From e6d5eda974acae6979ba2a922c3772a422d8ad4a Mon Sep 17 00:00:00 2001
From: dioph <dioph@protonmail.com>
Date: Wed, 7 Aug 2024 23:27:35 -0300
Subject: [PATCH] removed theano (temp fix)

---
 Pipfile               |    2 +-
 Pipfile.lock          | 1641 ++++++++++++++++++++---------------------
 pyproject.toml        |    2 +-
 setup.py              |    2 +-
 src/periodicity/gp.py |  201 +++--
 5 files changed, 910 insertions(+), 938 deletions(-)

diff --git a/Pipfile b/Pipfile
index 5a48652..23e2d94 100644
--- a/Pipfile
+++ b/Pipfile
@@ -9,7 +9,7 @@ celerite2 = "*"
 emcee = ">= 3.0"
 george = "*"
 pywavelets = ">= 0.5"
-pymc3_ext = "*"
+pymc_ext = "*"
 scipy = ">= 1.1"
 tqdm = "*"
 xarray = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index 8567dab..8e063e1 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "bbf95f8bcc370e2d061450e35c58960ab41e0d0349d029027825c1388e155ad5"
+            "sha256": "6fe21c41740509793b5fff0fcfd539e4a1c5e339bd4d380f2e6e4277c28569c9"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -21,22 +21,14 @@
                 "sha256:d225242bd2682530374a3fc4da2be0e929c2d52ca0a4ea44f87c7964ba4d0c53",
                 "sha256:fd4b0dd4848f0380c616e8e8a20566aa21b33a5f504ee9a0461cacf3f14dac5a"
             ],
-            "markers": "python_version >= '3.6'",
             "version": "==0.0.4"
         },
         "arviz": {
             "hashes": [
-                "sha256:a9d0eb32e84a0472aa78a488ba9b12b05e7be8c2c8fb34a1ba6286cc1254ee0d",
-                "sha256:f6a1389a90b53335f248d282c8142b8209150b9625459a85ec6d3d38786797c1"
+                "sha256:01872758eeabb9941479ec5dd378117337bf95d14cc2c298b437cfda1780b436",
+                "sha256:f434b223a7d0a74645d58f30800b3bfea821f8350ea55566814fef50cf32d37e"
             ],
-            "version": "==0.11.2"
-        },
-        "autograd": {
-            "hashes": [
-                "sha256:a15d147577e10de037de3740ca93bfa3b5a7cdfbc34cfb9105429c3580a33ec4"
-            ],
-            "index": "pypi",
-            "version": "==1.3"
+            "version": "==0.11.4"
         },
         "bottleneck": {
             "hashes": [
@@ -47,11 +39,10 @@
         },
         "cachetools": {
             "hashes": [
-                "sha256:2cc0b89715337ab6dbba85b5b50effe2b0c74e035d83ee8ed637cf52f12ae001",
-                "sha256:61b5ed1e22a0924aed1d23b478f37e8d52549ff8a961de2909c69bf950020cff"
+                "sha256:486471dfa8799eb7ec503a8059e263db000cdda20075ce5e48903087f79d5fd6",
+                "sha256:8fecd4203a38af17928be7b90689d8083603073622229ca7077b72d8e5a976e4"
             ],
-            "markers": "python_version ~= '3.5'",
-            "version": "==4.2.2"
+            "version": "==5.0.0"
         },
         "celerite2": {
             "hashes": [
@@ -74,51 +65,46 @@
         },
         "cftime": {
             "hashes": [
-                "sha256:0130b894e9e6ff288ed29d11b588c0050e1e795138d94482324821e2a3a92c7f",
-                "sha256:104c43bcb44de96d90b241b00e1c4ff042393ce4696a24150f862b7a37de4280",
-                "sha256:1688eb94d808d8ceb94471c129311ce3491ba34324108a23d328d6b1a16bda90",
-                "sha256:1ac7d6b47da6bee2e4bda31bcdfa8593187c2b0da962ae4855caf5de3e5a388b",
-                "sha256:2790c6ee5ed87b6327d13d9a6b2d80a2ce398d2b74db9a1e2290f1f2d46d2657",
-                "sha256:33019f2f0023616fe844a6fe37910fd56aa238c728179c01b058e35fb5e3869d",
-                "sha256:33c00274e6dbc0c7a27a9744574003f8fcbf343a4d25514e0cd4056bad37f0ee",
-                "sha256:428f43b17e184565f1e76112f0421f52692c59f0be0e9ad33bb119a0efa9f559",
-                "sha256:430d2140a4e96a2295fc7c1e29e547dbc69793b327db1f743613bda3d90d59a6",
-                "sha256:52442ee7cdf9b1c821032c47da93579dc1ab3fe79a5d1045a8f1e60aa2a7b3d8",
-                "sha256:532e634cced7fbdff3be836b07a2e10e6e3f4ebf5aa11b8c3da011c3aa97dfa1",
-                "sha256:57f899c5077ed9a14c991121c1173040f4dfb278ac35c61d40f1d3e958107455",
-                "sha256:6c96f79c3089dfa9445aa1b935ef3368c0a67dec9586bebcaa6a4919338792fa",
-                "sha256:6d1435a7cffd07fd603407027e864ff2de6b9948c32ce58ab06476cb0183fd7e",
-                "sha256:72e763612b74286ef1600705920713e7787b0eedc75f30890e3d9c27f3a9f52f",
-                "sha256:75ca6b42dbb5fe11713bc37f9d30b4c8e356de723fda0a63272ec30b7ae480e2",
-                "sha256:8a398caed78389b366f1037ca62939ff01af2f1789c77bce05eb903f19ffd840",
-                "sha256:918e31076d0704eca205af18b1209573a53fbf03e21e3b4ccc5d1c3a964cdda3",
-                "sha256:99d5d68bece39e4c5fce6863f7a72f4f39d7b24b50335699eccc4ed8e3281438",
-                "sha256:9ee3b49a7fa3bb20366cef0a11585eaa53a7f38071ab5851fb51001afa41f063",
-                "sha256:a08597a385c4730c61fc8ce4d2637700f8d8966a1eedfff3c5fae51544319da4",
-                "sha256:eb38bf59a0f1a67a6dce6e4ebc4ee3dd3e9ef40b05a5deeb44b71fb37009f996",
-                "sha256:ee889766a23542455c023cc9f1e516fa17901cddfa534b5b733dd673076f4bd2",
-                "sha256:efd6dd7c7e930b3425f37dd439928839cdeb1c22e50a3c16a1e7cf67ca2e56cf",
-                "sha256:f5c06642e497bc7c923f89e03fad3d980f5836543efd8fc94c8a4309aae80034",
-                "sha256:f7fffb88aadabdac9e41c6c1363387613555f034914693dcf84ae975ab6e18c9",
-                "sha256:fb6ca774d08cde9b5739b14e45a59ed3b831dc512562bca88b71bda978ea1074",
-                "sha256:fcd4f122473cb2cdbd0848373ada4aa9c83acb5d9791eaab0ffa30e9097f2473",
-                "sha256:feb6e83cf874245023f9612223ba1b13ee8baef6381563620405eb8f5955102d"
-            ],
-            "version": "==1.5.1"
+                "sha256:01882a6f540f61b87db7e6c1a389d278e97b63b8c40457df7d5b98a25413c1a7",
+                "sha256:06033c132beb5e560f1957c048fdfbb3c876b3cc2f3e5e0f664927d98b5fae44",
+                "sha256:2ebdc5b20fb113ad0aa41c61fa7f9d52cb3a22b4b847ed2bf653d7e1f81ae8e2",
+                "sha256:35cb5cbcbd9f8083265a3ce4283ebc62114e28d589d6bf62d98532ada6cfc890",
+                "sha256:40517a5c2352c847f919b7f29963015c37c071ebc2e89ecc4f62aa4b80d9a72e",
+                "sha256:4260ca466a445af7b243d84a7c96833b9261bdfd15ba86028d9710b7d700f490",
+                "sha256:5850ed2ae6c218d57249cf75fa27accd4e3436617a8ea9cd2bea6cb031f449fd",
+                "sha256:6db9129b4f26202c1ba4610b667693e686922011bd7166e7573dea668bb70c88",
+                "sha256:6dc4d76ec7fe5a2d3c00dbe6604c757f1319613b75ef157554ef3648bf102a50",
+                "sha256:72eff06667df45cc87c7b50c87ee67471a32b73cdfaac903bb65945823a4bdf3",
+                "sha256:75dc17b031e85feece5a5770f00342dedeb4349dc530bcd1cd25deb7064cab32",
+                "sha256:940fcaec3159e8ba6ad93bd939108c665c35650fcf6cd026687b40b48bca8110",
+                "sha256:a220f9652f34038c0d9ca275bbfb50efc52dbf867733746cf8a7dcd3b5b84cf1",
+                "sha256:ae7151d4c0759f7263d3baf4ca7283f715f99a02cf3bd4cf2403ae0e5312b64e",
+                "sha256:bcca217713f6468ea0fe18637ff2a113bcb5cf9c7328ba535a0323033110e419",
+                "sha256:bcdf2a3d52a36e81e30d07ae93afdeb057cca8eb0a28c21cfef1f523ade1fe74",
+                "sha256:c5d0985d99a4c749d90016e993dedd6c37c5b6d459b5747eb3a14e9ad63dc669",
+                "sha256:cd33cd2e220c1c283f6fe3273352d7754795f86835ad04520f26a441a5b45193",
+                "sha256:db62b4383815596a92acb6cfa3779b208292aa17f90c10669ee50806b2795cee",
+                "sha256:e3196615252312762ce2196664b2614d254d5e4d2b0cf5c7866c5916e45cec66",
+                "sha256:f549ad17102403984a9c865313f758c346cbb04dd4550946cab81a5fea132ff3",
+                "sha256:fae15273aafd0c9382637b03ef6fa51274c28c84dc3b459a969be2121f41c648",
+                "sha256:fd2311da209bbcff4221d344cb6ee217ce6b465832b013106e1ae21e947818f8",
+                "sha256:fdfc3f3da4efc22ae22096c2b20ac9d5da01b3038c4ed4407fcd501efab26099",
+                "sha256:ff292fcd4d2322fe28bcc0c9e70a37e048858aa68a9aaa43710a73100fa1e77d"
+            ],
+            "version": "==1.5.1.1"
         },
         "cycler": {
             "hashes": [
-                "sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d",
-                "sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"
+                "sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3",
+                "sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f"
             ],
-            "version": "==0.10.0"
+            "version": "==0.11.0"
         },
         "dill": {
             "hashes": [
                 "sha256:7e40e4a70304fd9ceab3535d36e58791d9c4a776b38ec7f7ec9afc8d3dca4d4f",
                 "sha256:9f9734205146b2b353ab3fec9af0070237b6ddae78452af83d2fca84d739e675"
             ],
-            "markers": "python_version >= '2.7' and python_version != '3.0'",
             "version": "==0.3.4"
         },
         "emcee": {
@@ -134,23 +120,21 @@
                 "sha256:474cd6a6e5b1c29a02383d709bf71f502477d0849bddc6ba5aa80b683f4ad16f",
                 "sha256:89e28ac1d2a5412aab18ee3f3dfd1ee8b5c1f2f7a44d0add0d0d4f69f0191bfe"
             ],
-            "markers": "python_version >= '3.6'",
             "version": "==1.0.0"
         },
         "filelock": {
             "hashes": [
-                "sha256:78925788ce8c8945fac28a68c1d05cf33a6a6c4fba14fe02835122c53268ceef",
-                "sha256:d9e9c7d8191e915339843c81c90d3e44f7c84e5fb03bdc6b1b4d019025cf953b"
+                "sha256:38b4f4c989f9d06d44524df1b24bd19e167d851f19b50bf3e3559952dddc5b80",
+                "sha256:cf0fc6a2f8d26bd900f19bf33915ca70ba4dd8c56903eeb14e1e7a2fd7590146"
             ],
-            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
-            "version": "==3.1.0"
+            "version": "==3.4.2"
         },
-        "future": {
+        "fonttools": {
             "hashes": [
-                "sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d"
+                "sha256:545c05d0f7903a863c2020e07b8f0a57517f2c40d940bded77076397872d14ca",
+                "sha256:edf251d5d2cc0580d5f72de4621c338d8c66c5f61abb50cf486640f73c8194d5"
             ],
-            "markers": "python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'",
-            "version": "==0.18.2"
+            "version": "==4.28.5"
         },
         "george": {
             "hashes": [
@@ -218,146 +202,144 @@
                 "sha256:f8eb7b6716f5b50e9c06207a14172cf2de201e41912ebe732846c02c830455b9",
                 "sha256:fc4453705b81d03568d5b808ad8f09c77c47534f6ac2e72e733f9ca4714aa75c"
             ],
-            "markers": "python_version >= '3.7'",
             "version": "==1.3.2"
         },
         "matplotlib": {
             "hashes": [
-                "sha256:01c9de93a2ca0d128c9064f23709362e7fefb34910c7c9e0b8ab0de8258d5eda",
-                "sha256:41b6e307458988891fcdea2d8ecf84a8c92d53f84190aa32da65f9505546e684",
-                "sha256:48e1e0859b54d5f2e29bb78ca179fd59b971c6ceb29977fb52735bfd280eb0f5",
-                "sha256:54a026055d5f8614f184e588f6e29064019a0aa8448450214c0b60926d62d919",
-                "sha256:556965514b259204637c360d213de28d43a1f4aed1eca15596ce83f768c5a56f",
-                "sha256:5c988bb43414c7c2b0a31bd5187b4d27fd625c080371b463a6d422047df78913",
-                "sha256:6a724e3a48a54b8b6e7c4ae38cd3d07084508fa47c410c8757e9db9791421838",
-                "sha256:6be8df61b1626e1a142c57e065405e869e9429b4a6dab4a324757d0dc4d42235",
-                "sha256:844a7b0233e4ff7fba57e90b8799edaa40b9e31e300b8d5efc350937fa8b1bea",
-                "sha256:85f0c9cf724715e75243a7b3087cf4a3de056b55e05d4d76cc58d610d62894f3",
-                "sha256:a78a3b51f29448c7f4d4575e561f6b0dbb8d01c13c2046ab6c5220eb25c06506",
-                "sha256:b884715a59fec9ad3b6048ecf3860f3b2ce965e676ef52593d6fa29abcf7d330",
-                "sha256:b8b53f336a4688cfce615887505d7e41fd79b3594bf21dd300531a4f5b4f746a",
-                "sha256:c70b6311dda3e27672f1bf48851a0de816d1ca6aaf3d49365fbdd8e959b33d2b",
-                "sha256:ebfb01a65c3f5d53a8c2a8133fec2b5221281c053d944ae81ff5822a68266617",
-                "sha256:eeb1859efe7754b1460e1d4991bbd4a60a56f366bc422ef3a9c5ae05f0bc70b5",
-                "sha256:f15edcb0629a0801738925fe27070480f446fcaa15de65946ff946ad99a59a40",
-                "sha256:f1c5efc278d996af8a251b2ce0b07bbeccb821f25c8c9846bdcb00ffc7f158aa",
-                "sha256:f72657f1596199dc1e4e7a10f52a4784ead8a711f4e5b59bea95bdb97cf0e4fd",
-                "sha256:fc4f526dfdb31c9bd6b8ca06bf9fab663ca12f3ec9cdf4496fb44bc680140318",
-                "sha256:fcd6f1954943c0c192bfbebbac263f839d7055409f1173f80d8b11a224d236da"
-            ],
-            "markers": "python_version >= '3.7'",
-            "version": "==3.4.3"
+                "sha256:14334b9902ec776461c4b8c6516e26b450f7ebe0b3ef8703bf5cdfbbaecf774a",
+                "sha256:2252bfac85cec7af4a67e494bfccf9080bcba8a0299701eab075f48847cca907",
+                "sha256:2e3484d8455af3fdb0424eae1789af61f6a79da0c80079125112fd5c1b604218",
+                "sha256:34a1fc29f8f96e78ec57a5eff5e8d8b53d3298c3be6df61e7aa9efba26929522",
+                "sha256:3e66497cd990b1a130e21919b004da2f1dc112132c01ac78011a90a0f9229778",
+                "sha256:40e0d7df05e8efe60397c69b467fc8f87a2affeb4d562fe92b72ff8937a2b511",
+                "sha256:456cc8334f6d1124e8ff856b42d2cc1c84335375a16448189999496549f7182b",
+                "sha256:506b210cc6e66a0d1c2bb765d055f4f6bc2745070fb1129203b67e85bbfa5c18",
+                "sha256:53273c5487d1c19c3bc03b9eb82adaf8456f243b97ed79d09dded747abaf1235",
+                "sha256:577ed20ec9a18d6bdedb4616f5e9e957b4c08563a9f985563a31fd5b10564d2a",
+                "sha256:6803299cbf4665eca14428d9e886de62e24f4223ac31ab9c5d6d5339a39782c7",
+                "sha256:68fa30cec89b6139dc559ed6ef226c53fd80396da1919a1b5ef672c911aaa767",
+                "sha256:6c094e4bfecd2fa7f9adffd03d8abceed7157c928c2976899de282f3600f0a3d",
+                "sha256:778d398c4866d8e36ee3bf833779c940b5f57192fa0a549b3ad67bc4c822771b",
+                "sha256:7a350ca685d9f594123f652ba796ee37219bf72c8e0fc4b471473d87121d6d34",
+                "sha256:87900c67c0f1728e6db17c6809ec05c025c6624dcf96a8020326ea15378fe8e7",
+                "sha256:8a77906dc2ef9b67407cec0bdbf08e3971141e535db888974a915be5e1e3efc6",
+                "sha256:8e70ae6475cfd0fad3816dcbf6cac536dc6f100f7474be58d59fa306e6e768a4",
+                "sha256:abf67e05a1b7f86583f6ebd01f69b693b9c535276f4e943292e444855870a1b8",
+                "sha256:b04fc29bcef04d4e2d626af28d9d892be6aba94856cb46ed52bcb219ceac8943",
+                "sha256:b19a761b948e939a9e20173aaae76070025f0024fc8f7ba08bef22a5c8573afc",
+                "sha256:b2e9810e09c3a47b73ce9cab5a72243a1258f61e7900969097a817232246ce1c",
+                "sha256:b71f3a7ca935fc759f2aed7cec06cfe10bc3100fadb5dbd9c435b04e557971e1",
+                "sha256:b8a4fb2a0c5afbe9604f8a91d7d0f27b1832c3e0b5e365f95a13015822b4cd65",
+                "sha256:bb1c613908f11bac270bc7494d68b1ef6e7c224b7a4204d5dacf3522a41e2bc3",
+                "sha256:d24e5bb8028541ce25e59390122f5e48c8506b7e35587e5135efcb6471b4ac6c",
+                "sha256:d70a32ee1f8b55eed3fd4e892f0286df8cccc7e0475c11d33b5d0a148f5c7599",
+                "sha256:e293b16cf303fe82995e41700d172a58a15efc5331125d08246b520843ef21ee",
+                "sha256:e2f28a07b4f82abb40267864ad7b3a4ed76f1b1663e81c7efc84a9b9248f672f",
+                "sha256:e3520a274a0e054e919f5b3279ee5dbccf5311833819ccf3399dab7c83e90a25",
+                "sha256:e3b6f3fd0d8ca37861c31e9a7cab71a0ef14c639b4c95654ea1dd153158bf0df",
+                "sha256:e486f60db0cd1c8d68464d9484fd2a94011c1ac8593d765d0211f9daba2bd535",
+                "sha256:e8c87cdaf06fd7b2477f68909838ff4176f105064a72ca9d24d3f2a29f73d393",
+                "sha256:edf5e4e1d5fb22c18820e8586fb867455de3b109c309cb4fce3aaed85d9468d1",
+                "sha256:fe8d40c434a8e2c68d64c6d6a04e77f21791a93ff6afe0dce169597c110d3079"
+            ],
+            "version": "==3.5.1"
         },
         "netcdf4": {
             "hashes": [
-                "sha256:02c2317f377f23005e447e4b187ebce3cdeae76b9d0a443d9d6f5156a3980f04",
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@@ -368,169 +350,161 @@
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-            "version": "==1.7.1"
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@@ -538,7 +512,6 @@
                 "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926",
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             "version": "==1.16.0"
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@@ -565,11 +538,11 @@
         },
         "xarray": {
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-            "version": "==0.19.0"
+            "version": "==0.20.2"
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     "develop": {
@@ -582,11 +555,10 @@
         },
         "anyio": {
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-                "sha256:c32da314c510b34a862f5afeaf8a446ffed2c2fde21583e654bd71ecfb5b744b"
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-            "markers": "python_full_version >= '3.6.2'",
-            "version": "==3.3.2"
+            "version": "==3.4.0"
         },
         "appdirs": {
             "hashes": [
@@ -597,27 +569,42 @@
         },
         "argon2-cffi": {
             "hashes": [
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-            "markers": "python_version >= '3.5'",
-            "version": "==21.1.0"
+                "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80",
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+            ],
+            "version": "==21.3.0"
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+        "argon2-cffi-bindings": {
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@@ -625,7 +612,6 @@
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@@ -635,14 +621,6 @@
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@@ -655,201 +633,196 @@
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@@ -857,43 +830,40 @@
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@@ -903,6 +873,7 @@
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@@ -915,6 +886,7 @@
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@@ -928,6 +900,8 @@
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@@ -945,32 +919,38 @@
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+            "markers": "python_version >= '3' and (platform_machine == 'aarch64' or (platform_machine == 'ppc64le' or (platform_machine == 'x86_64' or (platform_machine == 'amd64' or (platform_machine == 'AMD64' or (platform_machine == 'win32' or platform_machine == 'WIN32'))))))",
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@@ -981,19 +961,17 @@
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@@ -1011,34 +989,32 @@
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-            "version": "==0.18.0"
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@@ -1186,7 +1168,6 @@
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@@ -1284,27 +1258,24 @@
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@@ -1330,91 +1301,80 @@
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@@ -1440,7 +1400,6 @@
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@@ -1787,7 +1774,6 @@
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@@ -1795,9 +1781,15 @@
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@@ -1842,59 +1834,46 @@
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@@ -1906,19 +1885,17 @@
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             ],
-            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
-            "version": "==20.8.1"
+            "version": "==20.13.0"
         },
         "wcwidth": {
             "hashes": [
@@ -1936,34 +1913,32 @@
         },
         "websocket-client": {
             "hashes": [
-                "sha256:0133d2f784858e59959ce82ddac316634229da55b498aac311f1620567a710ec",
-                "sha256:8dfb715d8a992f5712fff8c843adae94e22b22a99b2c5e6b0ec4a1a981cc4e0d"
+                "sha256:1315816c0acc508997eb3ae03b9d3ff619c9d12d544c9a9b553704b1cc4f6af5",
+                "sha256:2eed4cc58e4d65613ed6114af2f380f7910ff416fc8c46947f6e76b6815f56c0"
             ],
-            "markers": "python_version >= '3.6'",
-            "version": "==1.2.1"
+            "version": "==1.2.3"
         },
         "wheel": {
             "hashes": [
-                "sha256:21014b2bd93c6d0034b6ba5d35e4eb284340e09d63c59aef6fc14b0f346146fd",
-                "sha256:e2ef7239991699e3355d54f8e968a21bb940a1dbf34a4d226741e64462516fad"
+                "sha256:4bdcd7d840138086126cd09254dc6195fb4fc6f01c050a1d7236f2630db1d22a",
+                "sha256:e9a504e793efbca1b8e0e9cb979a249cf4a0a7b5b8c9e8b65a5e39d49529c1c4"
             ],
-            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
-            "version": "==0.37.0"
+            "version": "==0.37.1"
         },
         "widgetsnbextension": {
             "hashes": [
-                "sha256:079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7",
-                "sha256:bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"
+                "sha256:763a9fdc836d141fa080005a886d63f66f73d56dba1fb5961afc239c77708569",
+                "sha256:e0731a60ba540cd19bbbefe771a9076dcd2dde90713a8f87f27f53f2d1db7727"
             ],
-            "version": "==3.5.1"
+            "version": "==3.5.2"
         },
         "zipp": {
             "hashes": [
-                "sha256:1fc9641b26f3bd81069b7738b039f2819cab6e3fc3399a953e19d92cc81eff4d",
-                "sha256:8dc6c4d5a809d659067cc713f76bcf42fae8ae641db12fddfa93694a15abc96b"
+                "sha256:9f50f446828eb9d45b267433fd3e9da8d801f614129124863f9c51ebceafb87d",
+                "sha256:b47250dd24f92b7dd6a0a8fc5244da14608f3ca90a5efcd37a3b1642fac9a375"
             ],
-            "markers": "python_version >= '3.6'",
-            "version": "==3.5.1"
+            "markers": "python_version < '3.10'",
+            "version": "==3.7.0"
         }
     }
 }
diff --git a/pyproject.toml b/pyproject.toml
index 10c4760..31252ee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [tool.black]
 line-length = 88
-target-version = ["py36", "py37", "py38"]
+target-version = ["py38", "py39", "py310"]
 
 [tool.isort]
 profile = "black"
diff --git a/setup.py b/setup.py
index c2e6616..8f40e71 100644
--- a/setup.py
+++ b/setup.py
@@ -18,7 +18,7 @@
     "emcee >= 3.0",
     "george",
     "PyWavelets >= 0.5",
-    "pymc3_ext",
+    "pymc_ext",
     "scipy >= 1.1",
     "tqdm",
     "xarray",
diff --git a/src/periodicity/gp.py b/src/periodicity/gp.py
index cc2d4e1..8047406 100644
--- a/src/periodicity/gp.py
+++ b/src/periodicity/gp.py
@@ -1,10 +1,10 @@
 import celerite2
-import celerite2.theano
+# import celerite2.pymc4
 import emcee
 import george
 import numpy as np
-import pymc3 as pm
-import pymc3_ext as pmx
+import pymc as pm
+import pymc_ext as pmx
 from scipy.optimize import minimize
 from scipy.stats import norm
 
@@ -13,12 +13,9 @@
 __all__ = [
     "GeorgeModeler",
     "CeleriteModeler",
-    "TheanoModeler",
     "QuasiPeriodicGP",
     "BrownianGP",
     "HarmonicGP",
-    "BrownianTheanoGP",
-    "HarmonicTheanoGP",
 ]
 
 
@@ -541,99 +538,99 @@ def prior_transform(self, u):
         return params
 
 
-class TheanoModeler(object):
-    def __init__(self, signal, err, init_period=None):
-        if not isinstance(signal, TSeries):
-            signal = TSeries(data=signal)
-        self.signal = signal
-        self.err = err
-        self.t = self.signal.time
-        self.y = self.signal.values
-        self.sigma = np.std(self.y)
-        self.jitter = np.min(self.err) ** 2
-        self.mean = np.mean(self.y)
-        if init_period is None:
-            init_period = np.sqrt(signal.size) * signal.median_dt
-        self.sigma_period = 0.5 * np.log(signal.size)
-        self.init_period = init_period
-
-    def mcmc(self, n_walkers=1, n_steps=2000, burn=1000, cores=1):
-        with self.model:
-            trace = pmx.sample(
-                tune=burn,
-                draws=n_steps - burn,
-                cores=cores,
-                chains=n_walkers,
-                random_seed=42,
-            )
-            self.period_samples = trace["period"]
-            return trace
-
-
-class BrownianTheanoGP(TheanoModeler):
-    def __init__(self, signal, err, init_period=None, predict_at=None, psd_at=None):
-        super().__init__(signal, err, init_period)
-        with pm.Model() as model:
-            # The mean flux of the time series
-            mean = pm.Normal("mean", mu=self.mean, sd=self.sigma)
-            # A jitter term describing excess white noise
-            log_jitter = pm.Normal("log_jitter", mu=np.log(self.jitter), sd=2.0)
-            # The parameters of the BrownianTerm kernel
-            sigma = pm.Lognormal("sigma", mu=np.log(self.sigma), sd=2.0)
-            period = pm.Lognormal(
-                "period", mu=np.log(self.init_period), sd=self.sigma_period
-            )
-            log_tau = pm.Uniform("log_tau", lower=0.0, upper=np.log(10))
-            tau = pm.math.exp(log_tau) * period
-            mix = pm.Uniform("mix", lower=0.0, upper=0.5)
-            Q = 0.01
-            sigma_1 = sigma * pm.math.sqrt(mix)
-            f = pm.math.sqrt(1 - 4 * Q ** 2)
-            w0 = 2 * Q / (tau * (1 - f))
-            S0 = (1 - mix) * sigma ** 2 / (0.5 * w0 * Q * (1 + 1 / f))
-            # Set up the Gaussian Process model
-            kernel1 = celerite2.theano.terms.SHOTerm(sigma=sigma_1, tau=tau, rho=period)
-            kernel2 = celerite2.theano.terms.SHOTerm(S0=S0, w0=w0, Q=Q)
-            kernel = kernel1 + kernel2
-            gp = celerite2.theano.GaussianProcess(kernel, mean=mean)
-            gp.compute(self.t, diag=self.err ** 2 + pm.math.exp(log_jitter), quiet=True)
-            gp.marginal("obs", observed=self.y)
-            if predict_at is not None:
-                pm.Deterministic("pred", gp.predict(self.y, predict_at))
-            if psd_at is not None:
-                pm.Deterministic("psd", kernel.get_psd(2 * np.pi * psd_at))
-        self.model = model
-
-
-class HarmonicTheanoGP(TheanoModeler):
-    def __init__(self, signal, err, init_period=None, predict_at=None, psd_at=None):
-        super().__init__(signal, err, init_period)
-        with pm.Model() as model:
-            # The mean flux of the time series
-            mean = pm.Normal("mean", mu=self.mean, sd=self.sigma)
-            # A jitter term describing excess white noise
-            log_jitter = pm.Normal("log_jitter", mu=np.log(self.jitter), sd=2.0)
-            # The parameters of the RotationTerm kernel
-            sigma = pm.Lognormal("sigma", mu=np.log(self.sigma), sd=2.0)
-            period = pm.Lognormal(
-                "period", mu=np.log(self.init_period), sd=self.sigma_period
-            )
-            Q0 = pm.Lognormal("Q0", mu=1.0, sd=5.0)
-            dQ = pm.Lognormal("dQ", mu=2.0, sd=5.0)
-            f = pm.Uniform("f", lower=0.0, upper=1.0)
-            # Set up the Gaussian Process model
-            kernel = celerite2.theano.terms.RotationTerm(
-                sigma=sigma,
-                period=period,
-                Q0=Q0,
-                dQ=dQ,
-                f=f,
-            )
-            gp = celerite2.theano.GaussianProcess(kernel, mean=mean)
-            gp.compute(self.t, diag=self.err ** 2 + pm.math.exp(log_jitter), quiet=True)
-            gp.marginal("obs", observed=self.y)
-            if predict_at is not None:
-                pm.Deterministic("pred", gp.predict(self.y, predict_at))
-            if psd_at is not None:
-                pm.Deterministic("psd", kernel.get_psd(2 * np.pi * psd_at))
-        self.model = model
+# class TheanoModeler(object):
+#     def __init__(self, signal, err, init_period=None):
+#         if not isinstance(signal, TSeries):
+#             signal = TSeries(data=signal)
+#         self.signal = signal
+#         self.err = err
+#         self.t = self.signal.time
+#         self.y = self.signal.values
+#         self.sigma = np.std(self.y)
+#         self.jitter = np.min(self.err) ** 2
+#         self.mean = np.mean(self.y)
+#         if init_period is None:
+#             init_period = np.sqrt(signal.size) * signal.median_dt
+#         self.sigma_period = 0.5 * np.log(signal.size)
+#         self.init_period = init_period
+
+#     def mcmc(self, n_walkers=1, n_steps=2000, burn=1000, cores=1):
+#         with self.model:
+#             trace = pmx.sample(
+#                 tune=burn,
+#                 draws=n_steps - burn,
+#                 cores=cores,
+#                 chains=n_walkers,
+#                 random_seed=42,
+#             )
+#             self.period_samples = trace["period"]
+#             return trace
+
+
+# class BrownianTheanoGP(TheanoModeler):
+#     def __init__(self, signal, err, init_period=None, predict_at=None, psd_at=None):
+#         super().__init__(signal, err, init_period)
+#         with pm.Model() as model:
+#             # The mean flux of the time series
+#             mean = pm.Normal("mean", mu=self.mean, sd=self.sigma)
+#             # A jitter term describing excess white noise
+#             log_jitter = pm.Normal("log_jitter", mu=np.log(self.jitter), sd=2.0)
+#             # The parameters of the BrownianTerm kernel
+#             sigma = pm.Lognormal("sigma", mu=np.log(self.sigma), sd=2.0)
+#             period = pm.Lognormal(
+#                 "period", mu=np.log(self.init_period), sd=self.sigma_period
+#             )
+#             log_tau = pm.Uniform("log_tau", lower=0.0, upper=np.log(10))
+#             tau = pm.math.exp(log_tau) * period
+#             mix = pm.Uniform("mix", lower=0.0, upper=0.5)
+#             Q = 0.01
+#             sigma_1 = sigma * pm.math.sqrt(mix)
+#             f = pm.math.sqrt(1 - 4 * Q ** 2)
+#             w0 = 2 * Q / (tau * (1 - f))
+#             S0 = (1 - mix) * sigma ** 2 / (0.5 * w0 * Q * (1 + 1 / f))
+#             # Set up the Gaussian Process model
+#             kernel1 = celerite2.theano.terms.SHOTerm(sigma=sigma_1, tau=tau, rho=period)
+#             kernel2 = celerite2.theano.terms.SHOTerm(S0=S0, w0=w0, Q=Q)
+#             kernel = kernel1 + kernel2
+#             gp = celerite2.theano.GaussianProcess(kernel, mean=mean)
+#             gp.compute(self.t, diag=self.err ** 2 + pm.math.exp(log_jitter), quiet=True)
+#             gp.marginal("obs", observed=self.y)
+#             if predict_at is not None:
+#                 pm.Deterministic("pred", gp.predict(self.y, predict_at))
+#             if psd_at is not None:
+#                 pm.Deterministic("psd", kernel.get_psd(2 * np.pi * psd_at))
+#         self.model = model
+
+
+# class HarmonicTheanoGP(TheanoModeler):
+#     def __init__(self, signal, err, init_period=None, predict_at=None, psd_at=None):
+#         super().__init__(signal, err, init_period)
+#         with pm.Model() as model:
+#             # The mean flux of the time series
+#             mean = pm.Normal("mean", mu=self.mean, sd=self.sigma)
+#             # A jitter term describing excess white noise
+#             log_jitter = pm.Normal("log_jitter", mu=np.log(self.jitter), sd=2.0)
+#             # The parameters of the RotationTerm kernel
+#             sigma = pm.Lognormal("sigma", mu=np.log(self.sigma), sd=2.0)
+#             period = pm.Lognormal(
+#                 "period", mu=np.log(self.init_period), sd=self.sigma_period
+#             )
+#             Q0 = pm.Lognormal("Q0", mu=1.0, sd=5.0)
+#             dQ = pm.Lognormal("dQ", mu=2.0, sd=5.0)
+#             f = pm.Uniform("f", lower=0.0, upper=1.0)
+#             # Set up the Gaussian Process model
+#             kernel = celerite2.theano.terms.RotationTerm(
+#                 sigma=sigma,
+#                 period=period,
+#                 Q0=Q0,
+#                 dQ=dQ,
+#                 f=f,
+#             )
+#             gp = celerite2.theano.GaussianProcess(kernel, mean=mean)
+#             gp.compute(self.t, diag=self.err ** 2 + pm.math.exp(log_jitter), quiet=True)
+#             gp.marginal("obs", observed=self.y)
+#             if predict_at is not None:
+#                 pm.Deterministic("pred", gp.predict(self.y, predict_at))
+#             if psd_at is not None:
+#                 pm.Deterministic("psd", kernel.get_psd(2 * np.pi * psd_at))
+#         self.model = model