temp probability density plot

Pipfile

@@ -11,6 +11,9 @@ gunicorn = "*"
 werkzeug = "*"
 pyyaml = "*"
 matplotlib = "*"
+plotly = "*"
+pandas = "*"
+scipy = "==1.8.1"
 
 [dev-packages]
 

Pipfile.lock

@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "51ddf9dc7440db9ddfb7bb7b45c97b133b214a6be4a7da1cf7fce0ddaabf6fd2"
+            "sha256": "3428842daebc7c8a255790fde5231377c05479a39d5ce2977f043e58c7c80826"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -413,7 +413,7 @@
                 "sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef",
                 "sha256:f76025acc8e2114bb664294a07ede0727aa75d63a06d2fae96bf29a81747e4a7"
             ],
-            "markers": "python_version >= '3.8'",
+            "markers": "python_version >= '3.10'",
             "version": "==1.23.4"
         },
         "opencv-python": {
@@ -437,6 +437,39 @@
             "markers": "python_version >= '3.6'",
             "version": "==21.3"
         },
+        "pandas": {
+            "hashes": [
+                "sha256:04e51b01d5192499390c0015630975f57836cc95c7411415b499b599b05c0c96",
+                "sha256:05c527c64ee02a47a24031c880ee0ded05af0623163494173204c5b72ddce658",
+                "sha256:0a78e05ec09731c5b3bd7a9805927ea631fe6f6cb06f0e7c63191a9a778d52b4",
+                "sha256:17da7035d9e6f9ea9cdc3a513161f8739b8f8489d31dc932bc5a29a27243f93d",
+                "sha256:249cec5f2a5b22096440bd85c33106b6102e0672204abd2d5c014106459804ee",
+                "sha256:2c25e5c16ee5c0feb6cf9d982b869eec94a22ddfda9aa2fbed00842cbb697624",
+                "sha256:32e3d9f65606b3f6e76555bfd1d0b68d94aff0929d82010b791b6254bf5a4b96",
+                "sha256:36aa1f8f680d7584e9b572c3203b20d22d697c31b71189322f16811d4ecfecd3",
+                "sha256:5b0c970e2215572197b42f1cff58a908d734503ea54b326412c70d4692256391",
+                "sha256:5cee0c74e93ed4f9d39007e439debcaadc519d7ea5c0afc3d590a3a7b2edf060",
+                "sha256:669c8605dba6c798c1863157aefde959c1796671ffb342b80fcb80a4c0bc4c26",
+                "sha256:66a1ad667b56e679e06ba73bb88c7309b3f48a4c279bd3afea29f65a766e9036",
+                "sha256:683779e5728ac9138406c59a11e09cd98c7d2c12f0a5fc2b9c5eecdbb4a00075",
+                "sha256:6bb391659a747cf4f181a227c3e64b6d197100d53da98dcd766cc158bdd9ec68",
+                "sha256:81f0674fa50b38b6793cd84fae5d67f58f74c2d974d2cb4e476d26eee33343d0",
+                "sha256:927e59c694e039c75d7023465d311277a1fc29ed7236b5746e9dddf180393113",
+                "sha256:932d2d7d3cab44cfa275601c982f30c2d874722ef6396bb539e41e4dc4618ed4",
+                "sha256:a52419d9ba5906db516109660b114faf791136c94c1a636ed6b29cbfff9187ee",
+                "sha256:b156a971bc451c68c9e1f97567c94fd44155f073e3bceb1b0d195fd98ed12048",
+                "sha256:bcf1a82b770b8f8c1e495b19a20d8296f875a796c4fe6e91da5ef107f18c5ecb",
+                "sha256:cb2a9cf1150302d69bb99861c5cddc9c25aceacb0a4ef5299785d0f5389a3209",
+                "sha256:d8c709f4700573deb2036d240d140934df7e852520f4a584b2a8d5443b71f54d",
+                "sha256:db45b94885000981522fb92349e6b76f5aee0924cc5315881239c7859883117d",
+                "sha256:ddf46b940ef815af4e542697eaf071f0531449407a7607dd731bf23d156e20a7",
+                "sha256:e675f8fe9aa6c418dc8d3aac0087b5294c1a4527f1eacf9fe5ea671685285454",
+                "sha256:eb7e8cf2cf11a2580088009b43de84cabbf6f5dae94ceb489f28dba01a17cb77",
+                "sha256:f340331a3f411910adfb4bbe46c2ed5872d9e473a783d7f14ecf49bc0869c594"
+            ],
+            "index": "pypi",
+            "version": "==1.5.1"
+        },
         "pillow": {
             "hashes": [
                 "sha256:0030fdbd926fb85844b8b92e2f9449ba89607231d3dd597a21ae72dc7fe26927",
@@ -501,6 +534,14 @@
             "markers": "python_version >= '3.7'",
             "version": "==9.2.0"
         },
+        "plotly": {
+            "hashes": [
+                "sha256:4efef479c2ec1d86dcdac8405b6ca70ca65649a77408e39a7e84a1ea2db6c787",
+                "sha256:52fd74b08aa4fd5a55b9d3034a30dbb746e572d7ed84897422f927fdf687ea5f"
+            ],
+            "index": "pypi",
+            "version": "==5.11.0"
+        },
         "pyparsing": {
             "hashes": [
                 "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb",
@@ -517,6 +558,13 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==2.8.2"
         },
+        "pytz": {
+            "hashes": [
+                "sha256:335ab46900b1465e714b4fda4963d87363264eb662aab5e65da039c25f1f5b22",
+                "sha256:c4d88f472f54d615e9cd582a5004d1e5f624854a6a27a6211591c251f22a6914"
+            ],
+            "version": "==2022.5"
+        },
         "pyyaml": {
             "hashes": [
                 "sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf",
@@ -579,6 +627,14 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==1.16.0"
         },
+        "tenacity": {
+            "hashes": [
+                "sha256:35525cd47f82830069f0d6b73f7eb83bc5b73ee2fff0437952cedf98b27653ac",
+                "sha256:e48c437fdf9340f5666b92cd7990e96bc5fc955e1298baf4a907e3972067a445"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==8.1.0"
+        },
         "werkzeug": {
             "hashes": [
                 "sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f",

flask_frontend.py

@@ -3,6 +3,8 @@ import numpy as np
 from ratio_pyrometry import ratio_pyrometry_pipeline
 import base64
 import cv2 as cv
+import plotly.figure_factory as ff
+from scipy import stats
 
 app = Flask(
     __name__, 
@@ -18,7 +20,7 @@ def index():
 def ratio_pyro():
     f = request.files['file']
     f_bytes = np.fromstring(f.read(), np.uint8)
-    img_orig, img_res, key = ratio_pyrometry_pipeline(
+    img_orig, img_res, key, ptemps = ratio_pyrometry_pipeline(
         f_bytes,
         ISO=float(request.form['iso']),
         I_Darkcurrent=float(request.form['i_darkcurrent']),
@@ -31,12 +33,34 @@ def ratio_pyro():
         eqn_scaling_factor=float(request.form['equation_scaling_factor'])
     )
 
+    # get base64 encoded images
     img_orig_b64 = base64.b64encode(cv.imencode('.png', img_orig)[1]).decode(encoding='utf-8')
     img_res_b64 = base64.b64encode(cv.imencode('.png', img_res)[1]).decode(encoding='utf-8')
 
+    # generate prob. distribution histogram & return embed
+    fig = ff.create_distplot(
+        [ptemps], 
+        group_labels=[f.name], 
+        show_rug=False,
+        show_hist=False,
+    )
+    fig.update_layout(
+        autosize=False,
+        width=800,
+        height=600,
+    )
+    fig.update_xaxes(
+        title_text="Temperature (°C)",
+    )
+    fig.update_xaxes(
+        title_text="Probability (1/°C)",
+    )
+    freq_plot = fig.to_html()
+
     return render_template(
         'results.jinja2',
         img_orig_b64=img_orig_b64,
         img_res_b64=img_res_b64,
-        legend=key
+        legend=key,
+        freq_plot=freq_plot
     )

ratio_pyrometry.py

@@ -13,11 +13,16 @@ def rg_ratio_normalize(
     ISO, 
     MIN_TEMP, 
     MAX_TEMP,
-    eqn_scaling_factor
+    eqn_scaling_factor,
 ):
+    """
+    Get normalized G/R -> temperature data + list of all temperatures
+    """
     # copy image into new array & chop off alpha values (if applicable)
     imgnew = imgarr.copy()[:,:,:3]
 
+    positive_temps = []
+
     for i in range(len(imgarr)):
         for j in range(len(imgarr[i])):
             px = imgarr[i][j]
@@ -32,12 +37,14 @@ def rg_ratio_normalize(
             # remove pixels outside calibration range
             if (MIN_TEMP != None and temp_C < MIN_TEMP) or (MAX_TEMP != None and temp_C > MAX_TEMP):
                 temp_C = MIN_TEMP
+            elif temp_C > MIN_TEMP:
+                positive_temps.append(temp_C)
 
             # scale light intensity to calculated temperature
             pix_i = scale_temp(temp_C, MIN_TEMP, MAX_TEMP)
             imgnew[i][j] = [pix_i, pix_i, pix_i]
 
-    return imgnew
+    return imgnew, positive_temps
 
 
 @jit(nopython=True)
@@ -83,7 +90,7 @@ def ratio_pyrometry_pipeline(
     # read image & crop
     img_orig = cv.imdecode(file_bytes, cv.IMREAD_UNCHANGED)
 
-    img = rg_ratio_normalize(
+    img, ptemps = rg_ratio_normalize(
         img_orig,
         I_Darkcurrent,
         f_stop,
@@ -91,7 +98,7 @@ def ratio_pyrometry_pipeline(
         ISO,
         MIN_TEMP,
         MAX_TEMP,
-        eqn_scaling_factor
+        eqn_scaling_factor,
     )
 
     # build & apply smoothing conv kernel
@@ -126,4 +133,4 @@ def ratio_pyrometry_pipeline(
         tempkey[temps[i]] = f"rgb({c[2]}, {c[1]}, {c[0]})"
 
     # original, transformed, legend
-    return img_orig, img_jet, tempkey
+    return img_orig, img_jet, tempkey, ptemps

templates/results.jinja2

@@ -35,6 +35,9 @@
         </td>
     </tr>
 </table>
+{# Temperature Frequency Plot #}
+<strong>Temperature Distribution</strong>
+{{freq_plot}}
 
 <style>
     .img-table-heading {