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‎docs/api-documentation.rst

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API Documentation
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*****************
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There are 3 modules in the geoglows package.
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.. toctree::
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:maxdepth: 3
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‎docs/api-documentation/bias.rst

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geoglows.bias
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=============
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Point Bias Correction
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~~~~~~~~~~~~~~~~~~~~~
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.. automodule:: geoglows.bias
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:members:
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correct_historical, correct_forecast, statistics_tables

‎docs/api-documentation/data.rst

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---------------------
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.. automodule:: geoglows.data
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:members:
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retrospective, return_periods, annual_averages, monthly_averages, daily_averages,
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:noindex:
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:members:
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retrospective, return_periods, annual_averages, monthly_averages, daily_averages,
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:noindex:
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GEOGLOWS Model Utilities
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------------------------
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.. automodule:: geoglows.data
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:members:
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metadata_tables
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:noindex:
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:members:
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metadata_tables
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:noindex:

‎docs/api-documentation/plots.rst

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geoglows.plots
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==============
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Plots for Streamflow Data
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. automodule:: geoglows.plots
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:members:
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forecast, forecast_stats, forecast_ensembles,
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retrospective, annual_averages, monthly_averages, daily_averages,
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daily_variance, flow_duration_curve
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:noindex:
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:members:
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forecast, forecast_stats, forecast_ensembles, retrospective, annual_averages, monthly_averages, daily_averages, flow_duration_curve, corrected_retrospective, corrected_month_average, corrected_day_average, corrected_scatterplots

‎docs/api-documentation/streamflow.rst

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THIS MODULE IS DEPRECATED. Please update your code to use the new GEOGLOWS model and data services. Analogous functions
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to everything in this module is found in the `geoglows.data` or `geoglows.streams` modules.
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The streamflow module provides a series of functions for requesting forecasted and historical data from the GEOGloWS
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The streamflow module provides a series of functions for requesting forecasted and historical data from the GEOGLOWS
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ECMWF Streamflow Service for Model and Data Services Version 1.
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Forecasted Streamflow
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historic_simulation, return_periods, daily_averages, monthly_averages
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:noindex:
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GEOGloWS Model Utilities
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GEOGLOWS Model Utilities
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------------------------
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.. automodule:: geoglows.streamflow

‎docs/conf.py

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# -- Project information -----------------------------------------------------
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project = 'geoglows'
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copyright = '2021, Riley Hales'
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author = 'Riley Hales'
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copyright = '2024, Riley Hales'
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author = 'Riley Hales, PhD'
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# The full version, including alpha/beta/rc tags
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release = '0.27.1'
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release = '1.2.0'
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master_doc = 'index'
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# -- General configuration ---------------------------------------------------
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ['_static']
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html_static_path = ['_static', ]

‎docs/index.rst

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These links will be maintained to reference the most updated versions of the tutorials.
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The tutorials are GitHub Gists which you can copy and launch in a Google Collaboratory setting directly from the GitHub.
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- Retrieve & plot GEOGloWS model data: `<https://gist.github.com/rileyhales/873896e426a5bd1c4e68120b286bc029>`_
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- Retrieve & plot GEOGLOWS model data: `<https://gist.github.com/rileyhales/873896e426a5bd1c4e68120b286bc029>`_
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- Finding Stream ID #'s programmatically: `<https://gist.github.com/rileyhales/ad92d1fce3aa36ef5873f2f7c2632d31>`_
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- Bias Evaluation and Calibration at a point: `<https://gist.github.com/rileyhales/d5290e12b5858d59960d0898fbd0ed69>`_
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- Generate/Download High Res Plot Images: `<https://gist.github.com/rileyhales/9b5bbb0c5f307eb14b9f1ced39d641e4>`_
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About GEOGloWS ECMWF Streamflow
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About GEOGLOWS ECMWF Streamflow
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===============================
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GEOGLOWS ECMWF Streamflow Project: This project provides access to the results of a hydrologic model that is run each
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day. The model is based on a group of unique weather forecasts, known as an ensemble, from ECMWF. Each unique

‎geoglows/__init__.py

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from ._constants import METADATA_TABLE_PATH
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__all__ = [
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'bias', 'plots', 'data', 'analyze', 'streams', 'tables',
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'bias', 'plots', 'data', 'analyze', 'streams', 'tables', 'streamflow',
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'METADATA_TABLE_PATH'
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]
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__version__ = '1.1.0'
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__version__ = '1.2.0'
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__author__ = 'Riley Hales'
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__license__ = 'BSD 3-Clause Clear License'

‎geoglows/_plots/__init__.py

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from .plots import *
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from .plots import (
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forecast,
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forecast_stats,
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forecast_ensembles,
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retrospective,
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daily_averages,
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monthly_averages,
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annual_averages,
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flow_duration_curve,
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corrected_retrospective,
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corrected_month_average,
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corrected_day_average,
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corrected_scatterplots,
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)
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__all__ = []
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__all__ = [
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'forecast',
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'forecast_stats',
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'forecast_ensembles',
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'retrospective',
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'daily_averages',
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'monthly_averages',
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'annual_averages',
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'flow_duration_curve',
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'corrected_retrospective',
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'corrected_month_average',
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'corrected_day_average',
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'corrected_scatterplots',
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]

‎geoglows/_plots/plotly_bias_corrected.py

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from .format_tools import build_title
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from .plotly_helpers import _rperiod_scatters
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__all__ = [
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'corrected_historical',
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'corrected_retrospective',
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'corrected_scatterplots',
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'corrected_day_average',
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'corrected_month_average',
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# BIAS CORRECTION PLOTS
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def corrected_historical(corrected: pd.DataFrame, simulated: pd.DataFrame, observed: pd.DataFrame,
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rperiods: pd.DataFrame = None, plot_titles: list = None,
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plot_type: str = 'plotly') -> go.Figure or str:
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def corrected_retrospective(corrected: pd.DataFrame, simulated: pd.DataFrame, observed: pd.DataFrame,
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rperiods: pd.DataFrame = None, plot_titles: list = None,
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plot_type: str = 'plotly') -> go.Figure or str:
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"""
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Creates a plot of corrected discharge, observed discharge, and simulated discharge
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rperiods: the csv response from return_periods
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plot_type: either 'plotly', or 'plotly_html' (default plotly)
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plot_titles: (dict) Extra info to show on the title of the plot. For example:
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{'Reach ID': 1234567, 'Drainage Area': '1000km^2'}
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{'River ID': 1234567, 'Drainage Area': '1000km^2'}
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Returns:
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plotly.GraphObject: plotly object, especially for use with python notebooks and the .show() method
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merged_sim_obs: (optional) if you have already computed it, hydrostats.data.merge_data(simulated, observed)
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merged_cor_obs: (optional) if you have already computed it, hydrostats.data.merge_data(corrected, observed)
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plot_titles: (dict) Extra info to show on the title of the plot. For example:
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{'Reach ID': 1234567, 'Drainage Area': '1000km^2'}
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{'River ID': 1234567, 'Drainage Area': '1000km^2'}
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plotly.GraphObject: plotly object, especially for use with python notebooks and the .show() method
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merged_sim_obs: pd.DataFrame = False, merged_cor_obs: pd.DataFrame = False,
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plot_titles: list = None, ) -> go.Figure or str:
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"""
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Calculates and plots the monthly average streamflow. This function uses
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Calculates and _plots the monthly average streamflow. This function uses
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hydrostats.data.merge_data on the 3 inputs. If you have already computed these because you are doing a full
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comparison of bias correction, you can provide them to save time
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merged_sim_obs: (optional) if you have already computed it, hydrostats.data.merge_data(simulated, observed)
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merged_cor_obs: (optional) if you have already computed it, hydrostats.data.merge_data(corrected, observed)
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plot_titles: (dict) Extra info to show on the title of the plot. For example:
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{'Reach ID': 1234567, 'Drainage Area': '1000km^2'}
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{'River ID': 1234567, 'Drainage Area': '1000km^2'}
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plotly.GraphObject: plotly object, especially for use with python notebooks and the .show() method
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def corrected_day_average(corrected: pd.DataFrame, simulated: pd.DataFrame, observed: pd.DataFrame,
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merged_sim_obs: pd.DataFrame = False, merged_cor_obs: pd.DataFrame = False,
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titles: dict = None, ) -> go.Figure or str:
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plot_titles: dict = None, ) -> go.Figure or str:
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"""
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Calculates and plots the daily average streamflow. This function uses
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Calculates and _plots the daily average streamflow. This function uses
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hydrostats.data.merge_data on the 3 inputs. If you have already computed these because you are doing a full
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comparison of bias correction, you can provide them to save time
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merged_sim_obs: (optional) if you have already computed it, hydrostats.data.merge_data(simulated, observed)
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merged_cor_obs: (optional) if you have already computed it, hydrostats.data.merge_data(corrected, observed)
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observed: the dataframe of observed data. Must have a datetime index and a single column of flow values
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titles: (dict) Extra info to show on the title of the plot. For example:
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{'Reach ID': 1234567, 'Drainage Area': '1000km^2'}
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plot_titles: (dict) Extra info to show on the title of the plot. For example:
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{'River ID': 1234567, 'Drainage Area': '1000km^2'}
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layout = go.Layout(
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title=build_title('Daily Average Streamflow Comparison', plot_titles),
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showlegend=True)
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"""
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Calculates and plots the cumulative volume output on each of the 3 datasets provided. This function uses
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comparison of bias correction, you can provide them to save time
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merged_cor_obs: (optional) if you have already computed it, hydrostats.data.merge_data(corrected, observed)
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plot_titles: (dict) Extra info to show on the title of the plot. For example:
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{'Reach ID': 1234567, 'Drainage Area': '1000km^2'}
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{'River ID': 1234567, 'Drainage Area': '1000km^2'}
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Returns:
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plotly.GraphObject: plotly object, especially for use with python notebooks and the .show() method

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