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).

In [5]:

case1 = la.distributions.Case(load_params, mat_props)      # instantiate a User Input Case Object through distributions
case1.apply(single_geo)
case1.plot(normalized=False)

Converting mat_props to Standard Form.
User input geometries have been converted and set to Case.

In [6]:

case1.plot(normalized=True, grayscale=True)

We can superimpose insets and adjust the colors for publications quality.

In [7]:

case1.plot(annotate=True, colorblind=True, inset=True)

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.

In [10]:

case2.plot(title, colorblind=True, annotate=True)

These distributions can be separated as desired into a panel of various plots.

In [11]:

case2.plot(title, colorblind=True, annotate=True, separate=True)

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:

• 1. constant total thickness; varied layer thicknesses
• 2. constant outer layer
• 3. constant inner layer
• 4. constant middle layer

halfplots

Panels of multi-plots are possible with a Cases object, a container for several Case objects.

In [13]:

cases1.plot(extrema=False)

---

Data Analysis

Using a prior case, we can analyze the data calculations based on a given theorical model.

In [15]:

case1.model

Out[15]:

'Wilson_LT'

Data for each laminate is contained in a pandas DataFrame, a powerful data structure for data analysis.

In [17]:

#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')

Accessing frames method.

Out[17]:

	layer	side	type	matl	...	strain_t	stress_r (Pa/N)	stress_t (Pa/N)	stress_f (MPa/N)
0	1	Tens.	outer	HA	...	5.965e-06	274182.824	378730.663	0.379
1	1	Tens.	outer	HA	...	3.579e-06	164509.695	227238.398	0.227
2	2	Tens.	inner	PSu	...	3.579e-06	9854.181	12915.334	0.013
3	2	Tens.	inner	PSu	...	2.386e-06	6569.454	8610.223	0.009
4	3	Tens.	middle	HA	...	2.386e-06	109673.130	151492.265	0.151
5	3	Comp.	middle	HA	...	-2.386e-06	-109673.130	-151492.265	-0.151
6	4	Comp.	inner	PSu	...	-2.386e-06	-6569.454	-8610.223	-0.009
7	4	Comp.	inner	PSu	...	-3.579e-06	-9854.181	-12915.334	-0.013
8	5	Comp.	outer	HA	...	-3.579e-06	-164509.695	-227238.398	-0.227
9	5	Comp.	outer	HA	...	-5.965e-06	-274182.824	-378730.663	-0.379

10 rows × 22 columns

Exporting Data

Finally, we can export data and parameters to Excel or .csv formats.

case1.to_xlsx()

ouput

Conclusion

With a few simple lines of code, we can use LamAna to quickly perform laminate analysis calculations, visulalize stress distributions and export data.

...and it’s FREE.