Input correction and output cleanup

- @mjsandells sent an email to note that snow pit 14 contained an error in the second density cutter measurement where it was reported as 2 cm in height. The error has been corrected and the sample is now 3 cm. Thanks for correction!
- Notebooks were rerun to make sure results were consistent. No issues indentified, the layer scaling appears to have compensated for the issue as intended. Annex materials also worked as expected.
- Noted that the files in the output directory were still named k19 instead of k20 named. Uploaded new files as this broke the binder service.

Co-Authored-By: mjsandells <[email protected]>

Annex_Matching.ipynb

@@ -55,12 +55,10 @@
     "MAX_STRETCH_OVERALL = 0.15 # Max profile change in % of total height\n",
     "NUM_TESTS = 10000\n",
     "\n",
-    "#coeffs = pickle.load(open('./output/density_kfolds_coeffs.sav', 'rb'))\n",
-    "\n",
     "axis_value_size = 12\n",
     "axis_label_size = 14\n",
     "\n",
-    "coeffs = pickle.load(open('./output/density_k19b_coeffs.pkl', 'rb'))"
+    "coeffs = pickle.load(open('./output/density_k20b_coeffs.pkl', 'rb'))"
    ]
   },
   {
@@ -170,7 +168,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "density_k2019 = coeffs[0] + coeffs[1] * np.log(scaled_force_median) \\\n",
+    "density_k2020 = coeffs[0] + coeffs[1] * np.log(scaled_force_median) \\\n",
     "          + coeffs[2] * np.log(scaled_force_median) * scaled_l \\\n",
     "          + coeffs[3] * scaled_l"
    ]

Part_3_Classification.ipynb

@@ -136,7 +136,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Reviewer 2 asked for a breakdown of the errors by layer type but confusion matrix in scipy does not play well with stratified CV. Its critical that we evaluate accurcy with stratified sampling else accuracy is reported as poor for round types due to under-trainiing rather than a breakdown of theory or physical snow properties. Method is slow and hacky but it works."
+    "Reviewer 2 asked for a breakdown of the errors by layer type but confusion matrix method in scipy does not play well with a stratified CV. Its critical that we evaluate accuracy with stratified sampling else accuracy is reported as poor for round types due to under-trainiing rather than a breakdown of theory or physical snow properties. The method is slow and hacky but it works."
    ]
   },
   {

data/Pit/pit_density.csv

@@ -124,7 +124,7 @@ ID,TOP,BOTTOM,RHO,NOTES,TYPE
 13,7,4,222,-9999,H
 13,3,0,301,-9999,H
 14,60,57,312,-9999,R
-14,57,55,235,-9999,F
+14,57,54,235,-9999,F
 14,55,52,354,-9999,F
 14,52,49,333,-9999,F
 14,49,46,331,-9999,F