def power(filename):
"""Return filename of plot of the damped_vibration function."""
df = read_file(filename)
labels = df.columns.tolist()
data = serialize(df, render_to='power', title='',
output_type='dict')
# data['subtitle'] = {'text': 'a subtitle here...'}
data['plotOptions'] = {'spline': {
'lineWidth': 2,
'states': {
'hover': {
'lineWidth': 3}
}}}
data['chart']['type'] = 'line'
data['chart']['zoomType'] = 'x'
data['chart']['panning'] = True
data['chart']['panKey'] = 'shift'
result = 'new Highcharts.StockChart({});'.format(json_encode(data))
return result
def datarate(filename):
"""Return filename of plot of the damped_vibration function."""
df = read_file(filename)
# add data volume columns
for column in df.columns.values.tolist():
if 'Accum' in column:
instr = column[0]
df[instr, 'Volume', 'Gbits'] = df[instr, 'Accum', 'Gbits'].sub(
df[instr, 'Accum', 'Gbits'].shift(), fill_value=0)
df = df.sort_index(axis=1)
insts = ['BELA','ISA','MERMAG','MERTIS','MGNS','MIXS_SIXS',
'PHEBUS','SERENA','SIMBIOSYS']
# slice out a few specific dataframes
df_insts_accum_gbits = {
'data': df[insts].filter(like='Accum'),
'title': ''}
df_insts_volume_gbits = {
'data': df[insts].filter(like='Volume'),
'title': 'Data Rate [Gbit/s]'}
df_insts_upload_kbitsps = {
'data': df[insts].filter(like='Upload'),
'title': ''}
df_ssmm_accum_gbits = {
'data': df.filter(like='SSMM').filter(like='Accum'),
'title': ''}
df_ssmm_volume_gbits = {
'data': df.filter(like='SSMM').filter(like='Volume'),
'title': ''}
# select dataframe to use
df = df_insts_volume_gbits
labels = insts # df.columns.tolist()
data = serialize(df['data'], render_to='datarate', title='',
output_type='dict')
for x in data['series']:
x['name'] = x['name'][0]
# data['subtitle'] = {'text': 'a subtitle here...'}
data['plotOptions'] = {'spline': {
'lineWidth': 2,
'states': {
'hover': {
'lineWidth': 3}
}}}
data['chart']['type'] = 'line'
data['chart']['zoomType'] = 'x'
data['chart']['panning'] = True
data['chart']['panKey'] = 'shift'
data['title']['text'] = df['title']
result = 'new Highcharts.StockChart({});'.format(json_encode(data))
return result
def compute_highcharts(A, b, w, T, resolution=10000):
"""Return filename of plot of the damped_vibration function."""
t = linspace(0, T, resolution+1)
u = damped_vibrations(t, A, b, T)
d = {'t': t, 'u': u}
df = pd.DataFrame(d)
df.set_index('t', inplace=True)
data = serialize(df, output_type='dict', chart_type='stock',
render_to='my-chart',
)
data['chart']['type'] = 'line'
data['chart']['zoomType'] = 'x'
data['chart']['panning'] = True
data['chart']['panKey'] = 'shift'
data['chart']['plotBackgroundColor'] = '#FCFFC5'
data["plotOptions"] = {
"spline": {
"color": "#FF0000",
"lineWidth": 1,
"states": { "hover": { "lineWidth": 1 } },
"marker": {"enabled": True }
}
}
chart = 'new Highcharts.Chart({});'.format(json_encode(data))
return chart
def timeline_pandas_highcharts():
"""Return filename of plot of the damped_vibration function."""
data = defaultdict(lambda: defaultdict(dict))
timeline_data = pd.read_csv('timeline.txt')
data['chart']['type'] = 'columnrange'
data['chart']['inverted'] = True
data['chart']['zoomType'] = 'y'
data['chart']['panning'] = True
data['chart']['panKey'] = 'shift'
data['chart']['renderTo'] = 'my-chart'
data['title']['text'] = 'BepiTimeline Test'
data['scrollbar']['enable'] = True
data['xAxis']['categories'] = list(set(timeline_data['Instrument'].values.tolist()))
data['yAxis']['type'] = 'datetime'
data['yAxis']['title']['text'] = 'Timespan'
data['plotOptions']['columnrange']['grouping'] = False
data['legend']['enabled'] = True
data['tooltip']['headerFormat'] = '<b>{series.name}</b><br/>'
data['tooltip']['pointFormat'] = '{point.low} - {point.high}'
data['series'] = []
grouped = timeline_data.groupby('Instrument')
grouped = [grouped.get_group(x) for x in grouped.groups]
for level, frame in enumerate(grouped):
df = {}
df['name'] = frame['Instrument'].values[0]
df['data'] = []
for row in frame.itertuples():
block = {}
block['x'] = level
st = dt.strptime(row[2], '%Y-%m-%d %H:%M')
st = int((st-dt(1970,1,1)).total_seconds()*1000)
en = dt.strptime(row[3], '%Y-%m-%d %H:%M')
en = int((en-dt(1970,1,1)).total_seconds()*1000)
block['low'] = st
block['high'] = en
block['mode'] = row[4]
block['power'] = row[5]
block['data_rate'] = row[6]
df['data'].append(block)
data['series'].append(df)
chart = 'new Highcharts.Chart({});'.format(json_encode(data))
print( data['title']['text'] )
return [data['series'], data['xAxis']['categories'], data['title']['text']]
