr(self, "cont"): if hasattr(self, "cont"):
for coll in self.cont.collections: self.cont.remove()
try:
coll.remove()
except AttributeError:
return
self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors="white", linewidths=1) self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors="white", linewidths=1)
if not self.embedded: if not self.embedded:
self.displayed.set_data(array) self.displayed.set_data(array)
@@ -2441,7 +2432,7 @@ class pol_map(object):
self.step_vec = step_vec self.step_vec = step_vec
self.scale_vec = scale_vec self.scale_vec = scale_vec
self.pa_err = pa_err self.pa_err = pa_err
self.conf = PCconf(self.Q / self.I, self.U / self.I, np.sqrt(self.IQU_cov[1, 1]) / self.I, np.sqrt(self.IQU_cov[2, 2]) / self.I) self.conf = PCconf(self.QN, self.UN, self.QN_ERR, self.UN_ERR)
# Get data # Get data
self.targ = self.Stokes[0].header["targname"] self.targ = self.Stokes[0].header["targname"]
@@ -2543,8 +2534,7 @@ class pol_map(object):
self.selected = False self.selected = False
self.region = deepcopy(self.select_instance.mask.astype(bool)) self.region = deepcopy(self.select_instance.mask.astype(bool))
self.select_instance.displayed.remove() self.select_instance.displayed.remove()
for coll in self.select_instance.cont.collections: self.select_instance.cont.remove()
coll.remove()
self.select_instance.lasso.set_active(False) self.select_instance.lasso.set_active(False)
self.set_data_mask(deepcopy(self.region)) self.set_data_mask(deepcopy(self.region))
self.pol_int() self.pol_int()
@@ -2587,8 +2577,7 @@ class pol_map(object):
self.select_instance.update_mask() self.select_instance.update_mask()
self.region = deepcopy(self.select_instance.mask.astype(bool)) self.region = deepcopy(self.select_instance.mask.astype(bool))
self.select_instance.displayed.remove() self.select_instance.displayed.remove()
for coll in self.select_instance.cont.collections: self.select_instance.cont.remove()
coll.remove()
self.select_instance.circ.set_visible(False) self.select_instance.circ.set_visible(False)
self.set_data_mask(deepcopy(self.region)) self.set_data_mask(deepcopy(self.region))
self.pol_int() self.pol_int()
@@ -2645,8 +2634,7 @@ class pol_map(object):
self.select_instance.update_mask() self.select_instance.update_mask()
self.region = deepcopy(self.select_instance.mask.astype(bool)) self.region = deepcopy(self.select_instance.mask.astype(bool))
self.select_instance.displayed.remove() self.select_instance.displayed.remove()
for coll in self.select_instance.cont.collections: self.select_instance.cont.remove()
coll.remove()
self.select_instance.rect.set_visible(False) self.select_instance.rect.set_visible(False)
self.set_data_mask(deepcopy(self.region)) self.set_data_mask(deepcopy(self.region))
self.pol_int() self.pol_int()
@@ -2923,14 +2911,42 @@ class pol_map(object):
def I(self): def I(self):
return self.Stokes["I_STOKES"].data return self.Stokes["I_STOKES"].data
@property
def I_ERR(self):
return np.sqrt(self.Stokes["IQU_COV_MATRIX"].data[0, 0])
@property @property
def Q(self): def Q(self):
return self.Stokes["Q_STOKES"].data return self.Stokes["Q_STOKES"].data
@property
def QN(self):
return self.Q / np.where(self.I > 0, self.I, np.nan)
@property
def Q_ERR(self):
return np.sqrt(self.Stokes["IQU_COV_MATRIX"].data[1, 1])
@property
def QN_ERR(self):
return self.Q_ERR / np.where(self.I > 0, self.I, np.nan)
@property @property
def U(self): def U(self):
return self.Stokes["U_STOKES"].data return self.Stokes["U_STOKES"].data
@property
def UN(self):
return self.U / np.where(self.I > 0, self.I, np.nan)
@property
def U_ERR(self):
return np.sqrt(self.Stokes["IQU_COV_MATRIX"].data[2, 2])
@property
def UN_ERR(self):
return self.U_ERR / np.where(self.I > 0, self.I, np.nan)
@property @property
def IQU_cov(self): def IQU_cov(self):
return self.Stokes["IQU_COV_MATRIX"].data return self.Stokes["IQU_COV_MATRIX"].data
@@ -3368,12 +3384,7 @@ class pol_map(object):
) )
if hasattr(self, "cont"): if hasattr(self, "cont"):
for coll in self.cont.collections: self.cont.remove()
try:
coll.remove()
except AttributeError:
del coll
del self.cont
if fig is None: if fig is None:
fig = self.fig fig = self.fig
if ax is None: if ax is None:
@@ -3390,17 +3401,17 @@ class pol_map(object):
+ r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg, np.ceil(PA_reg_err * 10.0) / 10.0) + r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg, np.ceil(PA_reg_err * 10.0) / 10.0)
+ str_conf + str_conf
) )
# self.str_cut = "" self.str_cut = ""
self.str_cut = ( # self.str_cut = (
"\n" # "\n"
+ r"$F_{{\lambda}}^{{cut}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format( # + r"$F_{{\lambda}}^{{cut}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(
self.pivot_wav, sci_not(I_cut * self.map_convert, I_cut_err * self.map_convert, 2) # self.pivot_wav, sci_not(I_cut * self.map_convert, I_cut_err * self.map_convert, 2)
) # )
+ "\n" # + "\n"
+ r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut * 100.0, np.ceil(P_cut_err * 1000.0) / 10.0) # + r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut * 100.0, np.ceil(P_cut_err * 1000.0) / 10.0)
+ "\n" # + "\n"
+ r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut, np.ceil(PA_cut_err * 10.0) / 10.0) # + r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut, np.ceil(PA_cut_err * 10.0) / 10.0)
) # )
self.an_int = ax.annotate( self.an_int = ax.annotate(
self.str_int + self.str_cut, self.str_int + self.str_cut,
color="white", color="white",
@@ -3426,17 +3437,17 @@ class pol_map(object):
+ r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg, np.ceil(PA_reg_err * 10.0) / 10.0) + r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg, np.ceil(PA_reg_err * 10.0) / 10.0)
+ str_conf + str_conf
) )
# str_cut = "" str_cut = ""
str_cut = ( # str_cut = (
"\n" # "\n"
+ r"$F_{{\lambda}}^{{cut}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format( # + r"$F_{{\lambda}}^{{cut}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(
self.pivot_wav, sci_not(I_cut * self.map_convert, I_cut_err * self.map_convert, 2) # self.pivot_wav, sci_not(I_cut * self.map_convert, I_cut_err * self.map_convert, 2)
) # )
+ "\n" # + "\n"
+ r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut * 100.0, np.ceil(P_cut_err * 1000.0) / 10.0) # + r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut * 100.0, np.ceil(P_cut_err * 1000.0) / 10.0)
+ "\n" # + "\n"
+ r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut, np.ceil(PA_cut_err * 10.0) / 10.0) # + r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut, np.ceil(PA_cut_err * 10.0) / 10.0)
) # )
ax.annotate( ax.annotate(
str_int + str_cut, str_int + str_cut,
color="white", color="white",