def plot_inflections(study, record, sensor, segment):
"""
"""
searchRange = retrieve_ref('searchRange')
searchRange = [int(x) for x in searchRange]
searchRange.append('unique')
row_num, col_num, plot_num = len(searchRange) + 2, 2, 0
row_width_mulp, col_width_mulp = 12, 5
plot_width, plot_height = col_num * row_width_mulp, row_num * col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
for range in searchRange:
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
path = [
study, 'analyzed', 'inflections', 'all_times',
str(range), record, segment
]
file = os.path.join(*path, sensor + ".csv")
if not os.path.isfile(file):
return
df = pd.read_csv(file)
for colName in df.columns:
if 'Minutes' in colName:
timeMinutes = list(df[colName])
if 'measurement' in colName:
measList = list(df[colName])
measMin = min(measList)
measMax = max(measList)
plt.scatter(timeMinutes, measList, label=str(colName))
if 'inflectionDecision' in colName or 'unique' in colName:
if 'inflectionDecision' in colName:
dfInflections = df.drop(df[(df[colName] != 'Yes')].index)
timeInflections = list(dfInflections['timeMinutes'])
if 'unique' in colName:
plt.scatter(timeMinutes, measList)
timeInflections = list(df[colName])
for time in timeInflections:
# multp = searchRange.index(range)/len(searchRange)
# colorScatter = [multp*x for x in [0,1,.5]]
colorScatter = [0, .9, .6]
xx = np.linspace(time, time, 100)
yy = np.linspace(measMin, measMax, 100)
plt.plot(xx, yy, color=colorScatter, linestyle='--')
plt.title('Record = ' + str(record) + ' Range = ' +
str(range) + ' seconds ' +
' Inflections Found = ' +
str(len(timeInflections)))
plt.xlabel('Time (Minutes)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit)
# plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
if 'polyfitEquation' in colName:
polyfitCoeff = list(dfInflections[colName])
coeffPolyList = []
for coeff in polyfitCoeff:
# print('coeff = ' + str(coeff))
coeff = str(coeff)
coeff = coeff.replace("x", ",")
coeff = coeff.replace("*", "")
coeff = coeff.replace("**2", "")
coeff = coeff.replace("**", "")
coeff = coeff.replace(" ", "")
coeff = coeff.replace(" ", "")
coeff = coeff.replace("+", "")
# print('coeff = ' + str(coeff))
coeffList = coeff.split(',')
coeffPoly = float(coeffList[0])
# print('coeffPoly = ' + str(coeffPoly))
coeffPolyList.append(coeffPoly)
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
plt.scatter(timeInflections, coeffPolyList)
plt.title('Time Infletions vs Coefficients')
plt.xlabel('Time (Minutes)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit)
# plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
path = [study, 'plotted', 'inflection', 'each_record', record]
path = build_path(path)
file = os.path.join(path, sensor + ".png")
plt.savefig(file, bbox_inches='tight')
print('inflection plot saved ' + file)
def plot_source(study):
"""
"""
study_list = retrieve_ref('study_list')
sensor_list = retrieve_ref('sensor_list')
segment_list = retrieve_ref('segment_list')
format_type = 'source'
source_path = os.path.join('studies', study, 'formatted', format_type)
format_folders = os.listdir(source_path)
for record in format_folders:
row_num, col_num, plot_num = len(sensor_list), 1, 0
row_width_mulp, col_width_mulp = 14, 5
plot_width, plot_height = col_num * row_width_mulp, row_num * col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
for sensor in sensor_list:
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
for segment in segment_list:
format_types = ['source', 'truncate']
for format_type in format_types:
source = os.path.join('studies', study, 'formatted',
format_type, record, segment,
sensor + '.csv')
if os.path.isfile(source):
print('source = ' + source)
df = pd.read_csv(source)
colNames = list(df.head())
print('colNames = ')
print(colNames)
for colName in colNames:
if str('measurement') in str(colName):
colNameSplit = colName.split('_')
labelName = format_type
print('labelName = ' + labelName)
valueColor = retrieve_ref_color(labelName)
plt.scatter(df['timeUnix'],
df[colName],
color=valueColor,
label=labelName)
plt.xlabel('time Unix')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit)
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2),
loc='upper left')
path = ['studies', study, 'plotted', format_type, record]
path = build_path(path)
file = os.path.join(path, sensor + ".png")
plt.savefig(file, bbox_inches='tight')
def plot_regression_segment(study, record, segment, sensor, degree):
"""
"""
df_coef = retrieve_regression(study, segment, sensor, degree)
colNamesCoef = list(df_coef.head())
format_type = 'clean'
source = os.path.join('studies', study, 'formatted', format_type, record,
segment, sensor + '.csv')
print('source = ' + source)
df = pd.read_csv(source)
colNames = list(df.head())
print('colNames = ')
print(colNames)
xx = list(df['timeMinutes'])
if len(xx) == 0:
return (0)
yyReal = list(df[colNames[-1]])
i = df_coef[df_coef['recordName'] == record].index.values[0]
coeff = df_coef.loc[i, colNamesCoef[-1]]
print('coeff = ')
print(coeff)
try:
float(coeff)
coeff = [coeff]
except:
coeff = coeff.split(' ')
print('coeff = ')
print(coeff)
# print('coeff =')
# print(coeff)
yy = []
for x in xx:
# print('x = ' + str(x))
# print('len(coeff) = ' + str(len(coeff)))
y = 0
coef_equation = []
for i in range(len(coeff)):
y = y + float(coeff[i]) * math.pow(x, len(coeff) - i - 1)
# y = y + float(coeff[i])*math.pow(x, i)
coef_equation.append(
str(
str(round(float(coeff[i]), 4)) + '*x^' +
str(len(coeff) - i - 1)))
yy.append(y)
# explain polyval https://numpy.org/devdocs/reference/generated/numpy.polyval.html
wearable_num = 1
colorWearableSegment = retrieve_ref_color_wearable_segment(
wearable_num, segment)
plt.scatter(xx, yyReal, color=colorWearableSegment, label='measured')
wearable_num = 2
colorWearableSegment = retrieve_ref_color_wearable_segment(
wearable_num, segment)
labelPolyfit = str('polyfit degree = ' + str(degree))
plt.plot(xx, yy, '--', color=colorWearableSegment, label=labelPolyfit)
plt.xlabel('Time (Minutes)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit)
plt.title(segment + ' polyfit for degree ' + str(degree))
coef_str = [str(x) for x in coef_equation]
coef_str = ' '.join(coef_str)
print('coef_str = ')
print(coef_str)
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
plt.title(segment + ' polyfit for degree ' + str(degree) + ' ' +
str(coef_str))
return (0)
def plot_inflections(study, record, sensor, segment):
"""
plot inflections
"""
if segment != 'All':
return
searchRange = retrieve_ref('searchRange')
searchRange = [int(x) for x in searchRange]
format_type, segment, range = 'trunate', 'All', searchRange[0]
path = [study, 'analyzed', 'inflections', 'all_times', str(range), record, segment]
file = os.path.join(*path, sensor + ".csv")
if not os.path.isfile(file):
return
row_num, col_num, plot_num = len(searchRange)+2, 1, 0
row_width_mulp, col_width_mulp = 12, 5
plot_width, plot_height = col_num*row_width_mulp, row_num*col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
for range in searchRange:
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
path = [study, 'analyzed', 'inflections', 'all_times', str(range), record, segment]
file = os.path.join(*path, sensor + ".csv")
if not os.path.isfile(file):
continue
# source = os.path.join(study, 'formatted', format_type, record, segment, sensor + '.csv')
# print('source = ' + source)
df = pd.read_csv(file)
for colName in df.columns:
if 'Unnamed' in colName:
del df[colName]
elif 'Minutes' in colName:
timeMinutes = list(df[colName])
elif 'measurement' in colName and '_' not in colName:
measList = list(df[colName])
measMin = min(measList)
measMax = max(measList)
plt.scatter(timeMinutes, measList, label = str(colName))
elif 'inflectionDecision' in colName:
dfInflections = df.drop(df[(df[colName] != 'Yes')].index)
timeInflections = list(dfInflections['timeMinutes'])
print('timeInflections = ')
print(timeInflections)
for time in timeInflections:
xx = np.linspace( time, time, 100)
yy = np.linspace( measMin, measMax, 100)
plt.plot(xx, yy, color=[0,.9,.6])
plt.xlabel('time Unix')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit )
# plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
plt.title('Record = ' + str(record) + ' Range = ' + str(range) + ' seconds ' + ' Inflections Found = ' + str(len(timeInflections)) )
path = [study, 'plotted', 'inflection', 'each_record', record]
path = build_path(path)
file = os.path.join(path, sensor + ".png")
plt.savefig(file, bbox_inches='tight')
print('inflection plot saved ' + file)
def plot_coregister(study):
"""
"""
segment = 'All'
study_list = retrieve_ref('study_list')
sensor_list = retrieve_ref('sensor_list')
segment_list = retrieve_ref('segment_list')
segment_list.reverse()
format_type = 'clean'
source_path = os.path.join('studies', study, 'formatted', format_type)
format_folders = os.listdir(source_path)
format_types = ['clean']
for record in format_folders:
row_num, col_num, plot_num = len(sensor_list), 1, 0
row_width_mulp, col_width_mulp = 14, 5
plot_width, plot_height = col_num * row_width_mulp, row_num * col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
for sensor in sensor_list:
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
for format_type in format_types:
for segment in segment_list:
source = os.path.join('studies', study, 'formatted',
format_type, record, segment,
sensor + '.csv')
if os.path.isfile(source):
print('source = ' + source)
df = pd.read_csv(source)
colNames = list(df.head())
print('colNames = ')
print(colNames)
for colName in colNames:
if str('measurement') in str(colName):
colNameSplit = colName.split('_')
labelName = str(format_type + ' ' +
colNameSplit[0])
print('labelName = ' + labelName)
index_col = df.columns.get_loc(colName)
wearable_num = len(colNames) - index_col
print('wearable_num = ' + str(wearable_num))
colorWearableSegment = retrieve_ref_color_wearable_segment(
wearable_num, segment)
plt.scatter(df['timeMinutes'],
df[colName],
color=colorWearableSegment,
label=labelName)
plt.xlabel('Record Time (minutes)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit)
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
path = ['studies', study, 'plotted', 'clean', record]
path = build_path(path)
file = os.path.join(path, sensor + ".png")
plt.savefig(file, bbox_inches='tight')
def plot_acc(study):
"""
"""
print('compare ACC sensors')
format_type, sensor, segment = 'truncate', 'ACC', 'All'
source_path = os.path.join('studies', study, 'formatted', format_type)
format_folders = os.listdir(source_path)
for record in format_folders:
row_num, col_num, plot_num = 5, 1, 0
row_width_mulp, col_width_mulp = 14, 5
plot_width, plot_height = col_num * row_width_mulp, row_num * col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
source = os.path.join('studies', study, 'formatted', format_type,
record, segment, sensor + '.csv')
if os.path.isfile(source):
print('source = ' + source)
df = pd.read_csv(source)
colNames = list(df.head())
print('colNames = ')
print(colNames)
for colName in colNames:
if str('eas') in str(colName):
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
labelName = colName
print('labelName = ' + labelName)
valueColor = retrieve_ref_color(
str('color_' + str(sensor) + '_' + str(colName)))
plt.scatter(df['timeUnix'],
df[colName],
color=valueColor,
label=labelName)
plt.xlabel('time Unix')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit)
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2),
loc='upper left')
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
for colName in colNames:
if str('eas') in str(colName):
labelName = colName
print('labelName = ' + labelName)
valueColor = retrieve_ref_color(
str('color_' + str(sensor) + '_' + str(colName)))
plt.scatter(df['timeUnix'],
df[colName],
color=valueColor,
label=labelName)
plt.xlabel('time (Unix)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit)
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
path = ['studies', study, 'plotted', format_type, record]
path = build_path(path)
file = os.path.join(path, sensor + ".png")
plt.savefig(file, bbox_inches='tight')
print('acc saved: ' + file)
def plot_inflections():
"""
"""
study_list = retrieve_ref('study_list')
sensor_list = retrieve_ref('sensor_list')
segment_list = retrieve_ref('segment_list')
searchRange = retrieve_ref('searchRange')
for study in study_list:
for sensor in sensor_list:
format_type = 'clean'
clean_path = os.path.join(study, 'formatted', format_type)
recordNames = os.listdir(clean_path)
for sensor in sensor_list:
for record in recordNames:
row_num, col_num, plot_num = len(searchRange), 2, 0
row_width_mulp, col_width_mulp = 7, 5
plot_width, plot_height = col_num * row_width_mulp, row_num * col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
for range in searchRange:
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
format_type = 'clean'
segment = 'All'
path = [
study, 'analyzed', 'inflections', 'all_times',
str(range), record, segment
]
path = build_path(path)
file = os.path.join(path, sensor + ".csv")
if os.path.isfile(file):
source = os.path.join(study, 'formatted',
format_type, record, segment,
sensor + '.csv')
print('source = ' + source)
df = pd.read_csv(source)
for colName in df.columns:
if 'timeMinutes' in colName:
timeMinutes = list(df[colName])
if 'meas' in colName:
measList = list(df[colName])
measMin = min(measList)
measMax = max(measList)
plt.scatter(timeMinutes,
measList,
label=str(colName))
df = pd.read_csv(file)
for colName in df.columns:
if 'inflection' in colName:
df = df.drop(
df[(df[colName] != 'Yes')].index)
timeInflections = list(df['timeMinutes'])
for time in timeInflections:
xx = np.linspace(time, time, 100)
yy = np.linspace(measMin, measMax, 100)
plt.plot(xx, yy, color=[0, .9, .6])
plt.xlabel('time Unix')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(sensor + ' ' + sensor_unit)
# plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
plt.title('Record = ' + str(record) + ' Range = ' +
str(range) + ' seconds')
path = [
study, 'plotted', 'inflection', 'each_record', record
]
path = build_path(path)
file = os.path.join(path, sensor + ".png")
plt.savefig(file, bbox_inches='tight')
print('inflection plot saved ' + file)
def plot_timestamp():
"""
compare the curves to verify the end of the record was properly found
plot the source measurements for temperature
plot the timestamped data for the temperature
plot the truncated data
plot the timestamped and truncated on the same plot
"""
print("begin plotting timestamped data")
study_list = retrieve_ref('study_list')
sensor_list = retrieve_ref('sensor_list')
analysis_list = retrieve_ref('analysis_list')
for study in study_list:
metadata_path = os.path.join(study, 'meta')
metadata_file = os.path.join(metadata_path, 'metadata.csv')
df_meta = pd.read_csv(metadata_file)
# print(df_meta)
# timestamp temp
sensor = 'TEMP'
for study in study_list:
df_meta = retrieve_meta(study)
source_path = list(df_meta['source_path'])
for record in source_path:
row_num, col_num, plot_num = 4, 1, 0
row_width_mulp, col_width_mulp = 14, 5
plot_width, plot_height = col_num*row_width_mulp, row_num*col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
# plot the timestamp in unix of timestamped record
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
analysis_type = 'source'
df = retrieve_analyzed(study, analysis_type, record, sensor)
valueColor = retrieve_ref_color(str('color_' + str(analysis_type)))
plt.scatter(df['count'], df['measurement'], color = valueColor, label = str(analysis_type))
plt.title( analysis_type + ' ' + record + ' ' + sensor)
plt.xlabel('Measurement Count - Before Timestamp')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(str(sensor) + ' ( ' + str(sensor_unit) + ' )')
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
# plot the timestamp in unix of timestamped record
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
analysis_type = 'timestamp'
df = retrieve_analyzed(study, analysis_type, record, sensor)
valueColor = retrieve_ref_color(str('color_' + str(analysis_type)))
plt.scatter(df['timeMinutes'], df['measurement'], color = valueColor, label = str(analysis_type))
plt.title( analysis_type + ' ' + record + ' ' + sensor)
plt.xlabel('Time (Unix)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(str(sensor) + ' ( ' + str(sensor_unit) + ' )')
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
# plot both the original and the truncated record
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
for analysis_type in analysis_list:
df = retrieve_analyzed(study, analysis_type, record, sensor)
valueColor = retrieve_ref_color(str('color_' + str(analysis_type)))
plt.scatter(df['timeMinutes'], df['measurement'], color = valueColor, label = str(analysis_type))
plt.title( analysis_type + ' ' + record + ' ' + sensor)
plt.xlabel('Time (minutes)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(str(sensor) + ' ( ' + str(sensor_unit) + ' )')
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
# plot the truncated record
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
analysis_type = 'truncate'
df = retrieve_analyzed(study, analysis_type, record, sensor)
valueColor = retrieve_ref_color(str('color_' + str(analysis_type)))
plt.scatter(df['timeMinutes'], df['measurement'], color = valueColor, label = str(analysis_type))
plt.title( analysis_type + ' ' + record + ' ' + sensor)
plt.xlabel('Time (minutes)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(str(sensor) + ' ( ' + str(sensor_unit) + ' )')
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2), loc='upper left')
# save the plot
plot_path = os.path.join(study, 'plot')
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_path = os.path.join(study, 'plot', 'timestamp')
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_path = os.path.join(study, 'plot', 'timestamp', record)
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_file = os.path.join(plot_path, sensor + '.png')
plt.savefig(plot_file, bbox_inches='tight')
print("completed plotting timestamped data")
def plot_segment():
"""
Clean the data
"""
print("plot segment data")
study_list = retrieve_ref('study_list')
sensor_list = retrieve_ref('sensor_list')
segment_list = retrieve_ref('segment_list')
timePreStudy = retrieve_ref('timePreStudy')
timePostStudy = retrieve_ref('timePostStudy')
for study in study_list:
df_meta = retrieve_meta(study)
source_path = list(df_meta['source_path'])
for record in source_path:
row_num, col_num, plot_num = 6, 1, 0
row_width_mulp, col_width_mulp = 14, 5
plot_width, plot_height = col_num * row_width_mulp, row_num * col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
for sensor in sensor_list:
# plot the timestamp in unix of timestamped record
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
for segment in segment_list[0:-1]:
# print('segment_list')
# print(segment_list)
analysis_type = segment
df = retrieve_analyzed(study, analysis_type, record,
sensor)
# print(df)
valueColor = retrieve_ref_color(
str('color_' + str(segment)))
plt.scatter(df['timeMinutes'],
df['measurement'],
color=valueColor,
label=str(segment))
plt.title(analysis_type + ' ' + record + ' ' + sensor)
plt.xlabel('Measurement Count - Before Timestamp')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(str(sensor) + ' ( ' + str(sensor_unit) + ' )')
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2),
loc='upper left')
# save the plot
plot_path = os.path.join(study, 'plot')
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_path = os.path.join(study, 'plot', 'segment')
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_path = os.path.join(study, 'plot', 'segment', record)
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_file = os.path.join(plot_path, sensor + '.png')
plt.savefig(plot_file, bbox_inches='tight')
def plot_acc():
"""
compare the curves to verify the end of the record was properly found
plot the source measurements for temperature
plot the timestamped data for the temperature
plot the truncated data
plot the timestamped and truncated on the same plot
"""
print("begin plotting acc data")
study_list = retrieve_ref('study_list')
sensor_list = retrieve_ref('sensor_list')
analysis_list = retrieve_ref('analysis_list')
for study in study_list:
metadata_path = os.path.join(study, 'meta')
metadata_file = os.path.join(metadata_path, 'metadata.csv')
df_meta = pd.read_csv(metadata_file)
# print(df_meta)
# timestamp ACC
sensor = 'ACC'
for study in study_list:
df_meta = retrieve_meta(study)
source_path = list(df_meta['source_path'])
for record in source_path:
row_num, col_num, plot_num = 5, 1, 0
row_width_mulp, col_width_mulp = 20, 5
plot_width, plot_height = col_num * row_width_mulp, row_num * col_width_mulp
plt.figure(figsize=(plot_width, plot_height))
analysis_type = 'truncate'
df = retrieve_analyzed(study, analysis_type, record, sensor)
for name in list(df.columns):
if 'time' not in name:
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
colorScatter = valueColor = retrieve_ref_color(
str('color_' + str(sensor) + '_' + str(name)))
plt.scatter(df['timeMinutes'],
df[name],
color=colorScatter,
label=str(name))
# plt.scatter(df['timeMinutes'], df['measurement'], label = str('vector'))
plt.title(analysis_type + ' ' + record + ' ' + sensor)
plt.xlabel('Time (Minutes)')
plt.ylabel(str(sensor + ' ' + name))
plt.xlim([0, 1.02 * max(list(df['timeMinutes']))])
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2),
loc='upper left')
plot_num += 1
plt.subplot(row_num, col_num, plot_num)
for name in list(df.columns):
if 'time' not in name:
colorScatter = valueColor = retrieve_ref_color(
str('color_' + str(sensor) + '_' + str(name)))
plt.scatter(df['timeMinutes'],
df[name],
color=colorScatter,
label=str(name))
plt.title(analysis_type + ' ' + record + ' ' + sensor +
' ' + name)
plt.xlabel('Time (Minutes)')
sensor_unit = retrieve_sensor_unit(sensor)
plt.ylabel(
str(sensor + ' ' + name + ' ( ' + str(sensor_unit) +
' )'))
plt.xlim([0, 1.02 * max(list(df['timeMinutes']))])
plt.legend(bbox_to_anchor=(1, 0.5, 0.3, 0.2),
loc='upper left')
# save the plot
plot_path = os.path.join(study, 'plot')
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_path = os.path.join(study, 'plot', 'timestamp')
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_path = os.path.join(study, 'plot', 'timestamp', record)
if not os.path.isdir(plot_path): os.mkdir(plot_path)
plot_file = os.path.join(plot_path, sensor + '.png')
plt.savefig(plot_file, bbox_inches='tight')
print('saved plotted acc figure - ' + str(plot_file))
print("completed plotting acc data")
