Wednesday, May 25, 2016

Using folium - 2: Use customized icon (Plot moment tensor)

In this script, we will try to use a customize icon for marker on the folium map. It is actulaly really simple to use a customized icon, you can either refer to a file on your local drive, or give it a url so that it will link the file. The following scripts are showing both, the first one is ploting iron man who visit Berkeley (link the file online), and the second one is plotting a moment tensor from a file we generated on our drive. You can find the script at Qingkai's Github.
In [1]:
import folium
In [2]:
url_file = 'images/6/67/Iron_Man.png/revision/latest?cb=20140720074813'
logo_url = url_base + url_folder + url_file
UCB_lat = 37.8719
UCB_lon = -122.2585

map_1 = folium.Map(location=[UCB_lat, UCB_lon], zoom_start=12,\
control_scale = True, tiles='Stamen Terrain')

icon = folium.features.CustomIcon(logo_url,\
icon_size=(200, 200))

folium.Marker([UCB_lat, UCB_lon],
popup='Iron man visit Berkeley',

map_1
Out[2]:

Plot moment tensor on the map¶

The following is how we can plot the moment tensors using obspy, and place it on the folium map using custom icon. Note that you need Obspy as the dependences. Let's plot the moment tensor for the 2014 south Napa earthquake here at Bay area, you can find details of this event at USGS. I won't show the maps here, but save it as a html file you can see it.
In [3]:
from obspy.imaging.beachball import beachball

# latitude and longitude of the earthquake
evla = 38.215
evlo = -122.312

# South Napa EQ moment tensor
mt = [247, 82, 8]
beachball(mt, size=200, linewidth=2, facecolor='b', outfile= './beachball.png')

# Add the USGS style map
url_base = 'http://server.arcgisonline.com/'
tiles = 'ArcGIS/rest/services/NatGeo_World_Map/MapServer/tile/{z}/{y}/{x}'
tileset = url_base + tiles
map_1 = folium.Map(location=[38, -122], zoom_start=8,\
control_scale = True, tiles=tileset, attr='USGS style')

# Add the Moment Tensor as an icon
icon_url = './beachball.png'
icon = folium.features.CustomIcon('/beachball.png',icon_size=(45, 45))

folium.Marker([evla, evlo],
popup='Napa Earthquake',

map_1.save('moment_tensors.html')

Friday, May 20, 2016

Using folium - 1: Use different map tiles (USGS style map)

Folium is an awesome tool that can make interactive maps built on Leaflet. I found it very easy to use. I will create a series of blog entries to show some of the plots that I usually use for my research.
Let's start with a simple map that change the tile to the style that USGS using for their earthquake page. I think the map tile looks good, here's an example from USGS. Also, there's a good website that you can checkout different map tiles that can be used in folium - Here. For the following code, you can download the notebook from Qingkai's Github
In [1]:
import folium
In [2]:
# Add the USGS style tile
url_base = 'http://server.arcgisonline.com/ArcGIS/rest/services/'
service = 'NatGeo_World_Map/MapServer/tile/{z}/{y}/{x}'
tileset = url_base + service

map_1 = folium.Map(location=[37.8716, -122.2727], zoom_start=10,\
control_scale = True, tiles=tileset, attr='USGS style')

folium.Marker([37.8716, -122.2727], popup = 'I am here'))
map_1
Out[2]:

Thursday, May 19, 2016

Today I want to add a progress bar on my python loops, after I searched online, I found a nice simple module - tqdm can do the job easily. All you need do is install it via 'pip install tqdm', and then you can use it. You can find the code below here.

(Gif grab from tqdm github)

Sunday, May 15, 2016

How to Write a Great Research Paper (great talk)

This is a great talk by Professor Simon Peyton Jones that I came across today! Even though it is aimed for Computer Science Conference papers, it is applicable to other fields as well! I will change my way to write paper!

Friday, May 6, 2016

Learning python basics

I have one friend ask me today for resources to learn python. The followings are the best ones I can think of, hope these will also useful to you.

Book:
My faverate book, and will let my students in the future to have as a reference is (if I become a faculty in the future ^)^):
This book not only covers a lot of things in python, but also a lot popular tools that used in science.

Another good book is: Automate boring stuff with python
This book is very practical, and you can automate a lot of boring stuff with python! Go python!

Webtutorial:
The notebook gallery has lots of interesting notebooks that you can play with, this is the best way to learn python in my opinion, learn from doing. You can start with the python tutorials.

Another great website that has a collection of good tutorials is: learning python

Videos:
Scipy conference has a lot of great tutorials online, you can checkout the following link, most of the tutoirals have a link to their repo on github, so you can do this following their video. Scipy 2015 videos

If you go through all these, I think you will be quite fluent in python!

Wednesday, May 4, 2016

Plotting historical moment tensors from GCMT directly

I created this script for our earthquake of this week (There are some other scripts for different purposes, I may add them sometime later, but you can find them at my Github). The purpose of this script is that, a lot of times, when we look at an earthquake occured recently, we want to see the historical moment tensors in this region. These historical moment tensors can tell us a lot of things, i.e. tectonic setting, fault information, etc. Then using this script, all you need do is to specify the starttime, endtime, and region (for region, right now, I just use one recent earthquake location as the center, and expand them both in latitude and longitude a certain degree).
For example, the 2016 M7.8 Ecuador, if we want to see the historical moment tensors in this region, we can do as the followings. You can find the script on Qingkai's Github.
In [1]:
import warnings
warnings.filterwarnings('ignore')
from obspy import UTCDateTime
import urllib
import urllib2
import numpy as np
from collections import OrderedDict
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from obspy.imaging.beachball import beach
from matplotlib.lines import Line2D
from BeautifulSoup import BeautifulSoup
%pylab inline
Populating the interactive namespace from numpy and matplotlib
In [3]:
def get_hist_mt(t1, t2, llat = '-90', ulat = '90', llon = '-180', ulon = '180',
lmw = 0, umw = 10, evla = None, evlo = None, step = 2.0, list_ = '6'):
'''
Function to query the GCMT and save the data for ploting. If evla and
evlo are provided, it will use this location as the center and adding
step to both latitude and longitude for the box.

t1 - starttime, example: UTCDateTime("1979-01-01")
t2 - endtime, example: UTCDateTime("2016-01-01")
llat - lower latitude
ulat - upper latitude
llon - left longitude
ulon - right longitude
lmw - the minimum magnitude to search
umw - the maximum magnitude to search
evla - latitude of the current earthquake
evlo - longitude of the current earthquake
step - from current earthquake location, expand lat/lon for a box
list_ - format of data you want to return from the GCMT
'''

yr = t1.year
mo = t1.month
day = t1.day
oyr = t2.year
omo = t2.month
oday = t2.day
mat = {}
locs = locals()

base_url = 'http://www.globalcmt.org/cgi-bin/globalcmt-cgi-bin/CMT4/form'

#note here, we must use the ordered Dictionary, so that the order in the
#url is exactly the same order
param = OrderedDict()
param['itype'] = 'ymd'
param['yr'] = yr
param['mo'] = mo
param['day'] = day
param['otype'] = 'ymd'
param['oyr'] = oyr
param['omo'] = omo
param['oday'] = oday
param['jyr'] = '1976'
param['jday'] = '1'
param['ojyr'] = '1976'
param['ojday'] = '1'

param['nday'] = '1'
param['lmw'] = str(lmw)
param['umw'] = str(umw)
param['lms'] = '0'
param['ums'] = '10'
param['lmb'] = '0'
param['umb'] = '10'

# now specify the region box
if evla and evlo is not None:
llat = evla - step
ulat = evla + step
llon = evlo - step
ulon = evlo + step

# save parameter for query
param['llat'] = llat
param['ulat'] = ulat
param['llon'] = llon
param['ulon'] = ulon

param['lhd'] = '0'
param['uhd'] = '1000'
param['lts'] = '-9999'
param['uts'] = '9999'
param['lpe1'] = '0'
param['upe1'] = '90'
param['lpe2'] = '0'
param['upe2'] = '90'
param['list'] = list_

# build the URL
url = "?".join((base_url, urllib.urlencode(param)))
print url

# grab data and parse it
page = urllib2.urlopen(url)
parsed_html = BeautifulSoup(page)
mecs_str = parsed_html.findAll('pre')[1].text.split('\n')

# string to array
def convertString(mecs_str):
return map(float, mecs_str.split()[:9])

psmeca = np.array(map(convertString, mecs_str))

# save the results for plotting
mat['psmeca'] = psmeca
mat['url'] = url
mat['range'] = (llat, ulat, llon, ulon)
mat['evloc'] = (evla, evlo)
return mat

def plot_hist_mt(psmeca_dict, figsize = (16,24), mt_size = 10, \
pretty = False, resolution='l'):
'''
Plot the historical moment tensor from the query of GCMT

psmeca_dict - dictionary that returned from get_hist_mt function
figsize - tuple of the size of the figure
mt_size - size of the moment tensor on the plot
pretty - boolean, whether want to plot nice maps
resolution - low or high as you want
'''

if psmeca_dict['psmeca'] != []:
psmeca = psmeca_dict['psmeca']
#get the latitudes, longitudes, and the 6 independent component
lats = psmeca[:,1]
lons = psmeca[:,0]
focmecs = psmeca[:,3:9]
depths =  psmeca[:,2]
(llat, ulat, llon, ulon) = psmeca_dict['range']
evla = psmeca_dict['evloc'][0]
evlo = psmeca_dict['evloc'][1]

plt.figure(figsize=figsize)
m = Basemap(projection='cyl', lon_0=142.36929, lat_0=38.3215,
llcrnrlon=llon,llcrnrlat=llat,urcrnrlon=ulon,urcrnrlat=ulat,\
resolution=resolution)

m.drawcoastlines()
m.drawmapboundary()

if pretty:
m.etopo()
else:
m.fillcontinents()

llat = float(llat)
ulat = float(ulat)
llon = float(llon)
ulon = float(ulon)

m.drawparallels(np.arange(llat, ulat, (ulat - llat) / 4.0), \
labels=[1,0,0,0])
m.drawmeridians(np.arange(llon, ulon, (ulon - llon) / 4.0), \
labels=[0,0,0,1])

ax = plt.gca()
x, y = m(lons, lats)

for i in range(len(focmecs)):

if depths[i] <= 50:
color = '#FFA500'
#label_
elif depths[i] > 50 and depths [i] <= 100:
color = 'g'
elif depths[i] > 100 and depths [i] <= 200:
color = 'b'
else:
color = 'r'

index = np.where(focmecs[i] == 0)[0]

#note here, if the mrr is zero, then you will have an error
#so, change this to a very small number
if focmecs[i][0] == 0:
focmecs[i][0] = 0.001

try:
b = beach(focmecs[i], xy=(x[i], y[i]),width=mt_size, \
linewidth=1, facecolor=color)
except:
pass

b.set_zorder(10)

x_0, y_0 = m(evlo, evla)
m.plot(x_0, y_0, 'r*', markersize=25, zorder = 10)

circ1 = Line2D([0], [0], linestyle="none", \
marker="o", alpha=0.6, markersize=10, markerfacecolor="#FFA500")
circ2 = Line2D([0], [0], linestyle="none", \
marker="o", alpha=0.6, markersize=10, markerfacecolor="g")
circ3 = Line2D([0], [0], linestyle="none", \
marker="o", alpha=0.6, markersize=10, markerfacecolor="b")
circ4 = Line2D([0], [0], linestyle="none", \
marker="o", alpha=0.6, markersize=10, markerfacecolor="r")
plt.legend((circ1, circ2, circ3, circ4), \
("depth $\leq$ 50 km", "50 km $<$ depth $\leq$ 100 km", \
"100 km $<$ depth $\leq$ 200 km", "200 km $<$ depth"), \
numpoints=1, loc=3)
plt.show()
else:
print 'No historical MT found!'
In [6]:
t1 = UTCDateTime("1979-01-01")
t2 = UTCDateTime("2016-01-01")

psmeca = get_hist_mt(t1, t2, evla = 0.371, evlo= -79.940, step = 8, lmw = 0)
plot_hist_mt(psmeca, figsize = (12, 10),  mt_size = 0.4, pretty = True, resolution='l')