system: You are an expert security code reviewer. You will be shown a source code file along with a multi-select question listing several CWE (Common Weakness Enumeration) categories. Your task is to identify which of the listed CWEs are present in the code.

OUTPUT FORMAT: Respond with the letter(s) of all correct options, sorted alphabetically and comma-separated, wrapped in a single \boxed{...}. Examples: `\boxed{A}` for one correct, `\boxed{A,C}` for two, `\boxed{A,C,E}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
"""Module that implements version 1 of an API to access
the data held in BMON.
Response are in JSON and implement the JSend specification;
see:  https://labs.omniti.com/labs/jsend .
"""

import logging
from datetime import datetime
from collections import Counter
import pytz
from django.http import JsonResponse
from dateutil.parser import parse
import pandas as pd
import numpy as np

from bmsapp import models
from bmsapp.readingdb import bmsdata

# Version number of this API
API_VERSION = 1.1

# Make a logger for this module
_logger = logging.getLogger('bms.' + __name__)

def api_version(request):
    """API method that returns the version number of the API
    """
    result = {
            'status': 'success',
            'data': {
                'version': API_VERSION,
            }
        }

    return JsonResponse(result)

def fail_payload(messages):
    """Helper routine. Returns a JSON response with success = fail
    indicating improper input data.

    Parameters
    ----------
    messages: a dictionary of messages describing the bad input data

    Returns
    -------
    A Django response object appropriate for a JSON payload.
    """
    result = {'status': 'fail'}
    if messages:
        result['data'] = messages
    else:
        result['data'] = None

    return JsonResponse(result, status=400)

def invalid_query_params(dj_request, valid_params):
    """Helper routine.  Determines if there are invalid query parameters
    in the Django request.

    Parameters
    ----------
    dj_request:      Django request object.
    valid_params:    A list of valid query parameter names.

    Returns
    -------
    A dictionary with keys being the invalid query parameter names, and the values
    of the keys being the string "Invalid query parameter."

    """
    extra_params = set(dj_request.GET.keys()) - set(valid_params)
    return {p: 'Invalid query parameter.' for p in extra_params}


def sensor_info(sensor_id):
    """Helper routine.  Return dictionary of properties related to
    the sensor with ID of 'sensor_id'.  Properties

    Parameters
    ----------
    sensor_id:  The Sensor ID of the sensor to look up

    Returns
    -------
    Dictionary of Sensor properties and the list of buildings
    associated with the sensor.  Properties are set to None
    if they are not available.

    """

    # the values to return if no Sensor object is present
    props = dict(
        sensor_id =  sensor_id,
        name = None,
        units = None,
        notes = None,
        calculated = None,
        tran_calc_func = None,
        calc_order = None,
        formatting_func = None,
        other_props = None,
        buildings = [],
    )

    qs = models.Sensor.objects.filter(sensor_id=sensor_id)
    if len(qs) > 0:
        sensor = qs[0]  # get the actual Sensor object (should only be one)
        props.update(
            name = sensor.title,
            units = sensor.unit.label,
            notes = sensor.notes,
            calculated = sensor.is_calculated,
            tran_calc_func = sensor.tran_calc_function,
            calc_order = sensor.calculation_order,
            formatting_func = sensor.formatting_function,
            other_props = sensor.other_properties
        )
        # see if this sensor has links to a building
        links = models.BldgToSensor.objects.filter(sensor=sensor)
        if len(links) > 0:
            # record the buildings and sensor groups that this is linked to
            bldgs = []
            for link in links:
                bldgs.append(
                    dict(
                        bldg_id = link.building.pk,
                        name = link.building.title,
                        sensor_group = link.sensor_group.title,
                        timezone = link.building.timezone,
                        latitude = link.building.latitude,
                        longitude = link.building.longitude,
                    )
                )
            props['buildings'] = bldgs

    return props

def check_sensor_reading_params(request):
    """Checks the validity of the most of the GET parameters used in the 'sensor_readings'
    and 'sensor_readings_multiple' methods. Returns error messages in a dictionary keyed
    on the erroneous parameter and returns variables gathered from the parameters.
    """
    
    # dictionary for error messages
    messages = {}

    # Valid 'start_ts' ?
    start_ts = request.GET.get('start_ts', None)
    if start_ts:
        try:
            start_ts = parse(start_ts)
        except:
            messages['start_ts'] = "'%s' is not a valid date/time." % start_ts

    # Valid 'end_ts' ?
    end_ts = request.GET.get('end_ts', None)
    if end_ts:
        try:
            end_ts = parse(end_ts)
        except:
            messages['end_ts'] = "'%s' is not a valid date/time." % end_ts

    # Valid timezone?
    timezone = request.GET.get('timezone', None)
    if timezone:
        try:
            timezone = pytz.timezone(timezone)
        except:
            messages['timezone'] = "'%s' is not a valid timezone name." % timezone

    # Test averaging and label_offset strings for validity
    st = datetime(2017, 1, 1)  # datetime used for testing only
    averaging = request.GET.get('averaging', None)
    if averaging:
        try:
            pd.date_range(st, periods=1, freq=averaging)
        except:
            messages['averaging'] = "'%s' is an invalid time averaging string." % averaging

    label_offset = request.GET.get('label_offset', None)
    if label_offset:
        # found that a label_offset of 0 causes hangs, so disallow
        if label_offset[0]=='0':
            messages['label_offset'] = "'%s' is an invalid time label_offset string." % label_offset
        else:
            try:
                pd.date_range(st, periods=1, freq=label_offset)
            except:
                messages['label_offset'] = "'%s' is an invalid time label_offset string." % label_offset

    return messages, start_ts, end_ts, timezone, averaging, label_offset

def sensor_readings(request, sensor_id):
    """API method. Returns a list of sensor readings for one sensor.  Time limits and
    time averaging can be requested for the returned readings.

    Parameters
    ----------
    request:    Django request object
    sensor_id:  The "sensor_id" of the sensor that readings are being requested for

    The 'request' object can have the following query parameters:
        start_ts: (optional) A date/time indicating the earliest reading to return.
            If not present, the earliest reading in the database is returned.
            Format is a string date/time, interpretable by dateutil.parser.parse.
        end_ts: (optional) A date/time indicating the latest reading to return.
            If not present records through the latest record in the database are returned.
            Format is a string date/time, interpretable by dateutil.parser.parse.
        timezone: (optional) A timezone name, present in the pytz.timezone database
            see https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
            e.g. "US/Alaska".  The timestamps for the sensor readings are returned
            consistent with this timezone.  Also, 'start_ts' and 'end_ts' are interpreted
            as being in this timezone.  If this parameter is not provided, the timezone of
            the first building associated with the requested sensor is used.  If no
            building is associated with the sensor, UTC is the assumed timezone.
        averaging: (optional) If provided, sensor readings are averaged into evenly spaced
            time intervals as indicated by this parameter.  The 'averaging' time interval
            must be provided as a string such as '4H' (4 hours), '2D' (2 days), or any interval
            describable by Pandas time offset notation:
            http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
        label_offset: (optional) Only used if an 'averaging' parameter is provided.  This
            parameter controls what point in the time averaging interval is used to produce
            the timestamp for the returned reading.  'label_offset' uses the same format as
            'averaging', i.e. Pandas time offset notation, and the value is the time distance
            from the start of averaging interval to the location of the timestamp. For example,
            a value of '30min' means place the timestamp 30 minutes after the start of the
            interval.
            If no 'label_offset' is provided, the 'label_offset' is assumed to be 0, i.e.
            the starting edge of the averaging interval is marked by the timestamp.  Note
            that the convention in all of the BMON timeseries plots is to place the timestamp
            at the *center* of the averaging interval; that is *not* the default in this
            function because of the difficulty in automatically calculating the proper
            label_offset for the middle of the interval.

    Returns
    -------
    A JSON response containing an indicator of success or failure, a list of
    sensor readings (date, reading), the timezone of the timestamps returned,
    and additional information about the sensor from the BMON Sensor object,
    if available.
    """
    try:
        db = bmsdata.BMSdata()  # reading database

        messages = {}   # used to store input validity messages.

        # Is the sensor_id in the database?
        if not db.sensor_id_exists(sensor_id):
            messages['sensor_id'] = "Sensor '%s' is not present in the reading database." % sensor_id

        # Check the input parameters and get the values
        param_messages, start_ts, end_ts, timezone, averaging, label_offset = \
            check_sensor_reading_params(request)
        messages.update(param_messages)

        # check for extra, improper query parameters
        messages.update(invalid_query_params(request,
                                             ['timezone', 'start_ts', 'end_ts', 'averaging', 'label_offset']))

        if messages:
            # Input errors occurred
            return fail_payload(messages)

        # ---- Get info about the sensor
        s_info = sensor_info(sensor_id)

        # if there is no requested timezone (or an invalid one), use the
        # timezone from the sensor information
        if timezone is None:
            timezone = pytz.timezone('UTC')   # default timezone if no valid building tz present
            if len(s_info['buildings']):
                tz_name = s_info['buildings'][0]['timezone']
                try:
                    timezone = pytz.timezone(tz_name)
                except:
                    # stick with default
                    pass
        # record the name of the final timezone
        tz_name = str(timezone)

        # ---- Get the Sensor Readings
        # if start and end timestamps are present, convert to Unix Epoch values
        if start_ts:
            ts_aware = timezone.localize(start_ts)
            start_ts = ts_aware.timestamp()

        if end_ts:
            ts_aware = timezone.localize(end_ts)
            end_ts = ts_aware.timestamp()

        # get the sensor readings
        df = db.dataframeForOneID(sensor_id, start_ts=start_ts, end_ts=end_ts, tz=timezone)

        # if averaging is requested, do it!
        if averaging:
            df = df.resample(rule = averaging, loffset = label_offset, label = 'left').mean().dropna()

        times = df.index.strftime('%Y-%m-%d %H:%M:%S')
        if len(df)>0 and np.abs(df.val.values).max() < 100000.:
            # needed the "tolist()" method to get formatting correctly of rounded floats
            values = np.char.mod('%.5g', df.val.values).astype(np.float).tolist()
        else:
            values = df.val.values.tolist()
        all_readings = list(zip(times, values))

        result = {
            'status': 'success',
            'data': {
                'readings': all_readings,
                'reading_timezone': tz_name,
                'sensor_info': s_info,
            }
        }

        return JsonResponse(result)

    except Exception as e:
        # A processing error occurred.
        _logger.exception('Error retrieving sensor readings.')
        result = {
            'status': 'error',
            'message': str(e)
        }
        return JsonResponse(result, status=500)

def sensor_list(request):
    """API Method.  Returns a list of all the sensors in the reading
    database, including sensor properties, if available.

    Parameters
    ----------
    request:  Django request object.

    Returns
    -------
    A JSON response containing an indicator of success or failure, a list of
    sensors including sensor properties and building association information
    if available.
    """

    try:
        #------ Check that there are no query parameters
        messages = invalid_query_params(request, [])
        if messages:
            return fail_payload(messages)

        db = bmsdata.BMSdata()  # reading database
        sensors = [sensor_info(sensor_id) for sensor_id in db.sensor_id_list()]

        result = {
            'status': 'success',
            'data': {
                'sensors': sensors,
            }
        }
        return JsonResponse(result)

    except Exception as e:
        # A processing error occurred.
        result = {
            'status': 'error',
            'message': str(e)
        }
        return JsonResponse(result, status=500)

```

Which of the following CWE vulnerability categories are present in this code? Multiple options may be correct.

  A) CWE-295: Improper Certificate Validation
  B) CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
  C) CWE-215: Insertion of Sensitive Information Into Debugging Code
  D) CWE-1333: Inefficient Regular Expression Complexity
  E) CWE-209: Generation of Error Message Containing Sensitive Information
  F) CWE-943: Improper Neutralization of Special Elements in Data Query Logic
Answer: E