# Betterfit
## Installation
`pip install betterfit`
## Getting Started
Refer to [getting_started.py](tests/getting_started.py):
```python
# getting_started.py
import matplotlib.pyplot as plt
import sympy as smp
from uncertainties import ufloat
from betterfit import Dataset, LinearFit
# create immutable datasets
resistance_data = Dataset.fromiter(symbol='R',
values=[2000, 1800, 1600, 1400, 1200, 1000, 900],
errors=10)
length_data = Dataset.fromiter(symbol=smp.Symbol('l'),
values=[160, 172, 192, 223, 254, 298, 327],
errors=[1.5, 2.4, 2, 2.3, 2.1, 3, 5])
length_data = length_data.multiply(1e-3)
# define quantities as sympy symbols
R, l, d = smp.symbols('R l d')
# define an expression depending on these symbols
phi = smp.atan(l / d)
# perform linear fit
linearfit = LinearFit()
linearfit.add_constant(symbol=d,
value=ufloat(0.535, 0.01))
linearfit.add_datasets(resistance_data, length_data)
slope, yintercept = linearfit.fit(x_expr=1 / phi,
y_expr=R)
# linearfit.fit return sympy symbols representing the slope and yintercept
# the values can be accessed through linearfit[slope] and linearfit[yintercept]
print(f'slope: {linearfit[slope]:.3g}')
print(f'yintercept: {linearfit[yintercept]:.3g}')
# similarily, values of expressions can be accessed through linearfit[expr]
print(f'phi: {linearfit[phi]}')
# plot setup
fig, ax = plt.subplots()
ax.set_title('Example Linear Fit with Weighted Least Squares')
ax.set_xlabel(r'$\frac{1}{\phi}$ [$s$]')
ax.set_ylabel(r'$R$ [$\Omega$]')
# plot fit
linearfit.plot_on(ax,
x=1 / phi,
y=R,
fmt='o',
label='data')
linearfit.plot_fit_on(ax)
ax.legend()
plt.show()
```
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"description": "# Betterfit\n## Installation\n`pip install betterfit`\n\n## Getting Started\nRefer to [getting_started.py](tests/getting_started.py):\n```python\n# getting_started.py\nimport matplotlib.pyplot as plt\nimport sympy as smp\nfrom uncertainties import ufloat\n\nfrom betterfit import Dataset, LinearFit\n\n# create immutable datasets\nresistance_data = Dataset.fromiter(symbol='R',\n values=[2000, 1800, 1600, 1400, 1200, 1000, 900],\n errors=10)\nlength_data = Dataset.fromiter(symbol=smp.Symbol('l'),\n values=[160, 172, 192, 223, 254, 298, 327],\n errors=[1.5, 2.4, 2, 2.3, 2.1, 3, 5])\nlength_data = length_data.multiply(1e-3)\n\n# define quantities as sympy symbols\nR, l, d = smp.symbols('R l d')\n\n# define an expression depending on these symbols\nphi = smp.atan(l / d)\n\n# perform linear fit\nlinearfit = LinearFit()\nlinearfit.add_constant(symbol=d,\n value=ufloat(0.535, 0.01))\nlinearfit.add_datasets(resistance_data, length_data)\nslope, yintercept = linearfit.fit(x_expr=1 / phi,\n y_expr=R)\n\n# linearfit.fit return sympy symbols representing the slope and yintercept\n# the values can be accessed through linearfit[slope] and linearfit[yintercept]\nprint(f'slope: {linearfit[slope]:.3g}')\nprint(f'yintercept: {linearfit[yintercept]:.3g}')\n\n# similarily, values of expressions can be accessed through linearfit[expr]\nprint(f'phi: {linearfit[phi]}')\n\n# plot setup\nfig, ax = plt.subplots()\nax.set_title('Example Linear Fit with Weighted Least Squares')\nax.set_xlabel(r'$\\frac{1}{\\phi}$ [$s$]')\nax.set_ylabel(r'$R$ [$\\Omega$]')\n\n# plot fit\nlinearfit.plot_on(ax,\n x=1 / phi,\n y=R,\n fmt='o',\n label='data')\nlinearfit.plot_fit_on(ax)\n\nax.legend()\n\nplt.show()\n```\n",
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