import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime
import os

import sys

sys.path.append('d:\guto\gpython\pytools')

import Hidroweb_tools as ht

# hidroweb_tools... sorry, in Portuguese! 
# Request me by email if you want. I'll put in GitHub in the future (????)

ht?

Object `ht` not found.

path_taquari = r'd:\GUTO\1_Trabs\ANA_bacias\Lagoa_dos_Patos\RioTaquari_Vazoes\\' 
file_taquari = 'vazoes_C_86510000.csv'

path_camaqua = r'd:\GUTO\1_Trabs\ANA_bacias\Lagoa_dos_Patos\RioCamaqua_Vazoes\\' 
file_camaqua = 'vazoes_T_87905000.txt'

path_jacui = r'd:\GUTO\1_Trabs\ANA_bacias\Lagoa_dos_Patos\RioJacui_Vazoes\\'
file_jacui = 'vazoes_C_85900000.csv'

path_cai = r'd:\GUTO\1_Trabs\ANA_bacias\Lagoa_dos_Patos\RioCai_Vazoes\\'
file_cai = 'vazoes_T_87170000.txt'

path_sinos = r'd:\GUTO\1_Trabs\ANA_bacias\Lagoa_dos_Patos\RioSinos_Vazoes\\'
file_sinos = 'vazoes_C_87382000.csv'

with open(path_camaqua + file_camaqua) as f:
    linhas_camaqua = f.readlines()
    
with open(path_jacui + file_jacui) as f:
    linhas_jacui = f.readlines()

with open(path_taquari + file_taquari) as f:
    linhas_taquari = f.readlines()
    
with open(path_cai + file_cai) as f:
    linhas_cai = f.readlines()
    
with open(path_sinos + file_sinos) as f:
    linhas_sinos = f.readlines()

linhas = [linhas_camaqua, linhas_jacui, linhas_taquari, linhas_cai, linhas_sinos]
rivers = ['Camaquã', 'Jacuí', 'Taquari', 'Caí','Sinos']

dados = []
time_n = []
for li in linhas:
    arruma = ht.arruma_vazao(li)
    time_n.append(mdates.date2num(arruma[:,0]))
    
    arruma[:,1] = [float(x) for x in arruma[:,1]]
    
    dados.append(arruma)

fig, ax = plt.subplots(figsize=(22,4))
for d in dados:
    ax.plot(d[:,0], d[:,1])
    ax.set_yscale('log')

for d in dados:
    print(d[0,0], d[-1,0])

1964-05-01 00:00:00 2021-04-30 00:00:00
1939-12-01 00:00:00 2021-07-31 00:00:00
1940-01-01 00:00:00 2021-03-31 00:00:00
1978-01-01 00:00:00 2021-07-31 00:00:00
1973-07-01 00:00:00 2021-07-31 00:00:00

t1 = datetime.date(1978, 1, 1)
t2 = datetime.date(2020, 12, 31)

t1n = mdates.date2num(t1)
t2n = mdates.date2num(t2)

data_synoptic = []
data_monthly_means = []

for i, d in enumerate(dados):
    g_time = time_n[i]
    idx = np.array(np.where(
                            (g_time >= t1n) 
                            & 
                            (g_time <= t2n)
                    )).squeeze()
    
    get_data = d[idx]
    
    # Detour to avoid the 'safe' problem!
    q_values = get_data[:,1]
    q_values = [float(x) for x in q_values]
    
    # find if there are nan 
    not_nan = np.array(np.where(np.isnan(q_values) == False)).squeeze()
    
    get_data = get_data[not_nan]
    print(len(get_data))
    
    data_synoptic.append(get_data)

    monthly_mean = ht.serie_mensal(get_data)
    print(len(monthly_mean))
    
    data_monthly_means.append(monthly_mean)
    # q_anual.append(ht.climatologia_mensal(arruma))

15497
513
15573
512
15665
515
15494
513
15355
506

for d in data_monthly_means:
    print(np.round( np.mean(d[:,1]), 1))

330.8
966.9
425.8
70.7
88.6

q_sum = []
q_t = []

# create a list of empty lists to save the months in each
save_months = []
for i in range(12):
    save_months.append([])

for year in range(1978, 2022):
    for month in range(1, 13):
        
        get_q = []
        q_t.append(datetime.date(year, month, 15))
        
        for qm in data_monthly_means:
            for q in qm:
                if q[0].year == year and q[0].month == month:
                    get_q.append(q[1])
                    
        
         
        make_sum = np.nansum(get_q)
        
        save_months[month-1].append(make_sum)
        
        q_sum.append(make_sum)

save_months = [np.array(x) for x in save_months]
    
# some bad data at the end of the series...
q_t = q_t[:-11]
q_sum = q_sum[:-11]

q_t[0]

datetime.date(1978, 1, 15)

'''
To find the flow for Feb and Mar/2020 (campaign period)

That's because we carried out a field experiment at the lagoon's mouth during this period, and neet to contextualize the hidrologic condition!
'''
for i, t in enumerate(q_t):
    if t.year == 2020 and t.month == 2:
        print(q_sum[i])
    if t.year == 2020 and t.month == 3:
        print(q_sum[i])

430.95109939148074
239.1316198924731

plt.rcParams.update({'font.size':14})
plt.figure(figsize=(16,4))
plt.plot(q_t, q_sum)
plt.axvline(datetime.date(2020,3,10), color='tab:orange')
plt.axhline(350, color='tab:orange')
plt.xlim(datetime.date(1977,11,1), datetime.date(2021,3,1))
plt.ylabel('Flow (m$^3$/s)')
plt.show()

# f, q, p, vp = flow_duration_curve(q_sum)
f, q, p, vp = ht.curva_permanencia(q_sum)

p_campanha = np.interp(350, vp, p)
print(p_campanha)


plt.rcParams.update({'font.size':12})
fig, ax = plt.subplots()

ax.plot(f, q)
for i in range(len(p)):
    ax.plot(p[i], vp[i], 'o', color='tab:blue')
    ax.text(p[i], vp[i]+150, str(int(np.round(vp[i]))))
    
ax.axhline(350, color='tab:orange')
ax.axvline(p_campanha, color='tab:orange')
    
ax.set_xlabel('Exceedance Probability')
ax.set_ylabel('Flow (monthly mean) (m$^3$/s)')
ax.text(0, 400, '350 m$^3$/s')
ax.text(0.9, 7000, '0.99')
plt.show()

0.99

plt.figure(figsize=(3,2))
for i in range(12):
    plt.plot(np.linspace(i+1, i+1, len(save_months[i])), save_months[i], '.')
    
    save_months[i] = save_months[i][save_months[i][:]>0]

plt.figure(figsize=(3,2))
for i in range(12):
    plt.plot(np.linspace(i+1, i+1, len(save_months[i])), save_months[i], '.')

a, b, c, d = [], [], [], []
for i in range(12):
    f, q, p, vp = ht.curva_permanencia(save_months[i])
    a.append(f)
    b.append(q)
    c.append(p)
    d.append(vp)
    
vp = np.array(d)
vp.shape

(12, 9)

p = np.array(p)
int(p[1]*100)

5

n_mes = np.arange(1,13)
l_mes = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

p_vp = vp[-1,:]
p_vp

array([4792.19, 4055.81, 2668.8 , 1381.69,  767.21,  610.95,  504.96,
        369.54,  332.2 ])

fig, ax = plt.subplots()

ax.plot(n_mes, vp[:,4], color='#8fd3fe', lw=4, zorder=5)
ax.plot(n_mes, vp[:,4], color='k', lw=3, zorder=5)
ax.fill_between(n_mes, vp[:,3], vp[:,5], zorder=4, color='#1134a6', edgecolor='#8fd3fe', lw=.5)
ax.fill_between(n_mes, vp[:,2], vp[:,6], zorder=3, color='#0052ff', edgecolor='#8fd3fe', lw=.5)
ax.fill_between(n_mes, vp[:,1], vp[:,7], zorder=2, color='#0080fe', edgecolor='w', lw=.5)
ax.fill_between(n_mes, vp[:,0], vp[:,8], zorder=1, color='#8fd3fe', edgecolor=[.7, .7, .7], lw=.5)

ax.set_ylabel('Flow (m$^3$/s)')
ax.set_xticks(np.arange(1,13))
ax.set_xticklabels(l_mes)

for i in range(9):
    if i < 5:
        ax.text(12.1, p_vp[i], str(int(p[i]*100))) 
    if i == 8:
        ax.text(12.1, p_vp[i], str(int(p[i]*100))) 
        
plt.show()

Hidroweb Data - River Flow Climatology¶

Gauged fresh water inflow to the Patos Lagoon¶

Worth to note that with the HidroWeb ASCII files (my guess) we have the 'safe' problem when using the np.isnan!¶

Gauge Stations Description (copy/paste from Hidroweb)¶

Drainagem Areas (km2)¶

Adressing...¶

Upload the data¶

Organize de data from the ASCII files¶

Check the start/end of each time-series¶

Separate the synoptic period and exclude the nans¶

Synoptic means and checking if there are nans¶

Summing the flows and separating the months¶

Check point!¶