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zM6;mM%4_<8u}zp4{vCr5$}=gOi3zlNg`xakt>HB{4!=YweoadBSK;BmHiiAFC2%H6 zSKQdzRFj>>VJE8q!eWT2X>QDkhtu?NX>#{8%L`+>F$2h6LB*Jvk0SVFCCWZi?FE3m z%$3lL`&dR$#$Aij7EJC&icIguK%267XRQMKo|8R# z2-xe9j0029j(Tj0Wq}172r>Vy^^Ui)^wn3M)F3o7HA_mcX;bnE`8?hqwkqRtO&P5x zKQP)4ptk_;mu_hOW6xkVr!v_eS^y`D+qUE$nwaz-)&VsWvJeJ-zZKvzzVKzKI+-?W a?H~BozPpVvesE$K&g!oj_WuXK-h4A-`(@1l From 110f9de7e780b23cda66fd0f01b21ccab4e321eb Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 25 Oct 2024 17:55:04 +0000 Subject: [PATCH 2/9] Delete file --- playground/git_api_test.ipynb | 1047 --------------------------------- 1 file changed, 1047 deletions(-) delete mode 100644 playground/git_api_test.ipynb diff --git a/playground/git_api_test.ipynb b/playground/git_api_test.ipynb deleted file mode 100644 index 0666d34a..00000000 --- a/playground/git_api_test.ipynb +++ /dev/null @@ -1,1047 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5b32e79f-6b2a-400c-8858-add19aa920a5", - "metadata": {}, - "source": [ - "# Git API test\n", - "This notebook serves testing if the gitlab and github APIs are compatible." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e429bc05-fb8b-4e02-83da-571d8520d746", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.chdir(\"../\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "96ad607d-2d96-4d13-b15a-c82440036a75", - "metadata": {}, - "outputs": [], - "source": [ - "import git_bob._gitlab_utilities as gu1\n", - "import git_bob._github_utilities as gu2" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0e5a8835-57bb-4058-bade-67a8e24d5bd7", - "metadata": {}, - "outputs": [], - "source": [ - "repository = \"haesleinhuepf/git-bob\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "33069017-7031-4acd-aa4c-1b4e22d3f391", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "\n", - "\n", - " \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu, \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f4582ea8-6b9c-4953-9705-e967ae625c6b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "main \n", - "\n", - "\n", - "main \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.get_default_branch_name(repository), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b86d90cf-e19a-4fb0-8ea4-1e2c2b921851", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> add_reaction_to_last_comment_in_issue(haesleinhuepf/git-bob, 1, +1)\n", - "None \n", - "\n", - "\n", - "-> add_reaction_to_last_comment_in_issue(haesleinhuepf/git-bob, 1, +1)\n", - "None \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.add_reaction_to_last_comment_in_issue(repository, 1, \"+1\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8e1cdbcf-3f6c-4232-9fc5-10b00688c9d3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> create_issue(haesleinhuepf/git-bob, test4fun, ...)\n", - "13\n", - "-> add_reaction_to_last_comment_in_issue(haesleinhuepf/git-bob, 13, +1)\n", - "None \n", - "\n", - "\n", - "-> add_comment_to_issue(haesleinhuepf/git-bob, 13, ...)\n", - "Comment added to issue #13 in repository haesleinhuepf/git-bob.\n", - "None \n", - "\n", - "\n", - "-> add_reaction_to_last_comment_in_issue(haesleinhuepf/git-bob, 13, +1)\n", - "None \n", - "\n", - "\n", - "-> create_issue(haesleinhuepf/git-bob, test4fun)\n", - "Issue created: #395\n", - "395\n", - "-> add_reaction_to_last_comment_in_issue(haesleinhuepf/git-bob, 395, +1)\n", - "None \n", - "\n", - "\n", - "-> add_comment_to_issue(haesleinhuepf/git-bob, 395, ...)\n", - "Comment added to issue #395 in repository haesleinhuepf/git-bob.\n", - "None \n", - "\n", - "\n", - "-> add_reaction_to_last_comment_in_issue(haesleinhuepf/git-bob, 395, +1)\n", - "None \n", - "\n", - "\n" - ] - } - ], - "source": [ - "from datetime import datetime\n", - "\n", - "current_datetime = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n", - "\n", - "for gu in [gu1, gu2]:\n", - " issue = gu.create_issue(repository, \"test4fun\", \"hello world again\")\n", - " print(issue)\n", - " print(gu.add_reaction_to_last_comment_in_issue(repository, issue, \"+1\"), \"\\n\\n\")\n", - " print(gu.add_comment_to_issue(repository, issue, \"hello world \" + current_datetime), \"\\n\\n\")\n", - " print(gu.add_reaction_to_last_comment_in_issue(repository, issue, \"+1\"), \"\\n\\n\")\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f10f007f-a52e-4132-a801-41550cd1d45e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> get_diff_of_pull_request(haesleinhuepf/git-bob, 2)\n", - "File: playground/test.txt -> playground/test.txt\n", - "@@ -0,0 +1 @@\n", - "+hello-world\n", - "\\ No newline at end of file\n", - "\n", - "\n", - "\n", - "-> get_diff_of_pull_request(haesleinhuepf/git-bob, 387)\n", - "https://github.com/haesleinhuepf/git-bob/pull/387.diff\n", - "diff --git a/playground/test.txt b/playground/test.txt\n", - "new file mode 100644\n", - "index 0000000..bdd51cc\n", - "--- /dev/null\n", - "+++ b/playground/test.txt\n", - "@@ -0,0 +1 @@\n", - "+hello-world\n", - "\\ No newline at end of file\n", - "\n" - ] - } - ], - "source": [ - "for gu, pr in zip([gu1, gu2], [2, 387]):\n", - " print(gu.get_diff_of_pull_request(repository, pr))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4a77f950-580b-4246-a201-0327f8b80517", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> create_branch(haesleinhuepf/git-bob, main)\n", - "-> create_branch(haesleinhuepf/git-bob, main)\n", - "git-bob-mod-9AsJU2OJ7D\n", - "git-bob-mod-88AwAVptDT\n" - ] - } - ], - "source": [ - "gu1_branch = gu1.create_branch(repository)\n", - "gu2_branch = gu2.create_branch(repository)\n", - "\n", - "print(gu1_branch)\n", - "print(gu2_branch)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1dc52b1b-71c6-44e3-aa5c-a6fe9a755608", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> write_file_in_branch(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/test.txt)\n", - "File playground/test.txt successfully created in repository haesleinhuepf/git-bob branch git-bob-mod-9AsJU2OJ7D. \n", - "\n", - "\n", - "-> write_file_in_new_branch(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/test.txt, ...)\n", - "-> check_if_file_exists(haesleinhuepf/git-bob, playground/test.txt)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/test.txt)\n", - "loading file content... playground/test.txt\n", - "File playground/test.txt successfully created in repository haesleinhuepf/git-bob branch git-bob-mod-88AwAVptDT. \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.write_file_in_branch(repository, branch, \"playground/test.txt\", \"hello-world\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "feb42d6c-1652-4b0e-b6b7-88dc1708411a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> get_diff_of_branches(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, main)\n", - "File:playground/test.txt -> playground/test.txt\n", - "----------------------------------------\n", - "@@ -0,0 +1 @@\n", - "+hello-world\n", - "\\ No newline at end of file\n", - " \n", - "\n", - "\n", - "\n", - "File: playground/test.txt\n", - "Status: added\n", - "----------------------------------------\n", - "@@ -0,0 +1 @@\n", - "+hello-world\n", - "\\ No newline at end of file \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.get_diff_of_branches(repository, branch, \"main\"), \"\\n\\n\")\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "717c69e7-efcf-45ab-8513-1f2f25408723", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> check_if_file_exists(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/test.txt)\n", - "True \n", - "\n", - "\n", - "-> check_if_file_exists(haesleinhuepf/git-bob, playground/test.txt)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/test.txt)\n", - "loading file content... playground/test.txt\n", - "True \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.check_if_file_exists(repository, branch, \"playground/test.txt\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e24f7926-b96a-4fa5-a9ae-8b660f95ca9a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> check_if_file_exists(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/blub.txt)\n", - "False \n", - "\n", - "\n", - "-> check_if_file_exists(haesleinhuepf/git-bob, playground/blub.txt)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/blub.txt)\n", - "loading file content... playground/blub.txt\n", - "False \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.check_if_file_exists(repository, branch, \"playground/blub.txt\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ffa99d44-aa29-49d2-8254-121d81f25f9d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> copy_file_in_repository(haesleinhuepf/git-bob, playground/test.txt, playground/blub2.txt, git-bob-mod-9AsJU2OJ7D)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/test.txt)\n", - "-> write_file_in_branch(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/blub2.txt)\n", - "None \n", - "\n", - "\n", - "-> copy_file_in_repository(haesleinhuepf/git-bob, playground/test.txt, playground/blub3.txt, git-bob-mod-9AsJU2OJ7D)\n", - "-> write_file_in_branch(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/blub3.txt)\n", - "None \n", - "\n", - "\n", - "-> copy_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/test.txt, playground/blub2.txt)\n", - "None \n", - "\n", - "\n", - "-> copy_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/test.txt, playground/blub3.txt)\n", - "None \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.copy_file_in_repository(repository, branch, \"playground/test.txt\", \"playground/blub2.txt\"), \"\\n\\n\")\n", - " print(gu.copy_file_in_repository(repository, branch, \"playground/test.txt\", \"playground/blub3.txt\"), \"\\n\\n\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "79cd1650-28fc-461b-921e-ec4e6a5dfdca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> rename_file_in_repository(haesleinhuepf/git-bob, playground/test.txt, playground/blub.txt, git-bob-mod-9AsJU2OJ7D)\n", - "None \n", - "\n", - "\n", - "-> rename_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/test.txt, playground/blub.txt)\n", - "None \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.rename_file_in_repository(repository, branch, \"playground/test.txt\", \"playground/blub.txt\"), \"\\n\\n\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b94bd5b1-1fdd-427d-9307-94d951ef0ed1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> check_if_file_exists(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/blub.txt)\n", - "False \n", - "\n", - "\n", - "-> check_if_file_exists(haesleinhuepf/git-bob, playground/blub.txt)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/blub.txt)\n", - "loading file content... playground/blub.txt\n", - "True \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.check_if_file_exists(repository, branch, \"playground/blub.txt\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "4d78779a-df02-428f-9f98-0e5be2c89263", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> download_to_repository(haesleinhuepf/git-bob, playground/image.png, https://github.com/user-attachments/assets/da6456bb-7bdf-4ad8-8f0d-95e38fa110e9, git-bob-mod-9AsJU2OJ7D)\n", - "-> write_file_in_branch(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/image.png)\n", - "None \n", - "\n", - "\n", - "-> download_to_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, https://github.com/user-attachments/assets/da6456bb-7bdf-4ad8-8f0d-95e38fa110e9, playground/image.png)\n", - "File 'playground/image.png' successfully uploaded.\n", - "None \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.download_to_repository(repository, branch, \"https://github.com/user-attachments/assets/da6456bb-7bdf-4ad8-8f0d-95e38fa110e9\", \"playground/image.png\"), \"\\n\\n\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4c3f6d60-0b88-410e-9a13-0352634cfb6b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> check_if_file_exists(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/image.png)\n", - "True \n", - "\n", - "\n", - "-> check_if_file_exists(haesleinhuepf/git-bob, playground/image.png)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/image.png)\n", - "loading file content... playground/image.png\n", - "True \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.check_if_file_exists(repository, branch, \"playground/image.png\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "4905226a-e114-4161-ac9d-140f019e2c55", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> check_if_file_exists(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/blub.txt)\n", - "False \n", - "\n", - "\n", - "-> check_if_file_exists(haesleinhuepf/git-bob, playground/blub.txt)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/blub.txt)\n", - "loading file content... playground/blub.txt\n", - "True \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.check_if_file_exists(repository, branch, \"playground/blub.txt\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "31ec2874-ac04-4b36-9a1a-d6997c8e937e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> delete_file_from_repository(haesleinhuepf/git-bob, playground/blub2.txt, git-bob-mod-9AsJU2OJ7D)\n", - "None \n", - "\n", - "\n", - "-> delete_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/blub2.txt)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/blub2.txt)\n", - "loading file content... playground/blub2.txt\n", - "None \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.delete_file_from_repository(repository, branch, \"playground/blub2.txt\"), \"\\n\\n\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7d641aca-57d0-41ee-bb65-53fd5eab36ca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> check_if_file_exists(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/blub.txt)\n", - "False \n", - "\n", - "\n", - "-> check_if_file_exists(haesleinhuepf/git-bob, playground/blub.txt)\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/blub.txt)\n", - "loading file content... playground/blub.txt\n", - "True \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.check_if_file_exists(repository, branch, \"playground/blub.txt\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "6a39c9c7-63a9-4dc3-9a13-6c89f2306a3a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, playground/blub3.txt)\n", - "hello-world \n", - "\n", - "\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, playground/blub3.txt)\n", - "loading file content... playground/blub3.txt\n", - "hello-world \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.decode_file(gu.get_file_in_repository(repository, branch, \"playground/blub3.txt\")), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "c7b0c3b7-33c7-41a1-b41b-7092eba53ac7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> send_pull_request(haesleinhuepf/git-bob, git-bob-mod-9AsJU2OJ7D, main)\n", - "Pull request created: https://gitlab.com/haesleinhuepf/git-bob/-/merge_requests/5 \n", - "\n", - "\n", - "-> send_pull_request(haesleinhuepf/git-bob, git-bob-mod-88AwAVptDT, main, ...)\n", - "Pull request created: https://github.com/haesleinhuepf/git-bob/pull/396 \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu, branch in zip([gu1, gu2], [gu1_branch, gu2_branch]):\n", - " print(gu.send_pull_request(repository, branch, \"main\", \"test-pr\", \"hello world pr\"), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fbf3868d-fcea-4376-bdcb-36f81a8ceb02", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "33dffa0d-32ca-4b2b-a4e9-b231a14c7783", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> check_access_and_ask_for_approval(haesleinhuepf, haesleinhuepf/git-bob, 1)\n", - "True \n", - "\n", - "\n", - "-> check_access_and_ask_for_approval(haesleinhuepf, haesleinhuepf/git-bob, 1)\n", - "True \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.check_access_and_ask_for_approval(\"haesleinhuepf\", repository, 1), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "fe337023-e7bf-4ca5-885c-8d5095c5a5cb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> check_access_and_ask_for_approval(ddd, haesleinhuepf/git-bob, 1)\n", - "User does not have access rights.\n", - "-> add_comment_to_issue(haesleinhuepf/git-bob, 1, ...)\n", - "Comment added to issue #1 in repository haesleinhuepf/git-bob.\n", - "False \n", - "\n", - "\n", - "-> check_access_and_ask_for_approval(ddd, haesleinhuepf/git-bob, 1)\n", - "User does not have access rights.\n", - "-> add_comment_to_issue(haesleinhuepf/git-bob, 1, ...)\n", - "Comment added to issue #1 in repository haesleinhuepf/git-bob.\n", - "False \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.check_access_and_ask_for_approval(\"ddd\", repository, 1), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "7a7a81bd-85ba-4c40-a324-852e8d2ebf7b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> get_contributors(haesleinhuepf/git-bob)\n", - "['github-actions[bot]', 'Robert Haase'] \n", - "\n", - "\n", - "-> get_contributors(haesleinhuepf/git-bob)\n", - "['haesleinhuepf', 'github-actions[bot]'] \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.get_contributors(repository), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d8cb48ac-5f1a-4e70-b13c-516427c0cf63", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> get_repository_file_contents(haesleinhuepf/git-bob, ['playground/test.py'], main)\n", - "{'playground/test.py': 'def faculty(number):\\n if number == 0:\\n return 1\\n else:\\n \n", - "\n", - "\n", - "-> get_repository_file_contents(haesleinhuepf/git-bob, ['playground/test.py'])\n", - "-> get_file_in_repository(haesleinhuepf/git-bob, main, playground/test.py)\n", - "loading file content... playground/test.py\n", - "{'playground/test.py': 'def faculty(number):\\n if number == 0:\\n return 1\\n else:\\n \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(str(gu.get_repository_file_contents(repository, \"main\", [\"playground/test.py\"]))[:100], \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "a5c1f71d-25f5-4c14-a64c-09fe27018b93", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> list_repository_files(haesleinhuepf/git-bob)\n", - "['.gitattributes', '.gitignore', 'LICENSE', 'README.md', 'pyproject.toml', 'requirements.txt', 'setup.cfg', 'setup.py', 'tests/test_github_utilities.py', 'tests/test_logger.py', 'tests/test_utilities.py', 'src/git_bob/__init__.py', 'src/git_bob/_ai_github_utilities.py', 'src/git_bob/_endpoints.py', 'src/git_bob/_github_utilities.py', 'src/git_bob/_logger.py', 'src/git_bob/_terminal.py', 'src/git_bob/_utilities.py', 'playground/blobs.tif', 'playground/long_file.java', 'playground/python_basics.ipynb', 'playground/readme.md', 'playground/test.py', 'playground/test3.py', 'docs/installation-tutorial.md', 'docs/images/banner.png', 'docs/images/demo_comment_code.png', 'docs/images/demo_fix_typos.png', 'docs/images/demo_question.png', 'docs/images/demo_review_pull_request.png', 'docs/images/install/img.png', 'docs/images/install/img4a.png', 'docs/images/install/img_1.png', 'docs/images/install/img_10.png', 'docs/images/install/img_2.png', 'docs/images/install/img_3.png', 'docs/images/install/img_4.png', 'docs/images/install/img_5.png', 'docs/images/install/img_6.png', 'docs/images/install/img_7.png', 'docs/images/install/img_8.png', 'docs/images/install/img_9.png', '.github/workflows/git-bob.yml', '.github/workflows/pytest.yml'] \n", - "\n", - "\n", - "-> list_repository_files(haesleinhuepf/git-bob)\n", - "['.gitattributes', '.gitignore', 'LICENSE', 'README.md', 'pyproject.toml', 'requirements.txt', 'setup.cfg', 'setup.py', 'docs/installation-tutorial.md', 'playground/blobs.tif', 'playground/long_file.java', 'playground/python_basics.ipynb', 'playground/readme.md', 'playground/test.py', 'playground/test3.py', 'tests/test_github_utilities.py', 'tests/test_logger.py', 'tests/test_utilities.py', '.github/workflows/git-bob.yml', '.github/workflows/pytest.yml', 'docs/images/banner.png', 'docs/images/demo_comment_code.png', 'docs/images/demo_fix_typos.png', 'docs/images/demo_question.png', 'docs/images/demo_review_pull_request.png', 'src/git_bob/__init__.py', 'src/git_bob/_ai_github_utilities.py', 'src/git_bob/_endpoints.py', 'src/git_bob/_github_utilities.py', 'src/git_bob/_logger.py', 'src/git_bob/_terminal.py', 'src/git_bob/_utilities.py', 'docs/images/install/img.png', 'docs/images/install/img4a.png', 'docs/images/install/img_1.png', 'docs/images/install/img_10.png', 'docs/images/install/img_2.png', 'docs/images/install/img_3.png', 'docs/images/install/img_4.png', 'docs/images/install/img_5.png', 'docs/images/install/img_6.png', 'docs/images/install/img_7.png', 'docs/images/install/img_8.png', 'docs/images/install/img_9.png'] \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.list_repository_files(repository), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "328bbb94-4059-4d00-befc-66b4f9455bb3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> get_gitlab_issue_details(haesleinhuepf/git-bob, 1)\n", - "\n", - "Issue #1: this is a test\n", - "State: opened\n", - "Created at: 2024-10-19T16:57:49.005Z\n", - "Updated at: 2024-10-20T08:31:43.504Z\n", - "Closed at: None\n", - "Author: haesleinhuepf\n", - "Assignees: \n", - "Labels: \n", - "Comments: 7\n", - "Description:\n", - "he \n", - "\n", - "\n", - "-> get_github_issue_details(haesleinhuepf/git-bob, 1)\n", - "\n", - "Issue #1: Testing conversational workflows\n", - "State: closed\n", - "Created at: 2024-08-01 13:09:09+00:00\n", - "Updated at: 2024-10-20 08:31:45+00:00\n", - "Closed at: 2024-08-01 14:47:48+00:00\n", - "Author: haesleinhuepf\n", - "Assigne \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(str(gu.get_issue_details(repository, 1))[:200], \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "c76d9fba-4516-4a99-b09b-a0661369ee19", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> list_issues(haesleinhuepf/git-bob, opened)\n", - "{13: 'test4fun', 12: 'Questions', 11: 'test4fun', 10: 'test4fun', 9: 'test4fun', 8: 'test4fun', 7: 'test4fun', 6: 'test4fun', 5: 'test4fun', 2: 'test2', 1: 'this is a test'} \n", - "\n", - "\n", - "-> list_issues(haesleinhuepf/git-bob, open)\n", - "{396: 'test-pr', 395: 'test4fun', 385: 'get_most_recently_commented_issue is super slow', 384: 'Bug: get_repository_file_contents does not have a branch parameter', 383: 'Add GitLab support with API-compatible utility functions.', 382: 'Gitlab support', 378: 'Also support Gitlab / Gitlab CI', 377: 'Integrate aider to solve complex issues', 362: 'plotting', 337: 'LLM-based task decision', 71: 'Halucinations in reviews'} \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.list_issues(repository), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f0adf7d9-f58f-4061-a5d8-d9722b8c0949", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> get_most_recent_comment_on_issue(haesleinhuepf/git-bob, 1)\n", - "('haesleinhuepf', \"\\nThis message was generated by [git-bob](https://github.com/haesleinhuepf/git-bob) (version: 0.10.0, model: None), an experimental AI-based assistant. It can make mistakes and has [limitations](https://github.com/haesleinhuepf/git-bob?tab=readme-ov-file#limitations). Check its messages carefully.\\n\\nHi @ddd, \\n\\nthanks for reaching out! Unfortunately, I'm not allowed to respond to you directly. \\nI need approval from a person who has access.\\n\\nBest,\\ngit-bob\") \n", - "\n", - "\n", - "-> get_most_recent_comment_on_issue(haesleinhuepf/git-bob, 1)\n", - "('haesleinhuepf', \"\\nThis message was generated by [git-bob](https://github.com/haesleinhuepf/git-bob) (version: 0.10.0, model: None), an experimental AI-based assistant. It can make mistakes and has [limitations](https://github.com/haesleinhuepf/git-bob?tab=readme-ov-file#limitations). Check its messages carefully.\\n\\nHi @ddd, \\n\\nthanks for reaching out! Unfortunately, I'm not allowed to respond to you directly. \\nI need approval from a person who has access.\\n\\nBest,\\ngit-bob\\n\") \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.get_most_recent_comment_on_issue(repository, 1), \"\\n\\n\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "98c59d84-f549-4750-a657-0e89027298a1", - "metadata": {}, - "outputs": [], - "source": [ - "## super slow, see https://github.com/haesleinhuepf/git-bob/issues/385\n", - "# for gu in [gu1, gu2]:\n", - "# print(gu.get_most_recently_commented_issue(repository), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "132c7c85-32bd-46c5-b91d-d66237af7439", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> get_conversation_on_issue(haesleinhuepf/git-bob, 1)\n", - "Issue Title: this is a test\n", - "\n", - "Issue Body:\n", - "hello world\n", - "\n", - "Comment by haesleinhuepf:\n", - "\n", - "This message w \n", - "\n", - "\n", - "-> get_conversation_on_issue(haesleinhuepf/git-bob, 1)\n", - "Issue Title: Testing conversational workflows\n", - "\n", - "Issue Body:\n", - "Hi, this is a test!\n", - "\n", - "Comment by haesleinh \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(gu.get_conversation_on_issue(repository, 1)[:100], \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "e6cbb2d0-efed-494e-bff9-2e4df3732bf9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-> add_comment_to_issue(haesleinhuepf/git-bob, 1, ...)\n", - "Comment added to issue #1 in repository haesleinhuepf/git-bob.\n", - "None \n", - "\n", - "\n", - "-> add_comment_to_issue(haesleinhuepf/git-bob, 1, ...)\n", - "Comment added to issue #1 in repository haesleinhuepf/git-bob.\n", - "None \n", - "\n", - "\n" - ] - } - ], - "source": [ - "from datetime import datetime\n", - "\n", - "current_datetime = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n", - "\n", - "for gu in [gu1, gu2]:\n", - " print(gu.add_comment_to_issue(repository, 1, \"hello world \" + current_datetime), \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "58c0e9ed-f9c4-494d-808a-b56948a2a394", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " => {' \n", - "\n", - "\n", - "Repository(full_name=\"haesleinhuepf/git-bob\") \n", - "\n", - "\n" - ] - } - ], - "source": [ - "for gu in [gu1, gu2]:\n", - " print(str(gu.get_repository_handle(repository))[:50], \"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "783b2898-bb3c-4e62-847e-9fdf95a565e9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7360a055-639c-438f-b542-191a87430e57", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53f8a081-49d9-454b-8bce-ec151caccf13", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "856d036e-790d-4862-91da-6ce78130721c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From adeb551fdbdfdfccf0ae1530fc5cd6225c9227da Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 25 Oct 2024 17:55:05 +0000 Subject: [PATCH 3/9] Delete file --- playground/long_file.java | 2502 ------------------------------------- 1 file changed, 2502 deletions(-) delete mode 100644 playground/long_file.java diff --git a/playground/long_file.java b/playground/long_file.java deleted file mode 100644 index e9009558..00000000 --- a/playground/long_file.java +++ /dev/null @@ -1,2502 +0,0 @@ - -/** - * This file is autogenerated. Do not edit manually. - */ -package net.clesperanto.kernels; - -import java.util.Objects; -import java.util.ArrayList; -import java.util.HashMap; - -import net.clesperanto.core.ArrayJ; -import net.clesperanto.core.DeviceJ; -import net.clesperanto.core.Utils; - -/** - * Class containing all functions of tier 1 category - */ -public class Tier1 { - - /** - * Computes the absolute value of every individual pixel x in a given image. - * 
f(x) = |x|
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The input image to be processed. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_absolute - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ absolute(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.absolute(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Calculates the sum of pairs of pixels x and y from images X and Y weighted with factors a and b. - * 
f(x, y, a, b) = x * a + y * b
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - The first input image to added. - * @param input1 ({@link ArrayJ}) - The second image to be added. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @param factor0 (float) - Multiplication factor of each pixel of src0 before adding it. (default: 1) - * @param factor1 (float) - Multiplication factor of each pixel of src1 before adding it. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_addImagesWeighted - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ addImagesWeighted(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output, float factor0, float factor1) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.add_images_weighted(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw(), factor0, factor1), device); - } - - /** - * Adds a scalar value s to all pixels x of a given image X. - * 
f(x, s) = x + s
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The input image where scalare should be added. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @param scalar (float) - The constant number which will be added to all pixels. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_addImageAndScalar - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ addImageAndScalar(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.add_image_and_scalar(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary AND operator &. - * All pixel values except 0 in the input images are interpreted as 1. - * 
f(x, y) = x & y
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - The first binary input image to be processed. - * @param input1 ({@link ArrayJ}) - The second binary input image to be processed. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_binaryAnd - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ binaryAnd(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.binary_and(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines pixels/voxels which are on the surface of binary objects and sets only them to 1 in the destination image. - * All other pixels are set to 0. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The binary input image where edges will be searched. - * @param output ({@link ArrayJ}) - The output image where edge pixels will be 1. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_binaryEdgeDetection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ binaryEdgeDetection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.binary_edge_detection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a binary image (containing pixel values 0 and 1) from an image X by negating its pixel values x using the binary NOT operator ! All pixel values except 0 in the input image are interpreted as 1. - * 
f(x) = !x
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The binary input image to be inverted. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_binaryNot - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ binaryNot(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.binary_not(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary OR operator |. - * All pixel values except 0 in the input images are interpreted as 1. - * 
f(x, y) = x | y
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - The first binary input image to be processed. - * @param input1 ({@link ArrayJ}) - The second binary input image to be processed. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_binaryOr - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ binaryOr(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.binary_or(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Subtracts one binary image from another. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - The first binary input image to be processed. - * @param input1 ({@link ArrayJ}) - The second binary input image to be subtracted from the first. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_binarySubtract - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ binarySubtract(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.binary_subtract(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary operators AND &, OR | and NOT ! implementing the XOR operator. - * All pixel values except 0 in the input images are interpreted as 1. - * 
f(x, y) = (x & !y) | (!x & y)
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - The first binary input image to be processed. - * @param input1 ({@link ArrayJ}) - The second binary input image to be processed. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_binaryXOr - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ binaryXor(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.binary_xor(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Compute the maximum of the erosion with plannar structuring elements. - * Warning: This operation is only supported BINARY data type images. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The binary input image to be processed. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ binarySupinf(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.binary_supinf(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Compute the minimum of the dilation with plannar structuring elements. - * Warning: This operation is only supported BINARY data type images. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The binary input image to be processed. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ binaryInfsup(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.binary_infsup(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Enumerates pixels with value 1 in a onedimensional image For example handing over the image [0, 1, 1, 0, 1, 0, 1, 1] would be processed to an image [0, 1, 2, 0, 3, 0, 4, 5] This functionality is important in connected component neccessary (see also sum_reduction_x). - * In the above example, with blocksize 4, that would be the sum array: [2, 3] labeling. - * Processing is accelerated by paralellization in blocks. - * Therefore, handing over precomputed block sums is Note that the block size when calling this function and sum_reduction must be identical. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - input binary vector image - * @param input1 ({@link ArrayJ}) - precomputed sums of blocks - * @param output ({@link ArrayJ}) - output enumerated vector image (default: None) - * @param blocksize (int) - (default: 256) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ blockEnumerate(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output, int blocksize) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.block_enumerate(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw(), blocksize), device); - } - - /** - * Convolve the image with a given kernel image. - * It is recommended that the kernel image has an odd size in X, Y and Z. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_convolve - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ convolve(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.convolve(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Copies an image. - * 
f(x) = x
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to copy. - * @param output ({@link ArrayJ}) - Output copy image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_copy - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ copy(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.copy(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * This method has two purposes: It copies a 2D image to a given slice z position in a 3D image stack or It copies a given slice at position z in an image stack to a 2D image. - * The first case is only available via ImageJ macro. - * If you are using it, it is recommended that the target 3D image already preexists in GPU memory before calling this method. - * Otherwise, CLIJ create the image stack with z planes. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to copy from. - * @param output ({@link ArrayJ}) - Output copy image slice. (default: None) - * @param slice (int) - (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_copySlice - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ copySlice(DeviceJ device, ArrayJ input, ArrayJ output, int slice) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.copy_slice(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), slice), device); - } - - /** - * This method has two purposes: It copies a 2D image to a given slice y position in a 3D image stack or It copies a given slice at position y in an image stack to a 2D image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to copy from. - * @param output ({@link ArrayJ}) - Output copy image slice. (default: None) - * @param slice (int) - (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_copySlice - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ copyHorizontalSlice(DeviceJ device, ArrayJ input, ArrayJ output, int slice) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.copy_horizontal_slice(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), slice), device); - } - - /** - * This method has two purposes: It copies a 2D image to a given slice x position in a 3D image stack or It copies a given slice at position x in an image stack to a 2D image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to copy from. - * @param output ({@link ArrayJ}) - Output copy image slice. (default: None) - * @param slice (int) - (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_copySlice - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ copyVerticalSlice(DeviceJ device, ArrayJ input, ArrayJ output, int slice) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.copy_vertical_slice(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), slice), device); - } - - /** - * Crops a given substack out of a given image stack. - * Note: If the destination image preexists already, it will be overwritten and keep it's dimensions. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param start_x (int) - Starting index coordicante x. (default: 0) - * @param start_y (int) - Starting index coordicante y. (default: 0) - * @param start_z (int) - Starting index coordicante z. (default: 0) - * @param width (int) - Width size of the region to crop. (default: 1) - * @param height (int) - Height size of the region to crop. (default: 1) - * @param depth (int) - Depth size of the region to crop. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_crop3D - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ crop(DeviceJ device, ArrayJ input, ArrayJ output, int start_x, int start_y, int start_z, int width, int height, int depth) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.crop(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), start_x, start_y, start_z, width, height, depth), device); - } - - /** - * Computes the cubic root of each pixel. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ cubicRoot(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.cubic_root(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Takes a labelmap and returns an image where all pixels on label edges are set to 1 and all other pixels to 0. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_detectLabelEdges - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ detectLabelEdges(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.detect_label_edges(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image. - * The dilation takes the Mooreneighborhood (8 pixels in 2D and 26 pixels in 3d) into account. - * The pixels in the input image with pixel value not equal to 0 will be interpreted as 1. - * This method is comparable to the 'Dilate' menu in ImageJ in case it is applied to a 2D image. - * The only difference is that the output image contains values 0 and 1 instead of 0 and 255. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. Input image to process. - * @param output ({@link ArrayJ}) - Output result image. Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_dilateBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ dilateBox(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.dilate_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image. - * The dilation takes the vonNeumannneighborhood (4 pixels in 2D and 6 pixels in 3d) into account. - * The pixels in the input image with pixel value not equal to 0 will be interpreted as 1. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. Input image to process. - * @param output ({@link ArrayJ}) - Output result image. Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_dilateSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ dilateSphere(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.dilate_sphere(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image. - * The dilation apply the Mooreneighborhood (8 pixels in 2D and 26 pixels in 3d) for the "box" connectivity and the vonNeumannneighborhood (4 pixels in 2D and 6 pixels in 3d) for a "sphere" connectivity. - * The pixels in the input image with pixel value not equal to 0 will be interpreted as 1. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. Input image to process. - * @param output ({@link ArrayJ}) - Output result image. Output result image. (default: None) - * @param connectivity (String) - Element shape, "box" or "sphere". (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_dilateBox - * @see https://clij.github.io/clij2-docs/reference_dilateSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ dilate(DeviceJ device, ArrayJ input, ArrayJ output, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.dilate(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), connectivity), device); - } - - /** - * Divides two images X and Y by each other pixel wise. - * 
f(x, y) = x / y
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_divideImages - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ divideImages(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.divide_images(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Divides a scalar by an image pixel by pixel. - * 
f(x, s) = s / x
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - (default: 0) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ divideScalarByImage(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.divide_scalar_by_image(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Determines if two images A and B equal pixel wise. - * 
f(a, b) = 1 if a == b; 0 otherwise.
-	 *
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - The first image to be compared with. - * @param input1 ({@link ArrayJ}) - The second image to be compared with the first. - * @param output ({@link ArrayJ}) - The resulting binary image where pixels will be 1 only if source1 (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_equal - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ equal(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.equal(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines if an image A and a constant b are equal. - * 
f(a, b) = 1 if a == b; 0 otherwise.
-	 *
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The image where every pixel is compared to the constant. - * @param output ({@link ArrayJ}) - The resulting binary image where pixels will be 1 only if source1 (default: None) - * @param scalar (float) - The constant where every pixel is compared to. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_equalConstant - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ equalConstant(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.equal_constant(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image. - * The erosion takes the Mooreneighborhood (8 pixels in 2D and 26 pixels in 3d) into account. - * The pixels in the input image with pixel value not equal to 0 will be interpreted as 1. - * This method is comparable to the 'Erode' menu in ImageJ in case it is applied to a 2D image. - * The only difference is that the output image contains values 0 and 1 instead of 0 and 255. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_erodeBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ erodeBox(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.erode_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image. - * The erosion takes the vonNeumannneighborhood (4 pixels in 2D and 6 pixels in 3d) into account. - * The pixels in the input image with pixel value not equal to 0 will be interpreted as 1. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_erodeSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ erodeSphere(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.erode_sphere(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image. - * The erosion apply the Mooreneighborhood (8 pixels in 2D and 26 pixels in 3d) for the "box" connectivity and the vonNeumannneighborhood (4 pixels in 2D and 6 pixels in 3d) for a "sphere" connectivity. - * The pixels in the input image with pixel value not equal to 0 will be interpreted as 1. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param connectivity (String) - Element shape, "box" or "sphere". (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_erodeBox - * @see https://clij.github.io/clij2-docs/reference_erodeSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ erode(DeviceJ device, ArrayJ input, ArrayJ output, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.erode(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), connectivity), device); - } - - /** - * Computes base exponential of all pixels values. - * f(x) = exp(x) Author(s): Peter Haub, Robert Haase. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_exponential - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ exponential(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.exponential(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Flips an image in X, Y and/or Z direction depending on boolean flags. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param flip_x (boolean) - Flip along the x axis if true. (default: True) - * @param flip_y (boolean) - Flip along the y axis if true. (default: True) - * @param flip_z (boolean) - Flip along the z axis if true. (default: True) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_flip3D - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ flip(DeviceJ device, ArrayJ input, ArrayJ output, boolean flip_x, boolean flip_y, boolean flip_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.flip(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), flip_x, flip_y, flip_z), device); - } - - /** - * Computes the Gaussian blurred image of an image given sigma values in X, Y and Z. - * Thus, the filter kernel can have nonisotropic shape. - * The implementation is done separable. - * In case a sigma equals zero, the direction is not blurred. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param sigma_x (float) - Sigma value along the x axis. (default: 0) - * @param sigma_y (float) - Sigma value along the y axis. (default: 0) - * @param sigma_z (float) - Sigma value along the z axis. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_gaussianBlur3D - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ gaussianBlur(DeviceJ device, ArrayJ input, ArrayJ output, float sigma_x, float sigma_y, float sigma_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.gaussian_blur(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), sigma_x, sigma_y, sigma_z), device); - } - - /** - * Computes the distance between all point coordinates given in two point lists. - * Takes two images containing pointlists (dimensionality n * d, n: number of points and d: dimensionality) and builds up a matrix containing the distances between these points. - * Convention: Given two point lists with dimensionality n * d and m * d, the distance matrix will be of size(n + 1) * (m + 1). - * The first row and column contain zeros. - * They represent the distance of the (see generateTouchMatrix). - * Thus, one can threshold a distance matrix to generate a touch matrix out of it for drawing objects to a theoretical background object. - * In that way, distance matrices are of the same size as touch matrices meshes. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_generateDistanceMatrix - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ generateDistanceMatrix(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.generate_distance_matrix(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the gradient of gray values along X. - * Assuming a, b and c are three adjacent pixels in X direction. - * In the target image will be saved as: 
b' = c a;
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_gradientX - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ gradientX(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.gradient_x(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the gradient of gray values along Y. - * Assuming a, b and c are three adjacent pixels in Y direction. - * In the target image will be saved as: 
b' = c a;
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_gradientY - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ gradientY(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.gradient_y(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the gradient of gray values along Z. - * Assuming a, b and c are three adjacent pixels in Z direction. - * In the target image will be saved as: 
b' = c a;
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_gradientZ - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ gradientZ(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.gradient_z(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines if two images A and B greater pixel wise. - * f(a, b) = 1 if a > b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_greater - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ greater(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.greater(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines if two images A and B greater pixel wise. - * f(a, b) = 1 if a > b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_greaterConstant - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ greaterConstant(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.greater_constant(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Determines if two images A and B greater or equal pixel wise. - * f(a, b) = 1 if a >= b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_greaterOrEqual - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ greaterOrEqual(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.greater_or_equal(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines if two images A and B greater or equal pixel wise. - * f(a, b) = 1 if a >= b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - Scalar value used in the comparison. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_greaterOrEqualConstant - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ greaterOrEqualConstant(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.greater_or_equal_constant(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Computes the eigenvalues of the hessian matrix of a 2d or 3d image. - * Hessian matrix or 2D images: [Ixx, Ixy] [Ixy, Iyy] Hessian matrix for 3D images: [Ixx, Ixy, Ixz] [Ixy, Iyy, Iyz] [Ixz, Iyz, Izz] Ixx denotes the second derivative in x. - * Ixx and Iyy are calculated by convolving the image with the 1d kernel [1 2 1]. - * Ixy is calculated by a convolution with the 2d kernel: [ 0. - * 25 0 0. - * 25] [ 0 0 0] [0. - * 25 0 0. - * 25] Note: This is the only clesperanto function that returns multiple images. - * This API might be subject to change in the future. - * Consider using small_hessian_eigenvalue() and/or large_hessian_eigenvalue() instead which return only one image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param small_eigenvalue ({@link ArrayJ}) - Output result image. (default: None) - * @param middle_eigenvalue ({@link ArrayJ}) - Output result image, null if input is 2D. (default: None) - * @param large_eigenvalue ({@link ArrayJ}) - Output result image. (default: None) - * @return ArrayList<{@link ArrayJ}> - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayList hessianEigenvalues(DeviceJ device, ArrayJ input, ArrayJ small_eigenvalue, ArrayJ middle_eigenvalue, ArrayJ large_eigenvalue) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return Utils.toArrayList(net.clesperanto._internals.kernelj.Tier1.hessian_eigenvalues(device.getRaw(), input.getRaw(), small_eigenvalue == null ? null : small_eigenvalue.getRaw(), middle_eigenvalue == null ? null : middle_eigenvalue.getRaw(), large_eigenvalue == null ? null : large_eigenvalue.getRaw())); - } - - /** - * Applies the Laplace operator (Box neighborhood) to an image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_laplaceBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ laplaceBox(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.laplace_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Applies the Laplace operator (Diamond neighborhood) to an image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_laplaceDiamond - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ laplaceDiamond(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.laplace_diamond(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Applies the Laplace operator with a "box" or a "sphere" neighborhood to an image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_laplaceDiamond - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ laplace(DeviceJ device, ArrayJ input, ArrayJ output, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.laplace(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), connectivity), device); - } - - /** - * Compute the cross correlation of an image to a given kernel. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ localCrossCorrelation(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.local_cross_correlation(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes base e logarithm of all pixels values. - * f(x) = log(x) Author(s): Peter Haub, Robert Haase. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_logarithm - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ logarithm(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.logarithm(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a masked image by applying a binary mask to an image. - * All pixel values x of image X will be copied to the destination image in case pixel value m at the same position in the mask image is not equal to zero. - * 
f(x,m) = (x if (m != 0); (0 otherwise))
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param mask ({@link ArrayJ}) - Mask image to apply. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_mask - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ mask(DeviceJ device, ArrayJ input, ArrayJ mask, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - Objects.requireNonNull(mask, "mask cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mask(device.getRaw(), input.getRaw(), mask.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes a masked image by applying a label mask to an image. - * All pixel values x of image X will be copied to the destination image in case pixel value m at the same position in the label_map image has the right index value i. - * f(x,m,i) = (x if (m == i); (0 otherwise)). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - Input Intensity image. - * @param input1 ({@link ArrayJ}) - Input Label image. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param label (float) - Label value to use. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maskLabel - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ maskLabel(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output, float label) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mask_label(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw(), label), device); - } - - /** - * Computes the maximum of a constant scalar s and each pixel value x in a given image X. - * 
f(x, s) = max(x, s)
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - Scalar value used in the comparison. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maximumImageAndScalar - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ maximumImageAndScalar(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.maximum_image_and_scalar(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Computes the maximum of a pair of pixel values x, y from two given images X and Y. - * 
f(x, y) = max(x, y)
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maximumImages - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ maximumImages(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.maximum_images(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the local maximum of a pixels cube neighborhood. - * The cubes size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maximum3DBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ maximumBox(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.maximum_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local maximum of a pixels neighborhood (box or sphere). - * The neighborhood size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 0) - * @param radius_y (int) - Radius size along y axis. (default: 0) - * @param radius_z (int) - Radius size along z axis. (default: 0) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maximum3DBox - * @see https://clij.github.io/clij2-docs/reference_maximum3DSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ maximum(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.maximum(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z, connectivity), device); - } - - /** - * Determines the maximum intensity projection of an image along X. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maximumXProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ maximumXProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.maximum_x_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the maximum intensity projection of an image along X. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maximumYProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ maximumYProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.maximum_y_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the maximum intensity projection of an image along Z. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maximumZProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ maximumZProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.maximum_z_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the local mean average of a pixels boxshaped neighborhood. - * The cubes size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_mean3DBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ meanBox(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mean_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local mean average of a pixels spherical neighborhood. - * The spheres size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_mean3DSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ meanSphere(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mean_sphere(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local mean average of a pixels neighborhood defined as a boxshaped or a sphereshaped. - * The shape size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_mean3DSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ mean(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mean(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z, connectivity), device); - } - - /** - * Determines the mean average intensity projection of an image along X. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_meanXProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ meanXProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mean_x_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the mean average intensity projection of an image along Y. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_meanYProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ meanYProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mean_y_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the mean average intensity projection of an image along Z. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_meanZProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ meanZProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mean_z_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the local median of a pixels box shaped neighborhood. - * The box is specified by its halfwidth and halfheight (radius). - * For technical reasons, the area of the box must have less than 1000 pixels. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_median3DBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ medianBox(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.median_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local median of a pixels sphere shaped neighborhood. - * The sphere is specified by its halfwidth and halfheight (radius). - * For technical reasons, the area of the box must have less than 1000 pixels. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_median3DSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ medianSphere(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.median_sphere(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local median of a pixels neighborhood. - * The neighborhood is defined as a box or a sphere shape. - * Its size is specified by its halfwidth, halfheight, and halfdepth (radius). - * For technical reasons, the area of the shpae must have less than 1000 pixels. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_median3DSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ median(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.median(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z, connectivity), device); - } - - /** - * Computes the local minimum of a pixels cube neighborhood. - * The cubes size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 0) - * @param radius_y (int) - Radius size along y axis. (default: 0) - * @param radius_z (int) - Radius size along z axis. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_minimum3DBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ minimumBox(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.minimum_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local minimum of a pixels cube neighborhood. - * The cubes size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 0) - * @param radius_y (int) - Radius size along y axis. (default: 0) - * @param radius_z (int) - Radius size along z axis. (default: 0) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_minimum3DBox - * @see https://clij.github.io/clij2-docs/reference_minimum3DSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ minimum(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.minimum(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z, connectivity), device); - } - - /** - * Computes the minimum of a constant scalar s and each pixel value x in a given image X. - * 
f(x, s) = min(x, s)
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - Scalar value used in the comparison. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_minimumImageAndScalar - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ minimumImageAndScalar(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.minimum_image_and_scalar(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Computes the minimum of a pair of pixel values x, y from two given images X and Y. - * 
f(x, y) = min(x, y)
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_minimumImages - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ minimumImages(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.minimum_images(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the minimum intensity projection of an image along Y. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_minimumXProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ minimumXProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.minimum_x_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the minimum intensity projection of an image along Y. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_minimumYProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ minimumYProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.minimum_y_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the minimum intensity projection of an image along Z. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_minimumZProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ minimumZProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.minimum_z_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the local mode of a pixels box shaped neighborhood. - * This can be used to postprocess and locally correct semantic segmentation results. - * The box is specified by its halfwidth and halfheight (radius). - * For technical reasons, the intensities must lie within a range from 0 to 255. - * In case multiple values have maximum frequency, the smallest one is returned. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ modeBox(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mode_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local mode of a pixels sphere shaped neighborhood. - * This can be used to postprocess and locally correct semantic segmentation results. - * The sphere is specified by its halfwidth and halfheight (radius). - * For technical reasons, the intensities must lie within a range from 0 to 255. - * In case multiple values have maximum frequency, the smallest one is returned. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ modeSphere(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mode_sphere(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local mode of a pixels neighborhood. - * This neighborhood can be shaped as a box or a sphere. - * This can be used to postprocess and locally correct semantic segmentation results. - * The shape size is specified by its halfwidth, halfheight, and halfdepth (radius). - * For technical reasons, the intensities must lie within a range from 0 to 255 (uint8). - * In case multiple values have maximum frequency, the smallest one is returned. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ mode(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.mode(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z, connectivity), device); - } - - /** - * Computes the remainder of a division of pairwise pixel values in two images. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ moduloImages(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.modulo_images(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Multiplies all pixel intensities with the x, y or z coordinate, depending on specified dimension. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param dimension (int) - Dimension (0,1,2) to use in the operation. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_multiplyImageAndCoordinate - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ multiplyImageAndPosition(DeviceJ device, ArrayJ input, ArrayJ output, int dimension) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.multiply_image_and_position(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), dimension), device); - } - - /** - * Multiplies all pixels value x in a given image X with a constant scalar s. - * 
f(x, s) = x * s
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The input image to be multiplied with a constant. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @param scalar (float) - The number with which every pixel will be multiplied with. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_multiplyImageAndScalar - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ multiplyImageAndScalar(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.multiply_image_and_scalar(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Multiplies all pairs of pixel values x and y from two image X and Y. - * 
f(x, y) = x * y
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - The first input image to be multiplied. - * @param input1 ({@link ArrayJ}) - The second image to be multiplied. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_multiplyImages - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ multiplyImages(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.multiply_images(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Copies all pixels instead those which are not a number (NaN), or positive/negative infinity which are replaced by a defined new value, default 0. - * This function aims to work similarly as its counterpart in numpy [1]. - * Default values for posinf and neginf may differ from numpy and even differ depending on compute hardware. - * It is recommended to specify those values. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @param nan (float) - Value to replace (default: 0) - * @param posinf (float) - Value to replace +inf with. (default: np.nan_to_num(float('inf'))) - * @param neginf (float) - Value to replace -inf with. (default: np.nan_to_num(float('-inf'))) - * @return {@link ArrayJ} - * @see https://numpy.org/doc/stable/reference/generated/numpy.nan_to_num.html - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ nanToNum(DeviceJ device, ArrayJ input, ArrayJ output, float nan, float posinf, float neginf) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.nan_to_num(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), nan, posinf, neginf), device); - } - - /** - * Apply a maximum filter (box shape) to the input image. - * The radius is fixed to 1 and pixels with value 0 are ignored. - * Note: Pixels with 0 value in the input image will not be overwritten in the output image. - * Thus, the result image should be initialized by copying the original image in advance. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag (0 or 1). - * @param output1 ({@link ArrayJ}) - Output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_nonzeroMaximumBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ nonzeroMaximumBox(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.nonzero_maximum_box(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw()), device); - } - - /** - * Apply a maximum filter (diamond shape) to the input image. - * The radius is fixed to 1 and pixels with value 0 are ignored. - * Note: Pixels with 0 value in the input image will not be overwritten in the output image. - * Thus, the result image should be initialized by copying the original image in advance. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag (0 or 1). - * @param output1 ({@link ArrayJ}) - Output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_nonzeroMaximumDiamond - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ nonzeroMaximumDiamond(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.nonzero_maximum_diamond(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw()), device); - } - - /** - * Apply a maximum filter of a neighborhood to the input image. - * The neighborhood shape can be a box or a sphere. - * The size is fixed to 1 and pixels with value 0 are ignored. - * Note: Pixels with 0 value in the input image will not be overwritten in the output image. - * Thus, the result image should be initialized by copying the original image in advance. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag (0 or 1). - * @param output1 ({@link ArrayJ}) - Output image where results are written into. (default: None) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_nonzeroMaximumBox - * @see https://clij.github.io/clij2-docs/reference_nonzeroMaximumDiamond - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ nonzeroMaximum(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.nonzero_maximum(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw(), connectivity), device); - } - - /** - * Apply a minimum filter (box shape) to the input image. - * The radius is fixed to 1 and pixels with value 0 are ignored. - * Note: Pixels with 0 value in the input image will not be overwritten in the output image. - * Thus, the result image should be initialized by copying the original image in advance. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag (0 or 1). - * @param output1 ({@link ArrayJ}) - Output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_nonzeroMinimumBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ nonzeroMinimumBox(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.nonzero_minimum_box(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw()), device); - } - - /** - * Apply a minimum filter (diamond shape) to the input image. - * The radius is fixed to 1 and pixels with value 0 are ignored. - * Note: Pixels with 0 value in the input image will not be overwritten in the output image. - * Thus, the result image should be initialized by copying the original image in advance. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag (0 or 1). - * @param output1 ({@link ArrayJ}) - Output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_nonzeroMinimumDiamond - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ nonzeroMinimumDiamond(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.nonzero_minimum_diamond(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw()), device); - } - - /** - * Apply a minimum filter of a neighborhood to the input image. - * The neighborhood shape can be a box or a sphere. - * The radius is fixed to 1 and pixels with value 0 are ignored. - * Note: Pixels with 0 value in the input image will not be overwritten in the output image. - * Thus, the result image should be initialized by copying the original image in advance. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag (0 or 1). - * @param output1 ({@link ArrayJ}) - Output image where results are written into. (default: None) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_nonzeroMinimumBox - * @see https://clij.github.io/clij2-docs/reference_nonzeroMinimumDiamond - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ nonzeroMinimum(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.nonzero_minimum(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw(), connectivity), device); - } - - /** - * Determines if two images A and B equal pixel wise. - * f(a, b) = 1 if a != b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - The first image to be compared with. - * @param input1 ({@link ArrayJ}) - The second image to be compared with the first. - * @param output ({@link ArrayJ}) - The resulting binary image where pixels will be 1 only if source1 (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_notEqual - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ notEqual(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.not_equal(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines if two images A and B equal pixel wise. - * f(a, b) = 1 if a != b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The image where every pixel is compared to the constant. - * @param output ({@link ArrayJ}) - The resulting binary image where pixels will be 1 only if source1 (default: None) - * @param scalar (float) - The constant where every pixel is compared to. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_notEqualConstant - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ notEqualConstant(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.not_equal_constant(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Pastes an image into another image at a given position. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param index_x (int) - Origin pixel coodinate in x to paste. (default: 0) - * @param index_y (int) - Origin pixel coodinate in y to paste. (default: 0) - * @param index_z (int) - Origin pixel coodinate in z to paste. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_paste3D - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ paste(DeviceJ device, ArrayJ input, ArrayJ output, int index_x, int index_y, int index_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.paste(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), index_x, index_y, index_z), device); - } - - /** - * Apply a local maximum filter to an image which only overwrites pixels with value 0. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag value, 0 or 1. - * @param output1 ({@link ArrayJ}) - Output image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_onlyzeroOverwriteMaximumBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ onlyzeroOverwriteMaximumBox(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.onlyzero_overwrite_maximum_box(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw()), device); - } - - /** - * Apply a local maximum filter to an image which only overwrites pixels with value 0. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag value, 0 or 1. - * @param output1 ({@link ArrayJ}) - Output image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_onlyzeroOverwriteMaximumDiamond - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ onlyzeroOverwriteMaximumDiamond(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.onlyzero_overwrite_maximum_diamond(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw()), device); - } - - /** - * Apply a local maximum filter to an image which only overwrites pixels with value 0. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output0 ({@link ArrayJ}) - Output flag value, 0 or 1. - * @param output1 ({@link ArrayJ}) - Output image. (default: None) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_onlyzeroOverwriteMaximumBox - * @see https://clij.github.io/clij2-docs/reference_onlyzeroOverwriteMaximumDiamond - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ onlyzeroOverwriteMaximum(DeviceJ device, ArrayJ input, ArrayJ output0, ArrayJ output1, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.onlyzero_overwrite_maximum(device.getRaw(), input.getRaw(), output0.getRaw(), output1 == null ? null : output1.getRaw(), connectivity), device); - } - - /** - * Computes all pixels value x to the power of a given exponent a. - * 
f(x, a) = x ^ a
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - Power value. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_power - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ power(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.power(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Calculates x to the power of y pixel wise of two images X and Y. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_powerImages - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ powerImages(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.power_images(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Crops an image according to a defined range and step size. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - First input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param start_x (int) - Range starting value in x (default: None) - * @param stop_x (int) - Range stop value in x (default: None) - * @param step_x (int) - Range step value in x (default: None) - * @param start_y (int) - Range starting value in y (default: None) - * @param stop_y (int) - Range stop value in y (default: None) - * @param step_y (int) - Range step value in y (default: None) - * @param start_z (int) - Range starting value in z (default: None) - * @param stop_z (int) - Range stop value in z (default: None) - * @param step_z (int) - Range step value in z (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ range(DeviceJ device, ArrayJ input, ArrayJ output, int start_x, int stop_x, int step_x, int start_y, int stop_y, int step_y, int start_z, int stop_z, int step_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.range(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), start_x, stop_x, step_x, start_y, stop_y, step_y, start_z, stop_z, step_z), device); - } - - /** - * Go to positions in a given image specified by a pointlist and read intensities of those pixels. - * The intensities are stored in a new vector. - * The positions are passed as a (x,y,z) coordinate per column. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param list ({@link ArrayJ}) - List of coordinate, as a 2D matrix. - * @param output ({@link ArrayJ}) - Output vector image of intensities. (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ readValuesFromPositions(DeviceJ device, ArrayJ input, ArrayJ list, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - Objects.requireNonNull(list, "list cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.read_values_from_positions(device.getRaw(), input.getRaw(), list.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Replaces integer intensities specified in a vector image. - * The values are passed as a vector of values. - * The vector index represents the old intensity and the value at that position represents the new intensity. - * s. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - Input image to process. - * @param input1 ({@link ArrayJ}) - List of intensities to replace, as a vector of values. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_replaceIntensities - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ replaceValues(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.replace_values(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Replaces a specific intensity in an image with a given new value. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar0 (float) - Old value. (default: 0) - * @param scalar1 (float) - New value. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_replaceIntensity - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ replaceValue(DeviceJ device, ArrayJ input, ArrayJ output, float scalar0, float scalar1) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.replace_value(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar0, scalar1), device); - } - - /** - * Computes the local maximum of a pixels spherical neighborhood. - * The spheres size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (float) - Radius size along x axis. (default: 1) - * @param radius_y (float) - Radius size along y axis. (default: 1) - * @param radius_z (float) - Radius size along z axis. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_maximum3DSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ maximumSphere(DeviceJ device, ArrayJ input, ArrayJ output, float radius_x, float radius_y, float radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.maximum_sphere(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local minimum of a pixels spherical neighborhood. - * The spheres size is specified by its halfwidth, halfheight and halfdepth (radius). - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (float) - Radius size along x axis. (default: 1) - * @param radius_y (float) - Radius size along y axis. (default: 1) - * @param radius_z (float) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_minimum3DSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ minimumSphere(DeviceJ device, ArrayJ input, ArrayJ output, float radius_x, float radius_y, float radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.minimum_sphere(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Multiplies two matrices with each other. - * Shape of matrix1 should be equal to shape of matrix2 transposed. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_multiplyMatrix - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ multiplyMatrix(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.multiply_matrix(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes 1/x for every pixel value This function is supposed to work similarly to its counter part in numpy [1]. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://numpy.org/doc/stable/reference/generated/numpy.reciprocal.html - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ reciprocal(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.reciprocal(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Sets all pixel values x of a given image X to a constant value v. - * 
f(x) = v
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param scalar (float) - Value to set. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_set - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ set(DeviceJ device, ArrayJ input, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set(device.getRaw(), input.getRaw(), scalar), device); - } - - /** - * Sets all pixel values x of a given column in X to a constant value v. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param column (int) - Column index. (default: 0) - * @param value (float) - Value to set. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setColumn - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setColumn(DeviceJ device, ArrayJ input, int column, float value) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_column(device.getRaw(), input.getRaw(), column, value), device); - } - - /** - * Sets all pixel values at the image border to a given value. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param value (float) - Value to set. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setImageBorders - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setImageBorders(DeviceJ device, ArrayJ input, float value) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_image_borders(device.getRaw(), input.getRaw(), value), device); - } - - /** - * Sets all pixel values x of a given plane in X to a constant value v. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param plane (int) - Plane index. (default: 0) - * @param value (float) - Value to set. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setPlane - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setPlane(DeviceJ device, ArrayJ input, int plane, float value) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_plane(device.getRaw(), input.getRaw(), plane, value), device); - } - - /** - * Sets all pixel values to their X coordinate. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setRampX - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setRampX(DeviceJ device, ArrayJ input) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_ramp_x(device.getRaw(), input.getRaw()), device); - } - - /** - * Sets all pixel values to their Y coordinate. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setRampY - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setRampY(DeviceJ device, ArrayJ input) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_ramp_y(device.getRaw(), input.getRaw()), device); - } - - /** - * Sets all pixel values to their Z coordinate. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setRampZ - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setRampZ(DeviceJ device, ArrayJ input) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_ramp_z(device.getRaw(), input.getRaw()), device); - } - - /** - * Sets all pixel values x of a given row in X to a constant value v. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param row (int) - (default: 0) - * @param value (float) - (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setRow - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setRow(DeviceJ device, ArrayJ input, int row, float value) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_row(device.getRaw(), input.getRaw(), row, value), device); - } - - /** - * Replaces all 0 value pixels in an image with the index of a pixel. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output image. (default: None) - * @param offset (int) - Offset value to start the indexing. (default: 1) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setNonzeroPixelsToPixelindex(DeviceJ device, ArrayJ input, ArrayJ output, int offset) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_nonzero_pixels_to_pixelindex(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), offset), device); - } - - /** - * Sets all pixel values a of a given image A to a constant value v in case its coordinates x == y. - * Otherwise the pixel is not overwritten. - * If you want to initialize an identity transfrom matrix, set all pixels to 0 first. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param value (float) - Value to set. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setWhereXequalsY - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setWhereXEqualsY(DeviceJ device, ArrayJ input, float value) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_where_x_equals_y(device.getRaw(), input.getRaw(), value), device); - } - - /** - * Sets all pixel values a of a given image A to a constant value v in case its coordinates x > y. - * Otherwise the pixel is not overwritten. - * If you want to initialize an identity transfrom matrix, set all pixels to 0 first. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param value (float) - Value to set. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setWhereXgreaterThanY - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setWhereXGreaterThanY(DeviceJ device, ArrayJ input, float value) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_where_x_greater_than_y(device.getRaw(), input.getRaw(), value), device); - } - - /** - * Sets all pixel values a of a given image A to a constant value v in case its coordinates x < y. - * Otherwise the pixel is not overwritten. - * If you want to initialize an identity transfrom matrix, set all pixels to 0 first. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param value (float) - Value to set. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_setWhereXsmallerThanY - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ setWhereXSmallerThanY(DeviceJ device, ArrayJ input, float value) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.set_where_x_smaller_than_y(device.getRaw(), input.getRaw(), value), device); - } - - /** - * Extracts the sign of pixels. - * If a pixel value < 0, resulting pixel value will be 1. - * If it was > 0, it will be 1. - * Otherwise it will be 0. - * This function aims to work similarly as its counterpart in numpy [1]. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ sign(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.sign(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines if two images A and B smaller pixel wise. - * f(a, b) = 1 if a < b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_smaller - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ smaller(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.smaller(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines if two images A and B smaller pixel wise. - * f(a, b) = 1 if a < b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - Scalar used in the comparison. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_smallerConstant - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ smallerConstant(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.smaller_constant(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Determines if two images A and B smaller or equal pixel wise. - * f(a, b) = 1 if a <= b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input0 ({@link ArrayJ}) - First input image to process. - * @param input1 ({@link ArrayJ}) - Second input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_smallerOrEqual - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ smallerOrEqual(DeviceJ device, ArrayJ input0, ArrayJ input1, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input0, "input0 cannot be null"); - Objects.requireNonNull(input1, "input1 cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.smaller_or_equal(device.getRaw(), input0.getRaw(), input1.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines if two images A and B smaller or equal pixel wise. - * f(a, b) = 1 if a <= b; 0 otherwise. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - Scalar used in the comparison. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_smallerOrEqualConstant - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ smallerOrEqualConstant(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.smaller_or_equal_constant(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Convolve the image with the Sobel kernel. - * Author(s): Ruth WhelanJeans, Robert Haase. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_sobel - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ sobel(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.sobel(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the square root of each pixel. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ squareRoot(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.square_root(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the standard deviation intensity projection of an image stack along Z. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_standardDeviationZProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ stdZProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.std_z_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Subtracts one image X from a scalar s pixel wise. - * 
f(x, s) = s x
. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param scalar (float) - Scalar used in the subtraction. (default: 0) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_subtractImageFromScalar - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ subtractImageFromScalar(DeviceJ device, ArrayJ input, ArrayJ output, float scalar) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.subtract_image_from_scalar(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), scalar), device); - } - - /** - * Takes an image and reduces it in width by factor blocksize. - * The new pixels contain the sum of the reduced pixels. - * For example, given the following image and block size 4: [0, 1, 1, 0, 1, 0, 1, 1] would lead to an image [2, 3]. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param blocksize (int) - Blocksize value. (default: 256) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ sumReductionX(DeviceJ device, ArrayJ input, ArrayJ output, int blocksize) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.sum_reduction_x(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), blocksize), device); - } - - /** - * Determines the sum intensity projection of an image along Z. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_sumXProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ sumXProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.sum_x_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the sum intensity projection of an image along Z. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_sumYProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ sumYProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.sum_y_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines the sum intensity projection of an image along Z. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_sumZProjection - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ sumZProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.sum_z_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Transpose X and Y axes of an image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The input image. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_transposeXY - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ transposeXy(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.transpose_xy(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Transpose X and Z axes of an image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The input image. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_transposeXZ - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ transposeXz(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.transpose_xz(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Transpose Y and Z axes of an image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - The input image. - * @param output ({@link ArrayJ}) - The output image where results are written into. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_transposeYZ - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ transposeYz(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.transpose_yz(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Copies all pixels instead those which are not a number (NaN) or infinity (inf), which are replaced by 0. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_undefinedToZero - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ undefinedToZero(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.undefined_to_zero(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Computes the local variance of a pixels box neighborhood. - * The box size is specified by its halfwidth, halfheight and halfdepth (radius). - * If 2D images are given, radius_z will be ignored. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_varianceBox - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ varianceBox(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.variance_box(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local variance of a pixels sphere neighborhood. - * The sphere size is specified by its halfwidth, halfheight and halfdepth (radius). - * If 2D images are given, radius_z will be ignored. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_varianceSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - @Deprecated - public static ArrayJ varianceSphere(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.variance_sphere(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z), device); - } - - /** - * Computes the local variance of a pixels neighborhood (box or sphere). - * The neighborhood size is specified by its halfwidth, halfheight and halfdepth (radius). - * If 2D images are given, radius_z will be ignored. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @param radius_x (int) - Radius size along x axis. (default: 1) - * @param radius_y (int) - Radius size along y axis. (default: 1) - * @param radius_z (int) - Radius size along z axis. (default: 1) - * @param connectivity (String) - Filter neigborhood (default: "box") - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_varianceBox - * @see https://clij.github.io/clij2-docs/reference_varianceSphere - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ variance(DeviceJ device, ArrayJ input, ArrayJ output, int radius_x, int radius_y, int radius_z, String connectivity) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.variance(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw(), radius_x, radius_y, radius_z, connectivity), device); - } - - /** - * Takes an image with three/four rows (2D: height = 3; 3D: height = 4): x, y [, z] and v and target image. - * The value v will be written at position x/y[/z] in the target image. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image to process. - * @param output ({@link ArrayJ}) - Output result image. (default: None) - * @return {@link ArrayJ} - * @see https://clij.github.io/clij2-docs/reference_writeValuesToPositions - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ writeValuesToPositions(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.write_values_to_positions(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines an Xposition of the maximum intensity along X and writes it into the resulting image. - * If there are multiple xslices with the same value, the smallest X will be chosen. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image stack - * @param output ({@link ArrayJ}) - altitude map (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ xPositionOfMaximumXProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.x_position_of_maximum_x_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines an Xposition of the minimum intensity along X and writes it into the resulting image. - * If there are multiple xslices with the same value, the smallest X will be chosen. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image stack - * @param output ({@link ArrayJ}) - altitude map (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ xPositionOfMinimumXProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.x_position_of_minimum_x_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines an Yposition of the maximum intensity along Y and writes it into the resulting image. - * If there are multiple yslices with the same value, the smallest Y will be chosen. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image stack - * @param output ({@link ArrayJ}) - altitude map (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ yPositionOfMaximumYProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.y_position_of_maximum_y_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines an Yposition of the minimum intensity along Y and writes it into the resulting image. - * If there are multiple yslices with the same value, the smallest Y will be chosen. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image stack - * @param output ({@link ArrayJ}) - altitude map (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ yPositionOfMinimumYProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.y_position_of_minimum_y_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines a Zposition of the maximum intensity along Z and writes it into the resulting image. - * If there are multiple zslices with the same value, the smallest Z will be chosen. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image stack - * @param output ({@link ArrayJ}) - altitude map (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ zPositionOfMaximumZProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.z_position_of_maximum_z_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - - /** - * Determines a Zposition of the minimum intensity along Z and writes it into the resulting image. - * If there are multiple zslices with the same value, the smallest Z will be chosen. - * @param device ({@link DeviceJ}) - Device to perform the operation on. - * @param input ({@link ArrayJ}) - Input image stack - * @param output ({@link ArrayJ}) - altitude map (default: None) - * @return {@link ArrayJ} - * @throws NullPointerException if any of the device or input parameters are null. - */ - public static ArrayJ zPositionOfMinimumZProjection(DeviceJ device, ArrayJ input, ArrayJ output) { - Objects.requireNonNull(device, "device cannot be null"); - Objects.requireNonNull(input, "input cannot be null"); - return new ArrayJ(net.clesperanto._internals.kernelj.Tier1.z_position_of_minimum_z_projection(device.getRaw(), input.getRaw(), output == null ? null : output.getRaw()), device); - } - -} \ No newline at end of file From d6c383fc8beab3f6dae11cd8db5d0c08f9ccebba Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 25 Oct 2024 17:55:07 +0000 Subject: [PATCH 4/9] Delete file --- playground/python_basics.ipynb | 131 --------------------------------- 1 file changed, 131 deletions(-) delete mode 100644 playground/python_basics.ipynb diff --git a/playground/python_basics.ipynb b/playground/python_basics.ipynb deleted file mode 100644 index 2df4f30c..00000000 --- a/playground/python_basics.ipynb +++ /dev/null @@ -1,131 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Python Basics\n", - "This notebook introduces basic Python concepts such as variables, functions, and loops." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Variables\n", - "Variables are used to store information to be referenced and manipulated in a program." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n", - "Hello\n" - ] - } - ], - "source": [ - "# This is a variable\n", - "x = 5\n", - "y = 'Hello'\n", - "print(x)\n", - "print(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Functions\n", - "Functions are blocks of code that only run when they are called." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello Alice\n" - ] - } - ], - "source": [ - "# This is a function\n", - "def greet(name):\n", - " return 'Hello ' + name\n", - "\n", - "print(greet('Alice'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loops\n", - "Loops are used to execute a block of code repeatedly." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n" - ] - } - ], - "source": [ - "# This is a loop\n", - "for i in range(5):\n", - " print(i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise\n", - "Write a function `sum_up_to(n)` that returns the sum of all integers from 1 to `n`." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 7226572062565ee74054a7d7bad02f2411d16dd8 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 25 Oct 2024 17:55:08 +0000 Subject: [PATCH 5/9] Delete file --- playground/readme.md | 14 -------------- 1 file changed, 14 deletions(-) delete mode 100644 playground/readme.md diff --git a/playground/readme.md b/playground/readme.md deleted file mode 100644 index bd488f73..00000000 --- a/playground/readme.md +++ /dev/null @@ -1,14 +0,0 @@ -# Playground - -This folder is a playground to test git-bob on a repository. - -This file contains a tpyo. Let's see what is going to happen with it. - -May it stay here forever? Or does an AI find it and fix it? - -Time will tell. - -Best, -Robert - - From fb1a129b862f9283534c781a773cb6bfff630769 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 25 Oct 2024 17:55:10 +0000 Subject: [PATCH 6/9] Delete file --- playground/real_cat.png | Bin 487090 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 playground/real_cat.png diff --git a/playground/real_cat.png b/playground/real_cat.png deleted file mode 100644 index 0d89cf0dbb8a0d7d90a2e38d2cbf222abf4a913a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 487090 zcmWh!byO5i7u^JQsRfpT{;9@S`d|z6eaxu zB7S~n&YU;zuQ_wh%zJO{y)VhsScekM0tWy9lzO_F<^TZb-x35M`@e}~(P;T+fFb5O zSitjXw!ME2^rDx@Gts%Rf66 z?86H`s84@gUQheys4J%P@09AWcx3TvYD1#^4X2%d-=|_?MPTi)WUyTR9p#R>RNn8W zIx1fe?E6TmkQxupo2;JAc44jld+YDz9vbNG9v` 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From a1db4c43d90f7d9ca95fcc9b69802cc7523173c1 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 25 Oct 2024 17:55:11 +0000 Subject: [PATCH 7/9] Delete file --- playground/test.py | 8 -------- 1 file changed, 8 deletions(-) delete mode 100644 playground/test.py diff --git a/playground/test.py b/playground/test.py deleted file mode 100644 index 3e744414..00000000 --- a/playground/test.py +++ /dev/null @@ -1,8 +0,0 @@ -def faculty(number): - if number == 0: - return 1 - else: - return number * faculty(number - 1) - -for i in range(10): - print("The faculty of", i, "is", faculty(i)) From c67bb4e1ff3755c241e2bfc4216b5cd93ea9e99a Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 25 Oct 2024 17:55:12 +0000 Subject: [PATCH 8/9] Delete file --- playground/test3.py | 6 ------ 1 file changed, 6 deletions(-) delete mode 100644 playground/test3.py diff --git a/playground/test3.py b/playground/test3.py deleted file mode 100644 index 00015f15..00000000 --- a/playground/test3.py +++ /dev/null @@ -1,6 +0,0 @@ -def sum_of_squares(numbers): - return sum([n**2 for n in numbers]) - - -def print_sum_of_squares(numbers): - print(f"Sum of squares of {numbers} is {sum_of_squares(numbers)}") From 07b9d052e962721672b930c18209cf25442b49f4 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 25 Oct 2024 17:55:14 +0000 Subject: [PATCH 9/9] Delete file --- playground/vision_api_test.ipynb | 164 ------------------------------- 1 file changed, 164 deletions(-) delete mode 100644 playground/vision_api_test.ipynb diff --git a/playground/vision_api_test.ipynb b/playground/vision_api_test.ipynb deleted file mode 100644 index eb753de3..00000000 --- a/playground/vision_api_test.ipynb +++ /dev/null @@ -1,164 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "4ef2bea1-ab89-405f-9eb8-2614d5956149", - "metadata": {}, - "outputs": [], - "source": [ - "from PIL import Image\n", - "from git_bob._endpoints import prompt_claude, prompt_azure, prompt_gemini, prompt_chatgpt\n", - "from git_bob._utilities import image_to_url" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "641874d4-7bf5-4a05-a633-bc031887cd60", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "image = Image.open(\"real_cat.png\")\n", - "image" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0b38fc8b-d5ed-48db-8f63-f02e229245a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'A white and black cat sitting next to a white microscope. The cat is looking to the right of the image. The microscope is on a wooden table. There is a red blanket in the background.'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prompt_gemini(\"What is in this image?\", image=image)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1e04ae21-adff-499b-a8a5-0b7fd9acc70c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"This image shows a black and white cat sitting next to a white microscope on what appears to be a wooden table or desk. The cat seems curious or interested in something off-camera. The microscope appears to be a modern digital or educational model in white plastic. There's also a glimpse of something red (possibly fabric or furniture) in the background against a white wall.\"" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prompt_claude(\"What is in this image?\", image=image)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c35caedf-2983-4de6-a01b-e3c050a915ed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model pt-4o-2024-08-06\n", - "base_url None\n", - "api_key 0\n", - "finish_reason stop\n", - "len 96\n" - ] - }, - { - "data": { - "text/plain": [ - "'The image shows a black and white cat positioned next to a white microscope on a wooden surface.'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prompt_chatgpt(\"What is in this image?\", image=image)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5cf3f043-311e-4de5-84cf-c0a50a95e573", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"That’s a cute image of a cat sitting next to a microscope. The cat's pose makes it look as if it's part of the microscope, adding a playful touch to the scene!\"" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prompt_azure(\"What is in this image?\", image=image, model=\"github_models:gpt-4o\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbea5197-33c8-4019-b6f9-db7a07c82955", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}