Illustrative example¶
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%matplotlib inline
%matplotlib inline
Importing libraries¶
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from pysigmap.energy import WangAndFrost
from pysigmap.energy import BeckerEtAl
from pysigmap.bilog import Bilog
from pysigmap.boone import Boone
from pysigmap.pachecosilva import PachecoSilva
from pysigmap.casagrande import Casagrande
import pandas as pd
from pysigmap.data import Data
from pysigmap.energy import WangAndFrost
from pysigmap.energy import BeckerEtAl
from pysigmap.bilog import Bilog
from pysigmap.boone import Boone
from pysigmap.pachecosilva import PachecoSilva
from pysigmap.casagrande import Casagrande
import pandas as pd
from pysigmap.data import Data
Block 1: Input loading data from an external file¶
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url = "https://raw.githubusercontent.com/eamontoyaa/data4testing/main/pysigmap/testData.csv"
df = pd.read_csv(url)
data = Data(df, sigmaV=75)
fig = data.plot() # Figure 2a
url = "https://raw.githubusercontent.com/eamontoyaa/data4testing/main/pysigmap/testData.csv"
df = pd.read_csv(url)
data = Data(df, sigmaV=75)
fig = data.plot() # Figure 2a
Block 2: $C_\mathrm{c}$ and $C_\mathrm{r}$ calculated following published criteria¶
2.1 - Default parameters: $C_\mathrm{c}$ (maximum slope) – $C_\mathrm{r}$ (opt=1)¶
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data.compressionIdx(range2fitCc=None)
data.recompressionIdx(opt=1)
fig = data.plot() # Figure 2a
data.compressionIdx(range2fitCc=None)
data.recompressionIdx(opt=1)
fig = data.plot() # Figure 2a
2.2: $C_\mathrm{c}$ (two last points) – $C_\mathrm{r}$ (opt=2)¶
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data.compressionIdx(range2fitCc=(3000, 8000))
data.recompressionIdx(opt=2)
fig = data.plot() # Figure 2b
data.compressionIdx(range2fitCc=(3000, 8000))
data.recompressionIdx(opt=2)
fig = data.plot() # Figure 2b
2.3: $C_\mathrm{c}$ (four last points) – $C_\mathrm{r}$ (opt=3)¶
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data.compressionIdx(range2fitCc=(700, 8000))
data.recompressionIdx(opt=3)
fig = data.plot() # Figure 2c
data.compressionIdx(range2fitCc=(700, 8000))
data.recompressionIdx(opt=3)
fig = data.plot() # Figure 2c
Block 3: Computation of $\sigma_{\mathrm{p}}$ via the Casagrande method¶
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method = Casagrande(data)
method = Casagrande(data)
3.1: Default parameters: cubic spline function¶
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fig = method.getSigmaP(mcp=None, range2fitFOP=None, loglog=True) # Figure 3a
fig = method.getSigmaP(mcp=None, range2fitFOP=None, loglog=True) # Figure 3a
3.2: Fourth order polynomial (FOP)¶
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fig = method.getSigmaP(range2fitFOP=[20, 5000], loglog=True) # Figure 3b
fig = method.getSigmaP(range2fitFOP=[20, 5000], loglog=True) # Figure 3b
3.3: MCP manually introduced¶
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fig = method.getSigmaP(mcp=200) # Not shown
fig = method.getSigmaP(mcp=200) # Not shown
Block 4: Computation of $\sigma_{\mathrm{p}}$ via the Pacheco Silva and Boone methods¶
4.1: Pacheco Silva method¶
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method = PachecoSilva(data)
fig = method.getSigmaP() # Figure 3c
method = PachecoSilva(data)
fig = method.getSigmaP() # Figure 3c
4.2: Boone method¶
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method = Boone(data)
fig = method.getSigmaP() # Figure 3d
method = Boone(data)
fig = method.getSigmaP() # Figure 3d
Block 5: Computation of $\sigma_{\mathrm{p}}$ via the bilogarithmic methods¶
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method = Bilog(data)
method = Bilog(data)
5.1: Butterfield method¶
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fig = method.getSigmaP(range2fitRR=None, range2fitCR=None, opt=1) # Figure 4a
fig = method.getSigmaP(range2fitRR=None, range2fitCR=None, opt=1) # Figure 4a
5.2: Oikawa method¶
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fig = method.getSigmaP(
range2fitRR=None, range2fitCR=[1000, 5000], opt=2) # Figure 4b
fig = method.getSigmaP(
range2fitRR=None, range2fitCR=[1000, 5000], opt=2) # Figure 4b
5.3: Onitsuka et al. method¶
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fig = method.getSigmaP(
range2fitRR=[0, 30], range2fitCR=[1000, 9000], opt=3) # Figure 4c
fig = method.getSigmaP(
range2fitRR=[0, 30], range2fitCR=[1000, 9000], opt=3) # Figure 4c
Block 6: Computation of $\sigma_{\mathrm{p}}$ via the strain energy methods¶
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method = BeckerEtAl(data)
method = BeckerEtAl(data)
6.1: Becker et al. method¶
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fig = method.getSigmaP(range2fitRR=None, range2fitCR=None,
morinFormulation=False, zoom=5.5) # Figure 5a
fig = method.getSigmaP(range2fitRR=None, range2fitCR=None,
morinFormulation=False, zoom=5.5) # Figure 5a
6.2: Morin method¶
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fig = method.getSigmaP(range2fitRR=[0, 100], range2fitCR=[700, 9000],
morinFormulation=True, zoom=5.5) # Figure 5b
fig = method.getSigmaP(range2fitRR=[0, 100], range2fitCR=[700, 9000],
morinFormulation=True, zoom=5.5) # Figure 5b
6.3: Wang and Frost method¶
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method = WangAndFrost(data)
fig = method.getSigmaP(range2fitCR=None) # Figure 5c
method = WangAndFrost(data)
fig = method.getSigmaP(range2fitCR=None) # Figure 5c
