Example Usage of the Field_Protocol_Reader.py Methods

Setup

Import modules and change current working directory to setup environment:

#!/usr/bin/env python
# -*- coding: utf-8 -*-from __future__ import print_function
from mni2017.field_protocol_reader import field_data, plot_field, create_multiindex_df, create_reasonable_multiindex_df, plot_from_reasonable_df
import os

# change current working directory to folder with field protocol:
path = '/media/thomas/NAS_Data/2017_MNI_campaign/field_data/MULTIPLY'
os.chdir(path)

# initialize field_data class object, load field data in 'MULTIPLY.xlsx
field = field_data(filename='MULTIPLY.xlsx')

UPDATE

added create_reasonable_multiindex_df to facilitate working with the actual data [STRIKEOUT:as default option when initializing the class object.]

[STRIKEOUT:One may set the option create_df False if additional .xlsx-files should be added to the class object as this only works with the old procedure.]

[STRIKEOUT:The dataframe can then afterwards be created via create_reasonable_multiindex_df(field.data.items()).]

field_df = create_reasonable_multiindex_df(field.data.items())
field_df

	301 (WT) high										...	542 (WT) medium
	BBCH	Chlorophyll measurement 1 [avarage of 10 SPAD Units]	Chlorophyll measurement 2 [avarage of 10 SPAD Units]	Chlorophyll measurement 3 [avarage of 10 SPAD Units]	Chlorophyll measurement 4 [avarage of 10 SPAD Units]	Chlorophyll measurement 5 [avarage of 10 SPAD Units]	Comments	Dry biomass fruit [g]	Dry biomass leaf [g]	Dry biomass stem [g]	...	Water content total [%]	Water loss fruit [g]	Water loss leaf [g]	Water loss stem [g]	Water loss total [g]	Weather	Wet biomass fruit [g]	Wet biomass leaf [g]	Wet biomass stem [g]	Wet biomass total [g]
2017-03-24	24.0	49.2	53.1	48.9	56.1	47.4	NaN	0.0	4.22	0.94	...	75.56	0.0	9.12	0.99	10.11	cloudy	0.0	11.96	1.42	13.38
2017-03-28	24.0	44.2	42.1	47.8	38.9	53.2	NaN	0.0	6.69	7.06	...	80.71	0.0	10.21	2.26	12.47	sunny	0.0	12.78	2.67	15.45
2017-04-05	28.0	56.4	57.5	55.6	51.3	52.1	NaN	0.0	6.88	3.14	...	84.76	0.0	22.24	6.19	28.43	cloudy	0.0	26.45	7.09	33.54
2017-04-10	29.0	50.1	55.9	45.5	56.2	54.6	NaN	0.0	18.51	9.71	...	84.01	0.0	7.90	1.98	9.88	sunny	0.0	9.52	2.24	11.76
2017-04-21	31.0	40.9	53.8	44.1	56.2	48.8	NaN	0.0	18.75	13.54	...	81.70	0.0	18.06	9.85	27.91	cloudy	0.0	22.42	11.74	34.16
2017-05-02	31.0	47.7	46.7	42.4	37.2	47.5	NaN	0.0	13.13	11.48	...	82.55	0.0	43.87	23.58	67.45	cloudy	0.0	53.85	27.86	81.71

6 rows × 705 columns

list(field_df['301 (WT) high'])  # List all available values

['BBCH',
 'Chlorophyll measurement 1 [avarage of 10 SPAD Units]',
 'Chlorophyll measurement 2 [avarage of 10 SPAD Units]',
 'Chlorophyll measurement 3 [avarage of 10 SPAD Units]',
 'Chlorophyll measurement 4 [avarage of 10 SPAD Units]',
 'Chlorophyll measurement 5 [avarage of 10 SPAD Units]',
 'Comments',
 'Dry biomass fruit [g]',
 'Dry biomass leaf [g]',
 'Dry biomass stem [g]',
 'Dry biomass total [g]',
 'Endtime field',
 'Estimated share of brown leaf area [%] lower layer',
 'Estimated share of brown leaf area [%] upper layer',
 'Field overview picture from',
 'Field overview picture to',
 'Height [cm]',
 'LAI file name',
 'LAI measurement [cm²]',
 'LAT-WGS84',
 'LON-WGS84',
 'Name',
 'Plant count',
 'Plants',
 'Plants per meter',
 'Row orientation [°]',
 'Row spacing [cm]',
 'Sky cover [x/8]',
 'Soil 5cm',
 'Soil surface',
 'Starttime ESU',
 'Starttime field',
 'UTM Zone 32 Hochwert',
 'UTM Zone 32 Rechtswert',
 'Water content fruit [%]',
 'Water content leaf [%]',
 'Water content stem [%]',
 'Water content total [%]',
 'Water loss fruit [g]',
 'Water loss leaf [g]',
 'Water loss stem [g]',
 'Water loss total [g]',
 'Weather',
 'Wet biomass fruit [g]',
 'Wet biomass leaf [g]',
 'Wet biomass stem [g]',
 'Wet biomass total [g]']

field_df['301 (WT) high'][['BBCH', 'Height [cm]']]  # Filter the data

	BBCH	Height [cm]
2017-03-24	24.0	12.0
2017-03-28	24.0	10.0
2017-04-05	28.0	28.0
2017-04-10	29.0	32.0
2017-04-21	31.0	37.0
2017-05-02	31.0	39.0

# Plot 3 values for field '301 (WT) high':
values = ['BBCH','Height [cm]', 'Chlorophyll measurement 1 [avarage of 10 SPAD Units]']
field_id = '301 (WT) high'

plot_from_reasonable_df(field_df, '301 (WT) high', values)

import matplotlib.pyplot as plt
import pandas as pd

# plot relative development of plant compartments
y = field_df[field_id][['Wet biomass total [g]']]
x = field_df[field_id][[ 'Wet biomass fruit [g]','Wet biomass leaf [g]','Wet biomass stem [g]']]

# make y a Series
y = y.iloc[:,0]
type(y)
# relative biomass:
x_new = x.div(y, axis='index')

# rename columns and plot
x_new.columns = ['wet biomass fruit [%]','wet biomass leaf [%]','wet biomass stem [%]']
x_new.plot(figsize=(10,8), fontsize = 14)
plt.legend(fontsize=14)
plt.grid()
plt.show()