def post_device_data(self, df, sensor_id, clean_na = 'drop'):
'''
POST data in the SmartCitizen API
Parameters
----------
df: pandas DataFrame
Contains data in a DataFrame format.
Data is posted regardless the name of the dataframe
It uses the sensor id provided, not the name
Data is posted in UTC TZ so dataframe needs to have located
timestamp
sensor_id: int
The sensor id
clean_na: string, optional
'drop'
'drop', 'fill'
Returns
-------
True if the data was posted succesfully
'''
if 'SC_ADMIN_BEARER' not in environ:
std_out('Cannot post without Auth Bearer', 'ERROR')
return
headers = {'Authorization':'Bearer ' + environ['SC_ADMIN_BEARER'], 'Content-type': 'application/json'}
# Get sensor name
sensor_name = list(df.columns)[0]
# Clean df of nans
df = clean(df, clean_na, how = 'all')
# Process dataframe
df['id'] = sensor_id
df.index.name = 'recorded_at'
df.rename(columns = {sensor_name: 'value'}, inplace = True)
df.columns = MultiIndex.from_product([['sensors'], df.columns])
j = (df.groupby('recorded_at', as_index = True)
.apply(lambda x: x['sensors'][['value', 'id']].to_dict('r'))
)
# Prepare json post
payload = {"data":[]}
for item in j.index:
payload["data"].append(
{
"recorded_at": localise_date(item, 'UTC').strftime('%Y-%m-%dT%H:%M:%SZ'),
"sensors": j[item]
}
)
payload_json = dumps(payload)
response = post(f'https://api.smartcitizen.me/v0/devices/{self.id}/readings', data = payload_json, headers = headers)
if response.status_code == 200 or response.status_code == 201:
return True
return False
def prepare(self, measurand, inputs, options=dict()):
"""
Prepares a test for a regression model
Parameters
----------
measurand: dict
measurand = {'8019043': ['NO2']}
inputs: dict
inputs per device and reading
inputs = {'devicename': ['reading-1', 'reading-2']}
options: dict
Options including data processing. Defaults in config._model_def_opt
Returns
-------
df = pandas Dataframe
measurand_name = string
"""
options = dict_fmerge(options, config._model_def_opt)
# Measurand
measurand_device = list(measurand.keys())[0]
measurand_metric = measurand[measurand_device][0]
measurand_name = measurand[measurand_device][0] + '_' + measurand_device
df = DataFrame()
df[measurand_name] = self.devices[measurand_device].readings[
measurand_metric]
for input_device in inputs.keys():
combined_df = self.combine(devices=[input_device],
readings=inputs[input_device])
df = df.combine_first(combined_df)
if options['common_avg']:
common_channels = inputs[list(inputs.keys())[0]]
for input_device in inputs.keys():
common_channels = list(
set(common_channels).intersection(set(inputs[input_device])))
std_out(f'Performing avg in common columns {common_channels}')
for channel in common_channels:
columns_list = [
channel + '_' + device for device in list(inputs.keys())
]
df[channel + '_AVG'] = df[columns_list].mean(axis=1)
df = df.loc[:,
df.columns.str.contains("_AVG")
| df.columns.str.contains(measurand_name)]
if options['clean_na'] is not None:
df = clean(df, options['clean_na'], how='any')
return df, measurand_name
def apply_regressor(dataframe, **kwargs):
'''
Applies a regressor model based on a pretrained model
Parameters
----------
model: sklearn predictor
Model with .predict method
options: dict
Options for data preprocessing. Defaults in config.model_def_opt
variables: dict
variables dictionary with:
{
'measurand': {
'measurand-device-name': ['measurand']
},
'inputs': {'input-device-names': ['input-1', 'input_2', 'input-3']
}
}
Returns
----------
pandas series containing the prediction
'''
inputs = list()
for device in kwargs['variables']['inputs']:
inputs = list(
set(inputs).union(set(kwargs['variables']['inputs'][device])))
try:
inputdf = dataframe[inputs].copy()
inputdf = inputdf.reindex(sorted(inputdf.columns), axis=1)
except KeyError:
std_out('Inputs not in dataframe', 'ERROR')
pass
return None
if 'model' not in kwargs:
std_out('Model not in inputs', 'ERROR')
else:
model = kwargs['model']
if 'options' not in kwargs:
options = config.model_def_opt
else:
options = dict_fmerge(config.model_def_opt, kwargs['options'])
# Remove na
inputdf = clean(inputdf, options['clean_na'], how='any')
features = array(inputdf)
result = DataFrame(model.predict(features)).set_index(inputdf.index)
return result
def get_device_data(self, start_date = None, end_date = None, frequency = '3Min', clean_na = None):
if start_date is not None: days_ago = (to_datetime(date.today())-to_datetime(start_date)).days
else: days_ago = 365 # One year of data
std_out(f'Requesting data from MUV API')
std_out(f'Device ID: {self.id}')
self.get_device_location()
self.get_device_sensors()
# Get devices
try:
if days_ago == -1: url = f'{self.API_BASE_URL}getSensorData?sensor_id={self.id}'
else: url = f'{self.API_BASE_URL}getSensorData?sensor_id={self.id}&days={days_ago}'
df = DataFrame(get(url).json())
except:
print_exc()
std_out('Failed sensor request request. Probably no connection', 'ERROR')
pass
return None
try:
# Rename columns
df.rename(columns = self.sensors, inplace = True)
df = df.set_index('time')
df.index = localise_date(df.index, self.location)
df = df[~df.index.duplicated(keep='first')]
# Drop unnecessary columns
df.drop([i for i in df.columns if 'Unnamed' in i], axis=1, inplace=True)
df.drop('id', axis=1, inplace=True)
# Check for weird things in the data
df = df.apply(to_numeric, errors='coerce')
# # Resample
df = df.resample(frequency).mean()
df = df.reindex(df.index.rename('Time'))
df = clean(df, clean_na, how = 'all')
self.data = df
except:
print_exc()
std_out('Problem closing up the API dataframe', 'ERROR')
pass
return None
std_out(f'Device {self.id} loaded successfully from API', 'SUCCESS')
return self.data
def ts_dendrogram(self, **kwargs):
"""
Plots dendrogram of devices and channels in matplotlib plot. Takes all the channels
in channels that are in the test `devices`
Parameters
----------
devices: list or string
'all'
If 'all', uses all devices in the test
channels: list
'all'
If 'all', uses all channels in the devices
metric: string
'correlation' for normal R2 or custom metric by callable
'method': string
'single'
Method for dendrogram
'options': dict
Options including data processing prior to plot. Defaults in config._plot_def_opt
formatting: dict
Name of auxiliary electrode found in dataframe. Defaults in config._ts_plot_def_fmt
Returns
-------
Dendrogram matrix, shows plot
"""
if 'metric' not in kwargs: metric = 'correlation'
else: metric = kwargs['metric']
if 'method' not in kwargs: method = 'single'
else: method = kwargs['method']
if 'devices' not in kwargs: devices = list(self.devices.keys())
else: devices = kwargs['devices']
if 'channels' not in kwargs: channels = 'all'
else: channels = kwargs['channels']
if 'options' not in kwargs:
std_out('Using default options')
options = config._plot_def_opt
else:
options = dict_fmerge(config._plot_def_opt, kwargs['options'])
if 'formatting' not in kwargs:
std_out('Using default formatting')
formatting = config._ts_plot_def_fmt['mpl']
else:
formatting = dict_fmerge(config._ts_plot_def_fmt['mpl'],
kwargs['formatting'])
# Style
if formatting['style'] is not None: style.use(formatting['style'])
else: style.use(config._plot_style)
# Palette
if formatting['palette'] is not None: set_palette(formatting['palette'])
# Size sanity check
if formatting['width'] > 50:
std_out('Reducing width to 12')
formatting['width'] = 12
if formatting['height'] > 50:
std_out('Reducing height to 10')
formatting['height'] = 10
# Font size
if formatting['fontsize'] is not None:
rcParams.update({'font.size': formatting['fontsize']})
df = DataFrame()
for device in devices:
dfd = self.devices[device].readings.copy()
dfd = dfd.resample(options['frequency']).mean()
if channels != 'all':
for channel in channels:
if channel in dfd.columns:
df = df.append(dfd[channel].rename(device + '_' + channel))
else:
df = df.append(dfd)
df = clean(df, options['clean_na'], how='any')
# if options['clean_na'] is not None:
# if options['clean_na'] == 'drop': df.dropna(axis = 1, inplace=True)
# if options['clean_na'] == 'fill': df = df.fillna(method='ffill')
# Do the clustering
Z = hac.linkage(df, method=method, metric=metric)
# Plot dendogram
plt.figure(figsize=(formatting['width'], formatting['height']))
plt.title(formatting['title'], fontsize=formatting['titlefontsize'])
plt.subplots_adjust(top=formatting['suptitle_factor'])
plt.xlabel(formatting['xlabel'])
plt.ylabel(formatting['ylabel'])
hac.dendrogram(
Z,
orientation=formatting['orientation'],
leaf_font_size=formatting[
'fontsize'], # font size for the x axis labels
labels=df.index)
plt.show()
return Z
def read_csv_file(file_path,
timezone,
frequency,
clean_na=None,
index_name='',
skiprows=None,
sep=',',
encoding='utf-8',
tzaware=True):
"""
Reads a csv file and adds cleaning, localisation and resampling and puts it into a pandas dataframe
Parameters
----------
file_path: String
File path for csv file
timezone: String
Time zone for the csv file
clean_na: String or None
None
Whether to perform clean_na or not. Either None, 'fill' or 'drop'
index_name: String
''
Name of the column to set an index in the dataframe
skiprows: list or None
None
List of rows to skip (same as skiprows in pandas.read_csv)
sep: String
','
Separator (same as sep in pandas.read_csv)
encoding: String
'utf-8'
Encoding of the csv file
Returns
-------
Pandas dataframe
"""
# Read pandas dataframe
df = read_csv(file_path,
verbose=False,
skiprows=skiprows,
sep=sep,
encoding=encoding,
encoding_errors='ignore')
flag_found = False
if type(index_name) == str:
# Single joint index
for column in df.columns:
if index_name in column:
df = df.set_index(column)
flag_found = True
break
elif type(index_name) == list:
# Composite index (for instance, DATE and TIME in different columns)
for iname in index_name:
if iname not in df.columns:
std_out(f'{iname} not found in columns', 'ERROR')
return None
joint_index_name = '_'.join(index_name)
df[joint_index_name] = df[index_name].agg(' '.join, axis=1)
df = df.set_index(joint_index_name)
df.drop(index_name, axis=1, inplace=True)
flag_found = True
if not flag_found:
std_out('Index not found. Cannot reindex', 'ERROR')
return None
# Set index
df.index = localise_date(df.index, timezone, tzaware=tzaware)
# Remove duplicates
df = df[~df.index.duplicated(keep='first')]
# Sort index
df.sort_index(inplace=True)
# Drop unnecessary columns
df.drop([i for i in df.columns if 'Unnamed' in i], axis=1, inplace=True)
# Check for weird things in the data
# df = df.apply(to_numeric, errors='coerce')
df = df.astype(float, errors='ignore')
# Resample
df = df.resample(frequency).mean()
# Remove na
df = clean(df, clean_na, how='all')
return df
def sdcard_concat(path,
output='CONCAT.CSV',
index_name='TIME',
keep=True,
ignore=['CONCAT.CSV', 'INFO.TXT']):
'''
Loads files from local directory in text format, for instance
SD card files with timestamp, sparse or concatenated
Parameters
----------
path: String
Directory containing the folder
output: String
CONCAT.CSV
Output name (csv file). If '' no output is saved, only
returns a pandas.DataFrame()
index_name: String
'TIME'
Name for the index of the pandas.DataFrame()
keep: boolean
True
Keeps the header in the output file
ignore: list
CONCAT.CSV
Ignores this file if present in the folder
Returns
-------
Pandas dataframe
'''
concat = DataFrame()
header_tokenized = dict()
marked_for_revision = False
for file in listdir(path):
if file != output and file not in ignore:
std_out(f'Loading file: {file}')
filename, _ = splitext(file)
src_path = join(path, file)
try:
with open(src_path, 'r', newline='\n',
errors='replace') as csv_file:
header = csv_file.readlines()[0:4]
except:
ignore_file = True
std_out(f'Ignoring file: {file}', 'WARNING')
pass
else:
ignore_file = False
if ignore_file: continue
if keep:
short_tokenized = header[0].strip('\r\n').split(',')
unit_tokenized = header[1].strip('\r\n').split(',')
long_tokenized = header[2].strip('\r\n').split(',')
id_tokenized = header[3].strip('\r\n').split(',')
for item in short_tokenized:
if item != '' and item not in header_tokenized.keys():
index = short_tokenized.index(item)
header_tokenized[short_tokenized[index]] = dict()
header_tokenized[short_tokenized[index]][
'unit'] = unit_tokenized[index]
header_tokenized[short_tokenized[index]][
'long'] = long_tokenized[index]
header_tokenized[
short_tokenized[index]]['id'] = id_tokenized[index]
temp = read_csv(src_path,
verbose=False,
skiprows=range(1, 4),
encoding_errors='ignore').set_index("TIME")
temp = clean(temp, clean_na='drop', how='all')
temp.index.rename(index_name, inplace=True)
concat = concat.combine_first(temp)
columns = concat.columns
## Sort index
concat.sort_index(inplace=True)
## Save it as CSV
if output.endswith('.CSV') or output.endswith('.csv'):
concat.to_csv(join(path, output))
if keep:
print('Updating header')
with open(join(path, output), 'r') as csv_file:
content = csv_file.readlines()
final_header = content[0].strip('\n').split(',')
short_h = []
units_h = []
long_h = []
id_h = []
for item in final_header:
if item in header_tokenized.keys():
short_h.append(item)
units_h.append(header_tokenized[item]['unit'])
long_h.append(header_tokenized[item]['long'])
id_h.append(header_tokenized[item]['id'])
content.pop(0)
for index_content in range(len(content)):
content[index_content] = content[index_content].strip('\n')
content.insert(0, ','.join(short_h))
content.insert(1, ','.join(units_h))
content.insert(2, ','.join(long_h))
content.insert(3, ','.join(id_h))
with open(join(path, output), 'w') as csv_file:
print('Saving file to:', output)
wr = csv.writer(csv_file, delimiter='\t')
for row in content:
wr.writerow([row])
return concat
def get_device_data(self,
start_date=None,
end_date=None,
frequency='1H',
clean_na=None):
'''
Based on code snippet from Marc Roig:
# I2CAT RESEARCH CENTER - BARCELONA - MARC ROIG ([email protected])
'''
std_out(f'Requesting data from Dades Obertes API')
std_out(f'Device ID: {self.id}')
self.get_device_sensors()
self.get_device_location()
request = self.API_BASE_URL
request += f'codi_eoi={self.id}'
if start_date is not None and end_date is not None:
request += "&$where=data between " + to_datetime(start_date).strftime("'%Y-%m-%dT%H:%M:%S'") \
+ " and " + to_datetime(end_date).strftime("'%Y-%m-%dT%H:%M:%S'")
elif start_date is not None:
request += "&$where=data >= " + to_datetime(start_date).strftime(
"'%Y-%m-%dT%H:%M:%S'")
elif end_date is not None:
request += "&$where=data < " + to_datetime(end_date).strftime(
"'%Y-%m-%dT%H:%M:%S'")
try:
s = get(request)
except:
print_exc()
std_out('Problem with sensor data from API', 'ERROR')
pass
return None
if s.status_code == 200 or s.status_code == 201:
df = read_csv(StringIO(s.content.decode('utf-8')))
else:
std_out('API reported {}'.format(s.status_code), 'ERROR')
pass
return None
# Filter columns
measures = ['h0' + str(i) for i in range(1, 10)]
measures += ['h' + str(i) for i in range(10, 25)]
# validations = ['v0' + str(i) for i in range(1,10)]
# validations += ['v' + str(i) for i in range(10,25)]
new_measures_names = list(range(1, 25))
columns = ['contaminant', 'data'] + measures # + validations
try:
df_subset = df[columns]
df_subset.columns = ['contaminant', 'date'] + new_measures_names
except:
print_exc()
std_out('Problem while filtering columns', 'Error')
return None
else:
std_out('Successful filtering', 'SUCCESS')
# Pivot
try:
df = DataFrame([])
for contaminant in self.sensors.keys():
if contaminant not in df_subset['contaminant'].values:
std_out(f'{contaminant} not in columns. Skipping',
'WARNING')
continue
df_temp = df_subset.loc[
df_subset['contaminant'] == contaminant].drop(
'contaminant',
1).set_index('date').unstack().reset_index()
df_temp.columns = ['hours', 'date', contaminant]
df_temp['date'] = to_datetime(df_temp['date'])
timestamp_lambda = lambda x: x['date'] + DateOffset(hours=int(
x['hours']))
df_temp['date'] = df_temp.apply(timestamp_lambda, axis=1)
df_temp = df_temp.set_index('date')
df[contaminant] = df_temp[contaminant]
except:
# print_exc()
std_out('Problem while filtering columns', 'Error')
pass
return None
else:
std_out('Successful pivoting', 'SUCCESS')
df.index = to_datetime(df.index).tz_localize('UTC').tz_convert(
self.location)
df.sort_index(inplace=True)
# Rename
try:
df.rename(columns=self.sensors, inplace=True)
except:
# print_exc()
std_out('Problem while renaming columns', 'Error')
pass
return None
else:
std_out('Successful renaming', 'SUCCESS')
# Clean
df = df[~df.index.duplicated(keep='first')]
# Drop unnecessary columns
df.drop([i for i in df.columns if 'Unnamed' in i],
axis=1,
inplace=True)
# Check for weird things in the data
df = df.apply(to_numeric, errors='coerce')
# Resample
df = df.resample(frequency).mean()
try:
df = df.reindex(df.index.rename('Time'))
df = clean(df, clean_na, how='all')
# if clean_na is not None:
# if clean_na == 'drop':
# # std_out('Cleaning na with drop')
# df.dropna(axis = 0, how='all', inplace=True)
# elif clean_na == 'fill':
# df = df.fillna(method='bfill').fillna(method='ffill')
# # std_out('Cleaning na with fill')
self.data = df
except:
std_out('Problem closing up the API dataframe', 'ERROR')
pass
return None
std_out(f'Device {self.id} loaded successfully from API', 'SUCCESS')
return self.data
def get_device_data(self,
start_date=None,
end_date=None,
frequency='1Min',
clean_na=None):
std_out(f'Requesting data from SC API')
std_out(f'Device ID: {self.id}')
rollup = self.convert_rollup(frequency)
std_out(f'Using rollup: {rollup}')
# Make sure we have the everything we need beforehand
self.get_device_sensors()
self.get_device_location()
self.get_device_last_reading()
self.get_device_added_at()
self.get_kit_ID()
if self.location is None: return None
# Check start date
# if start_date is None and self.added_at is not None:
# start_date = localise_date(to_datetime(self.added_at, format = '%Y-%m-%dT%H:%M:%SZ'), self.location)
# # to_datetime(self.added_at, format = '%Y-%m-%dT%H:%M:%SZ')
# elif start_date is not None:
# start_date = to_datetime(start_date, format = '%Y-%m-%dT%H:%M:%SZ')
if start_date is not None:
start_date = localise_date(
to_datetime(start_date, format='%Y-%m-%dT%H:%M:%SZ'),
self.location)
# if start_date.tzinfo is None: start_date = start_date.tz_localize('UTC').tz_convert(self.location)
std_out(f'Min Date: {start_date}')
# # Check end date
# if end_date is None and self.last_reading_at is not None:
# # end_date = to_datetime(self.last_reading_at, format = '%Y-%m-%dT%H:%M:%SZ')
# end_date = localise_date(to_datetime(self.last_reading_at, format = '%Y-%m-%dT%H:%M:%SZ'), self.location)
# elif end_date is not None:
# end_date = to_datetime(end_date, format = '%Y-%m-%dT%H:%M:%SZ')
if end_date is not None:
end_date = localise_date(
to_datetime(end_date, format='%Y-%m-%dT%H:%M:%SZ'),
self.location)
# if end_date.tzinfo is None: end_date = end_date.tz_localize('UTC').tz_convert(self.location)
std_out(f'Max Date: {end_date}')
# if start_date > end_date: std_out('Ignoring device dates. Probably SD card device', 'WARNING')
# Print stuff
std_out('Kit ID: {}'.format(self.kit_id))
# if start_date < end_date: std_out(f'Dates: from: {start_date}, to: {end_date}')
std_out(f'Device timezone: {self.location}')
if not self.sensors.keys():
std_out(f'Device is empty')
return None
else:
std_out(f'Sensor IDs: {list(self.sensors.keys())}')
df = DataFrame()
# Get devices in the sensor first
for sensor_id in self.sensors.keys():
# Request sensor per ID
request = self.API_BASE_URL + '{}/readings?'.format(self.id)
if start_date is None:
request += 'from=2001-01-01'
elif end_date is not None:
if start_date > end_date: request += 'from=2001-01-01'
else:
request += f'from={start_date}'
request += f'&to={end_date}'
request += f'&rollup={rollup}'
request += f'&sensor_id={sensor_id}'
request += '&function=avg'
# if end_date is not None:
# if end_date > start_date: request += f'&to={end_date}'
# Make request
sensor_req = get(request)
flag_error = False
try:
sensorjson = sensor_req.json()
except:
print_exc()
std_out('Problem with json data from API', 'ERROR')
flag_error = True
pass
continue
if 'readings' not in sensorjson.keys():
std_out(f'No readings key in request for sensor: {sensor_id}',
'ERROR')
flag_error = True
continue
elif sensorjson['readings'] == []:
std_out(f'No data in request for sensor: {sensor_id}',
'WARNING')
flag_error = True
continue
if flag_error: continue
# Put
try:
dfsensor = DataFrame(sensorjson['readings']).set_index(0)
dfsensor.columns = [self.sensors[sensor_id]]
# dfsensor.index = to_datetime(dfsensor.index).tz_localize('UTC').tz_convert(self.location)
dfsensor.index = localise_date(dfsensor.index, self.location)
dfsensor.sort_index(inplace=True)
dfsensor = dfsensor[~dfsensor.index.duplicated(keep='first')]
# Drop unnecessary columns
dfsensor.drop([i for i in dfsensor.columns if 'Unnamed' in i],
axis=1,
inplace=True)
# Check for weird things in the data
dfsensor = dfsensor.apply(to_numeric, errors='coerce')
# Resample
dfsensor = dfsensor.resample(frequency).mean()
df = df.combine_first(dfsensor)
except:
print_exc()
std_out('Problem with sensor data from API', 'ERROR')
flag_error = True
pass
continue
try:
df = df.reindex(df.index.rename('Time'))
df = clean(df, clean_na, how='all')
# if clean_na is not None:
# if clean_na == 'drop':
# # std_out('Cleaning na with drop')
# df.dropna(axis = 0, how='all', inplace=True)
# elif clean_na == 'fill':
# df = df.fillna(method='bfill').fillna(method='ffill')
# # std_out('Cleaning na with fill')
self.data = df
except:
std_out('Problem closing up the API dataframe', 'ERROR')
pass
return None
if flag_error == False:
std_out(f'Device {self.id} loaded successfully from API',
'SUCCESS')
return self.data
def read_csv_file(file_path, location, frequency, clean_na = None, index_name = '', skiprows = None, sep = ',', encoding = 'utf-8'):
"""
Reads a csv file and adds cleaning, localisation and resampling and puts it into a pandas dataframe
Parameters
----------
file_path: String
File path for csv file
location: String
Time zone for the csv file
clean_na: String or None
None
Whether to perform clean_na or not. Either None, 'fill' or 'drop'
index_name: String
''
Name of the column to set an index in the dataframe
skiprows: list or None
None
List of rows to skip (same as skiprows in pandas.read_csv)
sep: String
','
Separator (same as sep in pandas.read_csv)
encoding: String
'utf-8'
Encoding of the csv file
Returns
-------
Pandas dataframe
"""
# Read pandas dataframe
df = read_csv(file_path, verbose = False, skiprows = skiprows, sep = ',', encoding = encoding)
flag_found = False
for column in df.columns:
if index_name in column:
df = df.set_index(column)
flag_found = True
break
if not flag_found:
std_out('Index not found. Cannot reindex', 'ERROR')
return None
# Set index
df.index = localise_date(df.index, location)
# Remove duplicates
df = df[~df.index.duplicated(keep='first')]
# Sort index
df.sort_index(inplace=True)
# Drop unnecessary columns
df.drop([i for i in df.columns if 'Unnamed' in i], axis=1, inplace=True)
# Check for weird things in the data
df = df.apply(to_numeric, errors='coerce')
# Resample
df = df.resample(frequency).mean()
# Remove na
df = clean(df, clean_na, how = 'all')
return df
def path_plot(self,
channel=None,
map_type='dynamic',
devices='all',
start_date=None,
end_date=None,
options=dict()):
'''
Creates a folium map showing a path
Parameters
-------
channel: String
None
If None, shows path, otherwise, colored path with channel mapping
map_type: String
'dynamic'
'dynamic' or 'static'. Whether is a dinamic map or not
devices: list or 'all'
List of devices to include, or 'all' from self.devices
channel: String
The channel to make the map from
start_date, end_date: String
Date convertible string
options: dict()
Possible keys are (default otherwise)
location: list
[41.400818, 2.1825157]
Center map location
tiles: (String)
'Stamen Toner'
Tiles for the folium.Map
zoom: (float)
2.5
Zoom to start with in folium.Map
period: 'String'
'1W'
Period for 'dynamic' map
radius: float
10
Circle radius for icon
fillOpacity: float
1
(<1) Fill opacity for the icon
stroke: 'String'
'false'
'true' or 'false'. For icon's stroke
icon: 'String'
'circle'
A valid folium.Map icon style
Returns
-------
Folium.Map object
'''
# Set defaults
options = dict_fmerge(config._map_def_opt, options)
# Make features
features = []
if devices == 'all':
mdev = self.devices
else:
mdev = list()
for device in devices:
if device in self.devices: mdev.append(device)
else: std_out(f'Device {device} not found, ignoring', 'WARNING')
if len(mdev) == 0:
std_out('Requested devices not in test', 'ERROR')
return None
for device in mdev:
chs = ['GPS_LAT', 'GPS_LONG']
if channel is not None:
if channel not in self.devices[str(device)].readings.columns:
std_out(
f'Channel {channel} not in columns: {self.devices[str(device)].readings.columns}',
'ERROR')
return None
# Get bins
minmax = False
if not options['minmax']:
if all([key not in channel for key in config._channel_bins]):
std_out(
f'Requested channel {channel} not in config mapped bins {config._channel_bins.keys()}.Using min/max mapping',
'WARNING')
minmax = True
else:
minmax = True
if minmax:
bins = linspace(
self.devices[str(device)].readings[channel].min(),
self.devices[str(device)].readings[channel].max(),
config._channel_bin_n)
else:
for bname in config._channel_bins.keys():
if bname in channel:
bins = config._channel_bins[bname]
break
chs.append(channel)
# Create copy
dfc = self.devices[str(device)].readings[chs].copy()
# Resample and cleanup
# TODO THIS CAN INPUT SOME MADE UP READINGS
dfc = clean(dfc.resample(options['period']).mean(), 'fill')
# Make color column
legend_labels = None
if channel is not None:
dfc['COLOR'] = cut(dfc[channel], bins, labels =\
config._map_colors_palette)
# Make legend labels
legend_labels = {}
for ibin in range(len(bins) - 1):
legend_labels[f'{round(bins[ibin],2)} : {round(bins[ibin+1],2)}'] =\
config._map_colors_palette[ibin]
else:
dfc['COLOR'] = config._map_colors_palette[0]
if start_date is not None:
dfc = dfc[dfc.index > start_date]
if end_date is not None:
dfc = dfc[dfc.index < end_date]
# Add point for each date
for date in dfc.index:
if date == dfc.index[-1]: break
times = []
color = str(dfc.loc[date, 'COLOR'])
if color == 'nan' or isnan(dfc.loc[date, 'GPS_LONG'])\
or isnan(dfc.loc[date, 'GPS_LAT']):
std_out(f'Skipping point {date}', 'WARNING')
continue
geometry = {
'type':
'LineString',
'coordinates':
[[dfc.loc[date, 'GPS_LONG'], dfc.loc[date, 'GPS_LAT']],
[
dfc.loc[date + dfc.index.freq, 'GPS_LONG'],
dfc.loc[date + dfc.index.freq, 'GPS_LAT']
]],
}
properties = {
'icon':
options['icon'],
'iconstyle': {
'fillColor': color,
'fillOpacity': options['fillOpacity'],
'stroke': options['stroke'],
'radius': options['radius']
},
'device':
device,
'timestamp':
date.strftime('%Y-%m-%dT%H:%M:%S'),
"coordinates": [
dfc.loc[date + dfc.index.freq, 'GPS_LAT'],
dfc.loc[date + dfc.index.freq, 'GPS_LONG']
],
'style': {
'color': color,
'stroke-width': options['stroke-width'],
'fillOpacity': options['fillOpacity']
}
}
# Add reading to tooltip
if channel is not None:
properties['channel'] = channel
properties['value'] = dfc.loc[date, channel]
if map_type == 'dynamic':
properties['times'] = [
date.strftime('%Y-%m-%dT%H:%M:%S'),
(date + dfc.index.freq).strftime('%Y-%m-%dT%H:%M:%S')
]
features.append({
'type': 'Feature',
'geometry': geometry,
'properties': properties
})
featurecol = {'type': 'FeatureCollection', 'features': features}
# Make map
if options['location'] == 'average':
avg_long = dfc['GPS_LONG'].mean()
avg_lat = dfc['GPS_LAT'].mean()
loc = [avg_lat, avg_long]
else:
loc = options['location']
m = Map(
location=loc,
tiles=options['tiles'],
zoom_start=options['zoom'],
)
if map_type == 'static':
# TODO WORKAROUND UNTIL GEOJSON ACCEPTS MARKERS
if options['markers']:
for feature in features:
Circle(location=[
feature['geometry']['coordinates'][0][1],
feature['geometry']['coordinates'][0][0]
],
fill='true',
radius=feature['properties']['iconstyle']['radius'],
color=feature['properties']['iconstyle']['fillColor'],
fill_opacity=feature['properties']['iconstyle']
['fillOpacity']).add_to(m)
if channel is not None:
fields = ["device", "channel", "timestamp", "coordinates", "value"]
aliases = [
"Device:", "Sensor:", "Timestamp:", "Coordinates:", "Reading:"
]
else:
fields = ["device", "timestamp", "coordinates"]
aliases = ["Device:", "Timestamp:", "Coordinates:"]
popup = GeoJsonPopup(
fields=fields,
aliases=aliases,
localize=True,
labels=True,
max_width=800,
)
tooltip = GeoJsonTooltip(
fields=fields,
aliases=aliases,
localize=True,
sticky=True,
labels=True,
style="""
background-color: #F0EFEF;
border: 1px solid gray;
border-radius: 1px;
box-shadow: 2px;
""",
max_width=800,
)
GeoJson(
featurecol,
tooltip=tooltip,
popup=popup,
style_function=lambda x: {
'color': x['properties']['style']['color'],
'weight': x['properties']['style']['stroke-width'],
'fillOpacity': x['properties']['style']['fillOpacity']
},
).add_to(m)
elif map_type == 'dynamic':
TimestampedGeoJson(featurecol,
period='PT' + convert_rollup(options['period']),
add_last_point=True,
auto_play=False,
loop=False,
max_speed=options['max_speed'],
loop_button=True,
time_slider_drag_update=True).add_to(m)
else:
std_out(f'Not supported map type {map_type}', 'ERROR')
return None
if options['minimap']:
minimap = MiniMap(toggle_display=True, tile_layer=options['tiles'])
minimap.add_to(m)
if options['legend'] and not legend_labels is None:
templateLoader = FileSystemLoader(searchpath=join(dirname(__file__),\
'templates'))
templateEnv = Environment(loader=templateLoader)
template = templateEnv.get_template("map_legend.html")
filled_map_legend = template.render(legend_labels=legend_labels)
map_legend_html = '{% macro html(this, kwargs) %}'+\
filled_map_legend+\
'{% endmacro %}'
legend = element.MacroElement()
legend._template = element.Template(map_legend_html)
m.get_root().add_child(legend)
return m