def test_plot_symbol_fontsize():
"""Test changing fontsize in plotting of symbols."""
fig = plt.figure(figsize=(3, 3))
ax = plt.subplot(1, 1, 1)
sp = StationPlot(ax, [0], [0], fontsize=8, spacing=32)
sp.plot_symbol('E', [92], current_weather)
sp.plot_symbol('W', [96], current_weather, fontsize=100)
return fig
def test_plot_symbol_fontsize():
"""Test changing fontsize in plotting of symbols."""
fig = plt.figure(figsize=(3, 3))
ax = plt.subplot(1, 1, 1)
sp = StationPlot(ax, [0], [0], fontsize=8, spacing=32)
sp.plot_symbol('E', [92], current_weather)
sp.plot_symbol('W', [96], current_weather, fontsize=100)
return fig
def test_symbol_pandas_timeseries():
"""Test the usage of Pandas DatetimeIndex as a valid `x` input into StationPlot."""
pd.plotting.register_matplotlib_converters()
rng = pd.date_range('12/1/2017', periods=5, freq='D')
sc = [1, 2, 3, 4, 5]
ts = pd.Series(sc, index=rng)
fig, ax = plt.subplots()
y = np.ones(len(ts.index))
stationplot = StationPlot(ax, ts.index, y, fontsize=12)
stationplot.plot_symbol('C', ts, sky_cover)
ax.xaxis.set_major_locator(matplotlib.dates.DayLocator())
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%-d'))
return fig
def create_ornaments(n_ornaments,color='k'):
"""create ornaments and return the artist."""
ornaments_y=np.random.triangular(3,3.5,24.5,n_ornaments)
bounds=get_x_bounds(ornaments_y)
ornaments_x=np.random.rand(n_ornaments)*2*bounds-bounds
stationplot=StationPlot(ax,ornaments_x,ornaments_y,clip_on=True,fontsize=24)
symbols=np.array(list(wx_code_map.values()))
wx=symbols[np.random.randint(0,102,n_ornaments)]
return stationplot.plot_symbol('c',wx,current_weather,color=color)
def test_stationplot_clipping():
"""Test the that clipping can be enabled as a default parameter."""
fig = plt.figure(figsize=(9, 9))
# testing data
x = np.array([1, 5])
y = np.array([2, 4])
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16, clip_on=True)
sp.plot_barb([20, 0], [0, -50])
sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
sp.plot_symbol('S', [5, 7], high_clouds, color='green')
sp.ax.set_xlim(1, 5)
sp.ax.set_ylim(1.75, 4.25)
return fig
def test_stationplot_api():
"""Test the StationPlot API."""
fig = plt.figure(figsize=(9, 9))
# testing data
x = np.array([1, 5])
y = np.array([2, 4])
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16)
sp.plot_barb([20, 0], [0, -50])
sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
sp.plot_symbol('S', [5, 7], high_clouds, color='green')
sp.ax.set_xlim(0, 6)
sp.ax.set_ylim(0, 6)
return fig
def test_stationplot_api():
"""Test the StationPlot API."""
fig = plt.figure(figsize=(9, 9))
# testing data
x = np.array([1, 5])
y = np.array([2, 4])
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16)
sp.plot_barb([20, 0], [0, -50])
sp.plot_text("E", ["KOKC", "ICT"], color="blue")
sp.plot_parameter("NW", [10.5, 15], color="red")
sp.plot_symbol("S", [5, 7], high_clouds, color="green")
sp.ax.set_xlim(0, 6)
sp.ax.set_ylim(0, 6)
return fig
def test_stationplot_clipping():
"""Test the that clipping can be enabled as a default parameter."""
fig = plt.figure(figsize=(9, 9))
# testing data
x = np.array([1, 5])
y = np.array([2, 4])
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16, clip_on=True)
sp.plot_barb([20, 0], [0, -50])
sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
sp.plot_symbol('S', [5, 7], high_clouds, color='green')
sp.ax.set_xlim(1, 5)
sp.ax.set_ylim(1.75, 4.25)
return fig
def test_stationplot_api():
"""Test the StationPlot API."""
fig = plt.figure(figsize=(9, 9))
# testing data
x = np.array([1, 5])
y = np.array([2, 4])
# Make the plot
sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16)
sp.plot_barb([20, 0], [0, -50])
sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
sp.plot_symbol('S', [5, 7], high_clouds, color='green')
sp.ax.set_xlim(0, 6)
sp.ax.set_ylim(0, 6)
return fig
def plot_map_temperature(proj,
point_locs,
df_t,
area='EU',
west=-5.5,
east=32,
south=42,
north=62,
fonts=14,
cm='gist_ncar',
path=None,
SLP=False):
if path is None:
# set up the paths and test for existence
path = expanduser('~') + '/Documents/Metar_plots'
try:
os.listdir(path)
except FileNotFoundError:
os.mkdir(path)
else:
path = path
df = df_t
plt.rcParams['savefig.dpi'] = 300
# =========================================================================
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 16))
ax = fig.add_subplot(1, 1, 1, projection=proj)
if area == 'Antarctica':
df = df.loc[df['latitude'] < north]
ax.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
elif area == 'Arctic':
df = df.loc[df['latitude'] > south]
ax.set_extent([-180, 180, 60, 90], ccrs.PlateCarree())
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
else:
ax.set_extent((west, east, south, north))
# Set up a cartopy feature for state borders.
state_boundaries = feat.NaturalEarthFeature(category='cultural',
name='admin_0_countries',
scale='10m',
facecolor='#d8dcd6',
alpha=0.5)
ax.coastlines(resolution='10m', zorder=1, color='black')
ax.add_feature(state_boundaries, zorder=1, edgecolor='black')
# ax.add_feature(cartopy.feature.OCEAN, zorder=0)
# Set plot bounds
# reset index for easier loop
df = df.dropna(how='any', subset=['TT'])
df = df.reset_index()
cmap = matplotlib.cm.get_cmap(cm)
norm = matplotlib.colors.Normalize(vmin=-30.0, vmax=30.0)
# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
index = 0
a = np.arange(-30, 30, 1)
for x in a:
if index == 0:
df_min = df.loc[df['TT'] < min(a)]
df_max = df.loc[df['TT'] > max(a)]
j = 0
list_ex = [min(a) - 5, max(a) + 5]
for arr in [df_min, df_max]:
stationplot = StationPlot(ax,
arr['longitude'],
arr['latitude'],
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=fonts)
Temp = stationplot.plot_parameter('NW',
arr['TT'],
color=cmap(norm(list_ex[j])))
try:
Temp.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.Normal()
])
except AttributeError:
pass
j += 1
# slice out values between x and x+1
df_cur = df.loc[(df['TT'] < x + 1) & (df['TT'] >= x)]
stationplot = StationPlot(ax,
df_cur['longitude'],
df_cur['latitude'],
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=fonts)
# plot the sliced values with a different color for each loop
Temp = stationplot.plot_parameter('NW',
df_cur['TT'],
color=cmap(norm(x + 0.5)))
try:
Temp.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.Normal()
])
except AttributeError:
pass
print('x={} done correctly '.format(x))
index += 1
# fontweight = 'bold'
# More complex ex. uses custom formatter to control how sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of
# the pressure value in tenths of millibars.
stationplot = StationPlot(ax,
df['longitude'].values,
df['latitude'].values,
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=fonts)
try:
u, v = wind_components(((df['ff'].values) * units('knots')),
(df['dd'].values * units.degree))
cloud_frac = df['cloud_cover']
if area != 'Arctic':
stationplot.plot_barb(u, v, zorder=1000, linewidth=2)
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# stationplot.plot_text((2, 0), df['Station'])
for val in range(0, 2):
wx = df[['ww', 'StationType']]
if val == 0:
# mask all the unmanned stations
wx['ww'].loc[wx['StationType'] > 3] = np.nan
wx2 = wx['ww'].fillna(00).astype(int).values.tolist()
stationplot.plot_symbol('W', wx2, current_weather, zorder=2000)
else:
# mask all the manned stations
wx['ww'].loc[(wx['StationType'] <= 3)] = np.nan
# mask all reports smaller than 9
# =7 is an empty symbol!
wx['ww'].loc[wx['ww'] <= 9] = 7
wx2 = wx['ww'].fillna(7).astype(int).values.tolist()
stationplot.plot_symbol('W',
wx2,
current_weather_auto,
zorder=2000)
# print(u, v)
except (ValueError, TypeError) as error:
pass
if SLP is True:
lon = df['longitude'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)].values
lat = df['latitude'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)].values
xp, yp, _ = proj.transform_points(ccrs.PlateCarree(), lon, lat).T
sea_levelp = df['SLP'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)]
x_masked, y_masked, pres = remove_nan_observations(
xp, yp, sea_levelp.values)
slpgridx, slpgridy, slp = interpolate_to_grid(x_masked,
y_masked,
pres,
interp_type='cressman',
search_radius=400000,
rbf_func='quintic',
minimum_neighbors=1,
hres=100000,
rbf_smooth=100000)
Splot_main = ax.contour(slpgridx,
slpgridy,
slp,
colors='k',
linewidths=2,
extent=(west, east, south, north),
levels=list(range(950, 1050, 10)))
plt.clabel(Splot_main, inline=1, fontsize=12, fmt='%i')
Splot = ax.contour(slpgridx,
slpgridy,
slp,
colors='k',
linewidths=1,
linestyles='--',
extent=(west, east, south, north),
levels=[
x for x in range(950, 1050, 1)
if x not in list(range(950, 1050, 10))
])
plt.clabel(Splot, inline=1, fontsize=10, fmt='%i')
# stationplot.plot_text((2, 0), df['Station'])
# Also plot the actual text of the station id. Instead of cardinal
# directions, plot further out by specifying a location of 2 increments
# in x and 0 in y.stationplot.plot_text((2, 0), df['station'])
if (area == 'Antarctica' or area == 'Arctic'):
plt.savefig(path + '/CURR_SYNOP_color_' + area + '.png',
bbox_inches='tight',
pad_inches=0)
else:
plt.savefig(path + '/CURR_SYNOP_color_' + area + '.png',
bbox_inches='tight',
transparent="True",
pad_inches=0)
transform=ccrs.PlateCarree(), fontsize=12)
# Plot the temperature and dew point to the upper and lower left, respectively, of
# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', data['air_temperature'], color='red')
stationplot.plot_parameter('SW', data['dew_point_temperature'],
color='darkgreen')
# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE', data['slp'], formatter=lambda v: format(10 * v, '.0f')[-3:])
# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', wx, current_weather)
# Add wind barbs
stationplot.plot_barb(u, v)
# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), data['stid'])
plt.show()
def plot_station_data(date, site, sitetitle, df):
'''Given site station ID, the title of that site, and a dataframe of ASOS observation data from the last 3 days,
returns a plot of the last 3-days of weather at that site.
Parameters:
site (str): string of ASOS station ID
sitetitle (str): string of ASOS station full name
df (dataframe): dataframe containing last 72 hours (3 days) of ASOS station data
Returns:
None
*saves plots to plot_dir listed near top of script*
'''
#if isinstance(df, int): #Returns if the station is not reporting
# return
if df.empty:
print('dataframe is empty -- go to next plot')
return
lower_site = site.lower()
timestamp_end = str(df.index[-1].strftime('%Y%m%d%H%M'))
dt = df.index[:]
dt_array = np.array(dt.values)
graphtimestamp_start = dt[0].strftime("%m/%d/%y")
graphtimestamp = dt[-1].strftime("%m/%d/%y")
#now = datetime.datetime.utcnow()
now = datetime.strptime(date, '%Y%m%d')
today_date = dt[-1].strftime('%Y%m%d')
markersize = 1.5
linewidth = 1.0
#make figure and axes
fig = plt.figure()
fig.set_size_inches(18, 10)
if 'snow_depth_set_1' in df.keys(): #six axes if snow depth
ax1 = fig.add_subplot(6, 1, 1)
ax2 = fig.add_subplot(6, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(6, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(6, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(6, 1, 5, sharex=ax1)
ax6 = fig.add_subplot(6, 1, 6, sharex=ax1)
ax6.set_xlabel('Time (UTC)')
else:
ax1 = fig.add_subplot(5, 1, 1) #five axes if no snow depth
ax2 = fig.add_subplot(5, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(5, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(5, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(5, 1, 5, sharex=ax1)
ax5.set_xlabel('Time (UTC)')
#ax1.set_title(site+' '+sitetitle+' '+graphtimestamp_start+' - '+graphtimestamp+' '+now.strftime("%H:%MZ"))
ax1.set_title(site + ' ' + sitetitle + ' ' + graphtimestamp_start + ' - ' +
graphtimestamp)
#------------------
#plot airT and dewT
#------------------
if 'tmpc' in df.keys():
airT = df['tmpc']
airT_new = airT.dropna()
airT_list = list(airT_new.values)
airT_dt_list = []
for i in range(0, len(airT)):
if pd.isnull(airT[i]) == False:
airT_dt_list.append(dt[i])
#ax1.plot_date(airT_dt_list,airT_list,'o-',label="Temp",color="blue",linewidth=linewidth,markersize=markersize)
ax1.plot_date(airT_dt_list,
airT_list,
linestyle='solid',
label="Temp",
color="blue",
linewidth=linewidth,
marker='None')
#ax1.plot_date(dt,airT,'-',label="Temp",color="blue",linewidth=linewidth)
if 'dwpc' in df.keys():
dewT = df['dwpc']
dewT_new = dewT.dropna()
dewT_list = list(dewT_new.values)
dewT_dt_list = []
for i in range(0, len(dewT)):
if pd.isnull(dewT[i]) == False:
dewT_dt_list.append(dt[i])
#ax1.plot_date(dewT_dt_list,dewT_list,'o-',label="Dew Point",color="black",linewidth=linewidth,markersize=markersize)
ax1.plot_date(dewT_dt_list,
dewT_list,
linestyle='solid',
label="Dew Point",
color="black",
linewidth=linewidth,
marker='None')
if ax1.get_ylim()[0] < 0 < ax1.get_ylim()[1]:
ax1.axhline(0, linestyle='-', linewidth=1.0, color='deepskyblue')
trans = transforms.blended_transform_factory(
ax1.get_yticklabels()[0].get_transform(), ax1.transData)
ax1.text(0,
0,
'0C',
color="deepskyblue",
transform=trans,
ha="right",
va="center") #light blue line at 0 degrees C
ax1.set_ylabel('Temp ($^\circ$C)')
ax1.legend(loc='best', ncol=2)
axes = [ax1] #begin axes
#----------------------------
#plotting wind speed and gust
#----------------------------
if 'sknt' in df.keys():
wnd_spd = df['sknt']
#ax2.plot_date(dt,wnd_spd,'o-',label='Speed',color="forestgreen",linewidth=linewidth,markersize=markersize)
ax2.plot_date(dt,
wnd_spd,
linestyle='solid',
label='Speed',
color="forestgreen",
linewidth=linewidth,
marker='None')
if 'gust' in df.keys():
wnd_gst = df['gust']
max_wnd_gst = wnd_gst.max(skipna=True)
ax2.plot_date(dt,
wnd_gst,
'o-',
label='Gust (Max ' + str(round(max_wnd_gst, 1)) + 'kt)',
color="red",
linewidth=0.0,
markersize=markersize)
ax2.set_ylabel('Wind (kt)')
ax2.legend(loc='best', ncol=2)
axes.append(ax2)
#-----------------------
#plotting wind direction
#-----------------------
if 'drct' in df.keys():
wnd_dir = df['drct']
wnd_dir_new = wnd_dir.dropna()
wnd_dir_list = list(wnd_dir_new.values)
wnd_dir_dt_list = []
for i in range(0, len(wnd_dir)):
if pd.isnull(wnd_dir[i]) == False:
wnd_dir_dt_list.append(dt[i])
#ax3.plot_date(dt,wnd_dir,'o-',label='Direction',color="purple",linewidth=0.2, markersize=markersize)
#ax3.plot_date(wnd_dir_dt_list,wnd_dir_list,'o-',label='Direction',color="purple",linewidth=0.2, markersize=markersize)
ax3.plot_date(wnd_dir_dt_list,
wnd_dir_list,
linestyle='solid',
label='Direction',
color="purple",
linewidth=linewidth,
marker='None')
ax3.set_ylim(-10, 370)
ax3.set_ylabel('Wind Direction')
ax3.set_yticks([0, 90, 180, 270, 360])
axes.append(ax3)
#-------------
#plotting MSLP
#-------------
if 'mslp' in df.keys():
mslp = df['mslp']
mslp_new = mslp.dropna()
mslp_list = list(mslp_new.values)
mslp_dt_list = []
for i in range(0, len(mslp)):
if pd.isnull(mslp[i]) == False:
mslp_dt_list.append(dt[i])
max_mslp = mslp.max(skipna=True)
min_mslp = mslp.min(skipna=True)
labelname = 'Min ' + str(round(min_mslp, 1)) + 'hPa, Max ' + str(
round(max_mslp, 2)) + 'hPa'
#ax4.plot_date(mslp_dt_list,mslp_list,'o-',label=labelname,color='darkorange',linewidth=linewidth,markersize=markersize)
ax4.plot_date(mslp_dt_list,
mslp_list,
linestyle='solid',
label=labelname,
color='darkorange',
linewidth=linewidth,
marker='None')
ax4.legend(loc='best')
ax4.set_ylabel('MSLP (hPa)')
ax4.set_xlabel('Time (UTC)')
axes.append(ax4)
#-------------------------------------------
#plotting precip accumulation & precip types
#-------------------------------------------
# Move date_time from index to column
df = df.reset_index()
if 'p01m' in df.keys():
df['p01m'] = df['p01m'].fillna(0)
last_val = df.iloc[0]['p01m']
last_time = df.iloc[0]['time']
last_hour = last_time.strftime('%H')
last_minute = last_time.strftime('%M')
precip_inc = [last_val]
precip_accum = 0.0
precip_accum_list = [last_val]
num_ge_55_for_curr_hour = 0
for index in range(1, len(df)):
#for index in range(1,12):
val = df.iloc[index]['p01m']
time = df.iloc[index]['time']
hour = time.strftime('%H')
minute = time.strftime('%M')
#print('LAST: val=',last_val,' hour=',last_hour,' minute=',last_minute)
#print('CURR: val=',val,' hour=',hour,' minute=',minute)
if hour != last_hour:
num_ge_55_for_curr_hour = 0
#if val == last_val:
# increment = 0.0
#else:
# #if last_minute == '53':
# if last_minute > '50' and last_minute < '55':
# increment = val
# else:
# increment = val-last_val
if minute >= '55':
if num_ge_55_for_curr_hour == 0:
increment = val
else:
if val > last_val:
increment = val - last_val
else:
increment = 0
num_ge_55_for_curr_hour = num_ge_55_for_curr_hour + 1
else:
if val == last_val:
increment = 0
else:
if val > last_val:
increment = val - last_val
else:
increment = 0
precip_accum = precip_accum + increment
precip_accum_list.append(precip_accum)
precip_inc.append(increment)
last_val = val
last_hour = hour
last_minute = minute
#df['p01m_mod'] = precip_inc
max_precip = sum(precip_inc)
# max_precip is also precip_accum_list[-1]
#p01m_mod = list(df['p01m_mod'].values)
p01m_mod_dt = list(df['time'].values)
#max_precip = max(precip_accum_list)
labelname = 'Precip (' + str(round(max_precip, 2)) + 'mm)'
#ax5.plot_date(p01m_mod_dt,precip_accum_list,'o-',label=labelname,color='navy',linewidth=linewidth,markersize=markersize)
ax5.plot_date(p01m_mod_dt,
precip_accum_list,
linestyle='solid',
label=labelname,
color='navy',
linewidth=linewidth,
marker='None')
if max_precip > 0:
ax5.set_ylim(-0.1 * max_precip, max_precip + max_precip * 0.2)
else:
ax5.set_ylim(-0.1, 0.5)
# Add weather_code info to plot
if 'wxcodes' in df.keys():
df['wxcodes'] = df['wxcodes'].fillna('')
wxcodes_wto = []
for index in range(0, len(df)):
time = df.iloc[index]['time']
minute = time.strftime('%M')
#if minute == '53':
if minute > '50' and minute < '55':
# convert alphanumeric code to wto code number
wxcodes = df.iloc[index]['wxcodes']
# Added in a check in case of unexpected weather codes
# Used to resolve case when code set to -FZRAGR instead of -FZRA GR
# Unidata says this will work as well:
# wxcode_num = wx_code_map.get(wxcodes.split()[0], 0)
# Here is reference: https://docs.python.org/3/library/stdtypes.html#dict.get
if len(wxcodes) > 0 and wxcodes.split()[0] in wx_codes.keys():
wxcode_num = wx_code_map[wxcodes.split()[0]]
else:
wxcode_num = 0
else:
wxcode_num = 0
wxcodes_wto.append(wxcode_num)
#df['wxcodes_wto'] = wxcode_wto
#wxcodes_wto = list(df['wxcodes_wto'].values)
wxcodes_wto_dt = list(df['time'].values)
if max_precip > 0:
dummy_y_vals = np.ones(len(wxcodes_wto)) * (0.10 * max_precip)
else:
dummy_y_vals = np.ones(len(wxcodes_wto)) * (0.10 * 0.5)
sp = StationPlot(ax5, wxcodes_wto_dt, dummy_y_vals)
#ax.plot(dates, temps)
sp.plot_symbol('C',
wxcodes_wto,
current_weather,
fontsize=16,
color='red')
#sp.plot_symbol('C', wxcodes_wto, current_weather, fontsize=14, color='red')
ax5.legend(loc='best')
ax5.set_ylabel('Precip (mm)')
axes.append(ax5)
# Axes formatting
for ax in axes:
ax.spines["top"].set_visible(
False) #darker borders on the grids of each subplot
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.tick_params(axis='x', which='both', bottom='on',
top='off') #add ticks at labeled times
ax.tick_params(axis='y', which='both', left='on', right='off')
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_major_formatter(DateFormatter('%b-%d'))
ax.xaxis.set_minor_locator(HourLocator(np.linspace(6, 18, 3)))
ax.xaxis.set_minor_formatter(DateFormatter('%H'))
ax.fmt_xdata = DateFormatter('Y%m%d%H%M%S')
ax.yaxis.grid(linestyle='--')
ax.get_yaxis().set_label_coords(-0.06, 0.5)
# Write plot to file
plot_path = plot_dir + '/' + today_date
if not os.path.exists(plot_path):
os.makedirs(plot_path)
try:
catalogName = 'surface.Meteogram.' + timestamp_end + '.ASOS_' + asos_sites[
lower_site] + '.png'
#plt.savefig(plot_path+'/ops.asos.'+timestamp_end+'.'+lower_site+'.png',bbox_inches='tight')
plt.savefig(plot_path + '/' + catalogName, bbox_inches='tight')
except:
print("Problem saving figure for %s. Usually a maxticks problem" %
site)
plt.close()
# DON'T NEED THIS CHECK FOR RT PROCESSING
# ftp plot if in asos_for_cat list
#if lower_site in asos_for_cat:
# Open ftp connection
if test:
catalogFTP = FTP(ftpCatalogServer, ftpCatalogUser, ftpCatalogPassword)
catalogFTP.cwd(catalogDestDir)
else:
catalogFTP = FTP(ftpCatalogServer, ftpCatalogUser)
catalogFTP.cwd(catalogDestDir)
catalogFTP.set_pasv(False)
# ftp image
ftpFile = open(os.path.join(plot_path, catalogName), 'rb')
catalogFTP.storbinary('STOR ' + catalogName, ftpFile)
ftpFile.close()
# Close ftp connection
catalogFTP.quit()
def plot_station_data(date,site,sitetitle,df):
'''Given site station ID, the title of that site, and a dataframe of ASOS observation data from the last 3 days,
returns a plot of the last 3-days of weather at that site.
Parameters:
site (str): string of ASOS station ID
sitetitle (str): string of ASOS station full name
df (dataframe): dataframe containing last 72 hours (3 days) of ASOS station data
Returns:
None
*saves plots to plot_dir listed near top of script*
'''
if isinstance(df, int): #Returns if the station is not reporting
return
lower_site = site.lower()
timestamp_end=str(df.index[-1].strftime('%Y%m%d%H%M'))
dt = df.index[:]
dt_array = np.array(dt.values)
graphtimestamp_start=dt[0].strftime("%m/%d/%y")
graphtimestamp=dt[-1].strftime("%m/%d/%y")
#now = datetime.datetime.utcnow()
now = datetime.strptime(date,'%Y%m%d')
today_date = dt[-1].strftime('%Y%m%d')
markersize = 1.5
linewidth = 1.0
#make figure and axes
fig = plt.figure()
fig.set_size_inches(18,10)
if 'snow_depth_set_1' in df.keys(): #six axes if snow depth
ax1 = fig.add_subplot(6,1,1)
ax2 = fig.add_subplot(6,1,2,sharex=ax1)
ax3 = fig.add_subplot(6,1,3,sharex=ax1)
ax4 = fig.add_subplot(6,1,4,sharex=ax1)
ax5 = fig.add_subplot(6,1,5,sharex=ax1)
ax6 = fig.add_subplot(6,1,6,sharex=ax1)
ax6.set_xlabel('Time (UTC)')
else:
ax1 = fig.add_subplot(5,1,1) #five axes if no snow depth
ax2 = fig.add_subplot(5,1,2,sharex=ax1)
ax3 = fig.add_subplot(5,1,3,sharex=ax1)
ax4 = fig.add_subplot(5,1,4,sharex=ax1)
ax5 = fig.add_subplot(5,1,5,sharex=ax1)
ax5.set_xlabel('Time (UTC)')
#ax1.set_title(site+' '+sitetitle+' '+graphtimestamp_start+' - '+graphtimestamp+' '+now.strftime("%H:%MZ"))
ax1.set_title(site+' '+sitetitle+' '+graphtimestamp_start+' - '+graphtimestamp)
#------------------
#plot airT and dewT
#------------------
if 'tmpc' in df.keys():
airT = df['tmpc']
airT_new = airT.dropna()
airT_list = list(airT_new.values)
airT_dt_list = []
for i in range(0,len(airT)):
if pd.isnull(airT[i]) == False:
airT_dt_list.append(dt[i])
ax1.plot_date(airT_dt_list,airT_list,'o-',label="Temp",color="blue",linewidth=linewidth,markersize=markersize)
#ax1.plot_date(dt,airT,'-',label="Temp",color="blue",linewidth=linewidth)
if 'dwpc' in df.keys():
dewT = df['dwpc']
dewT_new = dewT.dropna()
dewT_list = list(dewT_new.values)
dewT_dt_list = []
for i in range(0,len(dewT)):
if pd.isnull(dewT[i]) == False:
dewT_dt_list.append(dt[i])
ax1.plot_date(dewT_dt_list,dewT_list,'o-',label="Dew Point",color="black",linewidth=linewidth,markersize=markersize)
if ax1.get_ylim()[0] < 0 < ax1.get_ylim()[1]:
ax1.axhline(0, linestyle='-', linewidth = 1.0, color='deepskyblue')
trans = transforms.blended_transform_factory(ax1.get_yticklabels()[0].get_transform(), ax1.transData)
ax1.text(0,0,'0C', color="deepskyblue", transform=trans, ha="right", va="center") #light blue line at 0 degrees C
ax1.set_ylabel('Temp ($^\circ$C)')
ax1.legend(loc='best',ncol=2)
axes = [ax1] #begin axes
#----------------------------
#plotting wind speed and gust
#----------------------------
if 'sknt' in df.keys():
wnd_spd = df['sknt']
ax2.plot_date(dt,wnd_spd,'o-',label='Speed',color="forestgreen",linewidth=linewidth,markersize=markersize)
if 'gust' in df.keys():
wnd_gst = df['gust']
max_wnd_gst = wnd_gst.max(skipna=True)
ax2.plot_date(dt,wnd_gst,'o-',label='Gust (Max ' + str(round(max_wnd_gst,1)) + 'kt)',color="red",linewidth=0.0,markersize=markersize)
ax2.set_ylabel('Wind (kt)')
ax2.legend(loc='best',ncol=2)
axes.append(ax2)
#-----------------------
#plotting wind direction
#-----------------------
if 'drct' in df.keys():
wnd_dir = df['drct']
ax3.plot_date(dt,wnd_dir,'o-',label='Direction',color="purple",linewidth=0.2, markersize=markersize)
ax3.set_ylim(-10,370)
ax3.set_ylabel('Wind Direction')
ax3.set_yticks([0,90,180,270,360])
axes.append(ax3)
#-------------
#plotting MSLP
#-------------
if 'mslp' in df.keys():
mslp = df['mslp']
mslp_new = mslp.dropna()
mslp_list = list(mslp_new.values)
mslp_dt_list = []
for i in range(0,len(mslp)):
if pd.isnull(mslp[i]) == False:
mslp_dt_list.append(dt[i])
max_mslp = mslp.max(skipna=True)
min_mslp = mslp.min(skipna=True)
labelname = 'Min ' + str(round(min_mslp,1)) + 'hPa, Max ' + str(round(max_mslp,2)) + 'hPa'
ax4.plot_date(mslp_dt_list,mslp_list,'o-',label=labelname,color='darkorange',linewidth=linewidth,markersize=markersize)
ax4.legend(loc='best')
ax4.set_ylabel('MSLP (hPa)')
ax4.set_xlabel('Time (UTC)')
axes.append(ax4)
#-------------------------------------------
#plotting precip accumulation & precip types
#-------------------------------------------
# Move date_time from index to column
df = df.reset_index()
# MODIFY THIS SO THAT WHEN READINGS COME IN WE KEEP THEM INSTEAD OF JUST ACCEPTING HOURLY ACCUMS
# WILL MAKE PLOT LESS CHOPPY
# Plot precip time series (use only values at minute 53)
if 'p01m' in df.keys():
df['p01m'] = df['p01m'].fillna(0)
precip_inc = list(df['p01m'].values)
precip_inc_dt = list(df['date_time'].values)
precip_accum = 0.0
precip_accum_list = []
precip_dt_list = []
precip_accum_indices = []
for i in range(0,len(precip_inc)):
time_obj = pd.to_datetime(precip_inc_dt[i])
minutes = time_obj.strftime('%M')
if minutes == '53':
precip_accum = precip_accum + precip_inc[i]
precip_accum_list.append(precip_accum)
precip_dt_list.append(precip_inc_dt[i])
precip_accum_indices.append(i)
times = precip_dt_list
precip_accums = precip_accum_list
"""
precip_accum = 0.0
precip_accum_list = []
last_increment = df.loc[0].p01m
precip_accum_indices = []
for index in range(1,len(df)):
if df.loc[index].p01m < last_increment:
precip_accum = precip_accum + last_increment
precip_accum_list.append(precip_accum)
precip_accum_indices.append(index)
else:
precip_accum_list.append(precip_accum)
last_increment = df.loc[index].p01m
# append last element in dataframe in case there's some precip there
precip_accum = precip_accum + last_increment
precip_accum_list.append(precip_accum)
# values to use for the plot
times = [precip_dt_list[i] for i in precip_accum_indices]
precip_accums = [precip_accum_list[i] for i in precip_accum_indices]
"""
max_precip = max(precip_accum_list)
labelname = 'Precip (' + str(round(max_precip,2)) + 'mm)'
ax5.plot_date(precip_dt_list,precip_accum_list,'o-',label=labelname,color='navy',linewidth=linewidth,markersize=markersize)
if max_precip > 0:
ax5.set_ylim(-0.1*max_precip,max_precip+max_precip*0.2)
else:
ax5.set_ylim(-0.5,5)
# Add weather_code info to plot
if 'wxcodes' in df.keys():
df['wxcodes'] = df['wxcodes'].fillna('')
wxcodes_list = list(df['wxcodes'].values)
#wxcodes_dt_list = list(df['date_time'].values)
wxcodes_num_list = []
for i in range(0,len(wxcodes_list)):
wxcodes = wxcodes_list[i]
if len(wxcodes) > 0:
wxcode_wto = wx_code_map[wxcodes.split()[0]]
else:
wxcode_wto = 0
wxcodes_num_list.append(wxcode_wto)
wxcodes = [wxcodes_num_list[i] for i in precip_accum_indices]
# Set y values for weather symbols on plot
dummy_y_vals = np.ones(len(wxcodes)) * (0.10*max_precip)
sp = StationPlot(ax5, times, dummy_y_vals)
#ax.plot(dates, temps)
#sp.plot_symbol('C', wxcodes, current_weather, fontsize=16, color='red')
sp.plot_symbol('C', wxcodes, current_weather, fontsize=14, color='red')
"""
wxcodes_num_list_hrly = []
wxcodes_dt_list_hrly = []
for i in range(0,len(wxcodes_num_list)):
time_obj = pd.to_datetime(wxcodes_dt_list[i])
minutes = time_obj.strftime('%M')
if minutes == '00':
wxcodes_num_list_hrly.append(wxcodes_num_list[i])
wxcodes_dt_list_hrly.append(wxcodes_dt_list[i])
dummy_y_vals = np.zeros(len(wxcodes_num_list_hrly)) # only to place on plot
sp = StationPlot(ax5, wxcodes_dt_list_hrly, dummy_y_vals)
#ax5.plot(precip_dt_list,precip_accum_list)
sp.plot_symbol('C', wxcodes_num_list_hrly, current_weather, fontsize=16, color='red')
"""
ax5.legend(loc='best')
ax5.set_ylabel('Precip (mm)')
axes.append(ax5)
"""
#-------------------
#plotting snow depth
#-------------------
if 'snow_depth_set_1' in df.keys():
snow_depth = df['snow_depth_set_1']
snow_depth_new = snow_depth.dropna()
snow_depth_dt_list = []
for i in range(0,len(snow_depth)):
if pd.isnull(snow_depth[i]) == False:
snow_depth_dt_list.append(dt[i])
max_snow_depth = snow_depth.max(skipna=True)
min_snow_depth = snow_depth.min(skipna=True)
labelname = 'Min Depth ' + str(round(min_snow_depth,2)) + 'mm, Max Depth ' + str(round(max_snow_depth,2)) + 'mm'
ax6.plot_date(snow_depth_dt_list,snow_depth_new,'o-',label=labelname,color='deepskyblue',linewidth=linewidth,markersize=markersize)
if max_snow_depth > 0:
ax6.set_ylim(-0.1*max_snow_depth,max_snow_depth+max_snow_depth*0.2)
else:
ax6.set_ylim(-0.5,5)
ax6.legend(loc='best')
ax6.set_ylabel('Snow Depth (mm)')
axes.append(ax6)
"""
# Axes formatting
for ax in axes:
ax.spines["top"].set_visible(False) #darker borders on the grids of each subplot
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.tick_params(axis='x',which='both',bottom='on',top='off') #add ticks at labeled times
ax.tick_params(axis='y',which='both',left='on',right='off')
ax.xaxis.set_major_locator( DayLocator() )
ax.xaxis.set_major_formatter( DateFormatter('%b-%d') )
ax.xaxis.set_minor_locator( HourLocator(np.linspace(6,18,3)) )
ax.xaxis.set_minor_formatter( DateFormatter('%H') )
ax.fmt_xdata = DateFormatter('Y%m%d%H%M%S')
ax.yaxis.grid(linestyle = '--')
ax.get_yaxis().set_label_coords(-0.06,0.5)
# Write plot to file
plot_path = plot_dir+'/'+today_date
if not os.path.exists(plot_path):
os.makedirs(plot_path)
try:
plt.savefig(plot_path+'/ops.asos.'+timestamp_end+'.'+lower_site+'.png',bbox_inches='tight')
except:
print("Problem saving figure for %s. Usually a maxticks problem" %site)
plt.close()
# Plot the temperature and dew point to the upper and lower left, respectively, of
# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', data['air_temperature'].values, color='red')
stationplot.plot_parameter('SW', data['dew_point_temperature'].values,
color='darkgreen')
# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE', data['air_pressure_at_sea_level'].values,
formatter=lambda v: format(10 * v, '.0f')[-3:])
# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', data['cloud_coverage'].values, sky_cover)
# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', data['present_weather'].values, current_weather)
# Add wind barbs
stationplot.plot_barb(data['eastward_wind'].values, data['northward_wind'].values)
# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), data['station_id'].values)
plt.show()
def makeStationPlot(plotTitle, plotFileName, maxDataAge, maxLat, minLat,
maxLon, minLon, stationDensity, textSize, figX, figY, dpi,
showCountryBorders, showStateBorders, showCountyBorders):
#
# Data Polling
# Get data from AWC TDS
dataURL = "https://www.aviationweather.gov/adds/dataserver_current/httpparam?dataSource=metars&requestType=retrieve&format=csv&minLat=" + str(
minLat) + "&minLon=" + str(minLon) + "&maxLat=" + str(
maxLat) + "&maxLon=" + str(maxLon) + "&hoursBeforeNow=" + str(
maxDataAge)
# First read in the data. We use pandas because it simplifies a lot of tasks, like dealing
# with strings
data = pd.read_csv(dataURL,
header=5,
usecols=(1, 3, 4, 5, 6, 7, 8, 12, 21, 22),
names=[
'stid', 'lat', 'lon', 'air_temperature',
'dew_point_temperature', 'wind_dir', 'wind_speed',
'slp', 'weather', 'cloud_fraction'
],
na_values=-99999)
#
# Data Handling
# convert T and Td from °C to °F
data['air_temperature'] = (data['air_temperature'] * (9 / 5.0)) + 32
data['dew_point_temperature'] = (data['dew_point_temperature'] *
(9 / 5.0)) + 32
# change sky category to %
data['cloud_fraction'] = data['cloud_fraction'].replace(
'SKC', 0.0).replace('CLR', 0.0).replace('CAVOK', 0.0).replace(
'FEW', 0.1875).replace('SCT',
0.4375).replace('BKN', 0.750).replace(
'OVC', 1.000).replace('OVX', 1.000)
# Drop rows with missing winds
data = data.dropna(how='any', subset=['wind_dir', 'wind_speed'])
# Set up the map projection
proj = ccrs.LambertConformal(
central_longitude=(minLon + (maxLon - minLon) / 2),
central_latitude=(minLat + (maxLat - minLat) / 2))
# Set station density, in x meter radius
point_locs = proj.transform_points(ccrs.PlateCarree(), data['lon'].values,
data['lat'].values)
data = data[reduce_point_density(point_locs, stationDensity * 1000)]
# Get the wind components, converting from m/s to knots as will be appropriate
u, v = wind_components(
(data['wind_speed'].values * units('m/s')).to('knots'),
data['wind_dir'].values * units.degree)
# Convert the fraction value into a code of 0-8 and compensate for NaN values
cloud_frac = (8 * data['cloud_fraction'])
cloud_frac[np.isnan(cloud_frac)] = 10
cloud_frac = cloud_frac.astype(int)
# Map weather strings to WMO codes. Only use the first symbol if there are multiple
wx = [
wx_code_map[s.split()[0] if ' ' in s else s]
for s in data['weather'].fillna('')
]
#
# Plot Setup
# Set DPI of the resulting figure
plt.rcParams['savefig.dpi'] = dpi
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(figX, figY))
ax = fig.add_subplot(1, 1, 1, projection=proj)
# Set plot bounds
ax.set_extent((minLon, maxLon, minLat, maxLat))
# Add geographic features
if showCountyBorders:
ax.add_feature(USCOUNTIES.with_scale('500k'),
edgecolor='gray',
linewidth=0.25)
if showStateBorders:
state_borders = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lakes',
scale='50m',
facecolor='none')
ax.add_feature(state_borders, edgecolor='gray', linewidth=0.5)
if showCountryBorders:
country_borders = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_0_countries',
scale='50m',
facecolor='none')
ax.add_feature(country_borders, edgecolor='black', linewidth=0.7)
#
# Create Station Plots
# Set station location, setup plot
stationplot = StationPlot(ax,
data['lon'].values,
data['lat'].values,
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=textSize)
# Plot the temperature and dew point
stationplot.plot_parameter('NW', data['air_temperature'], color='red')
stationplot.plot_parameter('SW',
data['dew_point_temperature'],
color='darkgreen')
# Plot pressure data
stationplot.plot_parameter('NE',
data['slp'],
formatter=lambda v: format(10 * v, '.0f')[-3:])
# Plot cloud cover
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# Plot current weather
stationplot.plot_symbol('W', wx, current_weather)
# Add wind barbs
stationplot.plot_barb(u, v)
# Plot station id
stationplot.plot_text((2, 0), data['stid'])
# Set a title and show the plot
ax.set_title(plotTitle)
# Export fig
fig.savefig('/home/CarterHumphreys/bin/send2web/' +
datetime.utcnow().strftime("%Y%m%d-%H00") + '_' +
plotFileName + '.png',
bbox_inches='tight')
fig.savefig('/home/CarterHumphreys/bin/send2web/' + plotFileName + '.png',
bbox_inches='tight')
# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', data['air_temperature'], color='red')
stationplot.plot_parameter('SW',
data['dew_point_temperature'],
color='darkgreen')
# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE',
data['slp'],
formatter=lambda v: format(10 * v, '.0f')[-3:])
# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', wx, current_weather)
# Add wind barbs
stationplot.plot_barb(u, v)
# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), data['stid'])
plt.show()
def main():
### START OF USER SETTINGS BLOCK ###
# FILE/DATA SETTINGS
# file path to input file
datafile = '/home/jgodwin/python/sfc_observations/surface_observations.txt'
timefile = '/home/jgodwin/python/sfc_observations/validtime.txt'
# file path to county shapefile
ctyshppath = '/home/jgodwin/python/sfc_observations/shapefiles/counties/countyl010g.shp'
# file path to ICAO list
icaopath = '/home/jgodwin/python/sfc_observations/icao_list.csv'
icaodf = pd.read_csv(icaopath, index_col='STATION')
# MAP SETTINGS
# map names (doesn't go anywhere (yet), just for tracking purposes)
maps = ['CONUS', 'Texas', 'Tropical Atlantic']
# minimum radius allowed between points (in km)
radius = [100.0, 50.0, 75.0]
# map boundaries (longitude/latitude degrees)
west = [-122, -108, -100]
east = [-73, -93, -60]
south = [23, 25, 10]
north = [50, 38, 35]
restart_projection = [True, False, True]
# use county map? (True/False): warning, counties load slow!
usecounties = [False, False, False]
# OUTPUT SETTINGS
# save directory for output
savedir = '/var/www/html/images/'
# filenames for output
savenames = ['conus.png', 'texas.png', 'atlantic.png']
# TEST MODE SETTINGS
test = False # True/False
testnum = 5 # which map are you testing? corresponds to index in "maps" above
### END OF USER SETTING SECTION ###
# if there are any missing weather codes, add them here
wx_code_map.update({
'-RADZ': 59,
'-TS': 17,
'VCTSSH': 80,
'-SGSN': 77,
'SHUP': 76,
'FZBR': 48,
'FZUP': 76
})
### READ IN DATA / SETUP MAP ###
# read in the valid time file
vt = open(timefile).read()
# read in the data
for i in range(len(maps)):
if test and i != testnum:
continue
with open(datafile) as f:
data = pd.read_csv(f,header=0,names=['siteID','lat','lon','elev','slp','temp','sky','dpt','wx','wdr',\
'wsp'],na_values=-99999)
# drop rows with missing winds
data = data.dropna(how='any', subset=['wdr', 'wsp'])
# remove data not within our domain
data = data[(data['lat'] >= south[i]-2.0) & (data['lat'] <= north[i]+2.0) \
& (data['lon'] >= west[i]-2.0) & (data['lon'] <= east[i]+2.0)]
# filter data (there seems to be one site always reporting a really anomalous temperature
data = data[data['temp'] <= 50]
print("Working on %s" % maps[i])
# set up the map projection central longitude/latitude and the standard parallels
cenlon = (west[i] + east[i]) / 2.0
cenlat = (south[i] + north[i]) / 2.0
sparallel = cenlat
if cenlat > 0:
cutoff = -30
flip = False
elif cenlat < 0:
cutoff = 30
flip = True
# create the projection
if restart_projection:
proj = ccrs.LambertConformal(central_longitude=cenlon,
central_latitude=cenlat,
standard_parallels=[sparallel],
cutoff=cutoff)
point_locs = proj.transform_points(ccrs.PlateCarree(),
data['lon'].values,
data['lat'].values)
data = data[reduce_point_density(point_locs, radius[i] * 1000)]
# state borders
state_boundaries = cfeature.NaturalEarthFeature(category='cultural',\
name='admin_1_states_provinces_lines',scale='50m',facecolor='none')
# county boundaries
if usecounties[i]:
county_reader = shpreader.Reader(ctyshppath)
counties = list(county_reader.geometries())
COUNTIES = cfeature.ShapelyFeature(counties, ccrs.PlateCarree())
### DO SOME CONVERSIONS ###
# get the wind components
u, v = wind_components(data['wsp'].values * units('knots'),
data['wdr'].values * units.degree)
# convert temperature from Celsius to Fahrenheit
data['temp'] = cToF(data['temp'])
data['dpt'] = cToF(data['dpt'])
# convert the cloud fraction value into a code of 0-8 (oktas) and compenate for NaN values
cloud_frac = (8 * data['sky'])
cloud_frac[np.isnan(cloud_frac)] = 10
cloud_frac = cloud_frac.astype(int)
# map weather strings to WMO codes (only use first symbol if multiple are present
data['wx'] = data.wx.str.split('/').str[0] + ''
wx = [
wx_code_map[s.split()[0] if ' ' in s else s]
for s in data['wx'].fillna('')
]
# get the minimum and maximum temperatures in domain
searchdata = data[(data['lat'] >= south[i]) & (data['lat'] <= north[i]) \
& (data['lon'] >= west[i]) & (data['lon'] <= east[i])]
min_temp = searchdata.loc[searchdata['temp'].idxmin()]
max_temp = searchdata.loc[searchdata['temp'].idxmax()]
max_dewp = searchdata.loc[searchdata['dpt'].idxmax()]
# look up the site names for the min/max temp locations
min_temp_loc = icaoLookup(min_temp['siteID'], icaodf)
max_temp_loc = icaoLookup(max_temp['siteID'], icaodf)
max_dewp_loc = icaoLookup(max_dewp['siteID'], icaodf)
text_str = "Min temp: %.0f F at %s (%s)\nMax temp: %.0f F at %s (%s)\nMax dewpoint: %.0f F at %s (%s)"\
% (min_temp['temp'],min_temp['siteID'],min_temp_loc,\
max_temp['temp'],max_temp['siteID'],max_temp_loc,\
max_dewp['dpt'],max_dewp['siteID'],max_dewp_loc)
### PLOTTING SECTION ###
# change the DPI to increase the resolution
plt.rcParams['savefig.dpi'] = 255
# create the figure and an axes set to the projection
fig = plt.figure(figsize=(20, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)
# add various map elements
ax.add_feature(cfeature.LAND, zorder=-1)
ax.add_feature(cfeature.OCEAN, zorder=-1)
ax.add_feature(cfeature.LAKES, zorder=-1)
ax.add_feature(cfeature.COASTLINE, zorder=2, edgecolor='black')
ax.add_feature(state_boundaries, edgecolor='black')
if usecounties[i]:
ax.add_feature(COUNTIES,
facecolor='none',
edgecolor='gray',
zorder=-1)
ax.add_feature(cfeature.BORDERS, linewidth=2, edgecolor='black')
# set plot bounds
ax.set_extent((west[i], east[i], south[i], north[i]))
### CREATE STATION PLOTS ###
# lat/lon of the station plots
stationplot = StationPlot(ax,data['lon'].values,data['lat'].values,clip_on=True,\
transform=ccrs.PlateCarree(),fontsize=6)
# plot the temperature and dewpoint
stationplot.plot_parameter('NW', data['temp'], color='red')
stationplot.plot_parameter('SW', data['dpt'], color='darkgreen')
# plot the SLP using the standard trailing three digits
stationplot.plot_parameter(
'NE', data['slp'], formatter=lambda v: format(10 * v, '.0f')[-3:])
# plot the sky condition
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# plot the present weather
stationplot.plot_symbol('W', wx, current_weather)
# plot the wind barbs
stationplot.plot_barb(u, v, flip_barb=flip)
# plot the text of the station ID
stationplot.plot_text((2, 0), data['siteID'])
# plot the valid time
plt.title('Surface Observations valid %s' % vt)
# plot the min/max temperature info and draw circle around warmest and coldest obs
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
plt.text(west[i],
south[i],
text_str,
fontsize=12,
verticalalignment='top',
bbox=props,
transform=ccrs.Geodetic())
projx1, projy1 = proj.transform_point(min_temp['lon'], min_temp['lat'],
ccrs.Geodetic())
ax.add_patch(
matplotlib.patches.Circle(xy=[projx1, projy1],
radius=50000,
facecolor="None",
edgecolor='blue',
linewidth=3,
transform=proj))
projx2, projy2 = proj.transform_point(max_temp['lon'], max_temp['lat'],
ccrs.Geodetic())
ax.add_patch(
matplotlib.patches.Circle(xy=[projx2, projy2],
radius=50000,
facecolor="None",
edgecolor='red',
linewidth=3,
transform=proj))
projx3, projy3 = proj.transform_point(max_dewp['lon'], max_dewp['lat'],
ccrs.Geodetic())
ax.add_patch(
matplotlib.patches.Circle(xy=[projx3, projy3],
radius=30000,
facecolor="None",
edgecolor='green',
linewidth=3,
transform=proj))
# save the figure
outfile_name = savedir + savenames[i]
plt.savefig(outfile_name, bbox_inches='tight')
# clear and close everything
fig.clear()
ax.clear()
plt.close(fig)
f.close()
print("Script finished.")
def plot_map_standard(proj,
point_locs,
df_t,
area='EU',
west=-9.5,
east=28,
south=35,
north=62,
fonts=14,
path=None,
SLP=False,
gust=False):
if path == None:
# set up the paths and test for existence
path = expanduser('~') + '/Documents/Metar_plots'
try:
os.listdir(path)
except FileNotFoundError:
os.mkdir(path)
else:
path = path
df = df_t.loc[(df_t['longitude'] >= west - 4)
& (df_t['longitude'] <= east + 4)
& (df_t['latitude'] <= north + 4) &
(df_t['latitude'] >= south - 4)]
plt.rcParams['savefig.dpi'] = 300
# =========================================================================
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 16))
ax = fig.add_subplot(1, 1, 1, projection=proj)
if area == 'Antarctica':
df = df.loc[df['latitude'] < north]
ax.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
elif area == 'Arctic':
df = df.loc[df['latitude'] > south]
ax.set_extent([-180, 180, 60, 90], ccrs.PlateCarree())
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
else:
ax.set_extent((west, east, south, north))
# Get the wind components, converting from m/s to knots as will
# be appropriate for the station plot.
df['dd'][df['dd'] > 360] = np.nan
u, v = wind_components(df['ff'].values * units('knots'),
df['dd'].values * units('deg'))
cloud_frac = df['cloud_cover']
# Change the DPI of the resulting figure. Higher DPI drastically improves
# look of the text rendering.
# Set up a cartopy feature for state borders.
# state_boundaries = feat.NaturalEarthFeature(category='cultural',
# name='admin_0_countries',
# scale='10m',
# facecolor='#d8dcd6',
# alpha=0.5)
# ax.coastlines(resolution='10m', zorder=0, color='black')
# ax.add_feature(feat.LAND)
ax.add_feature(feat.COASTLINE.with_scale('10m'),
zorder=2,
edgecolor='black')
ax.add_feature(feat.OCEAN.with_scale('50m'), zorder=0)
ax.add_feature(feat.STATES.with_scale('10m'),
zorder=1,
facecolor='white',
edgecolor='#5e819d')
# ax.add_feature(cartopy.feature.OCEAN, zorder=0)
# Set plot bounds
# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax,
df['longitude'].values,
df['latitude'].values,
clip_on=True,
transform=ccrs.PlateCarree(),
fontsize=fonts)
# Plot the temperature and dew point to the upper and lower left,
# respectively, of the center point. Each one uses a different color.
Temp = stationplot.plot_parameter('NW',
df['TT'],
color='#fd3c06',
fontweight='bold',
zorder=3)
Td = stationplot.plot_parameter('SW', df['TD'], color='#01ff07')
if gust == True:
maxff = stationplot.plot_parameter('SE',
df['max_gust'],
color='#cb416b',
fontweight='bold',
zorder=3)
maxff.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.Normal()
])
# fontweight = 'bold'
# More complex ex. uses custom formatter to control how sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of
# the pressure value in tenths of millibars.
if (area != 'Antarctica' and area != 'Arctic'):
p = stationplot.plot_parameter(
'NE',
df['SLP'],
formatter=lambda v: format(10 * v, '.0f')[-3:],
color="#a2cffe")
for x in [Temp, Td, p]:
x.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.Normal()
])
else:
for x in [Temp, Td]:
x.set_path_effects([
path_effects.Stroke(linewidth=1.5, foreground='black'),
path_effects.Normal()
])
# Add wind barbs
stationplot.plot_barb(u, v, zorder=3, linewidth=2)
# Plot the cloud cover symbols in the center location. This uses the codes
# made above and uses the `sky_cover` mapper to convert these values to
# font codes for the weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
for val in range(0, 2):
wx = df[['ww', 'StationType']]
if val == 0:
# mask all the unmanned stations
wx['ww'].loc[wx['StationType'] > 3] = np.nan
wx2 = wx['ww'].fillna(00).astype(int).values.tolist()
stationplot.plot_symbol('W', wx2, current_weather, zorder=4)
else:
# mask all the manned stations
wx['ww'].loc[(wx['StationType'] <= 3)] = np.nan
# mask all reports smaller than 9
# =7 is an empty symbol!
wx['ww'].loc[wx['ww'] <= 9] = 7
wx2 = wx['ww'].fillna(7).astype(int).values.tolist()
stationplot.plot_symbol('W', wx2, current_weather_auto, zorder=4)
if SLP == True:
lon = df['longitude'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)].values
lat = df['latitude'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)].values
xp, yp, _ = proj.transform_points(ccrs.PlateCarree(), lon, lat).T
sea_levelp = df['SLP'].loc[(df.PressureDefId == 'mean sea level')
& (df.Hp <= 750)]
x_masked, y_masked, pres = remove_nan_observations(
xp, yp, sea_levelp.values)
slpgridx, slpgridy, slp = interpolate_to_grid(x_masked,
y_masked,
pres,
interp_type='cressman',
search_radius=400000,
rbf_func='quintic',
minimum_neighbors=1,
hres=100000,
rbf_smooth=100000)
Splot_main = ax.contour(slpgridx,
slpgridy,
slp,
colors='k',
linewidths=2,
extent=(west, east, south, north),
levels=list(range(950, 1050, 10)))
plt.clabel(Splot_main, inline=1, fontsize=12, fmt='%i')
Splot = ax.contour(slpgridx,
slpgridy,
slp,
colors='k',
linewidths=1,
linestyles='--',
extent=(west, east, south, north),
levels=[
x for x in range(950, 1050, 1)
if x not in list(range(950, 1050, 10))
])
plt.clabel(Splot, inline=1, fontsize=10, fmt='%i')
# stationplot.plot_text((2, 0), df['Station'])
# Also plot the actual text of the station id. Instead of cardinal
# directions, plot further out by specifying a location of 2 increments
# in x and 0 in y.stationplot.plot_text((2, 0), df['station'])
if (area == 'Antarctica' or area == 'Arctic'):
plt.savefig(path + '/CURR_SYNOP_' + area + '.png',
bbox_inches='tight',
pad_inches=0)
else:
plt.savefig(path + '/CURR_SYNOP_' + area + '.png',
bbox_inches='tight',
transparent="True",
pad_inches=0)
