pymechlab 0.1.0

Python Package for Mechanical Laboratory Testing

pymechlab

Python Package for Mechanical Laboratory Testing

This package is used for calculating the A-basis and B-Basis mechanical properties from mechanical laboratory test results and data.

An example from CMH-17 is shown below.

Input file 'cmh17_pg_8-87.json':

{
    "Material": "Graphite/Epoxy",
    "Property": "Compression Strength",
    "Test Environment": "ETW",
    "Program": "Qualification Data",
    "batches": {
        "1": {
            "datasets": {
                "1": {
                    "coupons": {
                        "1": 106.358,
                        "2": 105.899,
                        "3": 88.464,
                        "4": 103.902,
                        "5": 80.206,
                        "6": 109.2,
                        "7": 61.014
                    }
                }
            }
        },
        "2": {
            "datasets": {
                "2": {
                    "coupons": {
                        "1": 99.321,
                        "2": 115.862,
                        "3": 82.613,
                        "4": 85.369,
                        "5": 115.802,
                        "6": 44.322,
                        "7": 117.328,
                        "8": 88.678
                    }
                }
            }
        },
        "3": {
            "datasets": {
                "3": {
                    "coupons": {
                        "1": 107.677,
                        "2": 108.960,
                        "3": 116.123,
                        "4": 80.233,
                        "5": 106.146,
                        "6": 104.668,
                        "7": 104.235
                    }
                }
            }
        }
    }
}

Python script 'cmh17_script.py':

#%%
# Import Dependencies
from IPython.display import display_markdown

from pymechlab.classes.cmh17statistics import pool_from_json

#%%
# Import JSON File
jsonfilepath  = '../files/cmh17_pg_8-87.json'
pool = pool_from_json(jsonfilepath)
display_markdown(pool)

#%%
# Import JSON File
jsonfilepath  = '../files/cmh17_pg_8-92.json'
pool = pool_from_json(jsonfilepath)
display_markdown(pool)

The output results in markdown are as follows:

Pool Information	Value
Material	Graphite/Epoxy
Property	Compression Strength
Test Environment	ETW
Program	Qualification Data
Number of Specimens	22
Number of Batches	3
Number of Data Sets	3
Minimum Value	44.322
Maximum Value	117.328

Batch ID	Data Set ID	Coupon ID	Data Values	Before Pooling	After Pooling
1	1	1	106.358
1	1	2	105.899
1	1	3	88.464
1	1	4	103.902
1	1	5	80.206
1	1	6	109.2
1	1	7	61.014
2	2	1	99.321
2	2	2	115.862
2	2	3	82.613
2	2	4	85.369
2	2	5	115.802
2	2	6	44.322		X
2	2	7	117.328
2	2	8	88.678
3	3	1	107.677
3	3	2	108.96
3	3	3	116.123
3	3	4	80.233	X
3	3	5	106.146
3	3	6	104.668
3	3	7	104.235

Anderson k-Sample Test	Value
ADK Statistic	0.328731
AD Critical (α = 0.250)	0.449259
AD Critical (α = 0.100)	1.30528
AD Critical (α = 0.050)	1.94342
AD Critical (α = 0.025)	2.57697
AD Critical (α = 0.010)	3.41635
AD Critical (α = 0.005)	4.0721
AD Critical (α = 0.001)	5.56419
ADK p-value	0.2888

Normal Distribution Statistics	Value
Observed Significance Level (OSL)	0.00605107
Mean	96.9264
Standard Deviation	18.8048
Coefficient of Variation (%)	19.4012%
B-Basis Value	61.4527
A-Basis Value	36.1265

Log Normal Distribution Statistics	Value
Observed Significance Level (OSL)	0.000307372
Log Mean	4.55097
Log Standard Deviation	0.234756
B-Basis Value	60.8328
A-Basis Value	44.3433

Two Param Weibull Distribution Statistics	Value
Observed Significance Level (OSL)	0.0218837
Scale Parameter	103.847
Shape Parameter	7.28576
B-Basis Value	66.864
A-Basis Value	43.1806

Non-Parametric Statistics	Value
B-Basis Method	Hans-Koop
A-Basis Method	Hans-Koop
B-Basis Rank	10
A-Basis Rank	N/A
B-Basis Hans-Koop k Factor	1.18418
A-Basis Hans-Koop k Factor	2.2602
B-Basis Value	37.8853
A-Basis Value	12.9966

Parameter	Value
Fcalc	1.50529
pcalc	0.247263
Fcrit	4.67443

ANOVA Statistics	Value
Sample Between-batch Mean Sq. (MSB)	257.302
Error Mean Square (MSE)	363.761
Estimate of Pop. Std. Deviation(s)	18.6873
B-Basis Tolerance Limit Factor (TB)	1.88641
A-Basis Tolerance Limit Factor (TA)	3.2332
B-Basis Value	61.6745
A-Basis Value	36.5067