def plot_heat_all(self, array_path, dep_lim = None, map_background = True):
"""
receives the path to a serialized array object
optionally receives a depth limit and a map background boolean, to
indicate wheter we use the map as background or the coast as background
in order to study the mainshocks plot heat map for all regions
"""
# obtain array of shocks within the depth limit
all_quakes = pickle.load(open(array_path, 'rb'))
useful_quakes = []
for index in range(len(all_quakes)):
current_quake = all_quakes[index]
if dep_lim != None and current_quake.depth > dep_lim:
continue
useful_quakes.append(current_quake)
# get instance of classes that we'll need methods
draw = Draw()
cat = Catalog()
# get dictionary that for every region name gives bound and zoom for all regions
regions = {}
regions["japan"] = [cat.get_catalog_bounds('../catalogs/new_jma.txt'), 5, 100]
regions["kanto"] = [cat.get_kanto_bounds(), 9, 25]
regions["kansai"] = [cat.get_kansai_bounds(), 9, 25]
regions["tohoku"] = [cat.get_tohoku_bounds(), 9, 25]
regions["east_japan"] = [cat.get_east_japan_bounds(), 9, 25]
# index to add legibility to the code
BOUNDS = 0
ZOOM = 1
BINS = 2
# set time bound on which to plot and which years to plot
year_bound = [[0.0, 12*366*24.0*3600]] # this way all years are considered
# iterate through the years
for time in year_bound:
# iterate through the regions
for region, info in regions.items():
# if we are using a map as background
if map_background:
# plot image for the region
draw.plot_image_quakes(useful_quakes, time, info[BOUNDS], info[ZOOM])
call(["pdftoppm", "-rx", "300", "-ry", "300", "-png", "Rplots.pdf", region + "_back_heat"])
call(["mv", region + "_back_heat-1.png", "../images/"])
call(["rm", "Rplots.pdf"])
input("Edit the image and then press Enter")
# plot heat map for the region in the given year
self.plot_heat(useful_quakes, time, info[BOUNDS], info[BINS], "../images/" + region + "_back_heat-1.png",\
only_main = False)
# save it in the correct format, and do cleaning
call(["mv", "temp.png", "../images/" + region + "_heat.png"])
else:
# plot image for the current region
draw.plot_quakes_coast(useful_quakes, time, info[BOUNDS], dep_lim = dep_lim)
call(["mv", "temp.png", "../images/" + region + "_back_heat_coast-1.png"])
input("Edit the image and then press Enter")
# plot heat map for the region in the given year
self.plot_heat(useful_quakes, time, info[BOUNDS], info[BINS], "../images/" + region + "_back_heat_coast-1.png",\
only_main = False)
# save it in the correct format, and do cleaning
call(["mv", "temp.png", "../images/" + region + "_heat_coast.png"])
def plot_clusters_all(self, array_path, dep_lim = None, map_background = True):
"""
receives a path to the array of quakes.
optionally it receives a depth limit and a map_background boolean
if map_background = True, then the cluster is plot with a background map
if map_background = False, then the cluster is plot with the coast of japan
as background
in order to plot study the mainshocks and it's associated clusters,
plot some clusters on the map
"""
# obtain array of shocks within the depth limit
all_quakes = pickle.load(open(array_path, 'rb'))
useful_quakes = []
for index in range(len(all_quakes)):
current_quake = all_quakes[index]
if dep_lim != None and current_quake.depth > dep_lim:
continue
useful_quakes.append(current_quake)
# get instance of classes so we can access methods
draw = Draw()
cat = Catalog()
# get dictionary that for every region name gives bound and zoom for all regions
regions = {}
regions["japan"] = [cat.get_catalog_bounds('../catalogs/new_jma.txt'), 5]
regions["kanto"] = [cat.get_kanto_bounds(), 9]
regions["kansai"] = [cat.get_kansai_bounds(), 9]
regions["tohoku"] = [cat.get_tohoku_bounds(), 9]
regions["east_japan"] = [cat.get_east_japan_bounds(), 9]
# index to add legibility to the code
BOUNDS = 0
ZOOM = 1
# set list of how many clusters to plot, and which years to plot
num_clusters = [10]
year_bound = [[0.0, 12*366*24.0*3600]] # this way we consider all years
# iterate through the list containing how many clusters we plot
for num in num_clusters:
# iterate through the years
for time in year_bound:
# iterate through the regions
for region, info in regions.items():
# if we need to plot a map in the background
if map_background:
# plot image for the current region
draw.plot_image_quakes(useful_quakes, time, info[BOUNDS], info[ZOOM], num_clusters = num, dep_lim = dep_lim)
call(["pdftoppm", "-rx", "300", "-ry", "300", "-png", "Rplots.pdf", region + "_back"])
call(["mv", region + "_back-1.png", "../images/"])
call(["rm", "Rplots.pdf"])
input("Edit the image and then press Enter")
# plot cluster for current region in the given year, for the current number of clusters
self.plot_clusters(useful_quakes, num, time, info[BOUNDS], "../images/" + region +"_back-1.png")
## save it in the correct format, and do cleaning
call(["mv", "temp.png", "../images/" + region + "_clusters_map.png"])
# if we dont want to plot a japan map in the background
else:
# plot image for the current region
draw.plot_quakes_coast(useful_quakes, time, info[BOUNDS], num_clusters = num, dep_lim = dep_lim)
call(["mv", "temp.png", "../images/" + region + "_back_coast-1.png"])
input("Edit the image and then press Enter")
# plot cluster for current region in the given year, for the current number of clusters
self.plot_clusters(useful_quakes, num, time, info[BOUNDS], "../images/" + region +"_back_coast-1.png")
## save it in the correct format, and do cleaning
call(["mv", "temp.png", "../images/" + region + "_clusters_coast.png"])
