import influxdb_client

from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS

bucket = "my-bucket"

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)

write_api.write(bucket=bucket, record=p)

## using Table structure
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for table in tables:
    print(table)
    for row in table.records:
        print (row.values)


## using csv library
csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
val_count = 0
for row in csv_result:
    for cell in row:
        val_count += 1

self.client = InfluxDBClient.from_config_file("config.ini")

self.client = InfluxDBClient.from_env_properties()

q = '''
    from(bucket: stringParam)
      |> range(start: -5m, stop: now())
      |> filter(fn: (r) => r._measurement == "mem")
      |> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "used")
      |> aggregateWindow(every: 1m, fn: mean)
      |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
'''
p = {
    "stringParam": "my-bucket",
}

query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"]))
csv_result = query_api.query(query=q, params=p)

class ProfilersCallback(object):
   def __init__(self):
       self.records = []

   def __call__(self, flux_record):
       self.records.append(flux_record.values)

callback = ProfilersCallback()

query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"], profiler_callback=callback))
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for profiler in callback.records:
   print(f'Custom processing of profiler result: {profiler}')

from datetime import datetime, timedelta, timezone

import pandas as pd
import reactivex as rx
from reactivex import operators as ops

from influxdb_client import InfluxDBClient, Point, WriteOptions

with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as _client:

    with _client.write_api(write_options=WriteOptions(batch_size=500,
                                                      flush_interval=10_000,
                                                      jitter_interval=2_000,
                                                      retry_interval=5_000,
                                                      max_retries=5,
                                                      max_retry_delay=30_000,
                                                      max_close_wait=300_000,
                                                      exponential_base=2)) as _write_client:

        """
        Write Line Protocol formatted as string
        """
        _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
        _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
                                                    "h2o_feet,location=coyote_creek water_level=3.0 3"])

        """
        Write Line Protocol formatted as byte array
        """
        _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1".encode())
        _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2".encode(),
                                                    "h2o_feet,location=coyote_creek water_level=3.0 3".encode()])

        """
        Write Dictionary-style object
        """
        _write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                    "fields": {"water_level": 1.0}, "time": 1})
        _write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                     "fields": {"water_level": 2.0}, "time": 2},
                                                    {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                     "fields": {"water_level": 3.0}, "time": 3}])

        """
        Write Data Point
        """
        _write_client.write("my-bucket", "my-org",
                            Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
        _write_client.write("my-bucket", "my-org",
                            [Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
                             Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])

        """
        Write Observable stream
        """
        _data = rx \
            .range(7, 11) \
            .pipe(ops.map(lambda i: "h2o_feet,location=coyote_creek water_level={0}.0 {0}".format(i)))

        _write_client.write("my-bucket", "my-org", _data)

        """
        Write Pandas DataFrame
        """
        _now = datetime.now(tz=timezone.utc)
        _data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
                                   index=[_now, _now + timedelta(hours=1)],
                                   columns=["location", "water_level"])

        _write_client.write("my-bucket", "my-org", record=_data_frame, data_frame_measurement_name='h2o_feet',
                            data_frame_tag_columns=['location'])

point_settings = PointSettings()
point_settings.add_default_tag("id", "132-987-655")
point_settings.add_default_tag("customer", "California Miner")
point_settings.add_default_tag("data_center", "${env.data_center}")

self.write_client = self.client.write_api(write_options=SYNCHRONOUS, point_settings=point_settings)

self.write_client = self.client.write_api(write_options=SYNCHRONOUS,
                                              point_settings=PointSettings(**{"id": "132-987-655",
                                                                              "customer": "California Miner"}))

self.client = InfluxDBClient.from_config_file("config.ini")

self.client = InfluxDBClient.from_env_properties()

from influxdb_client import InfluxDBClient, Point
from influxdb_client .client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=SYNCHRONOUS)

_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

write_api.write(bucket="my-bucket", record=[_point1, _point2])

client.close()

from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

"""
Prepare data
"""

_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

write_api.write(bucket="my-bucket", record=[_point1, _point2])

"""
Query: using Table structure
"""
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for table in tables:
    print(table)
    for record in table.records:
        print(record.values)

print()
print()

"""
Query: using Bind parameters
"""

p = {"_start": datetime.timedelta(hours=-1),
     "_location": "Prague",
     "_desc": True,
     "_floatParam": 25.1,
     "_every": datetime.timedelta(minutes=5)
     }

tables = query_api.query('''
    from(bucket:"my-bucket") |> range(start: _start)
        |> filter(fn: (r) => r["_measurement"] == "my_measurement")
        |> filter(fn: (r) => r["_field"] == "temperature")
        |> filter(fn: (r) => r["location"] == _location and r["_value"] > _floatParam)
        |> aggregateWindow(every: _every, fn: mean, createEmpty: true)
        |> sort(columns: ["_time"], desc: _desc)
''', params=p)

for table in tables:
    print(table)
    for record in table.records:
        print(str(record["_time"]) + " - " + record["location"] + ": " + str(record["_value"]))

print()
print()

"""
Query: using Stream
"""
records = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -10m)')

for record in records:
    print(f'Temperature in {record["location"]} is {record["_value"]}')

"""
Interrupt a stream after retrieve a required data
"""
large_stream = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -100d)')
for record in large_stream:
    if record["location"] == "New York":
        print(f'New York temperature: {record["_value"]}')
        break

large_stream.close()

print()
print()

"""
Query: using csv library
"""
csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)',
                                 dialect=Dialect(header=False, delimiter=",", comment_prefix="#", annotations=[],
                                                 date_time_format="RFC3339"))
for csv_line in csv_result:
    if not len(csv_line) == 0:
        print(f'Temperature in {csv_line[9]} is {csv_line[6]}')

"""
Close client
"""
client.close()

from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

"""
Prepare data
"""

_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

write_api.write(bucket="my-bucket", record=[_point1, _point2])

"""
Query: using Pandas DataFrame
"""
data_frame = query_api.query_data_frame('from(bucket:"my-bucket") '
                                        '|> range(start: -10m) '
                                        '|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") '
                                        '|> keep(columns: ["location", "temperature"])')
print(data_frame.to_string())

"""
Close client
"""
client.close()

"""
Import VIX - CBOE Volatility Index - from "vix-daily.csv" file into InfluxDB 2.0

https://datahub.io/core/finance-vix#data
"""

from collections import OrderedDict
from csv import DictReader

import reactivex as rx
from reactivex import operators as ops

from influxdb_client import InfluxDBClient, Point, WriteOptions

def parse_row(row: OrderedDict):
    """Parse row of CSV file into Point with structure:

        financial-analysis,type=ily close=18.47,high=19.82,low=18.28,open=19.82 1198195200000000000

    CSV format:
        Date,VIX Open,VIX High,VIX Low,VIX Close\n
        2004-01-02,17.96,18.68,17.54,18.22\n
        2004-01-05,18.45,18.49,17.44,17.49\n
        2004-01-06,17.66,17.67,16.19,16.73\n
        2004-01-07,16.72,16.75,15.5,15.5\n
        2004-01-08,15.42,15.68,15.32,15.61\n
        2004-01-09,16.15,16.88,15.57,16.75\n
        ...

    :param row: the row of CSV file
    :return: Parsed csv row to [Point]
    """

    """
     For better performance is sometimes useful directly create a LineProtocol to avoid unnecessary escaping overhead:
     """
     # from datetime import timezone
     # import ciso8601
     # from influxdb_client.client.write.point import EPOCH
     #
     # time = (ciso8601.parse_datetime(row["Date"]).replace(tzinfo=timezone.utc) - EPOCH).total_seconds() * 1e9
     # return f"financial-analysis,type=vix-daily" \
     #        f" close={float(row['VIX Close'])},high={float(row['VIX High'])},low={float(row['VIX Low'])},open={float(row['VIX Open'])} " \
     #        f" {int(time)}"

    return Point("financial-analysis") \
        .tag("type", "vix-daily") \
        .field("open", float(row['VIX Open'])) \
        .field("high", float(row['VIX High'])) \
        .field("low", float(row['VIX Low'])) \
        .field("close", float(row['VIX Close'])) \
        .time(row['Date'])


"""
Converts vix-daily.csv into sequence of datad point
"""
data = rx \
    .from_iterable(DictReader(open('vix-daily.csv', 'r'))) \
    .pipe(ops.map(lambda row: parse_row(row)))

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)

"""
Create client that writes data in batches with 50_000 items.
"""
write_api = client.write_api(write_options=WriteOptions(batch_size=50_000, flush_interval=10_000))

"""
Write data into InfluxDB
"""
write_api.write(bucket="my-bucket", record=data)
write_api.close()

"""
Querying max value of CBOE Volatility Index
"""
query = 'from(bucket:"my-bucket")' \
        ' |> range(start: 0, stop: now())' \
        ' |> filter(fn: (r) => r._measurement == "financial-analysis")' \
        ' |> max()'
result = client.query_api().query(query=query)

"""
Processing results
"""
print()
print("=== results ===")
print()
for table in result:
    for record in table.records:
        print('max {0:5} = {1}'.format(record.get_field(), record.get_value()))

"""
Close client
"""
client.close()

"""
Efficiency write data from IOT sensor - write changed temperature every minute
"""
import atexit
import platform
from datetime import timedelta

import psutil as psutil
import reactivex as rx
from reactivex import operators as ops

from influxdb_client import InfluxDBClient, WriteApi, WriteOptions

def on_exit(db_client: InfluxDBClient, write_api: WriteApi):
    """Close clients after terminate a script.

    :param db_client: InfluxDB client
    :param write_api: WriteApi
    :return: nothing
    """
    write_api.close()
    db_client.close()


def sensor_temperature():
    """Read a CPU temperature. The [psutil] doesn't support MacOS so we use [sysctl].

    :return: actual CPU temperature
    """
    os_name = platform.system()
    if os_name == 'Darwin':
        from subprocess import check_output
        output = check_output(["sysctl", "machdep.xcpm.cpu_thermal_level"])
        import re
        return re.findall(r'\d+', str(output))[0]
    else:
        return psutil.sensors_temperatures()["coretemp"][0]


def line_protocol(temperature):
    """Create a InfluxDB line protocol with structure:

        iot_sensor,hostname=mine_sensor_12,type=temperature value=68

    :param temperature: the sensor temperature
    :return: Line protocol to write into InfluxDB
    """

    import socket
    return 'iot_sensor,hostname={},type=temperature value={}'.format(socket.gethostname(), temperature)


"""
Read temperature every minute; distinct_until_changed - produce only if temperature change
"""
data = rx\
    .interval(period=timedelta(seconds=60))\
    .pipe(ops.map(lambda t: sensor_temperature()),
          ops.distinct_until_changed(),
          ops.map(lambda temperature: line_protocol(temperature)))

_db_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)

"""
Create client that writes data into InfluxDB
"""
_write_api = _db_client.write_api(write_options=WriteOptions(batch_size=1))
_write_api.write(bucket="my-bucket", record=data)


"""
Call after terminate a script
"""
atexit.register(on_exit, _db_client, _write_api)

input()

"""
Connect to InfluxDB 2.0 - write data and query them
"""

from datetime import datetime, timezone

from influxdb_client import Point, InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS

"""
Configure credentials
"""
influx_cloud_url = 'https://us-west-2-1.aws.cloud2.influxdata.com'
influx_cloud_token = '...'
bucket = '...'
org = '...'

client = InfluxDBClient(url=influx_cloud_url, token=influx_cloud_token)
try:
    kind = 'temperature'
    host = 'host1'
    device = 'opt-123'

    """
    Write data by Point structure
    """
    point = Point(kind).tag('host', host).tag('device', device).field('value', 25.3).time(time=datetime.now(tz=timezone.utc))

    print(f'Writing to InfluxDB cloud: {point.to_line_protocol()} ...')

    write_api = client.write_api(write_options=SYNCHRONOUS)
    write_api.write(bucket=bucket, org=org, record=point)

    print()
    print('success')
    print()
    print()

    """
    Query written data
    """
    query = f'from(bucket: "{bucket}") |> range(start: -1d) |> filter(fn: (r) => r._measurement == "{kind}")'
    print(f'Querying from InfluxDB cloud: "{query}" ...')
    print()

    query_api = client.query_api()
    tables = query_api.query(query=query, org=org)

    for table in tables:
        for row in table.records:
            print(f'{row.values["_time"]}: host={row.values["host"]},device={row.values["device"]} '
                  f'{row.values["_value"]} °C')

    print()
    print('success')

except Exception as e:
    print(e)
finally:
    client.close()

from influxdb_client import InfluxDBClient

_db_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", enable_gzip=True)

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086", token="my-token") as client

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086", username="my-user", password="my-password") as client

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086", auth_basic=True, token="my-proxy-secret") as client

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086",
                    token="my-token",
                    org="my-org",
                    proxy="http://localhost:3128") as client:

from urllib3 import Retry
Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT = frozenset()
Retry.DEFAULT.remove_headers_on_redirect = Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT

from influxdb_client import InfluxDBClient

client = InfluxDBClient(url="http://localhost:8086", token="my-token")

delete_api = client.delete_api()

"""
Delete Data
"""
start = "1970-01-01T00:00:00Z"
stop = "2021-02-01T00:00:00Z"
delete_api.delete(start, stop, '_measurement="my_measurement"', bucket='my-bucket', org='my-org')

"""
Close client
"""
client.close()

from influxdb_client import InfluxDBClient
from influxdb_client.client.exceptions import InfluxDBError
from influxdb_client.client.write_api import SYNCHRONOUS

with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    try:
        client.write_api(write_options=SYNCHRONOUS).write("my-bucket", record="mem,tag=a value=86")
    except InfluxDBError as e:
        if e.response.status == 401:
            raise Exception(f"Insufficient write permissions to 'my-bucket'.") from e
        raise

from influxdb_client import InfluxDBClient
from influxdb_client.client.exceptions import InfluxDBError


class BatchingCallback(object):

    def success(self, conf: (str, str, str), data: str):
        print(f"Written batch: {conf}, data: {data}")

    def error(self, conf: (str, str, str), data: str, exception: InfluxDBError):
        print(f"Cannot write batch: {conf}, data: {data} due: {exception}")

    def retry(self, conf: (str, str, str), data: str, exception: InfluxDBError):
        print(f"Retryable error occurs for batch: {conf}, data: {data} retry: {exception}")


with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    callback = BatchingCallback()
    with client.write_api(success_callback=callback.success,
                          error_callback=callback.error,
                          retry_callback=callback.retry) as write_api:
        pass

from urllib3 import Retry

from influxdb_client import InfluxDBClient

retries = Retry(connect=5, read=2, redirect=5)
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", retries=retries)

from influxdb_client import Point, InfluxDBClient
from influxdb_client.client.util.date_utils_pandas import PandasDateTimeHelper
from influxdb_client.client.write_api import SYNCHRONOUS

"""
Set PandasDate helper which supports nanoseconds.
"""
import influxdb_client.client.util.date_utils as date_utils

date_utils.date_helper = PandasDateTimeHelper()

"""
Prepare client.
"""
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

"""
Prepare data
"""

point = Point("h2o_feet") \
    .field("water_level", 10) \
    .tag("location", "pacific") \
    .time('1996-02-25T21:20:00.001001231Z')

print(f'Time serialized with nanosecond precision: {point.to_line_protocol()}')
print()

write_api.write(bucket="my-bucket", record=point)

"""
Query: using Stream
"""
query = '''
from(bucket:"my-bucket")
        |> range(start: 0, stop: now())
        |> filter(fn: (r) => r._measurement == "h2o_feet")
'''
records = query_api.query_stream(query)

for record in records:
    print(f'Temperature in {record["location"]} is {record["_value"]} at time: {record["_time"]}')

"""
Close client
"""
client.close()

from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


async with InfluxDBClientAsync(url="http://localhost:8086",
                               token="my-token",
                               org="my-org",
                               proxy="http://localhost:3128") as client:

import logging
import sys

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    for _, logger in client.conf.loggers.items():
        logger.setLevel(logging.DEBUG)
        logger.addHandler(logging.StreamHandler(sys.stdout))

client = InfluxDBClient(url="http://localhost:8086", token="my-token", debug=True)