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

# In[5]:


get_ipython().run_line_magic('matplotlib', 'inline')
from fatiando.gravmag import transform
from fatiando import gridder
from fatiando.vis import mpl
import numpy as np


# In[6]:


data = np.loadtxt('continued.txt')
X = np.loadtxt('X.txt')
Y = np.loadtxt('Y.txt')


# In[7]:


shape = data.shape


# In[8]:


# convert data, X, and Y arrays to 1D
d = data.ravel()
# x, y are switched because x in Fatiando is North-South. 
# So when plotting you pass contourf(y, x, ...)
y = X.ravel()
x = Y.ravel()

# calculate derivatives using Fatiando a Terra and reshape output arrays back
# to 2D
xderiv = transform.derivx(x, y, d, data.shape)
yderiv = transform.derivy(x, y, d, data.shape)
zderiv = transform.derivz(x, y, d, data.shape)
tga = transform.tga(x, y, data, data.shape)


# In[11]:


mpl.figure(figsize=(15, 4))
ax = mpl.subplot(141)
mpl.axis('scaled')
mpl.title('dz')
mpl.pcolor(y, x, zderiv, shape, cmap='cubehelix')
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
ax = mpl.subplot(142)
mpl.axis('scaled')
mpl.title('dy')
mpl.pcolor(y, x, yderiv, shape, cmap='cubehelix')
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
ax = mpl.subplot(143)
mpl.axis('scaled')
mpl.title('dx')
mpl.pcolor(y, x, xderiv, shape, cmap='cubehelix')
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
ax = mpl.subplot(144)
mpl.axis('scaled')
mpl.title('Total gradient amplitude')
mpl.pcolor(y, x, tga, shape, cmap='cubehelix')
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
mpl.tight_layout()