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Felipe Reyes Osorio
ejercicios-clase-03-datos
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fce5b237
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fce5b237
authored
4 years ago
by
Felipe Reyes Osorio
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First attempt at animation and code
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Ejercicio2_reyesf.ipynb
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Ejercicio2_reyesf.ipynb
animation.gif
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animation.gif
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fce5b237
{
"cells": [
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from matplotlib.pyplot import *\n",
"import matplotlib.animation as an\n",
"\n",
"%matplotlib inline\n",
"\n",
"giants = np.loadtxt(\"./data/giants.txt\",skiprows=1)\n",
"supGiants = np.loadtxt(\"./data/supergiants.txt\",skiprows=1)\n",
"dwarfs = np.loadtxt(\"./data/dwarfs.csv\",skiprows=1,delimiter=\",\")\n",
"ms = np.loadtxt(\"./data/ms.csv\",skiprows=1,delimiter=\",\")\n",
"\n",
"def tRange(listOfLists):\n",
" ans=[]\n",
" for i in range(len(listOfLists)):\n",
" ans.append([min(listOfLists[i]), max(listOfLists[i])])\n",
" ans = np.array(ans)\n",
" return min(ans[:,0]),max(ans[:,1])\n",
"\n",
"bigListT=[ms[:,1],giants[:,1],supGiants[:,1],dwarfs[:,1]]\n",
"temps = tRange(bigListT)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig1, ax1= subplots(figsize=(9,6))\n",
"ax1.tick_params(axis='both', which='major', labelsize=14)\n",
"\n",
"vmin, vmax = tRange(bigListT)\n",
"\n",
"cm = \"RdYlBu\"\n",
"\n",
"ylabel(\"Luminosity [L$_\\odot$]\", fontsize=20)\n",
"ax1.set_yscale('log')\n",
"ax1.set_ylim(0.3e-4, 5e7)\n",
"\n",
"xlabel(\"$T$ [K]\", fontsize=20)\n",
"ax1.set_xlim(vmin-900, vmax+500);"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"from celluloid import Camera\n",
"\n",
"cam = Camera(fig1)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"dMs = len(ms[:,0])\n",
"for j in range(dMs):\n",
" msTrash = np.delete(ms, slice(j+1, dMs-1), 0)\n",
" ax1.scatter(msTrash[:,1], msTrash[:,0], c=-msTrash[:,1], vmin=-vmax, vmax=0, s=1.7*msTrash[:,2]**1.5, cmap=cm, lw=0.8, ec=\"k\")\n",
" if j%3==0: cam.snap()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"MovieWriter Pillow unavailable; using Pillow instead.\n"
]
}
],
"source": [
"animation = cam.animate()\n",
"animation.save('animation.gif', writer='Pillow', fps=10)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"dGs = len(giants[:,0])\n",
"for j in range(dGs):\n",
" gsTrash = np.delete(giants, slice(j+1, dMs-1), 0)\n",
" ax1.scatter(ms[:,1], ms[:,0], c=-ms[:,1], vmin=-vmax, vmax=0, s=1.7*ms[:,2]**1.5, cmap=cm, lw=0.8, ec=\"k\")\n",
" ax1.scatter(gsTrash[:,1], gsTrash[:,0], c=-gsTrash[:,1], vmin=-vmax, vmax=0, s=1.7*gsTrash[:,2]**1.5, cmap=cm, lw=2, ec=\"k\")\n",
" cam.snap()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"dSg = len(supGiants[:,0])\n",
"for j in range(dSg):\n",
" sgTrash = np.delete(supGiants, slice(j+1, dMs-1), 0)\n",
" ax1.scatter(ms[:,1], ms[:,0], c=-ms[:,1], vmin=-vmax, vmax=0, s=1.7*ms[:,2]**1.5, cmap=cm, lw=0.8, ec=\"k\")\n",
" ax1.scatter(giants[:,1], giants[:,0], c=-giants[:,1], vmin=-vmax, vmax=0, s=1.7*giants[:,2]**1.5, cmap=cm, lw=2, ec=\"k\")\n",
" ax1.scatter(sgTrash[:,1], sgTrash[:,0], c=-sgTrash[:,1], vmin=-vmax, vmax=0, s=1.7*sgTrash[:,2]**1.5, cmap=cm, lw=2, ec=\"k\")\n",
" cam.snap()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"dDw = len(dwarfs[:,0])\n",
"for j in range(dDw):\n",
" dwTrash = np.delete(dwarfs, slice(j+1, dMs-1), 0)\n",
" ax1.scatter(ms[:,1], ms[:,0], c=-ms[:,1], vmin=-vmax, vmax=0, s=1.7*ms[:,2]**1.5, cmap=cm, lw=0.8, ec=\"k\")\n",
" ax1.scatter(giants[:,1], giants[:,0], c=-giants[:,1], vmin=-vmax, vmax=0, s=1.7*giants[:,2]**1.5, cmap=cm, lw=2, ec=\"k\")\n",
" ax1.scatter(supGiants[:,1], supGiants[:,0], c=-supGiants[:,1], vmin=-vmax, vmax=0, s=1.7*supGiants[:,2]**1.5, cmap=cm, lw=2, ec=\"k\")\n",
" ax1.scatter(dwTrash[:,1], dwTrash[:,0], c=-dwTrash[:,1], vmin=-vmax, vmax=0, s=1.7*dwTrash[:,2]**1.5, cmap=cm, lw=0.8, ec=\"k\")\n",
" cam.snap()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
%% Cell type:code id: tags:
```
python
import
numpy
as
np
from
matplotlib.pyplot
import
*
import
matplotlib.animation
as
an
%
matplotlib
inline
giants
=
np
.
loadtxt
(
"
./data/giants.txt
"
,
skiprows
=
1
)
supGiants
=
np
.
loadtxt
(
"
./data/supergiants.txt
"
,
skiprows
=
1
)
dwarfs
=
np
.
loadtxt
(
"
./data/dwarfs.csv
"
,
skiprows
=
1
,
delimiter
=
"
,
"
)
ms
=
np
.
loadtxt
(
"
./data/ms.csv
"
,
skiprows
=
1
,
delimiter
=
"
,
"
)
def
tRange
(
listOfLists
):
ans
=
[]
for
i
in
range
(
len
(
listOfLists
)):
ans
.
append
([
min
(
listOfLists
[
i
]),
max
(
listOfLists
[
i
])])
ans
=
np
.
array
(
ans
)
return
min
(
ans
[:,
0
]),
max
(
ans
[:,
1
])
bigListT
=
[
ms
[:,
1
],
giants
[:,
1
],
supGiants
[:,
1
],
dwarfs
[:,
1
]]
temps
=
tRange
(
bigListT
)
```
%% Cell type:code id: tags:
```
python
fig1
,
ax1
=
subplots
(
figsize
=
(
9
,
6
))
ax1
.
tick_params
(
axis
=
'
both
'
,
which
=
'
major
'
,
labelsize
=
14
)
vmin
,
vmax
=
tRange
(
bigListT
)
cm
=
"
RdYlBu
"
ylabel
(
"
Luminosity [L$_\odot$]
"
,
fontsize
=
20
)
ax1
.
set_yscale
(
'
log
'
)
ax1
.
set_ylim
(
0.3e-4
,
5e7
)
xlabel
(
"
$T$ [K]
"
,
fontsize
=
20
)
ax1
.
set_xlim
(
vmin
-
900
,
vmax
+
500
);
```
%% Output
%% Cell type:code id: tags:
```
python
from
celluloid
import
Camera
cam
=
Camera
(
fig1
)
```
%% Cell type:code id: tags:
```
python
dMs
=
len
(
ms
[:,
0
])
for
j
in
range
(
dMs
):
msTrash
=
np
.
delete
(
ms
,
slice
(
j
+
1
,
dMs
-
1
),
0
)
ax1
.
scatter
(
msTrash
[:,
1
],
msTrash
[:,
0
],
c
=-
msTrash
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
msTrash
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
0.8
,
ec
=
"
k
"
)
if
j
%
3
==
0
:
cam
.
snap
()
```
%% Cell type:code id: tags:
```
python
animation
=
cam
.
animate
()
animation
.
save
(
'
animation.gif
'
,
writer
=
'
Pillow
'
,
fps
=
10
)
```
%% Output
MovieWriter Pillow unavailable; using Pillow instead.
%% Cell type:code id: tags:
```
python
dGs
=
len
(
giants
[:,
0
])
for
j
in
range
(
dGs
):
gsTrash
=
np
.
delete
(
giants
,
slice
(
j
+
1
,
dMs
-
1
),
0
)
ax1
.
scatter
(
ms
[:,
1
],
ms
[:,
0
],
c
=-
ms
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
ms
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
0.8
,
ec
=
"
k
"
)
ax1
.
scatter
(
gsTrash
[:,
1
],
gsTrash
[:,
0
],
c
=-
gsTrash
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
gsTrash
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
2
,
ec
=
"
k
"
)
cam
.
snap
()
```
%% Cell type:code id: tags:
```
python
dSg
=
len
(
supGiants
[:,
0
])
for
j
in
range
(
dSg
):
sgTrash
=
np
.
delete
(
supGiants
,
slice
(
j
+
1
,
dMs
-
1
),
0
)
ax1
.
scatter
(
ms
[:,
1
],
ms
[:,
0
],
c
=-
ms
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
ms
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
0.8
,
ec
=
"
k
"
)
ax1
.
scatter
(
giants
[:,
1
],
giants
[:,
0
],
c
=-
giants
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
giants
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
2
,
ec
=
"
k
"
)
ax1
.
scatter
(
sgTrash
[:,
1
],
sgTrash
[:,
0
],
c
=-
sgTrash
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
sgTrash
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
2
,
ec
=
"
k
"
)
cam
.
snap
()
```
%% Cell type:code id: tags:
```
python
dDw
=
len
(
dwarfs
[:,
0
])
for
j
in
range
(
dDw
):
dwTrash
=
np
.
delete
(
dwarfs
,
slice
(
j
+
1
,
dMs
-
1
),
0
)
ax1
.
scatter
(
ms
[:,
1
],
ms
[:,
0
],
c
=-
ms
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
ms
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
0.8
,
ec
=
"
k
"
)
ax1
.
scatter
(
giants
[:,
1
],
giants
[:,
0
],
c
=-
giants
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
giants
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
2
,
ec
=
"
k
"
)
ax1
.
scatter
(
supGiants
[:,
1
],
supGiants
[:,
0
],
c
=-
supGiants
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
supGiants
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
2
,
ec
=
"
k
"
)
ax1
.
scatter
(
dwTrash
[:,
1
],
dwTrash
[:,
0
],
c
=-
dwTrash
[:,
1
],
vmin
=-
vmax
,
vmax
=
0
,
s
=
1.7
*
dwTrash
[:,
2
]
**
1.5
,
cmap
=
cm
,
lw
=
0.8
,
ec
=
"
k
"
)
cam
.
snap
()
```
%% Cell type:code id: tags:
```
python
``
`
%%
Cell
type
:
code
id
:
tags
:
```
python
```
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