Inverse Weighted Distance

In [1]:

# Inverse Weighted Distance

# Inverse Weighted Distance

In [1]:

from volumetricspy.stats import Dot,CloudPoints, IWD
import pandas as pd
import numpy as np
import geopandas as gpd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.spatial import distance_matrix

from volumetricspy.stats import Dot,CloudPoints, IWD import pandas as pd import numpy as np import geopandas as gpd import matplotlib.pyplot as plt import seaborn as sns from scipy.spatial import distance_matrix

In [2]:

x = [0,1,2,0,0,2]
y = [0,0,0,1,2,2]
v = [1,2,4,5,6,27]

dfp = pd.DataFrame({"x":x,"y":y,"v":v})

cp = CloudPoints().from_df(dfp, x='x',y='y',fields=['v'])
cp.df()

x = [0,1,2,0,0,2] y = [0,0,0,1,2,2] v = [1,2,4,5,6,27] dfp = pd.DataFrame({"x":x,"y":y,"v":v}) cp = CloudPoints().from_df(dfp, x='x',y='y',fields=['v']) cp.df()

Out[2]:

	x	y	z	crs	v
0	0.0	0.0	None	None	1.0
1	1.0	0.0	None	None	2.0
2	2.0	0.0	None	None	4.0
3	0.0	1.0	None	None	5.0
4	0.0	2.0	None	None	6.0
5	2.0	2.0	None	None	27.0

In [3]:

cp.plot(hue='v')

cp.plot(hue='v')

Out[3]:

<AxesSubplot:xlabel='x', ylabel='y'>

In [4]:

xu = [1,2,1,2,4,-1,8]
yu = [1,1,2,2,4,-1,8]
dfu = pd.DataFrame({"x":xu,"y":yu})
cpu = CloudPoints().from_df(dfu, x='x',y='y')
cpu.df()

xu = [1,2,1,2,4,-1,8] yu = [1,1,2,2,4,-1,8] dfu = pd.DataFrame({"x":xu,"y":yu}) cpu = CloudPoints().from_df(dfu, x='x',y='y') cpu.df()

Out[4]:

	x	y	z	crs
0	1.0	1.0	None	None
1	2.0	1.0	None	None
2	1.0	2.0	None	None
3	2.0	2.0	None	None
4	4.0	4.0	None	None
5	-1.0	-1.0	None	None
6	8.0	8.0	None	None

In [8]:

fig, ax = plt.subplots()

cp.plot(ax=ax, color='blue')
cpu.plot(ax=ax,color='red')

fig, ax = plt.subplots() cp.plot(ax=ax, color='blue') cpu.plot(ax=ax,color='red')

Out[8]:

<AxesSubplot:xlabel='x', ylabel='y'>

In [9]:

iwd = IWD(
    known_cp = cp,
    unknown_cp = cpu,
    p = 2
)

iwd

iwd = IWD( known_cp = cp, unknown_cp = cpu, p = 2 ) iwd

Out[9]:

IWD(known_cp=CloudPoints(points=[Dot(x=0.0, y=0.0, z=None, crs=None, fields={'v': 1.0}), Dot(x=1.0, y=0.0, z=None, crs=None, fields={'v': 2.0}), Dot(x=2.0, y=0.0, z=None, crs=None, fields={'v': 4.0}), Dot(x=0.0, y=1.0, z=None, crs=None, fields={'v': 5.0}), Dot(x=0.0, y=2.0, z=None, crs=None, fields={'v': 6.0}), Dot(x=2.0, y=2.0, z=None, crs=None, fields={'v': 27.0})]), unknown_cp=CloudPoints(points=[Dot(x=1.0, y=1.0, z=None, crs=None, fields=None), Dot(x=2.0, y=1.0, z=None, crs=None, fields=None), Dot(x=1.0, y=2.0, z=None, crs=None, fields=None), Dot(x=2.0, y=2.0, z=None, crs=None, fields=None), Dot(x=4.0, y=4.0, z=None, crs=None, fields=None), Dot(x=-1.0, y=-1.0, z=None, crs=None, fields=None), Dot(x=8.0, y=8.0, z=None, crs=None, fields=None)]), p=2.0, max_distance=None)

In [10]:

wm = iwd.forward('v')
wm.df()

wm = iwd.forward('v') wm.df()

/Users/scuervo91/Documents/dev/volumetricspy/volumetricspy/stats/krigging.py:33: RuntimeWarning: divide by zero encountered in true_divide
  inverse_distance = 1 / np.power(kudm,p)

Out[10]:

	x	y	z	crs	v
0	1.0	1.0	None	None	6.500000
1	2.0	1.0	None	None	11.000000
2	1.0	2.0	None	None	11.746032
3	2.0	2.0	None	None	3.926829
4	4.0	4.0	None	None	12.449814
5	-1.0	-1.0	None	None	3.807692
6	8.0	8.0	None	None	9.170956

In [12]:

xn = np.linspace(0,2,20)
yn = np.linspace(0,2,20)

xx, yy = np.meshgrid(xn, yn)

dfun = pd.DataFrame({"x":xx.flatten(order='F'),"y":yy.flatten(order='F')})
cp2 = CloudPoints().from_df(dfun, x='x',y='y')

iwd = IWD(
    known_cp = cp,
    unknown_cp = cp2,
    p = 2
)

wm = iwd.forward('v')

wm.plot_mesh('v')
cp.plot(color='red')

xn = np.linspace(0,2,20) yn = np.linspace(0,2,20) xx, yy = np.meshgrid(xn, yn) dfun = pd.DataFrame({"x":xx.flatten(order='F'),"y":yy.flatten(order='F')}) cp2 = CloudPoints().from_df(dfun, x='x',y='y') iwd = IWD( known_cp = cp, unknown_cp = cp2, p = 2 ) wm = iwd.forward('v') wm.plot_mesh('v') cp.plot(color='red')

/Users/scuervo91/Documents/dev/volumetricspy/volumetricspy/stats/krigging.py:33: RuntimeWarning: divide by zero encountered in true_divide
  inverse_distance = 1 / np.power(kudm,p)
/Users/scuervo91/Documents/dev/volumetricspy/volumetricspy/stats/points.py:228: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.
  d = ax.pcolormesh(xx,yy,df.values, **kwargs)

Out[12]:

<AxesSubplot:xlabel='x', ylabel='y'>

In [13]:

wm.df()

wm.df()

Out[13]:

	x	y	z	crs	v
0	0.0	0.000000	None	None	4.904762
1	0.0	0.105263	None	None	1.124016
2	0.0	0.210526	None	None	1.514603
3	0.0	0.315789	None	None	2.140078
4	0.0	0.421053	None	None	2.900200
...	...	...	...	...	...
395	2.0	1.578947	None	None	22.614441
396	2.0	1.684211	None	None	24.470115
397	2.0	1.789474	None	None	25.874443
398	2.0	1.894737	None	None	26.725222
399	2.0	2.000000	None	None	3.926829

400 rows × 5 columns

In [14]:

#df = pd.read_csv('sample_data_biased.csv')     # load our data table
df2 = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv')
dcp2 = CloudPoints().from_df(df2, x='X',y='Y', fields=['Facies','Porosity','Perm'])
dcp2.df()

#df = pd.read_csv('sample_data_biased.csv') # load our data table df2 = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv') dcp2 = CloudPoints().from_df(df2, x='X',y='Y', fields=['Facies','Porosity','Perm']) dcp2.df()

Out[14]:

	x	y	z	crs	Facies	Porosity	Perm
0	100.0	900.0	None	None	1.0	0.115359	5.736104
1	100.0	800.0	None	None	1.0	0.136425	17.211462
2	100.0	600.0	None	None	1.0	0.135810	43.724752
3	100.0	500.0	None	None	0.0	0.094414	1.609942
4	100.0	100.0	None	None	0.0	0.113049	10.886001
...	...	...	...	...	...	...	...
284	190.0	199.0	None	None	1.0	0.109411	3.247265
285	190.0	419.0	None	None	0.0	0.099541	1.528003
286	920.0	379.0	None	None	0.0	0.094099	2.514597
287	60.0	149.0	None	None	0.0	0.100167	3.636854
288	390.0	549.0	None	None	1.0	0.153335	165.699353

289 rows × 7 columns

In [24]:

xn = np.linspace(0,1000,50)
yn = np.linspace(0,1000,50)

xx, yy = np.meshgrid(xn, yn)

dfun = pd.DataFrame({"x":xx.flatten(order='F'),"y":yy.flatten(order='F')})
cp2 = CloudPoints().from_df(dfun, x='x',y='y')
iwd2 = IWD(
    known_cp = dcp2,
    unknown_cp = cp2,
    p = 2
)
wm2 = iwd2.forward('Porosity')

df_un = wm2.df()
df_un

xn = np.linspace(0,1000,50) yn = np.linspace(0,1000,50) xx, yy = np.meshgrid(xn, yn) dfun = pd.DataFrame({"x":xx.flatten(order='F'),"y":yy.flatten(order='F')}) cp2 = CloudPoints().from_df(dfun, x='x',y='y') iwd2 = IWD( known_cp = dcp2, unknown_cp = cp2, p = 2 ) wm2 = iwd2.forward('Porosity') df_un = wm2.df() df_un

Out[24]:

	x	y	z	crs	Porosity
0	0.0	0.000000	None	None	0.106607
1	0.0	20.408163	None	None	0.103739
2	0.0	40.816327	None	None	0.102452
3	0.0	61.224490	None	None	0.103183
4	0.0	81.632653	None	None	0.102330
...	...	...	...	...	...
2495	1000.0	918.367347	None	None	0.108932
2496	1000.0	938.775510	None	None	0.098912
2497	1000.0	959.183673	None	None	0.090253
2498	1000.0	979.591837	None	None	0.084839
2499	1000.0	1000.000000	None	None	0.095016

2500 rows × 5 columns

In [25]:

fig, ax = plt.subplots(1,2,figsize=(20,10))
wm2.plot_mesh('Porosity',ax=ax[0])
dcp2.plot(ax=ax[1], hue='Porosity', palette='viridis')

fig, ax = plt.subplots(1,2,figsize=(20,10)) wm2.plot_mesh('Porosity',ax=ax[0]) dcp2.plot(ax=ax[1], hue='Porosity', palette='viridis')

/Users/scuervo91/Documents/dev/volumetricspy/volumetricspy/stats/points.py:228: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.
  d = ax.pcolormesh(xx,yy,df.values, **kwargs)
/Users/scuervo91/Documents/dev/volumetricspy/volumetricspy/stats/points.py:229: MatplotlibDeprecationWarning: Starting from Matplotlib 3.6, colorbar() will steal space from the mappable's axes, rather than from the current axes, to place the colorbar.  To silence this warning, explicitly pass the 'ax' argument to colorbar().
  plt.colorbar(d)

Out[25]:

<AxesSubplot:xlabel='x', ylabel='y'>

In [26]:

data=wm2.df()
data.describe()

data=wm2.df() data.describe()

Out[26]:

	x	y	Porosity
count	2500.000000	2500.000000	2500.000000
mean	500.000000	500.000000	0.128193
std	294.566464	294.566464	0.021081
min	0.000000	0.000000	0.061128
25%	244.897959	244.897959	0.115918
50%	500.000000	500.000000	0.124371
75%	755.102041	755.102041	0.133730
max	1000.000000	1000.000000	0.217630

In [27]:

sns.displot(data=data.reset_index(), x='Porosity', kde=False)

sns.displot(data=data.reset_index(), x='Porosity', kde=False)

Out[27]:

<seaborn.axisgrid.FacetGrid at 0x7f9d645e2b80>

In [28]:

sns.displot(data=dcp2.df().reset_index(), x='Porosity', kde=False)

sns.displot(data=dcp2.df().reset_index(), x='Porosity', kde=False)

Out[28]:

<seaborn.axisgrid.FacetGrid at 0x7f9d6481c310>

In [29]:

wm.df().columns

wm.df().columns

Out[29]:

Index(['x', 'y', 'z', 'crs', 'v'], dtype='object')