def plot_map_pred_fish(self, model, year, rtime, fitdir, movdir, gname,
figname):
fig, ax = self.draw_water_and_polys()
survey_dates = self.get_survey_dates(year)
nsurveys = len(survey_dates)
self.set_fate_to_number(movdir, gname,
rtime) # fairly hardwired method
pred_data = self.get_pred_fish(fitdir, gname, rtime, survey_dates)
barxy = self.findSiteLoc(self.fates)
barboxsize = [10000., 10000.]
leg_args = {
'ylabel': 'Abundance',
'bars': ['January', 'February', 'March'],
'max': max_ab
}
map_w_bars.plot_bars(ax,
fig,
pred_data,
barxy,
barboxsize,
self.xylims,
leg_args,
frac=False)
plt.savefig(figname, dpi=900, facecolor='white', bbox_inches='tight')
plt.close()
plt.clf()
return
def plot_map_pred(self, model, year, gname, figname):
fig, ax = self.draw_water_and_polys()
survey_dates = self.get_survey_dates(year)
nsurveys = len(survey_dates)
pred_data, count_polys = self.get_pred(gname, model, survey_dates)
barxy = self.findSiteLoc(count_polys)
barboxsize = [10000., 10000.]
leg_args = {
'ylabel': 'Fraction',
'bars': ['January', 'February', 'March'],
'max': max_ab
}
map_w_bars.plot_bars(ax,
fig,
pred_data,
barxy,
barboxsize,
self.xylims,
leg_args,
frac=True)
plt.savefig(figname, dpi=900, facecolor='white', bbox_inches='tight')
plt.close()
plt.clf()
return
def plot_map_obs_pred(self, year, month, scenarios, labels, figname):
fig, ax = self.draw_water_and_polys()
ngroups = len(labels)
# get bardata (fraction in each region) for survey periods
survey_dates = self.get_survey_dates(year)
pred_data = {}
for label in labels:
scenario = scenarios[label]
# read predicted counts
gname = scenario['group_name']
rdir = scenario['run_dir']
model = scenario['model']
pred_data[label], count_polys = self.get_pred(gname,
model,
survey_dates,
rdir=rdir)
barxy = self.findSiteLoc(count_polys)
obs_data, obs_polys = self.get_obs_data(year)
# reorder obs_data to match order of count_polys
obs_data_frac = np.zeros_like(pred_data[labels[0]])
obs_total = np.sum(obs_data, axis=0)
for npol, poly in enumerate(obs_polys):
n = count_polys.index(poly)
obs_data_frac[n, :] = np.divide(obs_data[npol, :], obs_total)
survey = month_to_survey[month]
bardata = np.zeros((len(count_polys), ngroups + 1), np.float64)
# first bar is observed counts
bardata[:, 0] = obs_data_frac[:, survey]
for nl, label in enumerate(labels):
bardata[:, nl + 1] = pred_data[label][:, survey]
barboxsize = [10000., 10000.]
leg_args = {
'ylabel': 'Fraction',
'bars': ['Observed'] + labels,
'max': max_ab
}
map_w_bars.plot_bars(ax,
fig,
bardata,
barxy,
barboxsize,
self.xylims,
leg_args,
frac=True)
plt.savefig(figname, dpi=900, facecolor='white', bbox_inches='tight')
plt.close()
plt.clf()
return
def plot_map_obs(self,
longfile_path,
figname,
size_range,
Surveys,
normalize=False):
'''
Creates a bar plot of the Abundance for a particular year.
many surveys is untested and may look really ugly. Consider keeping 9 surveys max.
'''
self.obs_df = pd.read_csv(longfile_path, parse_dates=True)
fig, ax = self.draw_water_and_polys()
bars = []
for num in Surveys:
bars.append('Survey {0}'.format(num))
barboxsize = [10000., 10000.] #determines size of plots. Don't touch.
leg_args = {'ylabel': 'Density', 'bars': bars, 'max': ''}
if leg_args['ylabel'] == 'Density':
leg_args['max'] = self.max_den[self.year]
elif leg_args['ylabel'] == 'Abundance':
leg_args['max'] = self.max_ab[self.year]
plt.title('Observed {0} {1}'.format(leg_args['ylabel'], self.year))
# read observed counts
obs_data = self.get_obs_data(self.year, Surveys, size_range)
if leg_args['ylabel'] == 'Density':
obs_data = self.Abun_to_Density_Bar(
obs_data) #convert abundance data to density
barxy = self.findSiteLoc(self.poly_names) #get xy for each site
labels = self.findNoBarData(
Surveys, self.year) #find correct labels for surveys with no data
map_w_bars.plot_bars(ax,
fig,
obs_data,
barxy,
barboxsize,
self.xylims,
leg_args,
frac=False,
labels=labels)
figname = '_'.join([
figname,
str(self.year), '{0}mm-{1}mm.png'.format(size_range[0],
size_range[1])
])
plt.savefig(figname, dpi=900, facecolor='white', bbox_inches='tight')
plt.close()
plt.clf()
return
def plot_map_obs_pred_fish(self, obs_file, model, year, rtime, fitdir,
movdir, gname, figname):
reg_ab = fit_results.load_obs_regional_abundance(obs_file)
fig, ax = self.draw_water_and_polys()
survey_dates = self.get_survey_dates(year)
nsurveys = len(survey_dates)
self.set_fate_to_number(movdir, gname,
rtime) # fairly hardwired method
pred_data = self.get_pred_fish(fitdir, gname, rtime, survey_dates)
bardata = np.zeros((len(pred_data), nsurveys * 2), np.float64)
#bardata = np.ones((len(pred_data),nsurveys*2),np.float64)*np.nan
for nsur in range(nsurveys):
bardata[:, nsur * 2 + 1] = pred_data[:, nsur]
for nreg, pred in enumerate(pred_data):
if nreg < len(reg_ab): # can't set obs seaward loss or entrain
bardata[nreg, nsur * 2] = reg_ab[nreg, nsur]
# for nreg, pred in enumerate(pred_data):
# bardata[nreg*2,:] = pred_data[nreg,:]
# if nreg < len(reg_ab): # can't set observed seaward loss or entrain
# bardata[nreg*2+1,:] = reg_ab[nreg,:]
barxy = self.findSiteLoc(self.fates)
barboxsize = [10000., 10000.]
barboxsize = [10000., 8000.]
leg_args = {
'ylabel': 'Abundance',
'bars': ['January', 'February', 'March'],
'max': max_ab
}
map_w_bars.plot_bars(ax,
fig,
bardata,
barxy,
barboxsize,
self.xylims,
leg_args,
frac=False,
alt_hatch=True)
# x out losses and entrain for obs (15 and 16)?
plt.savefig(figname, dpi=900, facecolor='white', bbox_inches='tight')
plt.close()
plt.clf()
return
def plot_map_obs_fish(self, obs_file, year, movdir, gname, rtime, figname):
reg_ab = fit_results.load_obs_regional_abundance(obs_file)
nreg = len(reg_ab)
fig, ax = self.draw_water_and_polys()
survey_dates = self.get_survey_dates(year)
nsurveys = len(survey_dates)
self.set_fate_to_number(movdir, gname,
rtime) # fairly hardwired method
barxy = self.findSiteLoc(self.fates[:nreg])
barboxsize = [10000., 10000.]
barboxsize = [10000., 8000.]
leg_args = {
'ylabel': 'Abundance',
'bars': ['January', 'February', 'March'],
'max': max_ab
}
map_w_bars.plot_bars(ax,
fig,
reg_ab,
barxy,
barboxsize,
self.xylims,
leg_args,
frac=False,
alt_hatch=False)
# x out losses and entrain for obs (15 and 16)?
plt.savefig(figname, dpi=900, facecolor='white', bbox_inches='tight')
plt.close()
plt.clf()
return