fix SD typo

timothyas · Nov 27, 2023 · f293b19 · f293b19
1 parent 80658a9
commit f293b19
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diff --git a/docs/example_macro_training.ipynb b/docs/example_macro_training.ipynb
@@ -411,9 +411,11 @@
     "  \n",
     "  - $SD_j$ is the standard deviation of the sample forecast, taken over space and time\n",
     "   $$\n",
-    "   SD_j = \\dfrac{1}{(N_{\\text{steps}}-1)(N_v-1)}\n",
-    "    \\sqrt{\n",
+    "   SD_j = \\sqrt{\n",
+    "    \\dfrac{\n",
     "        \\sum_{i=1}^{N_v}\\sum_{n=1}^{N_{\\text{steps}}}\\left(v_j(i, n) - \\mu_j\\right)^2\n",
+    "       }\n",
+    "       {(N_{\\text{steps}}-1)(N_v-1)}\n",
     "        }\n",
     "   $$\n",
     "   where $\\mu_j$ is the sample average taken over space and time\n",

diff --git a/xesn/cost.py b/xesn/cost.py
@@ -55,7 +55,7 @@ class CostFunction():
     - The standard deviation used in the NRMSE calculation is
 
     .. math::
-        SD_j = \dfrac{1}{(N_{\\text{steps}}-1)(N_v-1)}\sqrt{\sum_{i=1}^{N_v}\sum_{n=1}^{N_{\\text{steps}}}\left(v_j(i, n) - \mu_j\\right)^2}
+        SD_j = \sqrt{\dfrac{\sum_{i=1}^{N_v}\sum_{n=1}^{N_{\\text{steps}}}\left(v_j(i, n) - \mu_j\\right)^2}{(N_{\\text{steps}}-1)(N_v-1)}}
 
     - :math:`SD_j(k)` used in the PSD_NRMSE calculation is defined similarly as above, but in spectral space, and note that each mode is normalized separately as different modes can vary by vastly different orders of magnitude