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Ramirez Saldana, Santiago
Calorimetry_Laboratory
Commits
c96918ee
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c96918ee
authored
1 year ago
by
Xia, Ning
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add template for notebook
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notebook/image/kalorimetrie_pruefstand.jpg
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notebook/image/kalorimetrie_pruefstand.jpg
notebook/notebook_PD_Kalorimetrie_Laborversuch.ipynb
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notebook/notebook_PD_Kalorimetrie_Laborversuch.ipynb
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notebook/image/kalorimetrie_pruefstand.jpg
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notebook/notebook_PD_Kalorimetrie_Laborversuch.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# E1: Kalorimetrie\n",
"<!-- verstecktes Kommentar -->\n",
"Kurzbeschreibung/Abstract\n",
"\n",
"Wichtige Punkte:\n",
"- wo liegen Rohdaten\n",
"- wo liegen Skripte für Datenerhebung\n",
"- welche andere relevante Dateien gibt es und wo liegen sie\n",
"- Prüfstand beschreiben "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zielsetzung/Hypothese\n",
"\n",
"- aus der Aufgabenstellung \n",
"- eigenes Verständnis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Versuchsaufbau und Durchführung\n",
"- Bild vom Prüfstand:\n",
"\n",
"<img src=\"image/kalorimetrie_pruefstand.jpg\" width=\"500\">\n",
"\n",
"- Beschreibung vom Prüfstand und vom Vorgehen"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Versuchsauswertung\n",
"\n",
"Verwenden von Gleichungen ist möglich sowie *inline*: $x^2 + y^2 = z^2$ \n",
"\n",
"als auch separat:\n",
"\\begin{equation}\n",
" x^n + y^n = z^n \n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Text der Teilaufgabe"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'TEMPERATURE in °C')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Lösung der Teilaufgabe\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt \n",
"\n",
"df = pd.DataFrame({'time': [0, 1, 2, 3],\n",
" 'temperature': [15.0, 15.2, 15.3, 15.4]})\n",
"\n",
"plt.plot(df['time'], df['temperature'])\n",
"plt.xlabel('TIME in s')\n",
"plt.ylabel('TEMPERATURE in °C')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Datenverfügbarkeit (title WIP)\n",
"\n",
"- Daten und Metadaten beschreiben inkl. Dateiennamen (wo sie zu finden sind, was sie enthalten etc.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diskussion\n",
"- wissenschaftliche Sicht"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fazit\n",
"- persönliche Sicht (was hat man gelernt usw.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "plot",
"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": "3.11.4"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
%% Cell type:markdown id: tags:
# E1: Kalorimetrie
<!-- verstecktes Kommentar -->
Kurzbeschreibung/Abstract
Wichtige Punkte:
-
wo liegen Rohdaten
-
wo liegen Skripte für Datenerhebung
-
welche andere relevante Dateien gibt es und wo liegen sie
-
Prüfstand beschreiben
%% Cell type:markdown id: tags:
## Zielsetzung/Hypothese
-
aus der Aufgabenstellung
-
eigenes Verständnis
%% Cell type:markdown id: tags:
## Versuchsaufbau und Durchführung
-
Bild vom Prüfstand:
<img
src=
"image/kalorimetrie_pruefstand.jpg"
width=
"500"
>
-
Beschreibung vom Prüfstand und vom Vorgehen
%% Cell type:markdown id: tags:
## Versuchsauswertung
Verwenden von Gleichungen ist möglich sowie
*inline*
: $x^2 + y^2 = z^2$
als auch separat:
\b
egin{equation}
x^n + y^n = z^n
\e
nd{equation}
%% Cell type:markdown id: tags:
Text der Teilaufgabe
%% Cell type:code id: tags:
```
python
# Lösung der Teilaufgabe
import
pandas
as
pd
import
matplotlib.pyplot
as
plt
df
=
pd
.
DataFrame
({
'
time
'
:
[
0
,
1
,
2
,
3
],
'
temperature
'
:
[
15.0
,
15.2
,
15.3
,
15.4
]})
plt
.
plot
(
df
[
'
time
'
],
df
[
'
temperature
'
])
plt
.
xlabel
(
'
TIME in s
'
)
plt
.
ylabel
(
'
TEMPERATURE in °C
'
)
```
%% Output
Text(0, 0.5, 'TEMPERATURE in °C')
%% Cell type:markdown id: tags:
## Datenverfügbarkeit (title WIP)
-
Daten und Metadaten beschreiben inkl. Dateiennamen (wo sie zu finden sind, was sie enthalten etc.)
%% Cell type:code id: tags:
```
python
``
`
%%
Cell
type
:
markdown
id
:
tags
:
## Diskussion
-
wissenschaftliche
Sicht
%%
Cell
type
:
markdown
id
:
tags
:
## Fazit
-
persönliche
Sicht
(
was
hat
man
gelernt
usw
.)
%%
Cell
type
:
code
id
:
tags
:
```
python
```
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