Merge pull request #97 from cgq-qgc/cleanup_weather_py

PR: Cleanup weather_reader.py

pyhelp/weather_reader.py

@@ -1,16 +1,16 @@
 # -*- coding: utf-8 -*-
-
-# Copyright © 2014-2018 GWHAT Project Contributors
-# https://github.com/jnsebgosselin/gwhat
+# -----------------------------------------------------------------------------
+# Copyright © PyHELP Project Contributors
+# https://github.com/cgq-qgc/pyhelp
 #
-# This file is part of GWHAT (Ground-Water Hydrograph Analysis Toolbox).
-# Licensed under the terms of the GNU General Public License.
+# This file is part of PyHELP.
+# Licensed under the terms of the MIT License.
+# -----------------------------------------------------------------------------
 
 
 # ---- Standard Library imports
 import os.path as osp
 import calendar
-from calendar import monthrange
 
 # ---- Third Party imports
 import numpy as np
@@ -91,7 +91,7 @@ def format_timeseries_for_HELP(years, data, year_format, data_format):
 
         year_data = data[indexes]
         year_data = np.hstack(
-                [year_data, np.zeros(10 - len(year_data) % 10)])
+            [year_data, np.zeros(10 - len(year_data) % 10)])
         year_data = year_data.reshape(37, 10).tolist()
 
         # Save the data in a format compatible with HELP :
@@ -102,161 +102,3 @@ def format_timeseries_for_HELP(years, data, year_format, data_format):
                 formated_line += data_format.format(line_data[i])
             fdata.append([formated_line])
     return fdata
-
-
-def save_data_to_HELP_format(filename, years, data, city, lat=None):
-    """Formats a time series to the HELP format."""
-    root, ext = osp.splitext(filename)
-    ext = ext[1:]
-    if ext == 'D4':  # precipitation
-        year_format = '{0:>10}'
-        data_format = '{0:>5.1f}'
-    elif ext == 'D7':  # air temperature
-        year_format = '{0:>5}'
-        data_format = '{0:>6.1f}'
-    elif ext == 'D13':  # global solar radiation
-        year_format = '{0:>5}'
-        data_format = '{0:>6.2f}'
-        if lat is None:
-            raise ValueError("A value must be specified for lat.")
-    else:
-        raise ValueError("%s is not a valid file extension." % ext)
-
-    # ---- Format Header
-
-    itype = 3   # Precipitation data for {city} was entered by the user.
-    iunits = 2  # 1 for IP and 2 for SI
-    fcontent = [['{0:>2}'.format(itype)],
-                ['{0:>2}'.format(iunits)],
-                ['{0:<40}'.format(city[:40])],
-                ]
-    if ext == 'D13':
-        # Append the latitude if the data are solar radiation.
-        fcontent.append(['{0:>6.2f}'.format(lat)])
-    else:
-        fcontent.append([])
-
-    # ---- Format Data
-
-    for year in np.unique(years):
-        # Selects the data and asserts that the data are complete for
-        # that year :
-
-        indexes = np.where(years == year)[0]
-        days_in_year = 366 if calendar.isleap(year) else 365
-        assert len(indexes) == days_in_year
-
-        # Adds zeros to complete de last row and reshape the data
-        # in a 37 x 10 grid:
-
-        year_data = data[indexes]
-        year_data = np.hstack(
-                [year_data, np.zeros(10 - len(year_data) % 10)])
-        year_data = year_data.reshape(37, 10).tolist()
-
-        # Save the data in a format compatible with HELP :
-
-        for line_data in year_data:
-            formated_line = year_format.format(year)
-            for i in range(10):
-                formated_line += data_format.format(line_data[i])
-            fcontent.append([formated_line])
-
-    save_content_to_csv(filename, fcontent)
-
-
-# ---- Base functions: monthly downscaling
-def calc_monthly_sum(yy_dly, mm_dly, x_dly):
-    """
-    Calcul monthly cumulative values from daily values, where yy_dly are the
-    years, mm_dly are the months (1 to 12), and x_dly are the daily values.
-    """
-    return calc_monthly(yy_dly, mm_dly, x_dly, np.sum)
-
-
-def calc_monthly_mean(yy_dly, mm_dly, x_dly):
-    """
-    Calcul monthly mean values from daily values, where yy_dly are the
-    years, mm_dly are the months (1 to 12), and x_dly are the daily values.
-    """
-    return calc_monthly(yy_dly, mm_dly, x_dly, np.mean)
-
-
-def calc_monthly(yy_dly, mm_dly, x_dly, func):
-    yy = np.unique(yy_dly)
-    mm = range(1, 13)
-
-    yy_mly = np.repeat(yy, len(mm))
-    mm_mly = np.tile(mm, len(yy))
-    x_mly = np.zeros(len(mm)*len(yy))
-
-    for i in range(len(mm)*len(yy)):
-        indx = np.where((yy_dly == yy_mly[i]) & (mm_dly == mm_mly[i]))[0]
-        if len(indx) < monthrange(yy_mly[i], mm_mly[i])[1]:
-            x_mly[i] = np.nan  # incomplete dataset for this month
-        else:
-            x_mly[i] = func(x_dly[indx])
-
-    return yy_mly, mm_mly, x_mly
-
-
-def calcul_monthly_normals(years, months, x_mly, yearmin=None, yearmax=None):
-    """Calcul the monthly normals from monthly values."""
-    if len(years) != len(months) != len(x_mly):
-        raise ValueError("The dimension of the years, months, and x_mly array"
-                         " must match exactly.")
-    if np.min(months) < 1 or np.max(months) > 12:
-        raise ValueError("Months values must be between 1 and 12.")
-
-    # Mark as nan monthly values that are outside the year range that is
-    # defined by yearmin and yearmax :
-    x_mly = np.copy(x_mly)
-    if yearmin is not None:
-        x_mly[years < yearmin] = np.nan
-    if yearmax is not None:
-        x_mly[years > yearmax] = np.nan
-
-    # Calcul the monthly normals :
-    x_norm = np.zeros(12)
-    for i, mm in enumerate(range(1, 13)):
-        indx = np.where((months == mm) & (~np.isnan(x_mly)))[0]
-        if len(indx) > 0:
-            x_norm[i] = np.mean(x_mly[indx])
-        else:
-            x_norm[i] = np.nan
-
-    return x_norm
-
-
-# ----- Base functions: yearly downscaling
-def calc_yearly_sum(yy_dly, x_dly):
-    """
-    Calcul yearly cumulative values from daily values, where yy_dly are the
-    years and x_dly are the daily values.
-    """
-    return calc_yearly(yy_dly, x_dly, np.sum)
-
-
-def calc_yearly_mean(yy_dly, x_dly):
-    """
-    Calcul yearly mean values from daily values, where yy_dly are the years
-    and x_dly are the daily values.
-    """
-    return calc_yearly(yy_dly, x_dly, np.mean)
-
-
-def calc_yearly(yy_dly, x_dly, func):
-    yy_yrly = np.unique(yy_dly)
-    x_yrly = np.zeros(len(yy_yrly))
-    for i in range(len(yy_yrly)):
-        indx = np.where(yy_dly == yy_yrly[i])[0]
-        x_yrly[i] = func(x_dly[indx])
-
-    return yy_yrly, x_yrly
-
-
-# ----- Base functions: secondary variables
-def calcul_rain_from_ptot(Tavg, Ptot, Tcrit=0):
-    rain = np.copy(Ptot)
-    rain[np.where(Tavg < Tcrit)[0]] = 0
-    return rain