RFC3261: SIP:17.1.1.2 形式描述

本文详细描述了INVITE客户端交易中的状态机工作原理,涉及定时器A和B的使用、请求重传策略、超时管理以及不同状态下的事务处理,包括从调用到完成和终止的转换过程。
17.1.1.2 Formal Description
17.1.1.2 形式描述

   The state machine for the INVITE client transaction is shown in Figure 5.  The initial state, "calling", MUST be entered when the TU initiates a new client transaction with an INVITE request.  The client transaction MUST pass the request to the transport layer for transmission (see Section 18).  If an unreliable transport is being used, the client transaction MUST start timer A with a value of T1. If a reliable transport is being used, the client transaction SHOULD NOT start timer A (Timer A controls request retransmissions).  For any transport, the client transaction MUST start timer B with a value of 64*T1 seconds (Timer B controls transaction timeouts).

​INVITE客户端事务的状态机如图5所示。当TU使用INVITE请求启动新的客户端事务时,必须输入初始状态“调用”。客户端事务必须将请求传递到传输层进行传输(见第18节)。如果正在使用不可靠的传输,则客户端事务必须启动值为T1的计时器A。如果正在使用可靠的传输,则客户端事务不应启动计时器a(计时器a控制请求重新传输)。对于任何传输,客户端事务必须以64*T1秒的值启动计时器B(计时器B控制事务超时)。

   When timer A fires, the client transaction MUST retransmit the request by passing it to the transport layer, and MUST reset the timer with a value of 2*T1.  The formal definition of retransmit within the context of the transaction layer is to take the message previously sent to the transport layer and pass it to the transport layer once more.

当定时器A触发时,客户端事务必须通过将请求传递到传输层来重新发送请求,并且必须用2*T1的值重置定时器。在事务层的上下文中重新传输的正式定义是将之前发送到传输层的消息再次传递到传输层。

   When timer A fires 2*T1 seconds later, the request MUST be retransmitted again (assuming the client transaction is still in this state).  This process MUST continue so that the request is retransmitted with intervals that double after each transmission. These retransmissions SHOULD only be done while the client transaction is in the "calling" state.

当计时器A在2*T1秒后触发时,必须再次重新发送请求(假设客户端事务仍处于此状态)。此过程必须继续,以便在每次传输后以双倍的间隔重新传输请求。这些重新传输应该只在客户端事务处于“调用”状态时进行。

   The default value for T1 is 500 ms.  T1 is an estimate of the RTT between the client and server transactions.  Elements MAY (though it is NOT RECOMMENDED) use smaller values of T1 within closed, private networks that do not permit general Internet connection.  T1 MAY be chosen larger, and this is RECOMMENDED if it is known in advance (such as on high latency access links) that the RTT is larger. Whatever the value of T1, the exponential backoffs on retransmissions described in this section MUST be used.

T1的默认值是500毫秒。T1是客户端和服务端事务之间的RTT的估计值。元素可能(尽管不建议)在不允许一般互联网连接的封闭专用网络中使用较小的T1值。T1可以选择得更大,并且如果事先知道(例如在高延迟接入链路上)RTT更大,则建议这样做。无论T1的值是多少,都必须使用本节中描述的重传的指数退避。

   If the client transaction is still in the "Calling" state when timer B fires, the client transaction SHOULD inform the TU that a timeout has occurred.  The client transaction MUST NOT generate an ACK.  The value of 64*T1 is equal to the amount of time required to send seven requests in the case of an unreliable transport.

如果定时器B触发时客户端事务仍处于“调用”状态,则客户端事务应通知TU发生超时。客户端事务不得生成ACK。64*T1的值等于在传输不可靠的情况下发送七个请求所需的时间。

   If the client transaction receives a provisional response while in the "Calling" state, it transitions to the "Proceeding" state. In the "Proceeding" state, the client transaction SHOULD NOT retransmit the request any longer. Furthermore, the provisional response MUST be passed to the TU.  Any further provisional responses MUST be passed up to the TU while in the "Proceeding" state.

如果客户端事务在“调用”状态下接收到临时响应,则会转换到“正在进行”状态。在“正在进行”状态下,客户端事务不应再重新发送请求。此外,临时响应必须传递给TU。任何进一步的临时响应必须在“正在进行”状态下传递给TU。

   When in either the "Calling" or "Proceeding" states, reception of a response with status code from 300-699 MUST cause the client transaction to transition to "Completed".  The client transaction MUST pass the received response up to the TU, and the client transaction MUST generate an ACK request, even if the transport is reliable (guidelines for constructing the ACK from the response are given in Section 17.1.1.3) and then pass the ACK to the transport layer for transmission.  The ACK MUST be sent to the same address, port, and transport to which the original request was sent.  The client transaction SHOULD start timer D when it enters the "Completed" state, with a value of at least 32 seconds for unreliable transports, and a value of zero seconds for reliable transports. Timer D reflects the amount of time that the server transaction can remain in the "Completed" state when unreliable transports are used. This is equal to Timer H in the INVITE server transaction, whose default is 64*T1.  However, the client transaction does not know the value of T1 in use by the server transaction, so an absolute minimum of 32s is used instead of basing Timer D on T1.

​当处于“呼叫”或“正在进行”状态时,从300-699接收到状态代码的响应必须使客户端事务转换为“已完成”。客户端事务必须将接收到的响应传递到TU,并且客户端事务必须生成ACK请求,即使传输是可靠的(根据响应构建ACK的指南在第17.1.1.3节中给出),然后将ACK传递到传输层进行传输。ACK必须发送到原始请求发送到的相同地址、端口和传输。客户端事务应在进入“完成”状态时启动计时器D,对于不可靠的传输,计时器D的值至少为32秒,对于可靠的传输则为0秒。计时器D反映了当使用不可靠的传输时,服务端事务可以保持在“完成”状态的时间量。这等于INVITE服务器事务中的Timer H,其默认值为64*T1。然而,客户端事务不知道服务端事务正在使用的T1的值,因此使用绝对最小值32秒,而不是将计时器D基于T1。

   Any retransmissions of the final response that are received while in the "Completed" state MUST cause the ACK to be re-passed to the transport layer for retransmission, but the newly received response MUST NOT be passed up to the TU.  A retransmission of the response is defined as any response which would match the same client transaction based on the rules of Section 17.1.3.

​在“完成”状态下接收的最终响应的任何重传都必须使ACK重新传递到传输层进行重传,但新接收的响应不得传递到TU。响应的重传定义为根据第17.1.3节的规则匹配相同客户端事务的任何响应。

                               |INVITE from TU
             Timer A fires     |INVITE sent
             Reset A,          V                      Timer B fires
             INVITE sent +-----------+                or Transport Err.
               +---------|           |---------------+inform TU
               |         |  Calling  |               |
               +-------->|           |-------------->|
                         +-----------+ 2xx           |
                            |  |       2xx to TU     |
                            |  |1xx                  |
    300-699 +---------------+  |1xx to TU            |
   ACK sent |                  |                     |
resp. to TU |  1xx             V                     |
            |  1xx to TU  -----------+               |
            |  +---------|           |               |
            |  |         |Proceeding |-------------->|
            |  +-------->|           | 2xx           |
            |            +-----------+ 2xx to TU     |
            |       300-699    |                     |
            |       ACK sent,  |                     |
            |       resp. to TU|                     |
            |                  |                     |      NOTE:
            |  300-699         V                     |
            |  ACK sent  +-----------+Transport Err. |  transitions
            |  +---------|           |Inform TU      |  labeled with
            |  |         | Completed |-------------->|  the event
            |  +-------->|           |               |  over the action
            |            +-----------+               |  to take
            |              ^   |                     |
            |              |   | Timer D fires       |
            +--------------+   | -                   |
                               |                     |
                               V                     |
                         +-----------+               |
                         |           |               |
                         | Terminated|<--------------+
                         |           |
                         +-----------+

                 Figure 5: INVITE client transaction
                图5:INVITE客户端事物

   If timer D fires while the client transaction is in the "Completed" state, the client transaction MUST move to the terminated state.

如果定时器D在客户端事务处于“完成”状态时触发,则客户端事务必须移动到终止状态。

   When in either the "Calling" or "Proceeding" states, reception of a 2xx response MUST cause the client transaction to enter the "Terminated" state, and the response MUST be passed up to the TU. The handling of this response depends on whether the TU is a proxy core or a UAC core.  A UAC core will handle generation of the ACK for this response, while a proxy core will always forward the 200 (OK) upstream.  The differing treatment of 200 (OK) between proxy and UAC is the reason that handling of it does not take place in the transaction layer.

当处于“正在调用”或“正在进行”状态时,2xx响应的接收必须使客户端事务进入“已终止”状态,并且该响应必须向上传递给TU。该响应的处理取决于TU是代理核心还是UAC核心。UAC核心将处理此响应的ACK生成,而代理核心将始终向上游转发200(OK)。代理和UAC之间对200(OK)的不同处理是它的处理不在事务层中进行的原因。

   The client transaction MUST be destroyed the instant it enters the "Terminated" state.  This is actually necessary to guarantee correct operation.  The reason is that 2xx responses to an INVITE are treated differently; each one is forwarded by proxies, and the ACK handling in a UAC is different.  Thus, each 2xx needs to be passed to a proxy core (so that it can be forwarded) and to a UAC core (so it can be acknowledged).  No transaction layer processing takes place. Whenever a response is received by the transport, if the transport layer finds no matching client transaction (using the rules of Section 17.1.3), the response is passed directly to the core.  Since the matching client transaction is destroyed by the first 2xx, subsequent 2xx will find no match and therefore be passed to the core.

​客户端事务必须在进入“已终止”状态时立即销毁。这实际上是保证正确操作所必需的。原因是对INVITE的2xx响应被区别对待;每一个都由代理转发,并且UAC中的ACK处理是不同的。因此,每个2xx都需要传递给一个代理核心(以便转发)和一个UAC核心(以便确认)。不进行事务层处理。每当传输接收到响应时,如果传输层没有发现匹配的客户端事务(使用第17.1.3节的规则),则将响应直接传递给核心。由于匹配的客户端事务被第一个2xx破坏,随后的2xx将找不到匹配项,因此将被传递到核心。

PS C:\Users\Administrator> conda install python==3.12 anaconda=custom 3 channel Terms of Service accepted Channels: - defaults Platform: win-64 Collecting package metadata (repodata.json): \ Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=t done Solving environment: done ## Package Plan ## environment location: D:\ANACONDA added / updated specs: - anaconda=custom - python==3.12 The following packages will be downloaded: package | build ---------------------------|----------------- _anaconda_depends-2025.06 | py312_mkl_2 64 KB defaults aiobotocore-2.25.0 | py312haa95532_0 171 KB defaults aiohappyeyeballs-2.6.1 | py312haa95532_0 37 KB defaults aiohttp-3.11.10 | py312h827c3e9_0 900 KB defaults aioitertools-0.7.1 | pyhd3eb1b0_0 20 KB defaults aiosignal-1.2.0 | pyhd3eb1b0_0 12 KB defaults alabaster-0.7.16 | py312haa95532_0 20 KB defaults altair-5.5.0 | py312haa95532_0 866 KB defaults anaconda-custom | py312_5 9 KB defaults anaconda-anon-usage-0.7.4 | pyhb46e38b_100 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py312haa95532_0 472 KB defaults pyqt-5.15.10 | py312h5da7b33_1 4.1 MB defaults pyqt5-sip-12.13.0 | py312h827c3e9_1 85 KB defaults pyqtwebengine-5.15.10 | py312h5da7b33_1 143 KB defaults pysocks-1.7.1 | py312haa95532_0 34 KB defaults pytables-3.10.2 | py312h0217527_2 2.0 MB defaults pytest-8.4.2 | py312haa95532_0 1.2 MB defaults python-3.12.0 | h1d929f7_0 16.2 MB defaults python-dateutil-2.9.0post0 | py312haa95532_2 319 KB defaults python-dotenv-1.1.0 | py312haa95532_0 71 KB defaults python-fastjsonschema-2.20.0| py312haa95532_0 260 KB defaults python-json-logger-3.2.1 | py312haa95532_0 29 KB defaults python-libarchive-c-5.1 | pyhd3eb1b0_0 50 KB defaults python-lmdb-1.6.2 | py312h5da7b33_0 160 KB defaults python-lsp-black-2.0.0 | py312haa95532_1 17 KB defaults python-lsp-jsonrpc-1.1.2 | pyhd3eb1b0_0 12 KB defaults python-lsp-server-1.12.2 | py312hbc747e5_0 210 KB defaults python-slugify-5.0.2 | pyhd3eb1b0_0 13 KB defaults python-tzdata-2025.2 | pyhd3eb1b0_0 141 KB defaults pytokens-0.2.0 | py312haa95532_0 33 KB defaults pytoolconfig-1.2.6 | py312haa95532_0 36 KB defaults pytz-2025.2 | py312haa95532_0 234 KB defaults pyuca-1.2 | py312haa95532_1 728 KB defaults pyviz_comms-3.0.6 | py312haa95532_0 55 KB defaults pywavelets-1.8.0 | py312h827c3e9_0 3.6 MB defaults pywin32-308 | py312h5da7b33_0 10.9 MB defaults pywin32-ctypes-0.2.2 | py312haa95532_0 54 KB defaults pywinpty-2.0.15 | py312h72d21ff_0 234 KB defaults pyyaml-6.0.2 | py312h827c3e9_0 196 KB defaults pyzmq-26.2.0 | py312h5da7b33_0 383 KB defaults qdarkstyle-3.2.3 | pyhd3eb1b0_0 615 KB defaults qstylizer-0.2.2 | py312haa95532_0 33 KB defaults qtawesome-1.4.0 | py312haa95532_0 1.9 MB defaults qtconsole-5.6.1 | py312haa95532_1 292 KB defaults qtpy-2.4.3 | py312haa95532_0 153 KB defaults queuelib-1.6.2 | py312haa95532_0 34 KB defaults readchar-4.2.1 | py312haa95532_0 20 KB defaults referencing-0.30.2 | py312haa95532_0 74 KB defaults regex-2024.11.6 | py312h827c3e9_0 392 KB defaults requests-2.32.5 | py312haa95532_0 175 KB defaults requests-file-2.1.0 | py312haa95532_0 16 KB defaults requests-toolbelt-1.0.0 | py312haa95532_0 83 KB defaults rfc3339-validator-0.1.4 | py312haa95532_0 10 KB defaults rfc3986-validator-0.1.1 | py312haa95532_0 10 KB defaults rich-14.2.0 | py312haa95532_0 583 KB defaults roman-numerals-py-3.1.0 | py312haa95532_0 18 KB defaults rope-1.13.0 | py312haa95532_0 586 KB defaults rpds-py-0.22.3 | py312h636fa0f_0 252 KB defaults rtree-1.4.1 | py312h1e5ca3c_0 79 KB defaults ruamel.yaml-0.18.10 | py312h827c3e9_0 272 KB defaults ruamel.yaml.clib-0.2.12 | py312h827c3e9_0 125 KB defaults ruamel_yaml-0.17.21 | py312h2bbff1b_0 237 KB defaults s3fs-2025.7.0 | py312haa95532_0 131 KB defaults scikit-image-0.25.2 | py312h094160a_0 10.7 MB defaults scikit-learn-1.6.1 | py312h585ebfc_0 9.5 MB defaults scipy-1.15.3 | py312h180bac5_0 26.0 MB defaults scrapy-2.13.3 | py312haa95532_0 930 KB defaults seaborn-0.13.2 | py312haa95532_3 715 KB defaults semver-3.0.2 | py312haa95532_1 108 KB defaults send2trash-1.8.2 | py312haa95532_1 97 KB defaults service_identity-24.2.0 | py312haa95532_0 38 KB defaults setuptools-72.1.0 | py312haa95532_0 2.9 MB defaults shellingham-1.5.0 | py312haa95532_0 21 KB defaults sip-6.7.12 | py312h5da7b33_1 626 KB defaults six-1.17.0 | py312haa95532_0 38 KB defaults sklearn-compat-0.1.3 | py312haa95532_0 32 KB defaults smmap-4.0.0 | pyhd3eb1b0_0 23 KB defaults sniffio-1.3.0 | py312haa95532_0 17 KB defaults snowballstemmer-2.2.0 | pyhd3eb1b0_0 61 KB defaults sortedcontainers-2.4.0 | pyhd3eb1b0_0 26 KB defaults soupsieve-2.5 | py312haa95532_0 86 KB defaults sphinx-8.2.3 | py312h827c3e9_0 3.1 MB defaults sphinxcontrib-applehelp-2.0.0| pyhd3eb1b0_1 27 KB defaults sphinxcontrib-devhelp-2.0.0| pyhd3eb1b0_0 23 KB defaults sphinxcontrib-htmlhelp-2.1.0| pyhd3eb1b0_0 34 KB defaults sphinxcontrib-jsmath-1.0.1 | pyhd3eb1b0_0 8 KB defaults sphinxcontrib-qthelp-2.0.0 | pyhd3eb1b0_1 25 KB defaults sphinxcontrib-serializinghtml-2.0.0| pyhd3eb1b0_0 27 KB defaults spyder-6.0.7 | py312haa95532_1 10.5 MB defaults spyder-kernels-3.0.5 | py312hbc747e5_0 375 KB defaults sqlalchemy-2.0.39 | py312h54f65d0_0 6.5 MB defaults stack_data-0.2.0 | pyhd3eb1b0_0 22 KB defaults statsmodels-0.14.4 | py312h827c3e9_0 11.7 MB defaults streamlit-1.50.0 | py312haa95532_0 8.7 MB defaults superqt-0.7.6 | py312hdfb3b6f_0 226 KB defaults sympy-1.14.0 | py312haa95532_0 15.3 MB defaults tabulate-0.9.0 | py312haa95532_0 79 KB defaults tblib-3.1.0 | py312haa95532_0 30 KB defaults tenacity-9.1.2 | py312haa95532_0 65 KB defaults terminado-0.18.1 | py312haa95532_0 32 KB defaults text-unidecode-1.3 | pyhd3eb1b0_0 65 KB defaults textdistance-4.6.3 | py312hbc747e5_1 74 KB defaults threadpoolctl-3.5.0 | py312hfc267ef_0 49 KB defaults three-merge-0.1.1 | pyhd3eb1b0_0 10 KB defaults tifffile-2025.10.4 | py312haa95532_0 593 KB defaults tinycss2-1.4.0 | py312haa95532_0 111 KB defaults tldextract-5.1.2 | py312haa95532_0 152 KB defaults toml-0.10.2 | pyhd3eb1b0_0 20 KB defaults tomli-2.2.1 | py312haa95532_0 35 KB defaults tomlkit-0.13.2 | py312haa95532_0 109 KB defaults toolz-1.0.0 | py312haa95532_0 142 KB defaults tornado-6.5.1 | py312h827c3e9_0 873 KB defaults tqdm-4.67.1 | py312hfc267ef_0 187 KB defaults traitlets-5.14.3 | py312haa95532_0 221 KB defaults truststore-0.10.1 | py312haa95532_0 50 KB defaults twisted-24.11.0 | py312haa95532_0 6.7 MB defaults twisted-iocpsupport-1.0.2 | py312h827c3e9_1 56 KB defaults typer-0.17.4 | py312haa95532_0 174 KB defaults typing-extensions-4.15.0 | py312haa95532_0 12 KB defaults typing-inspection-0.4.2 | py312haa95532_0 31 KB defaults typing_extensions-4.15.0 | py312haa95532_0 98 KB defaults uc-micro-py-1.0.3 | py312haa95532_0 12 KB defaults ujson-5.10.0 | py312h5da7b33_1 147 KB defaults unicodedata2-15.1.0 | py312h827c3e9_1 525 KB defaults unidecode-1.3.8 | py312haa95532_0 306 KB defaults urllib3-2.5.0 | py312haa95532_0 358 KB defaults w3lib-2.1.2 | py312haa95532_0 42 KB defaults watchdog-6.0.0 | py312haa95532_0 181 KB defaults wcwidth-0.2.5 | pyhd3eb1b0_0 26 KB defaults webencodings-0.5.1 | py312haa95532_2 25 KB defaults websocket-client-1.8.0 | py312haa95532_0 143 KB defaults werkzeug-3.1.3 | py312haa95532_0 411 KB defaults whatthepatch-1.0.7 | py312haa95532_0 55 KB defaults wheel-0.45.1 | py312haa95532_0 177 KB defaults widgetsnbextension-4.0.14 | py312haa95532_0 865 KB defaults win_inet_pton-1.1.0 | py312haa95532_0 10 KB defaults wrapt-1.17.0 | py312h827c3e9_0 68 KB defaults xarray-2025.10.1 | py312haa95532_0 2.7 MB defaults xlwings-0.32.1 | py312haa95532_1 1.3 MB defaults xyzservices-2025.4.0 | py312haa95532_0 64 KB defaults yapf-0.43.0 | py312haa95532_0 475 KB defaults yarl-1.18.0 | py312h827c3e9_0 154 KB defaults zict-3.0.0 | py312haa95532_0 110 KB defaults zipp-3.21.0 | py312haa95532_0 31 KB defaults zope-1.0 | py312haa95532_1 5 KB defaults zope.interface-7.1.1 | py312h827c3e9_0 407 KB defaults zstandard-0.23.0 | py312h4fc1ca9_1 351 KB defaults ------------------------------------------------------------ Total: 450.2 MB The following NEW packages will be INSTALLED: anaconda pkgs/main/win-64::anaconda-custom-py312_5 brotlicffi pkgs/main/win-64::brotlicffi-1.0.9.2-py312h5da7b33_1 m2w64-gcc-libgfor~ pkgs/msys2/win-64::m2w64-gcc-libgfortran-5.3.0-6 m2w64-gcc-libs pkgs/msys2/win-64::m2w64-gcc-libs-5.3.0-7 m2w64-gcc-libs-co~ pkgs/msys2/win-64::m2w64-gcc-libs-core-5.3.0-7 m2w64-gmp pkgs/msys2/win-64::m2w64-gmp-6.1.0-2 m2w64-libwinpthre~ pkgs/msys2/win-64::m2w64-libwinpthread-git-5.0.0.4634.697f757-2 mpi pkgs/main/win-64::mpi-1.0-msmpi mpi4py pkgs/main/win-64::mpi4py-4.0.3-py312h827c3e9_0 msmpi pkgs/main/win-64::msmpi-10.1.1-had4844c_0 pip pkgs/main/noarch::pip-25.2-pyhc872135_1 pytokens pkgs/main/win-64::pytokens-0.2.0-py312haa95532_0 setuptools pkgs/main/win-64::setuptools-72.1.0-py312haa95532_0 unicodedata2 pkgs/main/win-64::unicodedata2-15.1.0-py312h827c3e9_1 The following packages will be REMOVED: libmpdec-4.0.0-h827c3e9_0 python_abi-3.13-0_cp313 The following packages will be UPDATED: aiobotocore 2.19.0-py313haa95532_0 --> 2.25.0-py312haa95532_0 aiohappyeyeballs 2.4.4-py313haa95532_0 --> 2.6.1-py312haa95532_0 anaconda-anon-usa~ pkgs/main/win-64::anaconda-anon-usage~ --> pkgs/main/noarch::anaconda-anon-usage-0.7.4-pyhb46e38b_ anaconda-auth 0.8.6-py313haa95532_0 --> 0.9.1-py312haa95532_0 anaconda-cli-base 0.5.2-py313haa95532_0 --> 0.5.4-py312haa95532_0 anaconda-client 1.13.0-py313haa95532_1 --> 1.13.1-py312haa95532_0 annotated-types 0.6.0-py313haa95532_0 --> 0.6.0-py312haa95532_1 anyio 4.7.0-py313haa95532_0 --> 4.10.0-py312haa95532_0 arrow 1.3.0-py313haa95532_0 --> 1.4.0-py312haa95532_0 astroid 3.3.8-py313haa95532_0 --> 3.3.11-py312haa95532_0 astropy 7.0.0-py313h827c3e9_0 --> 7.1.0-py312hf9130e5_0 astropy-iers-data 0.2025.1.13.0.34.51-py313haa95532_0 --> 0.2025.10.20.0.39.8-py312haa95532_0 async-lru 2.0.4-py313haa95532_0 --> 2.0.5-py312haa95532_0 attrs 24.3.0-py313haa95532_0 --> 25.4.0-py312haa95532_0 beautifulsoup4 4.12.3-py313haa95532_0 --> 4.13.5-py312haa95532_0 black 24.10.0-py313haa95532_0 --> 25.9.0-py312haa95532_0 bokeh 3.6.2-py313haa95532_0 --> 3.8.0-py312haa95532_0 botocore 1.36.3-py313haa95532_0 --> 1.40.46-py312haa95532_0 ca-certificates 2025.2.25-haa95532_0 --> 2025.9.9-haa95532_0 certifi 2025.4.26-py313haa95532_0 --> 2025.10.5-py312haa95532_0 chardet 4.0.0-py313haa95532_1003 --> 5.2.0-py312haa95532_0 cloudpickle 3.0.0-py313haa95532_0 --> 3.1.1-py312haa95532_0 conda 25.5.1-py313haa95532_0 --> 25.9.1-py312haa95532_0 conda-anaconda-te~ pkgs/main/win-64::conda-anaconda-tele~ --> pkgs/main/noarch::conda-anaconda-telemetry-0.3.0-pyhd3e conda-anaconda-tos 0.2.1-py313haa95532_0 --> 0.2.2-py312haa95532_1 conda-build 25.5.0-py313hcfce1f1_0 --> 25.9.0-py312hcfce1f1_0 conda-index 0.6.1-py313haa95532_0 --> 0.7.0-py312haa95532_0 conda-repo-cli 1.0.165-py313haa95532_0 --> 1.0.173-py312haa95532_0 dask 2025.2.0-py313haa95532_0 --> 2025.9.1-py312haa95532_0 dask-core 2025.2.0-py313haa95532_0 --> 2025.9.1-py312haa95532_0 dask-expr 2.0.0-py313haa95532_0 --> 2.0.0-py312haa95532_1 datashader 0.18.0-py313haa95532_0 --> 0.18.2-py312haa95532_0 deprecated 1.2.13-py313haa95532_0 --> 1.2.18-py312haa95532_0 dill 0.3.8-py313haa95532_0 --> 0.4.0-py312haa95532_0 distributed 2025.2.0-py313haa95532_0 --> 2025.9.1-py312haa95532_0 docstring-to-mark~ 0.11-py313haa95532_0 --> 0.17-py312haa95532_0 et_xmlfile 1.1.0-py313haa95532_1 --> 2.0.0-py312haa95532_0 flask 3.1.0-py313haa95532_0 --> 3.1.2-py312haa95532_0 fsspec 2025.3.2-py313h4442805_0 --> 2025.7.0-py312hbc747e5_0 gitpython 3.1.43-py313haa95532_0 --> 3.1.45-py312haa95532_0 h5py 3.12.1-py313h535c9fb_1 --> 3.14.0-py312h6fc9867_0 holoviews 1.20.2-py313haa95532_0 --> 1.21.0-py312haa95532_0 httpx 0.28.1-py313haa95532_0 --> 0.28.1-py312haa95532_1 hvplot 0.11.3-py313haa95532_0 --> 0.12.1-py312haa95532_0 imbalanced-learn 0.13.0-py313haa95532_0 --> 0.14.0-py312haa95532_0 intake 2.0.7-py313haa95532_0 --> 2.0.8-py312haa95532_0 ipykernel 6.29.5-py313haa95532_1 --> 6.30.1-py312haa95532_0 ipywidgets 8.1.5-py313haa95532_0 --> 8.1.7-py312haa95532_0 isort 6.0.1-py313haa95532_0 --> 6.1.0-py312haa95532_0 joblib 1.4.2-py313haa95532_0 --> 1.5.2-py312haa95532_0 jsonschema 4.23.0-py313haa95532_0 --> 4.25.0-py312haa95532_0 jupyter_core 5.7.2-py313haa95532_0 --> 5.8.1-py312haa95532_0 jupyter_server 2.15.0-py313haa95532_0 --> 2.16.0-py312haa95532_0 jupyterlab 4.3.4-py313haa95532_0 --> 4.4.7-py312haa95532_0 jupyterlab_widgets 3.0.13-py313haa95532_0 --> 3.0.15-py312haa95532_0 linkify-it-py 2.0.0-py313haa95532_0 --> 2.0.3-py312haa95532_0 matplotlib-inline 0.1.6-py313haa95532_0 --> 0.1.7-py312haa95532_0 mdit-py-plugins 0.3.0-py313haa95532_0 --> 0.5.0-py312haa95532_0 mdurl 0.1.0-py313haa95532_0 --> 0.1.2-py312haa95532_0 more-itertools 10.3.0-py313haa95532_0 --> 10.8.0-py312haa95532_0 msgpack-python 1.0.3-py313h214f63a_0 --> 1.1.1-py312h5da7b33_0 multipledispatch 0.6.0-py313haa95532_0 --> 1.0.0-py312haa95532_0 mypy 1.14.1-py313h827c3e9_0 --> 1.16.0-py312h827c3e9_0 narwhals 1.31.0-py313haa95532_1 --> 2.7.0-py312haa95532_0 networkx 3.4.2-py313haa95532_0 --> 3.5-py312haa95532_0 nltk 3.9.1-py313hea850e4_0 --> 3.9.2-py312ha55a155_0 notebook 7.3.2-py313haa95532_1 --> 7.4.5-py312haa95532_0 numba 0.61.0-py313h5da7b33_1 --> 0.61.2-py312h5da7b33_0 numpy 2.1.3-py313he6dc315_0 --> 2.2.5-py312h5f75535_0 numpy-base 2.1.3-py313h6011491_0 --> 2.2.5-py312h23d94f8_0 numpydoc 1.7.0-py313haa95532_0 --> 1.9.0-py312haa95532_0 packaging 24.2-py313haa95532_0 --> 25.0-py312haa95532_1 panel 1.7.0-py313haa95532_0 --> 1.8.2-py312haa95532_0 param 2.2.0-py313haa95532_0 --> 2.2.1-py312haa95532_0 parsel 1.8.1-py313haa95532_0 --> 1.10.0-py312haa95532_0 pathspec 0.10.3-py313haa95532_0 --> 0.12.1-py312haa95532_0 plotly 5.24.1-py313h4442805_1 --> 6.3.0-py312hbc747e5_0 pyct 0.5.0-py313haa95532_0 --> 0.6.0-py312haa95532_0 pydantic 2.11.7-py313haa95532_0 --> 2.11.9-py312haa95532_0 pydantic-settings 2.6.1-py313haa95532_0 --> 2.10.1-py312haa95532_0 pydispatcher 2.0.5-py313haa95532_3 --> 2.0.7-py312haa95532_0 pylint 3.3.5-py313haa95532_0 --> 3.3.8-py312haa95532_0 pytest 8.3.4-py313haa95532_0 --> 8.4.2-py312haa95532_0 pytz 2024.1-py313haa95532_0 --> 2025.2-py312haa95532_0 pyviz_comms 3.0.2-py313haa95532_0 --> 3.0.6-py312haa95532_0 qtpy 2.4.1-py313haa95532_0 --> 2.4.3-py312haa95532_0 readchar 4.0.5-py313haa95532_0 --> 4.2.1-py312haa95532_0 requests 2.32.3-py313haa95532_1 --> 2.32.5-py312haa95532_0 rich 13.9.4-py313haa95532_0 --> 14.2.0-py312haa95532_0 rtree 1.0.1-py313h2eaa2aa_0 --> 1.4.1-py312h1e5ca3c_0 s3fs 2025.3.2-py313haa95532_0 --> 2025.7.0-py312haa95532_0 scikit-image 0.25.0-py313h5da7b33_0 --> 0.25.2-py312h094160a_0 scrapy 2.12.0-py313haa95532_1 --> 2.13.3-py312haa95532_0 streamlit 1.45.1-py313haa95532_1 --> 1.50.0-py312haa95532_0 superqt 0.7.3-py313h4442805_0 --> 0.7.6-py312hdfb3b6f_0 sympy 1.13.3-py313haa95532_1 --> 1.14.0-py312haa95532_0 tenacity 9.0.0-py313haa95532_0 --> 9.1.2-py312haa95532_0 terminado 0.17.1-py313haa95532_0 --> 0.18.1-py312haa95532_0 textdistance 4.6.3-py313h4442805_0 --> 4.6.3-py312hbc747e5_1 tifffile 2025.2.18-py313haa95532_0 --> 2025.10.4-py312haa95532_0 tomli 2.0.1-py313haa95532_1 --> 2.2.1-py312haa95532_0 truststore 0.10.0-py313haa95532_0 --> 0.10.1-py312haa95532_0 typer 0.9.0-py313haa95532_0 --> 0.17.4-py312haa95532_0 typing-extensions 4.12.2-py313haa95532_0 --> 4.15.0-py312haa95532_0 typing-inspection 0.4.0-py313haa95532_0 --> 0.4.2-py312haa95532_0 typing_extensions 4.12.2-py313haa95532_0 --> 4.15.0-py312haa95532_0 uc-micro-py 1.0.1-py313haa95532_0 --> 1.0.3-py312haa95532_0 urllib3 2.3.0-py313haa95532_0 --> 2.5.0-py312haa95532_0 watchdog 4.0.2-py313haa95532_0 --> 6.0.0-py312haa95532_0 whatthepatch 1.0.2-py313haa95532_0 --> 1.0.7-py312haa95532_0 widgetsnbextension 4.0.13-py313haa95532_0 --> 4.0.14-py312haa95532_0 xarray 2025.4.0-py313haa95532_0 --> 2025.10.1-py312haa95532_0 xyzservices 2022.9.0-py313haa95532_1 --> 2025.4.0-py312haa95532_0 yapf 0.40.2-py313haa95532_0 --> 0.43.0-py312haa95532_0 The following packages will be DOWNGRADED: python 3.13.5-h286a616_100_cp313 --> 3.12.0-h1d929f7_0 The following packages will be REVISED: _anaconda_depends 2025.06-py313_mkl_2 --> 2025.06-py312_mkl_2 aiohttp 3.11.10-py313h827c3e9_0 --> 3.11.10-py312h827c3e9_0 alabaster 0.7.16-py313haa95532_0 --> 0.7.16-py312haa95532_0 altair 5.5.0-py313haa95532_0 --> 5.5.0-py312haa95532_0 anaconda-catalogs 0.2.0-py313haa95532_2 --> 0.2.0-py312haa95532_2 anaconda-navigator 2.6.6-py313haa95532_2 --> 2.6.6-py312haa95532_2 anaconda-project 0.11.1-py313haa95532_1 --> 0.11.1-py312haa95532_1 argon2-cffi-bindi~ 21.2.0-py313h827c3e9_1 --> 21.2.0-py312h827c3e9_1 asttokens 3.0.0-py313haa95532_0 --> 3.0.0-py312haa95532_0 asyncssh 2.17.0-py313haa95532_0 --> 2.17.0-py312haa95532_0 automat 24.8.1-py313haa95532_0 --> 24.8.1-py312haa95532_0 babel 2.16.0-py313haa95532_0 --> 2.16.0-py312haa95532_0 bcrypt 4.3.0-py313h636fa0f_0 --> 4.3.0-py312h636fa0f_0 bleach 6.2.0-py313haa95532_0 --> 6.2.0-py312haa95532_0 blinker 1.9.0-py313haa95532_0 --> 1.9.0-py312haa95532_0 boltons 25.0.0-py313haa95532_0 --> 25.0.0-py312haa95532_0 bottleneck 1.4.2-py313h2cb717b_0 --> 1.4.2-py312h4b0e54e_0 brotli-python 1.0.9-py313h5da7b33_9 --> 1.0.9-py312h5da7b33_9 cachetools 5.5.1-py313haa95532_0 --> 5.5.1-py312haa95532_0 cffi 1.17.1-py313h827c3e9_1 --> 1.17.1-py312h827c3e9_1 click 8.1.8-py313haa95532_0 --> 8.1.8-py312haa95532_0 colorama 0.4.6-py313haa95532_0 --> 0.4.6-py312haa95532_0 colorcet 3.1.0-py313haa95532_0 --> 3.1.0-py312haa95532_0 comm 0.2.1-py313haa95532_0 --> 0.2.1-py312haa95532_0 conda-content-tru~ 0.2.0-py313haa95532_1 --> 0.2.0-py312haa95532_1 conda-pack 0.8.1-py313haa95532_0 --> 0.8.1-py312haa95532_0 conda-package-han~ 2.4.0-py313haa95532_0 --> 2.4.0-py312haa95532_0 conda-package-str~ 0.12.0-py313haa95532_0 --> 0.12.0-py312haa95532_0 constantly 23.10.4-py313haa95532_0 --> 23.10.4-py312haa95532_0 contourpy 1.3.1-py313h214f63a_0 --> 1.3.1-py312h214f63a_0 cryptography 44.0.1-py313hbd6ee87_0 --> 44.0.1-py312hbd6ee87_0 cssselect 1.2.0-py313haa95532_0 --> 1.2.0-py312haa95532_0 cytoolz 1.0.1-py313h827c3e9_0 --> 1.0.1-py312h827c3e9_0 debugpy 1.8.11-py313h5da7b33_0 --> 1.8.11-py312h5da7b33_0 distro 1.9.0-py313haa95532_0 --> 1.9.0-py312haa95532_0 docutils 0.21.2-py313haa95532_0 --> 0.21.2-py312haa95532_0 evalidate 2.0.3-py313haa95532_0 --> 2.0.3-py312haa95532_0 filelock 3.17.0-py313haa95532_0 --> 3.17.0-py312haa95532_0 flake8 7.1.1-py313haa95532_0 --> 7.1.1-py312haa95532_0 fonttools 4.55.3-py313h827c3e9_0 --> 4.55.3-py312h827c3e9_0 frozendict 2.4.2-py313haa95532_0 --> 2.4.2-py312haa95532_0 frozenlist 1.5.0-py313h827c3e9_0 --> 1.5.0-py312h827c3e9_0 gmpy2 2.2.1-py313h827c3e9_0 --> 2.2.1-py312h827c3e9_0 greenlet 3.1.1-py313h5da7b33_0 --> 3.1.1-py312h5da7b33_0 h11 0.16.0-py313haa95532_0 --> 0.16.0-py312haa95532_0 httpcore 1.0.9-py313haa95532_0 --> 1.0.9-py312haa95532_0 idna 3.7-py313haa95532_0 --> 3.7-py312haa95532_0 imageio 2.37.0-py313haa95532_0 --> 2.37.0-py312haa95532_0 imagesize 1.4.1-py313haa95532_0 --> 1.4.1-py312haa95532_0 importlib-metadata 8.5.0-py313haa95532_0 --> 8.5.0-py312haa95532_0 inflection 0.5.1-py313haa95532_1 --> 0.5.1-py312haa95532_1 ipython 8.30.0-py313haa95532_0 --> 8.30.0-py312haa95532_0 itemloaders 1.3.2-py313haa95532_0 --> 1.3.2-py312haa95532_0 itsdangerous 2.2.0-py313haa95532_0 --> 2.2.0-py312haa95532_0 jaraco.context 6.0.0-py313haa95532_0 --> 6.0.0-py312haa95532_0 jaraco.functools 4.1.0-py313haa95532_0 --> 4.1.0-py312haa95532_0 jedi 0.19.2-py313haa95532_0 --> 0.19.2-py312haa95532_0 jellyfish 1.1.3-py313h8ecf97c_0 --> 1.1.3-py312h8ecf97c_0 jinja2 3.1.6-py313haa95532_0 --> 3.1.6-py312haa95532_0 jmespath 1.0.1-py313haa95532_0 --> 1.0.1-py312haa95532_0 json5 0.9.25-py313haa95532_0 --> 0.9.25-py312haa95532_0 jsonpatch 1.33-py313haa95532_1 --> 1.33-py312haa95532_1 jsonschema-specif~ 2023.7.1-py313haa95532_0 --> 2023.7.1-py312haa95532_0 jupyter 1.1.1-py313haa95532_0 --> 1.1.1-py312haa95532_0 jupyter-lsp 2.2.5-py313haa95532_0 --> 2.2.5-py312haa95532_0 jupyter_client 8.6.3-py313haa95532_0 --> 8.6.3-py312haa95532_0 jupyter_console 6.6.3-py313haa95532_1 --> 6.6.3-py312haa95532_1 jupyter_events 0.12.0-py313haa95532_0 --> 0.12.0-py312haa95532_0 jupyter_server_te~ 0.5.3-py313haa95532_0 --> 0.5.3-py312haa95532_0 jupyterlab-variab~ 3.2.4-py313haa95532_0 --> 3.2.4-py312haa95532_0 jupyterlab_pygmen~ 0.3.0-py313haa95532_0 --> 0.3.0-py312haa95532_0 jupyterlab_server 2.27.3-py313haa95532_0 --> 2.27.3-py312haa95532_0 keyring 25.6.0-py313haa95532_0 --> 25.6.0-py312haa95532_0 kiwisolver 1.4.8-py313h5da7b33_0 --> 1.4.8-py312h5da7b33_0 lazy_loader 0.4-py313haa95532_0 --> 0.4-py312haa95532_0 libmambapy 2.0.5-py313h214f63a_1 --> 2.0.5-py312h214f63a_1 llvmlite 0.44.0-py313h8b1c7eb_1 --> 0.44.0-py312h8b1c7eb_1 locket 1.0.0-py313haa95532_0 --> 1.0.0-py312haa95532_0 lxml 5.3.0-py313h395c83e_1 --> 5.3.0-py312h395c83e_1 lz4 4.3.2-py313h827c3e9_1 --> 4.3.2-py312h827c3e9_1 markdown 3.8-py313haa95532_0 --> 3.8-py312haa95532_0 markdown-it-py 2.2.0-py313haa95532_1 --> 2.2.0-py312haa95532_1 markupsafe 3.0.2-py313h827c3e9_0 --> 3.0.2-py312h827c3e9_0 matplotlib 3.10.0-py313haa95532_1 --> 3.10.0-py312haa95532_0 matplotlib-base 3.10.0-py313h7aa5d4e_1 --> 3.10.0-py312he19b0ae_0 menuinst 2.3.0-py313h5da7b33_0 --> 2.3.0-py312h5da7b33_0 mistune 3.1.2-py313haa95532_0 --> 3.1.2-py312haa95532_0 mkl-service 2.4.0-py313h827c3e9_2 --> 2.4.0-py312h827c3e9_2 mkl_fft 1.3.11-py313h827c3e9_0 --> 1.3.11-py312h827c3e9_0 mkl_random 1.2.8-py313hce38976_0 --> 1.2.8-py312h0158946_0 mpmath 1.3.0-py313haa95532_0 --> 1.3.0-py312haa95532_0 multidict 6.1.0-py313h827c3e9_0 --> 6.1.0-py312h827c3e9_0 mypy_extensions 1.0.0-py313haa95532_0 --> 1.0.0-py312haa95532_0 navigator-updater 0.5.1-py313haa95532_0 --> 0.5.1-py312haa95532_0 nbclient 0.10.2-py313haa95532_0 --> 0.10.2-py312haa95532_0 nbconvert 7.16.6-py313haa95532_0 --> 7.16.6-py312haa95532_0 nbconvert-core 7.16.6-py313haa95532_0 --> 7.16.6-py312haa95532_0 nbconvert-pandoc 7.16.6-py313haa95532_0 --> 7.16.6-py312haa95532_0 nbformat 5.10.4-py313haa95532_0 --> 5.10.4-py312haa95532_0 nest-asyncio 1.6.0-py313haa95532_0 --> 1.6.0-py312haa95532_0 notebook-shim 0.2.4-py313haa95532_0 --> 0.2.4-py312haa95532_0 numexpr 2.10.1-py313h4cd664f_0 --> 2.10.1-py312h4cd664f_0 openpyxl 3.1.5-py313h827c3e9_1 --> 3.1.5-py312h827c3e9_1 overrides 7.4.0-py313haa95532_0 --> 7.4.0-py312haa95532_0 pandas 2.2.3-py313h5da7b33_0 --> 2.2.3-py312h5da7b33_0 parso 0.8.4-py313haa95532_0 --> 0.8.4-py312haa95532_0 partd 1.4.2-py313haa95532_0 --> 1.4.2-py312haa95532_0 patsy 1.0.1-py313haa95532_0 --> 1.0.1-py312haa95532_0 pillow 11.1.0-py313hea0d53e_1 --> 11.1.0-py312hea0d53e_1 pkce 1.0.3-py313haa95532_0 --> 1.0.3-py312haa95532_0 pkginfo 1.12.0-py313haa95532_0 --> 1.12.0-py312haa95532_0 platformdirs 4.3.7-py313haa95532_0 --> 4.3.7-py312haa95532_0 pluggy 1.5.0-py313haa95532_0 --> 1.5.0-py312haa95532_0 ply 3.11-py313haa95532_1 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3.2.1-py312haa95532_0 python-lmdb 1.6.2-py313h5da7b33_0 --> 1.6.2-py312h5da7b33_0 python-lsp-black 2.0.0-py313haa95532_1 --> 2.0.0-py312haa95532_1 python-lsp-server 1.12.2-py313h4442805_0 --> 1.12.2-py312hbc747e5_0 pytoolconfig 1.2.6-py313haa95532_0 --> 1.2.6-py312haa95532_0 pyuca 1.2-py313haa95532_1 --> 1.2-py312haa95532_1 pywavelets 1.8.0-py313h827c3e9_0 --> 1.8.0-py312h827c3e9_0 pywin32 308-py313h5da7b33_0 --> 308-py312h5da7b33_0 pywin32-ctypes 0.2.2-py313haa95532_0 --> 0.2.2-py312haa95532_0 pywinpty 2.0.15-py313h72d21ff_0 --> 2.0.15-py312h72d21ff_0 pyyaml 6.0.2-py313h827c3e9_0 --> 6.0.2-py312h827c3e9_0 pyzmq 26.2.0-py313h5da7b33_0 --> 26.2.0-py312h5da7b33_0 qstylizer 0.2.2-py313haa95532_0 --> 0.2.2-py312haa95532_0 qtawesome 1.4.0-py313haa95532_0 --> 1.4.0-py312haa95532_0 qtconsole 5.6.1-py313haa95532_1 --> 5.6.1-py312haa95532_1 queuelib 1.6.2-py313haa95532_0 --> 1.6.2-py312haa95532_0 referencing 0.30.2-py313haa95532_0 --> 0.30.2-py312haa95532_0 regex 2024.11.6-py313h827c3e9_0 --> 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24.2.0-py312haa95532_0 shellingham 1.5.0-py313haa95532_0 --> 1.5.0-py312haa95532_0 sip 6.7.12-py313h5da7b33_1 --> 6.7.12-py312h5da7b33_1 six 1.17.0-py313haa95532_0 --> 1.17.0-py312haa95532_0 sklearn-compat 0.1.3-py313haa95532_0 --> 0.1.3-py312haa95532_0 sniffio 1.3.0-py313haa95532_0 --> 1.3.0-py312haa95532_0 soupsieve 2.5-py313haa95532_0 --> 2.5-py312haa95532_0 sphinx 8.2.3-py313h827c3e9_0 --> 8.2.3-py312h827c3e9_0 spyder 6.0.7-py313haa95532_1 --> 6.0.7-py312haa95532_1 spyder-kernels 3.0.5-py313h4442805_0 --> 3.0.5-py312hbc747e5_0 sqlalchemy 2.0.39-py313h54f65d0_0 --> 2.0.39-py312h54f65d0_0 statsmodels 0.14.4-py313h827c3e9_0 --> 0.14.4-py312h827c3e9_0 tabulate 0.9.0-py313haa95532_0 --> 0.9.0-py312haa95532_0 tblib 3.1.0-py313haa95532_0 --> 3.1.0-py312haa95532_0 threadpoolctl 3.5.0-py313h4442805_0 --> 3.5.0-py312hfc267ef_0 tinycss2 1.4.0-py313haa95532_0 --> 1.4.0-py312haa95532_0 tldextract 5.1.2-py313haa95532_0 --> 5.1.2-py312haa95532_0 tomlkit 0.13.2-py313haa95532_0 --> 0.13.2-py312haa95532_0 toolz 1.0.0-py313haa95532_0 --> 1.0.0-py312haa95532_0 tornado 6.5.1-py313h827c3e9_0 --> 6.5.1-py312h827c3e9_0 tqdm 4.67.1-py313h4442805_0 --> 4.67.1-py312hfc267ef_0 traitlets 5.14.3-py313haa95532_0 --> 5.14.3-py312haa95532_0 twisted 24.11.0-py313haa95532_0 --> 24.11.0-py312haa95532_0 twisted-iocpsuppo~ 1.0.2-py313h827c3e9_1 --> 1.0.2-py312h827c3e9_1 ujson 5.10.0-py313h5da7b33_1 --> 5.10.0-py312h5da7b33_1 unidecode 1.3.8-py313haa95532_0 --> 1.3.8-py312haa95532_0 w3lib 2.1.2-py313haa95532_0 --> 2.1.2-py312haa95532_0 webencodings 0.5.1-py313haa95532_2 --> 0.5.1-py312haa95532_2 websocket-client 1.8.0-py313haa95532_0 --> 1.8.0-py312haa95532_0 werkzeug 3.1.3-py313haa95532_0 --> 3.1.3-py312haa95532_0 wheel 0.45.1-py313haa95532_0 --> 0.45.1-py312haa95532_0 win_inet_pton 1.1.0-py313haa95532_0 --> 1.1.0-py312haa95532_0 wrapt 1.17.0-py313h827c3e9_0 --> 1.17.0-py312h827c3e9_0 xlwings 0.32.1-py313haa95532_1 --> 0.32.1-py312haa95532_1 yarl 1.18.0-py313h827c3e9_0 --> 1.18.0-py312h827c3e9_0 zict 3.0.0-py313haa95532_0 --> 3.0.0-py312haa95532_0 zipp 3.21.0-py313haa95532_0 --> 3.21.0-py312haa95532_0 zope 1.0-py313haa95532_1 --> 1.0-py312haa95532_1 zope.interface 7.1.1-py313h827c3e9_0 --> 7.1.1-py312h827c3e9_0 zstandard 0.23.0-py313h4fc1ca9_1 --> 0.23.0-py312h4fc1ca9_1 Proceed ([y]/n)? y - D:\ANACONDA\Lib\site-packages\menuinst\platforms\win.py:69: UserWarning: Quick launch menus are not available for syss warnings.warn("Quick launch menus are not available for system level installs") menuinst Exception Traceback (most recent call last): File "D:\ANACONDA\Lib\site-packages\conda\gateways\disk\create.py", line 266, in make_menu import menuinst ^^^^^^^^^^^^^^^ ...<4 lines>... prefix=prefix, File "D:\ANACONDA\Lib\site-packages\menuinst\api.py", line 169, in _install_adapter install(metadata, target_prefix=prefix, **kwargs) ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\ANACONDA\Lib\site-packages\menuinst\utils.py", line 420, in wrapper_elevate return func( target_prefix=target_prefix, ...<3 lines>... **kwargs, ) File "D:\ANACONDA\Lib\site-packages\menuinst\api.py", line 61, in install paths += menu_item.create() ~~~~~~~~~~~~~~~~^^ File "D:\ANACONDA\Lib\site-packages\menuinst\platforms\win.py", line 158, in create from .win_utils.winshortcut import create_shortcut ModuleNotFoundError: No module named 'menuinst.platforms.win_utils.winshortcut'menuinst Exception Traceback (most recent call last): File "D:\ANACONDA\Lib\site-packages\conda\gateways\disk\create.py", line 266, in make_menu import menuinst ^^^^^^^^^^^^^^^ ...<4 lines>... prefix=prefix, File "D:\ANACONDA\Lib\site-packages\menuinst\api.py", line 169, in _install_adapter install(metadata, target_prefix=prefix, **kwargs) ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\ANACONDA\Lib\site-packages\menuinst\utils.py", line 420, in wrapper_elevate return func( target_prefix=target_prefix, ...<3 lines>... **kwargs, ) File "D:\ANACONDA\Lib\site-packages\menuinst\api.py", line 61, in install paths += menu_item.create() ~~~~~~~~~~~~~~~~^^ File "D:\ANACONDA\Lib\site-packages\menuinst\platforms\win.py", line 158, in create from .win_utils.winshortcut import create_shortcut ModuleNotFoundError: No module named 'menuinst.platforms.win_utils.winshortcut'menuinst Exception Traceback (most recent call last): File "D:\ANACONDA\Lib\site-packages\conda\gateways\disk\create.py", line 266, in make_menu import menuinst ^^^^^^^^^^^^^^^ ...<4 lines>... prefix=prefix, File "D:\ANACONDA\Lib\site-packages\menuinst\api.py", line 169, in _install_adapter install(metadata, target_prefix=prefix, **kwargs) ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\ANACONDA\Lib\site-packages\menuinst\utils.py", line 420, in wrapper_elevate return func( target_prefix=target_prefix, ...<3 lines>... **kwargs, ) File "D:\ANACONDA\Lib\site-packages\menuinst\api.py", line 61, in install paths += menu_item.create() ~~~~~~~~~~~~~~~~^^ File "D:\ANACONDA\Lib\site-packages\menuinst\platforms\win.py", line 158, in create from .win_utils.winshortcut import create_shortcut ModuleNotFoundError: No module named 'menuinst.platforms.win_utils.winshortcudone
10-30
【电力系统】单机无穷大电力系统短路故障暂态稳定Simulink仿真(带说明文档)内容概要:本文档围绕“单机无穷大电力系统短路故障暂态稳定Simulink仿真”展开,提供了完整的仿真模型与说明文档,重点研究电力系统在发生短路故障后的暂态稳定性问题。通过Simulink搭建单机无穷大系统模型,模拟不同类型的短路故障(如三相短路),分析系统在故障期间及切除后的动态响应,包括发电机转子角度、转速、电压和功率等关键参数的变化,进而评估系统的暂态稳定能力。该仿真有助于理解电力系统稳定性机理,掌握暂态过程分析方法。; 适合人群:电气工程及相关专业的本科生、研究生,以及从事电力系统分析、运行与控制工作的科研人员和工程师。; 使用场景及目标:①学习电力系统暂态稳定的基本概念与分析方法;②掌握利用Simulink进行电力系统建模与仿真的技能;③研究短路故障对系统稳定性的影响及提高稳定性的措施(如故障清除时间优化);④辅助课程设计、毕业设计或科研项目中的系统仿真验证。; 阅读建议:建议结合电力系统稳定性理论知识进行学习,先理解仿真模型各模块的功能与参数设置,再运行仿真并仔细分析输出结果,尝试改变故障类型或系统参数以观察其对稳定性的影响,从而深化对暂态稳定问题的理解。
本研究聚焦于运用MATLAB平台,将支持向量机(SVM)应用于数据预测任务,并引入粒子群优化(PSO)算法对模型的关键参数进行自动调优。该研究属于机器学习领域的典型实践,其核心在于利用SVM构建分类模型,同时借助PSO的全局搜索能力,高效确定SVM的最优超参数配置,从而显著增强模型的整体预测效能。 支持向量机作为一种经典的监督学习方法,其基本原理是通过在高维特征空间中构造一个具有最大间隔的决策边界,以实现对样本数据的分类或回归分析。该算法擅长处理小规模样本集、非线性关系以及高维度特征识别问题,其有效性源于通过核函数将原始数据映射至更高维的空间,使得原本复杂的分类问题变得线性可分。 粒子群优化算法是一种模拟鸟群社会行为的群体智能优化技术。在该算法框架下,每个潜在解被视作一个“粒子”,粒子群在解空间中协同搜索,通过不断迭代更新自身速度与位置,并参考个体历史最优解和群体全局最优解的信息,逐步逼近问题的最优解。在本应用中,PSO被专门用于搜寻SVM中影响模型性能的两个关键参数——正则化参数C与核函数参数γ的最优组合。 项目所提供的实现代码涵盖了从数据加载、预处理(如标准化处理)、基础SVM模型构建到PSO优化流程的完整步骤。优化过程会针对不同的核函数(例如线性核、多项式核及径向基函数核等)进行参数寻优,并系统评估优化前后模型性能的差异。性能对比通常基于准确率、精确率、召回率及F1分数等多项分类指标展开,从而定量验证PSO算法在提升SVM模型分类能力方面的实际效果。 本研究通过一个具体的MATLAB实现案例,旨在演示如何将全局优化算法与机器学习模型相结合,以解决模型参数选择这一关键问题。通过此实践,研究者不仅能够深入理解SVM的工作原理,还能掌握利用智能优化技术提升模型泛化性能的有效方法,这对于机器学习在实际问题中的应用具有重要的参考价值。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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