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Leonie Pick
ClassifyStorms
Commits
a45d9adf
Commit
a45d9adf
authored
Jun 12, 2020
by
Leonie Pick
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Adjust pl.Musical and pl.MusicalClassified for defense talk
parent
be6d8f0d
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3 changed files
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88 additions
and
729 deletions
+88
-729
.ipynb_checkpoints/ClassifyStorms-checkpoint.ipynb
.ipynb_checkpoints/ClassifyStorms-checkpoint.ipynb
+38
-619
ClassifyStorms.ipynb
ClassifyStorms.ipynb
+34
-100
Plots.py
Plots.py
+16
-10
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.ipynb_checkpoints/ClassifyStorms-checkpoint.ipynb
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a45d9adf
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ClassifyStorms.ipynb
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Plots.py
View file @
a45d9adf
...
...
@@ -793,8 +793,8 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
## Dummy for legend
#axs1.scatter(0,-50,s=20,marker='s',color='black',alpha=0.7,label=r'3 $\leq$ Kp $\leq$ 4+$^+$')
axs1
.
plot
([
0
,
1
],[
-
50
,
-
25
],
color
=
'dimgray'
,
linewidth
=
1.5
,
label
=
r
'SN'
)
axs1
.
plot
([
0
,
1
],[
-
50
,
-
25
],
color
=
'dimgray'
,
linewidth
=
1.5
,
linestyle
=
'--'
,
label
=
r
'HMC$_{11y}$'
)
axs1
.
scatter
(
0
,
-
50
,
s
=
20
,
marker
=
'
s'
,
color
=
'dimgray'
,
label
=
r
'Event
'
)
#
axs1.plot([0,1],[-50,-25],color='dimgray',linewidth=1.5,linestyle='--',label=r'HMC$_{11y}$')
axs1
.
scatter
(
0
,
-
50
,
s
=
20
,
marker
=
'
o'
,
color
=
'dimgray'
,
label
=
r
'Storm peak
'
)
# Plot storm events from catalogs (papers)
cm
=
plt
.
cm
.
get_cmap
(
'seismic'
)
...
...
@@ -807,7 +807,11 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
x_t
[
i
]
=
np
.
mod
(
diff_t
[
i
].
total_seconds
()
/
(
60
*
60
*
24
),
SolarRot
)
z
=
Reference
[
KnownE
,
0
]
sc
=
axs1
.
scatter
(
x_t
,
y_t
,
c
=
z
,
vmin
=
0.0
,
vmax
=
1.0
,
s
=
5
,
cmap
=
cm
,
zorder
=
3
)
znew
=
np
.
zeros
(
len
(
z
),
dtype
=
float
)
+
0.25
z1
=
np
.
where
(
z
==
1
)[
0
]
znew
[
z1
]
=
0.75
sc
=
axs1
.
scatter
(
x_t
,
y_t
,
c
=
znew
,
vmin
=
0.0
,
vmax
=
1.0
,
s
=
10
,
cmap
=
cm
,
zorder
=
3
)
cbar_axis
=
fig
.
add_axes
([
0.45
,
0.032
,
0.3
,
0.01
])
cbar
=
plt
.
colorbar
(
sc
,
cax
=
cbar_axis
,
use_gridspec
=
False
,
orientation
=
'horizontal'
,
ticks
=
[
0
,
0.5
,
1
])
#cbar.set_label('CME probability',fontsize=8)
...
...
@@ -820,10 +824,12 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
# Plots class occurrences
nip
=
0
c
=
[
0.75
,
0.25
]
for
i
in
[
1
,
0
]:
zi
=
np
.
where
(
z
==
i
)[
0
]
ni
,
bins
=
np
.
histogram
(
HTime
[
Reference
[
KnownE
[
zi
],
1
],
4
],
np
.
arange
(
1900
,
2017
,
1
))
axs2
.
barh
(
np
.
arange
(
1900.5
,
2016.5
,
1
),
ni
,
left
=
nip
,
height
=
0.9
,
color
=
cm
(
float
(
i
)),
zorder
=
5
)
#axs2.barh(np.arange(1900.5,2016.5,1), ni, left = nip, height=0.9, color=cm(float(i)),zorder=5)
axs2
.
barh
(
np
.
arange
(
1900.5
,
2016.5
,
1
),
ni
,
left
=
nip
,
height
=
0.9
,
color
=
cm
(
c
[
i
]),
zorder
=
5
)
nip
+=
ni
#Turner_CIR = np.where(np.logical_and(Reference[:,2] == b'Turner',Target['type'] ==0))[0]
...
...
@@ -864,7 +870,7 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
handles
,
labels
=
axs1
.
get_legend_handles_labels
()
order
=
[
0
,
1
]
axs1
.
legend
([
handles
[
idx
]
for
idx
in
order
],[
labels
[
idx
]
for
idx
in
order
],
ncol
=
5
,
frameon
=
True
,
fontsize
=
11
,
loc
=
'center left'
,
bbox_to_anchor
=
[
-
0.1
,
-
0.037
])
axs1
.
legend
([
handles
[
idx
]
for
idx
in
order
],[
labels
[
idx
]
for
idx
in
order
],
ncol
=
5
,
frameon
=
True
,
fontsize
=
11
,
loc
=
'center left'
,
bbox_to_anchor
=
[
-
0.1
5
,
-
0.037
])
axs1
.
set_ylim
([
np
.
nanmin
(
y
),
np
.
nanmax
(
y
)])
#handles, labels = axs1.get_legend_handles_labels()
...
...
@@ -886,7 +892,7 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
axs3
.
plot
((
SN
[
SN_start
:
SN_end
]
/
max
(
SN
[
SN_start
:
SN_end
]))
**
1
,
SNYears
[
SN_start
:
SN_end
],
color
=
'dimgray'
,
linewidth
=
1.5
,
label
=
'SN'
)
# Plot HMC threshold scaling
axs3
.
plot
((
HMC_filt
/
min
(
HMC_filt
))
**
1
,
HTime
[:,
4
],
color
=
'dimgray'
,
linestyle
=
'--'
,
linewidth
=
1.5
,
label
=
'HMC'
)
#
axs3.plot((HMC_filt/min(HMC_filt))**1,HTime[:,4],color='dimgray',linestyle='--',linewidth=1.5, label='HMC')
# Plot histogram
n
,
bins
,
patches
=
axs2
.
hist
(
x
=
HTime
[
Storms
,
4
],
bins
=
np
.
arange
(
1900
,
2017
,
1
),
orientation
=
'horizontal'
,
histtype
=
'stepfilled'
,
color
=
'silver'
,
alpha
=
1
)
...
...
@@ -981,8 +987,8 @@ def MusicalClassified(Time,HTime,Reference,Source,Storms,Class_Proba,Class,Kp_al
## Dummy for legend
#axs1.scatter(0,-50,s=20,marker='s',color='black',alpha=0.7,label=r'Kp $\geq$ 4$^+$')
axs1
.
plot
([
0
,
1
],[
-
50
,
-
25
],
color
=
'dimgray'
,
linewidth
=
1.5
,
label
=
r
'SN'
,
zorder
=
5
)
axs1
.
plot
([
0
,
1
],[
-
50
,
-
25
],
color
=
'dimgray'
,
linewidth
=
1.5
,
linestyle
=
'--'
,
label
=
r
'HMC$_{11y}$'
,
zorder
=
5
)
axs1
.
scatter
(
0
,
-
50
,
s
=
20
,
marker
=
'
s'
,
color
=
'black'
,
label
=
r
'Event
'
)
#
axs1.plot([0,1],[-50,-25],color='dimgray',linewidth=1.5,linestyle='--',label=r'HMC$_{11y}$', zorder=5)
axs1
.
scatter
(
0
,
-
50
,
s
=
20
,
marker
=
'
o'
,
color
=
'black'
,
label
=
r
'Storm peak
'
)
# Plot storm events (my picks)
CIRs_pred
=
np
.
where
(
Class_Proba
[:,
0
]
>
0.5
)[
0
]
...
...
@@ -1011,7 +1017,7 @@ def MusicalClassified(Time,HTime,Reference,Source,Storms,Class_Proba,Class,Kp_al
handles
,
labels
=
axs1
.
get_legend_handles_labels
()
order
=
[
0
,
1
]
axs1
.
legend
([
handles
[
idx
]
for
idx
in
order
],[
labels
[
idx
]
for
idx
in
order
],
ncol
=
5
,
frameon
=
True
,
fontsize
=
11
,
loc
=
'center left'
,
bbox_to_anchor
=
[
-
0.1
,
-
0.037
])
axs1
.
legend
([
handles
[
idx
]
for
idx
in
order
],[
labels
[
idx
]
for
idx
in
order
],
ncol
=
5
,
frameon
=
True
,
fontsize
=
11
,
loc
=
'center left'
,
bbox_to_anchor
=
[
-
0.1
5
,
-
0.037
])
axs1
.
set_ylim
([
np
.
nanmin
(
y
),
np
.
nanmax
(
y
)])
################################################
...
...
@@ -1048,7 +1054,7 @@ def MusicalClassified(Time,HTime,Reference,Source,Storms,Class_Proba,Class,Kp_al
SN_end
=
np
.
where
(
SNYears
==
2016.5
)[
0
][
0
]
#axs3.plot((SN[SN_start:SN_end,1]-np.min(SN[SN_start:SN_end,1]))/np.ptp(SN[SN_start:SN_end,1]),SN[SN_start:SN_end,0],color='black',linewidth=1.5, label='SN')
axs3
.
plot
((
SN
[
SN_start
:
SN_end
]
/
max
(
SN
[
SN_start
:
SN_end
]))
**
1
,
SNYears
[
SN_start
:
SN_end
],
color
=
'white'
,
linewidth
=
1.5
,
label
=
'SN'
)
axs3
.
plot
((
HMC_filt
/
min
(
HMC_filt
))
**
1
,
HTime
[:,
4
],
color
=
'white'
,
linestyle
=
'--'
,
linewidth
=
1.5
,
label
=
'HMC'
)
#
axs3.plot((HMC_filt/min(HMC_filt))**1,HTime[:,4],color='white',linestyle='--',linewidth=1.5, label='HMC')
####################################################################
#Class occurrence in separate plot
...
...
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