Module ttemtoolbox.core.plot

Functions

def generate_trace(data, *args)

Expand source code

def generate_trace(data, *args):
    # use plotly to add plot
    # define color bar
    # well log color bar fine, mix, coarse grained
    colorwell = [[0, 'rgb(0,204,255)'],
                 [0.5, 'rgb(204,204,0)'],
                 [1, 'rgb(255,0,0)']
                 ]
    # rock transform color bar fine, mix, coarse grained
    colorrock = [[0, 'rgb(30,144,255)'],
                 [0.5, 'rgb(255,255,0)'],
                 [1,'rgb(255,0,0)']]
    # tTEM data color bar, match color bar in aarhus workbench
    colorRes = [[0, 'rgb(0,0,190)'],
                [1 / 16, 'rgb(0,75,220)'],
                [2 / 16, 'rgb(0,150,235)'],
                [3 / 16, 'rgb(0,200,255)'],
                [4 / 16, 'rgb(80,240,255)'],
                [5 / 16, 'rgb(30,210,0)'],
                [6 / 16, 'rgb(180,255,30)'],
                [7 / 16, 'rgb(255,255,0)'],
                [8 / 16, 'rgb(255,195,0)'],
                [9 / 16, 'rgb(255,115,0)'],
                [10 / 16, 'rgb(255,0,0)'],
                [11 / 16, 'rgb(255,0,120)'],
                [12 / 16, 'rgb(140,40,180)'],
                [13 / 16, 'rgb(165,70,220)'],
                [14 / 16, 'rgb(195,130,240)'],
                [15 / 16, 'rgb(230,155,240)'],
                [1, 'rgb(230,155,255)']
                ]
    if np.isin("ttem", args):
        ttemfigdata = go.Scatter3d(x=data.UTMX.values, y=data.UTMY.values, z=data.Elevation_Cell.values,
                             customdata=data[["Layer_No", "Resistivity", "Line_No", "ID"]],
                             hovertemplate=
                             '<b>UTMX: %{x:.2f}</b><br>' +
                             '<b>UTMY: %{y:.2f}</b><br>' +
                             '<b>Elevation: %{z:.3f}</b><br>' +
                             '<b>ID: %{customdata[3]}</b><br>'
                             '<b>Layer_No: %{customdata[0]}</b><br>' +
                             '<b>Line_No: %{customdata[2]}</b><br>' +
                             '<b>Resistivity: %{customdata[1]}</b><br>',
                             mode='markers',
                             marker=dict(
                                 color=np.log10(data.Resistivity.values),
                                 cmin=0,
                                 cmax=3,
                                 showscale=True,
                                 size=8,
                                 colorscale=colorRes,
                                 colorbar=dict(
                                     ticks="outside",
                                     title="Resistivity",
                                     x=0.8,
                                     tickvals=[0, 1, 2, 3],
                                     ticktext=["1", "10", "100", "1000"],
                                     tickmode="array"
                                 ), ))
        layout = dict(scene=dict(xaxis=dict(title='UTMX'),
                                 yaxis=dict(title='UTMY'),
                                 zaxis=dict(title='Elevation (m)'),
                                 aspectmode='auto'),)
        trace = dict(data=[ttemfigdata], layout=layout)
        return trace
    elif np.isin("well", args):
        trace = go.Scatter3d(x=data["UTMX"], y=data["UTMY"], z=data["Elevation"], mode='markers',
                             text=data["Bore"],
                             hovertemplate=
                             '<b>WIN: %{text}</b><br>' +
                             '<b>Elevation: %{z:.2f}</b><br>',
                             marker=dict(
                                 color=data["Keyword_n"],
                                 cmin=1,
                                 cmax=3,
                                 showscale=True,
                                 size=10,
                                 colorscale=colorwell,
                                 colorbar=dict(
                                     ticks="outside",
                                     title="Lithology",
                                     x=0.1,
                                     tickvals=[1, 2, 3],
                                     ticktext=["Fine grain", "mix grain", "coarse grain"],
                                     tickmode="array"
                                 ),
                             ))
        return trace
    elif np.isin("rock",args):
        trace = go.Scatter3d(x=data.UTMX.values, y=data.UTMY.values, z=data.Elevation_Cell.values,
                             customdata=data[["Layer_No", "Resistivity", "Line_No","Identity","ID"]],
                             hovertemplate=
                             '<b>UTMX: %{x:.2f}</b><br>' +
                             '<b>UTMY: %{y:.2f}</b><br>' +
                             '<b>Elevation: %{z:.3f}</b><br>' +
                             '<b>ID: %{customdata[4]}</b><br>' +
                             '<b>Layer_No: %{customdata[0]}</b><br>' +
                             '<b>Line_No: %{customdata[2]}</b><br>' +
                             '<b>Resistivity: %{customdata[1]}</b><br>'+
                             '<b>Rocktype: %{customdata[3]}</b><br>',
                             mode="markers",
                             marker=dict(
                                 color=data.Identity_n.values,
                                 cmin=1,
                                 cmax=3,
                                 showscale=True,
                                 size=8,
                                 colorscale=colorrock,
                                 colorbar=dict(
                                     ticks="outside",
                                     title="Rock",
                                     x=0.8,
                                     tickvals=[1, 2, 3],
                                     ticktext=["Finegrain","Mixgrain","Coarsegrain"],
                                     tickmode="array"
                                 ),))
        return trace
    elif np.isin("geophysics", args):
        trace = data.plot(x=args, y="Depth")
        return trace
    elif np.isin('water', args):
        trace = go.Scatter3d(x=data['UTMX'].values,
                             y=data['UTMY'].values,
                             z=data['water_elevation'],
                             customdata=data[["wellname"]],
                             hovertemplate=
                             '<b>UTMX: %{x:.2f}</b><br>' +
                             '<b>UTMY: %{y:.2f}</b><br>' +
                             '<b>Water elevation: %{z:.3f}</b><br>' +
                             '<b>WellNo: %{customdata[0]}</b><br>'
                             #'<b>Well_elevation: %{customdata[1]}</b><br>'
                             )
        return trace
    else:
        raise TypeError('{} is not one of "ttem","well","rock",or,"geophysics"'.format(args))

def geophy_ttem_plot(df, **kwargs)

Expand source code

def geophy_ttem_plot(df,**kwargs):
    if np.isin(["x","y"],list(kwargs.keys())).all():
        fig = df.plot.scatter(x=df[kwargs["x"]],
                              y=df[kwargs["y"]])
        trend_trace = trendline(x=df[kwargs["x"]],
                                y=df[kwargs["y"]])
        fig.add_trace(trend_trace)
        fig.update_layout(title=df.loc[0, "comment"])
        return fig
    else:
        print("Select two columns you want to plot scatter plot")

def plot_rock_single(data)

Plot single trace of rock transform result for paper uses

:param data: The input data should be rock physics transform result from Rock_trans.rock_transform :return:Plotly Figure

Expand source code

def plot_rock_single(data):
    """
    Plot single trace of rock transform result for paper uses

    :param data: The input data should be rock physics transform result from Rock_trans.rock_transform
    :return:Plotly Figure
    """
    colorrock = [[0, 'rgb(30,144,255)'],
                 [0.5, 'rgb(255,255,0)'],
                 [1, 'rgb(255,0,0)']]

    y_shape = int(data.Depth_bottom.max()*10)
    empty_gird = np.full((y_shape,50), np.nan)
    for index,line in data.iterrows():
        y_start = int(line.Depth_top*10)
        y_stop = int(line.Depth_bottom*10)
        empty_gird[y_start:y_stop,:] = line.Identity_n
    fig_rock = px.imshow(empty_gird, range_color=(1, 3), color_continuous_scale=colorrock)
    return fig_rock

def plot_well_single(welllog, wellname: int = 0)

Plot single trace of well log result for paper uses

:param data: The input data should be well log from process_well.format_well :return: plotly fig

Expand source code

def plot_well_single(welllog, wellname:int=0):
    """
    Plot single trace of well log result for paper uses

    :param data: The input data should be well log from process_well.format_well
    :return: plotly fig
    """
    colorrock = [[0, 'rgb(30,144,255)'],
                 [0.5, 'rgb(255,255,0)'],
                 [1, 'rgb(255,0,0)']]
    if isinstance(welllog, pathlib.PurePath):
        ori_well = core.process_well.format_well(welllog, upscale=10)
        data = ori_well[ori_well.Bore == str(wellname)]
    elif isinstance(welllog, pd.DataFrame):
        data = welllog
    y_shape = int(data.Depth2.max()*0.3048*10)
    empty_gird = np.full((y_shape,50),np.nan)
    for index,line in data.iterrows():
        y_start = int(line.Depth1*3.048) #0.3048*10
        y_stop = int(line.Depth2*3.048)
        empty_gird[y_start:y_stop,:] = line.Keyword_n
    fig_well = px.imshow(empty_gird, range_color=(1, 3), color_continuous_scale=colorrock)
    return fig_well

def res_1d_plot(data, dash=False)

Plot tTEM single sounding in to 1d resistivity profile

:param data: tTEM Dataframe contains only a single ID, multiple ID input will plot multiple 1d profile in a single plot :return:Plotly trace

Expand source code

def res_1d_plot(data,dash=False):
    """
    Plot tTEM single sounding in to 1d resistivity profile

    :param data: tTEM Dataframe contains only a single ID, multiple ID input will plot multiple 1d profile in a single plot
    :return:Plotly trace
    """
    min_thickness = data.Depth_top.min()
    max_thickness = data.Depth_bottom.max()
    max_resistivity = data.Resistivity.max()
    min_resistivity = data.Resistivity.min()
    fig = go.Figure()
    ID_group = data.groupby('ID')
    for name, group in ID_group:
        print('ID :{}'.format(name))
        if dash is True:
            y_dash_number = list(set(group.Depth_top.tolist()+group.Depth_bottom.tolist()))
            for n in y_dash_number:
                trace = go.Scatter(
                    x=np.arange(min_resistivity,max_resistivity+5,1),
                    y=np.ones(100)*n,
                    mode='lines',
                    line=dict(
                        dash='dot'
                    )
                )
                fig.add_trace(trace)
        list1 = group.Depth_top.tolist()
        list2 = group.Depth_bottom.tolist()
        merged_list = []
        for ii in range(len(list1)):
            merged_list.append(list1[ii])
            merged_list.append(list2[ii])
        trace_resistivity = go.Scatter(
            x=[i for i in group.Resistivity for _ in range(2)],
                    # This repeat a list twice from [1,2,3] to [1,1,2,2,3,3])
            y=merged_list
        )
        fig.add_trace(trace_resistivity)
    fig.update_layout(xaxis=dict(
        type='log',
        title='Resistivity(Ohm.m)',
        titlefont=dict(
            family='Arial',
            size=30
        ),
        tickfont=dict(
            family='Arial',
            size=25
        ),
        # range = [np.log10(min_resistivity)-0.2, np.log10(max_resistivity)+0.2]
    ),
        yaxis=dict(
            title='Depth (m)',
            titlefont=dict(
                family='Arial',
                size=30
            ),
            tickfont=dict(
                family='Arial',
                size=25
            ),
            range=[min_thickness, max_thickness + 5],
            autorange='reversed',
            # tickmode='array',
            # tickvals=list(np.arange(0,max_thickness+5,10)),
            # ticktest=[]
        ),
        showlegend=False
    )
    return fig

def trendline(x, y)

Expand source code

def trendline(x,y):
    def length(n):
        import math
        if n > 0:
            digits = int(math.log10(n)) + 1
        elif n == 0:
            digits = 1
        else:
            digits = int(math.log10(-n)) + 2
        return digits
    trend_result = linregress(x, y) # result include slope, intercept, rvalue, pvalue, stderr, intercept_stderr
    x_limit = math.ceil(x.max()/10**(length(x.max())-1))*10**(length(x.max())-1)
    x_range = np.arange(x_limit)
    y_range = x_range*trend_result[0] + trend_result[1]
    slope = trend_result[0]; intercept = trend_result[1]; rvalue = trend_result[2]
    pvalue = trend_result[3];stderr = trend_result[4];intercept_stderr = trend_result.intercept_stderr
    rsquare = trend_result[2]**2
    df = pd.DataFrame({"x_range":x_range, "y_range":y_range})
    df[['slope','intercept','rvalue','pvalue','stderr','intercept_stderr','rsquare']] =[
        slope, intercept, rvalue, pvalue, stderr, intercept_stderr, rsquare
    ]
    trace = go.Scatter(x=x_range, y=y_range,
                       mode='lines',
                       name='trendline',
                       customdata=df[['slope','intercept','rvalue','pvalue','stderr','intercept_stderr','rsquare']],
                       hovertemplate=
                       '<b>y= %{customdata[0]:.3f}x+%{customdata[1]:.3f}</b><br>' +
                       '<b>r_value=: %{customdata[2]:.3f}</b><br>' +
                       '<b>p_value=: %{customdata[3]:.3f}</b><br>' +
                       '<b>r_square=: %{customdata[6]:.3f}</b><br>' +
                       '<b>stderr=: %{customdata[4]:.3f}</b><br>' +
                       '<b>intercept_stderr=: %{customdata[5]:.3f}</b><br>'
                       )
    return trace

def well_test_plot(data, window=10)

Plot gamma data vs depth with moving average :param data: Pandas Dataframe should contains Depth vs any well test result :return:

Expand source code

def well_test_plot(data, window=10):
    """
    Plot gamma data vs depth with moving average
    :param data: Pandas Dataframe should contains Depth vs any well test result
    :return:
    """
    def average_data(lst, n=10):
        sublist_n = [lst[i:i+n] for i in range(0, len(lst), n)]
        return [np.mean(sub_list) for sub_list in sublist_n]

    fig = go.Figure()
    fig.update_layout(
        xaxis=dict(
            title='Gamma Count',
            titlefont=dict(
                family='Arial',
                size=30
            ),
            tickfont=dict(
                family='Arial',
                size=25
            )
        ),
        yaxis=dict(
            title='Depth (m)',
            titlefont=dict(
                family='Arial',
                size=30
            ),
            tickfont=dict(
                family='Arial',
                size=25
            ),
            range=[0,50],
            autorange='reversed'

        )
    )
    average_gamma = average_data(data.GR,n=window)
    average_depth = average_data(data.Depth,n=window)
    trace = go.Scatter(
        x=average_gamma,
        y=average_depth,

    )
    fig.add_trace(trace)
    return fig