diff --git a/space/analysis/mva.py b/space/analysis/mva.py
new file mode 100644
index 0000000..4ecedf8
--- /dev/null
+++ b/space/analysis/mva.py
@@ -0,0 +1,100 @@
+import numpy as np
+
+# L,M,N constants
+L = 2
+M = 1
+N = 0
+
+
+class MVA(object):
+    """
+    Minimum Variance Analysis
+    """
+
+    def __init__(self, B):
+        """
+        @param B : magnetic field
+        """
+
+        self._B = B.copy()  # magnetic field
+
+        # not normalizing anymore...
+        # B2 = B[:,0]**2 + B[:,1]**2 + B[:,2]**2
+        # self._B[:,0]/=np.sqrt(B2)
+        # self._B[:,1]/=np.sqrt(B2)
+        # self._B[:,2]/=np.sqrt(B2)
+
+        self._LMN = np.ndarray(shape=(3, 3))  # LMN matrix
+        self._eigen = None
+
+        varM = np.ndarray(shape=(3, 3))
+
+        # build the variance matrix <BiBj> - <Bi><Bj>
+        for i in np.arange(3):
+            for j in np.arange(3):
+                varM[i, j] = np.mean(self._B[:, i] * self._B[:, j]) - np.mean(
+                    self._B[:, i]
+                ) * np.mean(self._B[:, j])
+
+        # find the eigenvalues of varM and the associated eigenvectors
+        w, v = np.linalg.eigh(varM)
+
+        # now we want to sort the eigenvalues
+        ind = np.argsort(w)  # return the indices that would sort 'w'
+        self._eigen = w[ind]
+
+        vt = v.transpose()  # eigh returns v[:,i] associated with w[i]
+        # and we want the line v[i,:] with w[i]
+        # so that vB is Blmn
+
+        # then put the vectors in the same order as the eigenvalues
+        for h in np.arange(3):
+            self._LMN[h, :] = vt[ind[h], :]
+
+        # legacy magnetopause : reverse N if pointing inside the magnetosphere
+        # can be convention that Nx >0
+        if self._LMN[N, 0] < 0:
+            self._LMN[N, :] *= -1
+
+        # same kind of convention we want Lz >0
+        if self._LMN[L, 2] < 0:
+            self._LMN[L, :] *= -1
+
+        # M defined as cross product LxN
+        self._LMN[1, 0] = (
+            self._LMN[0, 1] * self._LMN[2, 2] - self._LMN[0, 2] * self._LMN[2, 1]
+        )
+        self._LMN[1, 1] = (
+            self._LMN[2, 0] * self._LMN[0, 2] - self._LMN[2, 2] * self._LMN[0, 0]
+        )
+        self._LMN[1, 2] = (
+            self._LMN[0, 0] * self._LMN[2, 1] - self._LMN[0, 1] * self._LMN[2, 0]
+        )
+
+    # ==========================================================
+    # ==========================================================
+    def to_LMN(self, vector):
+        """
+        transforms vector[:,3] into the LMN coord. syst.
+        """
+        vlmn = np.ndarray(vector.shape)
+        for i in np.arange(3):
+            vlmn[:, i] = (
+                self._LMN[i, 0] * vector[:, 0]
+                + self._LMN[i, 1] * vector[:, 1]
+                + self._LMN[i, 2] * vector[:, 2]
+            )
+
+        return vlmn
+
+    # ==========================================================
+
+    # ==========================================================
+    # ==========================================================
+    def quality(self):
+        """
+        returns the quality of MVA as the ratio intermediate/min eigenvalues
+        """
+        return self._eigen[1] / self._eigen[0]
+
+    # ==========================================================