32 Cosas a calcular

Promedio mensual de To del máximo diario (memo)
Mes más cálido con la To promedio mensual (Lili)
Amplitud diaria de la To (Fer ipn)
Quartiles mensuales de la Ig (Nairo)
Calcular la Ib a partir de la Ig, Id (rodolfo)
Humedad promedio semanal (Luisa)
Promedio mensual de la To mínima (Mitsue)
Promedio diario mensual de la velocidad máxima del viento (Andre)
Promedios mensual de la Ig y su visualización (Laura)
Calcular la curva de la radiación solar del día más cálido (Fer ier)
Dia promedio de todas las variables con máximos, minimos y desviacion estándard (yadira)
Calculo diario/mensual/anual de la energia solar

import pandas as pd
import matplotlib.pyplot as plt

f = "../data/002_processed/esolmet_2018.parquet"
esolmet = pd.read_parquet(f)
To_diaria_promedio = esolmet.To.resample("D").mean()
esolmet["amplitud"] = (
    esolmet.To.resample("D").max() - 
    esolmet.To.resample("D").min()
).resample("MS").mean()
esolmet["Tpromedio"] = esolmet.To.resample("MS").mean()
esolmet.amplitud = esolmet.amplitud.ffill()
esolmet.Tpromedio = esolmet.Tpromedio.ffill()
esolmet

	Ib	Ig	Id	uv	To	hr	ws	p	amplitud	Tpromedio
Fecha
2018-01-01 00:00:00	0.057	0.0	0.0	0.001	18.93	41.57	1.253	879.0692	15.112258	20.019848
2018-01-01 00:10:00	0.002	0.0	0.0	0.001	18.76	41.00	0.418	879.4363	15.112258	20.019848
2018-01-01 00:20:00	0.170	0.0	0.0	0.001	18.92	40.96	0.955	879.5181	15.112258	20.019848
2018-01-01 00:30:00	0.371	0.0	0.0	0.001	18.52	42.46	1.823	879.5826	15.112258	20.019848
2018-01-01 00:40:00	0.305	0.0	0.0	0.001	18.49	42.43	2.149	879.6826	15.112258	20.019848
...	...	...	...	...	...	...	...	...	...	...
2018-12-31 23:10:00	0.125	0.0	0.0	0.000	18.88	59.60	2.145	875.5595	13.576452	20.362943
2018-12-31 23:20:00	0.000	0.0	0.0	0.000	18.71	59.67	1.638	875.5595	13.576452	20.362943
2018-12-31 23:30:00	0.044	0.0	0.0	0.000	18.52	58.75	1.923	875.2889	13.576452	20.362943
2018-12-31 23:40:00	0.170	0.0	0.0	0.000	18.36	60.62	2.089	875.0606	13.576452	20.362943
2018-12-31 23:50:00	0.003	0.0	0.0	0.000	17.99	60.76	0.744	875.1424	13.576452	20.362943

51173 rows × 10 columns

esolmet["Tpromedio"] = esolmet.To.resample("MS").mean()
esolmet["Tpromedio"] = esolmet.Tpromedio.ffill()
esolmet

	Ib	Ig	Id	uv	To	hr	ws	p	amplitud	Tpromedio
Fecha
2018-01-01 00:00:00	0.057	0.0	0.0	0.001	18.93	41.57	1.253	879.0692	15.112258	20.019848
2018-01-01 00:10:00	0.002	0.0	0.0	0.001	18.76	41.00	0.418	879.4363	15.112258	20.019848
2018-01-01 00:20:00	0.170	0.0	0.0	0.001	18.92	40.96	0.955	879.5181	15.112258	20.019848
2018-01-01 00:30:00	0.371	0.0	0.0	0.001	18.52	42.46	1.823	879.5826	15.112258	20.019848
2018-01-01 00:40:00	0.305	0.0	0.0	0.001	18.49	42.43	2.149	879.6826	15.112258	20.019848
...	...	...	...	...	...	...	...	...	...	...
2018-12-31 23:10:00	0.125	0.0	0.0	0.000	18.88	59.60	2.145	875.5595	13.576452	20.362943
2018-12-31 23:20:00	0.000	0.0	0.0	0.000	18.71	59.67	1.638	875.5595	13.576452	20.362943
2018-12-31 23:30:00	0.044	0.0	0.0	0.000	18.52	58.75	1.923	875.2889	13.576452	20.362943
2018-12-31 23:40:00	0.170	0.0	0.0	0.000	18.36	60.62	2.089	875.0606	13.576452	20.362943
2018-12-31 23:50:00	0.003	0.0	0.0	0.000	17.99	60.76	0.744	875.1424	13.576452	20.362943

51173 rows × 10 columns

esolmet.loc["2018-01-31 23:40":"2018-02-01 01:00"]

	Ib	Ig	Id	uv	To	hr	ws	p	amplitud	Tpromedio
Fecha
2018-01-31 23:40:00	0.081	0.0	0.0	0.001	15.28	53.26	2.083	878.6181	15.112258	20.019848
2018-01-31 23:50:00	0.075	0.0	0.0	0.001	15.32	52.38	2.500	878.3397	15.112258	20.019848
2018-02-01 00:00:00	0.003	0.0	0.0	0.001	15.38	53.71	1.531	878.2341	15.353929	23.522289
2018-02-01 00:00:00	0.003	0.0	0.0	0.001	15.38	53.71	1.531	878.2341	15.353929	23.522289
2018-02-01 00:10:00	0.313	0.0	0.0	0.001	15.71	50.03	2.339	878.1605	15.353929	23.522289
2018-02-01 00:20:00	0.029	0.0	0.0	0.001	15.57	51.52	2.836	877.9796	15.353929	23.522289
2018-02-01 00:30:00	0.013	0.0	0.0	0.001	15.23	52.00	2.780	877.9725	15.353929	23.522289
2018-02-01 00:40:00	0.001	0.0	0.0	0.001	15.28	51.80	2.541	878.0463	15.353929	23.522289
2018-02-01 00:50:00	0.065	0.0	0.0	0.001	15.38	50.84	2.434	878.1319	15.353929	23.522289
2018-02-01 01:00:00	0.086	0.0	0.0	0.001	15.41	51.53	2.271	878.2111	15.353929	23.522289

esolmet.loc["2018-01-31 00:00":"2018-02-01 01:00"]

	Ib	Ig	Id	uv	To	hr	ws	p	amplitud	Tpromedio
Fecha
2018-01-31 00:00:00	0.264	0.0	0.0	0.001	14.04	42.97	2.628	879.9633	15.112258	20.019848
2018-01-31 00:10:00	0.138	0.0	0.0	0.001	14.12	43.45	2.650	880.0983	15.112258	20.019848
2018-01-31 00:20:00	0.000	0.0	0.0	0.001	14.03	43.86	1.230	880.0222	15.112258	20.019848
2018-01-31 00:30:00	0.000	0.0	0.0	0.001	13.59	44.24	2.009	879.8823	15.112258	20.019848
2018-01-31 00:40:00	0.005	0.0	0.0	0.001	13.74	44.24	1.560	879.8358	15.112258	20.019848
...	...	...	...	...	...	...	...	...	...	...
2018-02-01 00:20:00	0.029	0.0	0.0	0.001	15.57	51.52	2.836	877.9796	15.353929	23.522289
2018-02-01 00:30:00	0.013	0.0	0.0	0.001	15.23	52.00	2.780	877.9725	15.353929	23.522289
2018-02-01 00:40:00	0.001	0.0	0.0	0.001	15.28	51.80	2.541	878.0463	15.353929	23.522289
2018-02-01 00:50:00	0.065	0.0	0.0	0.001	15.38	50.84	2.434	878.1319	15.353929	23.522289
2018-02-01 01:00:00	0.086	0.0	0.0	0.001	15.41	51.53	2.271	878.2111	15.353929	23.522289

152 rows × 10 columns

33 Promedio mensual de To del máximo diario (memo)

esolmet.To.resample("D").max()

Fecha
2018-01-01    29.30
2018-01-02    28.89
2018-01-03    28.12
2018-01-04    28.30
2018-01-05    29.57
              ...  
2018-12-27    26.92
2018-12-28    27.70
2018-12-29    30.01
2018-12-30    27.76
2018-12-31    27.31
Freq: D, Name: To, Length: 365, dtype: float64

esolmet.To.resample("D").max().resample("ME").mean()

Fecha
2018-01-31    28.455484
2018-02-28    31.873929
2018-03-31    34.029355
2018-04-30    33.984333
2018-05-31    31.782333
2018-06-30    30.436667
2018-07-31    29.096774
2018-08-31    28.932258
2018-09-30    28.838333
2018-10-31    29.023871
2018-11-30    27.871667
2018-12-31    27.880645
Freq: ME, Name: To, dtype: float64

fig, ax = plt.subplots(figsize=(12,4))


ax.plot(esolmet.To.resample("D").max().resample("ME").mean(),"ro-",label="Tmax")
ax.plot(esolmet.To.resample("D").max().resample("ME").max(),"ro-",label="",alpha=0.2)
ax.plot(esolmet.To,"r-",label="",alpha=0.1)

ax.legend()

esolmet

	Ib	Ig	Id	uv	To	hr	ws	p	amplitud	Tpromedio
Fecha
2018-01-01 00:00:00	0.057	0.0	0.0	0.001	18.93	41.57	1.253	879.0692	15.112258	20.019848
2018-01-01 00:10:00	0.002	0.0	0.0	0.001	18.76	41.00	0.418	879.4363	15.112258	20.019848
2018-01-01 00:20:00	0.170	0.0	0.0	0.001	18.92	40.96	0.955	879.5181	15.112258	20.019848
2018-01-01 00:30:00	0.371	0.0	0.0	0.001	18.52	42.46	1.823	879.5826	15.112258	20.019848
2018-01-01 00:40:00	0.305	0.0	0.0	0.001	18.49	42.43	2.149	879.6826	15.112258	20.019848
...	...	...	...	...	...	...	...	...	...	...
2018-12-31 23:10:00	0.125	0.0	0.0	0.000	18.88	59.60	2.145	875.5595	13.576452	20.362943
2018-12-31 23:20:00	0.000	0.0	0.0	0.000	18.71	59.67	1.638	875.5595	13.576452	20.362943
2018-12-31 23:30:00	0.044	0.0	0.0	0.000	18.52	58.75	1.923	875.2889	13.576452	20.362943
2018-12-31 23:40:00	0.170	0.0	0.0	0.000	18.36	60.62	2.089	875.0606	13.576452	20.362943
2018-12-31 23:50:00	0.003	0.0	0.0	0.000	17.99	60.76	0.744	875.1424	13.576452	20.362943

51173 rows × 10 columns

Mes más cálido con la To promedio mensual (Lili)

esolmet.To.resample("ME").mean().max(),  esolmet.To.resample("ME").mean().idxmax()

(np.float64(26.052599537037036), Timestamp('2018-04-30 00:00:00'))

def identifica_To_mes_calido(esolmet:pd.DataFrame, columna:str = "To")-> (float, float):
    """
    Recibe un df que tiene un indice que es datetime 
    y regresa en una tupla el valor máximo promedio To_max y el mes en el que sucede mes_max
    """
    To_max = esolmet[columna].resample("ME").mean().max()
    mes_calido = esolmet[columna].resample("ME").mean().idxmax().month
    return To_max, mes_calido

maximos = identifica_To_mes_calido(esolmet,columna="hr")
maximos

(np.float64(74.50158759546402), 9)

To_max, mes_max = identifica_To_mes_calido(esolmet,columna="hr")
# print(To_max)
# print(mes_max)

34 Amplitud diaria de la To (Fer ipn)

deltaTo_diaria = esolmet.To.resample("D").max() - esolmet.To.resample("D").min()

fig, ax = plt.subplots(figsize=(12,4))

ax.plot(esolmet.To.resample("D").mean() + deltaTo_diaria/2,"r-",label="")
ax.plot(esolmet.To.resample("D").mean() - deltaTo_diaria/2,"r-",label="")
ax.plot(esolmet.To.resample("D").mean())
ax.plot(esolmet.To, alpha=0.2)

esolmet

	Ib	Ig	Id	uv	To	hr	ws	p	amplitud	Tpromedio
Fecha
2018-01-01 00:00:00	0.057	0.0	0.0	0.001	18.93	41.57	1.253	879.0692	15.112258	20.019848
2018-01-01 00:10:00	0.002	0.0	0.0	0.001	18.76	41.00	0.418	879.4363	15.112258	20.019848
2018-01-01 00:20:00	0.170	0.0	0.0	0.001	18.92	40.96	0.955	879.5181	15.112258	20.019848
2018-01-01 00:30:00	0.371	0.0	0.0	0.001	18.52	42.46	1.823	879.5826	15.112258	20.019848
2018-01-01 00:40:00	0.305	0.0	0.0	0.001	18.49	42.43	2.149	879.6826	15.112258	20.019848
...	...	...	...	...	...	...	...	...	...	...
2018-12-31 23:10:00	0.125	0.0	0.0	0.000	18.88	59.60	2.145	875.5595	13.576452	20.362943
2018-12-31 23:20:00	0.000	0.0	0.0	0.000	18.71	59.67	1.638	875.5595	13.576452	20.362943
2018-12-31 23:30:00	0.044	0.0	0.0	0.000	18.52	58.75	1.923	875.2889	13.576452	20.362943
2018-12-31 23:40:00	0.170	0.0	0.0	0.000	18.36	60.62	2.089	875.0606	13.576452	20.362943
2018-12-31 23:50:00	0.003	0.0	0.0	0.000	17.99	60.76	0.744	875.1424	13.576452	20.362943

51173 rows × 10 columns

deltaTo_diaria = (
    esolmet.To.resample("D").max() - 
    esolmet.To.resample("D").min()
).resample("ME",closed="right").mean() 

fig, ax = plt.subplots(figsize=(12,4))

ax.plot(esolmet.To,"r-",alpha=0.3)
ax.fill_between(esolmet.index, 
                esolmet.Tpromedio + esolmet.amplitud/2,
                esolmet.Tpromedio -esolmet.amplitud/2,
               )

esolmet.To.hist(bins=50)

deltaTo_diaria

Fecha
2018-01-31    15.112258
2018-02-28    15.353929
2018-03-31    15.435806
2018-04-30    14.948667
2018-05-31    12.143333
2018-06-30    11.999667
2018-07-31    14.802258
2018-08-31    11.357097
2018-09-30    11.137667
2018-10-31    11.472581
2018-11-30    12.017333
2018-12-31    13.576452
Freq: ME, Name: To, dtype: float64