863 lines
31 KiB
Python
Executable File
863 lines
31 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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''' Utility to convert data from a glucometer into charts. '''
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__author__ = 'Timothy Allen'
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__email__ = 'tim@treehouse.org.za'
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__license__ = 'MIT'
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''' Included are the OpenSans and IcoGluco font sets.
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IcoGluco contains fonts from Noto Sans, which is licensed under the
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SIL Open Font License version 1.1
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<http://scripts.sil.org/cms/scripts/page.php?site_id=nrsi&id=OFL>,
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as well as a green apple character from
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Vectors Market <https://www.flaticon.com/authors/vectors-market>,
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licensed under Creative Commons BY 3.0, <http://creativecommons.org/licenses/by/3.0/>, and
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syringe and pushpin characters from FreePik, <http://www.freepik.com>,
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licensed under Creative Commons BY 3.0, <http://creativecommons.org/licenses/by/3.0/>.
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'''
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# TODO: weekly graph with each day's figures as a different-coloured line
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# TODO: Split each type of charts into a separate function and offer a means
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# of selecting which charts to generate
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import argparse
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import csv
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import datetime as dt
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from matplotlib import rcParams
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from matplotlib.figure import Figure
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from matplotlib.backends.backend_pdf import FigureCanvasPdf as FigureCanvas
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from matplotlib.backends.backend_pdf import PdfPages as FigurePDF
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from matplotlib.collections import LineCollection
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from matplotlib.colors import ListedColormap, BoundaryNorm
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from matplotlib import dates as mdates
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from matplotlib import font_manager as fm
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from matplotlib.patches import Circle, PathPatch
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from matplotlib.path import Path
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from matplotlib import ticker as mticker
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import numpy as np
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import os
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import re
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from scipy import interpolate
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from scipy.special import binom
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import sys
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''' Constants for units '''
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UNIT_MGDL = 'mg/dL'
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UNIT_MMOLL = 'mmol/L'
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VALID_UNITS = [UNIT_MGDL, UNIT_MMOLL]
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''' When averaging, set the period to this number of minutes '''
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INTERVAL = 15
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''' Set the default high and low in mmol/L; it will be reset to mg/dL if neccessary '''
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DEFAULT_HIGH = 8
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DEFAULT_LOW = 4
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''' Maximum glucose value to display '''
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GRAPH_MAX_MMOLL = 21
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GRAPH_MIN_MMOLL = 0
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GRAPH_MAX_MGDL = 400
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GRAPH_MIN_MGDL = 0
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''' Colour for below-target maxmins '''
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RED = '#d71920'
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'''' Colour for above-target maxmins '''
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YELLOW = '#f1b80e'
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''' Colour for graph lines '''
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BLUE = '#02538f'
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''' Colour for median glucose box '''
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GREEN = '#009e73'
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''' Colour for median A1c box '''
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BOXYELLOW = '#e69f00'
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def main():
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if sys.version_info < (3, 2):
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raise Exception(
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'Unsupported Python version, please use at least Python 3.2')
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args = parse_arguments()
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''' This could be done directly from glucometerutils instead of via CSV '''
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with open(args.input_file, 'r', newline='') as f:
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rows = from_csv(f)
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for row in rows:
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row = parse_entry(row, args.icons)
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''' Fill in gaps that might exist in the data, in order to smooth the curves and fills '''
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''' We're using 8 minute gaps in order to have more accurate fills '''
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rows = fill_gaps(rows, interval=dt.timedelta(minutes=10))
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''' If we're on the default values for units, highs and lows, check that the average
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value is under 35 (assuming that average mmol/L < 35 and average mg/dL > 35) '''
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if args.units == UNIT_MMOLL and (args.high == DEFAULT_HIGH or args.low == DEFAULT_LOW):
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mean = round(np.mean([l.get('value') for l in rows]), 1)
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if mean > 35:
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args.units = UNIT_MGDL
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args.high = convert_glucose_unit(args.high, UNIT_MMOLL)
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args.low = convert_glucose_unit(args.low, UNIT_MMOLL)
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''' Manually specify max and min for mg/dL '''
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args.graph_max = GRAPH_MAX_MGDL
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args.graph_min = GRAPH_MIN_MGDL
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''' Set some defaults '''
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rcParams['font.size'] = 8
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rcParams['axes.titlesize'] = 12
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rcParams['font.family'] = 'sans-serif'
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rcParams['font.sans-serif'] = ['Calibri','Verdana','Geneva','Arial','Helvetica','DejaVu Sans','Bitstream Vera Sans','sans-serif']
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rcParams['mathtext.default'] = 'regular'
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''' Load custom fonts for the icon sets
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At present, backend_pdf does not parse unicode correctly, and unicode
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characters from many fonts that lack proper glyph names are massed together
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and printed as the same character. The IcoGluco font, generated from Noto Sans and
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custom icons on IcoMoon, works around this. '''
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if args.icons:
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args.customfont = import_font('fonts/icogluco.ttf')
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#args.customfont = import_font('fonts/OpenSansEmoji.ttf') # Alternate font
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''' Calculate the days and weeks in which we are interested '''
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''' Note that trim_weeks should be adjusted based on the interval passed to fill_gaps() '''
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(days, weeks) = list_days_and_weeks(rows, trim_weeks=300)
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totalweeks = sum([len(weeks[y]) for y in weeks])
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totaldays = len(days)
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nrows = args.graphs_per_page
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ncols = 1
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plotnum = 1
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with FigurePDF(args.output_file) as pdf:
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''' Overall averages for all data by hour of the day '''
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title = 'Overall Average Glucose Summary'
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data = {}
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for row in rows:
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mpdate = dt.datetime.combine(rows[0]['date'], row.get('date').time())
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data[mdates.date2num(mpdate)] = {
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'value' : row.get('value'),
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'comment' : row.get('comment'),
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}
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''' Calculate max and min values for each 15 minute interval across the data set '''
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intervals = calculate_max_min(rows)
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intervaldata = {}
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for i in intervals:
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mpdate = dt.datetime.combine(rows[0]['date'], i)
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intervaldata[mdates.date2num(mpdate)] = {
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'max' : intervals.get(i).get('max'),
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'min' : intervals.get(i).get('min'),
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}
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''' Calculate the mean and median blood glucose and HbA1c levels '''
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(g_mean, g_median, a_mean, a_median) = calculate_averages(data, args)
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figure = Figure(figsize=args.pagesize)
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canvas = FigureCanvas(figure)
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ax = figure.add_subplot(nrows, ncols, plotnum)
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ax.set_title(title)
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figure.set_tight_layout({'pad':3})
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''' Draw the target range '''
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ax.axhspan(args.low, args.high, facecolor='#0072b2', edgecolor='#a8a8a8', alpha=0.1, zorder=5)
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''' The maxmin curve (maximum and minimum values for each 15 minute
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period of the data set, by day) '''
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generate_plot(intervaldata,
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ax=ax,
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transforms={'spline':False, 'maxmin':True},
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args=args,
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color='#979797',
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alpha=0.5,
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)
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''' The graph with a bezier curve applied, and a boundary transform to change line colour
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above and below the target values '''
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generate_plot(data,
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ax=ax,
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transforms={'bezier':True, 'avga1c':a_median, \
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'color':[RED, BLUE, RED], 'boundaries':[args.graph_min, args.low, args.high, args.graph_max]},
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args=args,
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color=BLUE,
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)
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''' Save the graph to the output PDF if we're at the end of the page '''
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pdf.savefig(figure)
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ax.clear()
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''' Overall averages for a week by hour of the dday '''
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cnt = 0
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for year in reversed(sorted(weeks.keys())):
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for week in reversed(sorted(weeks[year].keys())):
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''' Turn the year into a date (the first week of the year is the one containing January 4th) '''
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time = dt.datetime.combine(dt.date(year, 1, 4), dt.time(0, 0, 0))
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monday = time + dt.timedelta(days=-time.weekday(), weeks=week-1)
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sunday = monday + dt.timedelta(days=6)
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period = monday.strftime('%A, %-d %B %Y') + ' to ' + sunday.strftime('%A, %-d %B %Y');
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title = 'Average Glucose for ' + period
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weekrows = []
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for row in rows:
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for dow in range(7):
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day = monday + dt.timedelta(days=dow)
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if row.get('date').date() == day.date():
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weekrows.append(row)
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data = {}
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for row in weekrows:
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mpdate = dt.datetime.combine(monday, row.get('date').time())
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data[mdates.date2num(mpdate)] = {
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'value' : row.get('value'),
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'comment' : row.get('comment'),
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}
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''' Calculate the maximum and minimum value for each 15-minute period
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of the day, across the week '''
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intervals = calculate_max_min(weekrows)
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intervaldata = {}
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for i in intervals:
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mpdate = dt.datetime.combine(monday.date(), i)
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intervaldata[mdates.date2num(mpdate)] = {
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'max' : intervals.get(i).get('max'),
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'min' : intervals.get(i).get('min'),
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}
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''' Calculate the mean and median blood glucose levels for the week '''
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(g_mean, g_median, a_mean, a_median) = calculate_averages(data, args)
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if cnt % nrows == 0:
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figure = Figure(figsize=args.pagesize)
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canvas = FigureCanvas(figure)
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plotnum = (cnt % nrows) + 1
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ax = figure.add_subplot(nrows, ncols, plotnum)
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ax.set_title(title)
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figure.set_tight_layout({'pad':3})
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''' Draw the target range '''
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ax.axhspan(args.low, args.high, facecolor='#0072b2', edgecolor='#a8a8a8', alpha=0.1, zorder=5)
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''' The maxmined curve of maximum and minimum values '''
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generate_plot(intervaldata,
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ax=ax,
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transforms={'spline':False, 'maxmin':True, 'avga1c':a_median},
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args=args,
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color='#979797',
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alpha=0.5,
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)
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''' The graph with a bezier curve applied, and a boundary transform to change line colour
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above and below the target values '''
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generate_plot(data,
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ax=ax,
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transforms={'bezier':True, \
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'color':[RED, BLUE, RED], 'boundaries':[args.graph_min, args.low, args.high, args.graph_max]},
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args=args,
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color=BLUE,
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)
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''' Save the graph to the output PDF if we're at the end of the page or at the end of the data '''
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if (cnt + 1) % nrows == 0 or (cnt + 1) == totalweeks:
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pdf.savefig(figure)
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ax.clear()
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cnt += 1
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''' Daily graphs '''
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cnt = 0
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for day in reversed(sorted(days.keys())):
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title = 'Daily Glucose Summary for ' + day.strftime('%A, %-d %B %Y')
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data = {}
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for row in rows:
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if row.get('date').date() == day.date():
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mpdate = dt.datetime.combine(day.date(), row.get('date').time())
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data[mdates.date2num(mpdate)] = {
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'value' : row.get('value'),
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'comment' : row.get('comment'),
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}
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''' Calculate the mean and median blood glucose levels for the day '''
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(g_mean, g_median, a_mean, a_median) = calculate_averages(data, args)
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if cnt % nrows == 0:
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figure = Figure(figsize=args.pagesize)
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canvas = FigureCanvas(figure)
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plotnum = (cnt % nrows) + 1
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ax = figure.add_subplot(nrows, ncols, plotnum)
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ax.set_title(title)
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figure.set_tight_layout({'pad':3})
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''' Draw the target range '''
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ax.axhspan(args.low, args.high, facecolor='#0072b2', edgecolor='#a8a8a8', alpha=0.2, zorder=5)
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''' Draw graph with a spline tranform and labels '''
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generate_plot(data,
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ax=ax,
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transforms={'spline':True, 'label':True, 'avgglucose':g_median, 'avga1c':a_median},
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args=args,
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color=BLUE,
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)
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''' Fill the chart with colour when line is higher or lower than target range '''
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generate_plot(data,
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ax=ax,
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transforms={'spline':True, 'fill':True},
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args=args,
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)
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''' Save the graph to the output PDF if we're at the end of the page '''
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if (cnt + 1) % nrows == 0 or (cnt + 1) == totaldays:
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pdf.savefig(figure)
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ax.clear()
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cnt += 1
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return 1
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def generate_plot(data, ax=None, transforms={}, args=[], **plot_args):
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(x, y, z, p, q) = (list(), list(), list(), list(), list())
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for (key, value) in sorted(data.items()):
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''' Time '''
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a = key
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if 'maxmin' in transforms:
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''' If a max and a min exists, initialise them to y and z '''
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b = value.get('max')
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c = value.get('min')
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else:
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''' Glucose and comment '''
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b = value.get('value')
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c = value.get('comment', '')
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x.append(a)
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y.append(b)
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z.append(c)
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x = np.asarray(x)
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y = np.asarray(y)
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''' Don't convert z to a numpy array if it has text in it '''
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if len(z) > 0 and isinstance(z[0], (int, float)):
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z = np.asarray(z)
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''' Calculations the axis limits '''
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firstminute = mdates.num2date(x[0]).replace(hour=0, minute=0, second=0, microsecond=0)
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lastminute = mdates.num2date(x[-1]).replace(hour=23, minute=59, second=59, microsecond=59)
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x_min = mdates.date2num(firstminute)
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x_max = mdates.date2num(lastminute)
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ax.set_xlim(x_min, x_max)
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ax.set_ylim(args.graph_min, args.graph_max)
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''' Calculate the time intervals in 2 hour segments '''
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xtimes = []
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time = firstminute
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while time < lastminute:
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xtimes.append(time)
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time += dt.timedelta(hours=2)
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if args.units == UNIT_MMOLL:
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y_tick_freq = 2
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else:
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y_tick_freq = 50
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''' Formatting for axis labels, using date calculations from above '''
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ax.set_xlabel('Time', fontsize=9)
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ax.set_xbound(firstminute, lastminute)
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ax.grid(axis='x', color = '#f0f0f0', zorder=1)
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ax.set_xticks(xtimes)
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ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
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ax.xaxis.set_ticks_position('none')
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for tick in ax.xaxis.get_major_ticks():
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tick.label1.set_horizontalalignment('left')
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ax.set_ylabel('Blood Glucose (' + args.units + ')', fontsize=9)
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ax.set_ybound(args.graph_min, args.graph_max)
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ax.grid(axis='y', color = '#d0d0d0', linestyle = (1,(0.5,2)), zorder=1)
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ax.set_yticks([a for a in range(int(args.graph_min), int(args.graph_max), int(y_tick_freq))])
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ax.yaxis.set_major_formatter(mticker.FormatStrFormatter("%d"))
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ax.yaxis.set_ticks_position('none')
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if 'maxmin' in transforms and transforms.get('maxmin') is True:
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maxmin = True
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else:
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maxmin = False
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''' Transform points to apply smoothing and other fixups '''
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for transform in transforms:
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if transform == 'linear' and transforms.get(transform) is True:
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''' Use SciPy's interp1d for linear transforming '''
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if not maxmin:
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f = interpolate.interp1d(x, y, kind='linear')
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''' 50 is number of points to make between x.max & x.min '''
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x = np.linspace(x.min(), x.max(), 50)
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y = f(x)
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elif transform == 'spline' and transforms.get(transform) is True:
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''' Use SciPy's UnivariateSpline for transforming (s is transforming factor) '''
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''' An s of 8 (mmol/L) or 200 (mg/dL) was chosen by experimentation! '''
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if args.units == UNIT_MMOLL:
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s = 8
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else:
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s = 200
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if not maxmin:
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curve = interpolate.UnivariateSpline(x=x, y=y, k=3, s=s)
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y = curve(x)
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elif transform == 'bezier' and transforms.get(transform) is True:
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''' Create bezier function for transforming (s is transforming factor) '''
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def bezier(points, s=100):
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n = len(points)
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b = [binom(n - 1, i) for i in range(n)]
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r = np.arange(n)
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for t in np.linspace(0, 1, s):
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u = np.power(t, r) * np.power(1 - t, n - r - 1) * b
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yield t, u @ points
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''' The binomial calculation for the bezier curve overflows with arrays of 1020 or more elements,
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For large arrays, get a smaller slice of the full array.
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Do this by removing every nth element from the array. '''
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n = 5
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while len(x) > 1000:
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x = np.delete(x, np.arange(0, len(x), n), axis=0)
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y = np.delete(y, np.arange(0, len(y), n), axis=0)
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if not maxmin:
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curve = np.array([c for _, c in bezier(np.array([x,y]).T, 250)])
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(x, y) = (curve[:,0], curve[:,1])
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''' Add the mean or median glucose and A1c values in an annotation box '''
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if transform == 'avgglucose' and isinstance(transforms.get(transform), (int, float)):
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if args.units == UNIT_MMOLL:
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gmtext = 'Median glucose: %.1f%s' % (round(transforms.get('avgglucose'), 1), args.units)
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else:
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gmtext = 'Median glucose: %.0f%s' % (round(transforms.get('avgglucose'), 1), args.units)
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ax.annotate(gmtext, fontsize=9, xy=(0.95, 0.85),
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xycoords='axes fraction', verticalalignment='top', horizontalalignment='right',
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zorder=40, bbox=dict(facecolor=GREEN, edgecolor='#009e73', alpha=0.7, pad=8),
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)
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if transform == 'avga1c' and isinstance(transforms.get(transform), (int, float)):
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ax.annotate('Median HbA1c: %.1f%%' % round(transforms.get('avga1c'), 1), fontsize=9,
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xy=(0.05, 0.85), xycoords='axes fraction',
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verticalalignment='top', horizontalalignment='left',
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zorder=40, bbox=dict(facecolor=BOXYELLOW, edgecolor='#e69f00', alpha=0.7, pad=8),
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)
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if args.units == UNIT_MMOLL:
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y_offset = 6
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else:
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y_offset = convert_glucose_unit(6, UNIT_MMOLL)
|
|
|
|
if transform == 'label' and transforms.get(transform) is True and args.icons is True:
|
|
for x_pos, y_pos, label in zip(x, y, z):
|
|
if isinstance(label, dict) and len(label) > 0:
|
|
symbol = '$'
|
|
for key in label:
|
|
''' In the included IcoGluco font use for args.customfont,
|
|
\N{SYRINGE} is a straight syringe (modified from FreePik) for rotated labels,
|
|
\N{PUSHPIN} is a an angled syringe (from FreePik) for horizontal labels,
|
|
\N{DAGGER} is unused (reserved a different syringe icon),
|
|
\N{GREEN APPLE} is an apple (from Vectors Market). '''
|
|
if key == 'Insulin':
|
|
if isinstance(label.get(key), str):
|
|
symbol += '\N{SYRINGE}^{%s}' % label.get(key)
|
|
#symbol += '\N{PUSHPIN}^{%s}' % label.get(key)
|
|
#symbol += '\N{SYRINGE}'
|
|
else:
|
|
symbol += '\N{SYRINGE}'
|
|
elif key == 'Food':
|
|
symbol += '\N{GREEN APPLE}'
|
|
symbol += '$'
|
|
ax.annotate(symbol, xy=(x_pos, args.graph_max-y_offset),
|
|
rotation=45, zorder=25, fontsize=10,
|
|
fontproperties=args.customfont,
|
|
)
|
|
|
|
''' Create a line coloured according to the list in transforms['color'] '''
|
|
if transform == 'boundaries' and 'color' in transforms:
|
|
cmap = ListedColormap(transforms.get('color'))
|
|
norm = BoundaryNorm(transforms.get('boundaries'), cmap.N)
|
|
''' create an array of points on the plot, and split into segments '''
|
|
p = np.array([x, y]).T.reshape(-1, 1, 2)
|
|
segments = np.concatenate([p[:-1], p[1:]], axis=1)
|
|
''' Colour the line according to the values in norm and the colours in cmap '''
|
|
lc = LineCollection(segments, cmap=cmap, norm=norm)
|
|
lc.set_array(y)
|
|
|
|
|
|
if 'boundaries' in transforms and 'color' in transforms:
|
|
ax.add_collection(lc)
|
|
|
|
elif 'fill' in transforms and transforms.get('fill') is True:
|
|
z = np.clip(y, None, args.high)
|
|
ax.fill_between(x, y, z, interpolate=True, facecolor=YELLOW, alpha=0.7, zorder=12, **plot_args)
|
|
|
|
z = np.clip(y, args.low, None)
|
|
ax.fill_between(x, y, z, interpolate=True, facecolor=RED, alpha=0.7, zorder=12, **plot_args)
|
|
|
|
elif maxmin:
|
|
ax.fill_between(x, y, z, interpolate=True, zorder=10, **plot_args)
|
|
|
|
else:
|
|
ax.plot(x, y, '-', zorder=20, **plot_args)
|
|
|
|
return ax
|
|
|
|
def import_font(fontname):
|
|
''' Turns a relative font path into a matplotlib font property. '''
|
|
basedir = os.path.dirname(os.path.abspath(__file__))
|
|
fontpath = os.path.join(basedir, fontname)
|
|
if not os.path.exists(fontpath):
|
|
raise UserError("Font %s does not exist" % fontpath)
|
|
prop = fm.FontProperties(fname=fontpath)
|
|
return prop
|
|
|
|
def parse_entry(data, icons, fmt='%Y-%m-%d %H:%M:%S'):
|
|
''' Parse a row to create the icons and modify the timestamp
|
|
|
|
Args:
|
|
data: a dict containing the entries 'timestamp' and 'comment'
|
|
icons: bool indicating whether to display food/injection icons on the graph
|
|
date_format: the format of the timestamp in data
|
|
|
|
Returns:
|
|
data: the modified dict
|
|
|
|
Raises:
|
|
ValueError if an incorrectly-formatted date exists in data['timestamp']
|
|
'''
|
|
if icons:
|
|
''' Ignore comments that aren't relevant '''
|
|
rrelevant = re.compile('(Food|Rapid-acting insulin|Long-acting insulin)(?: \((.*?)\))', flags=re.IGNORECASE)
|
|
rduplicate = re.compile('^(I\$\^\{\d+\S?)(\}.*)$')
|
|
commentparts = {}
|
|
for part in data.get('comment').split('; '):
|
|
relevant = rrelevant.search(part)
|
|
if relevant is not None:
|
|
ctype = relevant.group(1)
|
|
cvalue = relevant.group(2)
|
|
|
|
''' Convert floating point-style strings (2.0) to integer-style strings (2) '''
|
|
try:
|
|
cvalue = int(float(cvalue))
|
|
except:
|
|
pass
|
|
cvalue = str(cvalue)
|
|
|
|
if re.search('Rapid', ctype) is not None:
|
|
cvalue += 'R'
|
|
if re.search('Long', ctype) is not None:
|
|
cvalue += 'L'
|
|
|
|
ctype = re.sub('Rapid-acting insulin', 'Insulin', ctype, flags=re.IGNORECASE)
|
|
ctype = re.sub('Long-acting insulin', 'Insulin', ctype, flags=re.IGNORECASE)
|
|
|
|
if ctype in commentparts:
|
|
commentparts[ctype] = commentparts[ctype] + '/' + cvalue
|
|
else:
|
|
commentparts[ctype] = cvalue
|
|
|
|
data['comment'] = commentparts
|
|
else:
|
|
data['comment'] = {}
|
|
|
|
''' Convert timestamp to ISO8601 (by default, at least), and store datetime object '''
|
|
try:
|
|
date = dt.datetime.strptime(data.get('timestamp'), fmt)
|
|
data['date'] = date
|
|
except ValueError:
|
|
raise ValueError('Invalid date: %s (should be of format %s)' % (data.get('timestamp'), fmt))
|
|
data['timestamp'] = date.strftime('%Y-%m-%dT%H:%M:%S')
|
|
|
|
''' Convert value from string to float '''
|
|
data['value'] = float(data.get('value'))
|
|
|
|
# XXX convert everything to mg/dL for testing
|
|
#data['value'] = float(round(data.get('value') * 18.0, 0))
|
|
|
|
return data
|
|
|
|
def list_days_and_weeks(data, trim_weeks=192):
|
|
''' Create a dictionary of the days and weeks that occur in the CSV
|
|
|
|
Args:
|
|
data: a dict containing a 'timestamp' entry
|
|
trim_weeks: the minimum number of entries a week should have in order to be considered for
|
|
a weekly average graph. A reading taken every 15 minutes over two days would yield 192 readings.
|
|
|
|
Returns:
|
|
seendays: a dict containing all days in data
|
|
seenweeks: a dict containing all weeks in data, subdivided by year
|
|
'''
|
|
seenweeks = {}
|
|
seendays = {}
|
|
for d in data:
|
|
date = d.get('date')
|
|
day = dt.datetime.combine(date.date(), dt.time.min)
|
|
(year, week, weekday) = date.isocalendar()
|
|
|
|
if not year in seenweeks:
|
|
seenweeks[year] = {}
|
|
if not week in seenweeks[year]:
|
|
seenweeks[year][week] = 0
|
|
else:
|
|
seenweeks[year][week] += 1
|
|
|
|
if not day in seendays:
|
|
seendays[day] = 1
|
|
else:
|
|
seendays[day] += 1
|
|
|
|
''' Remove weeks for which there is less than two days of results in that week. '''
|
|
''' Note that if we smooth the data to generate a reading every 10 minutes, there will be 144 readings per day '''
|
|
editedweeks = dict(seenweeks)
|
|
for year in seenweeks:
|
|
editedweeks = dict(seenweeks[year])
|
|
for week in seenweeks[year]:
|
|
if seenweeks[year][week] < trim_weeks:
|
|
del editedweeks[week]
|
|
seenweeks[year] = dict(editedweeks)
|
|
|
|
return (seendays, seenweeks)
|
|
|
|
def calculate_averages(data, args):
|
|
''' Return a dictionary with the maximum and mimimum values for each time interval
|
|
|
|
Args:
|
|
data: a dict with the element 'value'
|
|
args: a dict with the elements ;
|
|
|
|
Returns:
|
|
g_mean: The mean of all blood glucose 'value' elements
|
|
g_median: The median of all blood glucose 'value' elements
|
|
a_mean: The blood glucose mean converted to an HbA1c value
|
|
a_median: The blood glucose median converted to an HbA1c value
|
|
|
|
Raises:
|
|
ValueError if the blood glucose units can't be parsed or are unknown.
|
|
'''
|
|
g_mean = round(np.mean([data[k].get('value', 0) for k in data]), 1)
|
|
g_median = round(np.median([data[k].get('value', 0) for k in data]), 1)
|
|
if args.units == UNIT_MGDL:
|
|
a_median = (g_median + 46.7) / 28.7
|
|
a_mean = (g_mean + 46.7) / 28.7
|
|
elif args.units == UNIT_MMOLL:
|
|
a_median = (g_median + 2.59) / 1.59
|
|
a_mean = (g_mean + 2.59) / 1.59
|
|
else:
|
|
raise ValueError('Unknown blood glucose units for HbA1c calculations')
|
|
return (g_mean, g_median, a_mean, a_median)
|
|
|
|
|
|
def calculate_max_min(data):
|
|
''' Return a dictionary with the maximum and mimimum values for each time interval
|
|
|
|
Args:
|
|
datas: a dict with elements 'timestamp' and 'value'
|
|
|
|
Returns:
|
|
intervals: a dictionary of minimum and maximum values for a a time period
|
|
|
|
Raises:
|
|
ValueError if an incorrectly-formatted date exists in data['timestamp']
|
|
'''
|
|
intervals = {}
|
|
for d in data:
|
|
date = d.get('date')
|
|
date = date.replace(minute=int(date.minute/INTERVAL)*INTERVAL, second=0, microsecond=0, tzinfo=None)
|
|
time = date.time()
|
|
|
|
if not time in intervals:
|
|
intervals[time] = {}
|
|
intervals[time]['min'] = d.get('value')
|
|
intervals[time]['max'] = d.get('value')
|
|
|
|
if intervals[time]['min'] < d.get('value'):
|
|
intervals[time]['min'] = d.get('value')
|
|
|
|
if intervals[time]['max'] > d.get('value'):
|
|
intervals[time]['max'] = d.get('value')
|
|
|
|
return intervals
|
|
|
|
def fill_gaps(rows, interval, maxinterval=dt.timedelta(days=1)):
|
|
''' Fill in time gaps that may exist in a set of rows, in order to smooth drawn curves and fills
|
|
|
|
Args:
|
|
rows: a dict containing a 'date' entry (the result of parse_entry())
|
|
interval: a datetime.timedelta object that defines the maximum distance allowed between two entries
|
|
maxinterval: a datetime.timedelta object that defines the maximum amount of time, over which we ignore
|
|
the difference between two consecutive entries
|
|
|
|
Returns:
|
|
filledrows: a dict containing the rows with inserted items.
|
|
'''
|
|
filledrows = []
|
|
for i, row in enumerate(rows):
|
|
filledrows.append(row)
|
|
|
|
''' Don't check the distance between the last value and anything! '''
|
|
if i >= len(rows)-1:
|
|
continue
|
|
|
|
''' If the next row has a time gap, create new rows to insert '''
|
|
if rows[i+1].get('date') - rows[i].get('date') > interval and \
|
|
rows[i+1].get('date') - rows[i].get('date') < maxinterval:
|
|
|
|
n = (rows[i+1].get('date') - rows[i].get('date'))//interval
|
|
start = mdates.date2num(rows[i].get('date'))
|
|
end = mdates.date2num(rows[i+1].get('date'))
|
|
lower = rows[i].get('value')
|
|
upper = rows[i+1].get('value')
|
|
|
|
''' Calculate an range for each interval, assuming a straight line between the start and
|
|
end of the gap.
|
|
Use n+2 so we can remove the first and last value which overlap with existing values '''
|
|
periods = np.linspace(start, end, n+2)
|
|
periods = periods[1:n+1]
|
|
values = np.linspace(lower, upper, n+2)
|
|
values = values[1:n+1]
|
|
|
|
for j, val in enumerate(values):
|
|
period = mdates.num2date(periods[j])
|
|
period = period.replace(microsecond=0, tzinfo=None)
|
|
|
|
item = {
|
|
'date': period,
|
|
'meal': '',
|
|
'value': float('%.2f' % val),
|
|
'comment': '',
|
|
'timestamp': period.strftime('%Y-%m-%dT%H:%M:%S'),
|
|
'measure_method': 'Estimate',
|
|
}
|
|
filledrows.append(item)
|
|
|
|
return filledrows
|
|
|
|
def verify_pagesize(pagesize = None):
|
|
''' Check the page size '''
|
|
if re.search('a4', pagesize, flags=re.IGNORECASE) is not None:
|
|
pagesize = (11.69, 8.27)
|
|
elif re.search('letter', pagesize, flags=re.IGNORECASE) is not None:
|
|
pagesize = (11, 8.5)
|
|
elif re.search('\d+(cm|in),\d+/', pagesize, flags=re.IGNORECASE) is not None:
|
|
''' Do nothing '''
|
|
else: # A4 size default
|
|
pagesize = (11.69, 8.27)
|
|
return pagesize
|
|
|
|
def verify_units(units = None, high = None, low = None):
|
|
''' Standardise units for output and for the A1c calculations '''
|
|
if re.search('mg', units, flags=re.IGNORECASE) is not None:
|
|
units = UNIT_MGDL
|
|
elif re.search('mm', units, flags=re.IGNORECASE) is not None:
|
|
units = UNIT_MMOLL
|
|
elif isinstance(high, (int, float)) or isinstance(low, (int, float)):
|
|
''' If units are not specified by the arguments or calling function, let's assume they are
|
|
mg/dL if the high is more than 35 or the low more than 20 '''
|
|
if (isinstance(high, (int, float)) and (high > 35) or
|
|
isinstance(low, (int, float)) and (low > 20)):
|
|
units = UNIT_MGDL
|
|
else:
|
|
units = UNIT_MMOLL
|
|
else:
|
|
''' Leave empty so we can auto-detect based on input '''
|
|
units = ''
|
|
return units
|
|
|
|
def parse_arguments():
|
|
parser = argparse.ArgumentParser(description='Convert a CSV file containing blood sugar measurements into graphs')
|
|
|
|
parser.add_argument(
|
|
'--input', '-i', action='store', required=True, type=str, dest='input_file',
|
|
help='Select the CSV file exported by glucometerutils.')
|
|
parser.add_argument(
|
|
'--output', '-o', action='store', type=str, dest='output_file',
|
|
help=('Select the path for the output file.'))
|
|
|
|
parser.add_argument(
|
|
'--pagesize', action='store', required=False, type=str, default='',
|
|
help=('Page size of output PDF (currently, letter or A4).'))
|
|
parser.add_argument(
|
|
'--graphs', action='store', required=False, type=int, default=2, dest='graphs_per_page',
|
|
help=('Number of graphs to print per page.'))
|
|
parser.add_argument(
|
|
'--icons', action='store_true', required=False, default=True,
|
|
help=('Print food and injection indicators (default: true).'))
|
|
|
|
parser.add_argument(
|
|
'--units', action='store', required=False, type=str,
|
|
default='mmol/L', choices=(UNIT_MGDL, UNIT_MMOLL),
|
|
help=('The measurement units used (mmol/L or mg/dL).'))
|
|
parser.add_argument(
|
|
'--low', action='store', required=False, type=float, default=DEFAULT_LOW,
|
|
help=('Minimum of target glucose range.'))
|
|
parser.add_argument(
|
|
'--high', action='store', required=False, type=float, default=DEFAULT_HIGH,
|
|
help=('Maximum of target glucose range.'))
|
|
|
|
args = parser.parse_args()
|
|
|
|
args.pagesize = verify_pagesize(args.pagesize)
|
|
args.units = verify_units(args.units, args.high, args.low)
|
|
if args.units == UNIT_MMOLL:
|
|
args.graph_max = GRAPH_MAX_MMOLL
|
|
args.graph_min = GRAPH_MIN_MMOLL
|
|
else:
|
|
args.graph_max = GRAPH_MAX_MGDL
|
|
args.graph_min = GRAPH_MIN_MGDL
|
|
''' If the user specified the units but not the high or low targets, set them now '''
|
|
if args.high == DEFAULT_HIGH or args.low == DEFAULT_LOW:
|
|
args.high = convert_glucose_unit(args.high, UNIT_MMOLL)
|
|
args.low = convert_glucose_unit(args.low, UNIT_MMOLL)
|
|
|
|
''' Ensure we have a valid number of graphs_per_page '''
|
|
if not isinstance(args.graphs_per_page, int) or args.graphs_per_page < 1:
|
|
args.graphs_per_page = 2
|
|
|
|
return args
|
|
|
|
def from_csv(csv_file, newline=''):
|
|
'''Returns the reading as a formatted comma-separated value string.'''
|
|
data = csv.reader(csv_file, delimiter=',', quotechar='"')
|
|
fields = [ 'timestamp', 'value', 'meal', 'measure_method', 'comment' ]
|
|
rows = []
|
|
for row in data:
|
|
item = dict(zip(fields, row))
|
|
rows.append(item)
|
|
return rows
|
|
|
|
def convert_glucose_unit(value, from_unit, to_unit=None):
|
|
"""Convert the given value of glucose level between units.
|
|
|
|
Args:
|
|
value: The value of glucose in the current unit
|
|
from_unit: The unit value is currently expressed in
|
|
to_unit: The unit to conver the value to: the other if empty.
|
|
|
|
Returns:
|
|
The converted representation of the blood glucose level.
|
|
|
|
Raises:
|
|
exceptions.InvalidGlucoseUnit: If the parameters are incorrect.
|
|
Note that this is defined by the main glucometerutils package, from which
|
|
this function is duplicated, and is not a valid exception for this script.
|
|
So let's hope it doesn't get triggered!
|
|
"""
|
|
if from_unit not in VALID_UNITS:
|
|
raise exceptions.InvalidGlucoseUnit(from_unit)
|
|
|
|
if from_unit == to_unit:
|
|
return value
|
|
|
|
if to_unit is not None:
|
|
if to_unit not in VALID_UNITS:
|
|
raise exceptions.InvalidGlucoseUnit(to_unit)
|
|
|
|
if from_unit is UNIT_MGDL:
|
|
return round(value / 18.0, 2)
|
|
else:
|
|
return round(value * 18.0, 0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|
|
# vim: set expandtab shiftwidth=2 softtabstop=2 tw=0 :
|