def plot_bomb_frequency_typed(stormObj):
print("plot_bomb_frequency_typed()")
fig = plt.figure(figsize=(12, 9))
yrs = np.arange(1979, 2020, 1)
totals_year = np.zeros((len(yrs)))
for className in typeStr.keys():
count_list_ind = np.zeros((len(yrs)))
for y in yrs:
indCount = 0
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['classification'] == className):
if (stormObj[ed]['month'][0] in [1, 2, 3, 4, 10, 11, 12]
and stormObj[ed]['year'][0] == y):
#Calculate the pressure falls over the storm's lifetime
bergeron = storm.calculate_bergeron(stormObj[ed])
if (any(b >= 1 for b in bergeron)):
indCount += 1
count_list_ind[y - 1979] = indCount
#Draw the plot
plt.bar(yrs,
count_list_ind,
bottom=totals_year,
color=zoneColors[className])
totals_year += count_list_ind
plt.ylim(0, 60)
plt.xlabel("Year")
plt.ylabel("Number of Cyclones")
plt.legend(typeStr.values(), loc=0, framealpha=1.0)
plt.title("Number of Bomb Cyclones by Cyclone Type")
plt.savefig('figures/bomb_frequency_typed',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
print("plot_bomb_frequency_typed(): Done")
def plot_bomb_frequency(stormObj):
print("plot_bomb_frequency()")
fig = plt.figure(figsize=(12, 9))
yrs = np.arange(1979, 2020, 1)
counts = []
for y in yrs:
indCount = 0
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'):
if (stormObj[ed]['month'][0] in [1, 2, 3, 4, 10, 11, 12]
and stormObj[ed]['year'][0] == y):
#Calculate the pressure falls over the storm's lifetime
bergeron = storm.calculate_bergeron(stormObj[ed])
if (any(b >= 1 for b in bergeron)):
indCount += 1
counts.append(indCount)
#Draw the plot
plt.bar(yrs, counts)
plt.xlabel("Year")
plt.ylabel("Number of Cyclones")
plt.title("Number of Bomb Cyclones")
plt.savefig('figures/bomb_frequency',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
print("plot_bomb_frequency(): Done.")
def plot_cyclone_lifespan(stormObj, bombOnly=False):
print("plot_cyclone_lifespan(bombOnly = " + str(bombOnly) + ")")
fig = plt.figure(figsize=(12, 9))
months = [1, 2, 3, 4, 10, 11, 12]
years = np.arange(1979, 2020, 1)
dt = 3
# Find the longest cyclone.
longest = 0
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['month'][0] in months
and stormObj[ed]['year'][0] in years):
count = len(stormObj[ed]['amp'])
if (count > longest):
longest = count
lifespan_array = np.zeros((longest))
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['month'][0] in months
and stormObj[ed]['year'][0] in years):
bergeron = storm.calculate_bergeron(stormObj[ed])
if (bombOnly == True):
if (any(b >= 1 for b in bergeron)):
lifespan = len(stormObj[ed]['amp'])
lifespan_array[lifespan - 1] += 1
else:
lifespan = len(stormObj[ed]['amp'])
lifespan_array[lifespan - 1] += 1
idx_of_last_non_zero = np.max(np.nonzero(lifespan_array))
splitArray = lifespan_array[0:idx_of_last_non_zero + 1]
times = np.arange(0, (len(splitArray) * 3), 3)
plt.bar(times, splitArray)
plt.xlabel("Cyclone Lifespan (Hours)")
plt.ylabel("Count")
if (bombOnly == True):
plt.title("Lifespan of Cyclones (Bomb Cyclones Only)")
plt.savefig('figures/lifespan_bombs',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
else:
plt.yscale('log')
plt.title("Lifespan of Cyclones")
plt.savefig('figures/lifespan',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
print("plot_cyclone_lifespan(): Done")
def plot_strength_bars_bombs(stormObj):
print("plot_strength_bars_bombs()")
fig = plt.figure(figsize=(12, 9))
months = [1, 2, 3, 4, 10, 11, 12]
years = np.arange(1979, 2020, 1)
dt = 3
# Find the longest cyclone.
longest = 0
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['month'][0] in months
and stormObj[ed]['year'][0] in years):
count = len(stormObj[ed]['amp'])
if (count > longest):
longest = count
totals_column = np.zeros((longest))
for className in typeStr.keys():
fall_array = np.zeros((longest))
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['month'][0] in months
and stormObj[ed]['year'][0] in years
and stormObj[ed]['classification'] == className):
bergeron = storm.calculate_bergeron(stormObj[ed])
if (any(b >= 1 for b in bergeron)):
dPressure = np.diff(stormObj[ed]['amp'])
largestFall = np.nanmin(dPressure)
index_of_largest_fall = np.argmin(dPressure)
time_of_fall = index_of_largest_fall * dt
fall_array[index_of_largest_fall] += 1
times = np.arange(0, (len(fall_array) * 3), 3)
plt.bar(times + 1,
fall_array,
bottom=totals_column,
width=2,
color=zoneColors[className])
totals_column += fall_array
idx_of_last_non_zero = np.max(np.nonzero(fall_array))
plt.xlim(0, idx_of_last_non_zero * 3)
plt.legend(typeStr.values(), loc=0, framealpha=1.0)
plt.xlabel("Time after cyclogenesis (hours)")
plt.ylabel("Count")
plt.title("Time of Maximum 3-Hour Pressure Fall (Bomb Cyclones Only)")
plt.savefig('figures/pressure_fall_bombs_occurance',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
print("plot_strength_bars_bombs(): Done")
def plot_mean_pressure_bombs(stormObj):
print("plot_mean_pressure_bombs()")
fig = plt.figure(figsize=(12, 9))
months = [1, 2, 3, 4, 10, 11, 12]
years = np.arange(1979, 2020, 1)
cyclGroups = [
"Clipper", "Colorado", "GreatBasin", "GulfOfMexico", "EastCoast"
]
for year in years:
for type in cyclGroups:
meanPressure = 0
count = 0
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['classification'] == type
and stormObj[ed]['month'][0] in months
and stormObj[ed]['year'][0] == year):
bergeron = storm.calculate_bergeron(stormObj[ed])
if (any(b >= 1 for b in bergeron)):
#lat = stormObj[ed]['lat'][0]
lowestP = np.nanmin(stormObj[ed]['amp'])
meanPressure += lowestP
count += 1
if (count != 0):
meanPressure = (meanPressure / count) / 100
plt.plot(year, meanPressure, '-o', color=zoneColors[type])
# Add "Fake" Lines for Legend
for type in cyclGroups:
plt.plot(0,
0,
linestyle='-',
alpha=1,
color=zoneColors[type],
label=typeStr[type])
plt.xlim(1979, 2020)
plt.ylim(900, 1000)
plt.xlabel("Year")
plt.ylabel("Pressure")
plt.legend(framealpha=1.0)
plt.title('Cyclone Mean Lowest Pressure (Bomb Cyclones Only)')
plt.savefig('figures/mean_lowest_pressure_bomb',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
print("plot_mean_pressure_bombs(): Done")
def plot_bomb_tracks(stormObj):
print("plot_bomb_tracks()")
projObj = get_projection_object()
# Create our figure and axis object
fig = plt.figure(figsize=(12, 9))
ax = plt.axes(projection=projObj)
#ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(plotExtent, crs=ccrs.PlateCarree())
# Draw our plot, coastlines first, then the contours.
states = cartopy.feature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lakes',
scale='110m',
facecolor='none')
ax.add_feature(states, edgecolor='k')
ax.coastlines()
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'):
if (stormObj[ed]['month'][0] in [1, 2, 3, 4, 10, 11, 12]):
#Calculate the pressure falls over the storm's lifetime
bergeron = storm.calculate_bergeron(stormObj[ed])
if (any(b >= 1 for b in bergeron)):
lon, lat = stormObj[ed]['lon'], stormObj[ed]['lat']
lon[lon < 0] = 360 + lon[lon < 0]
plt.plot(lon,
lat,
'r-',
linewidth=0.5,
alpha=0.35,
transform=ccrs.PlateCarree())
# Show the plot.
plt.title('Storm Tracks')
plt.savefig('figures/storm_bomb_tracks',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
print("plot_bomb_tracks(): Done")
def plot_mean_track(stormObj, bombOnly=False):
print("plot_mean_track(bombOnly = " + str(bombOnly) + ")")
projObj = get_projection_object()
# Create our figure and axis object
fig = plt.figure(figsize=(12, 9))
ax = plt.axes(projection=projObj)
#ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(plotExtent, crs=ccrs.PlateCarree())
# Draw our plot, coastlines first, then the contours.
states = cartopy.feature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lakes',
scale='110m',
facecolor='none')
ax.add_feature(states, edgecolor='k')
ax.coastlines()
# Process Variables
monthList = [1, 2, 3, 4, 10, 11, 12]
yearList = np.arange(1979, 2020, 1)
cycloneList = [
'Clipper', 'Northwest', 'Colorado', 'GreatBasin', 'GulfOfMexico',
'EastCoast'
]
for classification in cycloneList:
# Find the longest cyclone.
longest = 0
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['month'][0] in monthList
and stormObj[ed]['year'][0] in yearList
and stormObj[ed]['classification'] == classification):
count = len(stormObj[ed]['amp'])
if (count > longest):
longest = count
# Test classification
meanLat = np.zeros((longest))
meanLon = np.zeros((longest))
count = np.zeros((longest))
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'):
# Test months
if (storms[ed]['month'][0] in monthList):
# Test years
if (storms[ed]['year'][0] in yearList):
if (storms[ed]['classification'] == classification):
bergeron = storm.calculate_bergeron(stormObj[ed])
if (bombOnly == True):
if (any(b >= 1 for b in bergeron)):
for i in range(len(stormObj[ed]['lat'])):
meanLat[i] += stormObj[ed]['lat'][i]
meanLon[i] += stormObj[ed]['lon'][i]
count[i] += 1
else:
for i in range(len(stormObj[ed]['lat'])):
meanLat[i] += stormObj[ed]['lat'][i]
meanLon[i] += stormObj[ed]['lon'][i]
count[i] += 1
meanLat[:] = meanLat[:] / count[:]
meanLon[:] = meanLon[:] / count[:]
print(classification + ": stdLat: %.3f" % np.std(meanLat))
#print(meanLat)
#print(meanLon)
#print(count)
# Draw the lines.
plt.plot(meanLon,
meanLat,
linestyle='-',
color=zoneColors[classification],
alpha=1,
markeredgewidth=0,
transform=ccrs.PlateCarree())
legendVals = list(typeStr.values())
legendVals.remove("Other Lows")
plt.legend(legendVals, loc=0, framealpha=1.0)
if (bombOnly == True):
plt.title("Mean Bomb Cyclone Track (" + str(yearList[0]) + " - " +
str(yearList[-1]) + ")")
plt.savefig('figures/mean_track_bomb',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
else:
plt.title("Mean Cyclone Track (" + str(yearList[0]) + " - " +
str(yearList[-1]) + ")")
plt.savefig('figures/mean_track',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
print("plot_mean_track(): Done.")
def plot_lifespan_panel(stormObj):
print("plot_lifespan_panel()")
fig, axs = plt.subplots(1, 2)
ax1 = axs[0]
ax2 = axs[1]
months = [1, 2, 3, 4, 10, 11, 12]
years = np.arange(1979, 2020, 1)
dt = 3
longest = 0
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['month'][0] in months
and stormObj[ed]['year'][0] in years):
count = len(stormObj[ed]['amp'])
if (count > longest):
longest = count
# Panel A
lifespan_array1 = np.zeros((longest))
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['month'][0] in months
and stormObj[ed]['year'][0] in years):
lifespan = len(stormObj[ed]['amp'])
lifespan_array1[lifespan - 1] += 1
idx_of_last_non_zero = np.max(np.nonzero(lifespan_array1))
splitArray1 = lifespan_array1[0:idx_of_last_non_zero + 1]
times1 = np.arange(0, (len(splitArray1) * 3), 3)
#plt.bar(times, splitArray)
ax1.scatter(times1, splitArray1, s=2)
ax1.set(xlabel='Cyclone Lifespan (Hours)', ylabel='Count')
#ax1.
# Panel B
lifespan_array2 = np.zeros((longest))
for ed in range(len(stormObj)):
if (stormObj[ed]['type'] == 'cyclonic'
and stormObj[ed]['month'][0] in months
and stormObj[ed]['year'][0] in years):
bergeron = storm.calculate_bergeron(stormObj[ed])
if (any(b >= 1 for b in bergeron)):
lifespan = len(stormObj[ed]['amp'])
lifespan_array2[lifespan - 1] += 1
idx_of_last_non_zero = np.max(np.nonzero(lifespan_array2))
splitArray2 = lifespan_array2[0:idx_of_last_non_zero + 1]
times2 = np.arange(0, (len(splitArray2) * 3), 3)
ax2.bar(times2, splitArray2)
#ax1.scatter(times2, splitArray2)
ax2.set(xlabel='Cyclone Lifespan (Hours)', ylabel='Count')
plt.suptitle('Lifespan of Cyclones')
plt.savefig('figures/lifespan_panel',
bbox_inches='tight',
pad_inches=0.05,
dpi=300)
print("plot_lifespan_panel(): Done")