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profile_class.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue May 22 18:33:19 2018
@author: ian
"""
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pdb
class profile(object):
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def __init__(self, df, use_T_var = None, use_P_var = None, site_alt = None):
'''
Docstring here!
'''
self.df = df
self.interval = self._get_data_interval_mins()
self._use_T_var = use_T_var
self._use_P_var = use_P_var
self.site_alt = site_alt
self.CO2_names = self.get_names()
self.n_levels = len(self.CO2_names)
self.T_names = self._check_integrity(self._use_T_var, 'Tair')
try:
self.P_names = self._check_integrity(self._use_P_var, 'ps')
except KeyError:
self.P_names = ['ps']
self._make_ps()
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def _check_integrity(self, use_var, name_str):
if use_var:
assert isinstance(use_var, str)
assert use_var in self.df.columns
return [use_var]
else:
names = self.get_names(name_str)
if len(names) == 0:
raise KeyError('No variables found in dataframe!')
elif len(names) == 1:
return names
elif len(names) == self.n_levels:
assert self.get_heights() == self.get_heights(name_str)
return names
else:
raise RuntimeError('Wrong number of variables; '
'must be same as CO2 or 1 (got {})'
.format(str(len(names))))
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def _get_data_interval_mins(self):
freq = pd.infer_freq(self.df.index)
if not freq: raise RuntimeError('Time series non-continuous... exiting')
if freq == 'H':
interval = 60
if 'T' in freq:
interval = int(filter(lambda x: x.isdigit(),
pd.infer_freq(self.df.index)))
assert interval % 30 == 0
return interval
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def get_diel_storage_average(self, write_to_filepath = None):
# How would this deal with 90 minutes? - CRASH!!!
df = self.get_storage_time_series()
diel_df = df.groupby([lambda x: x.hour, lambda y: y.minute]).mean()
if 60 / self.interval > 1:
diel_df.index = np.arange(48) / 2.0
else:
diel_df.index = diel_df.index.get_level_values(0)
if write_to_filepath: self.write_to_file(write_to_filepath)
return diel_df
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def get_heights(self, search_str = 'CO2'):
names_list = self.get_names(search_str)
return sorted(map(lambda x: float(x.split('_')[1][:-1]), names_list))
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def get_layer_depths(self):
heights_list = self.get_heights()
return list(np.array(heights_list - np.array([0] + heights_list[:-1])))
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def get_layer_names(self, prefix = 'CO2'):
heights_list = [0] + self.get_heights()
return map(lambda x: '{0}_{1}-{2}m'.format(prefix,
str(heights_list[x - 1]),
str(heights_list[x])),
range(1, len(heights_list)))
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def get_layer_series(self):
molar_df = self.get_molar_density_time_series()
layer_names = self.get_layer_names()
data_list = []
for i in range(self.n_levels):
level_name = self.CO2_names[i]
layer_name = layer_names[i]
if i == 0:
s = molar_df[level_name]
else:
prev_level_name = self.CO2_names[i - 1]
s = (molar_df[level_name] + molar_df[prev_level_name]) / 2
s.name = layer_name
data_list.append(s)
return pd.concat(data_list, axis = 1)
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def get_molar_density_time_series(self):
CO2_list = self.CO2_names
if len(self.T_names) == 1:
T_list = self.T_names * self.n_levels
else:
T_list = self.T_names
if len(self.P_names) == 1:
P_list = self.P_names * self.n_levels
else:
P_list = self.P_names
var_set = zip(CO2_list, T_list, P_list)
data_list = []
for this_set in var_set:
co2, T, P = this_set[0], this_set[1], this_set[2]
try:
molar_density_series = (self.df[P] * 10**3 /
(8.3143 * (273.15 + self.df[T])))
except:
pdb.set_trace()
CO2_molar_density_series = (molar_density_series *
self.df[co2] * 10**-6)
CO2_molar_density_series.name = co2
data_list.append(CO2_molar_density_series)
return pd.concat(data_list, axis = 1)
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def get_names(self, search_str = 'CO2'):
names_list = sorted(filter(lambda x: search_str in x, self.df.columns))
if len(names_list) == 1: return names_list
numbers_list = map(lambda x: float(x.split('_')[1][:-1]), names_list)
index_arr = np.argsort(np.array(numbers_list))
return list(np.array(names_list)[index_arr])
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def get_storage_time_series(self, write_to_filepath = None):
layer_df = self.get_layer_series()
diff_df = (layer_df - layer_df.shift())
layer_names = self.get_layer_names()
mult_dict = dict(zip(layer_names, self.get_layer_depths()))
name_dict = dict(zip(layer_names, self.get_layer_names('Sc')))
data_dict = []
for level_name in layer_names:
new_name = name_dict[level_name]
s = (diff_df[level_name] / (self.interval * 60) * 10**6 *
mult_dict[level_name])
s.name = new_name
data_dict.append(s)
output_df = pd.concat(data_dict, axis = 1)
output_df['Sc_total'] = output_df.sum(axis = 1)
nans = np.isnan(output_df[output_df.columns[:-1]]).sum(axis=1) != 0
output_df.loc[nans, 'Sc_total'] = np.nan
if write_to_filepath: self.write_to_file(write_to_filepath)
return output_df
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def plot_diel_average(self):
df = self.get_diel_storage_average()
vars_list = list(df.columns)
vars_list.remove('Sc_total')
strip_vars_list = [var.split('_')[1] for var in vars_list]
fig, ax = plt.subplots(1, 1, figsize = (12, 8))
fig.patch.set_facecolor('white')
colour_idx = np.linspace(0, 1, len(vars_list))
ax.set_xlim([0, 24])
ax.set_xticks([0,4,8,12,16,20,24])
ax.tick_params(axis = 'x', labelsize = 14)
ax.tick_params(axis = 'y', labelsize = 14)
ax.set_xlabel('$Time$', fontsize = 18)
ax.set_ylabel('$S_c\/(\mu mol\/CO_2\/m^{-2}\/s^{-1})$', fontsize = 18)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for i, var in enumerate(vars_list):
color = plt.cm.cool(colour_idx[i])
plt.plot(df.index, df[var], label = strip_vars_list[i], color = color)
plt.plot(df.index, df.Sc_total, label = 'Total', color = 'grey')
plt.legend(loc=[0.65, 0.18], frameon = False, ncol = 2)
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def plot_time_series(self):
df = self.get_storage_time_series()
vars_list = list(df.columns)
vars_list.remove('Sc_total')
strip_vars_list = [var.split('_')[1] for var in vars_list]
fig, ax = plt.subplots(1, 1, figsize = (12, 8))
fig.patch.set_facecolor('white')
colour_idx = np.linspace(0, 1, len(vars_list))
ax.tick_params(axis = 'x', labelsize = 14)
ax.tick_params(axis = 'y', labelsize = 14)
ax.set_xlabel('$Date$', fontsize = 18)
ax.set_ylabel('$S_c\/(\mu mol\/CO_2\/m^{-2}\/s^{-1})$', fontsize = 18)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.plot(df.index, df.Sc_total, label = 'Total', color = 'grey',
alpha = 0.5)
for i, var in enumerate(vars_list):
color = plt.cm.cool(colour_idx[i])
plt.plot(df.index, df[var], label = strip_vars_list[i],
color = color, alpha = 0.4)
plt.legend(loc='lower left', frameon = False, ncol = 2)
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def _make_ps(self):
if not self.site_alt:
print ('Warning: there are no pressure data available in the raw '
'data file and a site altitude has not been specified; '
'standard sea level pressure will be used for subsequent '
'calculations but may result in substantial storage '
'underestimation for high altitude sites (by a factor of '
'1-p/p0!')
self.df['ps'] = 101.3
else:
p0 = 101325
L = 0.0065
R = 8.3143
T0 = 288.15
g = 9.80665
M = 0.0289644
A = (g * M) / (R * L)
B = L / T0
p = (p0 * (1 - B * self.site_alt) ** A) / 1000
self.df['ps'] = p
return
#--------------------------------------------------------------------------
#--------------------------------------------------------------------------
def write_to_file(self, file_path):
path = os.path.split(file_path)[0]
assert os.path.isdir(path)
df = self.get_storage_time_series()
df.to_csv(file_path, index_label = 'Datetime')
#--------------------------------------------------------------------------