| Name | gym-cas JSON |
| Version |
0.4.19
JSON |
| download |
| home_page | None |
| Summary | CAS tools for danish high schools. |
| upload_time | 2025-08-11 12:24:50 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
cas
matematik
math
gymnasium
htx
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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|
| coveralls test coverage |
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|
# GYM CAS
[](https://pypi.org/project/gym-cas)
[](https://pypi.org/project/gym-cas)
Anvend Python som CAS (Computational Algebra System) i gymnasiet.
Bygger på følgende moduler:
- Algebra/beregninger:
- [SymPy](https://docs.sympy.org/latest/index.html)
- [NumPy](https://numpy.org/)
- Afbildninger:
- [SymPy Plot Backends](https://sympy-plot-backends.readthedocs.io/en/latest/modules/index.html)
- [Matplotlib](https://matplotlib.org/)
## Installation
```console
pip install gym-cas
```
eller
```console
py -m pip install gym-cas
```
## Cheatsheet
I nedenstående afsnit antages det at `gym_cas` først importeres således:
```py
from gym_cas import *
```
### B1. Tal- og bogstavregning
```py
expand( udtryk )
factor( udtryk )
```
### B2. Ligninger og uligheder
```py
solve( udtryk )
solve( [udtryk1, udtryk2] )
nsolve( udtryk, startgæt )
solve_interval( udtryk, start, slut )
```
Bemærk at den nemmeste måde at bruge `solve` i `SymPy` er ved at omforme sin ligning så en af siderne er lig 0. Hvis man fx vil løse ligningen `x/2 = 10` så kan det skrives `solve(x/2-10)`.
### B3. Geometri og trigonometri
```py
Sin( vinkel )
Cos( vinkel )
Tan( vinkel )
aSin( forhold )
aCos( forhold )
aTan( forhold )
```
### B4. Analytisk plangeometri
```py
plot_points( X_list ,Y_list)
plot( funktion )
plot_implicit( udtryk ,xlim=( x_min, x_max),ylim=( y_min, y_max))
plot_geometry( Geometrisk objekt )
```
#### Flere grafer i en afbildning
```py
p1 = plot( udtryk1 )
p2 = plot( udtryk2 )
p = p1 + p2
p.show()
```
### B5. Vektorer
```py
a = vector(x,y)
a.dot(b)
plot_vector( vektor )
plot_vector( start, vektor )
plot_vector( [vektor1, vektor2, ...])
```
### B6. Deskriptiv Statistik
#### Ugrupperet
```py
max( data )
min( data )
mean( data )
median( data )
var( data, ddof )
std( data, ddof )
kvartiler( data )
percentile( data , procenter )
frekvenstabel( data )
boxplot( data )
plot_sum( data )
```
#### Grupperet
```py
group_mean( data, grupper )
group_percentile( data, grupper, procenter )
group_var( data, grupper, ddof )
group_std( data, grupper, ddof )
frekvenstabel( data, grupper )
boxplot( data, grupper )
plot_sum( data, grupper )
plot_hist( data, grupper )
```
### B8. Funktioner
```py
def f(x):
return funktionsudtryk
f(3)
def f(x):
return Piecewise(( funktion1, betingelse1), (funktion2, betingelse2))
plot( funktion , yscale="log")
plot( funktion , (variabel, start, stop), xscale="log", yscale="log")
regression_poly(X,Y, grad)
regression_power(X,Y)
regression_exp(X,Y)
```
### B9. Differentialregning
```py
limit( udtryk, variabel, grænse, retning )
diff( funktion )
def df(xi):
return diff( funktion ).subs( variabel, xi )
```
### B10. Integralregning
```py
integrate( udtryk )
integrate( udtryk, ( variabel, start, slut ))
plot3d_revolution( udtryk , (x, a, b),parallel_axis="x")
```
### A1. Vektorer i rummet
```py
a = vector(1,2,3)
a.cross(b)
plot_vector( a )
plot3d_points( X, Y, Z )
plot3d_line( a + t * r )
plot3d_plane( a + s * r1 + t * r2 )
plot3d_sphere( radius, centrum )
plot3d_implicit( ligning, backend=PB ) # Kræver Plotly eller K3D
```
### A4. Differentialligninger
```py
f = Function('f')
dsolve( ode )
plot_ode( ode, (x, start, stop), (f, start, stop))
```
### A5. Diskret Matematik
```py
X = [ udregning for x in range(start,slut)]
X = [ startbetingelse ]
for i in range(start, slut):
X.append( rekursionsligning )
```
Raw data
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"description": "# GYM CAS\n\n[](https://pypi.org/project/gym-cas)\n[](https://pypi.org/project/gym-cas)\n\n\nAnvend Python som CAS (Computational Algebra System) i gymnasiet.\nBygger p\u00e5 f\u00f8lgende moduler:\n\n- Algebra/beregninger:\n - [SymPy](https://docs.sympy.org/latest/index.html)\n - [NumPy](https://numpy.org/)\n- Afbildninger:\n - [SymPy Plot Backends](https://sympy-plot-backends.readthedocs.io/en/latest/modules/index.html)\n - [Matplotlib](https://matplotlib.org/)\n\n## Installation\n\n```console\npip install gym-cas\n```\n\neller\n\n```console\npy -m pip install gym-cas\n```\n\n## Cheatsheet\n\nI nedenst\u00e5ende afsnit antages det at `gym_cas` f\u00f8rst importeres s\u00e5ledes:\n\n```py\nfrom gym_cas import *\n```\n\n### B1. Tal- og bogstavregning\n\n```py\nexpand( udtryk )\nfactor( udtryk )\n```\n\n### B2. Ligninger og uligheder\n\n```py\nsolve( udtryk )\nsolve( [udtryk1, udtryk2] )\nnsolve( udtryk, startg\u00e6t )\nsolve_interval( udtryk, start, slut )\n```\n\nBem\u00e6rk at den nemmeste m\u00e5de at bruge `solve` i `SymPy` er ved at omforme sin ligning s\u00e5 en af siderne er lig 0. Hvis man fx vil l\u00f8se ligningen `x/2 = 10` s\u00e5 kan det skrives `solve(x/2-10)`.\n\n### B3. Geometri og trigonometri\n\n```py\nSin( vinkel )\nCos( vinkel )\nTan( vinkel )\naSin( forhold )\naCos( forhold )\naTan( forhold )\n```\n\n### B4. Analytisk plangeometri\n\n```py\nplot_points( X_list ,Y_list)\nplot( funktion )\nplot_implicit( udtryk ,xlim=( x_min, x_max),ylim=( y_min, y_max))\nplot_geometry( Geometrisk objekt )\n```\n\n#### Flere grafer i en afbildning\n\n```py\np1 = plot( udtryk1 )\np2 = plot( udtryk2 )\np = p1 + p2\np.show()\n```\n\n### B5. Vektorer\n\n```py\na = vector(x,y)\na.dot(b)\nplot_vector( vektor )\nplot_vector( start, vektor )\nplot_vector( [vektor1, vektor2, ...])\n```\n\n### B6. Deskriptiv Statistik\n\n#### Ugrupperet\n\n```py\nmax( data )\nmin( data )\nmean( data )\nmedian( data )\nvar( data, ddof )\nstd( data, ddof ) \nkvartiler( data )\npercentile( data , procenter )\nfrekvenstabel( data )\nboxplot( data ) \nplot_sum( data )\n```\n\n#### Grupperet\n\n```py\ngroup_mean( data, grupper )\ngroup_percentile( data, grupper, procenter )\ngroup_var( data, grupper, ddof )\ngroup_std( data, grupper, ddof ) \nfrekvenstabel( data, grupper )\nboxplot( data, grupper ) \nplot_sum( data, grupper )\nplot_hist( data, grupper )\n```\n\n### B8. Funktioner\n\n```py\ndef f(x):\n return funktionsudtryk\nf(3)\n\ndef f(x):\n return Piecewise(( funktion1, betingelse1), (funktion2, betingelse2))\n\nplot( funktion , yscale=\"log\")\nplot( funktion , (variabel, start, stop), xscale=\"log\", yscale=\"log\")\nregression_poly(X,Y, grad)\nregression_power(X,Y)\nregression_exp(X,Y)\n```\n\n### B9. Differentialregning\n\n```py\nlimit( udtryk, variabel, gr\u00e6nse, retning )\ndiff( funktion )\ndef df(xi):\n return diff( funktion ).subs( variabel, xi )\n```\n\n### B10. Integralregning\n\n```py\nintegrate( udtryk )\nintegrate( udtryk, ( variabel, start, slut ))\nplot3d_revolution( udtryk , (x, a, b),parallel_axis=\"x\")\n```\n\n### A1. Vektorer i rummet\n\n```py\na = vector(1,2,3)\na.cross(b)\nplot_vector( a )\nplot3d_points( X, Y, Z )\nplot3d_line( a + t * r )\nplot3d_plane( a + s * r1 + t * r2 )\nplot3d_sphere( radius, centrum )\nplot3d_implicit( ligning, backend=PB ) # Kr\u00e6ver Plotly eller K3D\n```\n\n### A4. Differentialligninger\n\n```py\nf = Function('f')\ndsolve( ode )\nplot_ode( ode, (x, start, stop), (f, start, stop))\n```\n\n### A5. Diskret Matematik\n\n```py\nX = [ udregning for x in range(start,slut)]\nX = [ startbetingelse ]\nfor i in range(start, slut):\n X.append( rekursionsligning )\n```\n",
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