Package: hydrobr
HydroBrHydroBr is an open-source package to work with Brazilian hydrometeorological time series.IntroductionHydroBr is an open-source package for work with Brazilian hydrometeorological time series in Python. This package
provides a connection with the Brazilian  National Water Agency (Agência Nacional de Águas - ANA), the Brazilian
National Institute of Meteorology (Instituto Nacional de Meteorologia - INMET), and the National Electric System
Operator (Operador Nacional do Sistema Elétrico - ONS) databases in order to help users to select, download,
preprocess, and plot hydrometeorological data.InstallationThe released version of HydroBr is 0.1.1.To install the released version, usepip install hydrobr.You may install the latest development version by cloning theGitHubrepository and using the setup script::git clone https://github.com/wallissoncarvalho/hydrobr.git
cd hydrobr
python setup.py installModules - DocumentationCurrently, theHydroBrpackage has four modules:get_data - Functions that provide a connection with the Brazilian National Water Agency
(Agência Nacional de Águas - ANA), the Brazilian National Institute of Meteorology
(Instituto Nacional de Meteorologia - INMET), and the National Electric System Operator
(Operador Nacional do Sistema Elétrico - ONS) databases.Plot - You will have some data plot options, such as the Gantt (temporal data availability) graphic, Flow Duration
Cuve, and plot for spatial station availability.PreProcessing - Presents a function to filter your data by dates, number of years with data, and missing percentage.
Further, there is a function to convert your data.SaveAs - Provides functions to save your data into a ".txt" file in the ASCII standard.The modules will be updated with new functions/methods as soon as possible. Contributions are welcome!Import HydroBrimporthydrobrData from ANAhelp(hydrobr.get_data.ANA)Help on class ANA in module hydrobr.get_data:

class ANA(builtins.object)
 |  It provides a connection with the Brazilian National Water Agency (Agência Nacional de Águas - ANA) database
 |  
 |  Static methods defined here:
 |  
 |  flow_data(list_station, only_consisted=False)
 |      Get the flow station data series from a list of stations code of the Brazilian National Water Agency
 |      (ANA) database.
 |      
 |      Parameters
 |      ----------
 |      list_station : list of strings
 |          A list of with the stations code as strings.
 |      only_consisted : boolean, default False
 |          If True, returns only the data classified as consistent by the provider.
 |      
 |      Returns
 |      -------
 |      data_stations : pandas DataFrame
 |          The data os each station as a column in a pandas DataFrame
 |  
 |  list_flow_stations(state='', city='', source='ANAF')
 |      Searches for flow/stage stations registered at the Brazilian National Agency of Water inventory.
 |      
 |      Parameters
 |      ----------
 |      state : string
 |          Brazilian state name where the stations are located (e.g., Rio de Janeiro)
 |      city : string
 |          Brazilian city name where the stations are located (e.g., Rio de Itaperuna)
 |      source: string, default 'ANAF'
 |          The source to look for the data. 'ANA' to get the list of stations from the Brazilian National Water
 |          Agency (ANA) database, or 'ANAF' to get the filtered list of stations that contain only the stations
 |          from ANA with registered data.
 |          More information about ANAF: https://doi.org/10.5281/zenodo.3755065
 |      
 |      Returns
 |      -------
 |      list_stations : pandas DataFrame
 |          The selected list of stations as a pandas DataFrame
 |  
 |  list_prec_stations(state='', city='', source='ANAF')
 |      Searches for precipitation stations registered at the Brazilian National Agency of Water (ANA)
 |      
 |      Parameters
 |      ----------
 |      state : string
 |          Brazilian state name where the stations are located (e.g., Rio de Janeiro)
 |      city : string
 |          Brazilian city name where the stations are located (e.g., Rio de Itaperuna)
 |      source: string, default 'ANA'
 |          The source to look for the data. 'ANA' to get the list of stations from the Brazilian National Water
 |          Agency (ANA) database, or 'ANAF' to get the filtered list of stations that contain only the stations
 |          from ANA with registered data.
 |          More information about ANAF: https://doi.org/10.5281/zenodo.3755065
 |      
 |      Returns
 |      -------
 |      list_stations : pandas DataFrame
 |          The selected list of stations as a pandas DataFrame
 |  
 |  prec_data(list_station, only_consisted=False)
 |      Get the precipitation station data series from a list of stations code.
 |      
 |      Parameters
 |      ----------
 |      list_station : list of strings
 |          A list of with the stations code as strings.
 |      only_consisted : boolean, default False
 |          If True, returns only the data classified as consistent by the provider.
 |      
 |      Returns
 |      -------
 |      data_stations : pandas DataFrame
 |          The data os each station as a column in a pandas DataFrame
 |  
 |  stage_data(list_station, only_consisted=False)
 |      Get the stage station data series from a list of stations code of the Brazilian National Water Agency
 |      (ANA) database.
 |      
 |      Parameters
 |      ----------
 |      list_station : list of strings
 |          A list of with the stations code as strings.
 |      only_consisted : boolean, default False
 |          If True, returns only the data classified as consistent by the provider.
 |      
 |      Returns
 |      -------
 |      data_stations : pandas DataFrame
 |          The data os each station as a column in a pandas DataFrame
 |  
 |  ----------------------------------------------------------------------Data from INMEThelp(hydrobr.get_data.INMET)Help on class INMET in module hydrobr.get_data:

class INMET(builtins.object)
 |  It provides a connection with the  Brazilian National Institute of Meteorology (Instituto Nacional de
 |  Meteorologia - INMET) database.
 |  
 |  Static methods defined here:
 |  
 |  daily_data(station_code, filter=True)
 |      Searches for all the data of a station registered at the Brazilian National Institute of Meteorology
 |      (Instituto Nacional de Meteorologia - INMET) database.
 |      
 |      Returns a pandas daily DataFrame with six variables for each day:
 |          - Prec - Precipitation (mm)
 |          - Tmean - Daily mean Temperature (ºC)
 |          - Tmax - Maximum Temperature (ºC)
 |          - Tmin - Minimum Temperature (ºC)
 |          - RH - Relative Humidity (%)
 |          - SD - Sunshine Duration (hours)
 |      
 |      Parameters
 |      ----------
 |      station_code : string
 |          Code of the station as a string
 |      filter: boolean, default True
 |          There is stations with repeated registered data. If 'True' the function returns a panda DataFrame
 |          with the first occurrence of the date. If 'False' return a pandas DataFrame with, in some cases,
 |          repeated datetime index.
 |      
 |      Returns
 |      -------
 |      data : pandas DataFrame
 |          The data of the selected station as a pandas DataFrame
 |  
 |  hourly_data(station_code)
 |      Searches for all the data of a station registered at the Brazilian National Institute of Meteorology
 |      (Instituto Nacional de Meteorologia - INMET) database.
 |      
 |      Only works for Automatic Stations.
 |      
 |      Returns a pandas hourly DataFrame with 17 variables for each day:
 |          - Tins - Instant Temperature (ºC)
 |          - Tmax - Maximum Temperature (ºC)
 |          - Tmin - Minimum Temperature (ºC)
 |          - RHins - Instant Relative Humidity (%)
 |          - RHmax - Maximum Relative Humidity (%)
 |          - RHmin - Minimum Relative Humidity (%)
 |          - DPins - Instant Dew Point Temperature (ºC)
 |          - DPmax - Maximum Dew Point Temperature (ºC)
 |          - DPmin - Minimum Dew Point Temperature (ºC)
 |          - Pins - Instant Pressure (hPa)
 |          - Pmax - Maximum Pressure (hPa)
 |          - Pmin - Minimum Pressure (hPa)
 |          - Wspeed - Wind Speed (m/s)
 |          - Wdir - Wind direction (º)
 |          - Wgust - Wind gust (m/s)
 |          - Rad - Global Radiation (kJ/m²)
 |          - Prec - Precipitation (mm)
 |      
 |      Parameters
 |      ----------
 |      station_code : string
 |          Code of the station as a string.
 |      
 |      Returns
 |      -------
 |      data : pandas DataFrame
 |          The data of the selected station as a pandas DataFrame.
 |  
 |  list_stations(station_type='both')
 |      Searches for precipitation stations registered at the Brazilian National Agency of Water (ANA) or the INMET
 |      inventory.
 |      
 |      Parameters
 |      ----------
 |      station_type : string, default 'both'
 |          The type of station. 'both' to get the list of automatic and manual gauge stations, 'automatic' to get only
 |          the automatic gauge stations, and 'conventional' to get only the conventional gauge stations.
 |      Returns
 |      -------
 |      list_stations : pandas DataFrame
 |          The selected list of stations as a pandas DataFrame
 |  
 |  ----------------------------------------------------------------------Data from ONShelp(hydrobr.get_data.ONS)Help on class ONS in module hydrobr.get_data:

class ONS(builtins.object)
 |  Provide data from the National Electric System Operator (Operador Nacional do Sistema Elétrico - ONS) database.
 |  
 |  Static methods defined here:
 |  
 |  daily_data()
 |       Returns all the naturalized daily flow data of different reservoirs from the National Electric System
 |       Operator (Operador Nacional do Sistema Elétrico - ONS) database.
 |      
 |      Parameters
 |      ----------
 |      
 |      Returns
 |      -------
 |      data : pandas DataFrame
 |          All the naturalized daily flow data as a pandas DataFrame, where each column refers to a specific
 |          reservoir.  
 |  ----------------------------------------------------------------------PreProcessing methodshelp(hydrobr.PreProcessing)Help on class PreProcessing in module hydrobr.preprocessing:

class PreProcessing(builtins.object)
 |  Static methods defined here:
 |  
 |  daily_to_monthly(data, method='sum')
 |      Transform a time series of daily data into a time series monthly data.
 |      
 |      In the conversion process a month with a day missing data is considered as a missing month.
 |      
 |      Parameters
 |      ----------
 |      data : pandas DataFrame
 |          A Pandas daily DataFrame with DatetimeIndex where each column corresponds to a station.
 |      method: str, default sum
 |          The method used to convert. If 'sum', the monthly data will be the sum of the daily data. If 'mean', the
 |          monthly data will be the mean of the daily data.
 |      
 |      Returns
 |      -------
 |      monthly_data : pandas DataFrame
 |          The  monthly pandas DataFrame
 |  
 |  stations_filter(data, n_years=10, missing_percentage=5, start_date=False, end_date=False)
 |      A composed method to filter stations. 
 |      
 |      First, the method filters the stations data by the Start Date and the End Date, it its passed. After that,
 |      the is selected only the stations with at least a defined number of years between the first date and the
 |      last date of the station. At the end is selected the stations that contains at least one window of data with
 |      the number of years and a maximum missing data percentage. 
 |      
 |      Parameters
 |      ----------
 |      data : pandas DataFrame
 |          A Pandas daily DataFrame with DatetimeIndex where each column corresponds to a station.
 |      n_years: int, default 10
 |          The minimum number of years of registered data for the station between the first date and the end date.
 |      missing_percentage: int, default 5
 |           The maximum missing data percentage in a window with n_years.
 |           A number between 0 and 100
 |      start_date : int, float, str, default False
 |          The desired start date for the output DataFrame.
 |          See: pandas.to_datetime documentation if have doubts about the date format
 |      end_date: int, float, str, default False
 |          The desired end date for the output DataFrame.
 |          See: pandas.to_datetime documentation if have doubts about the date format
 |      
 |      Returns
 |      -------
 |      data : pandas DataFrame
 |          A pandas DataFrame with only the filtered stations
 |  
 |  ----------------------------------------------------------------------Plot methodshelp(hydrobr.Plot)Help on class Plot in module hydrobr.graphics:

class Plot(builtins.object)
 |  Static methods defined here:
 |  
 |  fdc(data, y_log_scale=True)
 |      Make a flow duration curve plot.
 |      
 |      Parameters
 |      ----------
 |      data : pandas DataFrame
 |          A Pandas daily DataFrame with DatetimeIndex where each column corresponds to a station..
 |      y_log_scale : boolean, default True
 |          Defines if the the plotting y-axis will be in the logarithmic scale.
 |      
 |      Returns
 |      -------
 |      fig : plotly Figure
 |  
 |  gantt(data, monthly=True)
 |      Make a Gantt plot, which shows the temporal data availability for each station.
 |      
 |      Parameters
 |      ----------
 |      data : pandas DataFrame
 |          A Pandas daily DataFrame with DatetimeIndex where each column corresponds to a station..
 |      monthly : boolean, default True
 |          Defines if the availability count of the data will be monthly to obtain a more fluid graph.
 |      
 |      Returns
 |      -------
 |      fig : plotly Figure
 |  
 |  spatial_stations(list_stations, mapbox_access_token)
 |      Make a spatial plot of the stations.
 |      
 |      Parameters
 |      ----------
 |      list_stations : pandas DataFrame
 |          A Pandas DataFrame that must contain Latitude, Longitude, Name, and Code columns.
 |      mapbox_access_token : str
 |          Mapbox access toke, which can be obtained at https://account.mapbox.com/access-tokens/
 |      
 |      Returns
 |      -------
 |      fig : plotly Figure
 |  
 |  ----------------------------------------------------------------------SaveAs methodshelp(hydrobr.SaveAs)Help on class SaveAs in module hydrobr.save:

class SaveAs(builtins.object)
 |  Static methods defined here:
 |  
 |  asc_daily_flow(data, path_save)
 |      Save each column of the flow stations DataFrame into a ".txt" file in the ASCII standard.
 |      
 |      Parameters
 |      ----------
 |      data : pandas DataFrame
 |          A Pandas daily DataFrame with DatetimeIndex where each column corresponds to a station.
 |      path_save: string
 |          The computer location where the ".txt" files will be saved.
 |      
 |      Returns
 |      -------
 |      Saved data in the path_save
 |
 |  asc_daily_prec(data, path_save)
 |      Save each column of the precipitation stations DataFrame into a ".txt" file in the ASCII standard.
 |      
 |      Parameters
 |      ----------
 |      data : pandas DataFrame
 |          A Pandas daily DataFrame with DatetimeIndex where each column corresponds to a station.
 |      path_save: string
 |          The computer location where the ".txt" files will be saved.
 |      
 |      Returns
 |      -------
 |      Saved data in the path_save
 |  ----------------------------------------------------------------------ModulesExamples of usage are available atHydroBr's page on my blog.DependenciesNumPyPandasPlotlyRequeststqdmLicenseBSD 3-Clause LicenseHow to citeWallisson Moreira de Carvalho. (2020, July 5). HydroBr: A Python package to work with Brazilian hydrometeorological time
series. (Version 0.1.1). Zenodo.http://doi.org/10.5281/zenodo.3931027