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Dockerfile (596, 2021-04-03)
Pipfile (268, 2021-04-03)
Pipfile.lock (24315, 2021-04-03)
app.py (490, 2021-04-03)
assets (0, 2021-04-03)
assets\stylesheet.css (333, 2021-04-03)
controllers (0, 2021-04-03)
controllers\filter_modals.py (1227, 2021-04-03)
controllers\filter_selections_div.py (3085, 2021-04-03)
controllers\material_property_table.py (3392, 2021-04-03)
controllers\material_recommendation.py (652, 2021-04-03)
controllers\material_table.py (684, 2021-04-03)
controllers\physical_property_filters.py (2936, 2021-04-03)
controllers\property_filters.py (3054, 2021-04-03)
controllers\toggle_label.py (483, 2021-04-03)
data (0, 2021-04-03)
data\material.pickle (3784, 2021-04-03)
data\material.py (1778, 2021-04-03)
data\material_links.py (3376, 2021-04-03)
data\material_properties.pickle (7099, 2021-04-03)
data\material_properties_impute.pickle (7668, 2021-04-03)
data\material_spec_links.py (2404, 2021-04-03)
data\predicted_mat_props.py (11276, 2021-04-03)
data\process_spec_links.py (988, 2021-04-03)
data\test_conditions.pickle (1129, 2021-04-03)
dataframe.py (4625, 2021-04-03)
docker-compose.yml (371, 2021-04-03)
index.py (714, 2021-04-03)
ml (0, 2021-04-03)
ml\recommendation.py (2127, 2021-04-03)
models (0, 2021-04-03)
models\Dataframe.py (1897, 2021-04-03)
nginx_container (0, 2021-04-03)
nginx_container\Dockerfile (123, 2021-04-03)
nginx_container\ncampapp.conf (156, 2021-04-03)
requirements.txt (489, 2021-04-03)
views (0, 2021-04-03)
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# NCAMP-dashboard NCAMP, the National Center for Advanced Materials Performance, works with the FAA and industry partners to qualify material systems and populate a shared materials database that can be viewed publicly. This is a dashboard for viewing NCAMP materials database. [NCAMP-dashboard](https://ncamp-dashboard.herokuapp.com/) **1. Select a material**
Select an NCAMP qualified material from the top dropdown. Tables will update below with metadata, physical properties, and mechanical properties of chosen material. If only one material is chosen, then a similar material will be recommended. Similarity is determined based off of minimum Euclidean Distance from selected material (i.e. nearest neighbor).


**2. Select all materials**



**3. Show predicted material properties**
We can infer unmeasured material properties using machine learning, more specifically collaborative filtering / matrix factorization. Use toggle switch to show or not show these predicted material properties.


**4. Filter materials**
Narrow down qualified materials and qualified material properties using filters.


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