diff --git a/.coveragerc b/.coveragerc index bc5c5271..3bbac921 100644 --- a/.coveragerc +++ b/.coveragerc @@ -1,6 +1,7 @@ [run] omit = - # _version.py doesn't count + # _version.py doesn't count, same for _deprecation.py rdtools/_version.py + rdtools/_deprecation.py # omit the test files themselves rdtools/test/* diff --git a/.travis.yml b/.travis.yml index 40d2cb1f..9fe6be85 100644 --- a/.travis.yml +++ b/.travis.yml @@ -7,18 +7,27 @@ python: - "3.7" - "3.8" -# Test two environments: -# 1) dependencies with pinned versions from requirements.txt -# 2) 'pip install --upgrade --upgrade-strategy=eager .' to install upgraded +# Test three environments: +# 1) "standard" dependencies with pinned versions from requirements.txt +# 2) "minimum" dependencies with pinned versions from requirements-min.txt +# (only on python 3.6) +# 3) 'pip install --upgrade --upgrade-strategy=eager .' to install upgraded # dependencies from PyPi using version ranges defined within setup.py env: - - REQ_ENV='-r requirements.txt .' - - REQ_ENV='--upgrade --upgrade-strategy=eager .' + - REQ_ENV='-r requirements.txt .[test]' + - REQ_ENV='-r requirements-min.txt .[test]' + - REQ_ENV='--upgrade --upgrade-strategy=eager .[test]' + +# PyPI doesn't have wheels built for the minimum requirements (e.g. numpy 1.12) +# for newer python versions. Rather than try to get Travis to build them +# from source, prefer to restrict the minimum-reqs build to only py 3.6: jobs: exclude: - - python: 2.7 - env: REQ_ENV='--upgrade --upgrade-strategy=eager .' + - python: 3.7 + env: REQ_ENV='-r requirements-min.txt .[test]' + - python: 3.8 + env: REQ_ENV='-r requirements-min.txt .[test]' install: - pip install $REQ_ENV diff --git a/docs/degradation_and_soiling_example.ipynb b/docs/degradation_and_soiling_example.ipynb index cf4de875..edaf4116 100644 --- a/docs/degradation_and_soiling_example.ipynb +++ b/docs/degradation_and_soiling_example.ipynb @@ -7,7 +7,7 @@ "# Degradation and soiling example with clearsky workflow\n", "\n", "\n", - "This juypter notebook is intended to the RdTools analysis workflow. In addition, the notebook demonstrates the effects of changes in the workflow. For a consistent experience, we recommend installing the specific versions of packages used to develop this notebook. This can be achieved in your environment by running `pip install -r requirements.txt` followed by `pip install -r docs/notebook_requirements.txt` from the base directory. (RdTools must also be separately installed.)\n", + "This juypter notebook is intended to the RdTools analysis workflow. In addition, the notebook demonstrates the effects of changes in the workflow. For a consistent experience, we recommend installing the specific versions of packages used to develop this notebook. This can be achieved in your environment by running `pip install -r requirements.txt` followed by `pip install -r docs/notebook_requirements.txt` from the base directory. (RdTools must also be separately installed.) These environments and examples are tested with Python 3.7.\n", "\n", "The calculations consist of several steps illustrated here:\n", "
    \n", @@ -95,7 +95,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -189,9 +189,19 @@ "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/opt/anaconda3/envs/release_test/lib/python3.7/site-packages/ipykernel_launcher.py:11: rdtoolsDeprecationWarning: The normalize_with_pvwatts function was deprecated in rdtools 2.0.0 and will be removed in 3.0.0. Use normalize_with_expected_power instead.\n", + " # This is added back by InteractiveShellApp.init_path()\n", + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/normalization.py:170: rdtoolsDeprecationWarning: The pvwatts_dc_power function was deprecated in rdtools 2.0.0 and will be removed in 3.0.0. Use normalize_with_expected_power instead.\n", + " power_dc = pvwatts_dc_power(**pvwatts_kws)\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -242,7 +252,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -287,7 +297,7 @@ "outputs": [ { "data": { - "image/png": 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uGc4ynDv/wWi/QVtNyTBUbBEG0pHxmqt7HJupcvhsiVrok4nHKdYa+L7ixCzmqg0mS3WcmEWlHjBTqeEGwlA2zvVbCji2RdK2mKk1CDWk4gZkHJuhbBIEjkyVePDMPK4XUKn5lGouH58qkbItsJW4FWNLf5qn7BmiWG/wzUMTVNyA5+3bxp6h/JKnNAyVyXKd6XKD/kycECVUPW+w4MRshftPzZNP2jx8dp6j40USMYvj03luHuvDsYWJ+Qa37+gnk3IiD5RzN/1CH5gqlkTGf7xY58hEiURcsBCGc0lqXsBUxSUZsyjWPcq1yBs9W3TZO5KiWAmYrzZ47EwRNwwpVWNMl138MMD3hC25OIcnS/QnHQoZZ8H7bH/46nWfg1MlcnGbpBNjMJMgHbej+2C2QskNmKu5C32UACdnyjwyXgKF67fk2TOURSxwvYCBVGJBVjutboZawycIlXwyTtqJEajiB+A3z0mrid/ygFpeYcK2mKt7K/Lk2iuTmhfgBgGlms/uvjRl/5z3v7jSqXkBk2WX4zMVZqp18k6cHf1RK2WhrzauC0Y5YVsL3nO3DHKnBs0DfhL438B3ReQFqvoY0JuhnB5Trns8NlGi4Xsci1XYW8+SiNmcnqtRrfvsHMpwcHyOHx6ZJRsXgtCmPwb9CYupeZcgUOK2UPcFCQMOni3j1gOOzlYp1xsM5xKM9KWJx6wL+mVaN6gtwlA2gWUJFden0ogubC5mkXdiBEFItRHw0PE5Ts+Vma74ZFPRDRKLCUcmKiRjii0WyZiNE4MjEyWmi3VG+tLkkjF8H3wJOXBinoFcHAuo+yEnpipMzruEwPXDKWaqDR45NU8tiAyF2MINIzlsW0jH4xw8U6buunwRePETYgwVUhc0J+oNn/tOzBEoFNJ5bBGqDZ9HJ8oMJOOkkjZ136fiepyaLXHg+BzzjRDPCzhwusx3Dk2hQIDFkakSt+8aZEsuSRiExEXwVKm6Ht8/NotlKRknTi4Z48x8ldPTFc7OVzk0UOTp14/Sl4rz2HiZXcNJHhkvMT5XxVdlYrbGdw+CF4RghVjYJBwbLwxISIxj01XCUAisgKMTNfYOZXjKNUOM5lMXeI8Hzsxz7/FZUjHYUshw7ZYMh8YruA2Pedej7gdUqwG+H5JK2XhhyJnpKoEqhXSSwZxDMm5Ta0TGdyib4MYteYZzyfNkpeI25bpHue5Tqnscm64wkInTn3LYkncYSido+AFHp6rsHk6TjMfwg5ATs9Vmn54yXqzTl3TYt6MfyxImyvWLetwV1490yiSibo6aT8MLOVmqMlf18LwUw7kkh2dKHJksY4mwJZfEsmC+6uHWfH5wukix4fOE3QNcN5Ll9GydZNzm5m0FCmmHuAiPjBeJicWWviSx5ojtWg3bSpY++cCrROTXgG+KyL9fk+TLSCMIUYHpkkfJrXB8Yo6yG1L1lIGMw3ylzoFTRSarDXb3JbmuP8NwLkGp6vHQmTluHstxx81bqDc8js/UmJqrcrpYZcdAEj+Ik4zHcSyLct3j2HS5aYSiGycVt6m4Pl4QLswBC0OlXPexUCZKNdxGQBAoY4NJ5uYbFGvKfM3HEpitNdg7kmWskGS62qDihSQSFumYzSNnSoQI2/pqWBKSdmKoWkzMVXnstM9czaXUUBKxkJxjk0qmSGYc8l6AHYuRj4VYzaZ4yQ144OgkrgeDGYeKneDIeJF7vnmIF+zbwbWj+QUj88CpeaarNR47Nc9gX5rhTBxfQx44Mcd8xcWybLYNJgGYmnc5Nl2i2AgYKzjELYfDk1VOTkdOfy6b4MRMhYliHce22T6UYjCTZCid5GypxuGJImeLdYazSRIOjM81CIOQM/Mu0+U6fhDgeiHTpToPnU5SrAYcmpij4gaEYQgCvsbJJm1G80lyGRv1oZC0cewYc/Uq/3zgLFUP6g2Xuhdw+54+3IbSn3TY2p9mulTnvlPT4PmoE+PR03Mcn5rn0NkS865H4MNU1cVtePSlEmzrT1Kqe8zXfDIJCw1Djk0oxYpHIWdzaqrCfNWhVGnwpGsGEbEWvPWK61P3fQ5NzXPg+Cy1RsBQNsVAPkG93iDuxNiSS3DgRJHBPoet+TSFdIKEbVNu+FS8Bg8cm6MvHWe66rKrP8PRmSpiwe07+hnMJC4YIAtDpVjz8P2AmC2cmatQSDicmXX54ck5Hss63Ly9wJcfPMu/Hp2iUg/IJWMknTjbBjLsHkoyWaygloXvhZydczk6UyFuCZZEfb3jpTqHp8ok4zb5dAzLip6PtTZBO917wWyq6odE5EHgE0B6TdIvE2P5NDduySNhSHXc5dGpGtMlj3QcbAsCDYGAa0ayPPW6QTS0GMkl+N7hKSquUnWVp9w4iG1bZOPCp75/kvlKnf5sihu25jk6WcV2hIPjRQ5Nlpgo1tk31r/QZzKQdjg+W6Hq+iRiFnN1l0Pj89QbPjMVj1CVbCJGOhHD7oNbJIsfZGiEQhAKtmWxeyTHWBDgq7KjP0vCFv7uh6dRz6Ncr+OFcHK2TtyCSiNkrtJgvu7hCGTTGfoKCVKxGFvzaW7Z2sfjtw/ihyG+hkyVG5ycLPHodJ1y1WPrQIYn7SnwvUcrPDYZGZQXPWkHjhVnplrjnw6cZbbs4gbKaNWjUqtzbKpGxfWjWe3A6WmbSgBZRxARbtqa43E7BkjFbQrHZnlsvEjdU/rTFrOlGrO1gFrDxz6ipOKCZTlcvyVD3VOmS3XOzFRxFSyUvUNpdhUSTFdd/vHBs5RqHnERbBvSiTghFpVGGHkBMYvdfQ637hzglq0FjkxVODVTZ6bqk7RDfnCsyOGJIllHsCXk5GSFbxwaZzidIOXEuGlHH67r8c1HJnHsqE9pttJALEBt0JC6H6AKcdumIXBqvoGGSsy2QWJMlj1OzNTIJctkUzYzpQaluseWgTRFz2U0l2W4EGdi3mWqWOfMfJVHzpQ4NlUmDEO2D2UYKSc5MlVjrlwBbFKJGCKQTcQYzCW4eTSLF4RMlj1qnsfpmQqPnC2ydzjDYDZFX8oBUQ6cmePQeJltfUlu3tZHzLao+z4Pn54n0IDZqsf0fJ2YDdWaFxlqt8FXDsSpeiEzZZeaHzJdbZCIx1EgFbcouUrSgVTCJpsUHCu6bt+rNpiquAznEsQtYSAReapOrDsjw50atGe1/1HVr4vIU4gWkG84AoFcMk427TDSl8UCBtNxRgopfD+kWvcYG8rzgn1j7BjM0vBDYiLYtpBNxulLRU3FG7dE/UnPvW2Me49OM5BOcNv2fvYMZZmcr3N4sszJ6Sonp6pMFOvs6M8y2u9w8GyZeqPBmfka8ZjFkfEavu9Rdj3cUMmlHK4bzdGfcZirCjeNpan5IRXPw0YYSCWoBD4pjXPTtjz5pEPJbfCEPcMcPFvi5GSRehBSSAoVF4byDvlEjP50jJGBDGP5NPl0nJzjMJxLEIvbZHc2+6lUmau5fPfwNANn5zg2W2drIUHdh0I2wdGZOg+fmefPv+lz87Y+vDDg7FwN1/MZzqfJJWwem6gyW6oT2hZb8wkm510OV3wyjs1INsm2gTz7dw5w7ZY8CNywNc/4fI2aG3J6rsJ3Hpsi7jWoekrJbTBRDrHEo+6HXDecIhGzAEECn2wyye7RAsWqz1zdp1zzsW2boVyC2WqA2hZPGsuTTsQZ7UtRcUN2D2Z5/I5+ZmsNzpY9+nIhnh8wWW4wmrYJh7LkMg7HJ8pMlevYIpxM2KRSCR4bnydQJVDwRHAsi0wyhmCxNR8jGU/g2NCXc0g7cWZLLqEGxOIxRvtS+B6Mz1c5PlVivOyR9JWUY1PxfE5PV/jmgZAtA2USTozj0xXKtcjY9adjXDuao9oIcP2QSt1j70CC+12fWr1BOlQScZvpkstcPWR8rk7FC0nHhS35JCHKyekK4/NVtg2kuWVbH//4oMtcqcaJmRp7tuQJwpBT81WOTFaoVGqcnKnjxG0UKNYDqp5PMmYzUfYpuTUGkg77dhTIJmyqno8tFoV0glxKeNzOPPlEAj9QHjpd5vh0nfH5MnFb0DBgfD5OqIAo9jQMZ5IkCqneNTlFRNrejv7/RGRxY/sU0HH4oCuJVNxmOJugVEhTSMaJjRXoSySohz73n5ijEYTsGMwwnEvRn4kiF4WhskdzFFLOeVMkAK4dyuHYFkGgpJ042aRDqdFgrlon6cDBk/M8cnqWHcNZRnJJDp4p0fB9qp4yXnQRDRnpSzOQtvEbATU7cuG9MEQERgtJtvalODlToy8Vp+oHTJVcECGfjJNPxRlMO+STDk/e088/HDjLbLFO3VdyKeXm7TluGCkw1pfGcaJ5V60pBEuNJuaScZzrbK4dyYEqrhcyX/PYWkhx3ZYc9x2Zo+h6HDxTYt+uAjduyROKcPvufio1jwdPzJNN2gzmUowUkpyYrDI9X2WoP8NTrhlgsuhTyDkU0g41L2BHf5ZdgzkAJks17JiN63vEbYvp+Tqh7zNXi85XNu2wbdDG90KSiRg3jBXYmk9RcwMem0yzdyTLQDbJk/cM8ODZEinbZt/2aJCh1ckNUdMmFbcJVbEpcGiyTD5dZzg7wN7BLPXQ56sPTnByusTh6Rq2gIQ+Z8pK3IJrRvLcsCUHlsXOwTTFqs/OwXSzmQ9jhTSZRIyJSp2ZkksiFmOkkMALlKQlfO3wJA8fn6Xk+Vy3Lcd80eXh00VOzNc4VaySilsknRiFTJyYCMOFDM9/wla8QHnkdIlsIsaOoTRPrdU5Nl7jcTvz3Hd0joliFRVw3ZBKw6OQSbJnJMNMqUHcFk7NuUzM1/H9WVzXY7LqEbcsJssexyZLPHSqRNVtkE451Bs+FhY3jWUZziUIFPYMp5gr1zkz1+C6sQLPuHYLuXSco9MV6l7A/SfnOD7tc9uODGMDGdKOxY1bcgxkY3zvUZczRZcDp4pkkzFSjs1sucEjVokdwxmedu0wo4VlA/F0xMU8tHnOLTvyuXAAoLV08MqeabcEliUMZ5PMVBqEqvSnHXb0Z0jYFsP5JIEfkk86DOeS5+2TdmJUvYCh5ryp1ghNLGaxezB73ryy09MuR6frhIFPsRFyZq7K6XmPoZxNzVMcCyyEhBWSTCa4dUuWXC7J9JxLteHj+iGjhTjlWkg6GSObdNg2INGIWCLGYDaBLXLeHK4tTnQz/NwTHA6Ol0g60HDhlq0F0uml12ouF0ppNJ8i3zQCYagL/SypuM2De+f4wr+cQoMQVeG5+8ZohML2gRSHp8qcmW9ww7Ysw5kU2USMJ+5STk5XuXE0TzYZ50iyynAmueTw/WAmyTOuH6Xs+tGgSXPax5HJEt87Ok1/xuGGkTxF1yfl2GwtpBau0zUj+fPmtV2/deCC8ubaOsEdx2bvcI4wVJy4zXQ5xY6BNIXmhNVrBgscnilxZrbK2bk6Z4s1TkyUCG2L5+0bY9tAhkw8GuH0VJecItOvCWzLwhahkDrXL3b7zgEG0g7j8y637SyQiMe478QMX/rXU0xUXDw/pC8e55rhAv05h75Egl39Obb2pblt+yB1L6A/5eCpkro1mp+YSyZxvYCGF3J4pkTOiZFPOBTrDZLxBHtHc0wXXWbKDRpeg4fmoqZxyrGxNOTIRBlblHwmxW1jWRohiFjcsDXP9oE0haTDWF8ayxbqXkA6HluYIzmQSVKsN1DgzFyN3SNZUrHIaA1kHNJzdQqZFNNVj5oXknEssok4Db/BiaLPjqEUyR43OW9p+71n2VwbFDcI6cs41L0QP4j+FzIOWzXNVNklv8Qwd/vi6sUP42LD8KPXDHG2WCMIQnbWXI4kY8x7AdmEzXDWJh6zGM4nCHxhuD/B7sEcW3IJHp2qMFOusyWfZiibALUZTCfODXVnEhc8NC1a3lYuEefWsb6uDYfHYtZ587J2FbI88ZpBfnisSMVTfBVu3d63MJzv2BbbC2lGC6mF1Qy7B3MLZUguEVyzhWVFUy+yyfO9xutH8nghDKUdBnLn4n22T1YtpB0KqyjfYgPewnFsbtzSx/UjhebIX42HT5fY2Z9m+0DmvFUmrTn7i++Z9vlrrbxRmsPwzsHzplQMJB1GCylOTJU5Ml4mmbRx4g77d/czmEktDBS0l7Ml1xKhP50g7djM1RvYts2uoeUyDosAABLCSURBVBTzNZ8hP4mGMJCJpsJMlGrcd2yGJ6cdsmmHm8cKfPORaY5OFhktpHjaDSPcMJwnm4pTrHv0JR2cuH3RicG5VJxMIkY2EV8wtm4Qzek7NVvF9QNu3dXPTWN5Tk5HXqTEhJMTAXFbSDoxcsnVB0hoIedalRub/fv367333nvJfIsnKrYmrLZuqlLNWxiyvlgEiktN/IwegDoT8y7bC0nm3AYnZyqcnauzeziDHwg7h1KgsnDDJJpzx+KW4IeKY0VziDqNhtGarJtNxNY8WnSxY52dr/HI2RIVz6VSDfmxG4cZzac7Oi9r4XIHiFwv+fPVBidmqti2Uq6FXDuaXej6uJRurdn8wHlN7FTcXqhcjs2UefhskbRjc/1onmwiztlSha89PMWt2wvcuq1/TdFX2qm4PjNVl1LN59qhaALw2VKNqWKdybLLeKmGhsJzbtrCSH9nY4wi8n1V3b/Utov1of0pHcwzU9UNNX1jwbNqe1BHnHO1cqchbi4WtqU1kdSxLAYyDtmUw9aBDIlYjFBjbO1Ls7WQXnJ5yEjbPKQwVBzn4qM/7Q9ZN9fbLXesMIwmRl47kqFYcyimPBxZevZ8t7ncoXLWS34uGWfPcPaC1QGd6tberG7/3fIedw1kySTipGL2QlCBvmSSO27a0pXKsJ1oVD/BWCF9zpNOOrh+SCYZYzSfYjiXYCi/tr6zFhfT/FBXJFxhXOqhX81Nu9SM6aoXRLHJ/MjtBtheSGOJsC2fwnHOl79Uf1Inuizer1s343Kya15A3Q+J2zaBeriB4oZhV2ReTi6399dO+7nvxTrN9i6E1sTvhG2RTca7Xv7l7iMLYSiX6tqE2hYXe9HwO7oi4QqjF7XsYqPSMpa+H+LGw4WObcex2T2UXfIYq/Wu1jsKQvuylbgtZBybgdT6RX3oFVfrW796bTyXor0VdFkWpwOIiAPcQBTosX2i7Ze7qtFlZjU19WKjshDpIa7EYp0F4VutoV3vZli7PCewSSfieKpsdJO2UcLjbAZ6ec92Gm3j6cCngATRVI4iUUjsE8Denmh2mVhNTb2sW92DC3clNY02kxG43P1zhu7Q6RX8feC9qvr7IjKrqgMi8lts0DenX4wr/SG9kppGxggYrjQ6bTRfz4XBHf8H8PruqnP5Wc+496uhl/HYDYaNzkpektJaNXBGRG4G+lkUmNHQe650g2swXE46NWifAX6m+fseovDY3wf+shdKGQwGw2ro9J0Cr2v7/Xsi8l0i7+zveqWYwWAwrJRV9eiq6te7rYjBYDCslU6nbewkevPS7Vz4QpPre6CXwWAwrJhOPbRPAQ8Dv0X0fk6DwWC44ujUoN0IPFVVN/6iPYPBsGnpdJTzb4Bn9FIRg8FgWCudemj/CfiWiDwGjLdvUNVf6bpWBoPBsAo6NWgfBgLgIUwfmsFguELp1KDdAWxT1dJqBYnIAHA38GxgCniTqn5siXxvB94CuG3Jj1fVw6uVbTAYrg467UO7Hxhco6wPAA1gFHgp8EcicssyeT+hqtm2jzFmBoPhknTqoX0Z+HsR+TAX9qHdc6mdRSQDvAi4VVXLwDdE5K+BlwFvXJnKBoPBsDSdGrSnE72H89mL0pVobeeluB7wVfVgW9p9LD9y+nwRmQHOAH+oqn/UoZ4Gg+Eq5pIGTUQEeAVwXFX9VcrJEgWFbGeeKEjkYj4J3EXkCT4F+LSIzKnqx5fQ7U7gToCdO3euUjWDwbBZuGQfWvPt6Q8Aa5lUW+Zc+KEWeeCCQQZVfVBVT6tqoKrfIorD9uJldLtLVfer6v7h4eE1qGcwGDYDnQ4K/AtRs3G1HARiInJdW9o+4EAH+ypt7zAwGAyG5ei0D+2rwJdE5CNE7xFYeF9nJ4MCqloRkc8A7xSRXwVuA34W+JHFeUXkZ4GvAXPAk4gm9b65Qz0NBsNVTKcG7WnAES7sxO90UADg1c28E8A08CpVPSAiPwp8UVVbUTz+XTNfAjgJvEdVP9qhDIPBcBXTaYDHn1irIFWdAV64RPrXaQtJpKq/sFZZBoPh6mQl7+XsB54PjBFN4fgbVZ3tlWIGg8GwUjoaFBCRpwKPAb8OPB74NeCxZrrBYDBcEXTqob0feLWq/kUrQUReAvwvoo57g8FguOys5L2cn1yU9pfAtd1Vx2AwGFZPpwbtUaLRx3b+LVEz1GAwGK4IOm1yvg74WxH5T8AxYDdwHfC8HullMBgMK6bTaRvfEpFrgOcC24hCcn+hORXDYDAYrgg6nrbRnKLxZz3UxWAwGNbERQ2aiHyFtmVOS6Cq+szuqmQwGAyr41Ie2nIe2RjRGst0d9UxGAyG1XNRg6aqd7f/F5FB4E3AK4FPAO/snWoGg8GwMjpdKZAXkd8BDhG9E+AJqnqnqp7sqXYGg8GwAi5q0EQkJSJvAg4DNwFPV9WXqaqZf2YwGK44LtWHdpTI6L0XuBcYFZHR9gyq+uXeqGYwGAwr41IGrUY0yvmqZbYrsLerGhkMBsMqudSgwO510sNgMBjWTKdrOQ0Gg+GKxxg0g8GwaTAGzWAwbBqMQTMYDJsGY9AMBsOmwRg0g8GwaTAGzWAwbBqMQTMYDJsGY9AMBsOmwRg0g8GwaTAGzWAwbBrWzaCJyICIfFZEKiJyTER+cZl8IiLvEZHp5uc9IiLrpafBYNi4dPySlC7wAaBBFCDyNuDzInKfqh5YlO9O4IXAPqJoHv8AHAE+uI66GgyGDci6eGgikgFeBLxNVcuq+g3gr4GXLZH9l4H/qaonVfUU8D+Bl6+HngaDYWOzXh7a9YCvqgfb0u4DnrFE3lua29rz3bLUQUXkTiKPDqAsIo+sUr8hYGqV+64WI3NzyDMy11/eruV2WC+DlgWKi9LmgdwyeecX5cuKiKjqea/UU9W7gLvWqpyI3Kuq+9d6HCPz8su8Gsp4tchcjbz1GhQoA/lFaXmg1EHePFBebMwMBoNhMetl0A4CMRG5ri1tH7B4QIBm2r4O8hkMBsN5rItBU9UK8BngnSKSEZGnAT8L/OkS2f8EeIOIjInINuC/AB/psYprbrYamVeMzKuhjFeLzBXLk/VqyYnIAHAP8CxgGnijqn5MRH4U+KKqZpv5BHgP8KvNXf8Y+G+myWkwGC7Fuhk0g8Fg6DVm6ZPBYNg0GINmMBg2DcagGQyGTcNVYdBExG773fOF7iKSavu9XsvLpP17nWQ6bb97Xk4Rya6nvKacvSKSb/5el3MrIk8SkRvWQ1abzB8XkTvWWeYzROQtrfPbDTa1QRORnSLyCeCDIvJagF6OlorIdhH5W+DjIvIHIpJS1bBX8poyx0Tk3cCPQG/L1yZzp4h8DLhLRN7VlNuzcjblfRb4sIjcIyKxXp/Xptz/CPwQeDb0/tyKyA4R+UfgE0BfL2W1yRwSkS8CnwYeJyLxdZC5Q0S+AHwF+B2WnmC/KjatQROR24GvAmeAh4DXisjHRSTdI3mDwN8CJ4D/AzwN+JiI7LvojmuT+QtEN8WbgJ8SkaFmes88CRH5deBeovP6ZeAlInJPc1vX7ycReQvwA6Lz+k6iaT8faG7rtce0D5gFnrxoUnjXaPOs30s0gfwhVd2rqt9t395DfgOYVtVBVf0DVfV6KUxEPkRUzoPAbqJn9Ke6dfz1DB+03jwL+Lqqvg5ARP4KeBT4roh8SFVrXZZ3O1BR1Vc15X0H+CTwUhEZV9WzXZYHUSim/49oudivA98BPt8rT0JE+oDrgNeo6iebafcDXxWR16nq4vW6a5UnQAj8lKre20z7BpBfam1vF+XaqhoQ3S+fAJ4CPCQix1XV7aastjL8JPA1VX1tU4cnEz30JSDopszm8QXIAI8nmveJiLy4ufleVT3aA5l7iUKI7VPVIyIyCgy267TWa7ppPDQR2baoLW4BaRGJNy/eaeAw8EvAji7ISzS/Wy56Cbipld58uO8GbmTpqCJrkdnqu7ob+EtV/ThwEniBiOzuhqwlZArgAh8HvtRMs4iaRg8BXfEk2uTFmjf3+1T1XhF5okTRVF5AdB1f2N6H1yWZNkDTmAE8Ffgwkef9s8CebshbJDPZTHoZcIeIvKpZGf5f4AvAn7T3AXdJpt08txngVqAoIh8H3gX8CvAZEVkqtNdaZIqqHlbV1zaNWVxVx4mCT/xEK/ta5W14gyYiBRH5PPBPwJdE5KXNk/gYUe3+i82Lt5OoebabZn/Tatx5EelvNrE+CNDmoo8D3wNe1Zb900Te0xPX8vAtIbPRvEFKqjrbzPb7RE2kH+1GP8gSMlVVa6p6r6oWm/JDIEFkzMtdluc3v1se0TbgD1U1A7wP+G3gLSKyVMSW1coMmumt5+IEUeV3N5AEfkFE3iUij++izHrTeB8g8gY/QLQ65ulEy/7uAP5zc99VPfBLlbNp1MaJwnPdBZxS1RtV9WeAvwSe2+Vyats2Afzm338CdolIohv9ohveoAH/HagRRcH9EvAS4O2q+ing+8DvNWuffwU+T9S/9VxYeSeviDwO+CzwJOB6Efn5ts0TwDeBHxORPc3jh8BfAS9S1cZqCncRmdKWR5pNsn8Gfg64eTWyLiWz3VNoO3cvBB5u82p6Je9vVPV/N8s6TeRNvJyo0uqmTKvtwbodeERVZwAPeAvwOCIPsWsyOfccvhJ4pqr+MVGEmW8DbwV+DVY3KHGJexaitdO3Aqm2tM8D1wCrqoQvdm5hoXJslSUAsqrqShf6YDesQRMRS6IO/l3AF5o1+buJarefF5Fnqep7gecRRce9XVU/B4wQdTKvphPbIVpQ/3KimuWVLc9LVavNtDrw39r2OQ4cF5HCqgq6jExVDdtq7Nb3+4nCLf2IiPxXEXm3rG4QZDmZQUtm8/zbwBOJPAtE5FdF5FXLHHNN8tpo9fuWiDrsVzvkf7Hz2nqQvwu8Q0QeaMr5BnCUqKnWTZmNpsfkE3WSw7lyhsBRaZuy0iWZrQrou8DfAT/e2kFV7wNsov6ubsoM2++fZt4vEDkBo10ZuVbVDfPhXH/UcPN/DvgX4MVtebJEXtvXl9j/8UQjdM9ZobyR5n8HKDR/P615Md7Qll+IRmyOAR8i8pbuJ+oHWmkZLykTsJbY/w+IHoIp4IW9ktksaz9R/9JLiEY8x4Hn9aqMgN38vomoef+7vTivLbnAnzXvr1c0036a6EEd7eW15Nwa6xuIDM4belXOZvoeoor3C0TN228TRcfJ9fqeBa4lqiie1WkZL6pLNw7S6w9RbfF/iToQv0zUCf1zzW2/B/xgUf5/Q9Rf9lPN/6PA55r7v3GV8p6/KE+2efG/BuxatO1pRN7St4jeo7DaMl5SJpFhsYA4UT+a1+kDsAaZLaP2LM4Zz9/qcRlzRPPB/orIM3tzL8vYTN8DpLt0v3ZazhTRqGrrfn1Tj2W2Kombgdc35fZapnDOaI8QdQfdutLzvKRO3ThIrz9EntVXiGKMx4iadMeIRqGGicJ7v6Qt/w7g68Cz29JeCuTXIO8w8GOL8t0K/AXw/ra0eNvv2BrL2KnMWPP75zstYxdk2kR9JL9F1AfSa3lCZGDupEPPoYvndcErXady7gVeu47lbL9nL/D6e3luF8tf66crB+nFByi03Uh3AocX3Vx/QTSkPgy8hqhT/tq2/b8H/HgX5X2caLRrb9s+DtFw/j8SNXO/SYfN2S7L/Ol1lvlt4I51ltdxk2QDX8urpZwrkrmSz7oaqQ5P2HVEnZR/SzRSsotoCPurwG1t+Vr9YS9s/v8sURPv/UTu7bdotul7Ia8t/XlEUxZOAS/vZRk3ksyroYxGZm9lruZzRY1yisgriNri/wL8JjAAvI3InR2nuaYOQFXvBx4A/kMz6U6iuUkA/6CqP6KqEz2Q97LmvraIPItozs7/UdUxVf1Ij8q4oWReDWU0Mnsrc9X0ylKu5kM0t+iVbf+3E1n1bUTt80/Q1tQBnk+0LizdltZRP0c35AFjQN96lnEjyLwaymhk9lbmaj9X2lrODxItr2ktmagSzfhPAZ8imuz3OhF5TFWPEXVK/71Gc8CAFU8+XJM8jd7svq5l3CAyr4YyGpm9lbk61sNqrvTDuSHd24ncV6f5/1aiYeUfEs1dmaRtJHOjyLtaZF4NZTQyeytzxTpeDqErOIGvJ1p83Z5mA/uBX97o8q4WmVdDGY3M3srsWLfLKfwiJ6w12e/jwK82f7+KaARzeKPLu1pkXg1lNDJ7K3OlnyutDw1YiAYQIxpNGRGRrxFFyfgVVZ3c6PKuFplXQxmNzN7KXDGX26JepDZ4HNGymjPAb2w2eVeLzKuhjEbmlfO57Apc5MQ5wBuA5GaUd7XIvBrKaGReOR/z5nSDwbBpuKJWChgMBsNaMAbNYDBsGoxBMxgMmwZj0AwGw6bBGDSDwbBpMAbNYDBsGoxBMxgMmwZj0AwGw6bh/wc7qUdI7AaH0wAAAABJRU5ErkJggg==\n", 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\n", 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    " ] @@ -322,7 +332,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -386,14 +396,24 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/soiling.py:15: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" + ] + } + ], "source": [ "# Calculate the daily insolation, required for the SRR calculation\n", "daily_insolation = filtered['insolation'].resample('D').sum()\n", "\n", "# Perform the SRR calculation\n", + "from rdtools.soiling import soiling_srr\n", "cl = 68.2\n", - "sr, sr_ci, soiling_info = rdtools.soiling_srr(daily, daily_insolation, confidence_level=cl)" + "sr, sr_ci, soiling_info = soiling_srr(daily, daily_insolation, confidence_level=cl)" ] }, { @@ -405,7 +425,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The P50 insolation-weighted soiling ratio is 0.973\n" + "The P50 insolation-weighted soiling ratio is 0.974\n" ] } ], @@ -422,7 +442,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The 68.2 confidence interval for the insolation-weighted soiling ratio is 0.965–0.979\n" + "The 68.2 confidence interval for the insolation-weighted soiling ratio is 0.970–0.978\n" ] } ], @@ -436,9 +456,17 @@ "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:151: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -449,7 +477,7 @@ ], "source": [ "# Plot Monte Carlo realizations of soiling profiles\n", - "fig = rdtools.soiling_monte_carlo_plot(soiling_info, daily, profiles=200);" + "fig = rdtools.plotting.soiling_monte_carlo_plot(soiling_info, daily, profiles=200);" ] }, { @@ -457,9 +485,17 @@ "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:211: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
    " ] @@ -471,7 +507,7 @@ "source": [ "# Plot the slopes for \"valid\" soiling intervals identified,\n", "# assuming perfect cleaning events\n", - "fig = rdtools.soiling_interval_plot(soiling_info, daily);" + "fig = rdtools.plotting.soiling_interval_plot(soiling_info, daily);" ] }, { @@ -502,9 +538,9 @@ " \n", " start\n", " end\n", - " slope\n", - " slope_low\n", - " slope_high\n", + " soiling_rate\n", + " soiling_rate_low\n", + " soiling_rate_high\n", " inferred_start_loss\n", " inferred_end_loss\n", " length\n", @@ -516,60 +552,60 @@ " 0\n", " 2008-11-13 00:00:00+09:30\n", " 2008-12-11 00:00:00+09:30\n", - " -0.001403\n", - " -0.004020\n", + " -0.001377\n", + " -0.003570\n", " 0.000000\n", - " 1.021948\n", - " 0.982670\n", + " 1.021835\n", + " 0.983288\n", " 28\n", " True\n", " \n", " \n", " 1\n", " 2008-12-12 00:00:00+09:30\n", - " 2009-01-01 00:00:00+09:30\n", - " -0.000641\n", - " -0.002886\n", - " 0.000000\n", - " 0.990297\n", - " 0.977467\n", - " 20\n", - " True\n", - " \n", - " \n", - " 2\n", - " 2009-01-02 00:00:00+09:30\n", " 2009-03-20 00:00:00+09:30\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.981110\n", - " 0.995768\n", - " 77\n", + " 0.979344\n", + " 0.993455\n", + " 98\n", " False\n", " \n", " \n", - " 3\n", + " 2\n", " 2009-03-21 00:00:00+09:30\n", " 2009-03-24 00:00:00+09:30\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.987695\n", - " 1.023433\n", + " 0.986470\n", + " 1.023188\n", " 3\n", " False\n", " \n", " \n", - " 4\n", + " 3\n", " 2009-03-25 00:00:00+09:30\n", - " 2009-05-28 00:00:00+09:30\n", - " -0.000559\n", - " -0.000880\n", - " -0.000244\n", - " 1.039308\n", - " 1.003510\n", - " 64\n", + " 2009-05-25 00:00:00+09:30\n", + " -0.000731\n", + " -0.001012\n", + " -0.000414\n", + " 1.042359\n", + " 0.997774\n", + " 61\n", + " True\n", + " \n", + " \n", + " 4\n", + " 2009-05-26 00:00:00+09:30\n", + " 2009-08-11 00:00:00+09:30\n", + " -0.000553\n", + " -0.000741\n", + " -0.000368\n", + " 1.040725\n", + " 0.998162\n", + " 77\n", " True\n", " \n", " \n", @@ -577,19 +613,26 @@ "" ], "text/plain": [ - " start end slope slope_low \\\n", - "0 2008-11-13 00:00:00+09:30 2008-12-11 00:00:00+09:30 -0.001403 -0.004020 \n", - "1 2008-12-12 00:00:00+09:30 2009-01-01 00:00:00+09:30 -0.000641 -0.002886 \n", - "2 2009-01-02 00:00:00+09:30 2009-03-20 00:00:00+09:30 0.000000 0.000000 \n", - "3 2009-03-21 00:00:00+09:30 2009-03-24 00:00:00+09:30 0.000000 0.000000 \n", - "4 2009-03-25 00:00:00+09:30 2009-05-28 00:00:00+09:30 -0.000559 -0.000880 \n", + " start end soiling_rate \\\n", + "0 2008-11-13 00:00:00+09:30 2008-12-11 00:00:00+09:30 -0.001377 \n", + "1 2008-12-12 00:00:00+09:30 2009-03-20 00:00:00+09:30 0.000000 \n", + "2 2009-03-21 00:00:00+09:30 2009-03-24 00:00:00+09:30 0.000000 \n", + "3 2009-03-25 00:00:00+09:30 2009-05-25 00:00:00+09:30 -0.000731 \n", + "4 2009-05-26 00:00:00+09:30 2009-08-11 00:00:00+09:30 -0.000553 \n", + "\n", + " soiling_rate_low soiling_rate_high inferred_start_loss \\\n", + "0 -0.003570 0.000000 1.021835 \n", + "1 0.000000 0.000000 0.979344 \n", + "2 0.000000 0.000000 0.986470 \n", + "3 -0.001012 -0.000414 1.042359 \n", + "4 -0.000741 -0.000368 1.040725 \n", "\n", - " slope_high inferred_start_loss inferred_end_loss length valid \n", - "0 0.000000 1.021948 0.982670 28 True \n", - "1 0.000000 0.990297 0.977467 20 True \n", - "2 0.000000 0.981110 0.995768 77 False \n", - "3 0.000000 0.987695 1.023433 3 False \n", - "4 -0.000244 1.039308 1.003510 64 True " + " inferred_end_loss length valid \n", + "0 0.983288 28 True \n", + "1 0.993455 98 False \n", + "2 1.023188 3 False \n", + "3 0.997774 61 True \n", + "4 0.998162 77 True " ] }, "execution_count": 15, @@ -608,9 +651,17 @@ "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:251: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -621,7 +672,7 @@ ], "source": [ "# View a histogram of the valid soiling rates found for the data set\n", - "fig = rdtools.soiling_rate_histogram(soiling_info, bins=15)" + "fig = rdtools.plotting.soiling_rate_histogram(soiling_info, bins=15)" ] }, { @@ -680,7 +731,18 @@ "cell_type": "code", "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/opt/anaconda3/envs/release_test/lib/python3.7/site-packages/ipykernel_launcher.py:8: rdtoolsDeprecationWarning: The normalize_with_pvwatts function was deprecated in rdtools 2.0.0 and will be removed in 3.0.0. Use normalize_with_expected_power instead.\n", + " \n", + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/normalization.py:170: rdtoolsDeprecationWarning: The pvwatts_dc_power function was deprecated in rdtools 2.0.0 and will be removed in 3.0.0. Use normalize_with_expected_power instead.\n", + " power_dc = pvwatts_dc_power(**pvwatts_kws)\n" + ] + } + ], "source": [ "clearsky_pvwatts_kws = {\"poa_global\" : df.clearsky_poa,\n", " \"power_dc_rated\" : meta['power_dc_rated'],\n", @@ -755,12 +817,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "The P95 exceedance level with the clear sky analysis is -0.35%/yr\n" + "The P95 exceedance level with the clear sky analysis is -0.21%/yr\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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    " ] @@ -793,7 +855,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -826,7 +888,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/degradation_and_soiling_example_pvdaq_4.ipynb b/docs/degradation_and_soiling_example_pvdaq_4.ipynb index fef481b9..0e8bdafd 100644 --- a/docs/degradation_and_soiling_example_pvdaq_4.ipynb +++ b/docs/degradation_and_soiling_example_pvdaq_4.ipynb @@ -7,7 +7,7 @@ "# Degradation and soiling example with clearsky workflow\n", "\n", "\n", - "This jupyter notebook is intended to the RdTools analysis workflow. In addition, the notebook demonstrates the effects of changes in the workflow. For a consistent experience, we recommend installing the specific versions of packages used to develop this notebook. This can be achieved in your environment by running `pip install -r requirements.txt` followed by `pip install -r docs/notebook_requirements.txt` from the base directory. (RdTools must also be separately installed.)\n", + "This jupyter notebook is intended to the RdTools analysis workflow. In addition, the notebook demonstrates the effects of changes in the workflow. For a consistent experience, we recommend installing the specific versions of packages used to develop this notebook. This can be achieved in your environment by running `pip install -r requirements.txt` followed by `pip install -r docs/notebook_requirements.txt` from the base directory. (RdTools must also be separately installed.) These environments and examples are tested with Python 3.7.\n", "\n", "The calculations consist of several steps illustrated here:\n", "
      \n", @@ -90,7 +90,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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      " ] @@ -175,7 +175,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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      " ] @@ -224,7 +224,7 @@ "outputs": [ { "data": { - "image/png": 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x6JTz54BfI08f5JGnD/p/gPftUr12lTiVLHYiLi12MJAESUovTPEtDU0DpMQzBEbFYcSzSLN8xcCgms8DUxXe3JE8MFXZ/ZtZx1FfKXDTRqgxzIgYIXLNb7PfdzfXWx70sI1UKbJUkR6CQXLQsI0Y+Cngp/pTzUV1yFWAxW7E1VaIrWtkUhEnikwDEWZYukRoBlXbBE3PA3Bl7lofpOFVSj6PzigqJX9bdRqG+/6o29DiNI9Dc00NxxryxQ91i949xss2uqYz4g+cnGff2PaQo5RaOOzCzDI0psoOp2sej0y4nKya2Lqi3Y1IkgTTEJDFvLbQYnapiaUpSrYxsJDIbSLbz6U2DA/lUbehGRpoYjiZGdZzRB/ZjjEMg4mqg2EcfIF28Gu4S5Qci+kRH11lLHZTwkyQAZEUBFHKucWAi0shaaaYGq3hO4MviQlTlceypdsTTMNMo3JUSTJFlAw/LGWYsVDb5aAH1h70+q3lWHadJFN0opi5Ro+VXkwURyy3e6g4YqnVYbYTorIUshTTAF1lrPSSgR0Dtp53kM3sx5sZ7qMk668nvHtv0lF3CuyWBrqfe2N2g4iX51p0g4NpdN/t2MZhttljKdBMXbDSjXhtvkWz02Ohk3B1ucdXrjZ5aa7DYiPAJ0EIRRZldOIUYxsG6DBVJJncVEPbrIE0uyHnZldodu8+3OOoB9a6Zp5NxN0kzc9hZHalx9cuLzO70rvzyfvAbmdwGWab3XTKKYR42yAXOIwpuJVSNFsdzs93yFKHLEsJ4xSRKkwzNzy/3Iq51IzQNJ03dGOm6wz8g1qGTsk1sTaRgpsZ7rtJnvWju0kKm0E46k6BpU7Ii3MddFFiqj68sJj99A5rKBKRvx5EdtsLO8w2u5UN7QPrPs+Q+4GWgFHyHIhXOIQpuDthyquNmGaYsNyWOJZOydbR7DzbRidVLC726CZg10PmVrrUfBvPquHad1Zqa57JTOZR87bX4WaqNlGqmKnefQqbgx4CsFOuLbf56sUGdUsyUvaHZtfZz4HAs3QqtoFnHcw4tN1mmG12U4GmlLpnTYE/Ry7Efl4p1RNCeORb2B3KlQJpmlIxJZNVi7pv0E0hTjOiFFAJvVjQ6EEnhZUwY66T4Cz1mKqVcO07C6kwBSU233Bjs52iwzRPCx6mcLeLpg6TAXeVNE1Z7qaM+MYdPWmdMGa5E7PSDelG6VB2286XNWUYGiil7bmXuBMmLHYiOuHGC+MLBmdQG9q7gfcopXoA/defBX56tyq2m0SZIsg0kiijHaaYaYSUCQudgFfnQy6sBCwF0EsgjVOmSgYVWxt4SjDIYuONvumGCddbPbo7aNhxKuns0iLq3eLifIM/efoyF+cbdzy35FjUyza+Yw1tgtYOEi4sdbja6NGJ9j5D8VK7y/nrXZba3bu+xlF3Bg3KoAKtC3zDumNfz607QB0ayrZOnKTM9yKWVwIutSTzKymLHbjeyP+WyIWOoUGYZMSZYtB2HqeSXrK5UNnMo5ab3MS2HBAbcsga95MvX+WPn77Mky9fveO5ni7JsoyqxdC8ku1uj+evLNHr9Uizvc960Q0zeklCdwcpqHczG8h2WC9Y93oT5UHj0H4e+LQQ4o+By8Ap4PuB/3m3KrabpBKSNCWNMlJDp9tp0WyDkLAMrDrPE+CFayCNa7zpHsnrT1QHun4vjLi40KJuC2ql2yePm9kMpFSkmUTKu+9Qpi4QCBSDr2zYa9ZPMc/Pd1hsBZyf79zxf5+d7fLSXJuv1S0evmc4TvpnXrnMHz29CA/XqNcq9GJ5W9aL3ZzKT9U9To0nTNV3lgz0IAxf+UqODL9vCtitDMObMejO6b8PfCPwAlABXgS+qX98IIQQPymEeFIIEQkhPnSHc98thJgTQjSFEB8UQgw10XuUpGRSYJkaMs3opRmtBOazm8JslesZvLoU8dpCm2sDLs5daAe8tthloR1sq15BktEJE4IdxKGlEgxDoImD6+m83gz42uwK15v583nDTJ2zEyXeMFPf8v+UUlgiRkmJqydD00I/+rnLfOFiwCe+cG3TrBd3Ci1QShElGUGU0OrFhHE6cN2m62W+7nSN6Xr5rqeOB2FznNVnECcpUZKhlNq1ZAKbMfBKAaXU80KIF4FJpdS1uyhrFvhl4B2Au9lJQoh3AO8B3tb/nz8gXwT/nrsoc0OSLCNOU3phj/muohnCAhuPcDHQbEMvCvH1wdTmTGaEsSST2xNMhi7QNQ1jgEa52vCBW4zYhgYxAs+6s2E7jmNmmzEnqhaWNeyFkRuTpimXFppcWOhxqmIAZR45Pco3BjqPnK5tXd9UEsUZcZLRDRPmVnqMl21c29zR5inL/R9+QeaCod1s8AevNHn7g3VGR0eBjb2ga7W2OJUstgOurfRQSlLzHCarHmXXvOPvEEUxc8td7q9ZJJm37fARKSXLnYhekqFhYRjGtjXJbrfLM9e6vGHax/dvXYM8qHaaC/2MZi9BKYVt5lraXm6iPGgK7poQ4iNACLzSP/ZfCCF+edCClFIfV0r9IXf2jP73wAeUUs8ppRrALwHvHLScQcgyyUIz4LXrigsrMBdura4vA0Gc0EkHayCOaeC7Jo65vZVlrmVxYsTDXSdcsixjuR0RRAlZltEJ8xFwud3lKxeXaHdvaoLbsaU8+9o1PvAXL/Oll64MZONYqz0kScKVpS7dICJN04HzZS22Y16bbzK3EhElaX6sl9IJEhZ76ZblSim5tByy2Al4ca7D5eUuK72YOI555Xqb6402C+3ojvcShiHPX10hDPMA5odKeQzSvX7eKf/wa/N88plrfOJr12/8z0YrFKIkY6kTstDsMb/S4fmL1/nMM6/x589c5cK1JeZbwQ1NZSut69xij1cXQ84t9u5K02r1Yp67usRzl5aZX+lwZalLox3QDgZPH/+JL36NX/6Tp/jEF79223eDBr7qQtELU8L05qqavXZWDNrjfhtoAGeA5/vHvgj8M+C9Q67To8Afrfn8NDAphBhVSt0iDIUQ7wLeBXD69OC76cVpylyjyeWN+8+GXG2meMSbfr92FCu7NtM1h7K7vZly3bdQaNTXZTWYa7T5zPPX8G2d+8Z9lroJ7V6P1xbazHcUzfvH+NZHT2EYBnEqibOMXizuuEHyXzx/lT97ukHcbfLYPSc2tPet3pcuFHPLbV5dCnhozOKrl1Z4eb7HI9MlzoxXmO9m3D/hM17xthzNdVKWOjHdOCVKcm9ulKR0QnlDwK0nyRRRmtHpBlxfadGNM2TQZq7RpmxKLi/B+fkWtiY4OV7F1qsb3ssqT712nU+92OR7H67yzY+cYepElenrTWYmHZIs47QHnm1wapPsEqvPJIhiXp1rEiYxzU7IXzx7iacuxRg2tNot3hJJHG0M33VoBClTFRtvgxQhD417zHdiHhr3BorJWi0/X/srubrU5P976RqxMlBRmys9mC4Z1CoVHp2pUi+5d9SwnnhpkZfm4Alzkf/6u2/9bpAYvSiKeGGuQxoFNCMY8fKNhcI4ZbEbU7ENyq5JKnPBFyRq6FsRwuAC7buAE0qpRIjcBaSUWhBCTAy1NjkloLnm8+r7Muu0O6XU+4H3Azz++OMDDwGtbsSlxe1VanYFvvjqIg/dc2rD76MkoxkkuKaOqYEpwNLuItuGzEfC1XAspRSf++o5fu3PFyibcMqFSx1YkbmL2QPCbpvxuk+t5FN3DSxdQ9e4o1NguRvTSODl5S6Ndvc2IaCUohOmJFk+wn72uct8+plrlC3J+dmMuRjeMgNvOFWlK3w8MUbFtenGGZauUXKM2zrQ5YUVvvDSLJpj0+zkTpaRksvMSMRIaRNLhEy5stjhxcvX+fxLCfPA03OKtNRkpdXi3LUmc+2EE57Oi/M9Soak6k8BbDgtv7DQ4cVryzwyqvPNj4Bj6mg6ZEpjrhlQKpf59ocd7pu5dQqslKIXxsy1QhxDo9Fs8adfeZXFRpcwlvzlqkKXQvR8j3NL55lbWuHeyRroBtrJEc7Yt09Bs0wSxhlZ/zlvJHzWTqnDOOV6K6bqCC4utPj0Uxf41JPLxCY878HVFMYsePDMKLqc4oGZCVIFNddE1/UNBVvdAkfkr+vZTMiuFazPXFzkPz03C3GIsH0MYjzbRkrJ3HKHWQH3jJVwbYs0zdu5Ujq2yVAdLYMKtCYwBtywnQkhTq/9PEQ65I6HVVbft4dVQC+Omd2evZ4u8JdPzvLO737zht+vtNt85qVFHpz0iFPFXCtioRszOXrznCzLWGwGdJKMSV9nMeAW+1U7jLm83ENXNmGiqLg6vTDmdz+7QAtoJXB1XYhaD/jYyzHjlfPUa2XuHS/x+tPjaJaz6QbJqw3xbfdX+ZOvdQlTOHd5jnumR5FS0mgHLPYSJn2DRqQQWUorzvjYpy/xlXX3/cRVeOJqE50mi5dmecdbA4JU8NhMhUq5wqhvEqeSVphScQyeu9bkmetQsiIuLXeQUjJRcbl3PGWi4m5oG/zKSxf4l391laWLPVYDO55agej8HFc0+OyNwSmhylWy9gI/9O0W01U3T6ktxC2dUmQRYZIhstzJo1kWlqXRTiStXsSJsoXULSq+c8PQ3QtjVsKUZqfDVy63GHU0XrpwjY88s7FndhaYvZ7y5evzTDDPtz1S4mTVpt0X2pah3dCgn77S5KuXVxixBbVaDYUCdEydG8KtE+aazphvsdLp9VdLZHzqq5f4zy8ELOe3z1x/+L/YhacbS7RXlvmBr09INYv7J31Krkfdt3CsW7v+D3zDA3StJX7gTaO3HF+vDSaZupG1d6WXkElJlqZ88gvP8PsvSizApMVXX73Gf/X1AQ9Ml/jSqw3iTLEy6eGXyzw84aGERZZBJAD03V8psI7fBT4mhPg/AE0I8c3APyafig6b54A3Av+h//mNwPX1082dEIfJXUnHJ1Y2/+6b/ukXbrx/ax3OnBnhgUrKX/QS3jBdolYpM7/S5U+fnUUqxcmKRVcavGGmzNmJGqYuSFJJECbMNzO6Wcj9Yz4vvHaJVwaYGv/2l1eAvILfOvUCP/iWUzx23zSTtQoVzyJNU2YbAWkacW2lh0RwbjliBWgvwV+fm+U73/QA5653ePHCLM/Mp3zjjElXGiwtN3hlIblNmK0lA/7gKvzBR1/mpA7fep/FyZNTPDbtYVo28ysRJ8dLmFISAiqGV681c3tgBpqC5VaX5661GfU0yp5P1TVZXG7wv374Za5vUObzyzftH6s0gd99NgbnVb7h3jEeOlFH6AYlE6Ik31w6SkEzIEkVjU7MA6MW03WXmZoFCBIl+oI1o9GNWekGPH9lmaVWwPmLl/j8ZcmEB3+9cOffBWAe+I8vdDj/wlP8vR+6F6FZPHZqlOnRCpYhmCzpOJoGScgLV5c4PeLhll3mGyEvXZ3jwkrKWyZNLnZN3nKyxJMvXOBffe4aVR2ebG5ebgr8yQXF+YvnMCd13nFviccevJfHTtSwTf2GsAyCgLl2yv0TLrbrEEQJlqERJLmpIUwVMk04t9DGEHD/RJVMwZXlDt0g5MsvXOBDL+aDUND/+5sFePVPL/DWs/DVCyAceH4EUsvlTTNlHjszwcxoiRP18lA9s4MKtH9C7hD4TcAEPgj8DvB/D1qQEMLol6cDuhDCAVKl1Pru+m+ADwkhPkyuAb4X+NCg5QzCbGNoyt6GfKEBX2gs85++ukwI/K0z8HPf/ya+fH6e3/j0LKM+/OAjHqI6yYSdcP7aPHMdyaMjgtmWQdBo86UFDe/ROv/uc+e3Xf7n5uBzf3qZOpc5MQ6/+B0n8EYn+a1Pf41PXrip4r2+/5oBn/1aTCf4zzy/CItNuA58/G9yzXS75twrGfy7czGcu3Tj2A8/6HNqqowjE0bJO/nLCz1evjJLlCjOzTb5rY9c5SngLPDO757h/X9+ldlt333O7/7NAr/7N7nE+a57ff7OgyUmZk4w4lokSmAojYWVNv/xK69hRiG+Jukk0G42mFvWqPkOs/MRf/XCFf7mlRWu9yAKYPWOXrmLoP6vAFc+fp57Twj+9htO831vPku97KHrGk8/PcdHnoJT5kX+wXeO0r66xC89l/9fGXi4AtMnR4m7Jf7pp65ta2r0vALmMp6ea6RHO4sAACAASURBVPJj3ZfptidxbBPNMHnjyRE++/QFfuMzF5muCQyZ8NpiF4uMTLN4cMJHmS5hr80nvzKLZpj8yJtjlkPJ51+8wheebvLcJgtbloE/udD/EMLlWYCAz1wI4PPzfOM4/Og33cN3v/kshuUMZeop9sr7IIT4ReAX1h1+H7lwfB54nVLqUv/cnwb+d/Lwjo8B/5NSassgsMcff1w9+eSTA9XlR3/hk3z+LlNPXfjV79vw+Nn3fHLb13KACW52EoA3jsLLS/lU8m0ePDGktRhvAJ4ZzqWGSgW4x4en737Vz67wVh+UhC9u0zSxHXTgX3zfSc4vrPDrX7pzULEGvMWHJ4f4rL7Nh54HTy6ABUzpcKnvrK4Abz0F5coIsrHMx2bzOj9egosdmBtSHf63t9b4nscf4eRIaUOnyXqEEF9WSj2+4XeDCDQhxHngI0qp9647/qxS6vWb/Nuesh2BdjfCZ5VhCrSCgoKct54y+ZnvvJe3vO7+O567lUAb1Gc6DfwtIcQfCyHKa46fHfD/CwoKCjblC5cTfunDL+34OoMKtAT4HvL8Z38thLivf/wgLB8rKCg4Ajw1hH2MB45qU0qlSqkfB34D+LwQ4u07L76goKBgeAzq5bzhelBK/Y4Q4nnyXdN3lh6goKCgYIgMKtC+Z+0HpdTnhBDfRL6AvKCgoOBAsNUmKWLNhsJfEkKsn55eBQZOH1RQUFCw22yloTW5uewo5XYHgOgfO547OxQUFBw4thJoj655f8+mZxUUFBQcELba9enymvcX96Y6BQUFBXfPVja032eAODOl1N8dao0OMJum2S0oKDgQbDXlfGXPanFI2CrphQYcno3jCgoOHsOIAdtqyvm+IVz/SLGV92McNkxxU1BQMBhnhnCNgZPeCyEs4CHyRI9rA22fGEI9DgVb7WZ+yoHr4Z5V5a5YdUsXFBxEJqZ2fo2BBJoQ4luAjwI2eShHizxN02Xg3p1X43CwVb7xapU8Y9y68+d3sT7bYRxwHeiFeY6zXcyKcxtnHOiGsM2s58cGv/93UNrKfuAAf/cbdp7Rf9C1nL8O/JpSagRo919/CfitHdfgEDExtvl3k+VbP48Do24u1DzykQBglDXq7R0YbFvjwZDkeeZP1fOca2e3tyHVXaMBUkFNg5P2/q6Vm9HgdL/FV8hTyOwHLrmQX6ULnBzbn4DOGtuYpu0ip+sQGpU7n3gHBhVoD3J7dtpfBd694xocIk5tIWGmxus3HqYGlB2454TJ1Eie4rcCnBRQcfM5+yDs/OfNqQG+2d9UpWRSr5iMVrfWOIeBDzzkgevC6ChM18WNexIMLtiHgQdYAkIJI8BkBdx92JN3Cjhdvb0t2abg5B7XRQMmS1An15BctpGtYkhY5P3C0KETbJL6dhsMWv+1qwauCSFeR/4cSjuuwWFiiw5wesRnxsmFxyhQ9cA1LSbKBqNG3qnPTEA3yDc1HgTH2vnoOa3BA3WwHEgSkEmK71hM1xxGd/nXqwqwbJgqGczUbCqWQd3JG80oucDbC+rkjVeZ4GhgiFwbsrx8sBnlpga9m4wBj8zAyTIo89YSq7bgodNwQs/rtBdI8valyD34KXvzHFYxyX8bz4GJSomZ0Z0HRg0q0D4O/O3++w8AnwG+TG5XOzZ0t8jX5Do25f6USgCmCQgNJTOwwbbA1LZnJ5Hm3U07BfloK4BY5oJx0s0bTsU1cU0TZEZ4l2nIB8EHLDNPY22ZOq5j4bo2vge+yAWLt+783aAElEwwTNAFOHq+IYuuQdXKBVvG8LThzagAMyNwol7Bdfy8XaxB120828a3tp6WD1PYOeRbIi72r2uzt7bVKZELU9OEUzWbscrOW8FAAk0p9VNKqY/03/8z4EeAv09/k9/jwri7uZXDskzKrgABQgMhQUpFlEHQAzIoe3fOl74WQ889L9tBByZF3kAVuZ9iIYBUywUMaChNEKcZnZ1r+JtiA44DmQadSCERVFwbQ881A03L94Fc3TWnTD4dG/aUxwQ8E3QdKh5YpbzAVIDp3ZxinKnsnk3NBUYtSCU0gpiaqZis3SpCS55JN8qI1dZa+U681CXgFPkzcYDT7k0/VkBe7nbb290yDpj9H78dwHwrIYz3bsp5C0qpzymlPqWUOjaxpAZQLm0+goz6FrWSR9nKRxzdygWIZ2qM1KDma6h1U9Y7aQWn6gZT22xhFeDMONT7v2wXWGmDp8GI61BzbTzLoNeRrJA37GFrJ4K804y4MFPRqTk6VdegYgksA3wbxitg23lnHxMwVgLfzTXSrcJjtosLZBJMHRwDJjyYqsDJssW9Iz5TJahXYLQE9VLeoYc97cqAbgydEBpBRKCZTFRu1bV83cAyBaYGo34+FduIAXY03BALGHGg1LeZjZhwZiwfRCAXBK7In/0JffftqwYgs/w1S2G+F3Blaee7vwwatnGafMemN7PObqaUenDHtdhHXAZTsw3A2GIn9E4KBgLTAS0GTYKpGZimxZgT4Pse4yUDQXxjlO2xeWyYCTwwNULLmOevt7HrnqvD2ZpNO4iw2xABmQB0jZpvU6s4jLgmHS3Pq26Re2OtYDhhFYK+cbUCZ2oWKwkkUoHSSBAEaX6SAnSjL7wULHTy/y0bYKW3RcDcFS7g+7mXVTPAtTQEuX2z7Nl4ns1ELWIlzlCGotefgpvkz21YxORGaMJ8Cj7iWlQ8myo3jdOZBoYQ+B6UDMgSaMS3X2s1Zmq7jAuYqkFJh1aQD7q2afG6ezLE5YzltO+N9iFToCJySbwLlAEEBDJ/NgKI4oQk3bmGNqjN+aPAi8D/yd5Os3edEoPdkAnE2eYKacUxqfom9W4+5bRtB9MAYViU/QzTMhj3Naqsbge89Wg7ocHDJ8o8G3TIRd9gTFRBs21cN6LezsvKJLR6kum6RtkxMTWJkeTCrAqMVgRGoli82+F/DVNA2cunlI0oYyHMUEqnm0hKpkIXuSDznPyZLpI/Bwl4Oph2bvOjd/crL1YHCRuw9fyDY0LFsWknkk4U4UqJa+qMlW1kDzw9IFXyhnY5TAxyAek6MF31qZQsTpRNHhuHzy/A/SMwVbWZawQYmsCzdap+ihnng85aRmwwI9jOrtsl4Ow0jJV8Flu5FlRyYbJi00p1TtZXMDv5lFzFsJCQzzSC28sfBg7g2hCloMtcsCnA0Hf+5AcVaA8D33wUp5glGxYGGI41QInNH1fdtym7NoZhULIzyo7NuGfjezqNhqKT6bRScYvwnBTQVbDRjoyeBykanj74Ix8HJmsOZS1jxHewJkMuLUKicg2gG4YsdG1OVHS8smAyUVRMKNsmK3IDdWAATHJNaFVrmKmDZkIzgV6U4WkajmtTsTWWuyErYf7MJ8suF8OAACgLuG9SUHUNFjsJUkLdg2jxpvDfbp10cs2z5OXBxM0MplXGyZqLZ5pUHIOSpVMtuaRahqcZjFaaxMu588aVwxm5VzXWCBjx4PRYiZpr0Eng5IjJ2SDhvnEH19DRVELJNpgu27SjDmVu7kS+yoSXX2xbAk2HmqWjaTpKgWVBzTWxHRunF+O7OjOuoGwaXFwICcJc+LoMX6A5wNRIbgLxPZM0SnjuOqCgZOxcvAwq0P4Y+HZy7+aRYjXoNWVrDXukCvdPbO5/ijLI0pgwVfgGmIZOrWxTFToyibGlhqnCW6YyusobzUa7k9tmbpd7EYtBJmA14MEpqHs2jUSiCYWt5dOrERfOTFZRukGUSAxh8NBUDdvu4Rs6GTqpuDuB9kAdpgx4YiHX9u4bE8TKQu/EnJ2oUqu4NIOMSsnh/LVFLrfgdA08yyJTARrg+XB6pMJU3cZvRCx3urTClO25UG7iAb4FI2WouDZZEpEBJdthsuKRqRDXNphrB8w1IkbKPg9POmRKomjT7YIb5ULxztv/5mxmOhgHJkoQ6TBdd/FcGyV0WkGCZ+nUvIQUjYVORDvJaISKsqnhmRo2kpRbBVoktj8TjDPophJPU5h6f7qpC0ZKNmGq0Y5bKKFzumbRaIbMAkrlbepuprdbMW3CmG8y7ts4jslycwXfU2i6wVy0c5fQoALtfwG+IIR4lXUzAaXU39txLfaR6ihML+Va2lYmyaptYJqbm4t1AUoJ4jgj0MAMAq63TBzLQEqNimcRBrc2xUyA6Au19ZPK8TKcqPlUzTuPkWXg9Sd1vuGeMZRhc21hhSiTaDqUSyZnJ0rcN11BZTGx5vDolIOpayxFgqoRE2Fz/1ibC3cxx6vaMFEx8BZSHAMs26HilWjJHmMVhxNVk3nb4r4RmyfPaXSRdAKwdMWoD4s98GzAAGEa1NyYhbZAZn1vKLlgEeT2lkGQ5FpI3QLX1AgsqJg203UXaRoYlkUsM5aaIdc6Ea7t4NkGVVvDEBDFuUY1qEfRIP8N15o6y/R3Ih+DUx4shhpTZYNJXyfNJC2gmwg6IXTChCiRZIni+hL0ehEnSvkgu16gauru7Itppkgy0ARICZqmYQjBiAt13yWTGQqB5+TOEdfoh9wkgz/3zXDI61whN4nUPJNelrHciHjpouIq8Hgl5c3TO19HMqhA+z3ygeEFjpgN7YG6g06I24GlVi7UVmNy1vbvRCm2iqzVNA3X0lGGIIoV7SijGWa0o4wwg5ouUOsGoETBiRpMZ/B8+6Z6nzsgLHRdZzG684KYcRceOznGdzx6glgKnnd0mG3R6kpEZlKyNOIUqo5H2TIJM43ldkirG6AsnZGKTrVqUr+e0NjW04NuCm7J52ytiWYanJ2ooek6c82EIJYsBBCmEKHzdWeqnFtsMFnVcGyLkudT9bqMexbTdQ/HMFhIBEmUEGUw7oMVgUo3D0YucWun1/rH4gwwNJq9kGs9eHBMQzdNXA1mqhZRkhK0BQYCISSNMOPCSsJKmBvFV2P51trG15e1igNM2rkhvUPedsZMsH2oOBYpMT00GqHkalehyQwhNHRSEgmOJqmXXKarFufmY9oBNPS8La6fhPXSrQfeVQxuDgZVG1zHoWYLWj0NTZNEqWSxHRGm4GiCzLAIwoRulq8qqVh5AHK5sb3p7Xo8cs19kb5zJIRymICmk0jJahbZp+YgUDtfhDXoFd4GnFBKbcPfditCiBHyoNy3k9/fz67Gtq07753989YKzu9XSn32bsveisyqcKJmYJoRZSehHUKWgelCezHXnMrARNnFtjZ/XJahUfFsJlyDxBGcHitxomrTCmKkBNvQ0EoeOks3OkjNg/smXWQqmW1HN9R7CzC03ID+4ITPZhMfh7zBuH5uE/FtF0cJ7hmPaQURy4Yi03TQoBdnlE2BIQ3iOGOxHbASJEw4UHJMVGjiWQlpvHFH2ggPOFvRed2JEUY8kx4Oj50sc6kZ49s9MgSGkjimjq0p7p0e41seEli6wYkRmzDNaMcpJ6o+FccgzsCxBRiCuKcwBUzVIQkhbHObsF1dAbD26ax2ZM8BV1Ms9RQiA88UlBwdqZuMGoLlMGNiNKUtNXzHwlASg4y6C1WtL0hlHlKwWq6zyS8xYuWhFqu2WBso+7kdyrN1XKWDzNBRWFpChoZraGiajmEkCF3DtTVGKw5lI0Z3YKYG1zq5EDDINRyHPKbO485uotH+q9BhtAwjvkO5bOL0IhwjwrZMHMdCJhKp6SQSdEPD0TWko6j6Fm4K3Tii0717r69L7iQz+jbJXgKmLig5FrYwbjzRBAjTvbOhPUP+jO5aoAG/Sf77TAJvAj4phHhaKfXcBud+USn1LTsoa2AenbQ5tyDxY0XFNlBoXGl06cQwrkGowz2l3DtV97ey6ggsQ6fiOqDpTNd8yo7JcpChGxLN1JmxdMrcNHTXPDhR9XG0jJdmI6Igb7gCkEojlYp6ycMnFzKrnXV1CmCRa3hVx8axLQwdLi8HBEFKqjRMy8YXKY0Qaq5CMw10AWGS0e71aAQZQVnHdw2inknVzRueSre2Hc2Qa0wTHjx6aozRWhnXtulKQazZVFzBZN1FKUHFNYl1kzSDS0tdGu2Qs+MVXN3ANAx83cAwNIJMyzu8ZeMYGugZSkKQwnQtH2S6vfzeTfIlXaMjYGuwPH+zYRrA5ChMj1UY9XS6hGhBhG7YGEKjbOlYhiBDoSkHpTRqZRvd1Cn7HonSCMKA+Q4IM++QDXJNZ7NOrekQp3mHFUDNgemajueUqPgWKu4xkqp8UDRsXENg6Do138MUIe1I0osljmExNaEzVSkxYme8fL2DntwMkHaAe+oaYUeyuMU80AVmqvnKliiF0ZJJ3TMIYihZFlN1jZm6x/1jHmGcMr/UJk4Tyq6N5ttEvRiVwUTFoduNmO2rhDUBoeqHoAyIQ+6QKsm8XiNlGPVthGGikps3MUq+fHCnDCrQngD+TAjxe9xuQ/vgnf5ZCOEDPww8ppTqAH8lhPgE8N8B79lelYeL47mUrZBlw+DMqEssJd04ZaxiMHoipaeVeWQMGplFJjaf/gkhsDRFIDV0ldJNwLclhgDXs6nYBomCByvwpVYu1UfKHrZlkmBT9RrMBnmH1YFMKZRSaLp5w3Hgk097rpI3jik/9waOV1zuGbERQkNISZikCAGeIUilgaZJTEvHN808NEEY6LoNoocSoOs6tYrPfVOKxcUWSaMfR9cvd63N5l4fHqzCs9dhrCoYLbtUfIfpqkMoBZO+SRBnBFHKQi/GMnSkVIRpxvn5Li83eiQyo+zm8RQRGcu9iImKzWjFwzMEc55LnAUESYZUGrrQMM0Uh/z5VICzYzAyUqekRTQ6PS72cmE/U4HXn6pxZrKGKTJKtkY7KVH3TUzLxHct0iQhkRo1x0YIHU0IbB18U6PZtyroFqRZvqDdUHmZFR3aG1jkZZavOhDk2plvw8xolcdOVEiFweyi5FovwjBByoya5+K7FvMNk4oNQjNBSkq+zb3jNU7WHQSSU1MBi52MRiMXDHUfJmo1rneWt4xpKetwelyw0FW0UhgveZi2TS+IcSyTccOk7jsYpkUnUCRKEAmDbqLopVo+2FgOrmXiuhoCSUwuzBLydjGo97MJmClM+7ljzbIsMnQcIbjSjSmR2wq/7SEXz9l5SPWgAu1byPvR29cdV8AdBRp5to5MKXVuzbGnyT2nG/FmIcQisEy+9+evKKWGECV1O0kiqdUrOF6Ma2iYAmzTYMQ1qXsWqaZTMRRXOlDzN3cKmLqgkwqUkhimiUlGmApGqx6eAVGicDTF/SddZs8FCAGGyqi4Jo6uqPs6/lJGh75RW8vtcjXfZMSDtJen/oljMLv5SoBTIzol16NWdkmEiW0alD2HIIzw7QxdSOI4RtctTtc9XNdGSoVAcqJqshjYPDBZ4XTFpOvoOKbgy1nM1XZISUHdyVcZXO3f47QBbzzpUHUsLvVamIaDa4Brmpys21hWrsE2uhGWqSPQKNk6aSzpxBkii/JlTraGZhhkaUSc5p432zKo+jamrjEzWkIKnbDXopnmsWSrsxETUBq4rsbZSReVWdwXJsRzCXUX3nR2jLecHaHqO1xrhnhlnToZvudwz6hDO1KEqUQmEYFuUDIF890UITU8xyAVGiXb4EQ1JZJ5MgG/mwuTmQq0ruWNci2+CzMjJsu9hEaQrxCpuBbVSgkp4eKCgW1IklSw2EnwbYPxqstKN6bZg7orqbkWUinqJRuh67iGwZtPj/Lq9RYvdUJ6SX/q6GlY4uZqhpjbnRclG6arFQwjw45SToyWuHfc49KKTicIaUQK1xRYpuBExeCRkzVWOgGNbsJI2cDEoBWBJhSjno1BgCK3Jap++9zMnrgWi1zzCg0wHChbJko3GC0ZGKZJq2tiE6GAqpYixM7Tn9xRoIm8lB8DLu1AqJS4XVNtsvHSsb8EHgMukm+l9+/JhfivbFC3d9FfT3r69Om7qphvwslRn8WWTiuWeJbOQxWPkucyVTZZ6EnGXEG5Jhgtbe6FiVNJyRKcGa9wsmrSlTpSZthIOrFAFxEdYVB2LdI0IAEanYj5TszrJsucnaxxeWWJdqufCcLUcU0dw9CZqAtsR3HfmMfico+rXSj5MFWvUPJtpmoeJ+sevm0wWbFJE5uFIIU0pYXNZEXH8xxGSza9KGOhFSA1E8tSSHTasaLsWCAEvtXCc0NUBmU3t8H0VvoGTQG2YTJdLzG60EXoOik6hqGRSI2ymWuwNUfDtUxmaoJ62aa52KMZRJi6wNbyuLeZsoUjMpaDBNcQCAW2AfVRH9/RcewWL16VGEFET0Ka9tMyAdUyPDRRYrJWwlQZSys96n7KAxNl3nh6hMmRCiOemQsuKUkzgUCghIFnQ6uX0Awly50WFccgQaPbS+h0ewjAM3Q6ekqU5tpZ1YSJqsaYA2VTsrxGPfEBz4KpeokwbfLsrMwrqmnUPBvXNvNBxQioOrASQpQJFBpBJkgU6EauObbCBENTuQarW8zUS1xfCTDsEDuBTgzXu7lQ0fplK3JtKSH/jUrA6XGHs2M+rSRiRDOplkwqJZexTNBLMkaFwncs4hQ0zeLecY0l18Cx8gEmTmPiXkKKzkTVxSdghZv2vDr8/+S9SaxlSXrf94vpzHd6Y85jTd3VzWqSTZmjCUM0aJmWJRoW7I13WliCQRiQvPRGCxuwN4KhhSFAgBaEIRiGQFu0LBqgTHOEm02yyR6ruiqzKseX+Yb77nTmiPAi7st6lVPlQDdF9wcUHuqem+fEjRPxxTf8v/9HLsMh0xNieqeV3Ul2emICTVIt4fw45dw4ZX/pQBgyrbh6puAbnyz5qIYb8w5rX7804XMVmvfeCyG+yevVrS55smRwyFNict77G6f+95tCiH8A/Fc8RaF57/8x8I8BvvrVr75a3a7QHJcOh8Qo2C4i8iTGC0kSGS4mmiKSjL0gS549XQ7BpMiItOHsOEEIKDtHXXd8fFTh8SgR0vM9Ad8zreDeccOb2ylfvLDB0aJiuSixMWxmMUJprBcUaY4SJUWecabQHDJnZAznJylfvLTJuY2C3VFOZz3zqqfpHRKIIsm5RFFECo9gVnZUvSPTIJRkEAW8UW0dZxPFVhFxdH7CtKy4ddTT+VBKNYohb2E4gu0iYTJIuLRV0BNxdpyyUyQMTxXuTyuLF4JhnrI7MHxysGIjj9hDclzBx4cl710TbKQxnZ3jveew7IhnHV84k3FtZ4J1UFY1HDoyYyjLloMGLmTwhSvbvHdlTJQXJMoxXdYcNI6tYcrmMGFnmFJEisSs8FJSKMdh7ci0IIkT6rbl9qFl77hmEQm2RxltV/PxcYUCVpGhcnC4NkH0mkfNCfEECeNIQpYYhIpJdcAMxlJweZJwcTNsGbszIo5jDB17i45zk4Tzo4Q3tnJuHzW8sTtgZxjjvOf2YY+rPEUUEWM5rjq6JiiTWMJmLriXSHLhyDUM8xC/a1cw9bCVw5XNgjzLGAxgjCAyMfOqxzo4O0zQSrORG4zR9M6DBaUjtgvP3qrD9I48idgZGBSKLALaoKQM678G4iYosVyGOtnGgesJNcsedjYV710YUImEjUwzThVSd2QRCKXJ44h4nfkvW5hVLZuj14ujvajL+ScEt/F7r/icDwAthHjTe//99WfvAU9LCDwuz8dLvIJoPi07qp1jI5MUseG47ImjiJ1hyqrzKKUZZjFZJOldcCufJZu5YWeUMZUlZWuJjGFnEDEVnizRGN+xX0mS2DyirBEOtoYJW4OUYZ7y0f6S4b2SVsLZzZztQUxZZ7xzbsjdQ00eG97cyhFxihCady9MePv8FltFRNV5Fm3D4arieNngvWczj2mdwCiJ8J6q6bFegBCkRhFHEVEU0ONpHLEzzLg4L/lGnGLiRbBWUtiNBIURbAzG7G4NGcSazUFOmkbsjAdMis8mS9R6QcdasGgtHslmrhhkMZIVrbP01iGlQAmF8z299ST0ODzDNMx7kSXs9JJJHpGpnka1fHk74r3rW1zYKehlRCIdG4OMy1uenY2MJA4VG957VlXPrLaMCs2kCBs4SzRGGQaxJlGSzkKqJd7F9P2cFhikjlFiaLKOWQOugbJ0tHFIAJwEkHYJWdjcaHLlGGaGjUHNJC/YHGYkkeG4arFekiWGRWlR2qBNRI/i+rkx95YtmTHYvkd4j7Owd3TArWnDj+9qyt7Secg0DBJDkqTs5BFG14xS2BoGPNmBsTSLML4k0lzcKnDSMMkkURSxanq2I42WgrZ3ZIlmkse0Xc/HZUMsPVFiUMuGWS+JpMcIQRzF7Izgzn5wIRXhGW4NbUkIJVNZDPvzkDjRAi5vwlvnNvjylTEog3eeSEuypOO4bELCK5b8zPWY1YcNu6OIpnn9Ct4XVWi/BfwrIcQ/JfQReGQNvUhSwHu/EkL8c+AfCCH+NiHL+TeAn378u0KIvwb8sff+gRDiHeC/5s+Zd+2035xrxSCJUMKyt+jorCXSijRWZJEiidQ64P/8e5ato+kt1gtiI2l7x+Gqo+0cUgis06TGkQ1TvnAW/D6cG8E4jdgeJOgoYmeQcHFi6FTKG9sFWmsubQ34mbfO8md3DtHK8M6FEW9fUlRdz/YgZZxFOCS960M9nFb0a0ttkii0Uszrnt1IghD01hEZSRZHRMajhGAji7kwzojjmCxL2B0m9G1DlKSMdMeDWnFtYtjdnvCVS2MsitqCNposeTKumGhB23XcP2rYyjVSSYpUc+3MBl86qnFC0DY9V88PeHM34d5xS2EkFknbWco2bGKJwGvN5Y2Mt3YyTLHk/EBzdpwyLjJQGt93bAwSvqQl13eHnNsYMswMzjl2RinTusNoSRYHHrhYaSZFyArvbqRoAZNxzkXfcuswpu0920VBvKXQ8ggx72hV0GHLDvzaK4qBCztwdWuEiWN6GeFEwzA2XNvJubhZIKQgNxqfR/TeIfH01uGtpeksq2XJr//+A1oJf+1tw86F86SxYtVHHDQNHydwfjRgsTqi7GAYBUaONInJ45pOgkWRakUSW7IGxrni/CRlNwBRlwAAIABJREFUe5RjZUyxXscPFjWxkijh+fDBgqpzRFqDVCRGc9y4QL6ZhppSj+Cw6jGtZ1KE+G5COITVmigzbgPP3+Utw2Zi8L5kWsPWAN7cGfHW2THXzkyI4qA452VL7xyzSqCMZFykvH3lAveaA0ykOKp+AC7nWn4GuMmTQfwXTQoA/N31dx8SsHp/x3v/7TWTx3eAL3rvbwF/FfinQoiCkMv5VeC/ecFnvLQkaUTTWequJ9YSJRXWg5YSpdQLByqrzlK2jiI1pMpzf9awkUaUwmBKS5YoJlJhvGVvZ8jSLtGZoeosjZdI70kjzdtncg76mI01RMQYw/WdIY11FLHmyu6ItrfcndWMUkO8jltlkUKJiKubkrZpuLdoKVtHpKG2Hut8CNQLQRpJdouI/XmLAPJUk0SaPNG8c2ZM0zlub+QclBbpWnQmubIVs7M5YGuYs1kkTLIELzyD9LPWmXOOe7OG+4smPF+GMqftImVrUOC8548+nlMLgZcRwyzh42OLEz4U0Vcdq7wjUpI0iRh7y2SYcXlrgEkzbOcYZjHDLEYpxbK0WCHZHOXsjoeMi5ApazpLEkXsDlOsdRgtSWPDMFVMV4rtIsL7gliB1JpRatgc5dw9qjm3mfPOuQHOQWuPaaKGHskgixgWNfEUzkTw5bMjvnx5g41BhneWr7cd2xPN1d0BkyIn1orMSJx3aO84mJccVZ6zA4NSkq9/MuUWgIPf+G7Hr1xX5Dqlbwz3ypj/6L0xKh3xxzcM3767pEgiyt6jpQcZCrtTY7iyk5NPFZHpubY74uruCC8Uxnhic/JuPdI7jlYVZdsziDTOC3ZTxbIyXEwdsVYcLWuKLApxOueQRrM7SBhlK+IAp6PqQ6mf0jDKYWsy4As7BWk64/a0ZKNIeffChItbBXmaYIzBKMmisbQ9WASJFKRGcGm74Ge7hmlvOD98feKmF1Jo3vt/53Uf5L0/Av7mUz6/xSlKIu/93wf+/us+73lyQsGiCW5R23u2iphzG4ZJajDrhfg8F/NxGaWGK1sF4Lmxd8R3H9ZksebcJKd1np1BwiRP+PDhjCSJGOUhrrA7iNjIDLOqY2kFwmT4HqanQE9CCPI45tw4IUsipkcl87pnnHoma4UbG0WkJZGW7BQxg8wwzg1aKagdaaSRUmFdT+slViikUTTC0HWeVevJUyiyhJ98Y5fLmzk3D1b4vmHeSs5PYoQOTocxhgubirJ1xOazpmvZhrDwKFJMEsO4iOkwJJEhjSOubKbcXnRcG8ecG2fMFiVb85ZxqvASvHX0FgZJxLtnhyw6ePfckCRJyONwAGSRJjYKKSV179FCMsljxnlga/A+WKhIwflxxmHZkmiJEGKN+eponeDCRkZvLdPKkxvFWDvuIdjNFdd2JjS9Z1xETOclh4uOLJKk2oLumMQSqYNb/N6VHerO0TrYXfb86MUNhlmEEIJlbZnVHffnDTcOa+4ed5wbRbyXGGz1aZ5wM4drZ0bcO1xwmI54bzthe+cM4yzmowfHKFkjhaTpPIcVpAaKSHBle8RXLo24P865ddTwI+cyNoqUxEh2dEJqFFkk8c7zcFFyOKuxzjHMDaNUcVB2dL3DJIbWQ917siRikEi8EGRa4fqC3XHH2EDVtdxdBYosncG5kSSLYq7sDNid5JiPjxmmEdfPTdgeDZjk4eBpOknTdhwYyXZqECoko/reIXVCqgRW/OBqORFCTIC/TsBV3gX+hff+ZStl/o2Q+NTfqvWMssBPBVD2nknk6d3LEf0lkWazSBHCsX8cUeiKxEiKNCJdVNyeloxTxblhQhqbQEGtQyyitxalFINIs7uTsWUV17Y+DY46LzBGoWSwsHaHYbPsDD5LtyKEoHcwax1GKHZGGUUSsTevYK34BolmEGtyDdZZemsZZopJpuisp3OeSGuu7o7ZHhX0fU/rQuZy3sBGHpZM7wAR/p52x1MjgkU2iDmueoaxppcG6+G46vjm92/zrz9YcTXeILt2lskw5dJmj7cdi7JlOzOMs1D2NRWC0UCRJAlSSkxk0FrTOU/TWax3JFqyPco4O0pQKlirnfUkkWSThN72LNpQxNj2llg5jsuG6bLC+4SzoxgrPE4Klq3nsOu4eVTxduu4ujNhY5hzf/+Yr92cIpTkzY2cNKtZVRYTK2or6axgd5Ty3uUztNaxMwzjbXtH0/W0vQ3JJiqOVjVdkyKEoFYZJzDpL11KGSSG+bLk1mHFj2QGECxby6xx9EjGuWYrj6hXmo085spmxnsXR7x1boMsWZLGLdfPFCgdqGAneYRRgs56GueZ1xatJYM1+Ptg1VNXJXuzhrd2MqIoHK5WSDZjiZeKWAn6puWdC1tcHSluHJToaYXvG6xMePtcxu6kYHtUBOLQDs6OE949v4Exhmh9kBglmJWGzVGGKxwIQaJgb9awbHuWfZiv15UXJXj8KeB/JyQFPgH+A+AfCiF+yXv/B689ih+wDAhI9xzYHkYM01CsvbcIOKlYa4ba01lPpF/MSuusxzqL9XB9Z0DZC7YGCXlk+O3/50/5Z9+Fv/UO/OK//RM0rWXWgVlZqoOSS5sr3j2/wYWtgt1MsbSSQf4pyHCcG6SUDJLw0o3WnN94OndUFkk2s4ijvCWPDVJ49qY1o1SQRBFJbLBe4AnQAa0VkTY0VhAJj5Fh8QkhGGYRnOK8OI17DNbrk1as9YI8NXROsLfoyOKWs5OALxPO849+b8YU+O9/4yHvvHmdqrVIYL/sqDqBkoosiUlNqJQ4CdZqGbJ5Sngq6wKUw0OqNZc3I7JTWtUoAZEh1o7DRR8AyiJAa2or8A5KK0l7S2M9eazYyhQ7w4ThtCFdV1TERjGRCWJzwM68I9GKr5wbcHFeszev2SkivnRxzGZhUEpxZpLRWf9oTk7mUSAZ5SnT0nNv3nLzIBQufelMzJXvwTtnJX/zq1eQQvDhfsNH0wU7o6AMxpnh3XNDnJBcHcdsDnO0EFQi5uwo4Y1zG2RJjFAN5zYMZ8YFUgXXWim5HgcUkWKziJikitZaqtaRGliuYGUl9+aW7aEniSNGviOLFSZKSJTl3iLiyqbkykaME4qNYYbyFq9jfvbqEG8ydkcpsVG8d1mzO4zIkrBuvPe0a/hMEinOj3OEd0yrnkQLjFasyhJtA0zpdeVFLbR/CPxd7/0/O/lACPGfAP8D8BOvPYofsJxskgaoWsvOIGye3AgSo9kZmBCDeAmXE6DqHXXb42xPpAXOhwX9v343lDv9i/fhP/x5R1m3eCznJimTjZxRGtF5yblxRq4FvnFE+tMNqpRiUija3q1JJuUzFa2UkmGWMMx6pFQcLGsO6x4hFVEksNYxL2vuTEvatieOI3YH65PceSIlXyhuKIR46hiMEhSx4dp2zqLuGCeKZk2MGRn9qOpgRoi3VU3PrO4pm44GQxYFXjQhBFmkH53anfW01iG9o+0ck0zhRXCnpPysqyKEQEvPcW1praO1IVEea0ViJHac0HQt3otQxeAcZa8oEsMwS8mStTIQIf64UaT8+OVN8khxebPgjbOe/VXHThGRp/Gj+Xp8TkKoQLNRxEg8rl2xLMF2FUYJ0nzIV673/MjFgt3tLSSORHVESjMwwWL2QnF5e4yXhnOjhM1Bxu4gIooN5ycJ5yd5sNpHKd5DlkSk8ZOWexobstYyKxuq9ZxWfSB5bCxc3ogxxtDbjrjXZKkhigwGSREZxnnEzjDm/syyNfCcGRdsDYNl3DpJFoV1c35Df2bfnLw37zxahQRE2fYI6VBKEXv/qLZV6x9ccfpbwP/82Gf/C/A/vvYI/gLkxObQhCD0cd1jnaPpA2TAC/UZhfJC99SSwkgezluO50umtWcjD67hj27A/SP4sS2YN5bGQR5nfPn8mIvndthIJSiItUIK8Dget779uhTqxIJ6mngfXLG+70nWisEIwyRRXN9KSdOMtrM8mJU0bceqdwxzz8Nly+YgRQj56DmvitoWQhAbxbjIuHrG03eh+UXbK8aR5ksK/tDCOxFEWqGEx+FJhKDsfMDpuUceMtZ/+ts661CEgLhDMngOLrBsg/KXiMAtZhRJJLEeZrVj2QbrO4skwgqU8Fg8wgcQr7Uu1OYmmlGiKdIUh6P1oZLgWv5iLdeClSdxziHjnDReUFkToBNGUWQm4AS9o3OOIjHEokHKUHXiCYH9cZGyUSSM8pjeea7tCrYH8SPltVUkzOun49699+H5+HWtqabuBcu6o1KCzWFKHK8DLEIxzCSx1qRGMYwUO+OcC+MECHO2Ncx448zkUZzwdFro8UPuxLJOjaDuBbHyNC1sZoYsUnz0YMW0sZwdaPLkB8dY+33gPwVOs2P8LeCj1x7BX4RIwAUcTRErciOpe4GRwV14SV0GhI3sEHR94J2KZShWz2PFf/ZLX2D83Tm/+EYOrE3vLKZIY6arFqMisjjEOpQWRPpJV+5RfOs5FlRnPbOq45Pjmrp1LDJLawVbo4wiTQLMwii0gumqZnVcc9NBZGp2R3nAJVmHeAlX+1kSm3AaH1UNs8pSRIrGwrmLMLoFX7weoaTnqOyRQpBnEccLS9X3OOeIjUIQMrJCCLwPms0Ix7SxbGfPd0+ySOK9RrieVWvpvadqHUpKhHCsGgsIVq2jsZJMe3oLve3pnWXR9BSJweiQJYy042DRkxhean2cWG3eC37hnW1uLBxfPD+i6XpGg5QLGw2xMuzNayIpaHpBh2O/7Gh7i7UBBhPJT9fEODNIIRmmn2bhpZQIKZ44CCGsi0VjKTu3XpOapG15MG8YRSCUItHBw4iNxEjx6CBBBrxaGhuEd+SJeZSAepFD7yTWumosi9aSKEFlHcILlArP1FKxUSQk5gdnof2XwK8LIX6FEEO7ArxJiKX9pZMr2/D+AxibQHxXJBEDIZjVXVAc1j9CML+MpEaylcdsppJZ44iVYFb1XNwe85OV5MJ2zv1FQxrHSAT7y45eLplkIwZDRW2DZZRH+gk36lkxqxM5sawSLdlKNXu2oW47nIfMaLQM1ug40WwVOWdHJe/fn5PTkEaCIpKf+4wXlZO4Cc6tLQPHdDrlz/ZbnA+p/jhJWDaWzlqsg7FeB/o7S9sHd0RrgbViHdiGSMl1vWRLFmnOPyNr471/BIReNKFQXopPY1LThSaPBIOIEElcx8uSSBMbTe+h7S298yjCelg1loNVyyg1ZAmfi0t8XIQQmGLI1e2aXsZYH9h0dwYpmQnvbpBGXNrOef+gIpM6WFZ4lmtrs+49OZ+GIE5LHqu1m/7kwIwSDGKFcxrnwr1CDEsitURKQdV5yibE1lQkEWKt+Eywar0HKQRSSpR4uuJ8mpysqXnTMV01bKQaoxRKgsSzXSRc3/ZsDpIfTC0ngPf+94UQ14FfAs4RKLn/5RqK8ZdOdgbAg9CrcX9W4S96EqMRTf9aNQlSSiKjWNYWowSrztOuGtrOM8gMi87jeouzPa1XWBtYOeq2e+TmRTrg3x5XKs+KWZ3IiQWXRJoiS0hbh1xnqcq+ZbpqKXuPEQ4pJaNYYq1DxIrUaBzykRX0utL2juOq5ajqgvsoNV+7scfvfrLkjIN3dgZ84eyETEuGSYTzPXcP53x4sOLyICxJLcNh4/xJciZYBKly9NazlT/bQmt7R9lalACtJEVqGKURWaSoe+i9Z944PJIik2SRYZzFXJik3Jn1bBfxI6uwsw7vPakJynAQ6xdS+H497pPEgPeerTzhrd2MK5PA/9Z0PUUsSY0kNQolYHeU8fZOQZIarLOUTU+mIU80qXm2FpVSUiRPvy7WikgrSec9XW8ReLRRbOeG2km09CyblsWyhjziwiTCKIV1Hi3Dumx7i5IhHpfHLxbAP1m3eawZJAYpQyJAIrDeoXQo1P/zsM7gJWAba4jGr/65PPUvWA7W1JGLGg7KlmXVkkYGFfoEv5LLCeHlCRGUSx4HsGZtPbGC1otQqN44ut4x7xzLTrGygk+OKs6MC0wUPVJqLyOn42uRluRGoIRgMze0vWV/0TA0jkUjwVnmjaeqeqTSCBNcCE+wQjrnySP1CLD7KuJ9QMQnSjAaxJQ9iK7leFEzSUJp1Lx1aK24sJnhpw237tY8nNbs75QYFSwA7z115yhi/2hjLHvNMIPOq+fCapwPVRDjNMJ6SLVal30FK1ZKgcSihGS8jsW1FoyWJFphlKTpLGbdujaJNDvrg+ZFLImTYDgEZTEre4aZ4a0zE84MQnXHSQZUa4VD8GDR0PSSnXFK7RXLxpGmDi2Dwnvcan9ZsS4cemmk6J3B04dGPLGm73ua1nLYWHrRszOCQgq8c2vYhQRvg4WdvPxYkkhzdiQfHdxawnEZzLxEnViBr3+gPlehCSH+L55Pre6993/1tUfxFyQa6JygbENqv+4dznry2Kz5ul5cnHM0nV0j4xWj1DDJQ42llqEc5GhZUyQlw9TQCUGsFF6qQGjIp65abF4uKP94fG3ZOMrO0vTQ9B7be2SsGOah4eOibMiV5+zIsJEYjPCUTc8w0a/cyPZk/J0NylVJEbJYRtAD4yJhNKiRsqW2lqr3SKmCa6kE1trQcX7tLp4YG+qxfZMaQddLUvPs+Ym0JLYK5x298xglySKFllC3PfcPj7nzcM6VrYSm6zgqw0YrGwvO0Tq/dvcEZm0Zfp6F/LgYFeJm3ntWjaPqOh7MWuquZ9YohplDS0EcCerOMUoEkVTUjWecaLTSjLOIPFJI4deZ2leXSEsSLSnrlo6glCyCRMtHdZkCh21bRCQZmNC1K9XukaW6qDuOVi1HZcvW6OXW6Emy6GSNdDa4voJwCC9bR9s70tcsFvg8C+1ZFtl5QuOU1+9q8BcgWRxe4JkxnB8n6DUI0q3hBa9ioZWtY1o27M8q2jUNSmxCHWNZrvjTe0sujRRIxezhfX5vD96IEt5+8x3GeUQWa8o+nOovg3+DT+MUIaPkMNLR9w6BJTeSItFsppJpJ1EebpcNnfbEcUwtJA9XPV51pEZhnuLuvqh01tP0lu5UgMV7T+ccSawoTMTbE4UebvDjlwZsZJpV5Tkq20elTudG8aN7QegpelqsFxgTyAKsd0+1mAJcQrFqoOkbDhctinBgPZxX/Nb39vna3TmrOsNJzWbek5uCXId4FbbHes8gVus5efX5cD5AFqzzKOGw1tP3luOypel7mjZcnzU9RaQ4nFV8eFjzk1dHbAzSABb2Ho1/dOC9qJX4uNsrpWTZBTf27ChlO4+ouzXxjxD0XuJQ1A72Vx1pIhEIeheeG7CWbp34erk1ejL2uu1DHan0HJcNWkAW6YBT/HOIoT1363rv/8np/4BfA74A/D3gnxPgHH/p5MpmwaYJLde2BilKSayzrJqe7jnNhJ8nWRSYVZu+Y29WcbCscc5hpOBffuMG/+g33+f//NNPkHh+9wHcmcGvfVDTOYEQIaPnrAuL9xUCzpEOm7zpLbPa4b0LOCwHSgnuLzqqumPRWGxvqZqOuqrwrmMUeZTwtL3F41844Pu4GBV+w7LuEM6uGWcdVd1zXIfWaSJKGWYJrY84WPXcP5zyrdvH7C8bvIdVR+is7v1nspynnxGtzbYT5f+sOcnjMK9V16/frSfWAo3Duo62a5muGm4eVczLmo9nDQ+OV3w8rSmbjumi5IMHc1bVyzPqd9bj8VgboCdaSVITUSSBsqezIfQQ4EI9dd2yanoerBpuHFQ8nDekUUQRa7I1QcKJG/us3/y0MZz+fh4rNrOIRMvQ8FnKNT4mYPTOTxImqWTV9szLmkhJikiCD0oxFR3TsiWT3Usr+RBX7cKhv6g5XDVMVw37yxZrQx3p0xIaLysvWikwJHCS/RfArwM/5r3/ywnZANy6ZmzZh7KjwDzg6J2jf0XTVwjBRm6YVwllY9eZNk/btvze9/Z4/75jS1ZsjYdcicGO4Ze/OKLr7Rp35ql6h1HyiXKiFxWjBG0vSE0YTyI9q6pmf1ayWyi6dQYzMYq6sezNWsTCspXHnDEJae/QyvPc1gmfMwe98yzqHmc7pouOCIkHxrFko0jJU0HZ9DR9S9m2/PGNI/703pKzzpKaOBAu9qGVtlLg3NOzridWx+dtLC0FsZakkWSYaHIjODuKSW9LtNTU8xnfnwlG0tLVDYu2Zzk74nc+kFxMKr5xqPiPf2SDn3r3+kvNRXA5JUYGZZIaSde2HC5KYqXYyAzCW6arFik8kdEMpGBkBINIspmacEgqRd36R/dU6sWz0I9nraWUbBQJrYPOeWLnA4yis3Sd5ajscd7TWuisQEtJ1fVMlw1bheZbeyUfPVzxR5nm8ln3qNTshedECpxWNDoc2kVicDa4+MY5qs4zeM3cwOfF0FICZOPvESiEfvYZTU3+UsnxsmTRwf4qmPFZFECEiZYsmoCDelnprA9p9STizMix6hx933P7sKRuHeME0jTm48OK4c4Gv7xVcPXSiM5JBOHE1CK4u6+TlMgiyYM1N/Ci8zycLvn4sGQU52yPI6aLHuUdzrbMy4YijR5BO5JIo7V4ZYUK6/hhHlGWHcu6YRBHpHHMxmjAm67hTOY59hG7wwTvYW86ZzHv2R0G8sEsSYLbaj2ub7m76Hh7O2O4Zgtue8eqtS+UuGh7R9U7hJD0FhaNJVbQCY0XEicVf3rzmD85AlPPeePyLt5ZbhzWrKbHfLtZ8sAqLhS8tEI7sSrLzpFHYZx35zW3j2tioymymGXVcrDqSKRjs0iITaA5yrOe0joWVcf4FDyj73uOVh07A4Mx5gmX8mljOHELT393nGiOy4ajVY31QbmVdceDwzm3jkouTVJ2RwlZrDicNxyXLfMq4uok4huZYRx7ZlXPRvHiCi1kqQ2jVJNFob41jzWN87i+Z7pq2cw0gULy1eXz9OFNAqfbfwd8HdgVQuye/oL3/l+/1gj+AqR1MrTNsoE3PYvU2mULsY7ehRPx8xbMaTEqZIJSo1gohbKske89b1/c5Op5ybu7KXemK5zzjGJJnsTEkSGPDJ21HC4bJJ5hFj+Bg3veWE6uaRkAjHVVcvNgyUaU0bY9deeIZFAEe0czvn67xtgF9+aWd/OYa9sDzk8yjBKPkhivKrFRjFNB31S0OKy1GCXZyQUfHDgSKcFJEqORWnE47Tko4Z0MLu4M2BwmRCqUM314UPKNOyuMcHzlFP35i9gnJzEbhUf40CN1JMAIxTjVnJ8UXNoc8JvfPGYFfO0O/JV3AvmjsSEOeiGG1qVcnTy7KuBp7+V0cuRkrG3viKVgEgdE/EZmMMJRRJJIBdBxs6aS8tZyd96xrFvGRRLu1zseVh2HZQdknN8wn8mkfl4863QJ0qoL7t/xqmWYKCZ5TCIkf7xsuXvcMsgiskgjhSSWlnnbkyhHJ2I2BxFSRyQvGD87PT/Bg/BYHwC13nlio2iExGFZtZ7nsNy/kHyeQqsJWc6/86zxAtdebwg/eNksIjJaCgOzsqdsLdb23DtaMcpCgBJ4qQUDQamtPCQKiFSgxPEZP/fuZTYzzb2jFV+7ccTd45ZrmymDWJMlEUJKFmXJrcMFRuacnTxJQ3wScG978QhEefpaax1tD03X8607M751e44RlqF0VLVjVfX0bc137q/4+HCBXbRMBbyzWZGnCfG6dhDhn2uhfZ6SPxlL2VnKqmfPBrqir/3xB/zaTctP78LV69cD84Wv+YPDUFP70QH88m7B5rAgi1QobTIC4S2nE86n2RueJ926W7BDcLhsEUIwSoNi0tpwfXfA9d2Cg/X391lTabsATYgjOFvEbBYTzm8/zh7/5O89vUZOPtMCjArJmtp7slizNYyJTGAfnq4aQDCOJVp4mrYnkZ5hahieqsc8ud8oDowr20XYti8DhD5d3I9zaBEUfd0LytZyXPYo4fDOoaVjWnWkcc/9paXuPPulYyNVSBlqbF8UtnF6fiD0h+2txdrQrGfVwCTVa1rw/48rBbz3V177Cf8GyrnNIWcnS/IIHpQtmp4Hs4YPHyz4wtnikSvzMgums56qczjvaawk0YLGCkZpwjhPKZdz/uSTA24+WHLQwb3pkv1VyxkVNu901TCte8rm6cCJk/iY58kM0+ksZ9MJvOvpOohF6Gw+q2qOK8V4lJAZRxYZ7s1bbgH3pvPPZM+azuOEw/vnK6xnKfmu67g/q/HW4oHDqsfKlv/pO5YF8L99DP/5pZ6qs3xwf/mo1+XFbdgZp4zSYHn03iK14fJWQZbGn8nuRTrAPOaVXbNKPOn6hDlRDGLNKI+ItQyU6KuGzgkGiSKNDJeAW4RTef/BPf7oZsuFHDbPKobjgnycMxk8m+f+aWvk5DPvwwFRdSHTe7RseTCrGaYx7dgyLwN1zjA2xNZTNw17pUXblqNKY7tApW5USPpoLRkaiV+3UwyKnfUBw3O9iN6B88GNndUtiyokIYYyAGa9d+ua0QglFcuqY5V2XNvI6L3g0jilsZ7dzAR3/QXDMo/PT2okdRvAvUaB947eSZIogLtfV/584Ll/yeTcOGUr1SSx5PC44v0HCyaZRmmF0Z9aPy+DPTJKkEUKb3sEIWtjlEDIkEX6/Y8O+b8/fMjxEdgIqqajqluWkaayUFc1i7LDu+6pAMOTrN1peprT107GmUWKS7tjGq+4tjvk1sMplhCQTSPNIE1JZMOHhO4837m7VlJ9UFL9Gnwp5dMV1ucp+aOyY29aMYo954cRyzKcwCfdcCygtcQ6x2IeCA5z4MyGZn/RkscRozywgnR1yc2DFe9sKlqbc1qJHi5qPjosub6ZsTN+usI5mcdJFjNKA8VP5wKu6sGDh7y/t+QLIziawU9fh3/yhy1TYLqCv7015McuFfhkwEbx7H6RT1sjn9ZveuiD2121FrAcNw6tepqu53hRMz1eQR846o4bT9V03J/1mFXFN+8v2N2c0K7dsxMIjPefbvznHTCPu3uLyvFwUTGvWrrOoZTGe8t05ci0RwqBlgJp6BPjAAAeHElEQVSJpLeOurecHeV88UyMVoLCWzbHGUYKqs6RvUAbzacxkPTOU3Y9XWVDBYMUpPL1qYPgc2Ab/3+Vu7OWu4c9Hz5sma0ayqYnjwxv7uaMc/NKSYETeTCd81vv73Hj3hTvPZFSREoyX5WUXagaSGNwruPmwYqyrpDC83BW8/27U+4cLJ+alncuFFRr+fyTGEKfgjPjjERLVp2niIKrcLSo2btzj298XHLyC3cSArPDGuekpXguW+8JRORZY0h1sNKatqX3MEgNQmk21tcjoFxboiuvGQCZgrZ13NxfQN+QxwGY/I27C/7gowN+/6Mp0WPZvaP5km98csTR/OndIdveMas69qZzvv7xPg+O54/eh3WeP7xf8scfH3Fr3SX+1z8KvPAnsjNM8SYhi/QjFpK2dy+FZj8B5C4aS9WGIvxJJMiMYFa13J1V3F50PDwu+dP7NXcOFpSNZRJZlISNRNLZsPnnVUf/FHKCExjL097XadiGEAIloOwssZZcHAdygKZpuTOreTBv6fo1O0kUmrokWrFsHbMmYMc6LxnFGi9CY+ZXkbDGJJmRHC8b5mVDogXZa+D9TssPpUJbNS17DdyfQ9s2bBSBTUAKRdkJ6v7lUdknG+g79+d869YRf3R7Rtv3FIkmiyRD6YlUaLIxLaFre2aV4860wSNYtj2r3jGv7VNLn8o2dHQPFNefldObrbOee8cVt6YVd2cVq6anaiwHy5p7xxW/fxf2TvWiGE1A+FAfaa1lVn5aV/oqEnB1guPS0XaeZd3w8eGSd4uQv/qRFKrWUbWOHzuXEQMrC/enjg8eLLh5uHwE4hwYi3eQR+4JJXpY9yxqy+FTKHNO5kMK+ORwxZ/dmvH1W8fUbY9RklVdUU0hjz0/fyXc8/ixe1gByrZ88GDGqq5eGgN2IkYFNH5nLXuHS+7Mm3XBfEdZd0jniaUA2yClZmtgqIgYpYY8TchjhRZiXcoVYnKnN/7zDpjHlZ2UksIoHHB3VnHzsORo1VEoj8LSuZa67Wl7x1ZmQs2mEY8y76mRSCXp+hBeeVURUuD6jo8PFzw8XrFqulea26fJD6XLOc6SwFoggys2ymK87ZlXLedGIbX8KqKk4MzAkMUR2RrNLkTIHN5pJG2/5luy8Ml9+MW/kjJJNRK4NjF8e5Dy3tn8qQotgA71U8GHn3U7JL5r+d6dQ2SXI4UA5Vm1jk7B4NTJeiaCnVzz/v1jzm3k5EZxb9kRqfwJksDH5VnZPeEDBc1mLHl/v+Jw0XDcCt64GtMtJT+RVdypGyYm4/aiYxqmg/ePIZ/UtE2I7ZSdY5DnvHtecn5z/MTz39oZsGoFb+0UT1w7IcKMteLCKKLvGo7mkuliRZzEPJyuWCm4WMScPzOCbz944h7LVcOtqeOjmefcyHB+a8KrMpEIIWjang8PV9x4sEDa0LneSEfVNcwqiRMRW4Ui1hIhPEbLwDgs5SMU/Zqn8oVhNY+7e0WiSWPN3VnF3dt3+M1Pav7dKznJcIOPjlbsTefcO66x3nN1O0dFESscs1WHTRR5nDFOIxbNy3dnOp35bXvLx4cle8c1s8RwfdUgVdgzPNH59OXkh1KhXT8z5N1zEgecGWbcmZYk0nFUOSzipQGDELJvuVdMBjlfvlCxWyRsZEEpJBquphZ96lA76uDi1oiBgZUVTFuBE4K9yj11wT6PTeEzcS0VGGJvT0uqxvGl3YhYKDZiwcI6zm7C2Q66JvRXXDjJjf0ly85zfmjYX1guDD8fWfus7N6qDzGeWeO48XDFcrXi/PaEqztnKBaK1eyYWw9X/P4thaHnZGvsGNBas+gVAkfZdORGcWkj5uzoyVz+5rDgp9/Inokul+tMqNAJWRxxb9bxyeGSKzuag0XFooK2a7g7faLXNQD3pivO5znjLGErfTVslHOOWdnTuxAze/Bgn4+Oes4MFFWV87UPDvmjIzgr4KtvGSbDlNmypmx6Lm9Lskg/AmhrFeJb+NfDKSZaEivBr/7OjJvA7VsN//6/Zbh/tOT+w5L7JWTJkjvHNTJqKEXPt++XXNxM2SxSJnlowl28JBnjoyx9G3qyBriIJk8jJI7jqmGcqs89SD9PfigVmvCeNImIpaCxHd9/uOSLm4GQMH/F1eK9Z1Z2zMqaWdmRKkXT9QyAqvN8MPPcnX/6/fsE1gnrQxFwojVbg5jtUfLSVsDpk9g5h/IW5RyjVPLNbxzwm0uYDWH7zTMM0oR3duHew5p7FRwuQiu7wgjauuKjhw1vbGjYftIqOi3Pyu4lwrGoW8bG4oTAytDH4OqZIcVEsH9/yYdHGiM8bd09+rdRAj/31iZfuTiiDdyLTBuIo4jKP3nAPE/BR1pireXhvEILxxtbMfeWoaHKrKyp6hajoW09v/Od8qn3aDpLoj06MhTJy2G+TqRsHY21eOvpnePuqme2gOPKclz13DgK/T7veej7Et9m3DsqaZuOxSpwxTVdKMkzawbb/Xn9Gc7+FxXvPYuqY9V0KKW4uf78LnB7b5+9yoXO5z54LRuppu0sdd8yr1qsjdfVDyHO+rJr9CRL37ueo9mK20dz5suaJFIgYFF1VJ1l8lJ3fVJ+KBXah4cN91cdibWUvecNGdGOCqJHWcmXl2Xdc7iquX1Y8p1bR3ycxFzYzBjkWeD26hoe3zqdDS5a3VreODvm4lnDtc1nE929CNB3UXXsl45ICuI45veWoa/Qb8zhF46XTCLFj725xYO7n7AAVlN4Y6fARDEf3jridz465nLu+OKVc8/9vc/K7h1WlofLjnQoeHc3485BT1l31L1lezAkYYef6Ja8tR3xvXvHhIaCcHYCP3Flg7woGCaBG6wdp2ij1vTPLy5CCGZVx/v3Znjv2MgTGiyDNOJo1bDsPYkKTVa+fapZtyEoGAM0tufm3gEflRXnUsu5rTFt//nW0el3FKxHg7WhEYxoQ0LojQ3DMBKsWazogRvTnnFWMa8tNz5u+PDoLl8YN/z8V7/Cwbyk6h22U9yZWSJVvLRC66ynbHv25zXHZfeZa3ul43v7MAZ+9FrCL7yzDSLUwJ7NFJNMs5mHyoS7xyUHixa5U7A9enH1cZKlP5w7vn1vxm9/e4+7q4Yf6xxfvbzJ5jAkYF5XfiiTAiPVkyvBZg6ddVgnGOQJRhl6/2pT0vc9h4uKg4Mjbk7L0P/SeqZlS2c9P3plwrnHDI2mtwjvEQJGRcoXz49Jkmdv3hcJTDdtT9t37E87vv7xA64TYBF/YzOwjjqpuDhKebD29T6uQSrDKJHcnjcsqoYb0/qZ93+eeO8pNGhpsV3PQdWxN2+5ebTiYF4RGclGZtgYxFzeGjCJPvXB7y9gUTXgw1zePlqxaHrGaWje8bLStw23jhYslgseLFusC0yv3/3Wt/jd+1A28HgkqAMua5gAR2XD+w8bbhwu+aM7CxZlxTfvTFmU1VOeduoep95RsCL1GggtGRaQJpoz4yG97Th9p/tTOKpaLk1ibgAfzeBX/o8DvPfsLxoezBv25jXLqsXal49hnSQnlqsV37l9zLvrz39OQlsGBL2TsDnMyYuCB7OKD/fmPDhccGvWcbSsqDrHYrniz+4dsyhXLz0GAK0EqVG0TcPDQziar4iU59IkX3cZez35obTQjmtLJyPivmcuJZdHnkuTjJaO3ecwoT5PWget9dyY9jR9wD596fyEnlArd/XsDpfGn3DnFDYg1pLOdhyuOsqme+a94dNGF9559HP2t5CC5ariRglJBT99VfGLb1zhTNrx298/pi0rPtib8++dgV/bg1++CvvLhq2B4a+/M2JvKfjFN5+NjH+edNazaHvmlaWsWr53b8lH9+YMx4aqySjrnv3DJR8+rLm+abh79CmLxUdTuP1giooz9l3P9x8uUAg2iuRRs9oXpc0BuH04488+WTCSC25OG7aGI37qcs5/+yfh+iFwTUou4Lhz6t9FEez30FjHUIB1LedNzXfuHvGvPzgGO+an3n5xoK33nkXtyGLFYgn7xz33Do5IhuPPcM9tKrg8zjhuPnuYCCHYHsYUnaMQDd9etHyx+3TeXrQ87+Tan3x4l3/1/ZK768+/52B3rQUaB2NZsTevaZuee0vH4cGcbz7s8N2YH7m0zcF8xft3Z7w1kVw7u/2cN/CkBEC44vqZAZM0VIjcmTfcmzW8cf71CSzhh9RCm65q5quahy3M2p4HK8/esuXm4f/b3rmHyVXWd/zzm8vOXmavSXaTsFlCSAgQGsBwEYiI9AEtGpEiVahUuVqtVauCPAKPLa39p1J4bEVLRa1WFCsIAopY7jRc5E4SQrK57CbZ2+zu7M7uzM7OzJlf/3jPZGc3k73Onjks5/s888yZmfec85n3nPd3fu/t9yZoH5idd7I4XMZxS2tZtzTIsoYKTj2igkW1YeorTQiY5obwIWF5Bnp7iY+maetP0D889ZN/JJ0lnZ08vE+4PEh77whJYFTBP2Lx4u4IB/a10xqJ8eKeNA+90cHz/ab61JqArqE0ZC0imRBVFUG6U7NrmA36hf6BGG/t62e4P8poJkVvDHZ1pWnd28Hr+6Ps7oqyKxIl0hfjzV1jDfIJ4P4n97EvMkRndx+R2AhLyrOkMkp8NDPjbv1ILE0ilWRvX5JtHcrL7QO82j5+cEZbT5aWCU2FOxOGxTeg9GWhbxC29WZIJEbpHEiQSEweSmjiMIq0pfj9IJbFy73GWD6+bYgL1i0dt9+ODDzT1sfzb473fEx0lAA1FUE6BkdpiyRo7x9rvJjJcBJL4e2eBG1xU831AevrocF+hg8CP30lwWNbDxAb7CMgglppuqJJtu4fZH9/jHg8yYHYEAODhdseJ1PQL5SXBakKBRkeNV5hJgPl/pm3yR1O70oPzcqMEo1m6QCq/BlY2k9vbzkvtQ6xpiZj5uHMUIFAgMW1lVRXhikPpMDvPxilE6CjK0p/bPw+V9+3hx9cfBTbD/Rz8mLBLNdQWLmZCLntw6m8LEDYZ7y9xT54sAsgxrgIAnljUSPdEAwKWXwEyJBIpJlt3FoRoTdukVIlli2jtkLpZgRS8IftWf5sSYZXX+vlhRiskmH2TOhgfCoOFa/sYH80TSjkZ+9ui5FgPZ997yKOW7tmRjf9uuZaWvsTpIey7O6Ok0jB7s7BcWl6slA+YQCavSAYW1JQ12XGp+3rGsI6xkItwcrOrLpnmAPs6uw7WMXsHILa2tpD0r4RGf/5BCCeTPL6vijhkI9MfIThRJIyHZssP5PpeXWVQcJ5z2sFEmUQzRu1Mgg82z6KDEHt0nKWB1OkRmBf/wg79/Wxv3eQfV0j7G8anHj4KSUihMsDBAMBGqorqe5MUFtp5jwXI7gjOOihiUiDiPxaROIi0iYil02S9u9EpEtEBkXkhyIyx8C84xVPmSpHAohYsC2aZkvHAFu7+3llT3TWxw36BX8wRDBYRlbG2gMikQif/O5rtBYoC63RFP0jKXb0zTyIYCGJCGl7rl/9oWXmEO0B1MpSV6ZmURIx77PVmasbOXttE5ed1sx7V495IW1pOGVlLXsSpqrxWBsU+seP7E6zJQovd1ncuxd+uzPK7Y+1TnvZtJwa66ppqa8iFK7hxKNCVATg7b5DZxVMvNr5zm/O1sVGgEyWeGqUmUa/zHlsvaNj7J3A8OjUx/niR5vYvj/C09s6aOuO0dYX4+2eAXZ2jj0Jppq5kS+/38+recsaKfBcN2yfkE6Bp6OwrSPK829k6QQGhqG9f4gX9sSIJuGtnslrFIeTiNDSUMmFp67klBZIqY9tBwZn1S5YSE5WOb+L6XBrAv4S+J6IrJuYSEQ+CNwA/ClmubxVwD8UEyRjjfdA6tKQzVokk5BMT96WNZlEhBOPrOf0o5ewbsVYXea677/IoUM3jc47rpGNRzdx3nGNkx57NG3ROzzKkL3U3qQcVpoA0BKe+gIngc2tPby2t5/+wQR7B4bon0Z1otBUoEwmQ8qCIxdV0rJ0ER8+acXBhUwqgNNXNXLJCX4aA/Dnx/qY7pJhT3RMM2GeEhnY/PIOfvBMJ0fKKCe3VBBPHVroC49CG68Vi2BbR4TWjhjbOiJT71BAZ69uGPd5UXjqav2Dm7vZ1tbLK3uG2N3dz0N/HGJnv8Vvnj3c3TS1ZlIl2zuovG5v9ypYQzG29sAI0OibXjnJv09y24FAgDOOW0FFCl7tTHP9w2088LtHZ/pXCsoRgyYiVcDFwM2qOqyqzwK/AS4vkPzTwF2qutVeaeofgc8Uk6dlSQPH1JkFEcqAeFk5RyypZ8WSGo6YYvzVlMdurGfT+mZaGsdG1OyY4Iq8bxFc+Z4lPP/lDaxc3sRnNq5h5fImJlMqkyWZthAmr3ICaHkVS8qgujbMtz7UwonLKzlcM3Y9MJQeZX8sSXdS8fmgOzm1h5bKZE3bVp7H0juc4tm9Ud44MMz2SJJQKMQXNoRoKoOrNoQoC/i48iMbufXyDVz90Y1TnmMuqikP8H+dEAceOAAXnNRMY+2hswqmUhhoWdZA57CQ8UHn8OyqRqubx1/fQGBq0/JgL7QNpMlY0BO3Dq7q/ersn7k0zaC/Z+Jw5vZUBblTt49Or0cyv40vfzsQCPB8z1i6f3lx9lOp8iXFWotx0pOInAxsVh2r/IvI14D3q+qmCWlfB/5ZVe+xPy/GhKtarKp9E9JeC1xrf1wLvH0YhMVwMPQVAL7y6jq1UqO+ippFViyyH0R85eHabHJ40AxwKK6kdtm6QFlIMgNduzUzmkR1hldQBJ/fT9aypsPnr6pvtOLRHgAJVVVrZjQZrFu2ykqnfT6/P50Z7NrrDy9aZg1FDvjKq+uyI7F+AF9FbUN2ZHAazpO96kY2mz3II+KTQFkI8fk0NXKwdVvKKsOaSuTX98z1EJ8/uGjFsZlYpC1Q27jSGurb7w83HIHP5yebtTSTSkgwVJWJRdp0ND4dZ2oCos8fqF+2KhPt2IVqVsoqKn2hcL2vIrxIUyOxrD9UK6l4RMrKq0X8gXS0YydAsH75mnS0Y6cvVFWNZrPZkVgUwB9uaLKG+2ftHklZRVWgtumozGD3Hk2NxCVUVS1V9StJDLRLqLLGF6yoBiT3/9PRjp1YmbQ/3NCUTQ4PqJVKBRua16T79u+Y/T0q4q9Z0iz+QJn4g+VWYrDbF6qstZLxlD9U4c+mRob8lXVN1shgRHzBMk0lhvzVi1dkk8N91nB/t688XCuhyhor1rN/+vewyBjv2LYEQxWBmsYWgHTfvlzZPaSsFtCRqlqwodupToEwpr0xX4NA9TTS5rarGR8QAVW9E7hzqpOLyEuqesq0aedBIvJSqsQMOQ7LBXlR6uvhFg43MOQ40okBV3DMJT+cakMbBiY6uzUUbsKYmDa3PfMntCdPnt5Vcsqg7QACIrIm77sTgUILrmy1f8tP1z2xuunJkydPE+WIQVPVOGYdz1tEpEpEzgIuBH5aIPlPgKtE5HgRqQduAn48R4Qpq6UOyA0M4A4ONzCAOzjcwAALhMORTgEw49CAHwLnYdrCblDVu0WkBdgGHK+q7XbarwBfx/T23wv8taoWZ6CWJ0+eFqwcM2iePHnyNN96V87l9OTJ08KUZ9A8efK0YOQZNE+ePC0YvaMNmojU5G0XZ7r+7Dj8pWawz1+Zt10SFjsIQaCUDPa5jxORpaXkEJH3iciGUpx7Ase5IvJxESlpeReRs0XkNhE5ar7O8Y40aCJyhIg8CtwnIr8UkSNKyHE7cAmAlqiHRURaRORe4CcicpeI1DrNIiIrROQh4GfAPSLSXML8uBQznvFycP662Hnxe+CXmMjWJZH9cHkYM1JgORx2Su98c6wQkUeAx4EvkYu7Pg96xxk0e6L7L4C9mMypB+4APuIwxybgUeCvgA+IyGr7e0e9ARG5AngeaMNENDkZuMtJFhG5AXgZE43o08BRwG32b6W4x9YCbwGrRGSjzeFUXtyIicjTqqrLVPUxJ857GF0BDKpqvap+R1WHbEbH7lERuRPYgplnHQSeAjZNutMc9I4zaMAazOJ9N6rqVuAioAP4hIgc7SBHHabQXg40Ah8CZ70Bu2q3GhPF5Cuq+gTwYeBjIrLcQZZR4EJV/VtV7cEYt0YREZ3xJPzZK8949gJ/xHgk54tIWFXVoYJ8GvCUqv6NzXS27aHMbKWXOcrOi7OAR+zPl4rI50VkPeAIi4iUYcLKnaSqX8IE8BhlHj1F1xs0Eamw33MBpJLAezBRhFHVYeDnmIn2H3eAIxc35T7gV6r6MKZ6c4aInG6nmbeCk8cRUNUMZrbFA3lslcBrmEHJ882QC3f2HVV9TkTWicgbmCr4K8Bl9sDp+eYIAOQZz7WYGSe/A04CzrB/L34UlTGGnJH4KnC0iPy9iLwM/DvwK+B+EVlR7PMX4MgFnAgDzUC3iPwYuBl4P/Aj4DoHOIKqmlLV61V1j/05jomrusFOU3T741qDJiL1IvIz4GEAVU3bGbATeBr4Rl7yZ4BW4BgRmTyw2Nw5UrbnEVfVXFDT/8Y89T4oIpXzVHAmcuQM+nZV7bWZUnBwacN9DjCM2u+5cKOLgdtUtQ4zZe0i4PpiG7XD5UVeARkC1gH3Y/LhEhG5U8yUu/liSNoPmVbMw+6LmGk8J2Gq4dXA5+wmk6KpUF6IiF9VY8Au4HYgpqrHq+ongG8DG0XknHnmSOf9JowtsvUQcLztNRfde3elQbOrjr8AjgSWi8g19k85Q3E3cI6IHA8Hn7xPY55As4+hPX2OcfmmqtuBJzE37weKdf6pOMTuXbUZcnlzAbDDNm5OMzylqj+yt+PA92yeooWpmowjr4C0AM+p6giwCNPOuRZ4cz4ZGLsvbsJUwf8Dc2m2YwzJX1A48vh8cdyK8dJW5tVwNtu/z3zhjBly5O4NNcpdGwsTUWdeqr2uNGi27gY+i3HZvyoi5XbGWBjjtQX417z02zGx0xY7wGHlvIG86uXPMW03Z4rIjbZHMPlqvcXlyBmW0xmrgl4lIt+UvOEc88mQU17h6cGEXS/2fXY4jtx59wC3ishrwDLg15hFwpvnmSFlG1YLeNZOlzPmccz9Uexez0IcOe9oG6ZJ4mRsA6aqbZhmiQMFjlVsjkLl5EngTEyVuPjNM7lY36V8AcdivKsl9ucAELa3m4HHgG9P2Gc1pjrxP8A1mHajuwCfExyFzgN8E7PORhdwkZMcgGAiiv8W+Dzwv5j1ODY5mRdA0H4/HtP7ekfuOwc4xH590z73Vfb3pwL/Bqx0OC/8efu/ANziVDnJuy+Cdtl4FrgFeA74fe4YDueHYDoENgNXzyUvDss2HwedQcb4gf/EeFaPY7raNxVIcxFmmMbaCTfKn2CicjwC3FQCDp/98mPa9NLAdaXgsN83YAxqL6bn0+m8KMc8fR/CVP1vdDov7O+XAxUlvD99mIfLeuBBe/9vlICjzH5fCVyK6RD4eonyIxcIo87Ok9Pnen0KMs7HQWeQQeuBJzDVxADGOO0Gzp6QrgHjOt+X911ZfiaWmgM4F6guIUcQs6LWDdhPyxIwBDBP4E+VOi/s91l760XKizBwVYnzIr+cSKmvSS5v5sIxKeN8HXiSTKlhzKO4Ftidf/Nh2qLuAlbl7ePHrLu6FTP2aztwmUs4PuUCjreBT7qA4VIX5MWc7g0vL9zJMW1eJ05i/8k1mLr7Q5ju9COBjdi9g3np1gMvAR+bsP9ZmEF67cAVHsfcOdzA4BYONzB4HHN/ObUu51WYOverwPWYsVI3Y9zWbuD8XFpVfQPTvX65va9fzDJ4f8Cs19mi9tAAj2P2HG5gcAuHGxg8jiLJCasJ/BNwTd7nZsxYlOWYevg9wLl5v2/CuKuV9ucqoMbjKB6HGxjcwuEGBo+jOC+n1uX8PvaAQnu6TAIzirkCM+ziaODLIrJLzTiZU4FHVTUBBwdpehzF5XADg1s43MDgcRRDTlpPxrpuT8a4qbkewhMw9fQtmPEyEeB8j2P+OdzA4BYONzB4HHN7OeWhAeOm55wDvK329BxV3SIiF9sZt05V/8vjcIbDDQxu4XADg8cxNzlq0PKmhZzGWFiTz2Hm2X1LVV/C9Jh4HA5xuIHBLRxuYPA45ianPTRLTHiTBky8rKcxo5ivVNWIx+E8hxsY3MLhBgaPY45yuo6Lma6Uxcwz/JrT5/c43MngFg43MHgcs385vtCwmCCEXwDuUNWkoyf3OFzL4BYONzB4HLOXt3K6J0+eFozcHA/NkydPnmYkz6B58uRpwcgzaJ48eVow8gyaJ0+eFow8g+bJk6cFI8+gefLkacHIM2iePHlaMPIMmidPnhaM/h+bzQ1D4GeYpAAAAABJRU5ErkJggg==\n", 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\n", 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      " ] @@ -271,7 +271,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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      " ] @@ -307,7 +307,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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      " ] @@ -350,7 +350,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The P95 exceedance level is -0.65%/yr\n" + "The P95 exceedance level is -0.63%/yr\n" ] } ], @@ -364,9 +364,9 @@ "source": [ "## 5: Soiling calculations \n", "\n", - "This section illustrates how the aggregated data can be used to estimate soiling losses using the stochastic rate and recovery (SRR) method.1 Since our example system doesn't experience much soiling, we apply an artificially generated soiling signal, just for the sake of example.\n", + "This section illustrates how the aggregated data can be used to estimate soiling losses using the stochastic rate and recovery (SRR) method.¹ Since our example system doesn't experience much soiling, we apply an artificially generated soiling signal, just for the sake of example.\n", "\n", - "1M. G. Deceglie, L. Micheli and M. Muller, \"Quantifying Soiling Loss Directly From PV Yield,\" IEEE Journal of Photovoltaics, vol. 8, no. 2, pp. 547-551, March 2018. doi: 10.1109/JPHOTOV.2017.2784682" + "¹ M. G. Deceglie, L. Micheli and M. Muller, \"Quantifying Soiling Loss Directly From PV Yield,\" IEEE Journal of Photovoltaics, vol. 8, no. 2, pp. 547-551, March 2018. doi: 10.1109/JPHOTOV.2017.2784682" ] }, { @@ -387,15 +387,25 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/soiling.py:15: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" + ] + } + ], "source": [ "# Calculate the daily insolation, required for the SRR calculation\n", "daily_insolation = filtered['insolation'].resample('D').sum()\n", "\n", "# Perform the SRR calculation\n", + "from rdtools.soiling import soiling_srr\n", "cl = 68.2\n", - "sr, sr_ci, soiling_info = rdtools.soiling_srr(soiled_daily, daily_insolation,\n", - " confidence_level=cl)" + "sr, sr_ci, soiling_info = soiling_srr(soiled_daily, daily_insolation,\n", + " confidence_level=cl)" ] }, { @@ -424,7 +434,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The 68.2 confidence interval for the insolation-weighted soiling ratio is 0.940–0.951\n" + "The 68.2 confidence interval for the insolation-weighted soiling ratio is 0.939–0.951\n" ] } ], @@ -438,9 +448,17 @@ "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:151: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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      " ] @@ -451,7 +469,7 @@ ], "source": [ "# Plot Monte Carlo realizations of soiling profiles\n", - "fig = rdtools.soiling_monte_carlo_plot(soiling_info, soiled_daily, profiles=200);" + "fig = rdtools.plotting.soiling_monte_carlo_plot(soiling_info, soiled_daily, profiles=200);" ] }, { @@ -459,9 +477,17 @@ "execution_count": 15, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:211: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
      " ] @@ -473,7 +499,7 @@ "source": [ "# Plot the slopes for \"valid\" soiling intervals identified,\n", "# assuming perfect cleaning events\n", - "fig = rdtools.soiling_interval_plot(soiling_info, soiled_daily);" + "fig = rdtools.plotting.soiling_interval_plot(soiling_info, soiled_daily);" ] }, { @@ -504,9 +530,9 @@ " \n", " start\n", " end\n", - " slope\n", - " slope_low\n", - " slope_high\n", + " soiling_rate\n", + " soiling_rate_low\n", + " soiling_rate_high\n", " inferred_start_loss\n", " inferred_end_loss\n", " length\n", @@ -517,36 +543,36 @@ " \n", " 0\n", " 2010-02-25 00:00:00-07:00\n", - " 2010-03-07 00:00:00-07:00\n", + " 2010-03-06 00:00:00-07:00\n", " 0.000000\n", " 0.000000\n", - " 0.0\n", - " 0.669120\n", - " 0.896014\n", - " 10\n", + " 0.000000\n", + " 0.685379\n", + " 0.863517\n", + " 9\n", " False\n", " \n", " \n", " 1\n", - " 2010-03-08 00:00:00-07:00\n", + " 2010-03-07 00:00:00-07:00\n", " 2010-03-11 00:00:00-07:00\n", " 0.000000\n", " 0.000000\n", - " 0.0\n", - " 1.048615\n", - " 1.011925\n", - " 3\n", + " 0.000000\n", + " 1.053439\n", + " 1.003025\n", + " 4\n", " False\n", " \n", " \n", " 2\n", " 2010-03-12 00:00:00-07:00\n", " 2010-04-08 00:00:00-07:00\n", - " -0.002423\n", - " -0.005208\n", - " 0.0\n", - " 1.058734\n", - " 0.993322\n", + " -0.002505\n", + " -0.005069\n", + " 0.000000\n", + " 1.058785\n", + " 0.991144\n", " 27\n", " True\n", " \n", @@ -556,42 +582,49 @@ " 2010-04-11 00:00:00-07:00\n", " 0.000000\n", " 0.000000\n", - " 0.0\n", - " 1.045265\n", - " 1.045265\n", + " 0.000000\n", + " 1.044975\n", + " 1.044975\n", " 2\n", " False\n", " \n", " \n", " 4\n", " 2010-04-12 00:00:00-07:00\n", - " 2010-04-20 00:00:00-07:00\n", - " 0.000000\n", - " 0.000000\n", - " 0.0\n", - " 1.036445\n", - " 1.026461\n", - " 8\n", - " False\n", + " 2010-06-15 00:00:00-07:00\n", + " -0.000594\n", + " -0.000997\n", + " -0.000174\n", + " 1.011211\n", + " 0.973207\n", + " 64\n", + " True\n", " \n", " \n", "\n", "" ], "text/plain": [ - " start end slope slope_low \\\n", - "0 2010-02-25 00:00:00-07:00 2010-03-07 00:00:00-07:00 0.000000 0.000000 \n", - "1 2010-03-08 00:00:00-07:00 2010-03-11 00:00:00-07:00 0.000000 0.000000 \n", - "2 2010-03-12 00:00:00-07:00 2010-04-08 00:00:00-07:00 -0.002423 -0.005208 \n", - "3 2010-04-09 00:00:00-07:00 2010-04-11 00:00:00-07:00 0.000000 0.000000 \n", - "4 2010-04-12 00:00:00-07:00 2010-04-20 00:00:00-07:00 0.000000 0.000000 \n", + " start end soiling_rate \\\n", + "0 2010-02-25 00:00:00-07:00 2010-03-06 00:00:00-07:00 0.000000 \n", + "1 2010-03-07 00:00:00-07:00 2010-03-11 00:00:00-07:00 0.000000 \n", + "2 2010-03-12 00:00:00-07:00 2010-04-08 00:00:00-07:00 -0.002505 \n", + "3 2010-04-09 00:00:00-07:00 2010-04-11 00:00:00-07:00 0.000000 \n", + "4 2010-04-12 00:00:00-07:00 2010-06-15 00:00:00-07:00 -0.000594 \n", "\n", - " slope_high inferred_start_loss inferred_end_loss length valid \n", - "0 0.0 0.669120 0.896014 10 False \n", - "1 0.0 1.048615 1.011925 3 False \n", - "2 0.0 1.058734 0.993322 27 True \n", - "3 0.0 1.045265 1.045265 2 False \n", - "4 0.0 1.036445 1.026461 8 False " + " soiling_rate_low soiling_rate_high inferred_start_loss \\\n", + "0 0.000000 0.000000 0.685379 \n", + "1 0.000000 0.000000 1.053439 \n", + "2 -0.005069 0.000000 1.058785 \n", + "3 0.000000 0.000000 1.044975 \n", + "4 -0.000997 -0.000174 1.011211 \n", + "\n", + " inferred_end_loss length valid \n", + "0 0.863517 9 False \n", + "1 1.003025 4 False \n", + "2 0.991144 27 True \n", + "3 1.044975 2 False \n", + "4 0.973207 64 True " ] }, "execution_count": 16, @@ -610,9 +643,17 @@ "execution_count": 17, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/Documents/GitHub/rdtools/rdtools/plotting.py:251: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" + ] + }, { "data": { - "image/png": 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      " ] @@ -623,7 +664,7 @@ ], "source": [ "# View a histogram of the valid soiling rates found for the data set\n", - "fig = rdtools.soiling_rate_histogram(soiling_info, bins=50)" + "fig = rdtools.plotting.soiling_rate_histogram(soiling_info, bins=50)" ] }, { @@ -633,6 +674,298 @@ "These plots show generally good results from the SRR method. In this example, we have slightly overestimated the soiling loss because we used the default behavior of the `method` key word argument in `rdtools.soiling_srr()`, which does not assume that every cleaning is perfect but the example artificial soiling signal did include perfect cleaning. We encourage you to adjust the options of `rdtools.soiling_srr()` for your application." ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      monthsoiling_rate_mediansoiling_rate_lowsoiling_rate_highinterval_count
      01-0.000942-0.001954-0.0006926
      12-0.001794-0.006180-0.0007527
      23-0.001096-0.002230-0.00039410
      34-0.000924-0.001899-0.0001229
      45-0.000305-0.000733-0.0000867
      56-0.000331-0.000777-0.0000918
      67-0.000404-0.001342-0.0001408
      78-0.000674-0.001779-0.0001827
      89-0.000856-0.001572-0.0001918
      910-0.000881-0.001413-0.0002038
      1011-0.000920-0.001894-0.0002298
      1112-0.000947-0.002455-0.0006916
      \n", + "
      " + ], + "text/plain": [ + " month soiling_rate_median soiling_rate_low soiling_rate_high \\\n", + "0 1 -0.000942 -0.001954 -0.000692 \n", + "1 2 -0.001794 -0.006180 -0.000752 \n", + "2 3 -0.001096 -0.002230 -0.000394 \n", + "3 4 -0.000924 -0.001899 -0.000122 \n", + "4 5 -0.000305 -0.000733 -0.000086 \n", + "5 6 -0.000331 -0.000777 -0.000091 \n", + "6 7 -0.000404 -0.001342 -0.000140 \n", + "7 8 -0.000674 -0.001779 -0.000182 \n", + "8 9 -0.000856 -0.001572 -0.000191 \n", + "9 10 -0.000881 -0.001413 -0.000203 \n", + "10 11 -0.000920 -0.001894 -0.000229 \n", + "11 12 -0.000947 -0.002455 -0.000691 \n", + "\n", + " interval_count \n", + "0 6 \n", + "1 7 \n", + "2 10 \n", + "3 9 \n", + "4 7 \n", + "5 8 \n", + "6 8 \n", + "7 7 \n", + "8 8 \n", + "9 8 \n", + "10 8 \n", + "11 6 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate and view a monthly soiling rate summary\n", + "from rdtools.soiling import monthly_soiling_rates\n", + "monthly_soiling_rates(soiling_info['soiling_interval_summary'],\n", + " confidence_level=cl)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      620160.9371500.9252130.944815
      \n", + "
      " + ], + "text/plain": [ + " year soiling_ratio_median soiling_ratio_low soiling_ratio_high\n", + "0 2010 0.961769 0.950512 0.969079\n", + "1 2011 0.944563 0.937086 0.950570\n", + "2 2012 0.939465 0.931211 0.945439\n", + "3 2013 0.954355 0.944595 0.961878\n", + "4 2014 0.949834 0.929179 0.965085\n", + "5 2015 0.950557 0.921117 0.966028\n", + "6 2016 0.937150 0.925213 0.944815" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate and view annual insolation-weighted soiling ratios and their confidence\n", + "# intervals based on the Monte Carlo simulation. Note that these losses include the\n", + "# assumptions of the cleaning assumptions associated with the method parameter\n", + "# of rdtools.soiling_srr(). For anything but 'perfect_clean', each year's soiling\n", + "# ratio may be impacted by prior years' soiling profiles. The default behavior of\n", + "# rdtools.soiling_srr uses method='half_norm_clean'\n", + "\n", + "from rdtools.soiling import annual_soiling_ratios\n", + "annual_soiling_ratios(soiling_info['stochastic_soiling_profiles'],\n", + " daily_insolation,\n", + " confidence_level=cl)" + ] + }, { "cell_type": "markdown", "metadata": { @@ -658,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -691,7 +1024,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -722,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -748,7 +1081,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -766,19 +1099,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The P95 exceedance level with the clear sky analysis is -0.91%/yr\n" + "The P95 exceedance level with the clear sky analysis is -0.81%/yr\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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      " ] @@ -813,17 +1146,17 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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      " ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/notebook_requirements.txt b/docs/notebook_requirements.txt index 7b74f3a4..386a2e5c 100644 --- a/docs/notebook_requirements.txt +++ b/docs/notebook_requirements.txt @@ -14,13 +14,13 @@ jedi==0.12.1 Jinja2==2.10.1 jsonschema==2.6.0 jupyter==1.0.0 -jupyter-client==5.2.3 +jupyter-client==6.1.7 jupyter-console==5.2.0 -jupyter-core==4.4.0 +jupyter-core==4.6.3 MarkupSafe==1.1.1 mistune==0.8.3 nbconvert==5.3.1 -nbformat==4.4.0 +nbformat==5.0.7 notebook==5.7.8 pandocfilters==1.4.2 parso==0.3.1 @@ -29,12 +29,12 @@ pickleshare==0.7.4 prometheus-client==0.3.0 prompt-toolkit==1.0.15 ptyprocess==0.6.0 -Pygments==2.2.0 +Pygments==2.7.1 pyzmq==17.1.0 qtconsole==4.3.1 Send2Trash==1.5.0 simplegeneric==0.8.1 -tables==3.4.4 +tables==3.6.1 terminado==0.8.1 testpath==0.3.1 tornado==5.1 diff --git a/docs/sphinx/source/_images/availability_summary.png b/docs/sphinx/source/_images/availability_summary.png new file mode 100644 index 00000000..9dc7dd6e Binary files /dev/null and b/docs/sphinx/source/_images/availability_summary.png differ diff --git a/docs/sphinx/source/api.rst b/docs/sphinx/source/api.rst index 4f4649d3..fe3be2e0 100644 --- a/docs/sphinx/source/api.rst +++ b/docs/sphinx/source/api.rst @@ -16,6 +16,7 @@ analysis workflow. degradation soiling + availability filtering normalization aggregation @@ -26,98 +27,119 @@ analysis workflow. Degradation =========== -Functions for estimating degradation rates from PV system data. +.. automodule:: rdtools.degradation + :noindex: .. autosummary:: :toctree: generated/ - degradation.degradation_classical_decomposition - degradation.degradation_ols - degradation.degradation_year_on_year + degradation_classical_decomposition + degradation_ols + degradation_year_on_year Soiling ======= -Functions for estimating soiling rates from PV system data. +.. automodule:: rdtools.soiling + :noindex: .. autosummary:: :toctree: generated/ - soiling.soiling_srr - soiling.SRRAnalysis - soiling.SRRAnalysis.run + soiling_srr + monthly_soiling_rates + annual_soiling_ratios + SRRAnalysis + SRRAnalysis.run + + +System Availability +=================== + +.. automodule:: rdtools.availability + :noindex: + +.. autosummary:: + :toctree: generated/ + + AvailabilityAnalysis + AvailabilityAnalysis.run + AvailabilityAnalysis.plot Filtering ========= -Functions to perform filtering on PV system data. +.. automodule:: rdtools.filtering + :noindex: .. autosummary:: :toctree: generated/ - filtering.clip_filter - filtering.csi_filter - filtering.poa_filter - filtering.tcell_filter - filtering.normalized_filter + clip_filter + csi_filter + poa_filter + tcell_filter + normalized_filter Normalization ============= -Functions for normalizing power measurements for further analysis. +.. automodule:: rdtools.normalization + :noindex: .. autosummary:: :toctree: generated/ - normalization.check_series_frequency - normalization.delta_index - normalization.energy_from_power - normalization.interpolate - normalization.interpolate_series - normalization.irradiance_rescale - normalization.normalize_with_expected_power - normalization.normalize_with_pvwatts - normalization.normalize_with_sapm - normalization.pvwatts_dc_power - normalization.sapm_dc_power - normalization.t_step_nanoseconds - normalization.trapz_aggregate + energy_from_power + interpolate + irradiance_rescale + normalize_with_expected_power + normalize_with_pvwatts + normalize_with_sapm + pvwatts_dc_power + sapm_dc_power + delta_index + check_series_frequency Aggregation =========== -Functions to aggregate PV system data. +.. automodule:: rdtools.aggregation + :noindex: .. autosummary:: :toctree: generated/ - aggregation.aggregation_insol + aggregation_insol Clear-Sky Temperature ===================== -Functions for modeling ambient temperature. +.. automodule:: rdtools.clearsky_temperature + :noindex: .. autosummary:: :toctree: generated/ - clearsky_temperature.get_clearsky_tamb + get_clearsky_tamb Plotting ======== -Functions to visualize degradation and soiling analysis results. +.. automodule:: rdtools.plotting + :noindex: .. autosummary:: :toctree: generated/ - plotting.degradation_summary_plots - plotting.soiling_monte_carlo_plot - plotting.soiling_interval_plot - plotting.soiling_rate_histogram + degradation_summary_plots + soiling_monte_carlo_plot + soiling_interval_plot + soiling_rate_histogram + availability_summary_plots diff --git a/docs/sphinx/source/changelog.rst b/docs/sphinx/source/changelog.rst index ed8a34b9..f99652cd 100644 --- a/docs/sphinx/source/changelog.rst +++ b/docs/sphinx/source/changelog.rst @@ -1,4 +1,6 @@ RdTools Change Log ================== -.. include:: changelog/v2.0.0b0.rst +.. include:: changelog/v2.0.0.rst +.. include:: changelog/pre_2.0.0.rst + diff --git a/docs/sphinx/source/changelog/pre_2.0.0.rst b/docs/sphinx/source/changelog/pre_2.0.0.rst new file mode 100644 index 00000000..8e41a75a --- /dev/null +++ b/docs/sphinx/source/changelog/pre_2.0.0.rst @@ -0,0 +1,126 @@ + +*********************** +v1.2.3 (April 12, 2020) +*********************** + +- Updates dependencies +- Versioneer bug fix +- Licence update + +Contributors +------------ +* Mike Deceglie (:ghuser:`mdeceglie`) + + +************************* +v1.2.2 (October 12, 2018) +************************* + +Patch that adds author email to enable pypi deployment + +Contributors +------------ +* Mike Deceglie (:ghuser:`mdeceglie`) + + +************************* +v1.2.1 (October 12, 2018) +************************* + +This update includes automated testing and deployment to support development +along with some bug fixes to the library itself, a documented environment for +the example notebook, and new example results to reflect changes in the example +dataset. It addresses :issue:`49`, :issue:`76`, :issue:`78`, :issue:`79`, +:issue:`80`, :issue:`85`, :issue:`86`, and :issue:`92`. + +Contributors +------------ +* Mike Deceglie (:ghuser:`mdeceglie`) +* Adam Shinn (:ghuser:`abshinn`) +* Chris Deline (:ghuser:`cdeline`) +* nb137 (:ghuser:`nb137`) + + +*********************** +v1.2.0 (March 30, 2018) +*********************** + +This incorporates changes including: + +- Enables users to control confidence intervals reported in degradation calculations (:issue:`59`) +- Adds python 3 support (:issue:`56` and :issue:`67`) +- Fixes bugs (:issue:`61` :issue:`57`) +- Improvements/typo fixes to docstrings +- Fixes error in check for two years of data in degradation_year_on_year +- Improves the calculations underlying irradiance_rescale + +Contributors +------------ +* Mike Deceglie (:ghuser:`mdeceglie`) +* Ambarish Nag (:ghuser:`ambarishnag`) +* Gregory Kimball (:ghuser:`GregoryKimball`) +* Chris Deline (:ghuser:`cdeline`) +* Mark Mikofski (:ghuser:`mikofski`) + + +************************* +v1.1.3 (December 6, 2017) +************************* + +This patch includes the following changes: + +1. Update the notebook for improved plotting with Pandas v.0.21.0 +2. Fix installation bug related to package data + +Contributors +------------ +* Mike Deceglie (:ghuser:`mdeceglie`) +* Chris Deline (:ghuser:`cdeline`) + + +************************* +v1.1.2 (November 6, 2017) +************************* + +This patch includes the following changes: + +1. Fix bugs in installation +2. Update requirements +3. Notebook plots made compatible with pandas v.0.21.0 + +Contributors +------------ +* Mike Deceglie (:ghuser:`mdeceglie`) + + +************************* +v1.1.1 (November 1, 2017) +************************* + +This patch: + +1. Improves documentation +2. Fixes installation requirements + +Contributors +------------ +* Mike Deceglie (:ghuser:`mdeceglie`) +* Adam Shinn (:ghuser:`abshinn`) +* Chris Deline (:ghuser:`cdeline`) + + +*************************** +v1.1.0 (September 30, 2017) +*************************** + +This update includes the addition of filters, functions to support a clear-sky +workflow, and updates to the example notebook. + +Contributors +------------ +* Mike Deceglie (:ghuser:`mdeceglie`) +* Adam Shinn (:ghuser:`abshinn`) +* Ambarish Nag (:ghuser:`ambarishnag`) +* Gregory Kimball (:ghuser:`GregoryKimball`) +* Chris Deline (:ghuser:`cdeline`) +* Jiyang Yan (:ghuser:`yjy1663`) diff --git a/docs/sphinx/source/changelog/v2.0.0b0.rst b/docs/sphinx/source/changelog/v2.0.0.rst similarity index 64% rename from docs/sphinx/source/changelog/v2.0.0b0.rst rename to docs/sphinx/source/changelog/v2.0.0.rst index c9901904..a9b98145 100644 --- a/docs/sphinx/source/changelog/v2.0.0b0.rst +++ b/docs/sphinx/source/changelog/v2.0.0.rst @@ -1,6 +1,7 @@ -************************ -v2.0.0b0 (July 31, 2020) -************************ +************************* +v2.0.0 (October 20, 2020) +************************* +Version 2.0.0 adds experimental soiling and availability modules, plotting capability, and includes updates to normalization work flow. This major release introduces some breaking changes to the API. Details below. API Changes ----------- @@ -10,15 +11,6 @@ API Changes right-labeled energy with :py:func:`~rdtools.normalization.energy_from_power` before being used with these normalization functions (:pull:`105`, :pull:`108`). * Remove ``low_power_cutoff`` parameter in :py:func:`~rdtools.filtering.clip_filter` (:issue:`84`). -* Rename `soiling.srr_analysis` to :py:class:`~rdtools.soiling.SRRAnalysis` (:pull:`168`). -* Double the default value of the ``max_timedelta`` in :py:func:`~rdtools.normalization.interpolate` - and :py:func:`~rdtools.normalization.interpolate_series` to be twice the - median timedelta (:pull:`182`). -* Support varying logic in soiling module for defining cleaning events from shifts and - precipitation with ``clean_criterion`` parameter. Behavior of ``clean_criterion='precip_and_shift'`` - has changed relative to prior versions using ``precip_clean_only=True`` (:pull:`176`). -* Remove ``random_seed`` parameter from soiling module. Functionality can be obtained by running - ``numpy.random.seed()`` outside of RdTools functions. (:pull:`176`) * Many kwargs have changed name (but not input order) to bring nomenclature into closer alignment with the `DuraMAT pv-terms project `_: (:pull:`185`) @@ -34,45 +26,68 @@ API Changes * :py:func:`~rdtools.filtering.csi_filter` first two kwargs are now ``poa_global_measured``, ``poa_global_clearsky``. * :py:func:`~rdtools.normalization.normalize_with_pvwatts` pvwatts_kws dictionary keys have been renamed. * :py:func:`~rdtools.normalization.pvwatts_dc_power` input kwargs are now ``poa_global``, ``power_dc_rated``, ``temperature_cell``, ``poa_global_ref``, ``temperature_cell_ref``, ``gamma_pdc``. + * :py:func:`~rdtools.normalization.irradiance_rescale` second kwarg is now ``irrad_sim`` + +Deprecations +------------ +* The functions :py:func:`~rdtools.normalization.pvwatts_dc_power`, + :py:func:`~rdtools.normalization.sapm_dc_power`, + :py:func:`~rdtools.normalization.normalize_with_pvwatts`, and + :py:func:`~rdtools.normalization.normalize_with_sapm` have been deprecated + in favor of :py:func:`~rdtools.normalization.normalize_with_expected_power`. + (:pull:`215`) +* :py:func:`~rdtools.normalization.delta_index` and :py:func:`~rdtools.normalization.check_series_frequency` (:pull:`222`) Enhancements ------------ * Add new :py:mod:`~rdtools.soiling` module to implement the stochastic rate and - recovery method (:pull:`112`). + recovery method: + + - Create new class :py:class:`~rdtools.soiling.SRRAnalysis` and helper function + :py:func:`~rdtools.soiling.soiling_srr` (:pull:`112`, :pull:`168`, :pull:`169`, + :pull:`176`, :pull:`208`, :pull:`213`) + - Create functions :py:func:`~rdtools.soiling.monthly_soiling_rates` and + :py:func:`~rdtools.soiling.annual_soiling_ratios` (:pull:`193`, :pull:`207`) + +* Create new module :py:mod:`~rdtools.availability` with the class + :py:class:`~rdtools.availability.AvailabilityAnalysis` for estimating + timeseries system availability (:pull:`131`) * Add new function :py:func:`~rdtools.normalization.normalize_with_expected_power` (:pull:`173`). * Add new functions :py:func:`~rdtools.normalization.energy_from_power` and - :py:func:`~rdtools.normalization.interpolate` (:pull:`105`, :pull:`108`). -* Add new function :py:func:`~rdtools.filtering.normalized_filter`. -* Add new :py:mod:`~rdtools.plotting` module for generating standard plots. + :py:func:`~rdtools.normalization.interpolate` (:pull:`105`, :pull:`108`, :pull:`182`, :pull:`212`). +* Add new function :py:func:`~rdtools.filtering.normalized_filter` (:pull:`139`) +* Add new :py:mod:`~rdtools.plotting` module for generating standard plots + (:pull:`138`, :pull:`131`) * Add parameter ``convergence_threshold`` to :py:func:`~rdtools.normalization.irradiance_rescale` (:pull:`152`). -* Add parameter ``warning_threshold`` to :py:func:`~rdtools.normalization.interpolate` - and :py:func:`~rdtools.normalization.interpolate_series` (:pull:`182`). Bug fixes --------- * Allow ``max_iterations=0`` in :py:func:`~rdtools.normalization.irradiance_rescale` (:pull:`152`). -* Fix a bug in :py:mod:`~rdtools.soiling` code that caused problems for soiling intervals - consisting solely of invalid data. (:pull:`169`) - Testing ------- * Add Python 3.7 and 3.8 to CI testing (:pull:`135`). +* Add CI configuration based on the minimum dependency versions (:pull:`197`) Documentation ------------- * Create sphinx documentation and set up ReadTheDocs (:pull:`125`). * Add guides on running tests and building sphinx docs (:pull:`136`). * Improve module-level docstrings (:pull:`137`). +* Update landing page and add new "Inverter Downtime" documentation page + based on the availability notebook (:pull:`131`) Requirements ------------ * Drop support for Python 2.7, minimum supported version is now 3.6 (:pull:`135`). -* Increase minimum pvlib version to 0.7.0. +* Increase minimum pvlib version to 0.7.0 (:pull:`170`) * Update requirements.txt and notebook_requirements.txt to avoid conflicting specifications. Taken together, they represent the complete environment for the notebook example (:pull:`164`). +* Add minimum matplotlib requirement of 3.0.0 (released September 18, 2018) (:pull:`197`) +* Increase minimum numpy version from 1.12 (released January 15, 2017) to + 1.15 (released July 23, 2018) (:pull:`197`) Example Updates --------------- @@ -80,14 +95,16 @@ Example Updates * Use :py:func:`~rdtools.filtering.normalized_filter` instead of manually filtering the normalized energy timeseries. Also updated the associated mask variable names (:pull:`139`). +* Add soiling section to the original example notebook. * Add a new example notebook that analyzes data from a PV system located at NREL's South Table Mountain campus (PVDAQ system #4) (:pull:`171`). * Explicitly register pandas datetime converters which were `deprecated `_. - +* Add new ``system_availability_example.ipynb`` notebook (:pull:`131`) + Contributors ------------ * Mike Deceglie (:ghuser:`mdeceglie`) * Kevin Anderson (:ghuser:`kanderso-nrel`) * Chris Deline (:ghuser:`cdeline`) - +* Will Vining (:ghuser:`wfvining`) diff --git a/docs/sphinx/source/conf.py b/docs/sphinx/source/conf.py index 420d26b3..12e9c8e9 100644 --- a/docs/sphinx/source/conf.py +++ b/docs/sphinx/source/conf.py @@ -53,7 +53,8 @@ # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] +exclude_patterns = ['changelog/*'] + source_suffix = ['.rst', '.md'] @@ -80,6 +81,7 @@ # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static', '_images'] +smartquotes = False master_doc = 'index' # A workaround for the responsive tables always having annoying scrollbars. @@ -158,6 +160,12 @@ def make_github_url(pagename): URL_BASE = "https://github.com/nrel/rdtools/blob/{}/".format(branch) + # map notebook pagenames to source files on github + notebook_map = { + 'rd_example': 'degradation_and_soiling_example_pvdaq_4.ipynb', + 'system_availability_example': 'system_availability_example.ipynb', + } + # is it an API autogen page? if pagename.startswith("generated/"): # pagename looks like "generated/rdtools.degradation.degradation_ols" @@ -170,9 +178,9 @@ def make_github_url(pagename): if start and end: target_url += '#L{}-L{}'.format(start, end) - # is it the example notebook? - elif pagename == "example": - target_url = URL_BASE + "docs/degradation_and_soiling_example.ipynb" + # is it an example notebook? + elif pagename in notebook_map: + target_url = URL_BASE + "docs/" + notebook_map[pagename] # is the the changelog page? elif pagename == "changelog": diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst index 40feeba8..af40e60b 100644 --- a/docs/sphinx/source/index.rst +++ b/docs/sphinx/source/index.rst @@ -17,7 +17,8 @@ time series data from photovoltaic energy systems. The library aims to provide best practice analysis routines along with the building blocks for users to tailor their own analyses. Current applications include the evaluation of PV production over several years to obtain -rates of performance degradation and soiling loss. RdTools can handle +rates of performance degradation and soiling loss. They also include the capability to +analyze systems for system- and subsystem-level availability. RdTools can handle both high frequency (hourly or better) or low frequency (daily, weekly, etc.) datasets. Best results are obtained with higher frequency data. @@ -27,11 +28,12 @@ Full examples are worked out in the example notebooks in the To report issues, contribute code, or suggest improvements to this documentation, visit the RdTools development repository on `github`_. -Workflow --------- +Degradation and Soiling +----------------------- -RdTools supports a number of workflows, but a typical analysis follows -the following: +Both degradation and soiling analyses are based on normalized yield, similar to performance +index. Usually, this is computed at the daily level although other aggregation periods are +supported. A typical analysis of soiling and degradation contains the following: 0. Import and preliminary calculations 1. Normalize data using a performance metric @@ -47,11 +49,11 @@ drift. .. image:: _images/RdTools_workflows.png :alt: RdTools workflow diagram -Degradation Results -------------------- +Degradation +^^^^^^^^^^^ The preferred method for degradation rate estimation is the year-on-year -(YOY) approach, available in :py:func:`.degradation.degradation_year_on_year`. +(YOY) approach (Jordan 2018), available in :py:func:`.degradation.degradation_year_on_year`. The YOY calculation yields in a distribution of degradation rates, the central tendency of which is the most representative of the true degradation. The width of the distribution provides information about @@ -77,8 +79,8 @@ analysis when details such as filtering are changed. We generally recommend that the clear-sky analysis be used as a check on the sensor-based results, rather than as a stand-alone analysis. -Soiling Results ---------------- +Soiling +^^^^^^^ Soiling can be estimated with the stochastic rate and recovery (SRR) method (Deceglie 2018). This method works well when soiling patterns @@ -96,6 +98,23 @@ identified soiling rates for the dataset. :width: 320 :height: 216 +Availability +------------ + +Evaluating system availability can be confounded by data loss from interrupted +datalogger or system communications. RdTools implements two methods +(Anderson & Blumenthal 2020) of distinguishing nuisance communication +interruptions from true production outages +with the :py:class:`.availability.AvailabilityAnalysis` class. In addition to +classifying data outages, it estimates lost production and calculates +energy-weighted system availability. + +.. image:: _images/availability_summary.png + :alt: RdTools availability analysis plot + :width: 696 + :height: 288 + + Install RdTools using pip ------------------------- @@ -126,7 +145,7 @@ Usage and examples ------------------ Full workflow examples are found in the notebooks in `example notebook`_. -The examples are designed to work with python 3.6. For a consistent +The examples are designed to work with python 3.7. For a consistent experience, we recommend installing the packages and versions documented in ``docs/notebook_requirements.txt``. This can be achieved in your environment by first installing RdTools as described above, then running @@ -143,10 +162,11 @@ The most frequently used functions are: .. code:: python - normalization.normalize_with_pvwatts(energy, pvwatts_kws) + normalization.normalize_with_expected_power(pv, power_expected, poa_global, + pv_input='power') ''' - Inputs: Pandas time series of raw energy, PVwatts dict for system analysis - (poa_global, power_dc_rated, temperature_cell, poa_global_ref, temperature_cell_ref, gamma_pdc) + Inputs: Pandas time series of raw power or energy, expected power, and + plane of array irradiance. Outputs: Pandas time series of normalized energy and POA insolation ''' @@ -186,6 +206,15 @@ The most frequently used functions are: `sr_ci`: Confidence interval `soiling_info`: associated analysis data ''' +.. code:: python + + availability.AvailabilityAnalysis(power_system, power_subsystem, + energy_cumulative, power_expected) + ''' + Inputs: Pandas time series system and subsystem power and energy data + Outputs: DataFrame of production loss and availability metrics + ''' + Citing RdTools -------------- @@ -200,6 +229,10 @@ appropriate: - M. G. Deceglie, L. Micheli and M. Muller, "Quantifying Soiling Loss Directly From PV Yield," in IEEE Journal of Photovoltaics, 8(2), pp. 547-551, 2018 + +- K. Anderson and R. Blumenthal, "Overcoming Communications Outages in + Inverter Downtime Analysis", 2020 IEEE 47th Photovoltaic Specialists + Conference (PVSC)." ‌‌ - RdTools, version x.x.x, https://github.com/NREL/rdtools, https://doi.org/10.5281/zenodo.1210316 @@ -251,7 +284,8 @@ Documentation Contents .. toctree:: :maxdepth: 2 - In-Depth Examples + Degradation and Soiling + Inverter Downtime API Reference Change Log Developer Notes @@ -266,6 +300,6 @@ Indices and tables .. links and references -.. _example notebook: https://rdtools.readthedocs.io/en/latest/example.html +.. _example notebook: rd_example.nblink .. _release: https://github.com/NREL/rdtools/releases .. _github: https://github.com/NREL/rdtools diff --git a/docs/sphinx/source/example.nblink b/docs/sphinx/source/rd_example.nblink similarity index 100% rename from docs/sphinx/source/example.nblink rename to docs/sphinx/source/rd_example.nblink diff --git a/docs/sphinx/source/system_availability_example.nblink b/docs/sphinx/source/system_availability_example.nblink new file mode 100644 index 00000000..dba44bc5 --- /dev/null +++ b/docs/sphinx/source/system_availability_example.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../system_availability_example.ipynb" +} \ No newline at end of file diff --git a/docs/system_availability_example.ipynb b/docs/system_availability_example.ipynb new file mode 100644 index 00000000..428e5653 --- /dev/null +++ b/docs/system_availability_example.ipynb @@ -0,0 +1,658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# System availability example\n", + "\n", + "This notebook shows example usage of the inverter availability functions. As with the degradation and soiling example, we recommend installing the specific versions of packages used to develop this notebook. This can be achieved in your environment by running `pip install -r requirements.txt` followed by `pip install -r docs/notebook_requirements.txt` from the base directory. (RdTools must also be separately installed.) These environments and examples are tested with Python 3.7.\n", + "\n", + "RdTools currently implements two methods of quantifying system availability. The first method compares power measurements from inverters and the system meter to distinguish subsystem communication interruptions from true outage events. The second method determines the uncertainty bounds around an energy estimate of a total system outage and compares with true production calculated from a meter's cumulative production measurements. The RdTools `AvailabilityAnalysis` class uses both methods to quantify downtime loss.\n", + "\n", + "These methods are described in K. Anderson and R. Blumenthal, \"Overcoming Communications Outages in Inverter Downtime Analysis\", 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import rdtools\n", + "import pvlib\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Quantifying the production impact of inverter downtime events is complicated by gaps in a system's historical data caused by communication interruptions. Although communication interruptions may prevent remote operation, they usually do not result in production loss. Accurate production loss estimates require the ability to distinguish true outages from communication interruptions.\n", + "\n", + "The first method focuses on partial outages where some of a system's inverters are reporting production and some are not. In these cases, the method examines the AC power measurements at the inverter and system meter level to classify each timestamp individually and estimate timeseries production loss. This level of granularity is made possible by comparing timeseries power measurements between inverters and the meter.\n", + "\n", + "## Create a test dataset\n", + "\n", + "First we'll generate a test dataset to demonstrate the method. This code block just puts together an artificial dataset to use for the analysis -- feel free to skip ahead to where it gets plotted." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def make_dataset():\n", + " \"\"\"\n", + " Make an example dataset with several types of data outages for availability analysis.\n", + " \n", + " Returns\n", + " -------\n", + " df_reported : pd.DataFrame\n", + " Simulated data as a data acquisition system would report it, including the\n", + " effect of communication interruptions.\n", + " df_secret : pd.DataFrame\n", + " The secret true data of the system, not affected by communication\n", + " interruptions. Only used for comparison with the analysis output.\n", + " expected_power : pd.Series\n", + " An \"expected\" power signal for this hypothetical PV system, simulating a\n", + " modeled power from satellite weather data or some other method.\n", + " \n", + " (This function creates instananeous data. SystemAvailability is technically designed\n", + " to work with right-labeled averages. However, for the purposes of the example, the\n", + " approximation is suitable.)\n", + " \"\"\"\n", + "\n", + " # generate a plausible clear-sky power signal\n", + " times = pd.date_range('2019-01-01', '2019-01-12', freq='15min', tz='US/Eastern',\n", + " closed='left')\n", + " location = pvlib.location.Location(40, -80)\n", + " clearsky = location.get_clearsky(times, model='haurwitz')\n", + " # just scale GHI to power for simplicity\n", + " base_power = 2.5*clearsky['ghi']\n", + " # but require a minimum irradiance to turn on, simulating start-up voltage\n", + " base_power[clearsky['ghi'] < 20] = 0\n", + "\n", + " df_secret = pd.DataFrame({\n", + " 'inv1_power': base_power,\n", + " 'inv2_power': base_power * 1.5,\n", + " 'inv3_power': base_power * 0.66,\n", + " })\n", + "\n", + " # set the expected_power to be pretty close to actual power,\n", + " # but with some autocorrelated noise and a bias:\n", + " expected_power = df_secret.sum(axis=1)\n", + " np.random.seed(2020)\n", + " N = len(times)\n", + " expected_power *= 0.9 - (0.3 * np.sin(np.arange(0, N)/7 +\n", + " np.random.normal(0, 0.2, size=N)))\n", + "\n", + " # Add a few days of individual inverter outages:\n", + " df_secret.loc['2019-01-03':'2019-01-05', 'inv2_power'] = 0\n", + " df_secret.loc['2019-01-02', 'inv3_power'] = 0\n", + " df_secret.loc['2019-01-07 00:00':'2019-01-07 12:00', 'inv1_power'] = 0\n", + "\n", + " # and a full system outage:\n", + " full_outage_date = '2019-01-08'\n", + " df_secret.loc[full_outage_date, :] = 0\n", + "\n", + " # calculate the system meter power and cumulative production, \n", + " # including the effect of the outages:\n", + " df_secret['meter_power'] = df_secret.sum(axis=1)\n", + " interval_energy = rdtools.energy_from_power(df_secret['meter_power'])\n", + " df_secret['meter_energy'] = interval_energy.cumsum()\n", + " # fill the first NaN from the cumsum with 0\n", + " df_secret['meter_energy'] = df_secret['meter_energy'].fillna(0)\n", + " # add an offset to reflect previous production:\n", + " df_secret['meter_energy'] += 5e5\n", + " # calculate cumulative energy for an inverter as well:\n", + " inv2_energy = rdtools.energy_from_power(df_secret['inv2_power'])\n", + " df_secret['inv2_energy'] = inv2_energy.cumsum().fillna(0)\n", + " \n", + " # now that the \"true\" data is in place, let's add some communications interruptions:\n", + " df_reported = df_secret.copy()\n", + " # in full outages, we lose all the data:\n", + " df_reported.loc[full_outage_date, :] = np.nan\n", + " # add a communications interruption that overlaps with an inverter outage:\n", + " df_reported.loc['2019-01-05':'2019-01-06', 'inv1_power'] = np.nan\n", + " # and a communication outage that affects everything:\n", + " df_reported.loc['2019-01-10', :] = np.nan\n", + "\n", + " return df_reported, df_secret, expected_power" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize the dataset before analyzing it with RdTools. The dotted lines show the \"true\" data that wasn't recorded by the datalogger because of interrupted communications." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df, df_secret, expected_power = make_dataset()\n", + "\n", + "fig, axes = plt.subplots(3, 1, sharex=True, figsize=(8,6))\n", + "colors = plt.rcParams['axes.prop_cycle'].by_key()['color'][:3]\n", + "\n", + "# inverter power\n", + "df_secret[['inv1_power', 'inv2_power', 'inv3_power']].plot(ax=axes[0],\n", + " legend=False, ls=':',\n", + " color=colors)\n", + "df[['inv1_power', 'inv2_power', 'inv3_power']].plot(ax=axes[0], legend=False)\n", + "# meter power\n", + "df_secret['meter_power'].plot(ax=axes[1], ls=':', color=colors[0])\n", + "df['meter_power'].plot(ax=axes[1])\n", + "# meter cumulative energy\n", + "df_secret['meter_energy'].plot(ax=axes[2], ls=':', color=colors[0])\n", + "df['meter_energy'].plot(ax=axes[2])\n", + "\n", + "axes[0].set_ylabel('Inverter Power [kW]')\n", + "axes[1].set_ylabel('Meter Power [kW]')\n", + "axes[2].set_ylabel('Cumulative\\nMeter Energy [kWh]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the solid lines show the data that would be available in our example while the dotted lines show the true underlying behavior that we normally wouldn't know.\n", + "\n", + "If we hadn't created this dataset ourselves, it wouldn't necessarily be obvious why the meter shows low or no production on some days -- maybe it was just cloudy weather, maybe it was a nuisance communication outage (broken cell modem power supply, for example), or maybe it was a true power outage. This example also shows how an inverter can appear to be offline while actually producing normally. For example, just looking at inverter power on the 5th, it appears that only the small inverter is producing. However, the meter shows two inverters' worth of production. Similarly, the 6th shows full meter production despite one inverter not reporting power. Using only the inverter-reported power would overestimate the production loss because of the communication interruption.\n", + "\n", + "## System availability analysis\n", + "\n", + "Now we'll hand this data off to RdTools for analysis:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/opt/anaconda3/envs/final_release_test/lib/python3.7/site-packages/rdtools/availability.py:18: UserWarning: The availability module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The availability module is currently experimental. The API, results, '\n" + ] + } + ], + "source": [ + "from rdtools.availability import AvailabilityAnalysis\n", + "aa = AvailabilityAnalysis(\n", + " power_system=df['meter_power'],\n", + " power_subsystem=df[['inv1_power', 'inv2_power', 'inv3_power']],\n", + " energy_cumulative=df['meter_energy'],\n", + " power_expected=expected_power,\n", + ")\n", + "# identify and classify outages, rolling up to daily metrics for this short dataset:\n", + "aa.run(rollup_period='D') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we can visualize the estimated power loss and outage information:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/opt/anaconda3/envs/final_release_test/lib/python3.7/site-packages/rdtools/plotting.py:320: UserWarning: The availability module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The availability module is currently experimental. The API, results, '\n" + ] + }, + { + "data": { + "image/png": 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\n", 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      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = aa.plot()\n", + "fig.set_size_inches(16, 7)\n", + "fig.axes[1].legend(loc='upper left');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examining the plot of estimated lost power, we can see that the estimated loss is roughly in proportion to the amount of offline capacity. In particular, the loss estimate is robust to mixed outage and communication interruption like on the 5th when only the smallest inverter is reporting production but the analysis correctly inferred that one of the other inverters is producing but not communicating.\n", + "\n", + "RdTools also reports rolled-up production and availability metrics:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      lost_productionactual_productionavailability
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      " + ], + "text/plain": [ + " lost_production actual_production availability\n", + "2019-01-01 00:00:00-05:00 0.000 19606.785 1.000\n", + "2019-01-02 00:00:00-05:00 4114.031 15583.450 0.791\n", + "2019-01-03 00:00:00-05:00 9396.788 10399.112 0.525\n", + "2019-01-04 00:00:00-05:00 9466.477 10476.235 0.525\n", + "2019-01-05 00:00:00-05:00 9522.325 10538.040 0.525\n", + "2019-01-06 00:00:00-05:00 0.000 20185.784 1.000\n", + "2019-01-07 00:00:00-05:00 2859.565 17459.339 0.859\n", + "2019-01-08 00:00:00-05:00 19448.084 0.000 0.000\n", + "2019-01-09 00:00:00-05:00 0.000 20607.950 1.000\n", + "2019-01-10 00:00:00-05:00 0.000 20763.718 1.000\n", + "2019-01-11 00:00:00-05:00 0.000 20926.869 1.000" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.set_option('precision', 3)\n", + "aa.results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `AvailabilityAnalysis` object has other attributes that may be useful to inspect as well. The `outage_info` dataframe has one row for each full system outage with several columns, perhaps the most interesting of which are `type` and `loss`.\n", + "\n", + "See `AvailabilityAnalysis?` or `help(AvailabilityAnalysis)` for full descriptions of the available attributes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      startenddurationintervalsdaylight_intervalserror_lowererror_upper
      02019-01-07 17:00:00-05:002019-01-09 08:00:00-05:001 days 15:00:0015735-0.240.25
      12019-01-09 17:00:00-05:002019-01-11 08:00:00-05:001 days 15:00:0015735-0.240.25
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      " + ], + "text/plain": [ + " start end duration \\\n", + "0 2019-01-07 17:00:00-05:00 2019-01-09 08:00:00-05:00 1 days 15:00:00 \n", + "1 2019-01-09 17:00:00-05:00 2019-01-11 08:00:00-05:00 1 days 15:00:00 \n", + "\n", + " intervals daylight_intervals error_lower error_upper \n", + "0 157 35 -0.24 0.25 \n", + "1 157 35 -0.24 0.25 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.set_option('precision', 2)\n", + "# Show the first half of the dataframe\n", + "N = len(aa.outage_info.columns)\n", + "aa.outage_info.iloc[:, :N//2]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      energy_expectedenergy_startenergy_endenergy_actualci_lowerci_uppertypeloss
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      " + ], + "text/plain": [ + " energy_expected energy_start energy_end energy_actual ci_lower \\\n", + "0 19448.08 604248.74 604248.74 0.00 14819.33 \n", + "1 25284.75 624856.69 645620.41 20763.72 19266.84 \n", + "\n", + " ci_upper type loss \n", + "0 24271.15 real 19448.08 \n", + "1 31555.29 comms 0.00 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Show the second half\n", + "aa.outage_info.iloc[:, N//2:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other use cases\n", + "\n", + "Although this demo applies the methods for an entire PV system (comparing inverters against the meter and comparing the meter against expected power), it can also be used at the individual inverter level. Because there are no subsystems to compare against, the \"full outage\" analysis branch is used for every outage. That means that instead of basing the loss off of the other inverters, it relies on the expected power time series being accurate, which in this example causes the loss estimates to lose some accuracy. In this case, because the expected power signal is somewhat inaccurate, it causes the loss estimate to be overestimated:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2019-01-01 00:00:00-05:00 0.00\n", + "2019-01-02 00:00:00-05:00 0.00\n", + "2019-01-03 00:00:00-05:00 9931.24\n", + "2019-01-04 00:00:00-05:00 11453.27\n", + "2019-01-05 00:00:00-05:00 11238.57\n", + "2019-01-06 00:00:00-05:00 0.00\n", + "2019-01-07 00:00:00-05:00 0.00\n", + "2019-01-08 00:00:00-05:00 9505.33\n", + "2019-01-09 00:00:00-05:00 0.00\n", + "2019-01-10 00:00:00-05:00 0.00\n", + "2019-01-11 00:00:00-05:00 0.00\n", + "Freq: D, Name: lost_production, dtype: float64\n" + ] + } + ], + "source": [ + "# make a new analysis object:\n", + "aa2 = rdtools.availability.AvailabilityAnalysis(\n", + " power_system=df['inv2_power'],\n", + " power_subsystem=df['inv2_power'].to_frame(),\n", + " energy_cumulative=df['inv2_energy'],\n", + " # okay to use the system-level expected power here because it gets rescaled anyway\n", + " power_expected=expected_power,\n", + ")\n", + "# identify and classify outages, rolling up to daily metrics for this short dataset:\n", + "aa2.run(rollup_period='D')\n", + "print(aa2.results['lost_production'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mdecegli/opt/anaconda3/envs/final_release_test/lib/python3.7/site-packages/rdtools/plotting.py:320: UserWarning: The availability module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The availability module is currently experimental. The API, results, '\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aa2.plot();" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/rdtools/__init__.py b/rdtools/__init__.py index 8bc6f2d6..6610128c 100644 --- a/rdtools/__init__.py +++ b/rdtools/__init__.py @@ -14,11 +14,16 @@ from rdtools.filtering import tcell_filter from rdtools.filtering import clip_filter from rdtools.filtering import normalized_filter -from rdtools.soiling import soiling_srr +# from rdtools.soiling import soiling_srr +# from rdtools.soiling import monthly_soiling_rates +# from rdtools.soiling import annual_soiling_ratios from rdtools.plotting import degradation_summary_plots -from rdtools.plotting import soiling_monte_carlo_plot -from rdtools.plotting import soiling_interval_plot -from rdtools.plotting import soiling_rate_histogram +# from rdtools.plotting import soiling_monte_carlo_plot +# from rdtools.plotting import soiling_interval_plot +# from rdtools.plotting import soiling_rate_histogram +# from rdtools.plotting import availability_summary_plots +# from rdtools.availability import AvailabilityAnalysis + from ._version import get_versions __version__ = get_versions()['version'] diff --git a/rdtools/_deprecation.py b/rdtools/_deprecation.py new file mode 100644 index 00000000..bca368b0 --- /dev/null +++ b/rdtools/_deprecation.py @@ -0,0 +1,318 @@ +"""Matplotlib license for the deprecation module. + +License agreement for matplotlib versions 1.3.0 and later +========================================================= + +1. This LICENSE AGREEMENT is between the Matplotlib Development Team +("MDT"), and the Individual or Organization ("Licensee") accessing and +otherwise using matplotlib software in source or binary form and its +associated documentation. + +2. Subject to the terms and conditions of this License Agreement, MDT +hereby grants Licensee a nonexclusive, royalty-free, world-wide license +to reproduce, analyze, test, perform and/or display publicly, prepare +derivative works, distribute, and otherwise use matplotlib +alone or in any derivative version, provided, however, that MDT's +License Agreement and MDT's notice of copyright, i.e., "Copyright (c) +2012- Matplotlib Development Team; All Rights Reserved" are retained in +matplotlib alone or in any derivative version prepared by +Licensee. + +3. In the event Licensee prepares a derivative work that is based on or +incorporates matplotlib or any part thereof, and wants to +make the derivative work available to others as provided herein, then +Licensee hereby agrees to include in any such work a brief summary of +the changes made to matplotlib . + +4. MDT is making matplotlib available to Licensee on an "AS +IS" basis. MDT MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR +IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, MDT MAKES NO AND +DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS +FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF MATPLOTLIB +WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. + +5. MDT SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF MATPLOTLIB + FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR +LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING +MATPLOTLIB , OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF +THE POSSIBILITY THEREOF. + +6. This License Agreement will automatically terminate upon a material +breach of its terms and conditions. + +7. Nothing in this License Agreement shall be deemed to create any +relationship of agency, partnership, or joint venture between MDT and +Licensee. This License Agreement does not grant permission to use MDT +trademarks or trade name in a trademark sense to endorse or promote +products or services of Licensee, or any third party. + +8. By copying, installing or otherwise using matplotlib , +Licensee agrees to be bound by the terms and conditions of this License +Agreement. + +License agreement for matplotlib versions prior to 1.3.0 +======================================================== + +1. This LICENSE AGREEMENT is between John D. Hunter ("JDH"), and the +Individual or Organization ("Licensee") accessing and otherwise using +matplotlib software in source or binary form and its associated +documentation. + +2. Subject to the terms and conditions of this License Agreement, JDH +hereby grants Licensee a nonexclusive, royalty-free, world-wide license +to reproduce, analyze, test, perform and/or display publicly, prepare +derivative works, distribute, and otherwise use matplotlib +alone or in any derivative version, provided, however, that JDH's +License Agreement and JDH's notice of copyright, i.e., "Copyright (c) +2002-2011 John D. Hunter; All Rights Reserved" are retained in +matplotlib alone or in any derivative version prepared by +Licensee. + +3. In the event Licensee prepares a derivative work that is based on or +incorporates matplotlib or any part thereof, and wants to +make the derivative work available to others as provided herein, then +Licensee hereby agrees to include in any such work a brief summary of +the changes made to matplotlib. + +4. JDH is making matplotlib available to Licensee on an "AS +IS" basis. JDH MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR +IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, JDH MAKES NO AND +DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS +FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF MATPLOTLIB +WILL NOT INFRINGE ANY THIRD PARTY RIGHTS. + +5. JDH SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF MATPLOTLIB + FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR +LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING +MATPLOTLIB , OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF +THE POSSIBILITY THEREOF. + +6. This License Agreement will automatically terminate upon a material +breach of its terms and conditions. + +7. Nothing in this License Agreement shall be deemed to create any +relationship of agency, partnership, or joint venture between JDH and +Licensee. This License Agreement does not grant permission to use JDH +trademarks or trade name in a trademark sense to endorse or promote +products or services of Licensee, or any third party. + +8. By copying, installing or otherwise using matplotlib, +Licensee agrees to be bound by the terms and conditions of this License +Agreement. +""" + +# modified from Matplotlib b97cd2d (post 2.2.2) in the following ways: +# 1. use module-level _projectName = 'rdtools' and +# _projectWarning = 'rdtoolsDeprecationWarning' in place of MPL specific +# string/Class. +# 2. remove keyword only argument requirement for removal +# 3. remove deprecated obj_type from deprecated function +# 4. if removal is empty, say 'soon' instead of assuming two minor releases +# later. + +import functools +import textwrap +import warnings + + +class rdtoolsDeprecationWarning(UserWarning): + """A class for issuing deprecation warnings for rdtools users. + + In light of the fact that Python builtin DeprecationWarnings are ignored + by default as of Python 2.7 (see link below), this class was put in to + allow for the signaling of deprecation, but via UserWarnings which are not + ignored by default. + + https://docs.python.org/dev/whatsnew/2.7.html#the-future-for-python-2-x + """ + + pass + + +# make it easier for others to copy paste this code into their projects +_projectName = 'rdtools' +_projectWarning = rdtoolsDeprecationWarning + + +def _generate_deprecation_message( + since, message='', name='', alternative='', pending=False, + obj_type='attribute', addendum='', removal=''): + + if removal == "": + removal = "soon" + elif removal: + if pending: + raise ValueError( + "A pending deprecation cannot have a scheduled removal") + removal = "in {}".format(removal) + + if not message: + message = ( + "The %(name)s %(obj_type)s" + + (" will be deprecated in a future version" + if pending else + (" was deprecated in %(projectName)s %(since)s" + + (" and will be removed %(removal)s" + if removal else + ""))) + + "." + + (" Use %(alternative)s instead." if alternative else "") + + (" %(addendum)s" if addendum else "")) + + return message % dict( + func=name, name=name, obj_type=obj_type, since=since, removal=removal, + alternative=alternative, addendum=addendum, projectName=_projectName) + + +def warn_deprecated( + since, message='', name='', alternative='', pending=False, + obj_type='attribute', addendum='', removal=''): + """ + Used to display deprecation in a standard way. + Parameters + ---------- + since : str + The release at which this API became deprecated. + message : str, optional + Override the default deprecation message. The format + specifier `%(name)s` may be used for the name of the function, + and `%(alternative)s` may be used in the deprecation message + to insert the name of an alternative to the deprecated + function. `%(obj_type)s` may be used to insert a friendly name + for the type of object being deprecated. + name : str, optional + The name of the deprecated object. + alternative : str, optional + An alternative API that the user may use in place of the deprecated + API. The deprecation warning will tell the user about this alternative + if provided. + pending : bool, optional + If True, uses a PendingDeprecationWarning instead of a + DeprecationWarning. Cannot be used together with *removal*. + removal : str, optional + The expected removal version. With the default (an empty string), a + removal version is automatically computed from *since*. Set to other + Falsy values to not schedule a removal date. Cannot be used together + with *pending*. + obj_type : str, optional + The object type being deprecated. + addendum : str, optional + Additional text appended directly to the final message. + Examples + -------- + Basic example:: + # To warn of the deprecation of "matplotlib.name_of_module" + warn_deprecated('1.4.0', name='matplotlib.name_of_module', + obj_type='module') + """ + message = '\n' + _generate_deprecation_message( + since, message, name, alternative, pending, obj_type, addendum, + removal=removal) + category = (PendingDeprecationWarning if pending + else _projectWarning) + warnings.warn(message, category, stacklevel=2) + + +def deprecated(since, message='', name='', alternative='', pending=False, + addendum='', removal=''): + """ + Decorator to mark a function or a class as deprecated. + Parameters + ---------- + since : str + The release at which this API became deprecated. This is + required. + message : str, optional + Override the default deprecation message. The format + specifier `%(name)s` may be used for the name of the object, + and `%(alternative)s` may be used in the deprecation message + to insert the name of an alternative to the deprecated + object. + name : str, optional + The name of the deprecated object; if not provided the name + is automatically determined from the passed in object, + though this is useful in the case of renamed functions, where + the new function is just assigned to the name of the + deprecated function. For example:: + def new_function(): + ... + oldFunction = new_function + alternative : str, optional + An alternative API that the user may use in place of the deprecated + API. The deprecation warning will tell the user about this alternative + if provided. + pending : bool, optional + If True, uses a PendingDeprecationWarning instead of a + DeprecationWarning. Cannot be used together with *removal*. + removal : str, optional + The expected removal version. With the default (an empty string), a + removal version is automatically computed from *since*. Set to other + Falsy values to not schedule a removal date. Cannot be used together + with *pending*. + addendum : str, optional + Additional text appended directly to the final message. + Examples + -------- + Basic example:: + @deprecated('1.4.0') + def the_function_to_deprecate(): + pass + """ + + def deprecate(obj, message=message, name=name, alternative=alternative, + pending=pending, addendum=addendum): + + if not name: + name = obj.__name__ + + if isinstance(obj, type): + obj_type = "class" + old_doc = obj.__doc__ + func = obj.__init__ + + def finalize(wrapper, new_doc): + obj.__doc__ = new_doc + obj.__init__ = wrapper + return obj + else: + obj_type = "function" + if isinstance(obj, classmethod): + func = obj.__func__ + old_doc = func.__doc__ + + def finalize(wrapper, new_doc): + wrapper = functools.wraps(func)(wrapper) + wrapper.__doc__ = new_doc + return classmethod(wrapper) + else: + func = obj + old_doc = func.__doc__ + + def finalize(wrapper, new_doc): + wrapper = functools.wraps(func)(wrapper) + wrapper.__doc__ = new_doc + return wrapper + + message = _generate_deprecation_message( + since, message, name, alternative, pending, obj_type, addendum, + removal=removal) + category = (PendingDeprecationWarning if pending + else _projectWarning) + + def wrapper(*args, **kwargs): + warnings.warn(message, category, stacklevel=2) + return func(*args, **kwargs) + + old_doc = textwrap.dedent(old_doc or '').strip('\n') + message = message.strip() + new_doc = (('\n.. deprecated:: %(since)s' + '\n %(message)s\n\n' % + {'since': since, 'message': message}) + old_doc) + if not old_doc: + # This is to prevent a spurious 'unexected unindent' warning from + # docutils when the original docstring was blank. + new_doc += r'\ ' + + return finalize(wrapper, new_doc) + + return deprecate diff --git a/rdtools/availability.py b/rdtools/availability.py new file mode 100644 index 00000000..2a7812af --- /dev/null +++ b/rdtools/availability.py @@ -0,0 +1,625 @@ +""" +Functions for detecting and quantifying production loss from photovoltaic +system downtime events. + +The availability module is currently experimental. The API, results, +and default behaviors may change in future releases (including MINOR +and PATCH releases) as the code matures. +""" + +import rdtools + +import pandas as pd +import numpy as np +from scipy.interpolate import interp1d +import warnings + +warnings.warn( + 'The availability module is currently experimental. The API, results, ' + 'and default behaviors may change in future releases (including MINOR ' + 'and PATCH releases) as the code matures.' +) + + +class AvailabilityAnalysis: + """ + A class to perform system availability and loss analysis. + + This class follows the analysis procedure described in [1]_, and + implements two distinct algorithms. One for partial (subsystem) outages + and one for system-wide outages. The :py:meth:`.AvailabilityAnalysis.run()` + method executes both algorithms and combines their results. + + The input timeseries don't need to be in any particular set of units as + long as all power and energy units are consistent, with energy units + being the hourly-integrated power (e.g., kW and kWh). The units of the + analysis outputs will match the inputs. + + Parameters + ---------- + power_system : pd.Series + Timeseries total system power. In the typical case, this is meter + power data. Should be a right-labeled interval average (this is what + is typically recorded in many DAS). + + power_subsystem : pd.DataFrame + Timeseries power data, one column per subsystem. In the typical case, + this is inverter AC power data. Each column is assumed to represent + a subsystem, so no extra columns may be included. The index must + match ``power_system``. Should be a right-labeled interval average. + + energy_cumulative : pd.Series + Timeseries cumulative energy data for the entire system (e.g. meter). + These values must be recorded at the device itself (rather than summed + by a downstream device like a datalogger or DAS provider) to preserve + its integrity across communication interruptions. Units must match + ``power`` integrated to hourly energy (e.g. if ``power`` is in kW then + ``energy`` must be in kWh). + + power_expected : pd.Series + Expected system power data with the same index as the measured data. + This can be modeled from on-site weather measurements if instruments + are well calibrated and there is no risk of data gaps. However, because + full system outages often cause weather data to be lost as well, it may + be more useful to use data from an independent weather station or + satellite-based weather provider. Should be a right-labeled interval + average. + + Attributes + ---------- + results : pd.DataFrame + Rolled-up production, loss, and availability metrics. The index is + a datetime index of the period passed to + :py:meth:`AvailabilityAnalysis.run`. The columns of the dataframe are + as follows: + + +----------------------+----------------------------------------------+ + | Column Name | Description | + +======================+==============================================+ + | 'lost_production' | Production loss from outages. Units match the| + | | input power units (e.g. if power is given in | + | | kW, 'lost_production' will be in kWh). | + +----------------------+----------------------------------------------+ + | 'actual_production' | System energy production. Same units as | + | | 'lost_production'. | + +----------------------+----------------------------------------------+ + | 'availability' | Energy-weighted system availability as a | + | | fraction (0-1). | + +----------------------+----------------------------------------------+ + + loss_system : pd.Series + Estimated timeseries lost power from system outages. + + loss_subsystem : pd.Series + Estimated timeseries lost power from subsystem outages. + + loss_total : pd.Series + Estimated total lost power from outages. + + reporting_mask : pd.DataFrame + Boolean mask indicating whether subsystems appear online or not. + + power_expected_rescaled : pd.Series + Expected power rescaled to better match system power during periods + where the system is performing normally. + + energy_expected_rescaled : pd.Series + Interval expected energy calculated from `power_expected_rescaled`. + + energy_cumulative_corrected : pd.Series + Cumulative system production after filling in data gaps from outages + with estimated production. + + error_info : pd.DataFrame + Records about the error between expected power and actual power. + + interp_lower, interp_upper : function + Functions to estimate the uncertainty interval bounds of expected + production based on outage length. + + outage_info : pd.DataFrame + Records about each detected system outage, one row per + outage. The primary columns of interest are ``type``, which can be + either ``'real'`` or ``'comms'`` and reports whether the outage + was determined to be a real outage with lost production or just a + communications interruption with no production impact; and ``loss`` + which reports the estimated production loss for the outage. The + columns are as follows: + + +----------------------+----------------------------------------------+ + | Column Name | Description | + +======================+==============================================+ + | 'start' | Timestamp of the outage start. | + +----------------------+----------------------------------------------+ + | 'end' | Timestamp of the outage end. | + +----------------------+----------------------------------------------+ + | 'duration' | Length of the outage (*i.e.* | + | | ``outage_info['end'] - outage_info['start']``| + | | ). | + +----------------------+----------------------------------------------+ + | 'intervals' | Total count of data intervals contained in | + | | the outage. | + +----------------------+----------------------------------------------+ + | 'daylight_intervals' | Count of data intervals contained in the | + | | outage occurring during the day. | + +----------------------+----------------------------------------------+ + | 'error_lower' | Lower error bound as a fraction of expected | + | | energy. | + +----------------------+----------------------------------------------+ + | 'error_upper' | Upper error bound as a fraction of expected | + | | energy. | + +----------------------+----------------------------------------------+ + | 'energy_expected' | Total expected production for the outage | + | | duration. | + +----------------------+----------------------------------------------+ + | 'energy_start' | System cumulative production at the outage | + | | start. | + +----------------------+----------------------------------------------+ + | 'energy_end' | System cumulative production at the outage | + | | end. | + +----------------------+----------------------------------------------+ + | 'energy_actual' | System production during the outage (*i.e.*, | + | | ``outage_info['energy_end'] - | + | | outage_info['energy_start']``). | + +----------------------+----------------------------------------------+ + | 'ci_lower' | Lower bound for the expected energy | + | | confidence interval. | + +----------------------+----------------------------------------------+ + | 'ci_upper' | Lower bound for the expected energy | + | | confidence interval. | + +----------------------+----------------------------------------------+ + | 'type' | Type of the outage ('real or 'comms'). | + +----------------------+----------------------------------------------+ + | 'loss' | Estimated production loss. | + +----------------------+----------------------------------------------+ + + Notes + ----- + This class's ability to detect short-duration outages is limited by + the resolution of the system data. For instance, 15-minute averages + would not be able to resolve the rapid power cycling of an intermittent + inverter. Additionally, the loss at the edges of an outage may be + underestimated because of masking by the interval averages. + + This class expects outages to be represented in the timeseries by NaN, + zero, or very low values. If your DAS does not record data from outages + (e.g., a three-hour outage results in three hours of omitted timestamps), + you should insert those missing rows before using this analysis. + + References + ---------- + .. [1] Anderson K. and Blumenthal R. "Overcoming communications outages in + inverter downtime analysis", 2020 IEEE 47th Photovoltaic Specialists + Conference (PVSC). + """ + + def __init__(self, power_system, power_subsystem, energy_cumulative, + power_expected): + for series in [power_subsystem, energy_cumulative, power_expected]: + if not power_system.index.equals(series.index): + raise ValueError("Input timeseries indexes must match") + + self.power_system = power_system + self.power_subsystem = power_subsystem + self.energy_cumulative = energy_cumulative + self.power_expected = power_expected + + def _calc_loss_subsystem(self, low_threshold, relative_sizes, + power_system_limit): + """ + Estimate timeseries production loss from subsystem downtime events. + + This implements the "power comparison" method from [1]_ of comparing + subsystem power data to total system power (e.g. inverter power to + meter power). + + Because this method is based on peer-to-peer comparison at each + timestamp, it is not suitable for full system outages (i.e., at least + one inverter must be reporting along with the system meter). + + Sets the `reporting_mask` and `loss_subsystem` attributes. + + Parameters + ---------- + low_threshold : float or pd.Series + An optional threshold used to naively classify subsystems as + online. If the threshold is a scalar, it will be used for all + subsystems. For subsystems with different capacities, a pandas + Series may be passed with index values matching the columns in + ``power_subsystem``. Units must match ``power_subsystem`` and + ``power_system``. If omitted, the limit is calculated for each + subsystem independently as 0.001 times the 99th percentile of its + power data. + + relative_sizes : dict or pd.Series + The production capacity of each subsystem, normalized by the mean + subsystem capacity. If not specified, it will be estimated from + power data. + + power_system_limit : float or pd.Series, optional + Maximum allowable system power. This parameter is used to account + for cases where online subsystems can partially mitigate the loss + of an offline subsystem, for example a system with a plant + controller and dynamic inverter setpoints. This constraint is + only applied to the subsystem loss calculation. + """ + power_subsystem = self.power_subsystem + power_system = self.power_system + power_subsystem = power_subsystem.fillna(0) + power_system = power_system.clip(lower=0) + + # Part A + if low_threshold is None: + # calculate the low-power threshold based on the upper edge of the + # power distribution so that low-power strangeness (snow cover, + # outages, shading etc) don't affect the estimate: + low_threshold = power_subsystem.quantile(0.99) / 1000 + + self.reporting_mask = looks_online = power_subsystem > low_threshold + reporting = power_subsystem[looks_online] + if relative_sizes is None: + # normalize by mean power and take the median across the timeseries + normalized = reporting.divide(reporting.mean(axis=1), axis=0) + relative_sizes = normalized.median() + else: + # convert dict to Series (no effect if already Series) + relative_sizes = pd.Series(relative_sizes) + + normalized_subsystem_powers = reporting.divide(relative_sizes, axis=1) + mean_subsystem_power = normalized_subsystem_powers.mean(axis=1) + + virtual_full_power = mean_subsystem_power * power_subsystem.shape[1] + + system_delta = 1 - power_system / virtual_full_power + + subsystem_fraction = relative_sizes / relative_sizes.sum() + smallest_delta = power_subsystem.le(low_threshold) \ + .replace(False, np.nan) \ + .multiply(subsystem_fraction) \ + .min(axis=1) \ + .fillna(1) # use safe value of 100% + is_downtime = system_delta > (0.75 * smallest_delta) + is_downtime[looks_online.all(axis=1)] = False + + # Part B + lowest_possible = looks_online.multiply(subsystem_fraction).sum(axis=1) + f_online = power_system / virtual_full_power + f_online = f_online.clip(lower=lowest_possible, upper=1) + p_loss = (1 - f_online) / f_online * power_system + p_loss[~is_downtime] = 0 + + if power_system_limit is not None: + limit_exceeded = p_loss + power_system > power_system_limit + loss = power_system_limit - power_system[limit_exceeded] + p_loss.loc[limit_exceeded] = loss.clip(lower=0) + + self.loss_subsystem = p_loss.fillna(0) + + def _calc_error_distributions(self, quantiles): + """ + Calculate the error distributions of Section II-A in [1]_. + + Sets the `power_expected_rescaled`, `energy_expected_rescaled`, + `error_info`, `interp_lower`, and `interp_upper` attributes. + + Parameters + ---------- + quantiles : 2-element tuple, default (0.01, 0.99) + The quantiles of the error distribution used for the expected + energy confidence interval. The lower bound is used to classify + outages as either (1) a simple communication interruption with + no production loss or (2) a power outage with an associated + production loss estimate. + """ + df = pd.DataFrame({ + 'Meter_kW': self.power_system, + 'Expected Power': self.power_expected, + 'Meter_kWh': self.energy_cumulative, + }) + + system_performing_normally = ( + (self.loss_subsystem == 0) & (self.power_system > 0) + ) + # filter out nighttime as well, since night intervals shouldn't count + subset = system_performing_normally & (df['Expected Power'] > 0) + + # rescale expected energy to better match actual production. + # this shifts the error distributions so that as interval length + # increases, error -> 0 + scaling_subset = df.loc[subset, ['Expected Power', 'Meter_kW']].sum() + scaling_factor = ( + scaling_subset['Expected Power'] / scaling_subset['Meter_kW'] + ) + df['Expected Power'] /= scaling_factor + self.power_expected_rescaled = df['Expected Power'] + df['Expected Energy'] = rdtools.energy_from_power(df['Expected Power']) + self.energy_expected_rescaled = df['Expected Energy'] + df['Meter_kWh_interval'] = rdtools.energy_from_power(df['Meter_kW']) + + df_subset = df.loc[subset, :] + + # window length is "number of daytime intervals". + # Note: these bounds are intended to provide good resolution + # across many dataset lengths + window_lengths = 2**np.arange(1, int(np.log2(len(df_subset))), 1) + + results_list = [] + for window_length in window_lengths: + rolling = df_subset.rolling(window=window_length, center=True) + window = rolling.sum() + actual = window['Meter_kWh_interval'] + expected = window['Expected Energy'] + # remove the nans at beginning and end because of minimum window + # length + actual = actual[~np.isnan(actual)] + expected = expected[~np.isnan(expected)] + temp = pd.DataFrame({ + 'actual': actual, + 'expected': expected, + 'window length': window_length + }) + results_list.append(temp) + + df_error = pd.concat(results_list) + df_error['error'] = df_error['actual'] / df_error['expected'] - 1 + + self.error_info = df_error + error = df_error.groupby('window length')['error'] + lower = error.quantile(quantiles[0]) + upper = error.quantile(quantiles[1]) + + # functions to predict the confidence interval for a given outage + # length. linear interp inside the range, nearest neighbor outside the + # range. + def interp(series): + return interp1d(series.index, series.values, + fill_value=(series.values[0], series.values[-1]), + bounds_error=False) + + # functions mapping number of intervals (outage length) to error bounds + def interp_lower(n_intervals): + return float(interp(lower)(n_intervals)) + + def interp_upper(n_intervals): + return float(interp(upper)(n_intervals)) + + self.interp_lower = interp_lower + self.interp_upper = interp_upper + + def _calc_loss_system(self): + """ + Estimate total production loss from system downtime events. + + See Section II-B in [1]_. + + This implements the "expected energy" method from [1]_ of comparing + system production recovered from cumulative production data with + expected production from an energy model. + + This function is useful for full system outages when no system data is + available at all. However, it does require cumulative production data + recorded at the device level and only reports estimated lost production + for entire outages rather than timeseries lost power. + + Sets the `outage_info`, `energy_cumulative_corrected`, and + `loss_system` attributes. + """ + # Calculate boolean series to indicate full outages. Considerations: + # - Multi-day outages need to span across nights + # - Full outages don't always take out communications, so the + # cumulative meter can either drop out or stay constant depending on + # the case. + # During a full outage, no inverters will report production: + looks_offline = ~self.reporting_mask.any(axis=1) + # Now span across nights: + all_times = self.power_system.index + masked = looks_offline[self.power_expected > 0].reindex(all_times) + # Note: in Series, (nan | True) is False, but (True | nan) is True + full_outage = ( + masked.ffill().fillna(False) | masked.bfill().fillna(False) + ) + + # Find expected production and associated uncertainty for each outage + diff = full_outage.astype(int).diff() + starts = all_times[diff == 1].tolist() + ends = all_times[diff.shift(-1) == -1].tolist() + steps = diff[~diff.isnull() & (diff != 0)] + if not steps.empty: + if steps[0] == -1: + # data starts in an outage + starts.insert(0, all_times[0]) + if steps[-1] == 1: + # data ends in an outage + ends.append(all_times[-1]) + + outage_data = [] + for start, end in zip(starts, ends): + outage_expected_power = self.power_expected_rescaled[start:end] + daylight_intervals = (outage_expected_power > 0).sum() + outage_expected_energy = self.energy_expected_rescaled[start:end] + + # self.cumulative_energy[start] is the first value in the outage. + # so to get the starting energy, need to get previous value: + start_minus_one = all_times[all_times.get_loc(start)-1] + + data = { + 'start': start, + 'end': end, + 'duration': end - start, + 'intervals': len(outage_expected_power), + 'daylight_intervals': daylight_intervals, + 'error_lower': self.interp_lower(daylight_intervals), + 'error_upper': self.interp_upper(daylight_intervals), + 'energy_expected': outage_expected_energy.sum(), + 'energy_start': self.energy_cumulative[start_minus_one], + 'energy_end': self.energy_cumulative[end], + } + outage_data.append(data) + + # specify columns in case no outages are found. Also specifies + # the order for pandas < 0.25.0 + cols = ['start', 'end', 'duration', 'intervals', 'daylight_intervals', + 'error_lower', 'error_upper', 'energy_expected', + 'energy_start', 'energy_end'] + df_outages = pd.DataFrame(outage_data, columns=cols) + + df_outages['energy_actual'] = ( + df_outages['energy_end'] - df_outages['energy_start'] + ) + # poor-quality cumulative meter data can create "negative production" + # outages. Set to nan so that negative value doesn't pollute other + # calcs. However, if using a net meter (instead of delivered), system + # consumption creates a legitimate decrease during some outages. Rule + # of thumb is that system consumption is about 0.5% of system + # production, but it'll be larger during winter. Choose 5% to be safer. + lower_limit = -0.05 * df_outages['energy_expected'] # Note the sign + below_limit = df_outages['energy_actual'] < lower_limit + df_outages.loc[below_limit, 'energy_actual'] = np.nan + + df_outages['ci_lower'] = ( + (1 + df_outages['error_lower']) * df_outages['energy_expected'] + ) + df_outages['ci_upper'] = ( + (1 + df_outages['error_upper']) * df_outages['energy_expected'] + ) + df_outages['type'] = np.where( + df_outages['energy_actual'] < df_outages['ci_lower'], + 'real', + 'comms') + df_outages.loc[df_outages['energy_actual'].isnull(), 'type'] = 'unknown' + df_outages['loss'] = np.where( + df_outages['type'] == 'real', + df_outages['energy_expected'] - df_outages['energy_actual'], + 0) + df_outages.loc[df_outages['type'] == 'unknown', 'loss'] = np.nan + + self.outage_info = df_outages + + # generate a best-guess timeseries loss for the full outages by + # scaling the expected power signal to match the actual + lost_power_full = pd.Series(0, index=self.loss_subsystem.index) + expected_power = self.power_expected + corrected_cumulative_energy = self.energy_cumulative.copy() + for i, row in self.outage_info.iterrows(): + start = row['start'] + end = row['end'] + subset = expected_power.loc[start:end].copy() + subset_energy = rdtools.energy_from_power(subset) + loss_fill = subset * row['loss'] / subset_energy.sum() + lost_power_full.loc[subset.index] += loss_fill + + # fill in the cumulative meter during the outages, again using + # the expected energy signal, but rescaled to match actual + # production this time: + production_fill = subset_energy.cumsum() + production_fill *= row['energy_actual'] / subset_energy.sum() + corrected_segment = row['energy_start'] + production_fill + corrected_cumulative_energy.loc[start:end] = corrected_segment + + self.energy_cumulative_corrected = corrected_cumulative_energy + self.loss_system = lost_power_full + + def _combine_losses(self, rollup_period='M'): + """ + Combine subsystem and system losses. + + Sets the `loss_total` and `results` attributes. + + Parameters + ---------- + rollup_period : pandas offset string, default 'M' + The period on which to roll up losses and calculate availability. + """ + + if ((self.loss_system > 0) & (self.loss_subsystem > 0)).any(): + msg = ( + 'Loss detected simultaneously at both system and subsystem ' + 'levels. This is unexpected and could indicate a problem with ' + 'the input time series data.' + ) + warnings.warn(msg, UserWarning) + + self.loss_total = self.loss_system + self.loss_subsystem + + # calculate actual production based on corrected cumulative meter + cumulative_energy = self.energy_cumulative_corrected + resampled_cumulative = cumulative_energy.resample(rollup_period) + actual_production = ( + resampled_cumulative.last() - resampled_cumulative.first() + ) + + lost_production = rdtools.energy_from_power(self.loss_total) + df = pd.DataFrame({ + 'lost_production': lost_production.resample(rollup_period).sum(), + 'actual_production': actual_production, + }) + loss_plus_actual = df['lost_production'] + df['actual_production'] + df['availability'] = 1 - df['lost_production'] / loss_plus_actual + self.results = df + + def run(self, low_threshold=None, relative_sizes=None, + power_system_limit=None, quantiles=(0.01, 0.99), + rollup_period='M'): + """ + Run the availability analysis. + + Parameters + ---------- + low_threshold : float or pd.Series, optional + An optional threshold used to naively classify subsystems as + online. If the threshold is a scalar, it will be used for all + subsystems. For subsystems with different capacities, a pandas + Series may be passed with index values matching the columns in + ``power_subsystem``. Units must match ``power_subsystem`` and + ``power_system``. If omitted, the limit is calculated for each + subsystem independently as 0.001 times the 99th percentile of its + power data. + + relative_sizes : dict or pd.Series, optional + The production capacity of each subsystem, normalized by the mean + subsystem capacity. If not specified, it will be estimated from + power data. + + power_system_limit : float or pd.Series, optional + Maximum allowable system power in the same units as the input + power timeseries. This parameter is used to account + for cases where online subsystems can partially mitigate the loss + of an offline subsystem, for example a system with a plant + controller and dynamic inverter setpoints. This constraint is + only applied to the subsystem loss calculation. + + quantiles : 2-element tuple, default (0.01, 0.99) + The quantiles of the error distribution used for the expected + energy confidence interval. The lower bound is used to classify + outages as either (1) a simple communication interruption with + no production loss or (2) a power outage with an associated + production loss estimate. + + rollup_period : pandas DateOffset or alias, default 'M' + The period on which to roll up losses and calculate availability. + """ + self._calc_loss_subsystem(low_threshold, relative_sizes, + power_system_limit) + self._calc_error_distributions(quantiles) + self._calc_loss_system() + self._combine_losses(rollup_period) + + def plot(self): + """ + Create a figure summarizing the availability analysis results. The + analysis must be run using the :py:meth:`.run` method before using + this method. + + Returns + ------- + fig : matplotlib Figure + """ + try: + self.loss_total + except AttributeError: + raise TypeError("No results to plot, use the `run` method first") + + return rdtools.plotting.availability_summary_plots( + self.power_system, self.power_subsystem, self.loss_total, + self.energy_cumulative, self.energy_expected_rescaled, + self.outage_info) diff --git a/rdtools/clearsky_temperature.py b/rdtools/clearsky_temperature.py index bbf64afa..4472d70c 100644 --- a/rdtools/clearsky_temperature.py +++ b/rdtools/clearsky_temperature.py @@ -22,7 +22,7 @@ def get_clearsky_tamb(times, latitude, longitude, window_size=40, latitude : float Coordinates in decimal degrees. longitude : float - Coordinates in decimal degrees. + Coordinates in decimal degrees. Positive is east of the prime meridian. window_size : int, default 40 The window size in days to use when calculating rolling averages. gauss_std : int, default 20 diff --git a/rdtools/degradation.py b/rdtools/degradation.py index 203c6c48..6489e997 100644 --- a/rdtools/degradation.py +++ b/rdtools/degradation.py @@ -21,7 +21,7 @@ def degradation_ols(energy_normalized, confidence_level=68.2): Returns ------- Rd_pct : float - Estimated degradation rate in units percent/year. + Estimated degradation relative to the year 0 system capacity [%/year] Rd_CI : np.array The calculated confidence interval bounds. calc_info : dict @@ -94,7 +94,7 @@ def degradation_classical_decomposition(energy_normalized, Returns ------- Rd_pct : float - Estimated degradation rate in units percent/year. + Estimated degradation relative to the year 0 system capacity [%/year] Rd_CI : np.array The calculated confidence interval bounds. calc_info : dict @@ -191,8 +191,10 @@ def degradation_year_on_year(energy_normalized, recenter=True, energy_normalized: pd.Series Daily or lower frequency time series of normalized system ouput. recenter : bool, default True - Specify whether data is centered to normalized yield of 1 based on - first year. + Specify whether data is internally recentered to normalized yield + of 1 based on first year median. If False, ``Rd_pct`` is calculated + assuming ``energy_normalized`` is passed already normalized to the + year 0 system capacity. exceedance_prob : float, default 95 The probability level to use for exceedance value calculation, in percent. @@ -201,10 +203,10 @@ def degradation_year_on_year(energy_normalized, recenter=True, Returns ------- - degradation_rate : float - rate of relative performance change in %/yr + Rd_pct : float + Estimated degradation relative to the year 0 median system capacity [%/year] confidence_interval : np.array - confidence interval (size specified by `confidence_level`) of + confidence interval (size specified by ``confidence_level``) of degradation rate estimate calc_info : dict diff --git a/rdtools/normalization.py b/rdtools/normalization.py index 3ad1f593..fe9c3665 100644 --- a/rdtools/normalization.py +++ b/rdtools/normalization.py @@ -5,7 +5,7 @@ import numpy as np from scipy.optimize import minimize import warnings - +from rdtools._deprecation import deprecated class ConvergenceError(Exception): '''Rescale optimization did not converge''' @@ -14,50 +14,52 @@ class ConvergenceError(Exception): def normalize_with_expected_power(pv, power_expected, poa_global, pv_input='power'): ''' - Normalize pv output based on expected PV power. + Normalize PV power or energy based on expected PV power. Parameters ---------- pv : pd.Series Right-labeled time series PV energy or power. If energy, should *not* - be cumulative, but only for preceding time step. + be cumulative, but only for preceding time step. Type (energy or power) + must be specified in the ``pv_input`` parameter. power_expected : pd.Series - Right-labeled time series of expected PV power. + Right-labeled time series of expected PV power. (Note: Expected energy + is not supported.) poa_global : pd.Series Right-labeled time series of plane-of-array irradiance associated with - `expected_power` - pv_input : str - 'power' or 'energy' to specify type of input used for pv parameter + ``expected_power`` + pv_input : {'power' or 'energy'} + Specifies the type of input used for ``pv`` parameter. Default: 'power' Returns ------- energy_normalized : pd.Series - Energy normalized based on `expected_power` + Energy normalized based on ``power_expected`` insolation : pd.Series Insolation associated with each normalized point ''' - freq = check_series_frequency(pv, 'pv') + freq = _check_series_frequency(pv, 'pv') if pv_input == 'power': - energy = energy_from_power(pv, freq) + energy = energy_from_power(pv, freq, power_type='right_labeled') elif pv_input == 'energy': energy = pv.copy() energy.name = 'energy_Wh' else: raise ValueError("Unexpected value for pv_input. pv_input should be 'power' or 'energy'.") - model_tds, mean_model_td = delta_index(power_expected) - measure_tds, mean_measure_td = delta_index(energy) + model_tds, mean_model_td = _delta_index(power_expected) + measure_tds, mean_measure_td = _delta_index(energy) # Case in which the model less frequent than the measurements if mean_model_td > mean_measure_td: power_expected = interpolate(power_expected, pv.index) poa_global = interpolate(poa_global, pv.index) - energy_expected = energy_from_power(power_expected, freq) - insolation = energy_from_power(poa_global, freq) + energy_expected = energy_from_power(power_expected, freq, power_type='right_labeled') + insolation = energy_from_power(poa_global, freq, power_type='right_labeled') energy_normalized = energy / energy_expected @@ -68,6 +70,8 @@ def normalize_with_expected_power(pv, power_expected, poa_global, return energy_normalized, insolation +@deprecated(since='2.0.0', removal='3.0.0', + alternative='normalize_with_expected_power') def pvwatts_dc_power(poa_global, power_dc_rated, temperature_cell=None, poa_global_ref=1000, temperature_cell_ref=25, gamma_pdc=None): @@ -84,7 +88,7 @@ def pvwatts_dc_power(poa_global, power_dc_rated, temperature_cell=None, Rated DC power of array in watts temperature_cell : pd.Series, optional Measured or derived cell temperature [degrees Celsius]. - Time series assumed to be same frequency as `poa_global`. + Time series assumed to be same frequency as ``poa_global``. If omitted, the temperature term will be ignored. poa_global_ref : float, default 1000 Reference irradiance at standard test condition [W/m**2]. @@ -116,6 +120,8 @@ def pvwatts_dc_power(poa_global, power_dc_rated, temperature_cell=None, return power_dc +@deprecated(since='2.0.0', removal='3.0.0', + alternative='normalize_with_expected_power') def normalize_with_pvwatts(energy, pvwatts_kws): ''' Normalize system AC energy output given measured poa_global and @@ -130,7 +136,7 @@ def normalize_with_pvwatts(energy, pvwatts_kws): Must be a right-labeled regular time series. pvwatts_kws : dict Dictionary of parameters used in the pvwatts_dc_power function. See - `Other Parameters`. + Other Parameters. Other Parameters ------------------ @@ -171,6 +177,8 @@ def normalize_with_pvwatts(energy, pvwatts_kws): return energy_normalized, insolation +@deprecated(since='2.0.0', removal='3.0.0', + alternative='normalize_with_expected_power') def sapm_dc_power(pvlib_pvsystem, met_data): ''' Use Sandia Array Performance Model (SAPM) and PVWatts to compute the @@ -183,8 +191,8 @@ def sapm_dc_power(pvlib_pvsystem, met_data): pvlib_pvsystem : pvlib-python LocalizedPVSystem object Object contains orientation, geographic coordinates, equipment constants (including DC rated power in watts). The object must also - specify either the `temperature_model_parameters` attribute or both - `racking_model` and `module_type` attributes to infer the temperature model parameters. + specify either the ``temperature_model_parameters`` attribute or both + ``racking_model`` and ``module_type`` attributes to infer the temperature model parameters. met_data : pd.DataFrame Measured irradiance components, ambient temperature, and wind speed. Expected met_data DataFrame column names: @@ -239,6 +247,8 @@ def sapm_dc_power(pvlib_pvsystem, met_data): return power_dc, effective_irradiance +@deprecated(since='2.0.0', removal='3.0.0', + alternative='normalize_with_expected_power') def normalize_with_sapm(energy, sapm_kws): ''' Normalize system AC energy output given measured met_data and @@ -255,15 +265,15 @@ def normalize_with_sapm(energy, sapm_kws): Must be a right-labeled regular time series. sapm_kws : dict Dictionary of parameters required for sapm_dc_power function. See - `Other Parameters`. + Other Parameters. Other Parameters --------------- pvlib_pvsystem : pvlib-python LocalizedPVSystem object Object contains orientation, geographic coordinates, equipment constants (including DC rated power in watts). The object must also - specify either the `temperature_model_parameters` attribute or both - `racking_model` and `module_type` to infer the model parameters. + specify either the ``temperature_model_parameters`` attribute or both + ``racking_model`` and ``module_type`` to infer the model parameters. met_data : pd.DataFrame Measured met_data, ambient temperature, and wind speed. Expected column names are ['DNI', 'GHI', 'DHI', 'Temperature', 'Wind Speed'] @@ -288,7 +298,7 @@ def normalize_with_sapm(energy, sapm_kws): return energy_normalized, insolation -def delta_index(series): +def _delta_index(series): ''' Takes a pandas series with a DatetimeIndex as input and returns (time step sizes, average time step size) in hours @@ -301,7 +311,7 @@ def delta_index(series): Returns ------- deltas : pd.Series - A timeseries representing the timestep sizes of `series` + A timeseries representing the timestep sizes of ``series`` mean : float The average timestep ''' @@ -323,6 +333,9 @@ def delta_index(series): return deltas, np.mean(deltas.dropna()) +delta_index = deprecated('2.0.0', removal='3.0.0')(_delta_index) + + def irradiance_rescale(irrad, irrad_sim, max_iterations=100, method='iterative', convergence_threshold=1e-6): ''' @@ -337,7 +350,7 @@ def irradiance_rescale(irrad, irrad_sim, max_iterations=100, modeled/simulated irradiance time series max_iterations : int, default 100 The maximum number of times to attempt rescale optimization. - Ignored if `method` = 'single_opt' + Ignored if ``method = 'single_opt'`` method : str, default 'iterative' The calculation method to use. 'single_opt' implements the irradiance_rescale of rdtools v1.1.3 and earlier. 'iterative' @@ -346,9 +359,9 @@ def irradiance_rescale(irrad, irrad_sim, max_iterations=100, convergence_threshold : float, default 1e-6 The acceptable iteration-to-iteration scaling factor difference to determine convergence. If the threshold is not reached after - `max_iterations`, raise + ``max_iterations``, raise :py:exc:`rdtools.normalization.ConvergenceError`. - Must be greater than zero. Only used if `method=='iterative'`. + Must be greater than zero. Only used if ``method=='iterative'``. Returns ------- @@ -416,10 +429,10 @@ def _rmse(fact): raise ValueError('Invalid method') -def check_series_frequency(series, series_description): +def _check_series_frequency(series, series_description): ''' Returns the inferred frequency of a pandas series, raises ValueError - using `series_description` if it can't. + using ``series_description`` if it can't. Parameters ---------- @@ -446,7 +459,10 @@ def check_series_frequency(series, series_description): return freq -def t_step_nanoseconds(time_series): +check_series_frequency = deprecated('2.0.0', removal='3.0.0')(_check_series_frequency) + + +def _t_step_nanoseconds(time_series): ''' return a series of right labeled differences in the index of time_series in nanoseconds @@ -457,25 +473,31 @@ def t_step_nanoseconds(time_series): return t_steps -def energy_from_power(power, target_frequency=None, max_timedelta=None): +def energy_from_power(power, target_frequency=None, max_timedelta=None, power_type='right_labeled'): ''' Returns a regular right-labeled energy time series in units of Wh per - interval from an instantaneous power time series. NaN is filled where the - gap between input data points exceeds `max_timedelta`. Power_series should + interval from a power time series. For instantaneous timeseries, a + trapezoidal sum is used. For right labeled time series, a rectangular sum + is used. NaN is filled where the gap between input data points exceeds + ``max_timedelta``. Power_series should be given in Watts. Parameters ---------- power : pd.Series - Instantaneous time series of power in Watts + Time series of power in Watts target_frequency : DatetimeOffset or frequency string, default None The frequency of the energy time series to be returned. - If omitted, use the median timestep of `power` + If omitted, use the median timestep of ``power``, or if ``power`` has + fewer than two elements, use ``power.index.freq`. max_timedelta : pd.Timedelta, default None The maximum allowed gap between power measurements. If the gap between - consecutive power measurements exceeds `max_timedelta`, NaN will be - returned for that interval. If omitted, `max_timedelta` is set - internally to the median time delta in `power`. + consecutive power measurements exceeds ``max_timedelta``, NaN will be + returned for that interval. If omitted, ``max_timedelta`` is set + internally to the median time delta in ``power``. Ignored when ``power`` + has fewer than two elements. + power_type : {'right_labeled', 'instantaneous'} + The labeling convention used in power. Default: 'right_labeled' Returns ------- @@ -487,7 +509,26 @@ def energy_from_power(power, target_frequency=None, max_timedelta=None): raise ValueError('power must be a pandas series with a ' 'DatetimeIndex') - t_steps = t_step_nanoseconds(power) + if len(power) <= 1: + # just one value, doesn't make sense to interpolate or trapz aggregate. + # use the index frequency to determine the appropriate timescale + if power_type == 'instantaneous': + raise ValueError("power_type='instantaneous' is incompatible with single element " + "power. Use power_type='right-labeled'") + if target_frequency is None: + if power.index.freq is None: + raise ValueError('Could not determine period of input power') + + target_frequency = power.index.freq + # just raise if it's a non-fixed frequency + interval_length_ns = \ + pd.tseries.frequencies.to_offset(target_frequency).nanos + + energy = power * interval_length_ns / 1e9 / 3600 # ns to s to h + energy.name = 'energy_Wh' + return energy + + t_steps = _t_step_nanoseconds(power) median_step_ns = t_steps.median() if target_frequency is None: @@ -498,7 +539,8 @@ def energy_from_power(power, target_frequency=None, max_timedelta=None): max_interval_nanoseconds = median_step_ns else: max_interval_nanoseconds = max_timedelta.total_seconds() * 10.0**9 - + # set max_timedelta for use in interpolate and _aggregate + max_timedelta = pd.to_timedelta(f'{max_interval_nanoseconds} nanos') try: freq_interval_size_ns = \ pd.tseries.frequencies.to_offset(target_frequency).nanos @@ -508,32 +550,14 @@ def energy_from_power(power, target_frequency=None, max_timedelta=None): power.index[-1], freq=target_frequency) temp_series = pd.Series(data=1, index=temp_ind) - temp_diffs = t_step_nanoseconds(temp_series) + temp_diffs = _t_step_nanoseconds(temp_series) freq_interval_size_ns = temp_diffs.median() else: raise - # Upsampling case if freq_interval_size_ns <= median_step_ns: - resampled = interpolate(power, target_frequency, max_timedelta) - - moving_average = (resampled + resampled.shift()) / 2.0 - - energy = moving_average * t_step_nanoseconds(moving_average) \ - / 10.0**9 / 3600.0 - - # Drop first row with work around for pandas issue #18031 - if energy.index.tz is None: - energy = energy.drop(energy.index[0]) - else: - tz = str(energy.index.tz) - energy.index = energy.index.tz_convert('UTC') - energy = energy.drop(energy.index[0]) - energy.index = energy.index.tz_convert(tz) - - # Downsampling case - elif freq_interval_size_ns > median_step_ns: - energy = trapz_aggregate(power, target_frequency, max_timedelta) + power = interpolate(power, target_frequency, max_timedelta) + energy = _aggregate(power, target_frequency, max_timedelta, power_type) # Set the frequency if we can try: @@ -550,12 +574,13 @@ def energy_from_power(power, target_frequency=None, max_timedelta=None): return energy -def trapz_aggregate(time_series, target_frequency, max_timedelta=None): +def _aggregate(time_series, target_frequency, max_timedelta, series_type): ''' Returns a right-labeled series with frequency target_frequency generated by - aggregating `time_series` with the trapezoidal rule (in units of hours). - If any interval in `time_series` is greater than `max_timedelta`, it is - ommitted from the sum. + aggregating ``time_series`` (in units of hours). For instantaneous timeseries, + a trapezoidal sum is used. For right labeled time series, a rectangular sum + is used. If any interval in ``time_series`` is greater than ``max_timedelta``, + it is omitted from the sum. Parameters ---------- @@ -564,45 +589,73 @@ def trapz_aggregate(time_series, target_frequency, max_timedelta=None): The frequency of the accumulated series to be returned. max_timedelta : pd.Timedelta, default None The maximum allowed gap between power measurements. If the gap between - consecutive power measurements exceeds `max_timedelta`, no energy value - will be returned for that interval. If omitted, `max_timedelta` is set - internally to the median time delta in `time_series`. + consecutive power measurements exceeds ``max_timedelta``, no energy value + will be returned for that interval. + series_type : {'right_labeled', 'instantaneous'} + The labeling convention of time_series + Returns ------- pd.Series - right-labeled energy in Wh per interval + right-labeled aggregated time_series in _*hours per interval ''' - values = time_series.values - timestamps = time_series.index.astype('int64').values + #series that has same index as desired output + output_dummy = time_series.resample(target_frequency, + closed='right', + label='right').sum() + + union_index = time_series.index.union(output_dummy.index) + time_series = time_series.dropna() - t_diffs = np.diff(timestamps) + values = time_series.values - if max_timedelta is None: - max_interval_nanoseconds = np.median(t_diffs) + # Identify gaps (including from nans) bigger than max_time_delta + timestamps = time_series.index.astype('int64').values + timestamps = pd.Series(timestamps, index=time_series.index) + t_diffs = timestamps.diff() + # Keep track of the gap size but with refilled NaNs and new + # timestamps from target freq + t_diffs = t_diffs.reindex(union_index, method='bfill') + + max_interval_nanoseconds = max_timedelta.total_seconds() * 10.0**9 + + gap_mask = t_diffs > max_interval_nanoseconds + + time_series = time_series.reindex(union_index) + t_diffs = np.diff(time_series.index.astype('int64').values) + t_diffs_hours = t_diffs / 10**9 / 3600.0 + if series_type == 'instantaneous': + # interpolate with trapz sum + time_series = time_series.interpolate(method='time') + time_series[gap_mask] = np.nan + values = time_series.values + series_sum = (values[1:] + values[:-1]) / 2 * t_diffs_hours + elif series_type == 'right_labeled': + # bfill and rectangular sum + time_series = time_series.bfill() + time_series[gap_mask] = np.nan + values = time_series.values + series_sum = values[1:] * t_diffs_hours else: - max_interval_nanoseconds = max_timedelta.total_seconds() * 10.0**9 - - # in x*hours - trap_sum = (values[1:] + values[:-1]) / 2 * t_diffs / 10**9 / 3600.0 - - trap_sum[t_diffs > max_interval_nanoseconds] = np.nan + raise ValueError("series_type must be either 'instantaneous' or 'right_labeled', " + "not '{}'".format(series_type)) - trap_sum = pd.Series(data=trap_sum, index=time_series.index[1:]) + series_sum = pd.Series(data=series_sum, index=time_series.index[1:]) - aggregated = trap_sum.resample(target_frequency, + aggregated = series_sum.resample(target_frequency, closed='right', label='right').sum(min_count=1) return aggregated -def interpolate_series(time_series, target_index, max_timedelta=None, +def _interpolate_series(time_series, target_index, max_timedelta=None, warning_threshold=0.1): ''' Returns an interpolation of time_series onto target_index, NaN is returned - for times associated with gaps in time_series longer `than max_timedelta`. + for times associated with gaps in time_series longer than ``max_timedelta``. Parameters ---------- @@ -612,13 +665,13 @@ def interpolate_series(time_series, target_index, max_timedelta=None, the index onto which the interpolation is to be made max_timedelta : pd.Timedelta, default None The maximum allowed gap between values in time_series. Times associated - with gaps longer than `max_timedelta` are excluded from the output. If - omitted, `max_timedelta` is set internally to two times the median - time delta in `time_series.` + with gaps longer than ``max_timedelta`` are excluded from the output. If + omitted, ``max_timedelta`` is set internally to two times the median + time delta in ``time_series``. warning_threshold : float, default 0.1 The fraction of data exclusion above which a warning is raised. With the default value of 0.1, a warning will be raised if the fraction - of data excluded because of data gaps longer than `max_timedelta` is + of data excluded because of data gaps longer than ``max_timedelta`` is above than 10%. Returns @@ -628,7 +681,7 @@ def interpolate_series(time_series, target_index, max_timedelta=None, Note ---- Timezone information in the DatetimeIndexes is handled automatically, - however both `time_series` and `target_index` should be time zone aware or + however both ``time_series`` and ``target_index`` should be time zone aware or they should both be time zone naive. ''' @@ -706,14 +759,14 @@ def interpolate(time_series, target, max_timedelta=None, warning_threshold=0.1): * If DatetimeOffset or frequency string: the frequency at which to resample and interpolate max_timedelta : pd.Timedelta, default None - The maximum allowed gap between values in `time_series`. Times - associated with gaps longer than `max_timedelta` are excluded from the - output. If omitted, `max_timedelta` is set internally to two times - the median time delta in `time_series`. + The maximum allowed gap between values in ``time_series``. Times + associated with gaps longer than ``max_timedelta`` are excluded from the + output. If omitted, ``max_timedelta`` is set internally to two times + the median time delta in ``time_series``. warning_threshold : float, default 0.1 The fraction of data exclusion above which a warning is raised. With the default value of 0.1, a warning will be raised if the fraction - of data excluded because of data gaps longer than `max_timedelta` is + of data excluded because of data gaps longer than ``max_timedelta`` is above than 10%. Returns @@ -723,7 +776,7 @@ def interpolate(time_series, target, max_timedelta=None, warning_threshold=0.1): Note ---- Timezone information in the DatetimeIndexes is handled automatically, - however both `time_series` and `target` should be time zone aware or they + however both ``time_series`` and ``target`` should be time zone aware or they should both be time zone naive. ''' @@ -740,13 +793,13 @@ def interpolate(time_series, target, max_timedelta=None, warning_threshold=0.1): 'both must be time-zone naive.') if isinstance(time_series, pd.Series): - out = interpolate_series(time_series, target_index, max_timedelta, + out = _interpolate_series(time_series, target_index, max_timedelta, warning_threshold) elif isinstance(time_series, pd.DataFrame): out_list = [] for col in time_series.columns: ts = time_series[col] - series = interpolate_series(ts, target_index, max_timedelta, + series = _interpolate_series(ts, target_index, max_timedelta, warning_threshold) out_list.append(series) out = pd.concat(out_list, axis=1) diff --git a/rdtools/plotting.py b/rdtools/plotting.py index c66a1736..ec71f3f1 100644 --- a/rdtools/plotting.py +++ b/rdtools/plotting.py @@ -1,7 +1,8 @@ '''Functions for plotting degradation and soiling analysis results.''' import matplotlib.pyplot as plt - +import numpy as np +import warnings def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield, hist_xmin=None, hist_xmax=None, bins=None, @@ -114,6 +115,11 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5, Create figure to visualize Monte Carlo of soiling profiles used in the SRR analysis. + .. warning:: + The soiling module is currently experimental. The API, results, + and default behaviors may change in future releases (including MINOR + and PATCH releases) as the code matures. + Parameters ---------- soiling_info : dict @@ -141,6 +147,11 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5, ------- fig : matplotlib Figure ''' + warnings.warn( + 'The soiling module is currently experimental. The API, results, ' + 'and default behaviors may change in future releases (including MINOR ' + 'and PATCH releases) as the code matures.' + ) fig, ax = plt.subplots() renormalized = normalized_yield / soiling_info['renormalizing_factor'] @@ -167,6 +178,11 @@ def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5, ''' Create figure to visualize valid soiling profiles used in the SRR analysis. + .. warning:: + The soiling module is currently experimental. The API, results, + and default behaviors may change in future releases (including MINOR + and PATCH releases) as the code matures. + Parameters ---------- soiling_info : dict @@ -191,6 +207,11 @@ def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5, ------- fig : matplotlib Figure ''' + warnings.warn( + 'The soiling module is currently experimental. The API, results, ' + 'and default behaviors may change in future releases (including MINOR ' + 'and PATCH releases) as the code matures.' + ) sratio = soiling_info['soiling_ratio_perfect_clean'] fig, ax = plt.subplots() @@ -209,6 +230,11 @@ def soiling_rate_histogram(soiling_info, bins=None): ''' Create histogram of soiling rates found in the SRR analysis. + .. warning:: + The soiling module is currently experimental. The API, results, + and default behaviors may change in future releases (including MINOR + and PATCH releases) as the code matures. + Parameters ---------- soiling_info : dict @@ -221,12 +247,120 @@ def soiling_rate_histogram(soiling_info, bins=None): ------- fig : matplotlib Figure ''' + warnings.warn( + 'The soiling module is currently experimental. The API, results, ' + 'and default behaviors may change in future releases (including MINOR ' + 'and PATCH releases) as the code matures.' + ) soiling_summary = soiling_info['soiling_interval_summary'] fig, ax = plt.subplots() - ax.hist(100.0 * soiling_summary.loc[soiling_summary['valid'], 'slope'], + ax.hist(100.0 * soiling_summary.loc[soiling_summary['valid'], 'soiling_rate'], bins=bins) ax.set_xlabel('Soiling rate (%/day)') ax.set_ylabel('Count') return fig + + +def availability_summary_plots(power_system, power_subsystem, loss_total, + energy_cumulative, energy_expected_rescaled, + outage_info): + """ + Create a figure summarizing the availability analysis results. + + Because all of the parameters to this function are products of an + AvailabilityAnalysis object, it is usually easier to use + :py:meth:`.availability.AvailabilityAnalysis.plot` instead of running + this function manually. + + .. warning:: + The availability module is currently experimental. The API, results, + and default behaviors may change in future releases (including MINOR + and PATCH releases) as the code matures. + + Parameters + ---------- + power_system : pd.Series + Timeseries total system power. + + power_subsystem : pd.DataFrame + Timeseries power data, one column per subsystem. + + loss_total : pd.Series + Timeseries system lost power. + + energy_cumulative : pd.Series + Timeseries system cumulative energy. + + energy_expected_rescaled : pd.Series + Timeseries expected energy, rescaled to match actual energy. This + reflects interval energy, not cumulative. + + outage_info : pd.DataFrame + A dataframe with information about system outages. + + Returns + ------- + fig : matplotlib Figure + + See Also + -------- + rdtools.availability.AvailabilityAnalysis.plot + + Examples + -------- + >>> aa = AvailabilityAnalysis(...) + >>> aa.run() + >>> fig = rdtools.plotting.availability_summary_plots(aa.power_system, + ... aa.power_subsystem, aa.loss_total, aa.energy_cumulative, + ... aa.energy_expected_rescaled, aa.outage_info) + """ + warnings.warn( + 'The availability module is currently experimental. The API, results, ' + 'and default behaviors may change in future releases (including MINOR ' + 'and PATCH releases) as the code matures.' + ) + + fig = plt.figure(figsize=(16, 8)) + gs = fig.add_gridspec(3, 2) + ax1 = fig.add_subplot(gs[0, 0]) + ax2 = fig.add_subplot(gs[1, 0], sharex=ax1) + ax3 = fig.add_subplot(gs[2, 0], sharex=ax1) + ax4 = fig.add_subplot(gs[:, 1], sharex=ax1) + + # inverter power + power_system.plot(ax=ax1) + ax1.set_ylabel('Inverter Power [kW]') + # meter power + power_subsystem.plot(ax=ax2) + ax2.set_ylabel('System power [kW]') + # lost power + loss_total.plot(ax=ax3) + ax3.set_ylabel('Estimated lost power [kW]') + + # cumulative energy + energy_cumulative.plot(ax=ax4, label='Reported Production') + + # we'll use the index value to only set legend entries for the first + # outage we plot. Just in case the index has some other values, we'll + # reset it here: + outage_info = outage_info.reset_index(drop=True) + for i, row in outage_info.iterrows(): + # matplotlib ignores legend entries starting with underscore, so we + # can use that to hide duplicate entries + prefix = "_" if i > 0 else "" + start, end = row[['start', 'end']] + start_energy = row['energy_start'] + expected_energy = row['energy_expected'] + lo, hi = np.abs(expected_energy - row[['ci_lower', 'ci_upper']]) + expected_curve = energy_expected_rescaled[start:end].cumsum() + expected_curve += start_energy + expected_curve.plot(c='tab:orange', ax=ax4, + label=prefix + 'Expected Production') + energy_end = expected_curve.iloc[-1] + ax4.errorbar([end], [energy_end], [[lo], [hi]], c='k', + label=prefix + 'Uncertainty') + ax4.legend() + ax4.set_ylabel('Cumulative Energy [kWh]') + return fig diff --git a/rdtools/soiling.py b/rdtools/soiling.py index d9327425..f62ccfc0 100644 --- a/rdtools/soiling.py +++ b/rdtools/soiling.py @@ -1,9 +1,21 @@ -'''Functions for calculating soiling metrics from photovoltaic system data.''' +''' +Functions for calculating soiling metrics from photovoltaic system data. +The soiling module is currently experimental. The API, results, +and default behaviors may change in future releases (including MINOR +and PATCH releases) as the code matures. +''' + +import warnings import pandas as pd import numpy as np from scipy.stats.mstats import theilslopes +warnings.warn( + 'The soiling module is currently experimental. The API, results, ' + 'and default behaviors may change in future releases (including MINOR ' + 'and PATCH releases) as the code matures.' +) # Custom exception class NoValidIntervalError(Exception): @@ -25,7 +37,7 @@ class SRRAnalysis(): In either case, data should be insolation-weighted daily aggregates. insolation_daily : pd.Series Daily plane-of-array insolation corresponding to - `energy_normalized_daily` + `energy_normalized_daily`. Arbitrary units. precipitation_daily : pd.Series, default None Daily total precipitation. (Ignored if ``clean_criterion='shift'`` in subsequent calculations.) @@ -147,7 +159,8 @@ def _calc_daily_df(self, day_scale=14, clean_threshold='infer', if clean_criterion == 'precip_and_shift': # Detect which cleaning events are associated with rain within a 3 day window - precip_event = precip_event.rolling(3, center=True, min_periods=1).apply(any).astype(bool) + precip_event = precip_event.rolling( + 3, center=True, min_periods=1).apply(any).astype(bool) df['clean_event'] = (df['clean_event_detected'] & precip_event) elif clean_criterion == 'precip_or_shift': df['clean_event'] = (df['clean_event_detected'] | precip_event) @@ -160,7 +173,8 @@ def _calc_daily_df(self, day_scale=14, clean_threshold='infer', '{"precip_and_shift", "precip_or_shift", "precip", "shift"}') df['clean_event'] = df.clean_event | out_start | out_end - df['clean_event'] = (df.clean_event) & (~df.clean_event.shift(-1).fillna(False)) + df['clean_event'] = (df.clean_event) & ( + ~df.clean_event.shift(-1).fillna(False)) df = df.fillna(0) @@ -248,7 +262,8 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, # Filter results for each interval, # setting invalid interval to slope of 0 - results['slope_err'] = (results.run_slope_high-results.run_slope_low)/abs(results.run_slope) + results['slope_err'] = ( + results.run_slope_high - results.run_slope_low) / abs(results.run_slope) # critera for exclusions filt = ( (results.run_slope > 0) | @@ -270,7 +285,7 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0, results.next_inferred_start_loss - results.inferred_end_loss, 0, 1) - # Don't consider data outside of first and last valid interverals + # Don't consider data outside of first and last valid intervals if len(results[results.valid]) == 0: raise NoValidIntervalError('No valid soiling intervals were found') new_start = results[results.valid].start.iloc[0] @@ -321,9 +336,10 @@ def _calc_monte(self, monte, method='half_norm_clean'): * 'random_clean' - a random recovery between 0-100% * 'perfect_clean' - each cleaning event returns the performance metric to 1 - * 'half_norm_clean' - The three-sigma lower bound of recovery is - inferred from the fit of the following interval, the upper bound - is 1 with the magnitude drawn from a half normal centered at 1 + * 'half_norm_clean' - The starting point of each interval is taken + randomly from a half normal distribution with its mode (mu) at 1 and + its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to + the interval. ''' monte_losses = [] @@ -368,10 +384,10 @@ def _calc_monte(self, monte, method='half_norm_clean'): end = inter_start + row.run_loss end_list.append(end) - # Use a half normal with the infered clean at the + # Use a half normal with the inferred clean at the # 3sigma point x = np.clip(end + row.inferred_recovery, 0, 1) - inter_start = 1 - abs(np.random.normal(0.0, (1 - x)/3)) + inter_start = 1 - abs(np.random.normal(0.0, (1 - x) / 3)) # Update the valid rows in results_rand valid_update = pd.DataFrame() @@ -440,7 +456,7 @@ def _calc_monte(self, monte, method='half_norm_clean'): df_rand['soil_insol'] = df_rand.loss * df_rand.insol - monte_losses.append(df_rand.soil_insol.sum() / df_rand.insol.sum()) + monte_losses.append(df_rand.soil_insol.sum() / df_rand.insol[~df_rand.soil_insol.isnull()].sum()) random_profile = df_rand['loss'].copy() random_profile.name = 'stochastic_soiling_profile' random_profiles.append(random_profile) @@ -456,7 +472,8 @@ def run(self, reps=1000, day_scale=14, clean_threshold='infer', ''' Run the SRR method from beginning to end. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented - in Deceglie et al. JPV 8(2) p547 2018. + in Deceglie et al. "Quantifying Soiling Loss Directly From PV Yield" + JPV 8(2) p547 2018. Parameters ---------- @@ -476,13 +493,13 @@ def run(self, reps=1000, day_scale=14, clean_threshold='infer', method : str, default 'half_norm_clean' How to treat the recovery of each cleaning event: - * `random_clean` - a random recovery between 0-100% - * `perfect_clean` - each cleaning event returns the performance + * 'random_clean' - a random recovery between 0-100% + * 'perfect_clean' - each cleaning event returns the performance metric to 1 - * `half_norm_clean` (default) - The three-sigma lower bound of - recovery is inferred from the fit of the following interval, the - upper bound is 1 with the magnitude drawn from a half normal - centered at 1 + * 'half_norm_clean' - The starting point of each interval is taken + randomly from a half normal distribution with its mode (mu) at 1 and + its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to + the interval. clean_criterion : {'precip_and_shift', 'precip_or_shift', 'precip', 'shift'} \ default 'shift' @@ -518,23 +535,55 @@ def run(self, reps=1000, day_scale=14, clean_threshold='infer', Returns ------- insolation_weighted_soiling_ratio : float - P50 insolation weighted soiling ratio based on stochastic rate and + P50 insolation-weighted soiling ratio based on stochastic rate and recovery analysis confidence_interval : np.array confidence interval (size specified by confidence_level) of - degradation rate estimate + insolation-weighted soiling ratio calc_info : dict - * `renormalizing_factor` - value used to recenter data - * `exceedance_level` - the insolation-weighted soiling ratio that + * 'renormalizing_factor' - value used to recenter data + * 'exceedance_level' - the insolation-weighted soiling ratio that was outperformed with probability of exceedance_prob - * `stochastic_soiling_profiles` - List of Pandas series + * 'stochastic_soiling_profiles' - List of Pandas series corresponding to the Monte Carlo realizations of soiling ratio profiles - * `soiling_interval_summary` - Pandas dataframe summarizing the - soiling intervals identified - * `soiling_ratio_perfect_clean` - Pandas series of the soiling + * 'soiling_ratio_perfect_clean' - Pandas series of the soiling ratio during valid soiling intervals assuming perfect cleaning - and P50 slopes. + and P50 slopes + * 'soiling_interval_summary' - Pandas dataframe summarizing the + soiling intervals identified. The columns of the dataframe are + as follows: + + +------------------------+----------------------------------------------+ + | Column Name | Description | + +========================+==============================================+ + | 'start' | Start timestamp of the soiling interval | + +------------------------+----------------------------------------------+ + | 'end' | End timestamp of the soiling interval | + +------------------------+----------------------------------------------+ + | 'soiling_rate' | P50 Soiling rate for interval, in day^−1 | + | | Negative value indicates soiling is | + | | occurring. E.g. a rate of −0.01 indicates 1% | + | | soiling loss per day. | + +------------------------+----------------------------------------------+ + | 'soiling_rate_low' | Low edge of confidence interval for soiling | + | | rate for interval, in day^−1 | + +------------------------+----------------------------------------------+ + | 'soiling_rate_high' | High edge of confidence interval for | + | | soiling rate for interval, in day^−1 | + +------------------------+----------------------------------------------+ + | 'inferred_start_loss' | Estimated performance metric at the start | + | | of the interval | + +------------------------+----------------------------------------------+ + | 'inferred_end_loss' | Estimated performance metric at the end | + | | of the interval | + +------------------------+----------------------------------------------+ + | 'length' | Number of days in the interval | + +------------------------+----------------------------------------------+ + | 'valid' | Whether the interval meets the criteria to | + | | be treated as a valid soiling interval | + +------------------------+----------------------------------------------+ + ''' self._calc_daily_df(day_scale=day_scale, clean_threshold=clean_threshold, @@ -562,9 +611,9 @@ def run(self, reps=1000, day_scale=14, clean_threshold='infer', ['start', 'end', 'run_slope', 'run_slope_low', 'run_slope_high', 'inferred_start_loss', 'inferred_end_loss', 'length', 'valid']].copy() - intervals_out.rename(columns={'run_slope': 'slope', - 'run_slope_high': 'slope_high', - 'run_slope_low': 'slope_low', + intervals_out.rename(columns={'run_slope': 'soiling_rate', + 'run_slope_high': 'soiling_rate_high', + 'run_slope_low': 'soiling_rate_low', }, inplace=True) df_d = self.analyzed_daily_df @@ -600,7 +649,7 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, In either case, data should be insolation-weighted daily aggregates. insolation_daily : pd.Series Daily plane-of-array insolation corresponding to - `energy_normalized_daily` + `energy_normalized_daily`. Arbitrary units. reps : int, default 1000 number of Monte Carlo realizations to calculate precipitation_daily : pd.Series, default None @@ -619,14 +668,15 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals method : str, default 'half_norm_clean' - how to treat the recovery of each cleaning event + How to treat the recovery of each cleaning event - * `random_clean` - a random recovery between 0-100% - * `perfect_clean` - each cleaning event returns the performance metric + * 'random_clean' - a random recovery between 0-100% + * 'perfect_clean' - each cleaning event returns the performance metric to 1 - * `half_norm_clean` (default) - The three-sigma lower bound of recovery - is inferred from the fit of the following interval, the upper bound - is 1 with the magnitude drawn from a half normal centered at 1 + * 'half_norm_clean'(default) - The starting point of each interval is taken + randomly from a half normal distribution with its mode (mu) at 1 and + its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to + the interval. clean_criterion : {'precip_and_shift', 'precip_or_shift', 'precip', 'shift'} \ default 'shift' The method of partitioning the dataset into soiling intervals. @@ -640,7 +690,7 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, The daily precipitation threshold for defining precipitation cleaning events. Units must be consistent with precip. min_interval_length : int, default 2 - The minimum duration for an interval to be considered + The minimum duration, in days, for an interval to be considered valid. Cannot be less than 2 (days). exceedance_prob : float, default 95.0 the probability level to use for exceedance value calculation in @@ -664,22 +714,51 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, P50 insolation weighted soiling ratio based on stochastic rate and recovery analysis confidence_interval : np.array - confidence interval (size specified by `confidence_level`) of + confidence interval (size specified by ``confidence_level``) of degradation rate estimate calc_info : dict - Calculation information from the SRR process. - - * `renormalizing_factor` - value used to recenter data - * `exceedance_level` - the insolation-weighted soiling ratio that + * 'renormalizing_factor' - value used to recenter data + * 'exceedance_level' - the insolation-weighted soiling ratio that was outperformed with probability of exceedance_prob - * `stochastic_soiling_profiles` - List of Pandas series - corresponding to the Monte Carlo realizations of soiling - ratio profiles - * `soiling_interval_summary` - Pandas dataframe summarizing the - soiling intervals identified - * `soiling_ratio_perfect_clean` - Pandas series of the soiling + * 'stochastic_soiling_profiles' - List of Pandas series + corresponding to the Monte Carlo realizations of soiling ratio + profiles + * 'soiling_ratio_perfect_clean' - Pandas series of the soiling ratio during valid soiling intervals assuming perfect cleaning - and P50 slopes. + and P50 slopes + * 'soiling_interval_summary' - Pandas dataframe summarizing the + soiling intervals identified. The columns of the dataframe are + as follows: + + +------------------------+----------------------------------------------+ + | Column Name | Description | + +========================+==============================================+ + | 'start' | Start timestamp of the soiling interval | + +------------------------+----------------------------------------------+ + | 'end' | End timestamp of the soiling interval | + +------------------------+----------------------------------------------+ + | 'soiling_rate' | P50 Soiling rate for interval, in day^−1 | + | | Negative value indicates soiling is | + | | occurring. E.g. a rate of −0.01 indicates 1% | + | | soiling loss per day. | + +------------------------+----------------------------------------------+ + | 'soiling_rate_low' | Low edge of confidence interval for soiling | + | | rate for interval, in day^−1 | + +------------------------+----------------------------------------------+ + | 'soiling_rate_high' | High edge of confidence interval for | + | | soiling rate for interval, in day^−1 | + +------------------------+----------------------------------------------+ + | 'inferred_start_loss' | Estimated performance metric at the start | + | | of the interval | + +------------------------+----------------------------------------------+ + | 'inferred_end_loss' | Estimated performance metric at the end | + | | of the interval | + +------------------------+----------------------------------------------+ + | 'length' | Number of days in the interval | + +------------------------+----------------------------------------------+ + | 'valid' | Whether the interval meets the criteria to | + | | be treated as a valid soiling interval | + +------------------------+----------------------------------------------+ ''' srr = SRRAnalysis(energy_normalized_daily, @@ -694,6 +773,7 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, method=method, clean_criterion=clean_criterion, precip_threshold=precip_threshold, + min_interval_length=min_interval_length, exceedance_prob=exceedance_prob, confidence_level=confidence_level, recenter=recenter, @@ -701,3 +781,210 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000, max_negative_step=max_negative_step) return sr, sr_ci, soiling_info + + +def _count_month_days(start, end): + '''Return a dict of number of days between start and end (inclusive) in each month''' + days = pd.date_range(start, end) + months = [x.month for x in days] + out_dict = {} + for month in range(1, 13): + out_dict[month] = months.count(month) + + return out_dict + + +def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confidence_level=68.2): + ''' + Return annualized soiling ratios and associated confidence intervals based + on stochastic soiling profiles from SRR. Note that each year may be affected + by previous years' profiles for all SRR cleaning assumptions (i.e. method) except + perfect_clean. + + Parameters + ---------- + stochastic_soiling_profiles : list + List of pd.Series representing profile realizations from the SRR monte carlo. + Typically ``soiling_interval_summary['stochastic_soiling_profiles']`` obtained with + :py:func:`rdtools.soiling.soiling_srr` or :py:meth:`rdtools.soiling.SRRAnalysis.run` + insolation_daily : pd.Series + Daily plane-of-array insolation with DatetimeIndex. Arbitrary units. + confidence_level : float, default 68.2 + The size of the confidence interval to use in determining the upper and lower + quantiles reported in the returned DataFrame. (The median is always included in + the result.) + + Returns + ------- + pd.DataFrame + DataFrame describing annual soiling rates. + + +------------------------+-------------------------------------------+ + | Column Name | Description | + +========================+===========================================+ + | 'year' | Calendar year | + +------------------------+-------------------------------------------+ + | 'soiling_ratio_median' | The median insolation-weighted soiling | + | | ratio for the year | + +------------------------+-------------------------------------------+ + | 'soiling_ratio_low' | The lower edge of the confidence interval | + | | for insolation-weighted soiling ratio for | + | | the year | + +------------------------+-------------------------------------------+ + | 'soiling_ratio_high' | The upper edge of the confidence interval | + | | for insolation-weighted soiling ratio for | + | | the year | + +------------------------+-------------------------------------------+ + ''' + + # Create a df with each realization as a column + all_profiles = pd.concat(stochastic_soiling_profiles, axis=1) + all_profiles = all_profiles.dropna() + + if not all_profiles.index.isin(insolation_daily.index).all(): + warnings.warn('The indexes of stochastic_soiling_profiles are not entirely contained ' + 'within the index of insolation_daily. Every day in stochastic_soiling_profiles ' + 'should be represented in insolation_daily. This may cause erroneous results.') + + insolation_daily = insolation_daily.reindex(all_profiles.index) + + # Weight each day by insolation + all_profiles_weighted = all_profiles.multiply(insolation_daily, axis=0) + + # Compute the insolation-weighted soiling ratio (IWSR) for each realization + annual_insolation = insolation_daily.groupby(insolation_daily.index.year).sum() + all_annual_weighted_sums = all_profiles_weighted.groupby(all_profiles_weighted.index.year).sum() + all_annual_iwsr = all_annual_weighted_sums.multiply(1/annual_insolation, axis=0) + + annual_soiling = pd.DataFrame({ + 'soiling_ratio_median': all_annual_iwsr.quantile(0.5, axis=1), + 'soiling_ratio_low': all_annual_iwsr.quantile(0.5 - confidence_level/2/100, axis=1), + 'soiling_ratio_high': all_annual_iwsr.quantile(0.5 + confidence_level/2/100, axis=1), + }) + annual_soiling.index.name = 'year' + annual_soiling = annual_soiling.reset_index() + + return annual_soiling + + +def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14, + max_relative_slope_error=500.0, reps=100000, + confidence_level=68.2): + ''' + Use Monte Carlo to calculate typical monthly soiling rates. Samples possible + soiling rates from soiling rate confidence intervals associated with soiling + intervals assuming a uniform distribution. Soiling intervals get samples + proportionally to their overlap with each calendar month. + + Parameters + ---------- + soiling_interval_summary : pd.DataFrame + DataFrame describing soiling intervals. Typically from + ``soiling_info['soiling_interval_summary']`` obtained with + :py:func:`rdtools.soiling.soiling_srr` or + :py:meth:`rdtools.soiling.SRRAnalysis.run` Must have columns + ``soiling_rate_high``, ``soiling_rate_low``, ``soiling_rate``, ``length``, ``valid``, + ``start``, and ``end``. + + min_interval_length : int, default 14 + The minimum number of days a soiling interval must contain to be + included in the calculation. Similar to the same parameter in soiling_srr() + and SRRAnalysis.run() but with a more conservative default value as a + starting point for monthly soiling rate analyses. + + max_relative_slope_error : float, default 500.0 + The maximum relative size of the slope confidence interval for an + interval to be included in the calculation (percentage). + + reps : int, default 100000 + The number of Monte Carlo samples to take for each month. + + confidence_level : float, default 68.2 + The size of the confidence interval, as a percentage, to use in determining the + upper and lower quantiles reported in the returned DataFrame. (The median is + always included in the result.) + + Returns + ------- + pd.DataFrame + DataFrame describing monthly soiling rates. + + +-----------------------+--------------------------------------------------+ + | Column Name | Description | + +=======================+==================================================+ + | 'month' | Integer month, January (1) to December (12) | + +-----------------------+--------------------------------------------------+ + | 'soiling_rate_median' | The median soiling rate for the month over | + | | the entire dataset, in units of day^−1. | + | | Negative value indicates soiling is occurring. | + | | E.g. a rate of −0.01 indicates 1% soiling loss | + | | per day. | + +-----------------------+--------------------------------------------------+ + | 'soiling_rate_low' | The lower edge of the confidence interval | + | | for the monthly soiling rate in units of | + | | day^−1 | + +-----------------------+--------------------------------------------------+ + | 'soiling_rate_high' | The upper edge of the confidence interval | + | | for the monthly soiling rate in units of | + | | day^−1 | + +-----------------------+--------------------------------------------------+ + | 'interval_count' | The number of soiling intervals contributing | + | | to the monthly calculation. If only a few | + | | intervals contribute, the confidence interval | + | | is likely to underestimate the true uncertainty. | + +-----------------------+--------------------------------------------------+ + ''' + + # filter to intervals of interest + rel_error = 100 * abs((soiling_interval_summary['soiling_rate_high'] - + soiling_interval_summary['soiling_rate_low']) / soiling_interval_summary['soiling_rate']) + intervals = soiling_interval_summary[(soiling_interval_summary['length'] >= min_interval_length) & + (soiling_interval_summary['valid']) & + (rel_error <= max_relative_slope_error) + ].copy() + + # count the overlap of each interval with each month + month_counts = [] + for _, row in intervals.iterrows(): + month_counts.append(_count_month_days(row['start'], row['end'])) + + # divy up the monte carlo reps based on overlap + for month in range(1, 13): + days_in_month = np.array([x[month] for x in month_counts]) + if days_in_month.sum() > 0: + intervals[f'samples_for_month_{month}'] = np.ceil( + days_in_month / days_in_month.sum() * reps) + else: + intervals[f'samples_for_month_{month}'] = 0 + intervals[f'samples_for_month_{month}'] = intervals[f'samples_for_month_{month}'].astype(int) + + # perform the monte carlo month by month + ci_quantiles = [0.5 - confidence_level/2/100, 0.5 + confidence_level/2/100] + monthly_rate_data = [] + relevant_interval_count = [] + for month in range(1, 13): + rates = [] + sample_col = f'samples_for_month_{month}' + + relevant_intervals = intervals[intervals[sample_col] > 0] + for _, row in relevant_intervals.iterrows(): + rates.append(np.random.uniform( + row['soiling_rate_low'], row['soiling_rate_high'], row[sample_col])) + + rates = [x for sublist in rates for x in sublist] + + if rates: + monthly_rate_data.append(np.quantile(rates, [0.5, ci_quantiles[0], ci_quantiles[1]])) + else: + monthly_rate_data.append(np.array([np.nan]*3)) + + relevant_interval_count.append(len(relevant_intervals)) + monthly_rate_data = np.array(monthly_rate_data) + + # make a dataframe out of the results + monthly_soiling_df = pd.DataFrame(data=monthly_rate_data, + columns=['soiling_rate_median', 'soiling_rate_low', 'soiling_rate_high']) + monthly_soiling_df.insert(0, 'month', range(1, 13)) + monthly_soiling_df['interval_count'] = relevant_interval_count + + return monthly_soiling_df diff --git a/rdtools/test/availability_test.py b/rdtools/test/availability_test.py new file mode 100644 index 00000000..9af3bd12 --- /dev/null +++ b/rdtools/test/availability_test.py @@ -0,0 +1,474 @@ +""" +Test suite for inverter availability functions. +""" + +import pytest +from pandas.testing import assert_series_equal +from conftest import assert_isinstance + +from rdtools.availability import AvailabilityAnalysis + +import pvlib +import pandas as pd +import numpy as np +import itertools +import datetime +import matplotlib.pyplot as plt + +# Values to parametrize power tests across. One test will be run for each +# combination. Can't be careless about expanding this list because of +# combinatorial explosion. +PARAMETER_SPACE = list(itertools.product( + [0, np.nan], # values that power data takes during comms outage + [0, np.nan, 0.001, -0.001], # values during real downtime + [1.0, 3.0], # relative inverter capacities + [1, 3], # the number of inverters per system + [False, True], # whether any systems are really offline + [False, True], # whether a comms outage occurs +)) + +# display names for the test cases. default is just 0..N +PARAMETER_IDS = ["_".join(map(str, p)) for p in PARAMETER_SPACE] + + +@pytest.fixture(params=PARAMETER_SPACE, ids=PARAMETER_IDS) +def power_data(request): + """ + Generate power test cases corresponding to cover different system designs + and data artifacts caused by outages. This fixture is parametrized across + many (~hundreds) combinations in the PARAMETER_SPACE list. + + The method is to generate some inverter power signals of varying scales, + introduce power outages to some of them, calculate the system meter power + as the summed inverter power, and then add inverter communication outages. + + Returns a tuple: + - inverter_power, dataframe + - meter_power, series + - expected_loss, series + """ + # unpack the parameters: + comms_value, outage_value, relative_sizing, n_inverter, \ + has_power_outage, has_comms_outage = request.param + + # a few days of clearsky irradiance for creating a plausible power signal + times = pd.date_range('2019-01-01', '2019-01-05 23:59', freq='15min', + tz='US/Eastern') + location = pvlib.location.Location(40, -80) + # use haurwitz to avoid dependency on `tables` + clearsky = location.get_clearsky(times, model='haurwitz') + + # just set base inverter power = ghi+clipping for simplicity + base_power = clearsky['ghi'].clip(upper=0.8*clearsky['ghi'].max()) + + inverter_power = pd.DataFrame({'inv1': base_power}) + if n_inverter == 3: + inverter_power['inv2'] = base_power / relative_sizing + inverter_power['inv3'] = base_power * relative_sizing + + expected_loss = pd.Series(0, index=times, dtype=float) + + if has_power_outage: + date = '2019-01-01' + # this expected_loss calculation is not exactly the same as what the + # function uses, but it is quite close. Need to do the comparison + # with appropriate precision. + expected_loss.loc[date] = inverter_power.loc[date, 'inv1'] + lim = inverter_power['inv1'].quantile(0.99) / 1000 + expected_loss[inverter_power['inv1'] < lim] = 0 + inverter_power.loc[date, 'inv1'] = outage_value + + # special case: if n_inv == 1, the method can't estimate the loss: + if n_inverter == 1: + expected_loss.loc[date] = 0 + + # meter_power reflects real inverter-level outages, but not + # inverter-level comms outages, so do the sum before adding comms outages: + meter_power = inverter_power.sum(axis=1) + + if has_comms_outage: + inverter_power.loc['2019-01-02', 'inv1'] = comms_value + + return inverter_power, meter_power, expected_loss + + +def test__calc_loss_subsystem(power_data): + # implicitly sweeps across the parameter space because power_data is + # parametrized + inverter_power, meter_power, expected_loss = power_data + # these values aren't relevant to this test, but the timeseries are + # checked for timestamp consistency so just pass in dummy data: + energy_cumulative = pd.Series(np.nan, meter_power.index) + power_expected = pd.Series(np.nan, meter_power.index) + aa = AvailabilityAnalysis(meter_power, + inverter_power, + energy_cumulative=energy_cumulative, + power_expected=power_expected) + aa._calc_loss_subsystem(low_threshold=None, relative_sizes=None, + power_system_limit=None) + actual_loss = aa.loss_subsystem + # pandas <1.1.0 as no atol/rtol parameters, so just use np.round instead: + assert_series_equal(np.round(expected_loss, 1), + np.round(actual_loss, 1)) + + +@pytest.fixture +def dummy_power_data(): + # one inverter off half the time, one always online + N = 10 + df = pd.DataFrame({ + 'inv1': [0] * (N//2) + [1] * (N//2), + 'inv2': [1] * N, + }, index=pd.date_range('2019-01-01', freq='h', periods=N)) + # return dummy data for cumulative energy and expected power + dummy = pd.Series(np.nan, df.index) + return df, df.sum(axis=1), dummy + + +def test_calc_loss_subsystem_threshold(dummy_power_data): + # test low_threshold parameter. + # negative threshold means the inverter is never classified as offline + inverter_power, meter_power, dummy = dummy_power_data + aa = AvailabilityAnalysis(meter_power, + inverter_power, + energy_cumulative=dummy, + power_expected=dummy) + aa._calc_loss_subsystem(low_threshold=-1, relative_sizes=None, + power_system_limit=None) + actual_loss = aa.loss_subsystem + assert actual_loss.sum() == 0 + + +def test_calc_loss_subsystem_limit(dummy_power_data): + # test system_power_limit parameter. + # set it unrealistically low to verify it constrains the loss. + # real max power is 2, real max loss is 1, so setting limit=1.5 sets max + # loss to 0.5 + inverter_power, meter_power, dummy = dummy_power_data + aa = AvailabilityAnalysis(meter_power, + inverter_power, + energy_cumulative=dummy, + power_expected=dummy) + aa._calc_loss_subsystem(low_threshold=None, relative_sizes=None, + power_system_limit=1.5) + actual_loss = aa.loss_subsystem + assert actual_loss.max() == pytest.approx(0.5, abs=0.01) + + +@pytest.fixture +def difficult_data(): + # a nasty dataset with lots of downtime and almost no periods where + # the two inverters are online simultaneously, so calculating the + # relative sizes automatically gives the wrong answer. + + # generate a plausible clear-sky power signal + times = pd.date_range('2019-01-01', '2019-01-06', freq='15min', + tz='US/Eastern', closed='left') + location = pvlib.location.Location(40, -80) + clearsky = location.get_clearsky(times, model='haurwitz') + # just scale GHI to power for simplicity + base_power = 2.5*clearsky['ghi'] + # but require a minimum irradiance to turn on, simulating start-up voltage + base_power[clearsky['ghi'] < 20] = 0 + + # 1 and 3, so the relative sizing is 0.5 and 1.5 (1/2 and 3/2) + df = pd.DataFrame({ + 'inv1_power': base_power, + 'inv2_power': base_power * 3, + }) + relative_sizes = {'inv1_power': 0.5, 'inv2_power': 1.5} + + # inv1 offline days 1 & 2; inv2 offline days 3 & 4. Both online on day 5 + df.loc['2019-01-01', 'inv1_power'] = 0 + df.loc['2019-01-02', 'inv1_power'] = 0 + df.loc['2019-01-03', 'inv2_power'] = 0 + df.loc['2019-01-04', 'inv2_power'] = 0 + + # no need for communication outages here, so just take meter=inv sum + expected_power = meter_power = df.sum(axis=1) + + return df, meter_power, expected_power, relative_sizes + +def test_calc_loss_subsystem_relative_sizes(difficult_data): + # test that manually passing in relative_sizes improves the results + # for pathological datasets with tons of downtime + invs, meter, expected, relative_sizes = difficult_data + aa = AvailabilityAnalysis(meter, + invs, + energy_cumulative=meter.cumsum()/4, + power_expected=expected) + # verify that results are bad by default -- without the correction, the + # two inverters are weighted equally, so availability will be 50% when + # only one is online + aa.run(rollup_period='d') + ava = aa.results['availability'] + assert np.allclose(ava.iloc[0:4], 0.5) + assert np.allclose(ava.iloc[4], 1.0) + + # now use the correct relative_sizes + aa.run(rollup_period='d', relative_sizes=relative_sizes) + ava = aa.results['availability'] + assert np.allclose(ava.iloc[0:2], 0.75) + assert np.allclose(ava.iloc[2:4], 0.25) + assert np.allclose(ava.iloc[4], 1.0) + +# %% + +ENERGY_PARAMETER_SPACE = list(itertools.product( + [0, np.nan], # outage value for power + [0, np.nan, None], # value for cumulative energy (None means real value) + [0, 0.25, 0.5, 0.75, 1.0], # fraction of comms outage that is power outage +)) +# display names for the test cases. default is just 0..N +ENERGY_PARAMETER_IDS = ["_".join(map(str, p)) for p in ENERGY_PARAMETER_SPACE] + + +def _generate_energy_data(power_value, energy_value, outage_fraction): + """ + Generate an artificial mixed communication/power outage. + """ + # a few days of clearsky irradiance for creating a plausible power signal + times = pd.date_range('2019-01-01', '2019-01-15 23:59', freq='15min', + tz='US/Eastern') + location = pvlib.location.Location(40, -80) + # use haurwitz to avoid dependency on `tables` + clearsky = location.get_clearsky(times, model='haurwitz') + + # just set base inverter power = ghi+clipping for simplicity + base_power = clearsky['ghi'].clip(upper=0.8*clearsky['ghi'].max()) + + inverter_power = pd.DataFrame({ + 'inv0': base_power, + 'inv1': base_power*0.7, + 'inv2': base_power*1.3, + }) + expected_power = inverter_power.sum(axis=1) + # dawn/dusk points + expected_power[expected_power < 10] = 0 + # add noise and bias to the expected power signal + np.random.seed(2020) + expected_power *= 1.05 + np.random.normal(0, scale=0.05, size=len(times)) + + # calculate what part of the comms outage is a power outage + comms_outage = slice('2019-01-03 00:00', '2019-01-06 00:00') + start = times.get_loc(comms_outage.start) + stop = times.get_loc(comms_outage.stop) + power_outage = slice(start, int(start + outage_fraction * (stop-start))) + expected_loss = inverter_power.iloc[power_outage, :].sum().sum() / 4 + inverter_power.iloc[power_outage, :] = 0 + meter_power = inverter_power.sum(axis=1) + meter_energy = meter_power.cumsum() / 4 + # add an offset because in practice cumulative meter data never + # actually starts at 0: + meter_energy += 100 + + meter_power[comms_outage] = power_value + if energy_value is not None: + meter_energy[comms_outage] = energy_value + inverter_power.loc[comms_outage, :] = power_value + + expected_type = 'real' if outage_fraction > 0 else 'comms' + + return (meter_power, + meter_energy, + inverter_power, + expected_power, + expected_loss, + expected_type) + + +@pytest.fixture(params=ENERGY_PARAMETER_SPACE, ids=ENERGY_PARAMETER_IDS) +def energy_data(request): + # fixture sweeping across the entire parameter space + power_value, energy_value, outage_fraction = request.param + return _generate_energy_data(power_value, energy_value, outage_fraction) + + +@pytest.fixture +def energy_data_outage_single(): + # fixture only using a single parameter combination, for simpler tests. + # has one real outage. + outage_value, outage_fraction = np.nan, 0.25 + return _generate_energy_data(outage_value, outage_value, outage_fraction) + + +@pytest.fixture +def energy_data_comms_single(): + # fixture only using a single parameter combination, for simpler tests. + # has one comms outage. + outage_value, outage_fraction = np.nan, 0 + return _generate_energy_data(outage_value, outage_value, outage_fraction) + + +def test__calc_loss_system(energy_data): + # test single outage + (meter_power, + meter_energy, + inverter_power, + expected_power, + expected_loss, + expected_type) = energy_data + + aa = AvailabilityAnalysis(meter_power, inverter_power, + meter_energy, expected_power) + aa.run() + outage_info = aa.outage_info + + # only one outage + assert len(outage_info) == 1 + outage_info = outage_info.iloc[0, :] + + # outage was correctly classified: + assert outage_info['type'] == expected_type + + # outage loss is accurate to 5% of the true value: + assert outage_info['loss'] == pytest.approx(expected_loss, rel=0.05) + + +def test__calc_loss_system_multiple(energy_data): + # test multiple outages + (meter_power, + meter_energy, + inverter_power, + expected_power, + _, _) = energy_data + + date = '2019-01-08' + meter_power.loc[date] = 0 + meter_energy.loc[date] = 0 + inverter_power.loc[date] = 0 + aa = AvailabilityAnalysis(meter_power, inverter_power, + meter_energy, expected_power) + aa.run() + outage_info = aa.outage_info + assert len(outage_info) == 2 + + +@pytest.mark.parametrize('side', ['start', 'end']) +def test__calc_loss_system_startend(side, energy_data_outage_single): + # data starts or ends in an outage + (meter_power, + meter_energy, + inverter_power, + expected_power, + _, _) = energy_data_outage_single + + if side == 'start': + # an outage all day on the 1st, so technically the outage extends to + # sunrise on the 2nd, but it doesn't wrap around to the previous dusk + date = '2019-01-01' + expected_start = '2019-01-01 00:00' + expected_end = '2019-01-02 07:45' + idx = 0 + else: + # last day doesn't have a "sunrise on the next day", so it doesn't + # wrap around + date = '2019-01-15' + expected_start = '2019-01-14 17:15' + expected_end = '2019-01-15 23:45' + idx = -1 + + meter_power.loc[date] = 0 + meter_energy.loc[date] = 0 + inverter_power.loc[date] = 0 + + aa = AvailabilityAnalysis(meter_power, inverter_power, + meter_energy, expected_power) + aa.run() + outage_info = aa.outage_info + actual_start = outage_info['start'].iloc[idx].strftime('%Y-%m-%d %H:%M') + actual_end = outage_info['end'].iloc[idx].strftime('%Y-%m-%d %H:%M') + assert actual_start == expected_start + assert actual_end == expected_end + + +def test__calc_loss_system_quantiles(energy_data_comms_single): + # exercise the quantiles parameter + (meter_power, + meter_energy, + inverter_power, + expected_power, + _, _) = energy_data_comms_single + + # first make sure it gets picked up as a comms outage with normal quantiles + aa = AvailabilityAnalysis(meter_power, inverter_power, + meter_energy, expected_power) + aa.run(quantiles=(0.01, 0.99)) + outage_info = aa.outage_info + assert outage_info['type'].values[0] == 'comms' + + # set the lower quantile very high so that the comms outage gets + # classified as a real outage + aa = AvailabilityAnalysis(meter_power, inverter_power, + meter_energy, expected_power) + aa.run(quantiles=(0.999, 0.9999)) + outage_info = aa.outage_info + assert outage_info['type'].values[0] == 'real' + + +# %% plotting + +@pytest.fixture +def availability_analysis_object(energy_data_outage_single): + (meter_power, + meter_energy, + inverter_power, + expected_power, + _, _) = energy_data_outage_single + + aa = AvailabilityAnalysis(meter_power, inverter_power, meter_energy, + expected_power) + aa.run() + return aa + + +def test_plot(availability_analysis_object): + result = availability_analysis_object.plot() + assert_isinstance(result, plt.Figure) + + +# %% errors + +def test_plot_norun(dummy_power_data): + _, _, dummy = dummy_power_data + aa = AvailabilityAnalysis(dummy, dummy, dummy, dummy) + # don't call run, just go straight to plot + with pytest.raises(TypeError, match="No results to plot"): + aa.plot() + + +def test_availability_analysis_index_mismatch(energy_data_outage_single): + # exercise the timeseries index check + (meter_power, + meter_energy, + inverter_power, + expected_power, + _, _) = energy_data_outage_single + + base_kwargs = { + 'power_system': meter_power, + 'power_subsystem': inverter_power, + 'energy_cumulative': meter_energy, + 'power_expected': expected_power, + } + # verify that the check works for any of the timeseries inputs + for key in base_kwargs.keys(): + kwargs = base_kwargs.copy() + value = kwargs.pop(key) + value_shortened = value.iloc[1:] + kwargs[key] = value_shortened + with pytest.raises(ValueError, match='timeseries indexes must match'): + aa = AvailabilityAnalysis(**kwargs) + + +def test_availability_analysis_doublecount_loss(availability_analysis_object): + # test that a warning is emitted when loss is found at both the + # system and subsystem levels. I don't know how to trigger the warning + # with real data, so we'll "hack" the analysis object: + loss = pd.Series(0, index=availability_analysis_object.power_system.index) + loss.iloc[0] = 1 + availability_analysis_object.loss_system = loss + availability_analysis_object.loss_subsystem = loss + match = 'Loss detected simultaneously at both system and subsystem levels.' + with pytest.warns(UserWarning, match=match): + availability_analysis_object._combine_losses() diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py new file mode 100644 index 00000000..93c785ef --- /dev/null +++ b/rdtools/test/conftest.py @@ -0,0 +1,34 @@ +from pkg_resources import parse_version +import pytest +from functools import wraps + +import rdtools + +rdtools_base_version = \ + parse_version(parse_version(rdtools.__version__).base_version) + + +# decorator takes one argument: the base version for which it should fail +# for example @fail_on_rdtools_version('3.0.0') will cause a test to fail +# on rdtools versions 3.0.0, 3.0.0-alpha, 3.1.0, etc +def fail_on_rdtools_version(version): + # second level of decorator takes the function under consideration + def wrapper(func): + # third level defers computation until the test is called + # this allows the specific test to fail at test runtime, + # rather than at decoration time (when the module is imported) + @wraps(func) + def inner(*args, **kwargs): + # fail if the version is too high + if rdtools_base_version >= parse_version(version): + pytest.fail('the tested function is scheduled to be ' + 'removed in %s' % version) + # otherwise return the function to be executed + else: + return func(*args, **kwargs) + return inner + return wrapper + + +def assert_isinstance(obj, klass): + assert isinstance(obj, klass), f'got {type(obj)}, expected {klass}' diff --git a/rdtools/test/energy_from_power_test.py b/rdtools/test/energy_from_power_test.py index 8827338c..7d61cf56 100644 --- a/rdtools/test/energy_from_power_test.py +++ b/rdtools/test/energy_from_power_test.py @@ -4,143 +4,98 @@ import pytest -# Tests for resampling at same frequency -def test_energy_from_power_calculation(): - power_times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:00', freq='15T') - result_times = power_times[1:] - power_series = pd.Series(data=4.0, index=power_times) - expected_energy_series = pd.Series(data=1.0, index=result_times) - expected_energy_series.name = 'energy_Wh' +@pytest.fixture +def times(): + return pd.date_range(start='20200101 12:00', end='20200101 13:00', freq='15T') - result = energy_from_power(power_series, max_timedelta=pd.to_timedelta('15 minutes')) - pd.testing.assert_series_equal(result, expected_energy_series) +@pytest.fixture +def power(times): + return pd.Series([1.0, 2.0, 3.0, 2.0, 1.0], index=times) -def test_energy_from_power_max_interval(): - power_times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:00', freq='15T') - result_times = power_times[1:] - power_series = pd.Series(data=4.0, index=power_times) - expected_energy_series = pd.Series(data=np.nan, index=result_times) - expected_energy_series.name = 'energy_Wh' +def test_energy_from_power_single_arg(power): + expected = power.iloc[1:]*0.25 + expected.name = 'energy_Wh' + result = energy_from_power(power) + pd.testing.assert_series_equal(result, expected) - result = energy_from_power(power_series, max_timedelta=pd.to_timedelta('5 minutes')) - # We expect series of NaNs, because max_interval_hours is smaller than the - # time step of the power time series - pd.testing.assert_series_equal(result, expected_energy_series) +def test_energy_from_power_instantaneous(power): + expected = (0.25*(power + power.shift())/2).dropna() + expected.name = 'energy_Wh' + result = energy_from_power(power, power_type='instantaneous') + pd.testing.assert_series_equal(result, expected) -def test_energy_from_power_validation(): - power_series = pd.Series(data=[4.0] * 4) - with pytest.raises(ValueError): - energy_from_power(power_series, max_timedelta=pd.to_timedelta('15 minutes')) +def test_energy_from_power_max_timedelta_inference(power): + expected = power.iloc[1:]*0.25 + expected.name = 'energy_Wh' + expected.iloc[:2] = np.nan + result = energy_from_power(power.drop(power.index[1])) + pd.testing.assert_series_equal(result, expected) -def test_energy_from_power_single_argument(): - power_times = pd.date_range('2018-04-01 12:00', '2018-04-01 15:00', freq='15T') - result_times = power_times[1:] - power_series = pd.Series(data=4.0, index=power_times) - missing = pd.to_datetime('2018-04-01 13:00:00') - power_series = power_series.drop(missing) +def test_energy_from_power_max_timedelta(power): + expected = power.iloc[1:]*0.25 + expected.name = 'energy_Wh' + result = energy_from_power(power.drop(power.index[1]), + max_timedelta=pd.to_timedelta('30 minutes')) + pd.testing.assert_series_equal(result, expected) - expected_energy_series = pd.Series(data=1.0, index=result_times) - expected_nan = [missing] - expected_nan.append(pd.to_datetime('2018-04-01 13:15:00')) - expected_energy_series.loc[expected_nan] = np.nan - expected_energy_series.name = 'energy_Wh' - # Test that the result has the expected missing timestamp based on median timestep - result = energy_from_power(power_series) - pd.testing.assert_series_equal(result, expected_energy_series) +def test_energy_from_power_upsample(power): + expected = power.resample('10T').asfreq().interpolate()/6 + expected = expected.iloc[1:] + expected.name = 'energy_Wh' + result = energy_from_power(power, target_frequency='10T') + pd.testing.assert_series_equal(result, expected) -# Tests for downsampling -def test_energy_from_power_downsample(): - times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:00', freq='15T') - time_series = pd.Series(data=[1.0, 2.0, 3.0, 4.0, 5.0], index=times) +def test_energy_from_power_downsample(power): + expected = power.resample('20T').asfreq() + expected = expected.iloc[1:] + expected = pd.Series([0.75, 0.833333333, 0.416666667], index=expected.index) + expected.name = 'energy_Wh' + result = energy_from_power(power, target_frequency='20T') + pd.testing.assert_series_equal(result, expected) - expected_energy_series = pd.Series(index=[pd.to_datetime('2018-04-01 13:00:00')], - data=3.0, name='energy_Wh') - expected_energy_series.index.freq = '60T' - result = energy_from_power(time_series, '60T') - pd.testing.assert_series_equal(result, expected_energy_series) +def test_energy_from_power_max_timedelta_edge_case(): + times = pd.date_range('2020-01-01 12:00', periods=4, freq='15T') + power = pd.Series(1, index=times) + power = power.drop(power.index[2]) + result = energy_from_power(power, '30T', max_timedelta=pd.to_timedelta('20 minutes')) + assert result.isnull().all() -def test_energy_from_power_downsample_max_timedelta_exceeded(): - times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:00', freq='15T') - time_series = pd.Series(data=[1.0, 2.0, 3.0, 4.0, 5.0], index=times) - expected_energy_series = pd.Series(index=[pd.to_datetime('2018-04-01 13:00:00')], - data=1.5, name='energy_Wh') - expected_energy_series.index.freq = '60T' - result = energy_from_power(time_series.drop(time_series.index[2]), '60T', pd.to_timedelta('15 minutes')) - pd.testing.assert_series_equal(result, expected_energy_series) +def test_energy_from_power_single_value_input(): + times = pd.date_range('2019-01-01', freq='15T', periods=1) + power = pd.Series([100.], index=times) + expected_result = pd.Series([25.], index=times, name='energy_Wh') + result = energy_from_power(power) + pd.testing.assert_series_equal(result, expected_result) -def test_energy_from_power_downsample_max_timedelta_not_exceeded(): - times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:00', freq='15T') - time_series = pd.Series(data=[1.0, 2.0, 3.0, 4.0, 5.0], index=times) - - expected_energy_series = pd.Series(index=[pd.to_datetime('2018-04-01 13:00:00')], - data=3.0, name='energy_Wh') - expected_energy_series.index.freq = '60T' - result = energy_from_power(time_series.drop(time_series.index[2]), '60T', pd.to_timedelta('60 minutes')) - pd.testing.assert_series_equal(result, expected_energy_series) - - -def test_energy_from_power_for_issue_107(): - times = pd.date_range('2018-04-01 12:00', '2018-04-01 16:00', freq='15T') - dc_power = pd.Series(index=times, data=1.0) - dc_power = dc_power.drop(dc_power.index[5:12]) - - expected_times = pd.date_range('2018-04-01 13:00', '2018-04-01 16:00', freq='60T') - expected_energy_series = pd.Series(index=expected_times, - data=[1.0, np.nan, np.nan, 1.0], - name='energy_Wh') - result = energy_from_power(dc_power, '60T') - pd.testing.assert_series_equal(result, expected_energy_series) - - -# Tests for upsampling -def test_energy_from_power_upsample(): - times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:30', freq='30T') - time_series = pd.Series(data=[1.0, 3.0, 5.0, 6.0], index=times) - - expected_result_times = pd.date_range('2018-04-01 12:15', '2018-04-01 13:30', freq='15T') - expected_energy_series = pd.Series(index=expected_result_times, - data=[0.375, 0.625, 0.875, 1.125, 1.3125, 1.4375], - name='energy_Wh') - - result = energy_from_power(time_series, '15T', pd.to_timedelta('30 minutes')) - pd.testing.assert_series_equal(result, expected_energy_series) - - -def test_energy_from_power_upsample_maxtimedelta_not_exceeded(): - times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:30', freq='30T') - time_series = pd.Series(data=[1.0, 3.0, 5.0, 6.0], index=times) - - expected_result_times = pd.date_range('2018-04-01 12:15', '2018-04-01 13:30', freq='15T') - expected_energy_series = pd.Series(index=expected_result_times, - data=[0.375, 0.625, 0.875, 1.125, 1.3125, 1.4375], - name='energy_Wh') - - result = energy_from_power(time_series.drop(time_series.index[1]), '15T', pd.to_timedelta('60 minutes')) - pd.testing.assert_series_equal(result, expected_energy_series) - - -def test_energy_from_power_upsample_maxtimedelta_exceeded(): - times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:30', freq='30T') - time_series = pd.Series(data=[1.0, 3.0, 5.0, 6.0], index=times) - - expected_result_times = pd.date_range('2018-04-01 12:15', '2018-04-01 13:30', freq='15T') - expected_energy_series = pd.Series(index=expected_result_times, - data=[np.nan, np.nan, np.nan, np.nan, 1.3125, 1.4375], - name='energy_Wh') - - result = energy_from_power(time_series.drop(time_series.index[1]), '15T', pd.to_timedelta('30 minutes')) - pd.testing.assert_series_equal(result, expected_energy_series) +def test_energy_from_power_single_value_input_no_freq(): + power = pd.Series([1], pd.date_range('2019-01-01', periods=1, freq='15T')) + power.index.freq = None + match = "Could not determine period of input power" + with pytest.raises(ValueError, match=match): + energy_from_power(power) +def test_energy_from_power_single_value_instantaneous(): + power = pd.Series([1], pd.date_range('2019-01-01', periods=1, freq='15T')) + power.index.freq = None + match = "power_type='instantaneous' is incompatible with single element power. Use power_type='right-labeled'" + with pytest.raises(ValueError, match=match): + energy_from_power(power, power_type='instantaneous') +def test_energy_from_power_single_value_with_target(): + times = pd.date_range('2019-01-01', freq='15T', periods=1) + power = pd.Series([100.], index=times) + expected_result = pd.Series([100.], index=times, name='energy_Wh') + result = energy_from_power(power, target_frequency='H') + pd.testing.assert_series_equal(result, expected_result) diff --git a/rdtools/test/normalization_pvwatts_test.py b/rdtools/test/normalization_pvwatts_test.py index 40d7cf1b..56071a3d 100644 --- a/rdtools/test/normalization_pvwatts_test.py +++ b/rdtools/test/normalization_pvwatts_test.py @@ -1,6 +1,7 @@ """ Energy Normalization with PVWatts Unit Tests. """ import unittest +import pytest import pandas as pd import numpy as np @@ -8,6 +9,10 @@ from rdtools.normalization import normalize_with_pvwatts from rdtools.normalization import pvwatts_dc_power +from conftest import fail_on_rdtools_version +from rdtools._deprecation import rdtoolsDeprecationWarning + + class PVWattsNormalizationTestCase(unittest.TestCase): ''' Unit tests for energy normalization module. ''' @@ -49,19 +54,21 @@ def setUp(self): # define an irregular pandas series times = pd.DatetimeIndex(['2012-01-01 12:00', '2012-01-01 12:05', '2012-01-01 12:06', - '2012-01-01 12:09']) + '2012-01-01 12:09']) data = [1, 2, 3, 4] self.irregular_timeseries = pd.Series(data=data, index=times) def tearDown(self): pass + @fail_on_rdtools_version('3.0.0') def test_pvwatts_dc_power(self): ''' Test PVWatts DC power caculation. ''' - dc_power = pvwatts_dc_power(self.poa_global, self.power, - temperature_cell=self.temp, - gamma_pdc=self.gamma_pdc) + with pytest.warns(rdtoolsDeprecationWarning): + dc_power = pvwatts_dc_power(self.poa_global, self.power, + temperature_cell=self.temp, + gamma_pdc=self.gamma_pdc) # Assert output has same frequency and length as input self.assertEqual(self.poa_global.index.freq, dc_power.index.freq) @@ -70,6 +77,8 @@ def test_pvwatts_dc_power(self): # Assert value of output Series is equal to value expected self.assertTrue((dc_power == 19.75).all()) + + @fail_on_rdtools_version('3.0.0') def test_normalization_with_pvw(self): ''' Test PVWatts normalization. ''' @@ -80,7 +89,9 @@ def test_normalization_with_pvw(self): 'gamma_pdc': self.gamma_pdc, } - corr_energy, insolation = normalize_with_pvwatts(self.energy, pvw_kws) + with pytest.warns(rdtoolsDeprecationWarning): + corr_energy, insolation = normalize_with_pvwatts(self.energy, pvw_kws) + corr_energy = corr_energy.reindex(self.energy.index) # Test output is same frequency and length as energy self.assertEqual(corr_energy.index.freq, self.energy.index.freq) @@ -94,13 +105,16 @@ def test_normalization_with_pvw(self): # Test expected behavior when energy has no explicit frequency self.energy.index.freq = None - corr_energy, insolation = normalize_with_pvwatts(self.energy, pvw_kws) + with pytest.warns(rdtoolsDeprecationWarning): + corr_energy, insolation = normalize_with_pvwatts(self.energy, pvw_kws) + corr_energy = corr_energy.reindex(self.energy.index) self.assertTrue(np.isnan(corr_energy.iloc[0])) # first value should be nan self.assertTrue((corr_energy.iloc[1:] == 1.0).all()) # rest should be 1 # Test for valueError when energy frequency can't be inferred with self.assertRaises(ValueError): - corr_energy, insolation = normalize_with_pvwatts(self.irregular_timeseries, pvw_kws) + with pytest.warns(rdtoolsDeprecationWarning): + corr_energy, insolation = normalize_with_pvwatts(self.irregular_timeseries, pvw_kws) if __name__ == '__main__': diff --git a/rdtools/test/normalization_sapm_test.py b/rdtools/test/normalization_sapm_test.py index 85c21923..07266743 100644 --- a/rdtools/test/normalization_sapm_test.py +++ b/rdtools/test/normalization_sapm_test.py @@ -1,6 +1,7 @@ """ Energy Normalization with SAPM Unit Tests. """ import unittest +import pytest import pandas as pd import numpy as np @@ -9,6 +10,9 @@ from rdtools.normalization import normalize_with_sapm from rdtools.normalization import sapm_dc_power +from conftest import fail_on_rdtools_version +from rdtools._deprecation import rdtoolsDeprecationWarning + class SapmNormalizationTestCase(unittest.TestCase): ''' Unit tests for energy normalization module. ''' @@ -70,20 +74,23 @@ def setUp(self): # define an irregular pandas series times = pd.DatetimeIndex(['2012-01-01 12:00', '2012-01-01 12:05', '2012-01-01 12:06', - '2012-01-01 12:09']) + '2012-01-01 12:09']) data = [1, 2, 3, 4] self.irregular_timeseries = pd.Series(data=data, index=times) def tearDown(self): pass + @fail_on_rdtools_version('3.0.0') def test_sapm_dc_power(self): ''' Test SAPM DC power. ''' - dc_power, poa = sapm_dc_power(self.pvsystem, self.irrad) + with pytest.warns(rdtoolsDeprecationWarning): + dc_power, poa = sapm_dc_power(self.pvsystem, self.irrad) self.assertEqual(self.irrad.index.freq, dc_power.index.freq) self.assertEqual(len(self.irrad), len(dc_power)) + @fail_on_rdtools_version('3.0.0') def test_normalization_with_sapm(self): ''' Test SAPM normalization. ''' @@ -92,15 +99,18 @@ def test_normalization_with_sapm(self): 'met_data': self.irrad, } - corr_energy, insol = normalize_with_sapm(self.energy, sapm_kws) + with pytest.warns(rdtoolsDeprecationWarning): + corr_energy, insol = normalize_with_sapm(self.energy, sapm_kws) # Test output is same frequency and length as energy self.assertEqual(corr_energy.index.freq, self.energy.index.freq) - self.assertEqual(len(corr_energy), len(self.energy)) + # Expected behavior is to have a nan at energy.index[0] + self.assertEqual(len(corr_energy.dropna()), len(self.energy)-1) # Test for valueError when energy frequency can't be inferred with self.assertRaises(ValueError): - corr_energy, insolation = normalize_with_sapm(self.irregular_timeseries, sapm_kws) + with pytest.warns(rdtoolsDeprecationWarning): + corr_energy, insolation = normalize_with_sapm(self.irregular_timeseries, sapm_kws) # TODO, test for: # incorrect data format diff --git a/rdtools/test/normalize_with_expected_power_test.py b/rdtools/test/normalize_with_expected_power_test.py index 70d82588..79d6ded4 100644 --- a/rdtools/test/normalize_with_expected_power_test.py +++ b/rdtools/test/normalize_with_expected_power_test.py @@ -48,24 +48,12 @@ def irradiance_30(times_30): def test_normalize_with_expected_power_uniform_frequency(pv_15, expected_15, irradiance_15): norm, insol = normalize_with_expected_power( pv_15, expected_15, irradiance_15) - expected_norm = pd.Series( - {Timestamp('2020-01-01 12:15:00', freq='15T'): 1.0, - Timestamp('2020-01-01 12:30:00', freq='15T'): 1.0784313725490198, - Timestamp('2020-01-01 12:45:00', freq='15T'): 1.0612244897959184, - Timestamp('2020-01-01 13:00:00', freq='15T'): 1.0487804878048783} - ) - expected_norm.name = 'energy_Wh' - expected_norm.index.freq = '15T' - expected_insol = pd.Series( - {Timestamp('2020-01-01 12:15:00', freq='15T'): 231.25, - Timestamp('2020-01-01 12:30:00', freq='15T'): 225.0, - Timestamp('2020-01-01 12:45:00', freq='15T'): 240.625, - Timestamp('2020-01-01 13:00:00', freq='15T'): 233.125} - ) + expected_norm = pv_15.iloc[1:]/expected_15.iloc[1:] + expected_norm.name = 'energy_Wh' + expected_insol = irradiance_15.iloc[1:]*0.25 expected_insol.name = 'energy_Wh' - expected_insol.index.freq = '15T' pd.testing.assert_series_equal(norm, expected_norm) pd.testing.assert_series_equal(insol, expected_insol) @@ -74,27 +62,13 @@ def test_normalize_with_expected_power_uniform_frequency(pv_15, expected_15, irr def test_normalize_with_expected_power_energy_option(pv_15, expected_15, irradiance_15): norm, insol = normalize_with_expected_power( pv_15, expected_15, irradiance_15, pv_input='energy') - expected_norm = pd.Series( - {Timestamp('2020-01-01 12:00:00', freq='15T'): np.nan, - Timestamp('2020-01-01 12:15:00', freq='15T'): 5.714285714285714, - Timestamp('2020-01-01 12:30:00', freq='15T'): 4.705882352941177, - Timestamp('2020-01-01 12:45:00', freq='15T'): 3.5918367346938775, - Timestamp('2020-01-01 13:00:00', freq='15T'): 4.097560975609756} - ) + expected_norm = pv_15/(0.25*expected_15.iloc[1:]) expected_norm.name = 'energy_Wh' - expected_norm.index.freq = '15T' - - expected_insol = pd.Series( - {Timestamp('2020-01-01 12:00:00', freq='15T'): np.nan, - Timestamp('2020-01-01 12:15:00', freq='15T'): 231.25, - Timestamp('2020-01-01 12:30:00', freq='15T'): 225.0, - Timestamp('2020-01-01 12:45:00', freq='15T'): 240.625, - Timestamp('2020-01-01 13:00:00', freq='15T'): 233.125} - ) + expected_insol = irradiance_15.iloc[1:]*0.25 + expected_insol = expected_insol.reindex(expected_norm.index) expected_insol.name = 'energy_Wh' - expected_insol.index.freq = '15T' pd.testing.assert_series_equal(norm, expected_norm) pd.testing.assert_series_equal(insol, expected_insol) @@ -104,21 +78,19 @@ def test_normalize_with_expected_power_low_freq_pv(pv_30, expected_15, irradianc norm, insol = normalize_with_expected_power( pv_30, expected_15, irradiance_15) - expected_norm = pd.Series( - {Timestamp('2020-01-01 12:30:00', freq='30T'): 0.9302325581395349, - Timestamp('2020-01-01 13:00:00', freq='30T'): 1.1333333333333333} - ) - + pv_energy = pv_30.iloc[1:]*0.5 + expected_energy = expected_15.iloc[1:]*0.25 + # aggregate to 30 min level + expected_energy = expected_energy.rolling(2).sum() + expected_energy = expected_energy.reindex(pv_energy.index) + expected_norm = pv_energy/expected_energy expected_norm.name = 'energy_Wh' - expected_norm.index.freq = '30T' - - expected_insol = pd.Series( - {Timestamp('2020-01-01 12:30:00', freq='30T'): 456.25, - Timestamp('2020-01-01 13:00:00', freq='30T'): 473.75} - ) + expected_insol = irradiance_15.iloc[1:]*0.25 + # aggregate to 30 min level + expected_insol = expected_insol.rolling(2).sum() + expected_insol = expected_insol.reindex(pv_energy.index) expected_insol.name = 'energy_Wh' - expected_insol.index.freq = '30T' pd.testing.assert_series_equal(norm, expected_norm) pd.testing.assert_series_equal(insol, expected_insol) @@ -128,25 +100,14 @@ def test_normalized_with_expected_power_low_freq_expected(pv_15, expected_30, ir norm, insol = normalize_with_expected_power( pv_15, expected_30, irradiance_30) - expected_norm = pd.Series( - {Timestamp('2020-01-01 12:15:00', freq='15T'): 1.09375, - Timestamp('2020-01-01 12:30:00', freq='15T'): 1.1458333333333335, - Timestamp('2020-01-01 12:45:00', freq='15T'): 1.0000000000000002, - Timestamp('2020-01-01 13:00:00', freq='15T'): 0.9772727272727274} - ) - + expected_15 = expected_30.reindex(pv_15.index).interpolate() + expected_energy = expected_15.iloc[1:]*0.25 + expected_norm = pv_15.iloc[1:]*0.25/expected_energy expected_norm.name = 'energy_Wh' - expected_norm.index.freq = '15T' - - expected_insol = pd.Series( - {Timestamp('2020-01-01 12:15:00', freq='15T'): 246.875, - Timestamp('2020-01-01 12:30:00', freq='15T'): 240.625, - Timestamp('2020-01-01 12:45:00', freq='15T'): 233.75, - Timestamp('2020-01-01 13:00:00', freq='15T'): 226.25} - ) + irradiance_15 = irradiance_30.reindex(pv_15.index).interpolate() + expected_insol = irradiance_15.iloc[1:]*0.25 expected_insol.name = 'energy_Wh' - expected_insol.index.freq = '15T' pd.testing.assert_series_equal(norm, expected_norm) pd.testing.assert_series_equal(insol, expected_insol) diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py index 5da59f41..3062b696 100644 --- a/rdtools/test/plotting_test.py +++ b/rdtools/test/plotting_test.py @@ -1,16 +1,21 @@ import pandas as pd import numpy as np +import rdtools from rdtools.degradation import degradation_year_on_year from rdtools.soiling import soiling_srr +from rdtools.normalization import energy_from_power from rdtools.plotting import ( degradation_summary_plots, soiling_monte_carlo_plot, soiling_interval_plot, - soiling_rate_histogram + soiling_rate_histogram, + availability_summary_plots, ) import matplotlib.pyplot as plt import pytest +from conftest import assert_isinstance + # bring in soiling pytest fixtures from soiling_test import ( times, # can't rename this or else the others can't find it @@ -18,10 +23,11 @@ insolation as soiling_insolation, ) - -def assert_isinstance(obj, klass): - assert isinstance(obj, klass), f'got {type(obj)}, expected {klass}' - +# availability pytest fixture +from availability_test import ( + energy_data_outage_single, + availability_analysis_object +) # can't import degradation fixtures because it's a unittest file. # roll our own here instead: @@ -162,3 +168,24 @@ def test_soiling_rate_histogram_kwargs(soiling_info): ) result = soiling_rate_histogram(soiling_info, **kwargs) assert_isinstance(result, plt.Figure) + + +def test_availability_summary_plots(availability_analysis_object): + aa = availability_analysis_object + result = availability_summary_plots( + aa.power_system, aa.power_subsystem, aa.loss_total, + aa.energy_cumulative, aa.energy_expected_rescaled, + aa.outage_info) + assert_isinstance(result, plt.Figure) + + +def test_availability_summary_plots_empty(availability_analysis_object): + # empty outage_info + aa = availability_analysis_object + empty = aa.outage_info.iloc[:0, :] + result = availability_summary_plots( + aa.power_system, aa.power_subsystem, aa.loss_total, + aa.energy_cumulative, aa.energy_expected_rescaled, + empty) + assert_isinstance(result, plt.Figure) + diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py index e194861a..9c7fa22b 100644 --- a/rdtools/test/soiling_test.py +++ b/rdtools/test/soiling_test.py @@ -1,6 +1,9 @@ import pandas as pd import numpy as np -from rdtools import soiling_srr +from rdtools.soiling import soiling_srr +from rdtools.soiling import SRRAnalysis +from rdtools.soiling import annual_soiling_ratios +from rdtools.soiling import monthly_soiling_rates from rdtools.soiling import NoValidIntervalError import pytest @@ -57,7 +60,7 @@ def test_soiling_srr(normalized_daily, insolation, times): 'soiling_info["stochastic_soiling_profiles"] is not a list' # Check soiling_info['soiling_interval_summary'] - expected_summary_columns = ['start', 'end', 'slope', 'slope_low', 'slope_high', + expected_summary_columns = ['start', 'end', 'soiling_rate', 'soiling_rate_low', 'soiling_rate_high', 'inferred_start_loss', 'inferred_end_loss', 'length', 'valid'] actual_summary_columns = soiling_info['soiling_interval_summary'].columns.values @@ -69,14 +72,14 @@ def test_soiling_srr(normalized_daily, insolation, times): "'{}' was expected as a column, but not in soiling_info['soiling_interval_summary']".format(x) assert isinstance(soiling_info['soiling_interval_summary'], pd.DataFrame),\ 'soiling_info["soiling_interval_summary"] not a dataframe' - expected_means = pd.Series({'slope': -0.002617290, - 'slope_low': -0.002828525, - 'slope_high': -0.002396639, + expected_means = pd.Series({'soiling_rate': -0.002617290, + 'soiling_rate_low': -0.002828525, + 'soiling_rate_high': -0.002396639, 'inferred_start_loss': 1.021514, 'inferred_end_loss': 0.9572880, 'length': 24.0, 'valid': 1.0}) - expected_means = expected_means[['slope', 'slope_low', 'slope_high', + expected_means = expected_means[['soiling_rate', 'soiling_rate_low', 'soiling_rate_high', 'inferred_start_loss', 'inferred_end_loss', 'length', 'valid']] pd.testing.assert_series_equal(expected_means, soiling_info['soiling_interval_summary'].mean(), @@ -103,7 +106,7 @@ def test_soiling_srr_with_precip(normalized_daily, insolation, times): sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, clean_criterion='precip_and_shift', **kwargs) assert 0.983270 == pytest.approx(sr, abs=1e-6),\ "Soiling ratio with clean_criterion='precip_and_shift' different from expected" - np.random.seed(1977) + np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, clean_criterion='precip_or_shift', **kwargs) assert 0.973228 == pytest.approx(sr, abs=1e-6),\ "Soiling ratio with clean_criterion='precip_or_shift' different from expected" @@ -176,6 +179,19 @@ def test_soiling_srr_method(normalized_daily, insolation): 'Soiling ratio with method="perfect_clean" different from expected value' +def test_soiling_srr_min_interval_length(normalized_daily, insolation): + 'Test that a long minimum interval length prevents finding shorter intervals' + with pytest.raises(NoValidIntervalError): + np.random.seed(1977) + # normalized_daily intervals are 25 days long, so min=26 should fail: + _ = soiling_srr(normalized_daily, insolation, confidence_level=68.2, + reps=10, min_interval_length=26) + + # but min=24 should be fine: + _ = soiling_srr(normalized_daily, insolation, confidence_level=68.2, + reps=10, min_interval_length=24) + + def test_soiling_srr_recenter_false(normalized_daily, insolation): np.random.seed(1977) sr, sr_ci, soiling_info = soiling_srr(normalized_daily, insolation, reps=10, @@ -212,6 +228,7 @@ def test_soiling_srr_max_negative_slope_error(normalized_daily, insolation): assert 0.952995 == pytest.approx(sr, abs=1e-6),\ 'Soiling ratio different from expected when max_relative_slope_error=50.0' + def test_soiling_srr_with_nan_interval(normalized_daily, insolation, times): ''' Previous versions had a bug which would have raised an error when an entire interval @@ -224,3 +241,183 @@ def test_soiling_srr_with_nan_interval(normalized_daily, insolation, times): sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt, insolation, reps=reps) assert 0.947416 == pytest.approx(sr, abs=1e-6),\ 'Soiling ratio different from expected value when an entire interval was NaN' + + +def test_soiling_srr_kwargs(monkeypatch, normalized_daily, insolation): + ''' + Make sure that all soiling_srr parameters get passed on to SRRAnalysis and + SRRAnalysis.run(), i.e. all necessary inputs to SRRAnalysis are provided by + soiling_srr. Done by removing the SRRAnalysis default param values + and making sure everything still runs. + ''' + # the __defaults__ attr is the tuple of default values in py3 + monkeypatch.delattr(SRRAnalysis.__init__, "__defaults__") + monkeypatch.delattr(SRRAnalysis.run, "__defaults__") + _ = soiling_srr(normalized_daily, insolation, reps=10) + + +# ########################### +# annual_soiling_ratios tests +# ########################### + + +@pytest.fixture() +def multi_year_profiles(): + times = pd.date_range('01-01-2018', '11-30-2019', freq='D') + data = np.array([0]*365 + [10]*334) + profiles = [pd.Series(x + data, times) for x in range(10)] + + # make insolation slighly longer to test for proper normalization + times = pd.date_range('01-01-2018', '12-31-2019', freq='D') + insolation = 350*[0.8] + (len(times)-350)*[1] + insolation = pd.Series(insolation, index=times) + + return profiles, insolation + + +def test_annual_soiling_ratios(multi_year_profiles): + expected_data = np.array([[2018, 4.5, 1.431, 7.569], + [2019, 14.5, 11.431, 17.569]]) + expected = pd.DataFrame(data=expected_data, + columns=['year', 'soiling_ratio_median', 'soiling_ratio_low', 'soiling_ratio_high']) + expected['year'] = expected['year'].astype(int) + + srr_profiles, insolation = multi_year_profiles + result = annual_soiling_ratios(srr_profiles, insolation) + + pd.testing.assert_frame_equal(result, expected) + + +def test_annual_soiling_ratios_confidence_interval(multi_year_profiles): + expected_data = np.array([[2018, 4.5, 0.225, 8.775], + [2019, 14.5, 10.225, 18.775]]) + expected = pd.DataFrame(data=expected_data, + columns=['year', 'soiling_ratio_median', 'soiling_ratio_low', 'soiling_ratio_high']) + expected['year'] = expected['year'].astype(int) + + srr_profiles, insolation = multi_year_profiles + result = annual_soiling_ratios(srr_profiles, insolation, confidence_level=95) + + pd.testing.assert_frame_equal(result, expected) + + +def test_annual_soiling_ratios_warning(multi_year_profiles): + srr_profiles, insolation = multi_year_profiles + insolation = insolation.iloc[:-200] + match = ('The indexes of stochastic_soiling_profiles are not entirely contained ' + 'within the index of insolation_daily. Every day in stochastic_soiling_profiles ' + 'should be represented in insolation_daily. This may cause erroneous results.') + with pytest.warns(UserWarning, match=match): + result = annual_soiling_ratios(srr_profiles, insolation) + + +# ########################### +# monthly_soiling_rates tests +# ########################### + + +@pytest.fixture() +def soiling_interval_summary(): + starts = ['2019/01/01', '2019/01/16', '2019/02/08', '2019/03/06'] + starts = pd.to_datetime(starts).tz_localize('America/Denver') + ends = ['2019/01/15', '2019/02/07', '2019/03/05', '2019/04/07'] + ends = pd.to_datetime(ends).tz_localize('America/Denver') + slopes = [-0.005, -0.002, -0.001, -0.002] + slopes_low = [-0.0055, -0.0025, -0.0015, -0.003] + slopes_high = [-0.004, 0, 0, -0.001] + valids = [True, True, False, True] + + soiling_interval_summary = pd.DataFrame() + soiling_interval_summary['start'] = starts + soiling_interval_summary['end'] = ends + soiling_interval_summary['soiling_rate'] = slopes + soiling_interval_summary['soiling_rate_low'] = slopes_low + soiling_interval_summary['soiling_rate_high'] = slopes_high + soiling_interval_summary['inferred_start_loss'] = np.nan + soiling_interval_summary['inferred_end_loss'] = np.nan + soiling_interval_summary['length'] = (ends - starts).days + soiling_interval_summary['valid'] = valids + + return soiling_interval_summary + + +def _build_monthly_summary(top_rows): + ''' + Convienience function to build a full monthly soiling summary + dataframe from the expected_top_rows which summarize Jan-April + ''' + + all_rows = np.vstack((top_rows, [[1, np.nan, np.nan, np.nan, 0]]*8)) + + df = pd.DataFrame(data=all_rows, + columns=['month', 'soiling_rate_median', 'soiling_rate_low', 'soiling_rate_high', 'interval_count']) + df['month'] = range(1, 13) + + return df + + +def test_monthly_soiling_rates(soiling_interval_summary): + np.random.seed(1977) + result = monthly_soiling_rates(soiling_interval_summary) + + expected = np.array([[1.00000000e+00, -2.42103810e-03, -5.00912766e-03, -7.68551806e-04, 2.00000000e+00], + [2.00000000e+00, -1.25092837e-03, -2.10091842e-03, -3.97354321e-04, 1.00000000e+00], + [3.00000000e+00, -2.00313359e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00], + [4.00000000e+00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e+00]]) + expected = _build_monthly_summary(expected) + + pd.testing.assert_frame_equal(result, expected, check_dtype=False) + + +def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary): + np.random.seed(1977) + result = monthly_soiling_rates(soiling_interval_summary, min_interval_length=20) + + expected = np.array([[1.00000000e+00, -1.24851539e-03, -2.10394564e-03, -3.98358211e-04, 1.00000000e+00], + [2.00000000e+00, -1.25092837e-03, -2.10091842e-03, -3.97330424e-04, 1.00000000e+00], + [3.00000000e+00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00], + [4.00000000e+00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e+00]]) + expected = _build_monthly_summary(expected) + + pd.testing.assert_frame_equal(result, expected, check_dtype=False) + + +def test_monthly_soiling_rates_max_slope_err(soiling_interval_summary): + np.random.seed(1977) + result = monthly_soiling_rates(soiling_interval_summary, max_relative_slope_error=120) + + expected = np.array([[1.00000000e+00, -4.74910923e-03, -5.26236739e-03, -4.23901493e-03, 1.00000000e+00], + [2.00000000e+00, np.nan, np.nan, np.nan, 0.00000000e+00], + [3.00000000e+00, -2.00074270e-03, -2.68073474e-03, -1.31786434e-03, 1.00000000e+00], + [4.00000000e+00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00]]) + expected = _build_monthly_summary(expected) + + pd.testing.assert_frame_equal(result, expected, check_dtype=False) + + +def test_monthly_soiling_rates_confidence_level(soiling_interval_summary): + np.random.seed(1977) + result = monthly_soiling_rates(soiling_interval_summary, confidence_level=95) + + expected = np.array([[1.00000000e+00, -2.42103810e-03, -5.42313113e-03, -1.21156562e-04, 2.00000000e+00], + [2.00000000e+00, -1.25092837e-03, -2.43731574e-03, -6.23842627e-05, 1.00000000e+00], + [3.00000000e+00, -2.00313359e-03, -2.94998476e-03, -1.04988760e-03, 1.00000000e+00], + [4.00000000e+00, -1.99729563e-03, -2.95063841e-03, -1.04869949e-03, 1.00000000e+00]]) + + expected = _build_monthly_summary(expected) + + pd.testing.assert_frame_equal(result, expected, check_dtype=False) + + +def test_monthly_soiling_rates_reps(soiling_interval_summary): + np.random.seed(1977) + result = monthly_soiling_rates(soiling_interval_summary, reps=3) + + expected = np.array([[1.00000000e+00, -2.88594088e-03, -5.03736679e-03, -6.47391131e-04, 2.00000000e+00], + [2.00000000e+00, -1.67359565e-03, -2.00504171e-03, -1.33240044e-03, 1.00000000e+00], + [3.00000000e+00, -1.22306993e-03, -2.19274892e-03, -1.11793240e-03, 1.00000000e+00], + [4.00000000e+00, -1.94675549e-03, -2.42574164e-03, -1.54850795e-03, 1.00000000e+00]]) + + expected = _build_monthly_summary(expected) + + pd.testing.assert_frame_equal(result, expected, check_dtype=False) diff --git a/requirements-min.txt b/requirements-min.txt new file mode 100644 index 00000000..05d5ffcd --- /dev/null +++ b/requirements-min.txt @@ -0,0 +1,7 @@ +h5py==2.7.1 +matplotlib==3.0.0 +numpy==1.15 +pandas==0.23.0 +pvlib==0.7.0 +scipy==0.19.1 +statsmodels==0.8.0 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 9344820c..d7d0590e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,9 @@ cycler==0.10.0 h5py==2.10.0 kiwisolver==1.2.0 -matplotlib==3.1.2 -nbsphinx==0.4.3 -nbsphinx-link==1.3.0 +matplotlib==3.3.2 numpy==1.17.3 -pandas==1.0.3 +pandas==1.1.0 patsy==0.5.1 pvlib==0.7.1 pyparsing==2.4.7 @@ -13,5 +11,4 @@ python-dateutil==2.8.1 pytz==2019.3 scipy==1.3.2 six==1.14.0 -sphinx-rtd-theme==0.4.3 -statsmodels==0.11.1 \ No newline at end of file +statsmodels==0.11.1 diff --git a/setup.py b/setup.py old mode 100755 new mode 100644 index 7956cd16..55107a98 --- a/setup.py +++ b/setup.py @@ -36,24 +36,26 @@ ] INSTALL_REQUIRES = [ - 'matplotlib >= 2.2.2', - 'numpy >= 1.12', - 'pandas >= 0.23.0,!=1.0.0,!=1.0.1', # exclude 1.0.0 & 1.0.1 for GH142 + 'matplotlib >= 3.0.0', + 'numpy >= 1.15', + # exclude pandas==1.0.0 & 1.0.1 for GH142, and 0.24.0 for GH114 + 'pandas >= 0.23.0,!=0.24.0,!=1.0.0,!=1.0.1', 'statsmodels >= 0.8.0', 'scipy >= 0.19.1', 'h5py >= 2.7.1', - 'pvlib >= 0.7.0, <0.8.0', + 'pvlib >= 0.7.0, <0.9.0', ] EXTRAS_REQUIRE = { 'doc': [ 'sphinx==1.8.5', - 'nbsphinx==0.4.3', + 'nbsphinx==0.5.0', 'nbsphinx-link==1.3.0', 'pandas==0.23.0', 'pvlib==0.7.1', 'sphinx_rtd_theme==0.4.3', - 'ipython', + 'ipython' + ], 'test': [ 'pytest',