In [ ]:
%pip install -q dimstack
import plotly
plotly.offline.init_notebook_mode()

Note: you may need to restart the kernel to use updated packages.

c:\Users\phcre\Documents\Python\dimstack\.venv\Scripts\python.exe: No module named pip
In [ ]:
import dimstack as ds
ds.display.mode("df")

m1 = ds.StatisticalDimension(
    nom=208,
    tol=ds.tol.SymmetricBilateral(0.036),
    process_sigma=6,
    k=0.25,
    name="a",
    desc="Shaft",
)
m2 = ds.StatisticalDimension(
    nom=-1.75,
    tol=ds.tol.UnequalBilateral(0, 0.06),
    process_sigma=3,
    name="b",
    desc="Retainer ring",
)
m3 = ds.StatisticalDimension(nom=-23, tol=ds.tol.UnequalBilateral(0, 0.12), process_sigma=3, name="c", desc="Bearing")
m4 = ds.StatisticalDimension(
    nom=20,
    tol=ds.tol.SymmetricBilateral(0.026),
    process_sigma=3,
    name="d",
    desc="Bearing Sleeve",
)
m5 = ds.StatisticalDimension(nom=-200, tol=ds.tol.SymmetricBilateral(0.145), process_sigma=3, name="e", desc="Case")
m6 = ds.BasicDimension(
    nom=20,
    tol=ds.tol.SymmetricBilateral(0.026),
    # process_sigma=3,
    name="f",
    desc="Bearing Sleeve",
)
m7 = ds.StatisticalDimension(nom=-23, tol=ds.tol.UnequalBilateral(0, 0.12), process_sigma=3, name="g", desc="Bearing")
items = [m1, m2, m3, m4, m5, m6, m7]

stack = ds.Stack(title="stacks on stacks", items=items)
stack
Out[ ]:
Stack: stacks on stacks
ID Name Description dir Nom. Tol. Sen. Relative Bounds Distribution Process Sigma k C_p C_pk μ σ μ_eff σ_eff Yield Probability Reject PPM
0 a Shaft + 208.0 ± 0.036 1 [207.964, 208.036] Normal ± 6σ 0.25 2.0 1.5 208.009 0.006 208.0 0.008 99.99932047 6.8
1 b Retainer ring - 1.75 + 0 / - 0.06 1 [1.69, 1.75] Normal ± 3σ 0 1.0 1.0 1.72 0.01 1.72 0.01 99.73002039 2699.8
2 c Bearing - 23.0 + 0 / - 0.12 1 [22.88, 23] Normal ± 3σ 0 1.0 1.0 22.94 0.02 22.94 0.02 99.73002039 2699.8
3 d Bearing Sleeve + 20.0 ± 0.026 1 [19.974, 20.026] Normal ± 3σ 0 1.0 1.0 20.0 0.00867 20.0 0.00867 99.73002039 2699.8
4 e Case - 200.0 ± 0.145 1 [199.855, 200.145] Normal ± 3σ 0 1.0 1.0 200.0 0.04833 200.0 0.04833 99.73002039 2699.8
5 f Bearing Sleeve + 20.0 ± 0.026 1 [19.974, 20.026] Uniform
6 g Bearing - 23.0 + 0 / - 0.12 1 [22.88, 23] Normal ± 3σ 0 1.0 1.0 22.94 0.02 22.94 0.02 99.73002039 2699.8
In [ ]:
ds.plot.StackPlot().add(stack).show()
In [ ]:
display(stack.Closed)
display(stack.WC)
display(stack.RSS)
display(stack.MRSS)
display(stack.SixSigma(at=4.5))
Dimension: stacks on stacks - Closed Analysis -
ID Name Description dir Nom. Tol. Sen. Relative Bounds Distribution
7 stacks on stacks - Closed Analysis + 0.25 + 0.533 / - 0.233 1 [0.017, 0.783] Uniform
Dimension: stacks on stacks - WC Analysis -
ID Name Description dir Nom. Tol. Sen. Relative Bounds Distribution
8 stacks on stacks - WC Analysis + 0.4 ± 0.383 1 [0.017, 0.783] Uniform 
WARNING:root:Converting BasicDimension (5: f Bearing Sleeve +20 ± 0.026) to StatisticalDimension
Dimension: stacks on stacks - RSS Analysis - (assuming inputs with Normal Distribution & ± 3σ)
ID Name Description dir Nom. Tol. Sen. Relative Bounds Distribution Process Sigma k C_p C_pk μ σ μ_eff σ_eff Yield Probability Reject PPM
10 stacks on stacks - RSS Analysis (assuming inputs with Normal Distribution & ± 3σ) + 0.4 ± 0.17825 1 [0.22175, 0.57825] Normal ± 3σ 0 1.0 1.0 0.4 0.05942 0.4 0.05942 99.73002039 2699.8 
WARNING:root:Converting BasicDimension (5: f Bearing Sleeve +20 ± 0.026) to StatisticalDimension
Dimension: stacks on stacks - MRSS Analysis - (assuming inputs with Normal Distribution & ± 3σ)
ID Name Description dir Nom. Tol. Sen. Relative Bounds Distribution Process Sigma k C_p C_pk μ σ μ_eff σ_eff Yield Probability Reject PPM
12 stacks on stacks - MRSS Analysis (assuming inputs with Normal Distribution & ± 3σ) + 0.4 ± 0.24046 1 [0.15954, 0.64046] Normal ± 3.76693σ 0 1.25564 1.25564 0.4 0.06383 0.4 0.06383 99.98347307 165.27 
WARNING:root:Converting BasicDimension (5: f Bearing Sleeve +20 ± 0.026) to StatisticalDimension
Dimension: stacks on stacks - '6 Sigma' Analysis - (assuming inputs with Normal Distribution)
ID Name Description dir Nom. Tol. Sen. Relative Bounds Distribution Process Sigma k C_p C_pk μ σ μ_eff σ_eff Yield Probability Reject PPM
14 stacks on stacks - '6 Sigma' Analysis (assuming inputs with Normal Distribution) + 0.4 ± 0.26433 1 [0.13567, 0.66433] Normal ± 4.5σ 0 1.5 1.5 0.4 0.05874 0.4 0.05874 99.99932047 6.8
In [ ]:
ds.plot.StackPlot().add(stack.Closed).add(stack.WC).add(stack.RSS).add(stack.MRSS).add(stack.SixSigma(at=4.5)).show()

WARNING:root:Converting BasicDimension (5: f Bearing Sleeve +20 ± 0.026) to StatisticalDimension
WARNING:root:Converting BasicDimension (5: f Bearing Sleeve +20 ± 0.026) to StatisticalDimension
WARNING:root:Converting BasicDimension (5: f Bearing Sleeve +20 ± 0.026) to StatisticalDimension
In [ ]:
spec = ds.Spec("stack spec", "", dim=stack.SixSigma(at=4.5), LL=0.05, UL=0.8)
spec

WARNING:root:Converting BasicDimension (5: f Bearing Sleeve +20 ± 0.026) to StatisticalDimension
Out[ ]:
Spec: stack spec
Name Description Dimension Spec. Limits Median k C_p C_pk Yield Probability Reject PPM
stack spec 24: stacks on stacks - '6 Sigma' Analysis (assuming inputs with Normal Distribution) +0.4 ± 0.26433 @ ± 4.5σ & k=0 [0.05, 0.8] 0.425 0.06667 2.12804 1.98617 100 0
In [ ]:
from scipy import stats

# Generate some data for this demonstration.
a=1
b=1
data = stats.norm.rvs(a, b, size=400, random_state=140)

# Fit a normal distribution to the data:
dim = ds.StatisticalDimension.from_data(data, sigma=3, name="import data", desc="")
dim
Out[ ]:
Dimension: import data - (from data)
ID Name Description dir Nom. Tol. Sen. Relative Bounds Distribution Process Sigma k C_p C_pk μ σ μ_eff σ_eff Yield Probability Reject PPM
25 import data (from data) + 1.08406 ± 2.84827 1 [-1.76421, 3.93233] Normal ± 3σ 0 1.0 1.0 1.08406 0.94942 1.08406 0.94942 99.73002039 2699.8
In [ ]:
ds.plot.StackPlot().add(dim).show()