def scale_data(data, syear):
"""Scales the input array by month lenghs starting from january of start year"""
scale = np.zeros(data.shape[0])
for m in range(len(scale)):
mo = np.int(m % 12 + 1)
scale[m] = 1E-3 / (mr(np.int(syear + np.floor(m / 12)), mo)[1] * 24 *
60 * 60)
# input is g(CH4) m-2 month-1
# output is kg(CH4) m-2 s-1
for m in range(len(scale)):
data[m, :, :] = data[m, :, :] * scale[m]
return data
def total_stat(total_dict, new_procate_dict):
total_file.writerow(
['\ufeffmonth', '#total_sku', 'total_sales', 'total_gmv', 'avg_price'])
for month in total_dict:
sku = len(total_dict[month]['sku'])
month_days = mr(int(month[:4]), int(month[-2:]))[1]
sales, gmv = month_sales('all_stat', total_dict[month]['vol'],
total_dict[month]['price'], month_days,
new_procate_dict)
if sales == 0:
print('sales 0')
print([month, sku, sales, gmv, avg_price])
avg_price = 0
else:
avg_price = gmv / sales
total_file.writerow([month, sku, sales, gmv, avg_price])
def sku_stat(stat_dict, new_procate_dict):
final_file.writerow(
['\ufeffcategory', 'month', '#sku', 'month_sales', 'gmv', 'avg_price'])
for category in stat_dict:
for month in stat_dict[category]:
sku = len(stat_dict[category][month]['sku'])
month_days = mr(int(month[:4]), int(month[-2:]))[1]
sales, gmv = month_sales(category,
stat_dict[category][month]['vol'],
stat_dict[category][month]['price'],
month_days, new_procate_dict)
if sales == 0:
print('sales 0')
print([category, month, sku, sales, gmv, avg_price])
avg_price = 0
else:
avg_price = gmv / sales
final_file.writerow([category, month, sku, sales, gmv, avg_price])
def last_months_last_day():
'''Return datetime object for last day of previous month
Ex: today = 2019-06-15
last_months_last_day()
>>> Timestamp (2019, 5, 31)
'''
t = dt.datetime.today()
m = t.month
y = t.year
if m == 1:
y -= 1
m = 12
else:
m -= 1
d = mr(y, m)[1]
return dt.datetime(y, m, d)
def get(self, *args, **kwargs):
t = date.today()
y, m = t.year, t.month
self.date_from_str = self.request.GET.get("date_from")
self.date_to_str = self.request.GET.get("date_to")
try:
self.date_from = dateutil.parser.parse(self.date_from_str)
self.date_to = dateutil.parser.parse(self.date_to_str)
except:
self.date_from_str = date(y, m, 1).isoformat()
self.date_to_str = date(y, m, mr(y, m)[1]).isoformat()
self.date_from = dateutil.parser.parse(self.date_from_str)
self.date_to = dateutil.parser.parse(self.date_to_str)
self.budget_pk = self.request.GET.get("budget")
self.budget = Budget.objects.filter(pk=self.budget_pk).first()
if self.budget is None:
self.budget = Budget.objects.last()
self.budget_pk = self.budget.pk
self.comparisons = [Comparison(self.date_from, self.date_to, en) for en in self.budget.entry_set.all()]
self.comparisons.append(UnbudgetedComparison(self.date_from, self.date_to, self.budget))
return super(Compare, self).get(*args, **kwargs)
def endofmonth(dates):
dts = np.array([
datetime(dt.year, dt.month,
mr(dt.year, dt.month)[1]) for dt in dates
])
return dts
plt.title('Annual CH4 fluxes from [' + str(lat_min) + 'N:90N]')
plt.ylabel('Tg', rotation=0)
ax = plt.gca()
ax.yaxis.set_label_coords(-0.02, 1)
ax.set_xlim([2000, 2015])
plt.xlabel('year')
plt.grid(linestyle='dotted')
outf = output_dir + 'CH4_timeseries_2000-' + str(ye) + ifmt
plt.savefig(outf)
print("Created image:", outf)
if plot_annual == True:
# Monthly averages (calculate month length)
mscale = np.zeros(12)
for m in range(12):
mscale[m] = mr(2001, m + 1)[1]
x = np.linspace(1, 12, 12)
obsmodels = map_obs
fig, axs = plt.subplots(1, 2, figsize=(15, 8))
for omodel in obsmodels:
col = obsmodels.index(omodel)
ofile = glob(base_dir + 'wetlandCH4*' + omodel + '*.nc')
odata = Dataset(ofile[0], 'r')
try:
olats = odata.variables['latitude'][:]
except:
olats = odata.variables['lat'][:]
try:
olons = odata.variables['longitude'][:]
except:
olons = odata.variables['lon'][:]