import seaborn as sns
import gapflow
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from datetime import timedelta
from rv_utilities import discrete_cmap
from matplotlib import rcParams
rcParams['xtick.labelsize'] = 15
rcParams['ytick.labelsize'] = 15
rcParams['axes.labelsize'] = 15
rcParams['mathtext.default'] = 'sf'
cmap = discrete_cmap(7, base_cmap='Set1')
color1 = cmap(0)
color2 = cmap(1)
# import os
# homed = os.path.expanduser('~')
homed = '/Users/raulvalenzuela/Data'
def get_subdates(gf, tta):
bool_list = list()
"mesowest obs have 55 minutes offset "
offset = timedelta(minutes=55)
gauge_res = 0.254
nbins = 100
H, xed, yed = np.histogram2d(xlist,
ylist,
bins=np.arange(-gauge_res / 2.,
gauge_res * 0.5 * nbins * 2,
gauge_res),
normed=False)
#Hm = np.ma.masked_where(H<=2,H)
fig, ax = plt.subplots(figsize=(10, 8))
im = ax.pcolormesh(xed, yed, H, norm=LogNorm(), cmap='inferno', vmax=1e3)
''' 1:3 line '''
cmap = discrete_cmap(7, base_cmap='Set3')
color = cmap(0)
ax.plot([-gauge_res / 2., 12], [4, -gauge_res / 2.],
color=color,
lw=3,
zorder=1000)
ax.text(6, 2, '1:3', rotation=-22, color=color, weight='bold', fontsize=18)
ax.set_xlabel('rain czd $[mm\,h^{-1}]$')
ax.set_ylabel('rain bby $[mm\,h^{-1}]$')
axlim = 15
ax.set_xlim([-gauge_res / 2., axlim + gauge_res])
ax.set_ylim([-gauge_res / 2., axlim + gauge_res])
plt.colorbar(im, label='count')
plt.grid()
def plot_colored_staff(ax=None,
wspd=None,
wdir=None,
time=None,
height=None,
cmap=None,
spd_range=None,
spd_delta=None,
vdensity=None,
hdensity=None,
title=None,
cbar=True):
'''
NOAA wind profiler files after year 2000 indicate
the start time of averaging period; so a timestamp of
13 UTC indicates average between 13 and 14 UTC
There is a bug that plots wrong staff when using
meridional winds only
'''
from matplotlib import rcParams
rcParams['mathtext.default'] = 'sf'
spd_array = wspd
time_array = time
height_array = height
''' make a color map of fixed colors '''
cmap = discrete_cmap(24, base_cmap=cmap, norm_range=[0.1, 0.9])
if len(spd_range) == 2:
bounds = range(spd_range[0], spd_range[1] + spd_delta, spd_delta)
# vmin = spd_range[0]
# vmax = spd_range[1]
else:
bounds = spd_range
# vmin = spd_range[0]
# vmax = spd_range[-1]
norm = colors.BoundaryNorm(bounds, cmap.N)
nrows, ncols = spd_array.shape
''' derive U and V components '''
U = -wspd * np.sin(wdir * np.pi / 180.)
V = -wspd * np.cos(wdir * np.pi / 180.)
x = np.array(range(len(time))) + 0.5 # wind staff in the middle of pixel
# wind staff in the middle of pixel
y = np.array(range(height_array.size)) + 0.5
X = np.tile(x, (y.size, 1)) # repeats x y.size times to make 2D array
Y = np.tile(y, (x.size, 1)).T # repeates y x.size times to make 2D array
Uzero = U - U
Vzero = V - V
''' change arrays density '''
if vdensity == 0 or hdensity == 0:
pass
else:
U = fill2D_with_nans(inarray=U,
start=[3, 0],
size=[vdensity, hdensity])
V = fill2D_with_nans(inarray=V,
start=[3, 0],
size=[vdensity, hdensity])
ax.barbs(X,
Y,
U,
V,
np.sqrt(U * U + V * V),
sizes={'height': 0},
length=5,
linewidth=0.5,
barb_increments={'half': 1},
cmap=cmap,
norm=norm)
barb = ax.barbs(X,
Y,
Uzero,
Vzero,
np.sqrt(U * U + V * V),
sizes={'emptybarb': 0.05},
fill_empty=True,
cmap=cmap,
norm=norm)
if isinstance(cbar, maxes._subplots.Axes):
hcbar = add_colorbar(cbar,
barb,
loc='right',
label='[m s-1]',
labelpad=20,
fontsize=12)
elif isinstance(cbar, bool) and cbar:
hcbar = add_colorbar(ax, barb, loc='right')
else:
hcbar = None
ax.set_xlim([-3.0, len(time_array) + 3.0])
format_xaxis(ax, time_array, delta_hours=1)
ax.invert_xaxis()
ax.set_xlabel(r'$\leftarrow UTC \left[\stackrel{day}{time}\right]$',
fontsize=12)
format_yaxis2(ax, height_array)
ax.set_ylabel('Altitude MSL [km]')
if title is not None:
ax.text(0., 1.01, title, transform=ax.transAxes)
plt.subplots_adjust(left=0.08, right=0.95)
plt.draw()
return [ax, hcbar]
catalog_df[year][hist.index] = hist.values
data = {'h': hours, 'year': range(1998, 2013), 'th': [th] * hours.size}
df = pd.DataFrame(data=data)
hours_df = hours_df.append(df)
data = {'h': events, 'year': range(1998, 2013), 'th': [th] * events.size}
df = pd.DataFrame(data=data)
events_df = events_df.append(df)
labs = [str(int(np.mod(y, 100.))).zfill(2) for y in range(1998, 2013)]
panels = ('(a)', '(b) Events of 1 or more hours', '(c)')
ylims = [[0, 250], [0, 100], [1.8, 4]]
# cls = sns.color_palette('Set3')
dcmap = discrete_cmap(7, base_cmap='Set1')
cls = [dcmap(0), dcmap(1), dcmap(2)]
scale = 1.3
fig, axes = plt.subplots(3, 1, figsize=(5 * scale, 6 * scale), sharex=True)
for n in range(3):
axes[n].set_gid(n)
sns.barplot(
x='year',
y='h',
# hue='th',
data=hours_df,
ax=axes[0],
# palette='GnBu',
color=cls[0])
for p in axes[0].patches:
def plot_time_height(ax=None,
wspd=None,
time=None,
height=None,
spd_range=None,
spd_delta=None,
cmap=None,
title=None,
cbar=None,
cbarinvi=False,
timelabstep=None,
kind='pcolormesh',
cbar_label=None):
''' NOAA wind profiler files after year 2000 indicate
the start time of averaging period; so a timestamp of
13 UTC indicates average between 13 and 14 UTC '''
''' make a color map of fixed colors '''
if len(spd_range) == 2:
bounds = range(spd_range[0], spd_range[1] + spd_delta, spd_delta)
else:
bounds = spd_range
cmap = discrete_cmap(len(bounds) + 1,
base_cmap=cmap,
norm_range=[0.2, 1.0])
norm = colors.BoundaryNorm(bounds, cmap.N)
x = np.linspace(0, len(time), len(time) + 1)
y = np.linspace(0, height.size, height.size + 1)
wspdm = ma.masked_where(np.isnan(wspd), wspd)
# wspdm = ma.masked_where(wspdm<0, wspdm)
if kind == 'contourf':
x, y = np.meshgrid(np.linspace(0, len(time), len(time)),
np.linspace(0, height.size, height.size))
img = ax.contourf(x, y, wspdm, cmap=cmap, norm=norm)
else:
img = ax.pcolormesh(x, y, wspdm, cmap=cmap, norm=norm)
hcbar = add_colorbar(cbar,
img,
loc='right',
label=cbar_label,
labelpad=20,
fontsize=12,
invisible=cbarinvi)
ax.set_xlim([-1.0, len(time) + 1.0])
if timelabstep is None:
timelabstep = '1H'
format_xaxis2(ax, time, timelabstep=timelabstep)
ax.invert_xaxis()
ax.set_xlabel(r'$\leftarrow UTC \left[\stackrel{day}{time}\right]$',
fontsize=20)
ax.set_ylabel('Altitude MSL [km]')
if title is not None:
ax.text(0., 1.01, title, transform=ax.transAxes)
plt.subplots_adjust(left=0.08, right=0.95)
return [ax, hcbar, img]