update code

Author: amanmdesai

.gitignore

@@ -25,6 +25,7 @@ share/python-wheels/
 *.egg-info/
 .installed.cfg
 *.egg
+*.dat
 MANIFEST
 
 # PyInstaller

MANIFEST.in

@@ -2,6 +2,7 @@ include LICENSE README.md
 include tests/test*.py
 exclude environment.yml _config.yml
 recursive-exclude .github/
+recursive-exclude .dat
 recursive-include pygrc/*.py
 include setup.cfg
 include setup.py

pygrc/fit.py

@@ -27,10 +27,18 @@ def __init__(
             data_y (tuple): input data on y axis
             *args (float): Takes initial parameter values
 
+
         """
         self.args = args
-        self.data_x = data_x.to_numpy()
-        self.data_y = data_y.to_numpy()
+        print(type(data_x))
+        if 'numpy' not in str(type(data_x)):
+            self.data_x = data_x.to_numpy()
+        else:
+            self.data_x = data_x
+        if 'numpy' not in str(type(data_y)):
+            self.data_y = data_y.to_numpy()
+        else:
+            self.data_y = data_y
 
     def fit_lsq(
         self,
@@ -49,6 +57,15 @@ def fit_lsq(
         Returns:
             iminuit object
 
+        Example:
+
+        ```python
+        pygrc.Fit(x, y, 1,1).fit_lsq(function,[(-1,10),(-2,10)],.1)
+        ```
+        here we have two variables and two parameters (initial values of parameters is 1.)
+        where [..] are the limits for the two parameter and .1 is the error.
+
+
         """
         least_square = LeastSquares(self.data_x, self.data_y, err, function)
         m = Minuit(least_square, *self.args)

pygrc/reader.py

@@ -46,7 +46,7 @@ def read(
             rawdata = np.loadtxt(filepath)
             data = pd.DataFrame(rawdata, columns=columns)
             return data
-        elif pathlib.PurePosixPath(filepath).suffix == "csv":
+        elif pathlib.PurePosixPath(filepath).suffix == ".csv":
             data = pd.read_csv(filepath)
             return data
         else:

tests/test_fit.py

@@ -0,0 +1,19 @@
+import pygrc
+import numpy as np
+import random
+import math
+
+def function(x, *args):
+    return args[0]*x + args[1]
+
+# this will the least square against two numpy arrays
+def test_fit_lsq():
+    x = np.linspace(0,10,10)
+    y = np.zeros(10)
+    for i in range(len(y)):
+        y[i] = 2*x[i] + 2*random.random()
+    m=pygrc.Fit(x, y, 1,1).fit_lsq(function,[(-1,10),(-2,10)],.1)
+    assert math.fabs(m.values["x0"]-2.0) <  0.1, \
+        "Least Square x0 parameter does not converge to true value"
+    assert math.fabs(m.values["x1"]-1.0) <  1., \
+        "Least Square x1 parameter does not converge to true value"