iam-git / WellMet (public) (License: MIT) (since 2021-08-31) (hash sha1)
WellMet is pure Python framework for spatial structural reliability analysis. Or, more specifically, for "failure probability estimation and detection of failure surfaces by adaptive sequential decomposition of the design domain".

/plotly_plot.py (1feb1d43e90e027f35bbd0a6730ab18501cef63a) (2807 bytes) (mode 100644) (type blob)

#!/usr/bin/env python
# coding: utf-8

import plotly.graph_objects as go


def tri_estimation_graph(bx, tri_estimation_name='TRI_current_estimations', filename=''):
    if not filename:
        filename = 'store/%s_%s_%s_graph'%(bx.gm_signature, repr(bx), tri_estimation_name)

    data = bx.guessbox.estimations[tri_estimation_name]
    x=data[0]
    # it can be effectively done with pandas
    p_f = []
    p_mix = []
    p_outside = []
    p_success = []
    for estimation in data[1]:
        # -1 = 'out', 0=success, 1=failure, 2=mix
        p_f.append(estimation[1])
        p_mix.append(estimation[2])
        p_outside.append(estimation[-1])
        p_success.append(estimation[0])
    
    
    # uplně hahoru - success
    # outside
    # mix
    # uplně dolu - failure
    
    fig = go.Figure()
    
    fig.add_trace(go.Scatter(
        x=x, y=p_f,
        mode='lines',
        line=dict(width=0.5, color='red'), #rgb(184, 247, 212)
        name="Failure",
        stackgroup='one',
        groupnorm='fraction' # sets the normalization for the sum of the stackgroup
    ))
    fig.add_trace(go.Scatter(
        x=x, y=p_mix,
        mode='lines',
        line=dict(width=0.5, color='orange'),
        name="Mixed",
        stackgroup='one'
    ))
    fig.add_trace(go.Scatter(
        x=x, y=p_outside,
        mode='lines',
        line=dict(width=0.5, color='white'),
        name="Outside",
        stackgroup='one'
    ))
    fig.add_trace(go.Scatter(
        x=x, y=p_success,
        mode='lines',
        line=dict(width=0.5, color='green'),
        name="Success",
        stackgroup='one'
    ))
    try:
        fig.add_trace(go.Scatter(x=(min(x),max(x)), y=(bx.pf_exact,bx.pf_exact),
                    mode='lines',
                    name=bx.pf_exact_method,
                    line=dict(color='blue')))
    except AttributeError:
        pass
    
    fig.update_layout(
        showlegend=True,
        #xaxis_type='category',
        yaxis=dict(
            type='linear',
            range=[0, 1],
            #ticksuffix='%'
            ))
            
    # zatím nechcu nikomu nic zobrazovat
    #fig.show()
    fig.write_html(filename + ".html")
    
    # kdyby někdo chtěl statické obrázky
    # musí mať psutil nainštalovany
    try:
        fig.write_image(filename + ".png")
    except:
        pass 
        
    # vratíme figuru,
    # uživatel by mohl s ní eště něčo udělat    
    return fig





# 3D plot requires WebGL support
# which is not currently availiable under Haiku.
#
#import plotly.graph_objects as go
#import numpy as np
#
## Helix equation
#t = np.linspace(0, 10, 50)
#x, y, z = np.cos(t), np.sin(t), t
#
#fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z,
#                                   mode='markers')])
#fig.show()
#


Mode Type Size Ref File
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100644 blob 72 458b7e2ca46acd9ec0d2caf3cc4d72e515bb73dc __main__.py
100644 blob 73368 3d245b8568158ac63c80fa0847631776a140db0f blackbox.py
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100644 blob 36886 d27b8a79485ea48402937e44102a2c44fcef4dc5 estimation.py
100644 blob 34394 3f0ab9294a9352a071de18553aa687c2a9e6917a f_models.py
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100644 blob 21043 ca09c8a3fdcda14efa46d4a98fc55855773dcbca ghull.py
100644 blob 2718 5d721d117448dbb96c554ea8f0e4651ffe9ac457 gp_plot.py
100644 blob 29393 96162a5d181b8307507ba2f44bafe984aa939163 lukiskon.py
100644 blob 3164 8aac057ad91bea3300c87b1ae30e717aab7e6002 misc.py
040000 tree - ea76ee9256f0f148427487c733a2dc4c00c9fc1a mplot
100644 blob 1462 437b0d372b6544c74fea0d2c480bb9fd218e1854 plot.py
100644 blob 2807 1feb1d43e90e027f35bbd0a6730ab18501cef63a plotly_plot.py
040000 tree - 195fa1f8abb1e3ea0913a85b10e7c51613b705ea qt_gui
100644 blob 8566 5c8f8cc2a34798a0f25cb9bf50b5da8e86becf64 reader.py
100644 blob 4284 a0e0b4e593204ff6254f23a67652804db07800a6 samplebox.py
100644 blob 6558 df0e88ea13c95cd1463a8ba1391e27766b95c3a5 sball.py
100644 blob 6739 0b6f1878277910356c460674c04d35abd80acf13 schemes.py
100644 blob 76 11b2fde4aa744a1bc9fa1b419bdfd29a25c4d3e8 shapeshare.py
100644 blob 54884 fbe116dab4fc19bb7568102de21f53f15a8fc6bf simplex.py
100644 blob 13090 2b9681eed730ecfadc6c61b234d2fb19db95d87d spring.py
100644 blob 10953 da8a8aaa8cac328ec0d1320e83cb802b562864e2 stm_df.py
040000 tree - 4cad9136391e4dd71294c2b0486ab93c31317313 testcases
100644 blob 2465 d829bff1dd721bdb8bbbed9a53db73efac471dac welford.py
100644 blob 25318 fcdabd880bf7199783cdb9c0c0ec88c9813a5b18 whitebox.py
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