process_improve/__init__.py,sha256=9H-uHhy77AS1vaIGrCvJ0SLyYDOoUHToyIE7v0azurI,1194
process_improve/batch/__init__.py,sha256=stQx6pjeuwwydg42RXlu8_JMunDhQ_rpUOFM7oUksz8,1250
process_improve/batch/alignment_helpers.py,sha256=NAQ8spyDam26saKViR4qSG4YYbs8UZCzrxn2vvs0CQU,2865
process_improve/batch/data_input.py,sha256=vAK9vKfBEpWyG7NlIUY8X9kWqADXCWalRFpGdyYFCMQ,6565
process_improve/batch/features.py,sha256=BI8KyJ7bVYzZeH1JYZJcH4MwMpiBZojP_w1Lo_sbRws,26087
process_improve/batch/plotting.py,sha256=kfw76DceGDma3644bfxD1bm1S1vDN26fuvg5MYSPq0M,24730
process_improve/batch/preprocessing.py,sha256=6h8WfUHYoICy_hR1UjxlO_nTSiQbbb6TeuV0DcWkP1E,23806
process_improve/batch/tools.py,sha256=gh7PGc056zIeubCZLfo5EPli6x93XiSq21nPq3QzlSs,6528
process_improve/bivariate/__init__.py,sha256=U7SADecVCW8gMJ88vM4mhnFWDKHAL5VyNLIM2EigB3g,222
process_improve/bivariate/methods.py,sha256=plShHH0yeYz9Oo7ht8MZcXgP8dguQo9eQl9DyAcNtqw,6026
process_improve/bivariate/tools.py,sha256=bLhw06lz_0Ky8QPeUgmVHfmjFGfOjw8gIbhEQ2be9rM,3041
process_improve/datasets/batch/batch-fake-data.csv,sha256=5RS_WmtQdjIpRgoFOxbh7Yrqns1DQ9gQNtx5lwEPSP4,26583
process_improve/datasets/batch/details.txt,sha256=yl-dRetr30JJi8jfDZaThzg9OFJQcxQQq8k1G3zJjr4,3579
process_improve/datasets/batch/dryer.csv,sha256=coItn2qQQjU8skD7jMW9cIPsh0JEqCLrZEGfND0Kx6o,667696
process_improve/datasets/batch/nylon.csv,sha256=FolPbTcpcH6mnuORLovhy7dMAU8ALQdhDKQBdKVDGhg,325044
process_improve/datasets/experiments/test_doe1.csv,sha256=4h1i0AHdKw4iZRjcDk4wXkrwZpIJifWX8TOtZRZsEzM,1622
process_improve/datasets/monitoring/batch-yield-and-purity.csv,sha256=SdcqC6zsDf9RkPD1may8z4NpoV5N2OoLxxt_NKjXeXg,2353
process_improve/datasets/monitoring/rubber-colour.csv,sha256=min91219TCYfqSmyEntsBMmVbdpeHBU2Sir0DpL6RbY,407
process_improve/datasets/multivariate/LDPE/C.csv,sha256=JjEYkRX26X21sQkilKL_R5sKhhAJ75sM6Cc9Y8Gewz0,317
process_improve/datasets/multivariate/LDPE/Hotellings_T2_A3.csv,sha256=memIPONi6zPZJ6udpRsH0xtgN34NQfx0xjm-2EN0Fnc,498
process_improve/datasets/multivariate/LDPE/Hotellings_T2_A6.csv,sha256=ZJck-jjCic74UHA5mTl7VimW0h_q4TGtifHgJA-FyQQ,483
process_improve/datasets/multivariate/LDPE/LDPE.csv,sha256=SQ6meQMO535EH630LZaagSSN-k2fhzoULf_0VOndwLI,6913
process_improve/datasets/multivariate/LDPE/P.csv,sha256=6Qz4BUGtvr-EAxb80gh7EVIASyqye80T40gMjYW3cN8,891
process_improve/datasets/multivariate/LDPE/R.csv,sha256=M-T6hTIxCg72n3Fm0-E3Vvh0YEHusjjgn30m_R4d4MY,903
process_improve/datasets/multivariate/LDPE/T.csv,sha256=v7D6t1kUBNGaUlUZZ6iQ7yJ4d4Xux5xpRK6C3U0XTjo,3257
process_improve/datasets/multivariate/LDPE/U.csv,sha256=EC9M5il4GZ07f9iGbzqsG_QAGgfW_KGZAGTg04TjqcQ,3232
process_improve/datasets/multivariate/LDPE/W.csv,sha256=-CMU4VMzFO_QiioVQMlyd_uepruWJyO7rgNTODfsYdM,906
process_improve/datasets/multivariate/LDPE/Yhat_A6.csv,sha256=p_xdQZgJ20Jjg6A68rUQGFc49uQzzTTS22kJdj4ughk,2700
process_improve/datasets/multivariate/kamyr.csv,sha256=5aAsB1o1ZtQRUtyjhx2dcjYn_4YBWYWCF-S2BGVvaOA,6546
process_improve/datasets/multivariate/tablet-spectra.csv,sha256=qVoq3jbdJTceli_m9m1Xdb14kvXz_9lj2gmog_jkwzs,2394760
process_improve/datasets/multivariate/tpls-pyphi/SOURCE,sha256=SWiQd2RCISLFv3KR7AjhAKSuU9gBy4qG8fEpVDQX4bk,85
process_improve/datasets/multivariate/tpls-pyphi/formulas_Group1.csv,sha256=ivC8Uft_eguXQvEWaYZgLtMc63R9RiHyrEioommptSI,72671
process_improve/datasets/multivariate/tpls-pyphi/formulas_Group2.csv,sha256=OOzUMea7xEsvg3CrGB6cqZvdW_bx75WQDp0hJtJLPJk,4444
process_improve/datasets/multivariate/tpls-pyphi/formulas_Group3.csv,sha256=SrdT-MCqeYHDpzfvthCsgpRmGEGIIrPwzyF0E6XRLNQ,9969
process_improve/datasets/multivariate/tpls-pyphi/formulas_Group4.csv,sha256=F_DpsRtbJjA-zJnpXRYcqJvyGdbGpP6xDcSk0k9Fekg,8750
process_improve/datasets/multivariate/tpls-pyphi/formulas_Group5.csv,sha256=E6kNZtZY5DqiMTtaKGSIHhh36HM7ATdlVGAwDzIEo4I,8401
process_improve/datasets/multivariate/tpls-pyphi/process_conditions.csv,sha256=C0H82zyx2shWAn98GyZrFWe7YZnpdMwYdSDPS-KP0tw,5760
process_improve/datasets/multivariate/tpls-pyphi/properties_Group1.csv,sha256=T4UyFKAFrPQi1Q7Lt32DOxvCtTdKbI_gJOL4hQ2u33g,4960
process_improve/datasets/multivariate/tpls-pyphi/properties_Group2.csv,sha256=z_VOOkJ-dyw9TWkCGeW6_CCn235n4utkVzhUrXpvTIk,351
process_improve/datasets/multivariate/tpls-pyphi/properties_Group3.csv,sha256=_OrkTwqL8p_gwXfyE4ZWVBtXKOGw5lH6YZ3v1xwU9Yc,922
process_improve/datasets/multivariate/tpls-pyphi/properties_Group4.csv,sha256=dDCTX53nIohPpoRQpUCgYvfPgNegADh215W2N3e6v7U,1101
process_improve/datasets/multivariate/tpls-pyphi/properties_Group5.csv,sha256=04GbgU3FeYsikA5Kqr5G4pH0Uqzfvez15pLEDGPmT0w,412
process_improve/datasets/multivariate/tpls-pyphi/quality_indicators.csv,sha256=EFCNsv5yfiCMJsmZRxnU0B4kka1er6ZO6OJPmfdEc9Y,9854
process_improve/docs/outline.txt,sha256=RUYIy4wjXZaGg4Uo4dFSotQUNkf6hyrMZe-d6Xns4DA,677
process_improve/experiments/__init__.py,sha256=zFf3G7p5GyGAecyet_pZdsYRAjcM-I4MER7lhg2C6Kc,1465
process_improve/experiments/analysis.py,sha256=_ni19F_iy1YDZK42q91smHmDCQG37Q45ccoXhyjHYBQ,26592
process_improve/experiments/augment.py,sha256=CHqraeTAZh5ivCvMl3XIzrdOPcq4UHr3-sbU91Kl-L4,27348
process_improve/experiments/datasets.py,sha256=YpcCjwWlr8mb3voIjhG627Z4YC2zhRH170oi6NRDpcQ,6182
process_improve/experiments/designs.py,sha256=6BZXYZ3aVvdFRv4Jt3nVHyW62mz2MRHNnhNabwGDwuo,13004
process_improve/experiments/designs_factorial.py,sha256=svWmQp9klYXZqC54mM-66IEzRWOTtpepY82mbaockX0,689
process_improve/experiments/designs_mixture.py,sha256=jUHlzPsiKKbSTN6OmW5elsOQbQF0eKVI6rHumKZzb-Y,3182
process_improve/experiments/designs_optimal.py,sha256=5XGBzbWWb4KYJZtGaY5zHppHgY01gIibCjBfruzkZ6Q,11748
process_improve/experiments/designs_response_surface.py,sha256=6j0b8T8-BcDNDJqJ_ewAj_PtWZXKD8UjMEyTQr1aS-s,9708
process_improve/experiments/designs_screening.py,sha256=Y69peRxaBx4Kvq6at34FEtbjwMTEusrWLuf9Rb_uy68,5204
process_improve/experiments/designs_utils.py,sha256=vGiWyuOCMpNnSBOOxWkfz85SUBf3BZZta2fhwkKKfd4,8643
process_improve/experiments/evaluate.py,sha256=AE5wcdE846bMGDhDK_fc_S0alZ6HmUAS0t_dv-7z7mM,29359
process_improve/experiments/factor.py,sha256=6IGP6Io-jO0fO3BARqDsntTt1feFchJvFeVciyxbdzA,7176
process_improve/experiments/knowledge/__init__.py,sha256=HaRE40GxfDSiioTZxuHOob6zyUHH3Am3oQ-F5yqgOKI,895
process_improve/experiments/knowledge/api.py,sha256=f97Nr8TkqurQkMd2qNx2KSlOu1jNrXT7OYq4a0fjPWc,4427
process_improve/experiments/knowledge/data/concepts.yaml,sha256=DDKHS9YdWGJwu9rzJKvx-1gOiJ9TJIOYoJfbnU2UUK4,10242
process_improve/experiments/knowledge/data/decision_rules.yaml,sha256=4ZpUZPFsBgLCgdyX4gPqunM4r8JTN4YY9fmCzEr1KJo,8949
process_improve/experiments/knowledge/data/design_types.yaml,sha256=fA96_f1bCgJCW681hxDtTE_zmxlspceLEaIzQ5J_gak,14312
process_improve/experiments/knowledge/data/diagnostics.yaml,sha256=Q7lyiCcProVrnu4NA8FXY5K3HzfyDijIcKBzme4YCF0,5640
process_improve/experiments/knowledge/engine.py,sha256=IprGOqX47vDc9WyxDJzp-CjotqTh6NNovGcBo1I-T9I,19273
process_improve/experiments/knowledge/models.py,sha256=8R7CuU02nSrlDDNmjdg3yGqj9DJOGSFILdmdjVzOuoc,3745
process_improve/experiments/models.py,sha256=Cn0mRSg2xql8eQWeGaI1rx5n8lq74u_ymylYhAOcWu8,11111
process_improve/experiments/optimal.py,sha256=2gDDXR_xG1FQRb1PgP1VtvgMDqdZDgB7skPeZhkdrsw,4822
process_improve/experiments/optimization.py,sha256=-I_hRUt_GhnnL50ZinFO13EivwpT-9bSqreb6Y2LUOA,27424
process_improve/experiments/simulations.py,sha256=Ghy2UsvwNFxK5QMaodixD8eLYFYWQk0NM9A9QO_RBBo,3037
process_improve/experiments/strategy/__init__.py,sha256=aeT7ztrJ1twgBOzswfQ0vlXZn2Hiuh_pCCGn6qY4MW8,731
process_improve/experiments/strategy/budget.py,sha256=Y-FMNyaKdjQTL4lnI3CII0m7AZlJIPf8SjYCy7uSMBY,10044
process_improve/experiments/strategy/domain_templates.py,sha256=2gH4d7kO0FK6ISQ-9FQO5KlTIu5O_3gS_aXmymU-awU,12818
process_improve/experiments/strategy/engine.py,sha256=WqNQMcydMDaa4WH4gdLP4GtPK0_YE6c3j2mgSokPCR8,29668
process_improve/experiments/strategy/models.py,sha256=jfQyoyDjug3OQ3YNgcd0hl5PTaP1JHfiPijsSi9LUxM,8387
process_improve/experiments/structures.py,sha256=aOxxuopavLMi_Zhe4HdxM25rd6otE0FcPPVTknK6Vk4,13221
process_improve/experiments/tools.py,sha256=dQnH9OE7TdhAXZorO2RWjq3hp5J1_O3dGI8AI7h6xV4,61510
process_improve/experiments/visualization/__init__.py,sha256=vLWYchu7vEziQx1KIQJ2tfBqVe_VVWy9OLA_yfKUG0Y,1059
process_improve/experiments/visualization/adapters/__init__.py,sha256=x9C1F3nBs865kJRjkXD1e-pfPFjtsduaU6xZzmA-Rds,290
process_improve/experiments/visualization/adapters/base.py,sha256=pEAFaxo1yQFebah9Lsy_wcoHC-OarWbaqek9u17U3NY,1217
process_improve/experiments/visualization/adapters/echarts_adapter.py,sha256=lujFqaKw15AZ6QtOqy1eOFrHR031KMeQH3bPCsjCbAk,16270
process_improve/experiments/visualization/adapters/plotly_adapter.py,sha256=mCsB0xGugOIX4QO6kRUIp1GoHcypLFKVkSIhGxrkeAo,13540
process_improve/experiments/visualization/api.py,sha256=z8sJD-nVqhzV4RK4lgzHFAD5hVVLxqXv2KEhx2DUkYA,2897
process_improve/experiments/visualization/colors.py,sha256=jHtxIc2NBigVXV_1BHoOPtMIc5q47h11tOVACJK0ER4,3154
process_improve/experiments/visualization/plots/__init__.py,sha256=oJeEo3qfNa_EEynMXe2NOV6rSg3AuqqbN5Wfk9eP6QM,551
process_improve/experiments/visualization/plots/cube_plot.py,sha256=PNiEQT03PYgAz7fLyRV1568_7MKbg_z3NPVOryJzRdo,5806
process_improve/experiments/visualization/plots/design_quality.py,sha256=sYpyfWrPN-R_5fj-SNKQgZ1PBd3LmUqMsbhjHM1h_i4,10599
process_improve/experiments/visualization/plots/diagnostics.py,sha256=aO9CopL0v_FEYBsWmGGsF-xDfxWcVXvEmUMvejZO8zE,13527
process_improve/experiments/visualization/plots/effects.py,sha256=eaX81VtN-Vzw5oLnnYFL0grCBvlKsizjbE6RkpIP9yo,11203
process_improve/experiments/visualization/plots/optimization_plots.py,sha256=7rvMGFNyXabR9GhB-xGPcrxE-wOXl7iurSINX88ZS4Y,23421
process_improve/experiments/visualization/plots/registry.py,sha256=5Yf6GdUZ3rqOLjclTz68EhW8xNisgPmNvaTwpl-mxis,8341
process_improve/experiments/visualization/plots/significance.py,sha256=_py1C2dgZ7AVd97MiCKft4PKenocfgLzj0480by4HQg,13163
process_improve/experiments/visualization/plots/surfaces.py,sha256=jYe7qwhp5J_aQHxwTufx17ZIn8qfIRlnqtRIleKiWZU,11812
process_improve/experiments/visualization/spec.py,sha256=tayVzgKqsBBovJliSE2TNwcdSoZSxnYx7hha187CcDs,10152
process_improve/experiments/visualization/types.py,sha256=GIWZBdvbV3xAwSiNt_QHHGwVKE3D3Pj0gvZIB0wllM4,1873
process_improve/mcp_server.py,sha256=zCVWzv6KvjDrEBA9yp4No2SMqJWvuPeNne3TtVx08Us,4270
process_improve/media/boilingpot.csv,sha256=nDl48ckW6zhhycPyw_o1l4t-pOwZcVXKvH1F7fc9WaQ,158
process_improve/media/distillate-flow.csv,sha256=DlyCBxwiV02iXh2oTKHCMqSMTM42NWvwJdwD4UpFb3Q,291213
process_improve/media/oil-company-doe.csv,sha256=qGwDgJ4Nx0qJNIGcP55ij7OVXN30mywoPs7YjDVyXiM,473
process_improve/media/trade-off-table.html,sha256=qoqhMvG0klUPqGIFC91YvB3ymyitMhb7_2zHi1XhAgs,287
process_improve/media/trade-off-table.pdf,sha256=4Ai-e2GMTmgqlel169OLBXRu_3GLND2kLxer_GA57Ms,495520
process_improve/media/trade-off-table.png,sha256=nMmxb-yhXwu1ZjnACdJpokdRk0uwEuSBScrkpAsbZZc,429438
process_improve/monitoring/__init__.py,sha256=Pe40bTVn5pdZjlC9KmPY8Kckivn3EHy5mP9PFu6To9M,250
process_improve/monitoring/control_charts.py,sha256=jQPNMwNss6oKqYosbzWdzveyeNKCcdOTnhFabHxwIAU,14445
process_improve/monitoring/metrics.py,sha256=adRDb991JQUIkWAaeyA0yhHzTsvNCoXmmgZZn9I6qbI,3004
process_improve/monitoring/tools.py,sha256=i5HfhANgqch7xy1FrRpYK0eGaz-h4OentmWTsNqiq1I,7919
process_improve/multivariate/__init__.py,sha256=PxRSDVgTfg8zBJzmkhJLBjpX1DjChb0fdDOkhGs7Xqo,472
process_improve/multivariate/methods.py,sha256=DYX58SaREqFj_OVziayqS_rUA3eudhCjWW5rZe8SHwo,147424
process_improve/multivariate/plots.py,sha256=Vldw-T0aY8FYSGRssG7pqJE99fRpPAt_9_H0BU3zNAQ,26813
process_improve/multivariate/tools.py,sha256=k5OefQkSS7_zonu_7qSUCB-uuPkvH3679Ff4-jGjI0U,22196
process_improve/notebooks_examples/Example notebook.ipynb,sha256=mxpOlSF-PEwcZtwBhvuO8UPBM__hnKluMNByCU9aGMk,24984
process_improve/notebooks_examples/Tablets.xlsx,sha256=v6IqbG9j7N2bLzOOpG0kI-pO-4DYTJ2Yu0Jy6KsFf00,930039
process_improve/notebooks_examples/batch/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
process_improve/notebooks_examples/batch/alignment-and-pca-example.ipynb,sha256=TrR4bYyLdUsf9AlqDp6TYHsuxyzoJv6kjkEIguErtr8,10674
process_improve/notebooks_examples/batch/batch-optimization.ipynb,sha256=WGtOCx1drtnACzWmCwoHkZ4ljB5Jj4EcNpJ-vYsrvNg,11332
process_improve/notebooks_examples/batch/batch_llm.py,sha256=pWAInktWraZZWHTN6RIPK45_blEXbQ-zrJ4HDaQ3CDk,7335
process_improve/notebooks_examples/batch/batch_llm_multivariate.py,sha256=dJFxuuaKAdojSw8-a_C0qpDuvVC91rq21RGaDzz8B98,5973
process_improve/notebooks_examples/batch/batch_music.py,sha256=Loqnf0tTMNpDHGwm5hph9VXjev6_YUGPGDpJJR0vkWk,5797
process_improve/notebooks_examples/batch/demo-multiplots.ipynb,sha256=-ZLrMag0BsaBQOKKNhTLPhU2VeaG_ojmSuP4dz2fBqc,26469
process_improve/notebooks_examples/experiments/case-studies.py,sha256=meRPXJsNBb5osWWUn5oI9s3Nx7EXdepgftSad1jbR-0,26352
process_improve/notebooks_examples/least-squares-modelling/Misguided-reliance-on-R2-alone.ipynb,sha256=giFHAXAgpjvSr04XVttzZ0BnOXZ5XVq-LlbYOgVuZL0,19037
process_improve/notebooks_examples/multivariate/pca_example.py,sha256=djM6LqwVzr0Q_qYgsepbPXFiOfr3QLd_Uq4fKCBMiF8,1311
process_improve/regression/__init__.py,sha256=pFGwgVgcRNHbamBEv64crvLFSINegqkkF3ptu-AmBVo,270
process_improve/regression/methods.py,sha256=NBb7Lmi4slO-j_IKPHzZfM8SZ98tEyEN6WGIqAxFNEI,15090
process_improve/regression/tools.py,sha256=YZC8O4gqcEy-5WdGsSJAaQ_1YDIx_Q2MNSlFZfYs9Mk,6520
process_improve/tool_safety.py,sha256=xHESTAyv5D6EAiC15B71A3Rl81SwDdkLEuxPSJCOfIs,13894
process_improve/tool_spec.py,sha256=AFTqMpbr6ppR1HHLG5u3SGe4wiCIjVejYSIfT7qgtrw,9114
process_improve/univariate/__init__.py,sha256=zWNy6R5Gq4jsE5ynpqV21ZQIEyoCrIa8omDOxn7crjQ,1341
process_improve/univariate/metrics.py,sha256=lwVUsO70vHZM3Glm8fFOIQg86y7tpCBWAFu-x2ALfPY,32180
process_improve/univariate/tools.py,sha256=O9aj_-HACzc6v9l-6rVl2yO05lljfxGJ0EFPYp9Exl4,25282
process_improve/visualization/__init__.py,sha256=QWI1C_cR913u_f3dqmiiFaN2TK27aWIJGxlSLoqpH0c,60
process_improve/visualization/plots.py,sha256=b-txr7YkWpaYX1sZuiVlidY55-wl5B85ss7ef7OMMFw,763
process_improve-1.4.1.dist-info/WHEEL,sha256=GuAqCqoyQuys5_R4zkHUJFlKXw4RpRLNzo31-ui90WQ,81
process_improve-1.4.1.dist-info/entry_points.txt,sha256=yhdpWPBFmkDYKad8XwGurmWRIin-aT8uQBMn-BipYkY,73
process_improve-1.4.1.dist-info/METADATA,sha256=rp0OgoZ4XtqVjP_FQTDB5jnhwldYPRR7dRLNjS4Udxs,7235
process_improve-1.4.1.dist-info/RECORD,,
