<jupyter_start><jupyter_text>This file is forked from *Demo - Academic 0.1.3.ipynb*. All cells above this level are hidden in readthedocs by the `nbsphinx` extension.   Quick View Here is a brief gallery of some stress distribution plots produced from LamAna.  Single Geometry Plots We can plot stress distributions for a single laminate as a function of *height*, *d* or *normalized thicknesses*, *k* (default).<jupyter_code>case1 = la.distributions.Case(load_params, mat_props)      # instantiate a User Input Case Object through distributions
case1.apply(single_geo)
case1.plot(normalized=False)
case1.plot(normalized=True, grayscale=True)<jupyter_output><empty_output><jupyter_text>We can superimpose insets and adjust the colors for publications quality.<jupyter_code>case1.plot(annotate=True, colorblind=True, inset=True)<jupyter_output><empty_output><jupyter_text>Multiple Geometry Plots With normalized layers, we can superimpose multiple stress distributions fpr laminates different geometries.  Data for multiple laminates are encapsulated in a `Case` object.<jupyter_code>title = 'Stress Distributions of HA/PSu for Multiple Geometries'
multiple_geos = [
    '350-400-500',  '400-200-800', '200-200-1200', '200-100-1400',
    '100-100-1600', '100-200-1400', '300-400-600'
]
case2 = la.distributions.Case(load_params, mat_props)      # instantiate a User Input Case Object through distributions
case2.apply(multiple_geos)
case2.plot(title, colorblind=True, annotate=True)<jupyter_output><empty_output><jupyter_text>These distributions can be separated as desired into a panel of various plots.<jupyter_code>case2.plot(title, colorblind=True, annotate=True, separate=True)<jupyter_output><empty_output><jupyter_text>Halfplots The following has not been fully implemented yet, but demonstrates several multi-plots of tensile data. Each plot shows some *pattern* of interest, for example:      - (a) constant total thickness; varied layer thicknesses- (b) constant outer layer- (c) constant inner layer- (d) constant middle layer![halfplots](../docs/_images/halfplots.png) Panels of multi-plots are possible with a `Cases` object, a container for several `Case` objects.<jupyter_code># Setup a list of geometry strings
const_total = [
    '350-400-500',  '400-200-800', '200-200-1200',
    '200-100-1400', '100-100-1600', '100-200-1400'
]

# Setup cases
cases1 = la.distributions.Cases(
    const_total, load_params=load_params, mat_props=mat_props,
    model='Wilson_LT', ps=[2, 3]
) 
cases1.plot(extrema=False)<jupyter_output><empty_output><jupyter_text>---  Data Analysis Using a prior case, we can analyze the data calculations based on a given theorical model.<jupyter_code>case1
case1.model
case1.LMs<jupyter_output><empty_output><jupyter_text>Data for each laminate is contained in a [pandas](http://pandas.pydata.org/) `DataFrame`, a powerful data structure for data analysis.<jupyter_code>#df = case1.frames
df = case1.frames[0]
df
#df.style                                                   # pandas 0.17.1, css on html table
#df.style.bar(subset=['stress_f (MPa/N)', 'strain'], color='#d65f5f')<jupyter_output>Accessing frames method.<jupyter_text>Exporting Data Finally, we can export data and parameters to Excel or .csv formats.<jupyter_code>case1.to_xlsx()<jupyter_output><empty_output>
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