Obtención de series de datos discretos climatológicos satelitales y correlación con datos terrestres
Keywords: Remote-sensing CHIRPS Correlation Pearson Kendall Spearman Scatter-plot pandas rasterio requests tabulate
Para la validación o el contraste de información terrestre, se pueden obtener datos satelitales de precipitación diaria total, temperatura y evapotranspiración sobre las localizaciones específicas de la red climatológica utilizada. A partir de la información recopilada y validada para la red estaciones a usar en la zona de estudio y la conformación de series a partir de datos satelitales en las localizaciones específicas de la red, se correlacionan estos datos para evaluar si existe correspondencia y homogeneidad entre ellos.
CHIRPS permite descargar datos de precipitación diaria con resoluciones espaciales de 0.05 y 0.25 grados (5.5 y 27.8 km aprox.) en formatos BIL, TIDD o NetCDF y con series de 30 o más años. La banda de descarga se ubica entre las latitudes 50°S a -50°N en todas las longitudes de la superficie terrestre, iniciando su captura desde 1981 y hasta la actualidad. CHIRPS combina imágenes satelitales (NASA y NOAA) con datos registrados en estaciones terrestres y es frecuentemente utilizado para análisis de tendencias y monitoreo de sequías debidas a cambios estacionales. Esta fusión de datos permite estimar valores en zonas en las que no existen estaciones terrestres, complementando valores obtenidos por otros métodos que tienen en cuenta la relación espacial entre estaciones próximas.
Desde el año 1999, el Servicio Geológico de los Estados Unidos de América – USGS y los científicos del Grupo de Amenazas Climáticas - CHG, con el apoyo de la Agencia Internacional para el Desarrollo de los Estados Unidos – USAID, la NASA y la NOAA, han desarrollado técnicas para producir mapas de precipitación especialmente en zonas donde existen pocos datos. Estimar espacial y temporalmente las variaciones de la precipitación, es un aspecto importante para el monitoreo del medio ambiente y para mitigar las sequías.
- Descargar grillas de precipitación mensual total usando el servicio CHIRPS - Climate hazards group infrared precipitation.
- Realizar la lectura de los valores de precipitación CHIRPS, en las localizaciones de la red de estaciones terrestres utilizadas para la obtención de datos del IDEAM - Colombia.
- Realizar análisis de correlación entre datos terrestres y datos obtenidos a partir de observaciones satelitales.
- Python 3+
- Pandas para Python
- Rasterio para Python
- Requests para Python
- Tabulate para Python, requerido para impresión de tablas en formato Markdown desde pandas.
- Series de datos discretos climatológicos de estaciones terrestres del IDEAM. 🎓Aprender.
Procedimiento general - Precipitación CHIRPS 1
Convenciones generales en diagramas: clases de entidad en azul, dataset en gris oscuro, grillas en color verde, geo-procesos en rojo, procesos automáticos o semiautomáticos en guiones rojos y procesos manuales en amarillo. Líneas conectoras con guiones corresponden a procedimientos opcionales.
- Para la descarga, lectura y análisis de correlación, descargue el script ChirpsGetValue.py y guárdelo en la carpeta local
D:\R.LTWB\.srcde su equipo.
Funcionalidades del script
- Descarga directa de archivos comprimidos de grillas CHIRPS de precipitación mensual total a partir de la definición de un rango de años, p.ej., entre 1981 y 2021.
- Descompresión de grillas .tif.
- Segmentación mensual por año del archivo integrado de registros discretos obtenidos del IDEAM para la Etiqueta = "PTPM_TT_M" correspondiente a datos de precipitación mensual total.
- Lectura de valores CHIRPS por mes en cada año sobre las localizaciones específicas de la red de estaciones terrestres del IDEAM. Para cada mes en cada año, se crea un archivo .csv que contiene los valores IDEAM más los valores leídos CHIRPS, p.ej., chirps-v2.0.1981.01.csv.
- Integración de archivos .csv en un único archivo, nombrado como IDEAMJoinedChirps.csv.
- Para cada mes de cada año, se calculan correlaciones utilizando los métodos de Pearson, Kendal y Spearman y se genera el archivo IDEAMJoinedChirpsCorrelationDate.csv.
- A partir de los valores de correlación estimados en cada mes para cada año, se calculan los valores promedio de las correlaciones y se genera el archivo IDEAMJoinedChirpsCorrelationDateMean.csv que contiene 3 valores.
- A partir de los valores de correlación estimados en cada mes para cada año, se calculan los valores promedio de las correlaciones por año y se genera el archivo IDEAMJoinedChirpsCorrelationYear.csv que contiene 3 valores por cada año.
- A partir de los valores de correlación estimados en cada mes para cada año, se calculan los valores promedio de las correlaciones por mes y se genera el archivo IDEAMJoinedChirpsCorrelationMonth.csv que contiene 3 valores para los 12 meses del año.
- Generación de 6 gráficas de análisis con análisis de series de correlación y cajas de bigotes.
- Generación de reporte científico integrado en formato Markdown, nombrado como RemoteSensingRainChirps.md
Atención: tenga en cuenta que para la correcta ejecución de este script, los valores almacenados en los campos fecha - hora deben mantener consistente el formato en todos los registros. Si la fecha de una columna específica está registrada en formato aaaa//mm/dd, todos los registros deben conservar el mismo formato.
Contenido del script ChirpsGetValue.py
# -*- coding: UTF-8 -*-
# Name: ChirpsGetValue.py
# Description: this script downloads the Chirps monthly rain geogrids and identify the specific values in the IDEAM - Colombia monthly stations series for calculate their correlations.
# Requirements: Python 3+, pandas, rasterio, requests, tabulate
# Attention: do not convert the .csv file into an Excel file because you would need process more than 1048576 records.
# Libraries
import os.path
import sys
import requests
import rasterio
from rasterio.plot import show
import pandas as pd
import gzip
import shutil
import glob
import matplotlib
import matplotlib.pyplot as plt
import tabulate # required for print tables in Markdown using pandas
from datetime import datetime
# Function for get the raster value in a specific coordinate
def chirps_value(raster_file, longitude, latitude):
raster = rasterio.open(path + raster_file)
row, col = raster.index(longitude, latitude)
return raster.read(1)[row, col]
# Function for print and show results in a file
def print_log(txt_print, on_screen=True, center_div=False):
if on_screen:
print(txt_print)
if center_div:
file_log.write('\n<div align="center">\n' + '\n')
file_log.write(txt_print + '\n')
if center_div:
file_log.write('\n</div>\n' + '\n')
# General variables
station_file = 'D:/R.LTWB/.datasets/IDEAM/IDEAMJoined.csv' # Current IDEAM records file
path = 'D:/R.LTWB/.datasets/CHIRPS/' # Your local output path, use ../.datasets/CHIRPS/ for relative path
station_file_chirps = 'IDEAMJoinedChirps.csv' # Output IDEAM records with the Chirps values
station_file_corr_date = 'IDEAMJoinedChirpsCorrelationDate.csv' # Output IDEAM correlations with Chirps for each date
station_file_corr_date_mean = 'IDEAMJoinedChirpsCorrelationDateMean.csv' # Output IDEAM correlations with Chirps - mean
station_file_corr_year = 'IDEAMJoinedChirpsCorrelationYear.csv' # Output IDEAM correlations with Chirps for each year
station_file_corr_month = 'IDEAMJoinedChirpsCorrelationMonth.csv' # Output IDEAM correlations with Chirps for each month
file_log_name = path + 'RemoteSensingRainChirps.md'
file_log = open(file_log_name, 'w+') # w+ create the file if it doesn't exist
url_server = 'https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/tifs/'
plot_raster = False # Plot every geogrid
remove_temp_file_comp = True # Remove all the compressed Chirps files downloaded after processing
remove_temp_file_geogrid = True # Remove all the Chirps geogrid files after processing
remove_temp_file_csv = False # Remove all .csv sliced files after processing
date_install = 'FechaInstalacion' # IDEAM installation date field name
date_suspend = 'FechaSuspension' # IDEAM suspension date field name
date_record = 'Fecha' # IDEAM date field name for the record values
parameter_name = 'Etiqueta == "PTPM_TT_M"' # IDEAM field name and specific monthly rain tag
latitude_name = 'Latitud' # IDEAM latitude name
longitude_name = 'Longitud' # IDEAM longitude name
value_name = 'Valor' # IDEAM value field name
geogrid_extension = '.tif'
compress_format = '.gz'
plot_colormap = 'tab20b' # Color theme for plot graphics, https://matplotlib.org/stable/tutorials/colors/colormaps.html
year_start = 1981 # Chirps values starts at 1981
year_end = 2021 # This value have to correspond with the end of the IDEAM series
# Header
print_log('## Obtención de series de datos discretos climatológicos satelitales y correlación con datos terrestres, IDEAM vs. CHIRPS')
print_log('\n* Archivo de resultados: ' + file_log_name +
'\n* Fecha y hora de inicio de ejecución: ' + str(datetime.now()) +
'\n* Python versión: ' + str(sys.version) +
'\n* Python rutas: ' + str(sys.path[0:5]) +
'\n* matplotlib versión: ' + str(matplotlib.__version__) +
'\n* Encuentra este script en https://github.com/rcfdtools/R.LTWB/tree/main/Section03/RemoteSensing'
'\n* Cláusulas y condiciones de uso en https://github.com/rcfdtools/R.LTWB/blob/main/LICENSE.md'
'\n* Créditos: [email protected]\n')
# Open the IDEAM station dataframe and show general information
# Learn more about the IDEAM file in https://github.com/rcfdtools/R.LTWB/tree/main/Section03/CNEStationDatasetDownload
station_df = pd.read_csv(station_file, low_memory=False, parse_dates=[date_install, date_suspend, date_record]) # , infer_datetime_format=True
station_df[date_install] = pd.to_datetime(station_df[date_install], dayfirst=True, infer_datetime_format=True) #, format='%d/%m/%Y'
station_df[date_suspend] = pd.to_datetime(station_df[date_suspend], dayfirst=True, infer_datetime_format=True)
station_df[date_record] = pd.to_datetime(station_df[date_record], dayfirst=False, infer_datetime_format=True)
print_log('\n### General dataframe information\n')
print(station_df.info())
print('\nStation records sample\n')
print(station_df)
ideam_regs = station_df.shape[0]
print_log('* IDEAM records: %s' % (str(ideam_regs)))
station_df = station_df.query(parameter_name) # Filter for the monthly rain values
ideam_regs_query = station_df.shape[0]
print_log('* Filtered records for %s: %i (%s%%)' % (parameter_name, ideam_regs_query, str(round((ideam_regs_query/ideam_regs)*100, 2))))
# Processing Chrips values per month in each year (displayed only in Python console)
print('\n\n### Processing Chrips values per month in each year\n')
cols = ['Date', 'Year', 'Month', 'Pearson', 'Kendall', 'Spearman']
correlation_df = pd.DataFrame(columns=cols)
for year in range(year_start, year_end+1, 1):
for month in range(12):
year_month = str(year).zfill(4) + '-' + str(month+1).zfill(2)
date = year_month + '-01'
chirps_file = 'chirps-v2.0.' + str(year).zfill(4) + '.' + str(month+1).zfill(2)
url_file = url_server + chirps_file + geogrid_extension + compress_format
compress_file = path + chirps_file + geogrid_extension + compress_format
print('\nProcessing geogrid %s from %s' % (year_month, url_file))
# Request the compress geogrid Chirps file if the processed sliced .csv doesn't exist
if os.path.isfile(compress_file) is False and os.path.isfile(path + chirps_file + '.csv') is False:
print('Saving compressed file as %s' % compress_file)
file_request = requests.get(url_file)
if file_request:
open(compress_file, 'wb').write(file_request.content)
else:
print('Compressed file %s is already in the directory.' % compress_file)
# Uncompress the Chirps file if the geodrid and the processed sliced .csv doesn't exist
if os.path.isfile(path + chirps_file + geogrid_extension) is False and os.path.isfile(path + chirps_file + '.csv') is False:
with gzip.open(compress_file, 'rb') as f_in:
with open(path + chirps_file + geogrid_extension, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
print('Uncompressing geogrid as %s' % (path + chirps_file + geogrid_extension))
else:
print('Geogrid %s is already in the directory.' % (path + chirps_file + geogrid_extension))
# Plot raster in screen if the geogrid file still
if plot_raster and remove_temp_file_geogrid is False:
raster = rasterio.open(path + chirps_file + geogrid_extension)
# print(raster.crs) # Print coordinate reference system - CRS
# print(raster.count) # Print number of bands
show(raster) # Plot the raster file
# Slice the IDEAM file per year and month and get the Chirps values
if os.path.isfile(path + chirps_file + '.csv') is False:
#station_df_filter = station_df[station_df[date_record].dt.strftime('%Y-%m') == year_month]
station_df_filter = station_df[station_df[date_record].dt.strftime('%Y-%m') == year_month]
stations = station_df_filter.shape[0]
print('Slicing .csv serie for %s with %d records' % (year_month, stations))
station_df_filter['SatValue'] = chirps_value(chirps_file + geogrid_extension, station_df_filter[longitude_name], station_df_filter[latitude_name])
station_df_filter['SatDesc'] = chirps_file + geogrid_extension
station_df_filter.to_csv(path + chirps_file + '.csv')
else:
print('Sliced .csv serie %s with Chirps values is already in the directory.' % (path + chirps_file + '.csv'))
# Correlation analysis
df = pd.read_csv(path + chirps_file + '.csv', low_memory=False)
correlation_pearson = df[value_name].corr(df['SatValue'], method='pearson')
correlation_kendall = df[value_name].corr(df['SatValue'], method='kendall')
correlation_spearman = df[value_name].corr(df['SatValue'], method='spearman')
print('Correlation analysis. Pearson = %f, Kendall = %f, Spearman = %f' % (correlation_pearson, correlation_kendall, correlation_spearman))
#df2 = pd.DataFrame([[pd.to_datetime(date, format='%Y/%m/%d'), year, month + 1, correlation_pearson, correlation_kendall, correlation_spearman]], columns=cols)
df2 = pd.DataFrame([[pd.to_datetime(date, format='%Y-%m-%d'), year, month + 1, correlation_pearson, correlation_kendall, correlation_spearman]], columns=cols)
# correlation_df = pd.concat([correlation_df, df2], ignore_index = True)
correlation_df = pd.concat([correlation_df, df2])
# Join .csv files and plot
print_log('\n\n### General IDEAM vs. CHIRPS - Plots\n')
csv_files = glob.glob(path + 'chirps-v2.0.*.csv')
df = pd.concat(map(pd.read_csv, csv_files), ignore_index=True)
df.to_csv(path + station_file_chirps, encoding='utf-8', index=False)
df = pd.read_csv(path + station_file_chirps, low_memory=False)
print_log('\nProcessed .csv file [%s](%s)\n' % (station_file_chirps, station_file_chirps))
fig = df.plot.scatter(x=value_name, y='SatValue', alpha=0.75, figsize=(6, 6), c='black', cmap=None)
plt.title('IDEAM vs. CHIRPS - Scatter plot')
fig.figure.savefig(path + 'PlotDateScatterIdeamChirps.png')
plt.show()
print_log('', center_div=True)
fig = df.boxplot(column=[value_name, 'SatValue'], figsize=(6, 6), grid=False)
plt.title('IDEAM & CHIRPS - Boxplot')
fig.figure.savefig(path + 'PlotDateIdeamChirpsBoxplot.png')
print_log('', center_div=True)
plt.show()
# Correlation save & plot
print_log('\n\n### Correlation Analysis\n\nThe correlation methods used for the analysis are:\n')
print_log('* [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) ')
print_log('* [Kendall rank correlation coefficient](https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient) ')
print_log('* [Spearman’s rank correlation coefficient](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) ')
correlation_df.to_csv(path + station_file_corr_date, encoding='utf-8', index=False)
correlation_df.set_index('Date', inplace=True)
print_log('\n\n#### Correlation values for the date records\n\nThe following table, shows the monthly average correlation values obtained from the IDEAM records and the correspondent Chirps values.\nGet the table [%s](%s) \n' % (station_file_corr_date, station_file_corr_date))
print_log(correlation_df.to_markdown(), center_div=True)
df = correlation_df.iloc[:, [2, 3, 4]].mean(axis=0) # iloc for get only the required attributes
df.to_csv(path + station_file_corr_date_mean, encoding='utf-8', index=True)
fig = correlation_df.iloc[:, [2, 3, 4]].plot(figsize=(10, 6), rot=90, colormap=plot_colormap)
plt.title('IDEAM vs. CHIRPS - Correlations per date')
fig.figure.savefig(path + 'PlotDateCorrelationTimeSerie.png')
print_log('\n')
print_log('\n\n#### Average correlations per method\n\nThe values shown below, correspond to the average correlation values in each date processed.\nGet the table [%s](%s) \n' % (station_file_corr_date_mean, station_file_corr_date_mean))
print_log(df.to_markdown(), center_div=True)
correlation_df.iloc[:, [2, 3, 4]].plot.box(figsize=(6, 6))
fig = plt.title('IDEAM vs. CHIRPS - Correlations boxplot')
fig.figure.savefig(path + 'PlotDateCorrelationBoxplot.png')
print_log('\n', center_div=True)
df = correlation_df.groupby('Year').mean()
df.to_csv(path + station_file_corr_year, encoding='utf-8', index=True)
print_log('\n\n#### Average yearly correlation and method\n\nThis table shows the average correlation values obtained for each method in every year in the record set.\nGet the table [%s](%s) \n' % (station_file_corr_year, station_file_corr_year))
print_log(df.to_markdown(), center_div=True)
fig = df.plot(figsize=(10, 6), rot=90, colormap=plot_colormap)
plt.title('IDEAM vs. CHIRPS - Correlations per year')
fig.figure.savefig(path + 'PlotYearCorrelationTimeSerie.png')
print_log('\n')
df = correlation_df.groupby('Month').mean()
df.to_csv(path + station_file_corr_month, encoding='utf-8', index=True)
print_log('\n#### Average monthly correlation and method\n\nThis table shows the average correlation values obtained in every month in the record set.\nGet the table [%s](%s) \n' % (station_file_corr_month, station_file_corr_month))
print_log(df.to_markdown(), center_div=True)
fig = df.plot(figsize=(10, 6), rot=0, colormap=plot_colormap)
plt.title('IDEAM vs. CHIRPS - Correlations per month')
plt.xticks(range(1, 13, 1))
fig.figure.savefig(path + 'PlotMonthCorrelationTimeSerie.png')
print_log('\n')
plt.show()
# Remove temporal files
if remove_temp_file_comp:
chirps_files = glob.glob(path + 'chirps-v2.0.*.gz')
for chirps_file in chirps_files:
os.remove(chirps_file)
if remove_temp_file_geogrid:
geogrid_files = glob.glob(path + 'chirps-v2.0.*.tif')
for geogrid_file in geogrid_files:
os.remove(geogrid_file)
if remove_temp_file_csv: # csv glob.glob created before
for csv_file in csv_files:
os.remove(csv_file)
print('\nProcess accomplished, check the results files like: %s' % (path + station_file_chirps))
-
Cree una nueva carpeta en blanco con el nombre
CHIRPSen su directorio de proyecto localD:\R.LTWB\.datasets. Dentro de la carpetaD:\R.LTWB\Section03\RemoteSensing\, cree una subcarpeta con el nombre Graph. Verifique que la carpetaD:\R.LTWB\.datasets\IDEAM, contenga el archivo IDEAMJoined.csv que fue procesado en la actividad denominada CNEStationDatasetDownload. -
En Microsoft Windows, ejecute el Command Prompt o CMD, ingrese
D:y de Enter para cambiar a la unidad D:\ donde se encuentra el repositorio R.LTWB. Utilizando el comandoCD D:\R.LTWB\.datasets\CHIRPSubíquese dentro de la carpeta CHIRPS.
- En él
CMD, ejecute la instrucciónC:\Python3.10.5\python.exe "D:\R.LTWB\.src\ChirpsGetValue.py"que realizará la descarga y procesamiento de los datos de precipitación de CHIRPS. Durante la ejecución, podrá observar que en la consola se presenta el detalle de los procesos ejecutados para los registros de estaciones de cada mes en cada año y la previsualización de las diferentes tablas y gráficas de análisis.
Durante el proceso de ejecución del script, se genera automáticamente un reporte científico integrado de resultados en formato Markdown con el nombre D:\R.LTWB.datasets\CHIRPS\RemoteSensingRainChirps.md que contiene los siguientes resultados:
- Archivo de resultados: D:/R.LTWB/.datasets/CHIRPS/RemoteSensingRainChirps.md
- Fecha y hora de inicio de ejecución: 2022-10-23 08:19:23.420585
- Python versión: 3.10.5 (tags/v3.10.5:f377153, Jun 6 2022, 16:14:13) [MSC v.1929 64 bit (AMD64)]
- Python rutas: ['D:\R.LTWB\.src', 'C:\Python3.10.5\python310.zip', 'C:\Python3.10.5\DLLs', 'C:\Python3.10.5\lib', 'C:\Python3.10.5']
- matplotlib versión: 3.6.0
- IDEAM records: 514927
- Filtered records for Etiqueta == "PTPM_TT_M": 69603 (13.52%)
Processed .csv file IDEAMJoinedChirps.csv
The correlation methods used for the analysis are:
- Pearson correlation coefficient
- Kendall rank correlation coefficient
- Spearman’s rank correlation coefficient
The following table, shows the monthly average correlation values obtained from the IDEAM records and the correspondent CHIRPS values. Get the table IDEAMJoinedChirpsCorrelationDate.csv
| Date | Year | Month | Pearson | Kendall | Spearman |
|---|---|---|---|---|---|
| 1981-01-01 00:00:00 | 1981 | 1 | 0.612058 | 0.282219 | 0.383146 |
| 1981-02-01 00:00:00 | 1981 | 2 | 0.808769 | 0.378524 | 0.522209 |
| 1981-03-01 00:00:00 | 1981 | 3 | 0.539775 | 0.400649 | 0.569205 |
| 1981-04-01 00:00:00 | 1981 | 4 | 0.808314 | 0.377949 | 0.531696 |
| 1981-05-01 00:00:00 | 1981 | 5 | 0.643781 | 0.45438 | 0.627324 |
| 1981-06-01 00:00:00 | 1981 | 6 | 0.688421 | 0.552399 | 0.734658 |
| 1981-07-01 00:00:00 | 1981 | 7 | 0.495693 | 0.518113 | 0.703578 |
| 1981-08-01 00:00:00 | 1981 | 8 | 0.671228 | 0.431941 | 0.590448 |
| 1981-09-01 00:00:00 | 1981 | 9 | 0.609723 | 0.389817 | 0.537805 |
| 1981-10-01 00:00:00 | 1981 | 10 | 0.586131 | 0.423803 | 0.596277 |
| 1981-11-01 00:00:00 | 1981 | 11 | 0.641861 | 0.476287 | 0.671685 |
| 1981-12-01 00:00:00 | 1981 | 12 | 0.647182 | 0.489283 | 0.65906 |
| 1982-01-01 00:00:00 | 1982 | 1 | 0.774137 | 0.570714 | 0.754119 |
| 1982-02-01 00:00:00 | 1982 | 2 | 0.644174 | 0.458109 | 0.640362 |
| 1982-03-01 00:00:00 | 1982 | 3 | 0.71613 | 0.572578 | 0.749782 |
| 1982-04-01 00:00:00 | 1982 | 4 | 0.70992 | 0.538742 | 0.716901 |
| 1982-05-01 00:00:00 | 1982 | 5 | 0.630528 | 0.426531 | 0.602797 |
| 1982-06-01 00:00:00 | 1982 | 6 | 0.508836 | 0.408061 | 0.590496 |
| 1982-07-01 00:00:00 | 1982 | 7 | 0.68605 | 0.457934 | 0.637591 |
| 1982-08-01 00:00:00 | 1982 | 8 | 0.649016 | 0.470221 | 0.641039 |
| 1982-09-01 00:00:00 | 1982 | 9 | 0.612098 | 0.356416 | 0.490405 |
| 1982-10-01 00:00:00 | 1982 | 10 | 0.774179 | 0.417979 | 0.581164 |
| 1982-11-01 00:00:00 | 1982 | 11 | 0.795591 | 0.483623 | 0.665238 |
| 1982-12-01 00:00:00 | 1982 | 12 | 0.935416 | 0.382682 | 0.503019 |
| 1983-01-01 00:00:00 | 1983 | 1 | 0.665475 | 0.24156 | 0.322341 |
| 1983-02-01 00:00:00 | 1983 | 2 | 0.527949 | 0.456305 | 0.592618 |
| 1983-03-01 00:00:00 | 1983 | 3 | 0.603798 | 0.489475 | 0.675917 |
| 1983-04-01 00:00:00 | 1983 | 4 | 0.694043 | 0.529692 | 0.712004 |
| 1983-05-01 00:00:00 | 1983 | 5 | 0.72913 | 0.604743 | 0.776147 |
| 1983-06-01 00:00:00 | 1983 | 6 | 0.72672 | 0.481779 | 0.661273 |
| 1983-07-01 00:00:00 | 1983 | 7 | 0.684421 | 0.365716 | 0.510268 |
| 1983-08-01 00:00:00 | 1983 | 8 | 0.754224 | 0.554741 | 0.732593 |
| 1983-09-01 00:00:00 | 1983 | 9 | 0.805387 | 0.592026 | 0.77741 |
| 1983-10-01 00:00:00 | 1983 | 10 | 0.630253 | 0.492188 | 0.676457 |
| 1983-11-01 00:00:00 | 1983 | 11 | 0.550808 | 0.411868 | 0.569796 |
| 1983-12-01 00:00:00 | 1983 | 12 | 0.60451 | 0.442048 | 0.6168 |
| 1984-01-01 00:00:00 | 1984 | 1 | 0.703276 | 0.467201 | 0.619773 |
| 1984-02-01 00:00:00 | 1984 | 2 | 0.346839 | 0.330128 | 0.4867 |
| 1984-03-01 00:00:00 | 1984 | 3 | 0.599954 | 0.48701 | 0.660373 |
| 1984-04-01 00:00:00 | 1984 | 4 | 0.773793 | 0.647765 | 0.831608 |
| 1984-05-01 00:00:00 | 1984 | 5 | 0.795266 | 0.538248 | 0.720341 |
| 1984-06-01 00:00:00 | 1984 | 6 | 0.699226 | 0.452794 | 0.620636 |
| 1984-07-01 00:00:00 | 1984 | 7 | 0.700787 | 0.463871 | 0.65488 |
| 1984-08-01 00:00:00 | 1984 | 8 | 0.794719 | 0.559449 | 0.758433 |
| 1984-09-01 00:00:00 | 1984 | 9 | 0.623824 | 0.430889 | 0.594237 |
| 1984-10-01 00:00:00 | 1984 | 10 | 0.693342 | 0.477892 | 0.642265 |
| 1984-11-01 00:00:00 | 1984 | 11 | 0.578909 | 0.48281 | 0.660115 |
| 1984-12-01 00:00:00 | 1984 | 12 | 0.651398 | 0.302424 | 0.385755 |
| 1985-01-01 00:00:00 | 1985 | 1 | 0.488746 | 0.405176 | 0.522471 |
| 1985-02-01 00:00:00 | 1985 | 2 | 0.635806 | 0.321056 | 0.416477 |
| 1985-03-01 00:00:00 | 1985 | 3 | 0.692804 | 0.534202 | 0.711781 |
| 1985-04-01 00:00:00 | 1985 | 4 | 0.594484 | 0.559128 | 0.739981 |
| 1985-05-01 00:00:00 | 1985 | 5 | 0.739281 | 0.492348 | 0.669182 |
| 1985-06-01 00:00:00 | 1985 | 6 | 0.625975 | 0.460868 | 0.645639 |
| 1985-07-01 00:00:00 | 1985 | 7 | 0.597452 | 0.358122 | 0.498039 |
| 1985-08-01 00:00:00 | 1985 | 8 | 0.776426 | 0.467046 | 0.638565 |
| 1985-09-01 00:00:00 | 1985 | 9 | 0.699464 | 0.417397 | 0.592547 |
| 1985-10-01 00:00:00 | 1985 | 10 | 0.717803 | 0.456283 | 0.627897 |
| 1985-11-01 00:00:00 | 1985 | 11 | 0.83466 | 0.468738 | 0.642226 |
| 1985-12-01 00:00:00 | 1985 | 12 | 0.833099 | 0.43697 | 0.605685 |
| 1986-01-01 00:00:00 | 1986 | 1 | 0.733781 | 0.329713 | 0.425532 |
| 1986-02-01 00:00:00 | 1986 | 2 | 0.417436 | 0.336475 | 0.487919 |
| 1986-03-01 00:00:00 | 1986 | 3 | 0.632095 | 0.507068 | 0.694976 |
| 1986-04-01 00:00:00 | 1986 | 4 | 0.799844 | 0.501621 | 0.672769 |
| 1986-05-01 00:00:00 | 1986 | 5 | 0.802564 | 0.614403 | 0.810932 |
| 1986-06-01 00:00:00 | 1986 | 6 | 0.615454 | 0.351303 | 0.502667 |
| 1986-07-01 00:00:00 | 1986 | 7 | 0.728537 | 0.464694 | 0.637048 |
| 1986-08-01 00:00:00 | 1986 | 8 | 0.687403 | 0.354998 | 0.488718 |
| 1986-09-01 00:00:00 | 1986 | 9 | 0.821828 | 0.509026 | 0.696896 |
| 1986-10-01 00:00:00 | 1986 | 10 | 0.720635 | 0.43199 | 0.605987 |
| 1986-11-01 00:00:00 | 1986 | 11 | 0.674264 | 0.356495 | 0.506092 |
| 1986-12-01 00:00:00 | 1986 | 12 | 0.729148 | 0.321366 | 0.446529 |
| 1987-01-01 00:00:00 | 1987 | 1 | 0.518886 | 0.305535 | 0.426745 |
| 1987-02-01 00:00:00 | 1987 | 2 | 0.305346 | 0.311934 | 0.442172 |
| 1987-03-01 00:00:00 | 1987 | 3 | 0.487254 | 0.442464 | 0.598201 |
| 1987-04-01 00:00:00 | 1987 | 4 | 0.656947 | 0.462388 | 0.634917 |
| 1987-05-01 00:00:00 | 1987 | 5 | 0.611737 | 0.416172 | 0.579122 |
| 1987-06-01 00:00:00 | 1987 | 6 | 0.718846 | 0.457015 | 0.629619 |
| 1987-07-01 00:00:00 | 1987 | 7 | 0.742986 | 0.392704 | 0.559629 |
| 1987-08-01 00:00:00 | 1987 | 8 | 0.837654 | 0.454 | 0.620948 |
| 1987-09-01 00:00:00 | 1987 | 9 | 0.685293 | 0.441497 | 0.599652 |
| 1987-10-01 00:00:00 | 1987 | 10 | 0.662518 | 0.457267 | 0.633838 |
| 1987-11-01 00:00:00 | 1987 | 11 | 0.820482 | 0.544815 | 0.722334 |
| 1987-12-01 00:00:00 | 1987 | 12 | 0.771382 | 0.455196 | 0.626507 |
| 1988-01-01 00:00:00 | 1988 | 1 | 0.401901 | 0.308269 | 0.377737 |
| 1988-02-01 00:00:00 | 1988 | 2 | 0.494672 | 0.379542 | 0.51565 |
| 1988-03-01 00:00:00 | 1988 | 3 | 0.766699 | 0.509115 | 0.65973 |
| 1988-04-01 00:00:00 | 1988 | 4 | 0.528855 | 0.36198 | 0.505979 |
| 1988-05-01 00:00:00 | 1988 | 5 | 0.456579 | 0.346691 | 0.489466 |
| 1988-06-01 00:00:00 | 1988 | 6 | 0.5616 | 0.339761 | 0.475122 |
| 1988-07-01 00:00:00 | 1988 | 7 | 0.551247 | 0.368784 | 0.51192 |
| 1988-08-01 00:00:00 | 1988 | 8 | 0.771229 | 0.550388 | 0.738351 |
| 1988-09-01 00:00:00 | 1988 | 9 | 0.708319 | 0.491151 | 0.670569 |
| 1988-10-01 00:00:00 | 1988 | 10 | 0.76466 | 0.496802 | 0.692301 |
| 1988-11-01 00:00:00 | 1988 | 11 | 0.733081 | 0.432356 | 0.611407 |
| 1988-12-01 00:00:00 | 1988 | 12 | 0.884582 | 0.606868 | 0.788617 |
| 1989-01-01 00:00:00 | 1989 | 1 | 0.765013 | 0.439401 | 0.553926 |
| 1989-02-01 00:00:00 | 1989 | 2 | 0.572073 | 0.446785 | 0.614623 |
| 1989-03-01 00:00:00 | 1989 | 3 | 0.636633 | 0.478612 | 0.642165 |
| 1989-04-01 00:00:00 | 1989 | 4 | 0.661843 | 0.489631 | 0.662125 |
| 1989-05-01 00:00:00 | 1989 | 5 | 0.769166 | 0.532754 | 0.714012 |
| 1989-06-01 00:00:00 | 1989 | 6 | 0.834254 | 0.532417 | 0.714423 |
| 1989-07-01 00:00:00 | 1989 | 7 | 0.82225 | 0.447267 | 0.607109 |
| 1989-08-01 00:00:00 | 1989 | 8 | 0.715435 | 0.508522 | 0.694358 |
| 1989-09-01 00:00:00 | 1989 | 9 | 0.761334 | 0.583143 | 0.756538 |
| 1989-10-01 00:00:00 | 1989 | 10 | 0.822943 | 0.470085 | 0.634433 |
| 1989-11-01 00:00:00 | 1989 | 11 | 0.793241 | 0.491555 | 0.663919 |
| 1989-12-01 00:00:00 | 1989 | 12 | 0.415336 | 0.343698 | 0.48893 |
| 1990-01-01 00:00:00 | 1990 | 1 | 0.949898 | 0.35012 | 0.440971 |
| 1990-02-01 00:00:00 | 1990 | 2 | 0.665387 | 0.411503 | 0.543279 |
| 1990-03-01 00:00:00 | 1990 | 3 | 0.81449 | 0.573598 | 0.755102 |
| 1990-04-01 00:00:00 | 1990 | 4 | 0.732254 | 0.551283 | 0.737486 |
| 1990-05-01 00:00:00 | 1990 | 5 | 0.685783 | 0.385938 | 0.545466 |
| 1990-06-01 00:00:00 | 1990 | 6 | 0.662761 | 0.450147 | 0.616051 |
| 1990-07-01 00:00:00 | 1990 | 7 | 0.770828 | 0.365082 | 0.508908 |
| 1990-08-01 00:00:00 | 1990 | 8 | 0.708579 | 0.468585 | 0.635076 |
| 1990-09-01 00:00:00 | 1990 | 9 | 0.72072 | 0.42716 | 0.600328 |
| 1990-10-01 00:00:00 | 1990 | 10 | 0.582037 | 0.396558 | 0.555771 |
| 1990-11-01 00:00:00 | 1990 | 11 | 0.81389 | 0.499032 | 0.684661 |
| 1990-12-01 00:00:00 | 1990 | 12 | 0.729745 | 0.488282 | 0.664802 |
| 1991-01-01 00:00:00 | 1991 | 1 | 0.776482 | 0.245452 | 0.307058 |
| 1991-02-01 00:00:00 | 1991 | 2 | 0.783911 | 0.361024 | 0.486838 |
| 1991-03-01 00:00:00 | 1991 | 3 | 0.604412 | 0.525286 | 0.696318 |
| 1991-04-01 00:00:00 | 1991 | 4 | 0.860527 | 0.556787 | 0.732087 |
| 1991-05-01 00:00:00 | 1991 | 5 | 0.804223 | 0.470712 | 0.63364 |
| 1991-06-01 00:00:00 | 1991 | 6 | 0.701146 | 0.417707 | 0.556104 |
| 1991-07-01 00:00:00 | 1991 | 7 | 0.720612 | 0.446038 | 0.619762 |
| 1991-08-01 00:00:00 | 1991 | 8 | 0.570461 | 0.25304 | 0.365589 |
| 1991-09-01 00:00:00 | 1991 | 9 | 0.687495 | 0.4419 | 0.613858 |
| 1991-10-01 00:00:00 | 1991 | 10 | 0.671376 | 0.466408 | 0.642418 |
| 1991-11-01 00:00:00 | 1991 | 11 | 0.83504 | 0.410277 | 0.555026 |
| 1991-12-01 00:00:00 | 1991 | 12 | 0.890222 | 0.397691 | 0.507564 |
| 1992-01-01 00:00:00 | 1992 | 1 | 0.504782 | 0.330327 | 0.441734 |
| 1992-02-01 00:00:00 | 1992 | 2 | 0.704688 | 0.441491 | 0.577433 |
| 1992-03-01 00:00:00 | 1992 | 3 | 0.518449 | 0.445418 | 0.608973 |
| 1992-04-01 00:00:00 | 1992 | 4 | 0.71175 | 0.440021 | 0.609573 |
| 1992-05-01 00:00:00 | 1992 | 5 | 0.790649 | 0.533506 | 0.709343 |
| 1992-06-01 00:00:00 | 1992 | 6 | 0.728636 | 0.542014 | 0.729545 |
| 1992-07-01 00:00:00 | 1992 | 7 | 0.724407 | 0.440018 | 0.605182 |
| 1992-08-01 00:00:00 | 1992 | 8 | 0.722114 | 0.470109 | 0.652991 |
| 1992-09-01 00:00:00 | 1992 | 9 | 0.680463 | 0.432109 | 0.6151 |
| 1992-10-01 00:00:00 | 1992 | 10 | 0.626425 | 0.495928 | 0.670354 |
| 1992-11-01 00:00:00 | 1992 | 11 | 0.818989 | 0.610594 | 0.802671 |
| 1992-12-01 00:00:00 | 1992 | 12 | 0.832356 | 0.460367 | 0.629215 |
| 1993-01-01 00:00:00 | 1993 | 1 | 0.63213 | 0.410253 | 0.568793 |
| 1993-02-01 00:00:00 | 1993 | 2 | 0.401512 | 0.404081 | 0.554146 |
| 1993-03-01 00:00:00 | 1993 | 3 | 0.748287 | 0.473544 | 0.636207 |
| 1993-04-01 00:00:00 | 1993 | 4 | 0.760026 | 0.498384 | 0.661623 |
| 1993-05-01 00:00:00 | 1993 | 5 | 0.61397 | 0.452096 | 0.614362 |
| 1993-06-01 00:00:00 | 1993 | 6 | 0.68335 | 0.445871 | 0.613565 |
| 1993-07-01 00:00:00 | 1993 | 7 | 0.686704 | 0.361518 | 0.51016 |
| 1993-08-01 00:00:00 | 1993 | 8 | 0.693199 | 0.435343 | 0.612356 |
| 1993-09-01 00:00:00 | 1993 | 9 | 0.737537 | 0.445794 | 0.610741 |
| 1993-10-01 00:00:00 | 1993 | 10 | 0.706175 | 0.381976 | 0.523018 |
| 1993-11-01 00:00:00 | 1993 | 11 | 0.853748 | 0.486714 | 0.656401 |
| 1993-12-01 00:00:00 | 1993 | 12 | 0.806019 | 0.484756 | 0.652424 |
| 1994-01-01 00:00:00 | 1994 | 1 | 0.478124 | 0.345874 | 0.45086 |
| 1994-02-01 00:00:00 | 1994 | 2 | 0.656187 | 0.51411 | 0.693328 |
| 1994-03-01 00:00:00 | 1994 | 3 | 0.683528 | 0.561825 | 0.740097 |
| 1994-04-01 00:00:00 | 1994 | 4 | 0.788554 | 0.569722 | 0.74691 |
| 1994-05-01 00:00:00 | 1994 | 5 | 0.743518 | 0.437366 | 0.604119 |
| 1994-06-01 00:00:00 | 1994 | 6 | 0.715697 | 0.481754 | 0.64269 |
| 1994-07-01 00:00:00 | 1994 | 7 | 0.884902 | 0.540581 | 0.720412 |
| 1994-08-01 00:00:00 | 1994 | 8 | 0.716336 | 0.438143 | 0.599986 |
| 1994-09-01 00:00:00 | 1994 | 9 | 0.575235 | 0.442741 | 0.621881 |
| 1994-10-01 00:00:00 | 1994 | 10 | 0.810031 | 0.523409 | 0.711881 |
| 1994-11-01 00:00:00 | 1994 | 11 | 0.87004 | 0.416391 | 0.588776 |
| 1994-12-01 00:00:00 | 1994 | 12 | 0.869152 | 0.509068 | 0.668436 |
| 1995-01-01 00:00:00 | 1995 | 1 | 0.421771 | 0.34404 | 0.444198 |
| 1995-02-01 00:00:00 | 1995 | 2 | 0.290628 | 0.254758 | 0.345397 |
| 1995-03-01 00:00:00 | 1995 | 3 | 0.769214 | 0.515049 | 0.679385 |
| 1995-04-01 00:00:00 | 1995 | 4 | 0.688668 | 0.395187 | 0.554973 |
| 1995-05-01 00:00:00 | 1995 | 5 | 0.694521 | 0.497543 | 0.670855 |
| 1995-06-01 00:00:00 | 1995 | 6 | 0.654819 | 0.495509 | 0.676535 |
| 1995-07-01 00:00:00 | 1995 | 7 | 0.679759 | 0.469661 | 0.636964 |
| 1995-08-01 00:00:00 | 1995 | 8 | 0.596455 | 0.477233 | 0.659062 |
| 1995-09-01 00:00:00 | 1995 | 9 | 0.738295 | 0.466662 | 0.634143 |
| 1995-10-01 00:00:00 | 1995 | 10 | 0.824304 | 0.594947 | 0.766799 |
| 1995-11-01 00:00:00 | 1995 | 11 | 0.777246 | 0.521621 | 0.697318 |
| 1995-12-01 00:00:00 | 1995 | 12 | 0.844204 | 0.528593 | 0.699155 |
| 1996-01-01 00:00:00 | 1996 | 1 | 0.630333 | 0.415458 | 0.565816 |
| 1996-02-01 00:00:00 | 1996 | 2 | 0.609415 | 0.431977 | 0.598356 |
| 1996-03-01 00:00:00 | 1996 | 3 | 0.668055 | 0.549808 | 0.732378 |
| 1996-04-01 00:00:00 | 1996 | 4 | 0.756006 | 0.42176 | 0.586827 |
| 1996-05-01 00:00:00 | 1996 | 5 | 0.641742 | 0.290231 | 0.400951 |
| 1996-06-01 00:00:00 | 1996 | 6 | 0.614275 | 0.434552 | 0.583628 |
| 1996-07-01 00:00:00 | 1996 | 7 | 0.575827 | 0.353093 | 0.492406 |
| 1996-08-01 00:00:00 | 1996 | 8 | 0.764897 | 0.504518 | 0.669675 |
| 1996-09-01 00:00:00 | 1996 | 9 | 0.727969 | 0.479382 | 0.650495 |
| 1996-10-01 00:00:00 | 1996 | 10 | 0.823677 | 0.489864 | 0.666789 |
| 1996-11-01 00:00:00 | 1996 | 11 | 0.716095 | 0.452582 | 0.625447 |
| 1996-12-01 00:00:00 | 1996 | 12 | 0.962944 | 0.483129 | 0.640817 |
| 1997-01-01 00:00:00 | 1997 | 1 | 0.844213 | 0.490192 | 0.645076 |
| 1997-02-01 00:00:00 | 1997 | 2 | 0.880127 | 0.434474 | 0.552278 |
| 1997-03-01 00:00:00 | 1997 | 3 | 0.552475 | 0.533542 | 0.699973 |
| 1997-04-01 00:00:00 | 1997 | 4 | 0.650535 | 0.519535 | 0.700194 |
| 1997-05-01 00:00:00 | 1997 | 5 | 0.750352 | 0.527255 | 0.683398 |
| 1997-06-01 00:00:00 | 1997 | 6 | 0.786564 | 0.470392 | 0.645563 |
| 1997-07-01 00:00:00 | 1997 | 7 | 0.717971 | 0.516667 | 0.710349 |
| 1997-08-01 00:00:00 | 1997 | 8 | 0.727493 | 0.446399 | 0.618208 |
| 1997-09-01 00:00:00 | 1997 | 9 | 0.726954 | 0.368227 | 0.513542 |
| 1997-10-01 00:00:00 | 1997 | 10 | 0.844451 | 0.471428 | 0.642856 |
| 1997-11-01 00:00:00 | 1997 | 11 | 0.650808 | 0.4207 | 0.559427 |
| 1997-12-01 00:00:00 | 1997 | 12 | 0.633663 | 0.427011 | 0.564723 |
| 1998-01-01 00:00:00 | 1998 | 1 | 0.450178 | 0.222843 | 0.291759 |
| 1998-02-01 00:00:00 | 1998 | 2 | 0.745154 | 0.44211 | 0.594577 |
| 1998-03-01 00:00:00 | 1998 | 3 | 0.542292 | 0.4661 | 0.642656 |
| 1998-04-01 00:00:00 | 1998 | 4 | 0.859498 | 0.494558 | 0.669107 |
| 1998-05-01 00:00:00 | 1998 | 5 | 0.68868 | 0.420054 | 0.57833 |
| 1998-06-01 00:00:00 | 1998 | 6 | 0.627155 | 0.352127 | 0.499255 |
| 1998-07-01 00:00:00 | 1998 | 7 | 0.722671 | 0.52814 | 0.710129 |
| 1998-08-01 00:00:00 | 1998 | 8 | 0.657937 | 0.43758 | 0.611132 |
| 1998-09-01 00:00:00 | 1998 | 9 | 0.607246 | 0.487909 | 0.664723 |
| 1998-10-01 00:00:00 | 1998 | 10 | 0.635692 | 0.411372 | 0.585066 |
| 1998-11-01 00:00:00 | 1998 | 11 | 0.807348 | 0.517551 | 0.683097 |
| 1998-12-01 00:00:00 | 1998 | 12 | 0.884989 | 0.496647 | 0.66575 |
| 1999-01-01 00:00:00 | 1999 | 1 | 0.720544 | 0.596692 | 0.761227 |
| 1999-02-01 00:00:00 | 1999 | 2 | 0.774642 | 0.487387 | 0.691569 |
| 1999-03-01 00:00:00 | 1999 | 3 | 0.648219 | 0.52767 | 0.718096 |
| 1999-04-01 00:00:00 | 1999 | 4 | 0.688904 | 0.491552 | 0.663338 |
| 1999-05-01 00:00:00 | 1999 | 5 | 0.60313 | 0.474502 | 0.643408 |
| 1999-06-01 00:00:00 | 1999 | 6 | 0.644271 | 0.450623 | 0.632611 |
| 1999-07-01 00:00:00 | 1999 | 7 | 0.716489 | 0.521354 | 0.71087 |
| 1999-08-01 00:00:00 | 1999 | 8 | 0.438784 | 0.396256 | 0.551317 |
| 1999-09-01 00:00:00 | 1999 | 9 | 0.535081 | 0.424232 | 0.595874 |
| 1999-10-01 00:00:00 | 1999 | 10 | 0.684105 | 0.532494 | 0.70259 |
| 1999-11-01 00:00:00 | 1999 | 11 | 0.527076 | 0.41239 | 0.558573 |
| 1999-12-01 00:00:00 | 1999 | 12 | 0.875243 | 0.403543 | 0.554844 |
| 2000-01-01 00:00:00 | 2000 | 1 | 0.675203 | 0.382908 | 0.527618 |
| 2000-02-01 00:00:00 | 2000 | 2 | 0.90952 | 0.502114 | 0.684508 |
| 2000-03-01 00:00:00 | 2000 | 3 | 0.829564 | 0.568528 | 0.735972 |
| 2000-04-01 00:00:00 | 2000 | 4 | 0.610903 | 0.5142 | 0.706957 |
| 2000-05-01 00:00:00 | 2000 | 5 | 0.656033 | 0.502854 | 0.678942 |
| 2000-06-01 00:00:00 | 2000 | 6 | 0.733829 | 0.52733 | 0.710272 |
| 2000-07-01 00:00:00 | 2000 | 7 | 0.659653 | 0.445326 | 0.616425 |
| 2000-08-01 00:00:00 | 2000 | 8 | 0.651259 | 0.478336 | 0.665369 |
| 2000-09-01 00:00:00 | 2000 | 9 | 0.660458 | 0.529973 | 0.722265 |
| 2000-10-01 00:00:00 | 2000 | 10 | 0.731057 | 0.537327 | 0.723003 |
| 2000-11-01 00:00:00 | 2000 | 11 | 0.753406 | 0.58711 | 0.790455 |
| 2000-12-01 00:00:00 | 2000 | 12 | 0.641708 | 0.416232 | 0.573785 |
| 2001-01-01 00:00:00 | 2001 | 1 | 0.387503 | 0.307371 | 0.385734 |
| 2001-02-01 00:00:00 | 2001 | 2 | 0.523137 | 0.272272 | 0.340436 |
| 2001-03-01 00:00:00 | 2001 | 3 | 0.548027 | 0.511773 | 0.68219 |
| 2001-04-01 00:00:00 | 2001 | 4 | 0.569129 | 0.511511 | 0.675592 |
| 2001-05-01 00:00:00 | 2001 | 5 | 0.708522 | 0.513645 | 0.707593 |
| 2001-06-01 00:00:00 | 2001 | 6 | 0.640188 | 0.477459 | 0.664513 |
| 2001-07-01 00:00:00 | 2001 | 7 | 0.643413 | 0.412871 | 0.579972 |
| 2001-08-01 00:00:00 | 2001 | 8 | 0.639037 | 0.392568 | 0.54897 |
| 2001-09-01 00:00:00 | 2001 | 9 | 0.701943 | 0.453082 | 0.620898 |
| 2001-10-01 00:00:00 | 2001 | 10 | 0.611718 | 0.460986 | 0.632925 |
| 2001-11-01 00:00:00 | 2001 | 11 | 0.822868 | 0.549965 | 0.733715 |
| 2001-12-01 00:00:00 | 2001 | 12 | 0.852402 | 0.512093 | 0.668945 |
| 2002-01-01 00:00:00 | 2002 | 1 | 0.759308 | 0.45527 | 0.560717 |
| 2002-02-01 00:00:00 | 2002 | 2 | 0.787637 | 0.532488 | 0.681192 |
| 2002-03-01 00:00:00 | 2002 | 3 | 0.7955 | 0.507256 | 0.676584 |
| 2002-04-01 00:00:00 | 2002 | 4 | 0.844043 | 0.540159 | 0.732437 |
| 2002-05-01 00:00:00 | 2002 | 5 | 0.763381 | 0.455412 | 0.618601 |
| 2002-06-01 00:00:00 | 2002 | 6 | 0.727392 | 0.547125 | 0.742635 |
| 2002-07-01 00:00:00 | 2002 | 7 | 0.607905 | 0.34573 | 0.481351 |
| 2002-08-01 00:00:00 | 2002 | 8 | 0.465199 | 0.334605 | 0.476087 |
| 2002-09-01 00:00:00 | 2002 | 9 | 0.531921 | 0.414349 | 0.573628 |
| 2002-10-01 00:00:00 | 2002 | 10 | 0.707212 | 0.523313 | 0.707218 |
| 2002-11-01 00:00:00 | 2002 | 11 | 0.745463 | 0.402559 | 0.562468 |
| 2002-12-01 00:00:00 | 2002 | 12 | 0.342911 | 0.182286 | 0.253838 |
| 2003-01-01 00:00:00 | 2003 | 1 | 0.591989 | 0.359627 | 0.450194 |
| 2003-02-01 00:00:00 | 2003 | 2 | 0.53225 | 0.447312 | 0.577403 |
| 2003-03-01 00:00:00 | 2003 | 3 | 0.538609 | 0.463753 | 0.626168 |
| 2003-04-01 00:00:00 | 2003 | 4 | 0.616448 | 0.425757 | 0.578369 |
| 2003-05-01 00:00:00 | 2003 | 5 | 0.592451 | 0.392092 | 0.518069 |
| 2003-06-01 00:00:00 | 2003 | 6 | 0.658468 | 0.531049 | 0.71553 |
| 2003-07-01 00:00:00 | 2003 | 7 | 0.642935 | 0.464661 | 0.619013 |
| 2003-08-01 00:00:00 | 2003 | 8 | 0.628367 | 0.434462 | 0.603248 |
| 2003-09-01 00:00:00 | 2003 | 9 | 0.70104 | 0.553425 | 0.741784 |
| 2003-10-01 00:00:00 | 2003 | 10 | 0.54086 | 0.440565 | 0.61076 |
| 2003-11-01 00:00:00 | 2003 | 11 | 0.608547 | 0.375855 | 0.52797 |
| 2003-12-01 00:00:00 | 2003 | 12 | 0.674638 | 0.424366 | 0.590572 |
| 2004-01-01 00:00:00 | 2004 | 1 | 0.584864 | 0.317559 | 0.41584 |
| 2004-02-01 00:00:00 | 2004 | 2 | 0.405823 | 0.482006 | 0.632094 |
| 2004-03-01 00:00:00 | 2004 | 3 | 0.675883 | 0.58916 | 0.763641 |
| 2004-04-01 00:00:00 | 2004 | 4 | 0.787225 | 0.547558 | 0.725924 |
| 2004-05-01 00:00:00 | 2004 | 5 | 0.819884 | 0.475249 | 0.640409 |
| 2004-06-01 00:00:00 | 2004 | 6 | 0.581799 | 0.368793 | 0.515354 |
| 2004-07-01 00:00:00 | 2004 | 7 | 0.572282 | 0.38793 | 0.539984 |
| 2004-08-01 00:00:00 | 2004 | 8 | 0.687835 | 0.368826 | 0.51636 |
| 2004-09-01 00:00:00 | 2004 | 9 | 0.565788 | 0.350164 | 0.498991 |
| 2004-10-01 00:00:00 | 2004 | 10 | 0.806276 | 0.525116 | 0.701564 |
| 2004-11-01 00:00:00 | 2004 | 11 | 0.659081 | 0.349478 | 0.486526 |
| 2004-12-01 00:00:00 | 2004 | 12 | 0.819109 | 0.578401 | 0.733723 |
| 2005-01-01 00:00:00 | 2005 | 1 | 0.828806 | 0.545906 | 0.708626 |
| 2005-02-01 00:00:00 | 2005 | 2 | 0.664031 | 0.532248 | 0.71063 |
| 2005-03-01 00:00:00 | 2005 | 3 | 0.662458 | 0.544412 | 0.731437 |
| 2005-04-01 00:00:00 | 2005 | 4 | 0.752153 | 0.509313 | 0.680948 |
| 2005-05-01 00:00:00 | 2005 | 5 | 0.723379 | 0.498101 | 0.658098 |
| 2005-06-01 00:00:00 | 2005 | 6 | 0.682411 | 0.417689 | 0.575387 |
| 2005-07-01 00:00:00 | 2005 | 7 | 0.453912 | 0.304503 | 0.43842 |
| 2005-08-01 00:00:00 | 2005 | 8 | 0.654151 | 0.43112 | 0.601214 |
| 2005-09-01 00:00:00 | 2005 | 9 | 0.608523 | 0.461849 | 0.65485 |
| 2005-10-01 00:00:00 | 2005 | 10 | 0.796556 | 0.586955 | 0.77083 |
| 2005-11-01 00:00:00 | 2005 | 11 | 0.768298 | 0.545535 | 0.734988 |
| 2005-12-01 00:00:00 | 2005 | 12 | 0.772221 | 0.438518 | 0.590113 |
| 2006-01-01 00:00:00 | 2006 | 1 | 0.716326 | 0.521011 | 0.69309 |
| 2006-02-01 00:00:00 | 2006 | 2 | 0.273905 | 0.374441 | 0.501875 |
| 2006-03-01 00:00:00 | 2006 | 3 | 0.708128 | 0.58612 | 0.76812 |
| 2006-04-01 00:00:00 | 2006 | 4 | 0.739043 | 0.612467 | 0.800348 |
| 2006-05-01 00:00:00 | 2006 | 5 | 0.713791 | 0.490519 | 0.67402 |
| 2006-06-01 00:00:00 | 2006 | 6 | 0.488415 | 0.315236 | 0.460454 |
| 2006-07-01 00:00:00 | 2006 | 7 | 0.496998 | 0.382648 | 0.53615 |
| 2006-08-01 00:00:00 | 2006 | 8 | 0.501048 | 0.416868 | 0.584234 |
| 2006-09-01 00:00:00 | 2006 | 9 | 0.672591 | 0.430708 | 0.59544 |
| 2006-10-01 00:00:00 | 2006 | 10 | 0.760279 | 0.476165 | 0.660496 |
| 2006-11-01 00:00:00 | 2006 | 11 | 0.70232 | 0.39002 | 0.539005 |
| 2006-12-01 00:00:00 | 2006 | 12 | 0.747422 | 0.43257 | 0.600399 |
| 2007-01-01 00:00:00 | 2007 | 1 | 0.791345 | 0.140251 | 0.180015 |
| 2007-02-01 00:00:00 | 2007 | 2 | 0.537122 | 0.406642 | 0.532443 |
| 2007-03-01 00:00:00 | 2007 | 3 | 0.709219 | 0.51597 | 0.69981 |
| 2007-04-01 00:00:00 | 2007 | 4 | 0.687407 | 0.460963 | 0.642047 |
| 2007-05-01 00:00:00 | 2007 | 5 | 0.656411 | 0.442481 | 0.607694 |
| 2007-06-01 00:00:00 | 2007 | 6 | 0.575299 | 0.319003 | 0.4573 |
| 2007-07-01 00:00:00 | 2007 | 7 | 0.676161 | 0.522524 | 0.686086 |
| 2007-08-01 00:00:00 | 2007 | 8 | 0.642877 | 0.476246 | 0.65002 |
| 2007-09-01 00:00:00 | 2007 | 9 | 0.580575 | 0.43429 | 0.600848 |
| 2007-10-01 00:00:00 | 2007 | 10 | 0.64072 | 0.492587 | 0.649002 |
| 2007-11-01 00:00:00 | 2007 | 11 | 0.657051 | 0.347693 | 0.495809 |
| 2007-12-01 00:00:00 | 2007 | 12 | 0.914392 | 0.537073 | 0.712266 |
| 2008-01-01 00:00:00 | 2008 | 1 | 0.714356 | 0.480858 | 0.602596 |
| 2008-02-01 00:00:00 | 2008 | 2 | 0.772238 | 0.516013 | 0.682774 |
| 2008-03-01 00:00:00 | 2008 | 3 | 0.670054 | 0.547636 | 0.735196 |
| 2008-04-01 00:00:00 | 2008 | 4 | 0.738211 | 0.513624 | 0.685999 |
| 2008-05-01 00:00:00 | 2008 | 5 | 0.754173 | 0.518209 | 0.686474 |
| 2008-06-01 00:00:00 | 2008 | 6 | 0.682412 | 0.409455 | 0.581233 |
| 2008-07-01 00:00:00 | 2008 | 7 | 0.669913 | 0.428906 | 0.601516 |
| 2008-08-01 00:00:00 | 2008 | 8 | 0.673397 | 0.472985 | 0.651923 |
| 2008-09-01 00:00:00 | 2008 | 9 | 0.673355 | 0.491285 | 0.68109 |
| 2008-10-01 00:00:00 | 2008 | 10 | 0.677184 | 0.459983 | 0.633231 |
| 2008-11-01 00:00:00 | 2008 | 11 | 0.669105 | 0.409257 | 0.573469 |
| 2008-12-01 00:00:00 | 2008 | 12 | 0.567983 | 0.512906 | 0.678252 |
| 2009-01-01 00:00:00 | 2009 | 1 | 0.609333 | 0.423242 | 0.57377 |
| 2009-02-01 00:00:00 | 2009 | 2 | 0.827019 | 0.269238 | 0.357818 |
| 2009-03-01 00:00:00 | 2009 | 3 | 0.820737 | 0.615038 | 0.789561 |
| 2009-04-01 00:00:00 | 2009 | 4 | 0.798052 | 0.639287 | 0.81045 |
| 2009-05-01 00:00:00 | 2009 | 5 | 0.733001 | 0.429809 | 0.601411 |
| 2009-06-01 00:00:00 | 2009 | 6 | 0.578139 | 0.397017 | 0.562861 |
| 2009-07-01 00:00:00 | 2009 | 7 | 0.746881 | 0.394591 | 0.548057 |
| 2009-08-01 00:00:00 | 2009 | 8 | 0.656939 | 0.394454 | 0.540795 |
| 2009-09-01 00:00:00 | 2009 | 9 | 0.679189 | 0.402984 | 0.551845 |
| 2009-10-01 00:00:00 | 2009 | 10 | 0.652365 | 0.421434 | 0.594925 |
| 2009-11-01 00:00:00 | 2009 | 11 | 0.583653 | 0.390773 | 0.536701 |
| 2009-12-01 00:00:00 | 2009 | 12 | 0.639453 | 0.386426 | 0.53576 |
| 2010-01-01 00:00:00 | 2010 | 1 | 0.00408852 | 0.114963 | 0.145435 |
| 2010-02-01 00:00:00 | 2010 | 2 | 0.372217 | 0.399682 | 0.568576 |
| 2010-03-01 00:00:00 | 2010 | 3 | 0.560785 | 0.387724 | 0.542961 |
| 2010-04-01 00:00:00 | 2010 | 4 | 0.663673 | 0.317138 | 0.469267 |
| 2010-05-01 00:00:00 | 2010 | 5 | 0.63067 | 0.436126 | 0.579533 |
| 2010-06-01 00:00:00 | 2010 | 6 | 0.660678 | 0.454842 | 0.632635 |
| 2010-07-01 00:00:00 | 2010 | 7 | 0.638788 | 0.47464 | 0.65564 |
| 2010-08-01 00:00:00 | 2010 | 8 | 0.526662 | 0.405191 | 0.567096 |
| 2010-09-01 00:00:00 | 2010 | 9 | 0.641334 | 0.458212 | 0.641255 |
| 2010-10-01 00:00:00 | 2010 | 10 | 0.62896 | 0.461829 | 0.622605 |
| 2010-11-01 00:00:00 | 2010 | 11 | 0.756064 | 0.571506 | 0.763323 |
| 2010-12-01 00:00:00 | 2010 | 12 | 0.718266 | 0.451311 | 0.605804 |
| 2011-01-01 00:00:00 | 2011 | 1 | 0.28446 | 0.461286 | 0.616807 |
| 2011-02-01 00:00:00 | 2011 | 2 | 0.779287 | 0.460147 | 0.635964 |
| 2011-03-01 00:00:00 | 2011 | 3 | 0.56763 | 0.454265 | 0.620128 |
| 2011-04-01 00:00:00 | 2011 | 4 | 0.828787 | 0.67829 | 0.853732 |
| 2011-05-01 00:00:00 | 2011 | 5 | 0.63612 | 0.483543 | 0.653853 |
| 2011-06-01 00:00:00 | 2011 | 6 | 0.636367 | 0.448447 | 0.63395 |
| 2011-07-01 00:00:00 | 2011 | 7 | 0.472576 | 0.428807 | 0.597468 |
| 2011-08-01 00:00:00 | 2011 | 8 | 0.540888 | 0.408805 | 0.582813 |
| 2011-09-01 00:00:00 | 2011 | 9 | 0.556029 | 0.412377 | 0.575249 |
| 2011-10-01 00:00:00 | 2011 | 10 | 0.620834 | 0.465351 | 0.632568 |
| 2011-11-01 00:00:00 | 2011 | 11 | 0.67976 | 0.444555 | 0.616394 |
| 2011-12-01 00:00:00 | 2011 | 12 | 0.7493 | 0.456404 | 0.62975 |
| 2012-01-01 00:00:00 | 2012 | 1 | 0.691602 | 0.474192 | 0.590327 |
| 2012-02-01 00:00:00 | 2012 | 2 | 0.797643 | 0.495612 | 0.641643 |
| 2012-03-01 00:00:00 | 2012 | 3 | 0.850767 | 0.59663 | 0.771738 |
| 2012-04-01 00:00:00 | 2012 | 4 | 0.800804 | 0.590615 | 0.774618 |
| 2012-05-01 00:00:00 | 2012 | 5 | 0.440817 | 0.355042 | 0.495638 |
| 2012-06-01 00:00:00 | 2012 | 6 | 0.52089 | 0.428129 | 0.617037 |
| 2012-07-01 00:00:00 | 2012 | 7 | 0.604635 | 0.432666 | 0.609772 |
| 2012-08-01 00:00:00 | 2012 | 8 | 0.633807 | 0.473348 | 0.65687 |
| 2012-09-01 00:00:00 | 2012 | 9 | 0.599548 | 0.416439 | 0.584445 |
| 2012-10-01 00:00:00 | 2012 | 10 | 0.583431 | 0.461876 | 0.660022 |
| 2012-11-01 00:00:00 | 2012 | 11 | 0.693271 | 0.428615 | 0.597416 |
| 2012-12-01 00:00:00 | 2012 | 12 | 0.759241 | 0.565813 | 0.737312 |
| 2013-01-01 00:00:00 | 2013 | 1 | 0.281355 | 0.245968 | 0.30427 |
| 2013-02-01 00:00:00 | 2013 | 2 | 0.291143 | 0.340506 | 0.485853 |
| 2013-03-01 00:00:00 | 2013 | 3 | 0.634031 | 0.555407 | 0.752386 |
| 2013-04-01 00:00:00 | 2013 | 4 | 0.65835 | 0.567745 | 0.75323 |
| 2013-05-01 00:00:00 | 2013 | 5 | 0.750027 | 0.512956 | 0.689948 |
| 2013-06-01 00:00:00 | 2013 | 6 | 0.485205 | 0.385702 | 0.533675 |
| 2013-07-01 00:00:00 | 2013 | 7 | 0.581357 | 0.370342 | 0.508034 |
| 2013-08-01 00:00:00 | 2013 | 8 | 0.74024 | 0.551764 | 0.732503 |
| 2013-09-01 00:00:00 | 2013 | 9 | 0.714735 | 0.528413 | 0.707523 |
| 2013-10-01 00:00:00 | 2013 | 10 | 0.666992 | 0.468431 | 0.645082 |
| 2013-11-01 00:00:00 | 2013 | 11 | 0.816854 | 0.494946 | 0.659182 |
| 2013-12-01 00:00:00 | 2013 | 12 | 0.828207 | 0.62044 | 0.801774 |
| 2014-01-01 00:00:00 | 2014 | 1 | 0.506077 | 0.458606 | 0.606577 |
| 2014-02-01 00:00:00 | 2014 | 2 | 0.540584 | 0.445735 | 0.603514 |
| 2014-03-01 00:00:00 | 2014 | 3 | 0.583536 | 0.517943 | 0.694143 |
| 2014-04-01 00:00:00 | 2014 | 4 | 0.474498 | 0.38975 | 0.530169 |
| 2014-05-01 00:00:00 | 2014 | 5 | 0.626314 | 0.434524 | 0.610786 |
| 2014-06-01 00:00:00 | 2014 | 6 | 0.119051 | 0.0708604 | 0.0958695 |
| 2014-07-01 00:00:00 | 2014 | 7 | 0.0415518 | 0.0152779 | 0.0248425 |
| 2014-08-01 00:00:00 | 2014 | 8 | 0.377705 | 0.214143 | 0.304601 |
| 2014-09-01 00:00:00 | 2014 | 9 | 0.252189 | 0.10767 | 0.163646 |
| 2014-10-01 00:00:00 | 2014 | 10 | 0.470251 | 0.321469 | 0.491359 |
| 2014-11-01 00:00:00 | 2014 | 11 | 0.455694 | 0.385248 | 0.540489 |
| 2014-12-01 00:00:00 | 2014 | 12 | 0.308629 | 0.229434 | 0.324164 |
| 2015-01-01 00:00:00 | 2015 | 1 | 0.249946 | -0.0154679 | -0.0195618 |
| 2015-02-01 00:00:00 | 2015 | 2 | 0.581023 | 0.445228 | 0.608193 |
| 2015-03-01 00:00:00 | 2015 | 3 | 0.271012 | 0.484298 | 0.685415 |
| 2015-04-01 00:00:00 | 2015 | 4 | 0.251294 | 0.223172 | 0.335489 |
| 2015-05-01 00:00:00 | 2015 | 5 | 0.36231 | 0.288798 | 0.41566 |
| 2015-06-01 00:00:00 | 2015 | 6 | 0.201967 | 0.147981 | 0.218256 |
| 2015-07-01 00:00:00 | 2015 | 7 | 0.358108 | 0.193392 | 0.284694 |
| 2015-08-01 00:00:00 | 2015 | 8 | 0.368403 | 0.281194 | 0.400049 |
| 2015-09-01 00:00:00 | 2015 | 9 | 0.0667572 | 0.082961 | 0.120218 |
| 2015-10-01 00:00:00 | 2015 | 10 | 0.48689 | 0.30801 | 0.428979 |
| 2015-11-01 00:00:00 | 2015 | 11 | 0.498597 | 0.201927 | 0.289052 |
| 2015-12-01 00:00:00 | 2015 | 12 | 0.474719 | 0.165457 | 0.223821 |
| 2016-01-01 00:00:00 | 2016 | 1 | 0.146072 | 0.105635 | 0.141231 |
| 2016-02-01 00:00:00 | 2016 | 2 | 0.361717 | 0.299697 | 0.399486 |
| 2016-03-01 00:00:00 | 2016 | 3 | 0.342631 | 0.304381 | 0.428085 |
| 2016-04-01 00:00:00 | 2016 | 4 | 0.429708 | 0.31078 | 0.423981 |
| 2016-05-01 00:00:00 | 2016 | 5 | 0.444559 | 0.259401 | 0.379042 |
| 2016-06-01 00:00:00 | 2016 | 6 | 0.333509 | 0.247415 | 0.359475 |
| 2016-07-01 00:00:00 | 2016 | 7 | 0.505867 | 0.292487 | 0.425945 |
| 2016-08-01 00:00:00 | 2016 | 8 | 0.570064 | 0.413147 | 0.574427 |
| 2016-09-01 00:00:00 | 2016 | 9 | 0.454328 | 0.332538 | 0.486366 |
| 2016-10-01 00:00:00 | 2016 | 10 | 0.473113 | 0.409051 | 0.547146 |
| 2016-11-01 00:00:00 | 2016 | 11 | 0.624592 | 0.497739 | 0.693197 |
| 2016-12-01 00:00:00 | 2016 | 12 | 0.685437 | 0.487183 | 0.656253 |
| 2017-01-01 00:00:00 | 2017 | 1 | 0.718272 | 0.159715 | 0.211359 |
| 2017-02-01 00:00:00 | 2017 | 2 | 0.403057 | 0.177263 | 0.241979 |
| 2017-03-01 00:00:00 | 2017 | 3 | 0.628844 | 0.444864 | 0.595388 |
| 2017-04-01 00:00:00 | 2017 | 4 | 0.583575 | 0.283389 | 0.412926 |
| 2017-05-01 00:00:00 | 2017 | 5 | 0.473963 | 0.212074 | 0.306337 |
| 2017-06-01 00:00:00 | 2017 | 6 | 0.381841 | 0.31133 | 0.461404 |
| 2017-07-01 00:00:00 | 2017 | 7 | 0.202616 | 0.164892 | 0.263217 |
| 2017-08-01 00:00:00 | 2017 | 8 | 0.580356 | 0.414853 | 0.572272 |
| 2017-09-01 00:00:00 | 2017 | 9 | 0.407914 | 0.316274 | 0.44466 |
| 2017-10-01 00:00:00 | 2017 | 10 | 0.342383 | 0.227909 | 0.3396 |
| 2017-11-01 00:00:00 | 2017 | 11 | 0.397926 | 0.244965 | 0.346889 |
| 2017-12-01 00:00:00 | 2017 | 12 | 0.474661 | 0.426832 | 0.575854 |
| 2018-01-01 00:00:00 | 2018 | 1 | 0.357488 | 0.251639 | 0.338708 |
| 2018-02-01 00:00:00 | 2018 | 2 | 0.589456 | 0.0598689 | 0.0849494 |
| 2018-03-01 00:00:00 | 2018 | 3 | 0.512859 | 0.308461 | 0.43661 |
| 2018-04-01 00:00:00 | 2018 | 4 | 0.415993 | 0.285607 | 0.408119 |
| 2018-05-01 00:00:00 | 2018 | 5 | 0.331704 | 0.233192 | 0.332332 |
| 2018-06-01 00:00:00 | 2018 | 6 | 0.365834 | 0.280107 | 0.410734 |
| 2018-07-01 00:00:00 | 2018 | 7 | 0.318405 | 0.282568 | 0.415399 |
| 2018-08-01 00:00:00 | 2018 | 8 | 0.513883 | 0.36253 | 0.531865 |
| 2018-09-01 00:00:00 | 2018 | 9 | 0.428194 | 0.288788 | 0.41911 |
| 2018-10-01 00:00:00 | 2018 | 10 | 0.578641 | 0.387952 | 0.560485 |
| 2018-11-01 00:00:00 | 2018 | 11 | 0.63635 | 0.340611 | 0.490507 |
| 2018-12-01 00:00:00 | 2018 | 12 | 0.24116 | 0.0940691 | 0.120331 |
| 2019-01-01 00:00:00 | 2019 | 1 | 0.335199 | 0.303435 | 0.424376 |
| 2019-02-01 00:00:00 | 2019 | 2 | 0.00454535 | 0.221041 | 0.30082 |
| 2019-03-01 00:00:00 | 2019 | 3 | 0.657212 | 0.591263 | 0.779523 |
| 2019-04-01 00:00:00 | 2019 | 4 | 0.538835 | 0.238328 | 0.336366 |
| 2019-05-01 00:00:00 | 2019 | 5 | 0.473454 | 0.307928 | 0.427628 |
| 2019-06-01 00:00:00 | 2019 | 6 | 0.304595 | 0.243178 | 0.366383 |
| 2019-07-01 00:00:00 | 2019 | 7 | 0.329229 | 0.283461 | 0.408716 |
| 2019-08-01 00:00:00 | 2019 | 8 | 0.172448 | 0.205273 | 0.29102 |
| 2019-09-01 00:00:00 | 2019 | 9 | 0.188766 | 0.147484 | 0.21177 |
| 2019-10-01 00:00:00 | 2019 | 10 | 0.419301 | 0.278028 | 0.417699 |
| 2019-11-01 00:00:00 | 2019 | 11 | 0.470149 | 0.441572 | 0.607997 |
| 2019-12-01 00:00:00 | 2019 | 12 | 0.50841 | 0.413844 | 0.556981 |
| 2020-01-01 00:00:00 | 2020 | 1 | 0.477407 | 0.363899 | 0.498484 |
| 2020-02-01 00:00:00 | 2020 | 2 | 0.330905 | 0.42126 | 0.556288 |
| 2020-03-01 00:00:00 | 2020 | 3 | 0.31138 | 0.443959 | 0.592622 |
| 2020-04-01 00:00:00 | 2020 | 4 | 0.379751 | 0.280442 | 0.392357 |
| 2020-05-01 00:00:00 | 2020 | 5 | 0.405277 | 0.321466 | 0.44775 |
| 2020-06-01 00:00:00 | 2020 | 6 | 0.300411 | 0.23695 | 0.333389 |
| 2020-07-01 00:00:00 | 2020 | 7 | 0.582328 | 0.398011 | 0.569045 |
| 2020-08-01 00:00:00 | 2020 | 8 | 0.550445 | 0.411359 | 0.598834 |
| 2020-09-01 00:00:00 | 2020 | 9 | 0.576424 | 0.414181 | 0.609336 |
| 2020-10-01 00:00:00 | 2020 | 10 | 0.501594 | 0.369041 | 0.531319 |
| 2020-11-01 00:00:00 | 2020 | 11 | 0.398955 | 0.331139 | 0.479983 |
| 2020-12-01 00:00:00 | 2020 | 12 | 0.651758 | 0.392057 | 0.539636 |
| 2021-01-01 00:00:00 | 2021 | 1 | 0.712238 | 0.347116 | 0.45007 |
| 2021-02-01 00:00:00 | 2021 | 2 | 0.295655 | 0.317158 | 0.444964 |
| 2021-03-01 00:00:00 | 2021 | 3 | 0.433052 | 0.263416 | 0.374493 |
| 2021-04-01 00:00:00 | 2021 | 4 | 0.547263 | 0.35799 | 0.489321 |
| 2021-05-01 00:00:00 | 2021 | 5 | 0.395312 | 0.264118 | 0.398981 |
| 2021-06-01 00:00:00 | 2021 | 6 | 0.186817 | 0.0452113 | 0.0486043 |
| 2021-07-01 00:00:00 | 2021 | 7 | 0.472043 | 0.261918 | 0.387846 |
| 2021-08-01 00:00:00 | 2021 | 8 | 0.416536 | 0.355568 | 0.527069 |
| 2021-09-01 00:00:00 | 2021 | 9 | 0.523481 | 0.402299 | 0.577946 |
| 2021-10-01 00:00:00 | 2021 | 10 | 0.286252 | 0.154729 | 0.226714 |
| 2021-11-01 00:00:00 | 2021 | 11 | 0.735614 | 0.453125 | 0.631615 |
| 2021-12-01 00:00:00 | 2021 | 12 | 0.546456 | 0.398528 | 0.564 |
The values shown below, correspond to the average correlation values in each date processed. Get the table IDEAMJoinedChirpsCorrelationDateMean.csv
| 0 | |
|---|---|
| Pearson | 0.625075 |
| Kendall | 0.423064 |
| Spearman | 0.576582 |
This table shows the average correlation values obtained for each method in every year in the record set. Get the table IDEAMJoinedChirpsCorrelationYear.csv
| Year | Pearson | Kendall | Spearman |
|---|---|---|---|
| 1981 | 0.646078 | 0.43128 | 0.593924 |
| 1982 | 0.703006 | 0.461966 | 0.631076 |
| 1983 | 0.664727 | 0.471845 | 0.635302 |
| 1984 | 0.663444 | 0.47004 | 0.63626 |
| 1985 | 0.686333 | 0.448111 | 0.609207 |
| 1986 | 0.696916 | 0.423262 | 0.581339 |
| 1987 | 0.651611 | 0.428416 | 0.589474 |
| 1988 | 0.635285 | 0.432642 | 0.586404 |
| 1989 | 0.714127 | 0.480322 | 0.645547 |
| 1990 | 0.736364 | 0.447274 | 0.607325 |
| 1991 | 0.742159 | 0.416027 | 0.559688 |
| 1992 | 0.696976 | 0.470159 | 0.637676 |
| 1993 | 0.693555 | 0.440028 | 0.60115 |
| 1994 | 0.732609 | 0.481749 | 0.649115 |
| 1995 | 0.66499 | 0.4634 | 0.622065 |
| 1996 | 0.707603 | 0.442196 | 0.601132 |
| 1997 | 0.730467 | 0.468818 | 0.627966 |
| 1998 | 0.685737 | 0.439749 | 0.599632 |
| 1999 | 0.654707 | 0.476558 | 0.648693 |
| 2000 | 0.709383 | 0.499353 | 0.677964 |
| 2001 | 0.637324 | 0.447966 | 0.603457 |
| 2002 | 0.673156 | 0.436713 | 0.588896 |
| 2003 | 0.61055 | 0.442744 | 0.59659 |
| 2004 | 0.663821 | 0.44502 | 0.597534 |
| 2005 | 0.697242 | 0.484679 | 0.654628 |
| 2006 | 0.626689 | 0.452398 | 0.617803 |
| 2007 | 0.672382 | 0.424644 | 0.576112 |
| 2008 | 0.688532 | 0.480093 | 0.649479 |
| 2009 | 0.69373 | 0.430358 | 0.583663 |
| 2010 | 0.566849 | 0.411097 | 0.566178 |
| 2011 | 0.61267 | 0.466856 | 0.637389 |
| 2012 | 0.664705 | 0.476581 | 0.644737 |
| 2013 | 0.620708 | 0.470218 | 0.631122 |
| 2014 | 0.39634 | 0.299222 | 0.415847 |
| 2015 | 0.347586 | 0.233912 | 0.332522 |
| 2016 | 0.447633 | 0.329955 | 0.459553 |
| 2017 | 0.466284 | 0.28203 | 0.397657 |
| 2018 | 0.440831 | 0.264616 | 0.379096 |
| 2019 | 0.366845 | 0.306236 | 0.42744 |
| 2020 | 0.455553 | 0.365314 | 0.51242 |
| 2021 | 0.46256 | 0.301765 | 0.426802 |
This table shows the average correlation values obtained in every month in the record set. Get the table IDEAMJoinedChirpsCorrelationMonth.csv
| Month | Pearson | Kendall | Spearman |
|---|---|---|---|
| 1 | 0.573048 | 0.348927 | 0.456331 |
| 2 | 0.557186 | 0.390725 | 0.527545 |
| 3 | 0.622841 | 0.497446 | 0.667158 |
| 4 | 0.669266 | 0.468433 | 0.631433 |
| 5 | 0.641126 | 0.432756 | 0.590292 |
| 6 | 0.576671 | 0.394815 | 0.547976 |
| 7 | 0.597248 | 0.39189 | 0.544946 |
| 8 | 0.620613 | 0.423809 | 0.586744 |
| 9 | 0.606082 | 0.41603 | 0.576095 |
| 10 | 0.647893 | 0.442117 | 0.608432 |
| 11 | 0.688458 | 0.44099 | 0.605399 |
| 12 | 0.700465 | 0.428826 | 0.576633 |
En este momento, dispone de registros IDEAM de precipitación con el registro de valores CHIRPS y diferentes análisis de correlación.
En la siguiente tabla se listan las actividades complementarias que deben ser desarrolladas y documentadas por el estudiante en un único archivo de Adobe Acrobat .pdf. El documento debe incluir portada (mostrar nombre completo, código y enlace a su cuenta de GitHub), numeración de páginas, tabla de contenido, lista de tablas, lista de ilustraciones, introducción, objetivo general, capítulos por cada ítem solicitado, conclusiones y referencias bibliográficas.
| Actividad | Alcance |
|---|---|
| 1 | Investigue y documente servicios en Internet desde los cuales se puedan obtener datos satelitales de temperatura media diaria y evaporación total diaria. |
| 2 | Cree scripts en Python que permitan descargar, leer los valores de temperatura y evaporación satelitales en las localizaciones de la red de estaciones IDEAM utilizadas en este curso y que permita realizar análisis de correlación como los presentados en esta actividad. |
| 3 | Analice los resultados de las correlaciones e indique si a partir de los datos obtenidos satelitalmente, se pueden obtener valores precisos para la realización de balances hidrológicos de largo plazo. |
- https://www.earthdata.nasa.gov/learn/backgrounders/remote-sensing
- https://www.chc.ucsb.edu/data/chirps
- https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/tifs/
- https://hatarilabs.com/ih-en/extract-point-value-from-a-raster-file-with-python-geopandas-and-rasterio-tutorial
- Python Scripting for Exporting Multiple Rasters into Time Series
- https://sparkbyexamples.com/pandas/pandas-dataframe-filter/
- https://towardsdatascience.com/8-ways-to-filter-pandas-dataframes-d34ba585c1b8
- https://towardsdatascience.com/4-tricks-you-should-know-to-parse-date-columns-with-pandas-read-csv-27355bb2ad0e
- https://www.codegrepper.com/code-examples/python/how+to+extract+gz+file+python
- https://www.geeksforgeeks.org/python-pandas-dataframe-corr/
- https://stackoverflow.com/questions/42579908/use-corr-to-get-the-correlation-between-two-columns
- https://stackoverflow.com/questions/36454494
- https://www.geeksforgeeks.org/python-pandas-dataframe-set_index/
- https://pandas.pydata.org/docs/getting_started/intro_tutorials/05_add_columns.html
- https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html
- https://pandas.pydata.org/docs/reference/api/pandas.concat.html
- https://pandas.pydata.org/docs/getting_started/intro_tutorials/04_plotting.html
- https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.mean.html
- https://www.w3schools.com/python/ref_func_range.asp
- https://matplotlib.org/stable/tutorials/colors/colormaps.html
- Pearson correlation coefficient
- Kendall rank correlation coefficient
- Spearman’s rank correlation coefficient
| Versión | Descripción | Autor | Horas |
|---|---|---|---|
| 2023.09.01 | Actualización de script para compatibilidad con Python 3.11.5 y Pandas 2.1.1. | rcfdtools | 2.0 |
| 2023.02.08 | Guión, audio, video, edición y publicación. | rcfdtools | 2.5 |
| 2022.10.23 | Documentación y procedimiento general. Diagrama de procesos. | rcfdtools | 6 |
| 2022.10.06 | Análisis de correlación para cada mes. Promedio de correlaciones. Gráficas de análisis. Pruebas manuales de escritorio a partir de lectura en ArcGIS para verificar obtención correcta de valores mediante script. Exportación de gráficas a .png. | rcfdtools | 8 |
| 2022.10.05 | Descarga automática de archivos CHIRPS desde https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/tifs/. Lectura automática de valores mensuales por estación en cada localización específica utilizando rasterio. Incluye las columnas SatValue y SatDesc que contienen el valor obtenido y el nombre del archivo chirps utilizado. Generación de series segmentadas por año y mes en formato .csv para cada imagen satelital a partir de archivo a partir de archivo IDEAMJoined.csv. Integración de series obtenidas con resultados obtenidos en archivo IDEAMJoinedChirps.csv. Tiempo aproximado de ejecución para 514926 registros en 41 años (1981 a 2021) y 492 meses: 00:25:45. | rcfdtools | 8 |
| 2022.10.03 | Inicio Script Python procesamiento series y descarga datos precipitación CHIRPS D:\R.LTWB.src\ChirpsGetValue.py. Segmentación de series de precipitación para registros IDEAM en slices por año y mes a partir de archivo IDEAMJoined.csv. | rcfdtools | 8 |
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Este curso guía, ha sido desarrollado con el apoyo de la Escuela Colombiana de Ingeniería - Julio Garavito. Encuentra más contenidos en https://github.com/uescuelaing