def write_data(protocol_dict, DEnM_df, data_dict, outname):
"""
Write activity or sleep data to disk.
write_data(protocol_dict, DEnM_df, data_dict, outname) -> None
input protocol_dict: protocol settings stored as key->value pairs
input DEnM_df: pd.dataframe of DEnM data
input data_dict: activity_dict or sleep_dict
input outname: name to use for output file
"""
(dates, start_date, end_date) = analyze.calculate_dates(protocol_dict, DEnM_df)
# create string used to control binning size
resample_freq = str(protocol_dict['bin']) + 'Min'
# create the time series to be used for the dataframes
t_index = DEnM_df.ix[start_date:end_date].resample(resample_freq, how='sum').index
output_df = pd.DataFrame(index=t_index)
output_df['date'] = [i.strftime("%Y-%m-%d") for i in output_df.index]
output_df['time'] = [i.strftime("%H:%M:%S") for i in output_df.index]
for genotype in data_dict:
df = data_dict[genotype][start_date:end_date].resample(resample_freq,
how='sum')
output_df[genotype + '_mean'] = df.mean(axis=1)
output_df[genotype + '_sem'] = df.std(axis=1) / math.sqrt(df.shape[1])
output_df[genotype + '_N'] = df.shape[1]
output_df.to_excel(outname)
def write_data(protocol_dict, DEnM_df, data_dict, outname):
"""
Write activity or sleep data to disk.
write_data(protocol_dict, DEnM_df, data_dict, outname) -> None
input protocol_dict: protocol settings stored as key->value pairs
input DEnM_df: pd.dataframe of DEnM data
input data_dict: activity_dict or sleep_dict
input outname: name to use for output file
"""
(dates, start_date,
end_date) = analyze.calculate_dates(protocol_dict, DEnM_df)
# create string used to control binning size
resample_freq = str(protocol_dict['bin']) + 'Min'
# create the time series to be used for the dataframes
t_index = DEnM_df.ix[start_date:end_date].resample(resample_freq,
how='sum').index
output_df = pd.DataFrame(index=t_index)
output_df['date'] = [i.strftime("%Y-%m-%d") for i in output_df.index]
output_df['time'] = [i.strftime("%H:%M:%S") for i in output_df.index]
for genotype in data_dict:
df = data_dict[genotype][start_date:end_date].resample(resample_freq,
how='sum')
output_df[genotype + '_mean'] = df.mean(axis=1)
output_df[genotype + '_sem'] = df.std(axis=1) / math.sqrt(df.shape[1])
output_df[genotype + '_N'] = df.shape[1]
output_df.to_excel(outname)
def data(protocol_dict, DEnM_df, data_dict, genotype_list, data_type):
"""
Plot data for arbitrarily many lines on one graph.
data(protocol_dict, DEnM_df, data_dict, genotype_list, data_type) -> None
input protocol_dict: information about the protocol used for this experiment
input DEnM_df: pd.dataframe of data from DEnM file
input data_dict: activity_dict or sleep_dict
input genotype_list: list of genotypes to plot
input data_type: 'activity' or 'sleep'
"""
(dates, start_date, end_date) = analyze.calculate_dates(protocol_dict, DEnM_df)
# create string used to control binning size
resample_freq = str(protocol_dict['bin']) + 'Min'
# create the time series to be used for the dataframes
t_index = DEnM_df.ix[start_date:end_date].resample(resample_freq, how='sum').index
# create temporary dataframes to hold the mean and sem
mean_df = pd.DataFrame(index=t_index)
sem_df = pd.DataFrame(index=t_index)
for genotype in genotype_list:
df = data_dict[genotype][start_date:end_date].resample(resample_freq,
how='sum')
mean_df[genotype] = df.mean(axis=1)
sem_df[genotype] = df.std(axis=1) / math.sqrt(df.shape[1])
# plot decorations/parameters based on plot type
plot_decorations = {'activity': ('beam crossings per ' + str(protocol_dict['bin']) + ' minutes', (0, 100)),
'sleep': ('minutes sleep per ' + str(protocol_dict['bin']) + ' minutes', (0, 30))}
(ylabel, ylim) = plot_decorations[data_type]
light_bar = ylim[1]
# other plot parameters
xlabel = 'time (h)'
gender_labels = {'f': ur'$\u2640$', 'm': ur'$\u2642$'}
if protocol_dict['gender'] in gender_labels:
gender = gender_labels[protocol_dict['gender']]
else:
gender = ur''
# create name for pdf and open multi-page pdf object
savename = '_'.join([genotype_list[-1], protocol_dict['effector'], protocol_dict['gender'], data_type + '.pdf'])
pdf = PdfPages(savename)
for day in range(1, len(dates)):
start = start_date + dt.timedelta(days=(day - 1))
end = start_date + dt.timedelta(day)
if end > end_date: continue # exit if on the partial last day
__, ax = plt.subplots()
# plot each genotype as well as any controls on a graph
for gen_index, genotype in enumerate(genotype_list):
if len(genotype_list) > 1:
color = COLOR_CYCLE[gen_index % len(COLOR_CYCLE)]
else:
color = 'r'
legend_label = ' '.join([genotype, 'N=' + str(data_dict[genotype].shape[1])])
ax.plot_date(mean_df[start:end].index, mean_df[start:end][genotype], '-', label=legend_label,
color=color)
for i in mean_df[start:end].index:
ax.errorbar(x=i,
y=mean_df[genotype][i], yerr=sem_df[genotype][i],
color=color)
mean_temp = round(DEnM_df['Tavg'].ix[start:end].mean(), 1)
ax.set_title(' '.join([genotype_list[-1], 'x', protocol_dict['effector'], gender, data_type, 'Day', str(day), '(' + str(mean_temp) + '$^\circ$C' + ')']))
ax.xaxis.set_major_locator(mpld.HourLocator(interval=1))
ax.xaxis.set_major_formatter(mpld.DateFormatter('%H'))
ax.xaxis.grid(True, which='major')
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_ylim(ylim)
ax.set_xlim(start, end)
# Shink current axis's height by 10% on the bottom
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
# Put a legend below current axis
if len(genotype_list) > 1:
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), prop={'size': 'x-small'})
# plot dark line for D phase
if day < protocol_dict['DD']:
ax.axhline(y=light_bar, xmin=0.5, xmax=1, linewidth=3, color='k')
else: ax.axhline(y=light_bar, xmin=0, xmax=1, linewidth=3, color='k')
# save plot to pdf and close figure
pdf.savefig()
plt.close()
pdf.close()
def metadata(protocol_dict, DEnM_df):
"""
Plot Lavg, Tavg, and Havg daily.
metadata(protocol_dict, DEnM_df) -> None
input protocol_dict: information about the protocol used for this experiment
input DEnM_df: pd.dataframe of data from DEnM file
"""
(dates, start_date, end_date) = analyze.calculate_dates(protocol_dict, DEnM_df)
# create name for pdf and open multi-page pdf object
savename = '_'.join(['DEnM', str(protocol_dict['DEnM']) + '.pdf'])
pdf = PdfPages(savename)
for day in range(1, len(dates)):
start = start_date + dt.timedelta(days=(day - 1))
end = start_date + dt.timedelta(day)
if end > end_date:
continue # exit if on the partial last day
fig, ax = plt.subplots(3, sharex=True)
# plot the average light intensity, temperature, and relative humidity
L = DEnM_df['Lavg'].ix[start:end]
T = DEnM_df['Tavg'].ix[start:end]
H = DEnM_df['Havg'].ix[start:end]
ax[0].plot_date(L.index, L, '-', color='k')
ax[1].plot_date(L.index, T, '-', color='r')
ax[2].plot_date(L.index, H, '-', color='b')
# set title
ax[0].set_title(' '.join(['DEnM',
str(protocol_dict['DEnM']),
'Day',
str(day)]))
# set decorations for Lavg graphs (and title)
ax[0].set_ylabel('light (lux)')
ax[0].set_ylim(0, 400)
ax[0].set_xlim(start, end)
ax[0].xaxis.grid(True, which='major')
ax[0].yaxis.set_ticks(np.arange(0, 400, 100))
# set decorations for Tavg graphs
ax[1].set_ylabel('temp ($^\circ$C)')
ax[1].set_ylim(18, 36)
ax[0].set_xlim(start, end)
ax[1].xaxis.grid(True, which='major')
ax[1].yaxis.set_ticks(np.arange(18, 36, 5))
# set decorations for Havg graphs (and shared x axis)
ax[2].set_ylabel('rel hum (%)')
ax[2].set_ylim(55, 75)
ax[0].set_xlim(start, end)
ax[2].xaxis.grid(True, which='major')
ax[2].yaxis.set_ticks(np.arange(55, 75, 5))
# make the x axis shared and pretty
ax[2].xaxis.set_major_locator(mpld.HourLocator(interval=1))
ax[2].xaxis.set_major_formatter(mpld.DateFormatter('%H'))
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
fig.subplots_adjust(hspace=0)
# save plot to pdf and close figure
pdf.savefig()
plt.close()
pdf.close()
