In [1]:

    

import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt

import gpstools as gps

import warnings; warnings.simplefilter('ignore')


    

    




/Users/scott/Documents/GitHub/gpstools/gpstools/data/ungl

In [5]:

    

isinstance(gps.__version__, str)


    

    
Out[5]:





True

In [2]:

    

plt.style.use('seaborn-white')
#Non-interactive figures:
%matplotlib inline
# Interactive!
#%matplotlib notebook
#%qtconsole


    

In [3]:

    

# We'll use station TPW2
station = 'TPW2'
ref1 = 'ITRF2008'
# Reference frame is ITRF2008
# Download data
gps.io.panga.download_data(station,overwrite=True)
# Load the timeseries as pandas dataframe
df1 = gps.io.panga.load_panga(station)
title1 = 'UNGL:  {} ({}) [mm]'.format(station, ref1)
axes = df1.plot(y=['east','north','up'], style='.' ,sharex=True, subplots=True, title=title1)


    

    




Downloading http://www.geodesy.org/panga/timeseries_data.php/?n=panga&s=TPW2&p=raw&f=daily&c=lon ...
Downloading http://www.geodesy.org/panga/timeseries_data.php/?n=panga&s=TPW2&p=raw&f=daily&c=lat ...
Downloading http://www.geodesy.org/panga/timeseries_data.php/?n=panga&s=TPW2&p=raw&f=daily&c=rad ...






    

In [4]:

    

# Load UNGL
ref2= 'NA12'
# Download data
tenv3 = gps.io.ungl.download_data(station, ref2, overwrite=True)
df2 = gps.io.ungl.load_tenv3(tenv3)
title2 = 'PANGA:  {} ({}) [mm]'.format(station, ref2)
axes = df2.plot(y=['east','north','up'], style='.' ,sharex=True, subplots=True, title=title2)


    

    




Overwriting TPW2
Downloading http://geodesy.unr.edu/gps_timeseries/tenv3//NA12/TPW2.NA12.tenv3 ...






    

In [5]:

    

# Subtract mean from component to compare on same plot
#comp = 'east'
#comp = 'north'
comp = 'up'

# crop to target date range
tmp1 = df1['2010/01/01':'2017/06/01']#specific time range
tmp2 = df2['2010/01/01':'2017/06/01']

# subtract mean
pangaData = tmp1[comp] - tmp1[comp].mean()
unglData = tmp2[comp] - tmp2[comp].mean()

df = pd.DataFrame(dict(panga=pangaData)) 
df['ungl'] = unglData    
axes = df.plot(subplots=True, style='.', figsize=(11,6))
plt.subplots_adjust(hspace=0, wspace=0)
for ax in axes:
    ax.grid()
    #ax.legend(loc='upper left', frameon=True, fontsize='large')
    ax.legend(loc='upper left', frameon=True, prop=dict(size=16, weight='bold'))
    
axes[0].set_title('PANGA vs. UNGL {} ({}) [mm]'.format(station,comp))
#print(df.describe())


    

    
Out[5]:





<matplotlib.text.Text at 0x1175ee2b0>






    

In [6]:

    

# Subtract mean from component to compare on same plot
comp = 'east'
#comp = 'north'
#comp = 'up'

# crop to target date range
tmp1 = df1['2010/01/01':'2017/06/01']#specific time range
tmp2 = df2['2010/01/01':'2017/06/01']

# subtract mean
#pangaData = df1[comp]-df1[comp].mean()
#unglData = df2[comp]-df2[comp].mean()

# linear detrend 
# Ensure no nans in time series
tmp1 = tmp1.dropna()
tmp1 = gps.analysis.detrend(tmp1, comp)
pangaData = tmp1['detrend']
tmp2 = gps.analysis.detrend(tmp2, comp)
unglData = tmp2['detrend']

df = pd.DataFrame(dict(panga=pangaData)) 
df['ungl'] = unglData

axes = df.plot(subplots=True, style='.', figsize=(11,6))
plt.subplots_adjust(hspace=0, wspace=0)
for ax in axes:
    ax.grid()
    ax.legend(loc='upper left', frameon=True, prop=dict(size=16, weight='bold'))
    
axes[0].set_title('PANGA vs. UNGL {} ({}) [mm]'.format(station,comp))
#print(df.describe())


    

    




Rate [mm/yr]=9.098591855851716
Rate [mm/yr]=8.600864154864505






    
Out[6]:





<matplotlib.text.Text at 0x114c2be80>






    

In [7]:

    

# Subtract mean from component to compare on same plot
#comp = 'east'
comp = 'north'
#comp = 'up'

# crop to target date range
tmp1 = df1['2010/01/01':'2017/06/01']#specific time range
tmp2 = df2['2010/01/01':'2017/06/01']

# subtract mean
#pangaData = df1[comp]-df1[comp].mean()
#unglData = df2[comp]-df2[comp].mean()

# linear detrend 
# Ensure no nans in time series
tmp1 = tmp1.dropna()
tmp1 = gps.analysis.detrend(tmp1, comp)
pangaData = tmp1['detrend']
tmp2 = gps.analysis.detrend(tmp2, comp)
unglData = tmp2['detrend']

df = pd.DataFrame(dict(panga=pangaData)) 
df['ungl'] = unglData

axes = df.plot(subplots=True, style='.', figsize=(11,6))
plt.subplots_adjust(hspace=0, wspace=0)
for ax in axes:
    ax.grid()
    ax.legend(loc='upper left', frameon=True, prop=dict(size=16, weight='bold'))
    
axes[0].set_title('PANGA vs. UNGL {} ({}) [mm]'.format(station,comp))
#print(df.describe())


    

    




Rate [mm/yr]=7.396542796555013
Rate [mm/yr]=6.671898691051421






    
Out[7]:





<matplotlib.text.Text at 0x114b3dda0>