MikrofonSensor und TemperaturSenor die zwei Python programme funktionieren. mit den jeweiligen 2 json Datein. Beim TemperaturSensor wird im Terminal keine Wertre ausgegeben aber in der json Datei kann man die Temp und Hum sehen.

lib/python3.11/site-packages/pip/_vendor/tenacity/wait.py

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+# Copyright 2016–2021 Julien Danjou
+# Copyright 2016 Joshua Harlow
+# Copyright 2013-2014 Ray Holder
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+import random
+import typing
+from datetime import timedelta
+
+from pip._vendor.tenacity import _utils
+
+if typing.TYPE_CHECKING:
+    from pip._vendor.tenacity import RetryCallState
+
+wait_unit_type = typing.Union[int, float, timedelta]
+
+
+def to_seconds(wait_unit: wait_unit_type) -> float:
+    return float(wait_unit.total_seconds() if isinstance(wait_unit, timedelta) else wait_unit)
+
+
+class wait_base(abc.ABC):
+    """Abstract base class for wait strategies."""
+
+    @abc.abstractmethod
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        pass
+
+    def __add__(self, other: "wait_base") -> "wait_combine":
+        return wait_combine(self, other)
+
+    def __radd__(self, other: "wait_base") -> typing.Union["wait_combine", "wait_base"]:
+        # make it possible to use multiple waits with the built-in sum function
+        if other == 0:
+            return self
+        return self.__add__(other)
+
+
+class wait_fixed(wait_base):
+    """Wait strategy that waits a fixed amount of time between each retry."""
+
+    def __init__(self, wait: wait_unit_type) -> None:
+        self.wait_fixed = to_seconds(wait)
+
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        return self.wait_fixed
+
+
+class wait_none(wait_fixed):
+    """Wait strategy that doesn't wait at all before retrying."""
+
+    def __init__(self) -> None:
+        super().__init__(0)
+
+
+class wait_random(wait_base):
+    """Wait strategy that waits a random amount of time between min/max."""
+
+    def __init__(self, min: wait_unit_type = 0, max: wait_unit_type = 1) -> None:  # noqa
+        self.wait_random_min = to_seconds(min)
+        self.wait_random_max = to_seconds(max)
+
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        return self.wait_random_min + (random.random() * (self.wait_random_max - self.wait_random_min))
+
+
+class wait_combine(wait_base):
+    """Combine several waiting strategies."""
+
+    def __init__(self, *strategies: wait_base) -> None:
+        self.wait_funcs = strategies
+
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        return sum(x(retry_state=retry_state) for x in self.wait_funcs)
+
+
+class wait_chain(wait_base):
+    """Chain two or more waiting strategies.
+
+    If all strategies are exhausted, the very last strategy is used
+    thereafter.
+
+    For example::
+
+        @retry(wait=wait_chain(*[wait_fixed(1) for i in range(3)] +
+                               [wait_fixed(2) for j in range(5)] +
+                               [wait_fixed(5) for k in range(4)))
+        def wait_chained():
+            print("Wait 1s for 3 attempts, 2s for 5 attempts and 5s
+                   thereafter.")
+    """
+
+    def __init__(self, *strategies: wait_base) -> None:
+        self.strategies = strategies
+
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        wait_func_no = min(max(retry_state.attempt_number, 1), len(self.strategies))
+        wait_func = self.strategies[wait_func_no - 1]
+        return wait_func(retry_state=retry_state)
+
+
+class wait_incrementing(wait_base):
+    """Wait an incremental amount of time after each attempt.
+
+    Starting at a starting value and incrementing by a value for each attempt
+    (and restricting the upper limit to some maximum value).
+    """
+
+    def __init__(
+        self,
+        start: wait_unit_type = 0,
+        increment: wait_unit_type = 100,
+        max: wait_unit_type = _utils.MAX_WAIT,  # noqa
+    ) -> None:
+        self.start = to_seconds(start)
+        self.increment = to_seconds(increment)
+        self.max = to_seconds(max)
+
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        result = self.start + (self.increment * (retry_state.attempt_number - 1))
+        return max(0, min(result, self.max))
+
+
+class wait_exponential(wait_base):
+    """Wait strategy that applies exponential backoff.
+
+    It allows for a customized multiplier and an ability to restrict the
+    upper and lower limits to some maximum and minimum value.
+
+    The intervals are fixed (i.e. there is no jitter), so this strategy is
+    suitable for balancing retries against latency when a required resource is
+    unavailable for an unknown duration, but *not* suitable for resolving
+    contention between multiple processes for a shared resource. Use
+    wait_random_exponential for the latter case.
+    """
+
+    def __init__(
+        self,
+        multiplier: typing.Union[int, float] = 1,
+        max: wait_unit_type = _utils.MAX_WAIT,  # noqa
+        exp_base: typing.Union[int, float] = 2,
+        min: wait_unit_type = 0,  # noqa
+    ) -> None:
+        self.multiplier = multiplier
+        self.min = to_seconds(min)
+        self.max = to_seconds(max)
+        self.exp_base = exp_base
+
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        try:
+            exp = self.exp_base ** (retry_state.attempt_number - 1)
+            result = self.multiplier * exp
+        except OverflowError:
+            return self.max
+        return max(max(0, self.min), min(result, self.max))
+
+
+class wait_random_exponential(wait_exponential):
+    """Random wait with exponentially widening window.
+
+    An exponential backoff strategy used to mediate contention between multiple
+    uncoordinated processes for a shared resource in distributed systems. This
+    is the sense in which "exponential backoff" is meant in e.g. Ethernet
+    networking, and corresponds to the "Full Jitter" algorithm described in
+    this blog post:
+
+    https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
+
+    Each retry occurs at a random time in a geometrically expanding interval.
+    It allows for a custom multiplier and an ability to restrict the upper
+    limit of the random interval to some maximum value.
+
+    Example::
+
+        wait_random_exponential(multiplier=0.5,  # initial window 0.5s
+                                max=60)          # max 60s timeout
+
+    When waiting for an unavailable resource to become available again, as
+    opposed to trying to resolve contention for a shared resource, the
+    wait_exponential strategy (which uses a fixed interval) may be preferable.
+
+    """
+
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        high = super().__call__(retry_state=retry_state)
+        return random.uniform(0, high)
+
+
+class wait_exponential_jitter(wait_base):
+    """Wait strategy that applies exponential backoff and jitter.
+
+    It allows for a customized initial wait, maximum wait and jitter.
+
+    This implements the strategy described here:
+    https://cloud.google.com/storage/docs/retry-strategy
+
+    The wait time is min(initial * (2**n + random.uniform(0, jitter)), maximum)
+    where n is the retry count.
+    """
+
+    def __init__(
+        self,
+        initial: float = 1,
+        max: float = _utils.MAX_WAIT,  # noqa
+        exp_base: float = 2,
+        jitter: float = 1,
+    ) -> None:
+        self.initial = initial
+        self.max = max
+        self.exp_base = exp_base
+        self.jitter = jitter
+
+    def __call__(self, retry_state: "RetryCallState") -> float:
+        jitter = random.uniform(0, self.jitter)
+        try:
+            exp = self.exp_base ** (retry_state.attempt_number - 1)
+            result = self.initial * exp + jitter
+        except OverflowError:
+            result = self.max
+        return max(0, min(result, self.max))