Initial QOIs

xpdtools/pipelines/qoi.py

@@ -1,6 +1,7 @@
 import scipy.signal as sig
-
+import numpy as np
 from .raw_pipeline import mean, pdf, q
+from bluesky.callbacks import LivePlot
 
 r = pdf.pluck(0)
 true_pdf = pdf.pluck(1)

xpdtools/tools.py

@@ -17,6 +17,8 @@
 
 import numpy as np
 from scipy.integrate import simps
+import scipy.stats as stats
+import scipy.signal as sig
 from skbeam.core.accumulators.binned_statistic import BinnedStatistic1D
 from skbeam.core.mask import margin
 from xpdtools.jit_tools import mask_ring_median, mask_ring_mean, ring_zscore
@@ -581,3 +583,90 @@ def inner(x, *args, **kwargs):
         return func(*args, **kwargs)
 
     return inner
+
+
+def max_value(g):
+    """Returns largest value
+
+    Parameters
+    ----------
+    g : ndarray
+        g(r) of the pdf
+
+    Returns
+    -------
+    float :
+        Maximum g value
+
+    """
+    return np.amax(g)
+
+
+def tallest_peak(g, r):
+    """Returns r,g(r) of the tallest peak
+
+       Parameters
+       ----------
+       g : ndarray
+            g(r) of the pdf
+
+       r : ndarray
+            corresponding r values
+
+       Returns
+       -------
+       float :
+           r value of the tallest peak
+        float :
+            g(r) value of the tallest peak
+
+    """
+    peaks = sig.find_peaks(g)
+    height = []
+    r_val = []
+    for i in peaks[0]:
+        height.append(g[i])
+        r_val.append(r[i])
+    return r_val[np.argmax(height)], np.amax(height)
+
+
+def total_counts(g):
+    """Returns total number of data points in graph
+
+       Parameters
+       ----------
+       g : ndarray
+            g(r) of the pdf
+
+       Returns
+       -------
+       int :
+           total number of data points
+
+    """
+    return len(g)
+
+
+def average_pearson(group, g, r):
+    """Computes the average pearson of this PDF with the rest of the group
+
+      Parameters
+       ----------
+       g : ndarray
+            g(r) of the pdf
+
+       r : ndarray
+            corresponding r values
+       group : ndarray
+            Group of PDFs (PDFs as tuples (g,r))
+       Returns
+       -------
+       float :
+           Average pearson's coefficient
+
+    """
+    val = 0
+    for i in group:
+        r, p = stats.pearsonr((g, r), i)
+        val = val + r
+    return val/len(group)