Directory Lister (64-bit)

最新版本 NumPy 1.16.0

NumPy 1.16.0

NumPy 1.16.0
目錄 Lister 64bit 是一個用於從硬盤,CD-ROM,軟盤,USB 存儲器上的用戶選擇目錄生成文件列表的工具。列表可以是 HTML,TXT 或 CSV 格式。這就像老的指揮,但更方便。安裝目錄列表器,並進行 30 天免費試用!

目錄列表器特點:

列表文件夾內容
目錄列表器允許您列出& 打印文件夾的內容,即創建,然後保存,打印或通過電子郵件發送從硬盤上,CD-ROM,DVD-ROM,軟盤,USB 存儲和網絡共享選定的文件夾中的文件列表。列表可以是 HTML,文本,Microsoft Excel,CSV 格式或直接存儲到數據庫中。目錄列表程序是來自各種目錄打印機的最佳應用程序。目錄列表也可以集成到 Windows 資源管理器的上下文菜單中,所以你甚至不需要打開應用程序來生成列表。命令行界面支持從 Windows Task Scheduler 運行的自動化列表.

打印文件夾列表
當您打印文件列表時,可以包括文件名,擴展名,類型,所有者和屬性等標准文件信息以及可執行文件信息( EXE,DLL,OCX)像文件版本,描述,公司。此外,還可以列出音軌,標題,藝術家,專輯,流派,視頻格式,每像素比特率,每秒幀數,音頻格式,每通道比特等多媒體屬性(MP3,AVI,WAV,JPG,GIF,BMP)。您可以打印的另一組列是 Microsoft Office 和 Open Office 文件(DOC,XLS,PPT),因此您可以在不打開這些文件的情況下查看文檔標題,作者,關鍵字等。對於每個文件和文件夾,還可以獲得 CRC32,MD5,SHA-1,SHA-256,SHA-512 和 Whirlpool 哈希碼,以便驗證文件未被修改。

打印文件夾中的文件
大量選項允許你完全自定義輸出的外觀。您可以設置文件和文件夾的排序方式,以便隨時顯示它們。您可以定義列順序,使最重要的列立即可見。國際顯示格式選項允許您根據當地需要調整輸出。列表可以包含指向實際文件和目錄的鏈接,因此您可以將列表放在具有可點擊內容的網頁上。 HTML 顯示樣式完全自定義 - 您可以更改背景顏色,標題,目錄行,奇數和偶數文件行以及周圍框架的單獨樣式。您可以通過對文件名,日期,大小或屬性應用過濾器來限製文件列表。

檢查文件夾大小或查找大文件夾
目錄列表程序 64 位,您還可以找出給定的目錄大小是什麼,按文件夾大小進行排序並檢查哪些文件夾佔用了磁盤上最多的空間。您還可以使用尺寸過濾器選項在 PC 上找到最大的文件.

Windows 10 支持聲明
目錄 Lister 在以下版本的 Windows 10 中受到 KRKsoft 的支持– Windows 10 Pro,Windows 10 Education 和 Windows 10 Enterprise。目錄列表支持 Windows 10 的支持服務的分支機構,包括 - 當前分支機構,當前分支機構以及以下長期服務分支機構 - Windows 10 Enterprise LTSB.

系統要求
目錄列表程序可在 Windows XP,Windows 2003,Windows 2008,Windows Vista,Windows 7,Windows 8 和 Windows 10 操作系統。它適用於 32 位和 64 位 Windows 版本。

注意:30 天試用版.

ScreenShot

軟體資訊
檔案版本 NumPy 1.16.0

檔案名稱 numpy-1.16.0.zip
檔案大小 4.82 MB
系統 Windows 7 / Windows 7 64 / Windows 8 / Windows 8 64 / Windows 10 / Windows 10 64
軟體類型 未分類
作者 KRKsoft
官網 https://www.krksoft.com/
更新日期 2019-01-14
更新日誌

What's new in this version:

Highlights:
- Experimental support for overriding numpy functions, see __array_function__ below.
- The matmul function is now a ufunc. This provides better performance and allows overriding with __array_ufunc__.
- Improved support for the ARM and POWER architectures.
- Improved support for AIX and PyPy.
- Improved interop with ctypes.
- Improved support for PEP 3118.

New functions:
- New functions added to the numpy.lib.recfuntions module to ease the structured assignment changes: assign_fields_by_name, structured_to_unstructured, unstructured_to_structured, apply_along_fields, require_fields

New deprecations:
- The type dictionaries numpy.core.typeNA and numpy.core.sctypeNA are deprecated. They were buggy and not documented and will be removed in the 1.18 release. Usenumpy.sctypeDict instead.
- The numpy.asscalar function is deprecated. It is an alias to the more powerful numpy.ndarray.item, not tested, and fails for scalars.
- The numpy.set_array_ops and numpy.get_array_ops functions are deprecated.
- As part of NEP 15, they have been deprecated along with the C-API functions :c:func:PyArray_SetNumericOps and :c:func:PyArray_GetNumericOps. Users who wish to override the inner loop functions in built-in ufuncs should use :c:func:PyUFunc_ReplaceLoopBySignature.
- The numpy.unravel_index keyword argument dims is deprecated, use shape instead.
- The numpy.histogram normed argument is deprecated. It was deprecated previously, but no warning was issued.
- The positive operator (+) applied to non-numerical arrays is deprecated. See below for details.
- Passing an iterator to the stack functions is deprecated

Expired deprecations:
- NaT comparisons now return False without a warning, finishing a deprecation cycle begun in NumPy 1.11.
- np.lib.function_base.unique was removed, finishing a deprecation cycle begun in NumPy 1.4. Use numpy.unique instead.
- multi-field indexing now returns views instead of copies, finishing a deprecation cycle begun in NumPy 1.7. The change was previously attempted in NumPy 1.14 but reverted until now.
- np.PackageLoader and np.pkgload have been removed. These were deprecated in 1.10, had no tests, and seem to no longer work in 1.15.

Future changes:
NumPy 1.17 will drop support for Python 2.7

NumPy 1.16.0 相關參考資料
NumPy User Guide - Numpy and Scipy Documentation - SciPy.org

NumPy User Guide, Release 1.16.0. This guide is intended as an introductory overview of NumPy and explains how to install and make use of ...

https://docs.scipy.org

numpy1.16.0-notes.rst at master · numpynumpy · GitHub

NumPy 1.16.0 Release Notes. This NumPy release is the last one to support Python 2.7 and will be maintained as a long term release with bug fixes until 2020.

https://github.com

Releases · numpynumpy · GitHub

Commonly numpy.broadcast_arrays returns a writeable array with internal ...... The NumPy 1.16.1 release fixes bugs reported against the 1.16.0 release, and

https://github.com

numpynumpy - GitHub

I've got numpy version updated automatically from PyPI to 1.16.0 version today and my tests have failed with the following error on numpy ...

https://github.com

numpy · PyPI

NumPy is the fundamental package for array computing with Python.

https://pypi.org

Release Notes — NumPy v1.17 Manual

The NumPy 1.16.1 release fixes bugs reported against the 1.16.0 release, and also backports several enhancements from master that seem appropriate for a ...

https://docs.scipy.org

Release Notes — NumPy v1.16 Manual

NumPy 1.16.0 Release Notes¶. This NumPy release is the last one to support Python 2.7 and will be maintained as a long term release with ...

https://docs.scipy.org

Overview — NumPy v1.16 Manual - Numpy and Scipy Documentation

NumPy v1.16 Manual. Welcome! This is the documentation for NumPy 1.16.0, last updated Jan 31, 2019. Parts of the documentation: ...

https://docs.scipy.org

NumPy Reference - Numpy and Scipy Documentation

NumPy Reference, Release 1.16.0 itemsize [int] Length of one array element in bytes. nbytes [int] Total bytes consumed by the elements of the ...

https://docs.scipy.org