Team aaML

aaML/libtools.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+import pprint
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+# turn off ipython warnings 
+import warnings
+warnings.filterwarnings('ignore')
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+
+def applyPCA(df):
+    """
+    Receiver a dataset of features
+
+    Return:
+        Print of variance_ratio_ in percent
+    """
+
+    columns = df.columns
+    scaler = StandardScaler()  
+    pca = PCA()
+
+    scaler.fit(df)
+    df = scaler.transform(df)
+
+    pca.fit(df)
+    x_pca = pca.transform(df)
+    p = pca.explained_variance_ratio_
+    result = {}
+    for key, perc in zip(columns, p):
+        result[key] = "{:.2f}%".format(perc*100)
+
+    pp = pprint.PrettyPrinter(indent=4)
+    pp.pprint(result)
+
+def litologia(facies):
+    if facies == 1:
+        return 'Nonmarine sandstone'
+    if facies == 2:
+        return 'Nonmarine coarse siltstone'
+    if facies == 3:
+        return 'Nonmarine fine siltstone'
+    if facies == 4:
+        return 'Marine siltstone and shale'
+    if facies == 5:
+        return 'Mudstone (limestone)'
+    if facies == 6:
+        return 'Wackestone (limestone)'
+    if facies == 7:
+        return 'Dolomite'
+    if facies == 8:
+        return 'Packstone-grainstone (limestone)'
+    if facies == 9:
+        return 'Phylloid-algal bafflestone (limestone)'
+
+# Function to obtain Density Porosity    
+def phi_n(df):
+    return (df['DeltaPHI']+2*df['PHIND'])/2
+
+# Function to obtain Density Neutronic
+def phi_d(df):
+    return (-df['DeltaPHI']+2*df['PHIND'])/2
+
+# Function used by Brendom to put new label in facies
+def label_facies(row, labels):
+    return labels[ row['Facies'] -1]
+
+# Bredom's function that shows the well logs
+def make_facies_log_plot(logs, facies_colors):
+    #make sure logs are sorted by depth
+    logs = logs.sort_values(by='Depth')
+    cmap_facies = colors.ListedColormap(facies_colors[0:len(facies_colors)], 'indexed')
+
+    ztop=logs.Depth.min(); zbot=logs.Depth.max()
+
+    cluster=np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)
+
+    f, ax = plt.subplots(nrows=1, ncols=6, figsize=(8, 12))
+    ax[0].plot(logs.GR, logs.Depth, '-g')
+    ax[1].plot(logs.ILD_log10, logs.Depth, '-')
+    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')
+    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')
+    ax[4].plot(logs.PE, logs.Depth, '-', color='black')
+    im=ax[5].imshow(cluster, interpolation='none', aspect='auto',
+                    cmap=cmap_facies,vmin=1,vmax=9)
+
+    divider = make_axes_locatable(ax[5])
+    cax = divider.append_axes("right", size="20%", pad=0.05)
+    cbar=plt.colorbar(im, cax=cax)
+    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', 
+                                'SiSh', ' MS ', ' WS ', ' D  ', 
+                                ' PS ', ' BS ']))
+    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')
+
+    for i in range(len(ax)-1):
+        ax[i].set_ylim(ztop,zbot)
+        ax[i].invert_yaxis()
+        ax[i].grid()
+        ax[i].locator_params(axis='x', nbins=3)
+
+    ax[0].set_xlabel("GR")
+    ax[0].set_xlim(logs.GR.min(),logs.GR.max())
+    ax[1].set_xlabel("ILD_log10")
+    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())
+    ax[2].set_xlabel("DeltaPHI")
+    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())
+    ax[3].set_xlabel("PHIND")
+    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())
+    ax[4].set_xlabel("PE")
+    ax[4].set_xlim(logs.PE.min(),logs.PE.max())
+    ax[5].set_xlabel('Facies')
+
+    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])
+    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])
+    ax[5].set_xticklabels([])
+    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)
+
+def label_formation(df,formacao):
+    list_form = []
+    for df_form in df:
+        for i in range (0,len(formacao)):
+            if df_form == formacao[i]:
+                list_form.append(i+1)
+    return list_form
+
+def label_two_groups_formation(formacao):
+    formacao
+    if formacao[:][-2:] == 'SH':
+        return 1
+    if formacao[:][-2:] == 'LM':
+        return 2
+
+def NM_M_TOPO(df):
+
+    RELPOS_NM_M = []
+    name_well = df['Well Name'].unique()
+
+    for well in name_well:
+
+        NM_M = df[df['Well Name'] == well]['NM_M']
+        NM_total = len(NM_M[NM_M == 1])
+        M_total = len(NM_M[NM_M == 2])
+        count_NM = 0 ; count_M = 0
+
+        for i in NM_M:
+            if i == 1:
+                aux_NM = 1-(count_NM/(NM_total-1))
+                RELPOS_NM_M.append(aux_NM)
+                count_NM = count_NM+1
+            if i == 2:
+                aux_M = 1-(count_M/(M_total-1))
+                RELPOS_NM_M.append(aux_M)
+                count_M = count_M+1
+
+    return RELPOS_NM_M
+
+def SH_LM_TOPO(df):
+
+    SH_LM = []
+    name_well = df['Well Name'].unique()
+
+    for well in name_well:
+
+        S_L = df[df['Well Name'] == well]['Label_Form_SH_LM']
+        S_total = len(S_L[S_L == 1])
+        L_total = len(S_L[S_L == 2])
+        count_S = 0 ; count_L = 0
+
+        for i in S_L:
+            if i == 1:
+                aux_S = 1-(count_S/(S_total-1))
+                SH_LM.append(aux_S)
+                count_S = count_S+1
+            if i == 2:
+                aux_L = 1-(count_L/(L_total-1))
+                SH_LM.append(aux_L)
+                count_L = count_L+1
+
+    return SH_LM
+
+def divisao_sh(df):
+    return df[df['Label_Form_SH_LM']==1].drop(['Label_Form_SH_LM'],axis=1)
+
+def divisao_lm(df):
+    return df[df['Label_Form_SH_LM']==2].drop(['Label_Form_SH_LM'],axis=1)
+
+def divisao_nm(df):
+    return df[df['NM_M']==1].drop(['NM_M'],axis=1)
+
+def divisao_m(df):
+    return df[df['NM_M']==2].drop(['NM_M'],axis=1)
+
+def divisao_topo(df):
+    return df[df['RELPOS']>=0.5].drop(['RELPOS'],axis=1)
+
+def divisao_base(df):
+    return df[df['RELPOS']<0.5].drop(['RELPOS'],axis=1)
+
+def divisao_topo_sh_lm(df):
+    return df[df['RELPOS_SH_LM']>=0.5].drop(['RELPOS_SH_LM'],axis=1)
+
+def divisao_base_sh_lm(df):
+    return df[df['RELPOS_SH_LM']<0.5].drop(['RELPOS_SH_LM'],axis=1)
+
+def divisao_topo_nm(df):
+    return df[df['RELPOS_NM_M']>=0.5].drop(['RELPOS_NM_M'],axis=1)
+
+def divisao_base_nm(df):
+    return df[df['RELPOS_NM_M']<0.5].drop(['RELPOS_NM_M'],axis=1)