diff --git a/NAMESPACE b/NAMESPACE index e8130f5..32cc360 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -33,6 +33,7 @@ importFrom(ggplot2,Stat) importFrom(ggplot2,aes) importFrom(ggplot2,aes_) importFrom(ggplot2,aes_string) +importFrom(ggplot2,alpha) importFrom(ggplot2,coord_flip) importFrom(ggplot2,element_blank) importFrom(ggplot2,expr) diff --git a/NEWS.md b/NEWS.md index a71d925..67870a0 100644 --- a/NEWS.md +++ b/NEWS.md @@ -13,7 +13,7 @@ Changes: New features: - Annotations can now access data for any of the available modes by adding `upset_mode()` layer. By default the annotations are given data corresponding to the same mode as the mode of the passed in the `upset()` call. -- It is now possible to display all intersections, even if those are not present in the data by passing `intersections='all'` to `upset()`; this is only feasible for <20 sets, but filtering by degree can allow to explore a subset of all intersections when there are many more sets +- It is now possible to display all intersections, even if those are not present in the data by passing `intersections='all'` to `upset()`; this is only feasible for <20 sets, but filtering by degree can allow to explore a subset of all intersections when there are many more sets; this is only useful for modes different from the default exclusive intersection. - If filtering leads to no intersections, an informative error is shown (#80) diff --git a/R/data.R b/R/data.R index d430db8..9808767 100644 --- a/R/data.R +++ b/R/data.R @@ -231,7 +231,7 @@ binary_grid = function(n, m) { #' @param mode region selection mode for sorting and trimming by size. See `get_size_mode()` for accepted values. #' @param size_columns_suffix suffix for the columns to store the sizes (adjust if conflicts with your data) #' @param encode_sets whether set names (column in input data) should be encoded as numbers (set to TRUE to overcome R limitations of max 10 kB for variable names for datasets with huge numbers of sets); default TRUE for upset() and FALSE for upset_data(). -#' @param intersections whether only the intersections present in data (`observed`, default), or all intersections (`all`) should be computed; using all intersections for a high number of sets is not computationally feasible - use `min_degree` and `max_degree` to narrow down the selection +#' @param intersections whether only the intersections present in data (`observed`, default), or all intersections (`all`) should be computed; using all intersections for a high number of sets is not computationally feasible - use `min_degree` and `max_degree` to narrow down the selection; this is only useful for modes different from the default exclusive intersection. #' @param max_combinations_n the limit preventing accidental use of `intersections='all'` with a high number of sets #' @export diff --git a/man/upset.Rd b/man/upset.Rd index 2e99d88..72c1da8 100644 --- a/man/upset.Rd +++ b/man/upset.Rd @@ -82,7 +82,7 @@ upset( \item{\code{sort_ratio_denominator}}{the mode for denominator when sorting by ratio} \item{\code{group_by}}{the mode of grouping intersections; one of: \code{'degree'}, \code{'sets'}} \item{\code{size_columns_suffix}}{suffix for the columns to store the sizes (adjust if conflicts with your data)} - \item{\code{intersections}}{whether only the intersections present in data (\code{observed}, default), or all intersections (\code{all}) should be computed; using all intersections for a high number of sets is not computationally feasible - use \code{min_degree} and \code{max_degree} to narrow down the selection} + \item{\code{intersections}}{whether only the intersections present in data (\code{observed}, default), or all intersections (\code{all}) should be computed; using all intersections for a high number of sets is not computationally feasible - use \code{min_degree} and \code{max_degree} to narrow down the selection; this is only useful for modes different from the default exclusive intersection.} \item{\code{max_combinations_n}}{the limit preventing accidental use of \code{intersections='all'} with a high number of sets} }} } diff --git a/man/upset_data.Rd b/man/upset_data.Rd index 6c11cb5..fbdba32 100644 --- a/man/upset_data.Rd +++ b/man/upset_data.Rd @@ -69,7 +69,7 @@ upset_data( \item{max_combinations_n}{the limit preventing accidental use of \code{intersections='all'} with a high number of sets} -\item{intersections}{whether only the intersections present in data (\code{observed}, default), or all intersections (\code{all}) should be computed; using all intersections for a high number of sets is not computationally feasible - use \code{min_degree} and \code{max_degree} to narrow down the selection} +\item{intersections}{whether only the intersections present in data (\code{observed}, default), or all intersections (\code{all}) should be computed; using all intersections for a high number of sets is not computationally feasible - use \code{min_degree} and \code{max_degree} to narrow down the selection; this is only useful for modes different from the default exclusive intersection.} } \description{ Prepare data for UpSet plots diff --git a/tests/figs/examples/example-0-3-displaying-all-intersections-1.svg b/tests/figs/examples/example-0-3-displaying-all-intersections-1.svg new file mode 100644 index 0000000..a10a78b --- /dev/null +++ b/tests/figs/examples/example-0-3-displaying-all-intersections-1.svg @@ -0,0 +1,1671 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +Short +Documentary +Animation +Romance +Action +Comedy +Drama +group +Example: 0.3 Displaying all intersections: 1 + diff --git a/vignettes/Examples.ipynb b/vignettes/Examples.ipynb index 2dce4d2..d761431 100644 --- a/vignettes/Examples.ipynb +++ b/vignettes/Examples.ipynb @@ -327,6 +327,50 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0.3 Displaying all intersections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Too display all possible intersections (rather than only the observed ones) use `intersections='all'`.\n", + "\n", + "Note 1: it is usually desired to filter all the possible intersections down with `max_degree` and/or `min_degree` to avoid generating all combinations as those can easily use up all available RAM memory when dealing with multiple sets (e.g. all human genes) due to sheer number of possible combinations\n", + "\n", + "Note 2: using `intersections='all'` is only reasonable for mode different from the default *exclusive intersection*." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5WwRZAlsCPMs6cCXnbhFkCWwJ8CzrwJWcu0WQJbAlwLOsA1dy7hZBlsCWAM+yDlzJuVsEWQJbAjzLOnAl524RZAlsCfAs68CVnLtFkCWwJcCzrANXcu7lBAqxCLKoeJZN4ErOvZxAIe8rgkzfft1j0l0mPMsmcCXnXu6gkIolSP1GctuwcMsIz2UJXMm5lzsopGIJ8kQnecsm3DLCc1kCV3LulQhKkZUxQTb4iO0N5uQiKvVF8RY53BIEruTcKxGUfKEVljFBHqdPkGrm5KeZxaampUUz4IVz0slG4KOpGHipOHgTSfDGJ8BLToIXT4MXKwTOQuAwfJSJgpdWgSdV4Ilx8LA6RSKlMla5R+AUuRKBi3JPqMBYBVanSKjcYyr3KGRcCpTM/KCowPODEpkXFHWKhYISKwUK5L5QUMbnB2W+ljIPKEWtsIwJUv8KuXO48J+F12pH1Q8oMbudnXduovA6CaxeJ5FQe/xjhWnzGQgcho9y6q15GRU4rQJPToA3ngIvqQTIYqVeCQ4fLfR1fOPzvwpjPlDmn7CZULnPD0pkXlBU7gsFxXpP+m7a9N23PVn0nxZBLIKo3C2CzDaLIBZBVO4WQWabRRCLICp3iyCzzSKIRRCVu0WQ2TaufiyGC0US6mcr7KYVdll1eabU5UkrFCcVihPqQmHVyJIQOK+wm1bY5VTgjLo8hcAp1QIKgQu5JyH3QuCZQuBC7ipwUe4qcCH33QAlUwKU1OKCMrXsoKjcS4GiTlEKFLDKIYhlli2DWQSxzLJ5zCKIZZbNYxZBLLNsHrMIYpll85hFEMssm8csglhm2TxmEcQyy+YxiyCWWTaPWQSxzLJ5zCKIZZbNYxZBLLNsHrMIYpll81gpgkAJ5Ixy4C9QiZmHT3L5nY7ZYT/vopo6FckWPDLLy6tSzpmCp5IqpDetflQhcG5hgfMlAs+UClziFKUCLzT3mdmeBcpigFIqdzDrCWKZZfNYpRAkbzy7Veyhybme3i62CEzVP10ndhxMVD/tFHeG+DvPeMStZGzzcy3iWP9rL5rC63/plUHhdbzwRkB43mffFvt/ZrRntonNAjMxv9x/NFnztEPsM8iF/XFxP8qE/HJPwdToiNysMPjKS/3CmwiMyl0QLc9tHhPhPM+8I3b9ZBuftom9B2n70/ViKwPe/rRL3MGiW581xClG33y+TQQZ+vvGHuH1bvz7kPDwyJjcBZHwh8TPzsf9YRFkJgq5T0cg42zYL/dDTIX8chdEejQgd0FMBoJyF8T4yKgMnPSHMjsHjvkjKnBsZgGgpIKByZ1Bwf6Qyj2scpeBWe47B86G/HKTyNTYCFaBZ+W+cFBiO4NSyJ2ConafSKsQgnScgqjdQT3nMcx7gnpbjmAeU856njnr6qh3L/NOpMSYuYZ5nx+guP6ceV8PUeS+w7xLKASBc5j3G4pj92nMu4kipR/HvIdokIhHN3VbkHqvsI/WMu0hn103PG52qXrrdcPtoRco3+kwDJeXXpbxS9j3vkP/mvG6DKOxjV7lgc+zz66hXsuJzLuXHlq/jnnPUm8Tc454m3pPMu8YJ/XuYN4pHfQiXsm8L1I6pH7MvAsilDwXMO+/aYNL65qhN3TRjMc9HkOv66OHxplXyyRgQjz3UeqN1OiG7mE3lcE6mrGbtoF8d4NuaDoNMtPeKHPPNTsNw9lMG9KUQXN3dNDAEx63odczYibcNLDdx0Bx08A17KYStBmmx8NuKr7a0qBoXkqH6RYeOCtBcTBQJlnu9SY9BXbTU9jp5SExt24YNX7qjdnoKTz0QhF/DQvMdvz11zGPEiPf1UAD65QO0600d2cTJXC2ycnQnpagODp3BIXlXjsEoFTvBIqdZcyIwUHxcFDgghbZbIJcuHv2ntq/ecn6Kx/Izf33OBJ2DxmQ3svEI70G8qb0uslD0guR9cJZmyLfF94pM+SLwjuXTB8vvJ+ScXnAH8mI9J4luN5k5gqT7fKzVuL38M9q06TX4F51jrR5uefJk3PF175I8uJrTc0kfaT4bD0JySAPkW7pvUkapOchL0tvgNwjvTi5VDhHZ8i3hXdGPn+G8L5FZtz8FN5OkrVxT+8nKTv33AGScHDPGSVjLuZ01U+QIZGULUNMkbt7mrQ0CS9PdO6YBsnLwG1kerv4qJdM1Yif6Ce4QQQOkwgPbNZj4nfPB4pwTJ0QBYrM3TBJRgTWfWSijnvaGIk1cs8RJ0GNe3WTZEAGzpIuEc89Q5pFYA8ENptU7h0kV10CFJF7Y5SEZOBxMqR3Ma9minR7dwJFyxc3vTIhyAP0MfB60WzATna7bDxo5hfSOZZ8VXpnkROl98M8fO26NHiPQcNHW4BRqP018CL3g5f9nXTWEgETu7SnIWiW8lKYnfla4XgHMg2yaQU7IIg7JJtbY/px+Cx1PXj5H0rnBKAq+io5Vno/n4Gv3QakRc/1g1dbD17fiGgyZn2uT+ZUk283oc14Ve6QcSuxSc+cqZMZ+6Yc8oBQQjQt04VHZe4NmUEZpTbfpQI3K09+jTZLeajZUQAlq0AZFzwy3fGwAmVYBq6b7pGH2kir9NwqsFfl3paH3HtzkLs/1SgzicTk91wTAchdgVI7LyjNpFqBYt8ZFH1sboLMLApB5mrm8xMEr998759j19y4gWy9ev0f77mqgfsiq69A+xg4AbwMOEjR4tgx8M4wwPuv18G76UHwnrgGvK0Xgtd+CnhJ2QJNO4GP1uUcgPa4Bu0DrrvZ9SR87wG47kbwx/CZ5/PgBY8DT5EBZcE53gfe2Q7w/vdF8O64G7znO+BqR4HIrknIpGEGGpSd1EEqmUY4IgktunUMfmO3TzreIWgxRqhFem4MgRuzcEAdkQ2qy5hWoExA4AIoncNwxECvAgXIoMfha64plXu+Rnq1+Qb4XsoFx8bge20BCNzbD7n7ARQjCmTQFCiOGQhsJ/UQOOOE3BUoLQoUc26CTC8jQcivNm8gAya5dWzrC+TS/tgfuS8Icja0jz5FEPWMKCJIELzT1dPiEvW0uOEB8B67Cry3LwCv9WTw4kYXtAX4aF0OGpkHq7YQAjw7n4DvbehWbeG/4DPtDPBG4GmBplXuiubHQ98RfVE9LX7xAni33QXes3Bj1CNwfm1i3rYwpdpCQt1AR1VbGISMffC0MELqaaEI4pxNENMocdfwhuF7HQWCFO4aJQiSBq8eHkP0iTibILoiSPtsghiKIBQU+TO0CfixjunZdw296K5RAhQyrzbv7hPk3RsjyPj6zdVk9LZ7fhncaifXZrM3cF/8/WZoH7n/ls5R5EvSO4PAnfkiRZWrJsB7cBC8N9Sd2bsRvNE/gzf1a+kcrp7IHgKk+brqprRDN8XsgyeyEWiCIA1j8jI6J/8Kn41fDd7MxdI5lvwnkIEcJb0fKdLcBAMu9IQJ3jvV4HVCN6UhCw3Pnm9VGUMzKu5NQO5d0zJ37yB0U4xR6KZoCeimOKag4dXNQMOrLgIFAhd6Qu3QTSkCZSQpSeOOFkDxqe4hNMWa/Oy+W1H3MF8j23t3ThLeGJqQpPGEItDFwn6ZniMLj6tiUGZ3sZoUKJ3TMrB3IC3vJEawuGmWD0FeeG2znTzcRm4GgnBf/B0GujeRLuk9Seqkt41Ae28hd0rPT34lPUy+IVtlLn+a8M4iGdksv0ti8mu/V6P/B0lcXGRtlLwhP9OIT1w9OmyUg0VbFrrl2gz5gvjaafm8Bo0HBhK/UqP/O0mL9F4g26RXR56SXhe5SXpj5H+kl4Ku5YkzM3KcdQ6Z3iYucRsMdI0eMiEusj5MouIiayESEI2hIUn6RbO0p0mnyJ2OpWX3jI5H3dB45AiaMkqO/r1dMNA1BmH0T0EZE7+xMV4EilkCFNne3TCCpqDkxOjf206mxOPC6IPRv3uEhEXLd0VhSqRhnPSKwLVTpE2kvH2aSJa58xDYIDOzQOmG0b9nCEb/LgqKOMKRJAP6nKC45xmk7/Cn92zvPor5g2uv2pClBGm7/pY7npAE4b6M5z6aNY8raOdtK28obML3b9x7inobuPcm/SYfXRzVQEj2p8w7vp0+mfhU0Bk+QsZ4T+2rUUL6Psf5kSKkifd7fkX7ljV83ulmIuYxDT63+QgP/Cr1ehsMU/dE6Ck6HLrpYfOD080uw3Sz+cEon8b6Mu0Ppjwe03CxSdN23hn8aZYQB6fjNfRXvMXD3UfDPc29vxE5uYu20o4kn9w9WiMk8yPmnUS7wonzmXfmCCEBTsJvJNgcLD2Fs3WGzWPqpuFgE77happxPZvbDNSy3NmE76CdZmxjfdRuljubNM23NerUwzJ3jyfFJndpg9eMDCGTHjfLnUKRZKdoZBO+UQ89BZ/wDdqoV8dAGaKBjZrgLFD0HUHJellgfYqQNA/cREHBLJyzjQaOMc/BevshHpitIvlZ7jX0x5L+OpY7m/DtYoHdcQpPWyPN2D1OSK6JMsmtp2nuOj2Fy5sFUFpm2MT0HKDUFkAZVaDQtpBvnw1Kkc0eg+y+vfsxyK7j5d5BH/Tx/0q9hY4c5V7yKfQbsQAXPhNtEktHw59AW8QyUe9+yCYyaV+BHCKSB1V5uZOvR3t1cm96O9pfrPFNvY3+RSwe5kb7gyJI9PvodbE6NTXSHxLhJv39UqdvfGhACgB6q5BHeLHBIXHAjA3t28u97Bb0MSFTP7EJnRriXvwPaINYnRo9Br0pronvw+gdMdfdvTeqFadoWYFcIrDrwYflwmd0YFj82Hy43y8OnR7rD4j5+1wQcs8E+sfEBU0X5T4gX36BhwalsmDC50tC4KFxGXjAL9b4ZkL9I2kAJSACZ4P9o7kFgJL0+RI7gZKPDAyLwHkVmOWegcBBGVjlnlKBJ4YhMPYNSrnF+GAh91mgzBRAGS0CZXTXoMw5BilfgtBMPwH/jb4knRB6Xno/Ub/j35V3oPL2Ul5VlQqyt/I+pLyjZ5379gWupBYCFwwdoLx/Vd73pLOZ9qqEnatO8U/KW628lRWylPu+sgokyIwdfUgMSDLb0Tqhrzq+EV0pbkyxL6Ft4nY0+klUK25CQ6tRo7z17IXkbQZXVclSggRaJe9kUXSwODQfQUeIO9nk1gc3i3tf7joUFqCEXnvIJm5C/pceaRA/se9vj8nnxnhVlSyDMB5/XtaGxND+KQj8L+JONhNGF4ggueeRQ+SZ+RKSSsBTn0RRkXFqPyRvkeMrIffksF/m7nr0xQERzv7wywEJyl/fkKBsefBtcUTszb9KUMZefQhA2fiIBKXn+ccNEa71qWc6JChDIxKUhkde8nMva3voNfHMmw4PjokgudCgBCUz6pP33nTAJ3OfHBmSz41x/7AEJTk0ImtDYr6ABCXqCwIovrEsBA7lIDCAEoTAqcCQBGXCPzwLlPhQYHJ24FFxQadV4OxYUeAIBPbJh+mEzH1ugmQWwxabIDQrNy8wWT+VybzDC0zuoad5iffen6UenzI6fDP1bmTeOkcmk+Kj9JPbqNfkXIk0N85kxnVXVZXLO5nJJD1utMrZnM5kopoHHdTQTgOPaTr697oeGkQsnTxOvcHa3yDDNkI9vlS5tjaTSV/BBzfeTAbzqdwzejOZSS8LrI/TA/hQ4bvxTCauudFqRysNHKKBD6k3aZCgZqCz7f3UG6q5F71aM0y9XvtpSNeC1Ous/xj1IpnMVEvjvsjtTrDAzhVIY4HHWS2Fk+XedSo7xf9MFIOynYNytwLlmR1A4XOA6xoAlJNaM5kEry/4wiDN6Wt8cDPGQWG1IRSUJl5pcDn17GuZdxsNEmB1ILWDHBTay69moPSwoYJGD53qcNDeuxal8LDKFU1L0txZ5QoHZZxVrji9KQ4KHSAwUMIaHTQAKKZu7+Og0MA2DgoPPMpAYUMFLcxAcRisroVfUHoKzw6gJDSPYTSy3CMaGzV10ENHmWdnF9TPc/dRr7+WjTwC1DPpKTyuEA3cTnN3azEZ2M1aypwEyeEd7T3MYtGj3hVBXFf+Zn2i1B+22xVB8KCc17kBw5TqYxhmgt7Cz0lPw7Di3oP/V3qj2GOaeyHTdCYxHdtVVdGBI05SD60ymwwct1HvYNNowSE79Q4zPd0YpsdewT6XeQU91DGCN8jPOjCsuPuxnLU9Koo1EdiF48eIz87HiWoabrXpbcGRGuodaupdOFhPvXNNdz8ecpkPo82m04973V2fp6eoG8Pthvlx6tmi2Ntk7ssyTrBFl5XUc/Hc+QwLDh8uTvEjBcp1uFl6j2IojtmEn5eeC98hvW4MU3tB/E3hHBPHpwnvTJwUE2BNOoZ5tysw1AjciwNyFmuQgcJnggK4W0w22cO4Vcws2eLYEFNBrgSWE3pOCGx6cELOYjVzUNjcWRcelbNY/XhYfI+DIqbHxnCHDMxB2SlwEjsBlITM3YtjcgauHYfkLFYvHpGzWD48II5tCOIuEbg2glvlLFYce1TgMiJI5Mo0sd23K4JcJy8UisMC3Fp8lvROx7JVoguT8LXfqTX1+0JOQRB9cMQtCRLo1zlBTEek0+QEMe0Jhj8lCKXP1+ShJ7ILwAhCPztMfvaLCAS+TdWBPB2UBHH7obIKdbCyJ0oQsz7B5uMpQcwavuxGCUKJRNsYIwgNTA9lBDH1ZK3JCWK2xymPGEG8PWNOQRCPbwim/McegVMMqxqW2I+kcySGxaHTMKxKfkuBcoUC5R6YLEcbbeDV98M6SAQYhcLAqP8HjYflDktxQFqzKQnrIJ1RKNfoC8Jyhd8HpSZhWQtm1iVgkcKGYXXOBQ2/KLCRhMXDtrhcPPT2hORfdd8wlMmMylowsyEG66i1SVj9cKm1XRdWa6YY1kFa4rA41BWWi0P6wNwEySwKQeapOpxlm7bRHl8wsf6Ku/KywmTyxj/cPT161U1/sN8aIetZvzKLoXYJtarV6DA4SLWAdb3gnQpPAfRdVu7GCGK2sgvL2rHRzWopGEEMfu0YQdz8ejKCNMbXwrHYLgniDsBHJ7jBO1ct0V/aY4jAZucf4LPn2OVhBNFH2HVnBHFFuHcuW5uolQiY/DwAACAASURBVASpTTZIgmhsFYATxO3XBUFML7vuKzlpOqHJ9P4cTrH5y+A1K1BgvagYFEXkAigXqRqWq9VK6QYoMDF834TPnFDlhoagkdVhWCl1x2E12jUKbdsDZDBbeqDld7VB4H74yDMCBzjD4DUoMnBQuGlhoAoHRTTkPohSBIoqn1N5amMQ2BFXK+kqdy2qcg/AEfogBG4pJ4I83sT+fXo7edAlK0xesJONW+/VyS122+YYW5agBIEb4/wEOerdE4TdGN8bQb4+L0Gef+8E0RRBjL5FIMhR8xNEFXndvxgEcZcgSGeFEMSzEIIsfRfrdfYEefK2AKl9Va4P3n3rffe5rkqRv9vHb9pUw74zjW+ACxWH3sRa9VQ5Q/UmvlOqixWe3cUKqi5WlJFGdLHYErDoYsniddrFcgFBsOz4o/8tdLFU8dQzqos18gp81tE9q4tlK9HFcjNvVherQXaxellvgnexhoZVF+sxOMXwjQoUqIw8EgNpTsNQf/PtEqDco2pYXqoBrwGaryMC9QgoAvU3hxV1sQo9IVW3qdp2Z1S27R26WPLYxnC36mIBaXbVxYLasrY4VISoLpanZBdLFsd01SZVLafqYhUCezBU87Ym3lUXa+kJMnZ5mjjufNJGHmqUBNloIzb/fR5yu51cf2VaEARKXW9S49HHMZQnbcZ/k54bw6Xtxb+R3ljRIF1Tg3SnGKR7cbyaE8TbisO1nCB6N4Ziwb/DIL0hgP8CDR9DoeMIlhtN1kWxGwbpCcnVC/h4VA7Sa8UgvROPikG6xsejjCCNI7jXwwjSVRdi41EYpHthkK4Jgjgx9J41HJYbTX6sQLkBt0rvMQxDijcxFDpqGHpRPfi30hvFcqPJsXEsaynPUoN0A8Pz8koMY5UNMEinA10YpI/gHlmuEcZtaqArx9IUbVVHn4T6dBwXFSFy5oIxQIDCfpgapDf6cZ+gFB2kd4qWXxPFTeIUYuZCnmLnQbrXgEG60c5nXdgf6SBd5j4Eg/T6IDZF7jURmGDYHse6yn3BBHlv9q5mseovv+r6WOKq3985IwkycfMfHySjl111q528cQsRBMEOPhC/Oonx22JGk55GNIGnqceb7+FvUI8/atbZMY7zaayTvNQzGlciTYtgHNO0qiqXJ45xSNPQqkYjgfGo040OamilgUdcHvTvdV00iHgOPEK93ho2zeuj3m3so7XbMU5czrzjNIzDfH/HGR30FB5XVZWmxTDu5tO8F49hPEbDrXa00MABl44OqWunQXyajs62d1Ovj03z2gaoZ9pPQ7prmHptDR9DHmcA42QTm+Z1hWhgnU3zumnuUbfGJk1p7q1imjeKcSMH5Sp6ii2cNXcpUJ4CUA57nXo3KlD4oPtEg2bH2X2mSXPicxLn+envcdFTcFA8vEzmMupV8zKZW1nu1WyqtJeBwqZ5bYPU66qjn7noobilwTA9ziCFh0YwNC3EQdENl5uCEtFoYCfLfczpMQ1HMwPFyWZjGShDGgvMQOlnRSe2fg4KC8xBqWeBOShsNpaBktDZNK8AxSVBCTndhuHw0sBBF5s/bqWHDrPA/IIO2gxTr+mjXg/NXdfYBe2ggXXXCA3cyqaonfSSJbxsmlcL47IiyHz2Sj0QBCe2og/28uiRTehIH/dGn0aXBrk3/AX0Rph7fZ9Ab8e51/n0/bYk90J7oYDILFhVNSq8EbQqJDw/OjjCneQQOizKvbHX0bdHuBe/GvlEuIHPordi3Os+EG0Tgdv2QnUiyGhVlcgEN6AVzXCKD4icksPoUHGKhA+dJ4LEn0DVCe7FzkRDwot8HA2LwOH9kDg/HluJZOBgT++Y8GrR3h0i3FZ0cA/3om+iIyQoL6Jfi187/GUApV+B0rUaVYtTtKxEDSKchqo8MuPuXgFKcjva3xTZbb73hQGRsa9LQlHwogPmkAgX6Tf9Ikior1uiPdrbI3MP9MjA9BR9AIrZD6CYgzEVWIIyaAIo/aYEhQYGUFTgAiiB2YGTQ+ZgVJ4CMo75THkKmjug3WfKwKFekXuFEKT6hmlFEIzRP0J8dKZ0+minQtgPEfzxs8or2F7qs6oqFWSV8j6ovLXKu1I6N6tDj1Xeh5W3T6nAK5W3v/I+rbwLpfMy0qV3jgpyiPJWK29lid+D9lPex5T3n9Lxo4ek9xN16L8p7yDlrSqRe9EpDp792f8dqxCCgDGCRF9FB7fx6IkB9Dlxh4h50N3i1hO5EA2IO0T402hQ3CHGejp94g7hWIG2iMyqUZWdO8m30F6aCPIa2l/c8kMb0aFd3Bt5Dp0nbpvR36N++UD6NHpJ3AWbD0CvidNqe6HN4hSBqip5E9qCVjSKU/jQB8TdLTGIDhV3t/gA+rrM/TH0jggcORMNiIxDH0cy49H9kLwzB1YieWf2d5nizkyfdPuMwik+EgJQTpaBm9CdEpTvK1D+VYFyAJwiuBcaFoFHqqpk4M13bNAg8BoZeLCjV+QeeO6WpwQoA0/d8pz4tV2P3bJRgvLQnwCUDXe8JU5hv+fPNgnKnfcJUBKv3/6gIbJ7+ZZHxXNw9G+3PCE6B7G+jn6Ze28HgNILl3G0uwtAMbsKoEjch7q6AZTOXgAFco/3d/TJwH0dAEpvx6yWEjQ7OShzEmRmUUpN3lvR/DV3iv/HY2+7d/xLPpOp53Ovv05nMoFqHZ1Y381LHmrQLfYBVplQ/w2k1/ip19FwKOI1ClPNjaxGIZrJRHjJ+Kk9mcwwHyB8OZDJdJ7EvG/HMxkXFxr56WQms5XPVP2BBhGL0PdRr8/+W2TUDFHvWj7M2ZrJTPI6+nXOTCbO9+ue3MlKHtx0DOIez2R6+ADh/IioYVnd2DzFSh4MdEgDK37w1xjonPpe6g3YN6BXeeFGdz0dg1QHWMlDIys1CWUy6SbnvsjDih8mdG0lcms4k8EeDysen8hkYnQgs4+zKS1qWP6hkRVuBGw6Or6BFW74KCg38mqW3roL6fhJgPIpBcoBiIMy6XXtJapZxj10YMYLNzpPBlAiNPBBjS1Tslyjgf7EjBjc3Es9sY//OQXKlkwm9XMAJcFBOZH+2gDfXPAFCl4vB+U8CorBR00/pCer4UPJK+gp/s7D3cFAqfWahgSFVaUHZB0Ir2ahoDCPgjKpu+iIX4DilqDEWZ4urwBFVrOw4hhDgsL2APTz4hh6CgGKQ1azTLWwOnpW4sOKY0y3RkEpanzvqVZ0vlms90QOxovLfybWT5pemv1HmKC8noyzmaBT2TaYqNN0obvYNpgRj/ltxLfB9BnmpxHfBtMuZiSqc+Tr4tBjcvlThfd5MiWXOr5DojLwFQSWC+4nTum9RkY1PovVGCcg7tBHrpBelHxHOEdNsb1BbBbLQ3JyUf/rXF0DrTab2knazmexjD6SdIhSkxESEqUmriiTHGHTvPXjpFvMYtXwvUFsFkvsDWKzWMV7g5jkCNrHbGrlm47QR9jeoPEG6p1k6hQUl+lGt3FQ3OZ3S4ByAJsoy7FNdnvxwGyTncgddpFdyDdMoTVMOGWyTk58Ek3+7L8T2OTfSGAPcy+5UoFykXDWThG5F/+UfAEULL/2c7WL7E9qF9lzRC6JOCNkWG2YkqsphQ1T1dOwQbAAig46LE0tJCN3YplqF5mveBeZ3DCVULvIUqRDBBagiMDFTa9sCPLOxg06iV+9/qk7fvuwU6ys33jNtXLNEXbboRxf4zuVXVD6CxlBxEZKRhCzidSYvC2YXbBD0zuoliteB60dZKh1sqCa8k9DVcXhwCh0EgvM10G8anf6/06Bd8coeC8kYR0kClsQ0QBTBuHrIDm+1MJKTfK8muVcubuUr4MY7BR8HaQlD+sg3Vm5DmIMjcM6SAjEEFzjbMstJYjp4FtuKUHk7lJKEBGYEUSAwggittxyUDrZlltGEO9ASi4OiS23fO47ofYhB9iWW0qQoi23NnKe/OOJah/y18gR0vuF2ll/uwLlebUPuQ5UmVC/2oeM14M3813pHFO05VaJS6h96t1KLkVtudVLbbnNwD7k2tn7kLs8RVtuYYGlU+1DHoB9yPo8W24XWO6+C4K8pzHIdX7XfeTRRvKk+6VNTrGy/iB5spmQHUUbdEmQhplaSRCuT8AJwvUJeFvwJADt5r/DodcriZ9H5xdtWAce2+PPCWKoxbYT28E7fyt4vxuClfQBtXb3Cm+zlCAerk/AV9LThiRIQ94uCVIz44CFwklNEsSIGnIlvX0EVtJ7Qc3G8LNiI0YQIdrACCJEGxhBHNM1kiAclO/uDIpHEEToE/DyAi7awMsLfKrNbm6WBCkWbSiAAs5R8DxAJ3aCd9474F3+BHj3KkGBl0FQADlhUz7yF5QsFibaUKRqUgBlftGGgpJFKamXdyXasMCmvQcIMnH+rddfPHNDjHWxNjnFynoNeVUXWSnZn0GPIkiNJEhtCYLoSsCjRd3US8n+vKNkfzoKsj8lCKKkqk5WbeECKJxFVw0DQQb/CJ+9pgiSAII4p2AlnQt4CILkodSEtwVBkBiUmnABD06QPqVwM9IBBOGyP4wgQvaHE4TJ/kiC1EmC8LYgQGEKN5wgXPaHE4TL/rDcvUOqTGZriyRIsezP0QoUcI5Swhgnq4X5bypxiSufBu/+W8H7O2y2R+4zwQsogihFll3I/gQXJPtTrIWkApeU/VEtZSGyPwu0PdDF2v4MIbe2PqSRR10bNzlhZV0QBDZuMxTbTehieSVBDPYI5QRpUb2JbtDZ8A6pMqqtuiKD0sqKKCmg7O+lsxbkDNFpLPAVou8G4iO/y6nrDsMX9Nq4rKN3J9QNdGRYdrHsM92SIDbSYkIXy5jVxWpnGmmcIH1MI413sUYmG+UYJAriI87JEV0QRAjHMYII4TjRxfLO6mK1Mo20AigH8L7bVKOsvwklVKm+upNEePdwDeu7FYTjLlKgwM3/AgJ3kssVKBtABAO9qgqG3epO4lcCYin1VMlDmcyJqotlFITjVE+oF8RHCsJxFBRYrC8SjgPS1OSBNKVUTVrmE47zhMqRIDcOEFL318jvb3rZf+lDTlhZB4Ik4AJwnQ1KEHeQCUoygjTGmKAkI0jdBBOUZG3BlgFBSW2G/EAceuoMkYVK55Fpqa71cwJ3wT8R0NR6nrRJz0YiTk6Qekxgd0WA/El640SWDZ7Ah428m0Ly8jn0AzKjiVKTbpJhQ95Du+gImulssFITrrLJCNKQIEE3JwgdLA7onCDVOdJlcIIIlU0+BiHQepq5RggliNEl5gE+wkbQqVpOEC3IxEcYQRgoLk4QDgonSA0HhRHEPU1amzhBPHkin35eBcrPxDzAGi49Kro9bj+BnSHVpFZ6rQRKfEaU8uU4kcqXx+dATeZLBCZHLiFT8j6zXk2OPEz6pPcmqMnQEbQsfqSgDIrfbcsSuYGDgtICDR9AaSJSTaYgPWoMgCKLFiCy+NHJQWFGQRlWeqw9SnpUFsyUo6rJLuN1cRmSuwjXaUYn1rL9oCFPNbqlmsl2BGq+gYTW8aD9UCS0jrvrdaF1PPVLdug3woQk+YDwhxOEBLlu6aU5Qnp5LdIf6Sm8vIE8QoPU8OvItEyCtt8g3c32sj7KL7uXBr6Feaf3EJK7jHlfDXI55aqqRqZlEubN4pdThKR0Da1u6GY6zTTjQ+xMVT7m9qCzuWzHWPW96BWh01x7GvJ42F7SfvvHkNszTk9h1u+LNCbbMd3mWIFcTVlCsmynnqNV6DSjfaR4tRv9A9cDYaAcX8OEIcIUlBu4lknAdiGFQug0f0qAQnrqD0BuIV7t2Au5vBkmXk1zd7bQ3Ed5leZvs0zfREMH1THFCbbLUIhX13JQXqEeL2Nea1Og0JtY/lYFCq/E+UqAdpr51p2LEzQnPtH+CwqKj0+0X0v74G18zMc2Ajl47+1vHBQlXl1rGACKTj0BCrugaaZvwjSmFShCvNpwgaK37i4Wr5ageKSWCZv6FaAwNXK3BIV6HJSOXYpXl++e9Lz32a1i43A+jv5TquWb6GFxrulLkFTLz30Wyf2JmRCo5Y+sRHKb+CCqkq8QMNFecrKiGR0odnPPeJ7ZLo7INKIfSLWQ65FU+sf/geR7FSIHoWZxaHAVkv3VVFWVVIvp2fj6iPAw2l9OqCfQv+Qg92/J3F9Amsz9S5B79pOw/3xqPyS3WqdXIvkKgclAUL5CYBztA28pQB8FpX90ulT6H0R/kYH/W4Hy/ylQDkTw+oNVaGLn3AugYLRGvv4gAa8QyDrRReJ7qV+hRvFXfDiAEj24CBQpRzFUhQaE14tWSFDa0H6iB5M30D/Kff/a0zUilZkovFdhh1c3ACgheK9CejQwGxT1+oNk0esPIurVDfK9CtNRCJwL+1VLgXdCTMl3QlSgaEOxLVDVpGALVTUpOsW10imompygvI8ob58SgYuC7ClVE7Sf8v5ReV+QTpJLhTH7pTr0P5R3kPJWlQJllfI+WOK0V0rnHnXoKcr76LygoJXK2195n5p9ijKxCiTITLDHJ+4fuRF0mriThR5APxMKWamL0Yi4a0z8KwqIX5Ec7BkVP9S5Am0VXqSqSkgr5UNolbh/zATRweL+kQt0+8SNKfIcOk/c8UYuRM+J58vkOgiMP4SC4ifG90YhEdhehWpk4N4B+bKc19G+Qshq8iX00W7x2Qg6T+Q+9TSqFXfh1BfQiLjPjX8cAif2Q2MicGwlEq/3IeHefvEIzW1CewtBkvQraI1Q90q+gP5DvBoo9Cj6iXgMpH+gQPmsAuVAJEGJ7wW5K1BmNqOVbgBljQBlWoESfQ6dLUH5DoDS/xn0orgq+MOzQYlWIfE1eidbKUDJj6LVSRkY/dOEBKV7SIAy+vQNzwhpdd+jN74gvtf115tfEQ8J7wO3bBJAOe65fRs/RX777Xc3iHBv3nq/IXMvtBQInPF3D4vnS3qoG0Dx9cAFVS0lPtDLc5+7Fiu9GLZAgvR/b/1V15i7/h6ZTqeZ9IXZ2JJKp301XnSio4OeRuzRuJt6XQ3nI6PGl06nWho/zWRI0ulJLx2P6VqIHsrH5se3pdMJt7uqyuNJptOslmKV5hlPp0epd7DLSwP7a7ym4Win4cSepFupJ2YoH6Nee+Ph9BR+eoom1wdZ4UY6Pe7R9kYeLUJz4gOZz/Wm01GNDhw1D06nG/lq/YWJdHozD/LzSfq9Wi86p6GLhuup24BetfdRr8Nxmsy9ufHjyNCC6fSE17Uv4rljt3slKzpJp+MaHVXSPnM67eaTR+dH0+ntvDjmv+mvEFomv1eg/JmBUn8hMmp3BuUAEZjmvhcrOqGgaACKEJn8Kv0rE3hZ4/LSjP02CkpjKw33hAKFV/6jR2lgPghDr3JQPlwEiq6F0+mk21PFqlnSaVYcs5KjHdIMtJoHDtgMdIhTXlDT29BJA4s3pNxPPbEm8wKFgk9yHb6VBuFLVus0eihXIziJHmHygcyZw+m0hw9kvkF/T9BNW4qzmQYe4oHZBe2vo159NweFV7NwUEymzSJbiqGN8dxZm6FBFqdYsfQsFl5wsWL/3YRoty2EIDgsZiS8rTjo4LNYeheGLa8bcY8oNWkM4DZRVVEXAnUNWwx2Hq6NsB1BbMLGKXYzrWIrYdFasWGqGY+JqQ6jE2+RgZ/EMC25hW3cOZJtnRrDTU3mB6lXG2U7sfZmkiPxpFxkPC6RUFt4huWhl2BYWrwGBxr5LJbegwc1UWriw6bBZ7EcQaauwWax7GEs10Gq41iupDuToAziwjDb9i0M7+P5LXZJ7y4ME9gvsN1MbBarcYTJpRRA4aUmMbbpiO8iS7DdTAIU2K77NQHKGrafbExMrhodGCawn8CwwPE2hgk9HTc3mR9GfF+TBGV7nO1mqlK5o5VssxfXkFnNAofZhN4hptEGO7E83fglGe7v+F7pOTAU9nRhWLEKYDnPvC4Wl4szp2NYwL8AR2RLaeEthc3tdjINGX5RBnCvmPdyUlDEjFW9aik1MbUTK7FI1byLQRD9wWK9BvF2EFZhwmpOuHqDIAjs0LQnYSVdg61w6GS2746vg7jZ1k8+5d+sRDA6odYHPTfsgZ2xbIcmI0hjmK8gHExbTxwmy21q2+qxatvql5kIxpF82Y3t0PwgX7hgOzT35jtj7XCKzaAM4gypGpYR2LaK+Ltqdtxy6yracmuX6yCtMSg1MYOSILoP9nprQbVyM6Bk46NQrnEYNB50EgvM10E8uEaBUi9LTbrYdCdfB+kHUDwj6pU/XXwtkpWaxGHbag2GWoZjFChfVCvpP2KgfJiDElGgBDRBEH2IqQIwgrjGuuTiUEOcLwkdwvafF/bywlLtaRhqWL6jllV+NwTeferNFi8rVQCHWgPuhwITe0K9HULt5XWpLbcahgWWpgQUJXWGQd29b26CTC0KQRYo2tB/ybVXXZIo1msQbwdhFSas5uQZpt7Avpgt7Crm79BiBHHA0ghCbJqbE0RjbwfjbcE1ogp7YAUD/Z7t8efLFSavpeCiDW5JEA9soTadsSPhCNUCjmD6BJwgWtAtCeJmWv578/VJ2I+LbofLY/RDm0W2U1XDA4LUJ1WpSQJKTbSQEm0YNmarmqil4t6fQLg3QfcIeUH3CKld5whDqYk7AuUFrgCUmugllCzMy1XD80iCuAMKlGhByUKdQr2767hRtyRIAZQmrmTBS8DY3Y0RhGtQCCULvyYJ4grDpW1Q5TxILcEeDUtS6AxV2PNfarXxulvAe+hn4G0qiDZA7o4EkKEOw0o6B0V8bwQYqg8ApVqLb/Hzq5rsWYLcTWaee6lYr0G8HYRVmLCaE67esCNBXGEgSBGeTKtCECQKVRWa0qowVA3eH0zZFrw9rZIgulI10ZQwhyM+uy0cydoxJwgX8OAE8QzpQJCH4Xt/VgIeA/C6NVSnXqDTB6om9ez+JQiSrJcEcSmCFMn+KFWTDhDw8Pb+EsJtPQe8NvXqKiUpwUH5rgDFqUABghiDiiAKlG4oJUCv6UCQUdd8oCg2nsAURD68IyjN7MUFnCBd7UCQASCIhzOPEcQZAdzrCxdUvSDlGFXYc5aqYfnJs+DdrF7M95h6Sm9RLSVUIMhsVRN3TKmaBIEghq9AkPLpYnU/UKzXAG8HeVVnNSfPMPUG9sVpDO3DhpskQZxK3+0s9uAUtVh4u2wLbQkoIOhVUkBvBGRVhXNsUKqa1Me4vtvBrKXCzd+FvyUPOA2fLr0L2FOaE6SV9eI+yPs/rCfE2oI+6IVT1IPqjCOi9N3C10nnsCJVE7cSjnNKgjRhKDXpTMhSE70vBKUm/hHoH4efgcBB1T4SICN5tFJk+YLqYhnYpUCplV2sHqbvxlfSfUFZJuMaVYosQxyUNaz/06JAkbX96HOqa/tNDKS5jIHyYd53iwMoA+wOzgiijTDxEUYQR6RXSr3YE+2SINWgatLlUl3bs1Uv7qeKNH9UbHxS1bBsrQGvFbQ1UVC+vsW0JZtV7kCBQmfLUF47iN4JUJh5fGVEkMilxXoN8HaQV3VWc8LVGwRB4qLe32PicA0niGsQN0pM7Njn5ASxh5hGCGsL1THcLodjCSzv5Z9LSvGRJg9IjxrNOM4GkAd30TF/VDyI3f0Y3m+4FcNzXcf9bk6Qmgim32UEccZxiyEG6Ul8PlxZ2cn1enFEVlVci6H3/AgO1XKCOH1sZEoJ0tUQxIMuTpCaMKa/7uNieNsiSk2cSax7hfQolhe0ycAx2Z+6XA3X78FQLLgRw16WWjzUyAmyMygH8KE5E07hg3QMiiwenJAiWD8XoKxhw9uouPlqfRhuAluU5KuO4V7eizt1ThBXAkChuXuaOEHcHBRKEK/BxUcoQfQ2HKvmBHH3wvwLBQVkuxowcLVTSb6OYLnl5JgYlr3X/8QgrnkhjsuSx8ugpegmCKe4Brjkq8l1WIbEg4OBIu44thjuEC3FxUHhqVTiltuQphl6LdMRCDg1dKKNKVS8zdrgkUzLZKD6POR2sX2jPbWHIo0pVOAOu2443WGMo1zs5ltDTHHDUVXV4I3TzzyNaFUd0zIJay50kJ3ttA266Clq2N7P7Wwi9fBXqPcqm0hdt516vbbDkeZiUgGd9g8iF5O+SLbWrUKNnijGY7xM9ZJRjGN6o643MC0Tkw9rb6KewUchf2G5u9zo7Gq2b3XIdQ96ict29NtOoxmz3M3aj9HAbLdoe92+yMl0WBLNDStQoxFn2iwOGrg5QTPhj85rqNfKh+RM4MXBy2SYwMsWBsoRr3NQvo3czhEOyqdo7mxDakfdAcjFQEm2NOyFnHpMguJgoAxynv+Oi74oUJwASrUC5e8clG3U4+P6E10clA8pUPZGTgZKwuuoQg6WOwUFraxnoITdLrTaznbajjld6JM13fyCug0PF3jZxEZ/azdTj1PvOCYqcR/zTm+mga9j3ld6MI7wB+ZFfnro9zij6Wn7zgVQQuwyFnLnLWXY6TE8LiZqMVjtMSQoNbohQOmsY+orPPd6XXd6YpVIEIpo/7DYS0zvnGeIvcTx+ufqxE7jxMUoKHYVJz6DpG5JfHhAKmkMvv5yp/AiVVVi5zYOoVUx8A6WgUf7R6RsR8NzdvG9yM9Rg/xsnQr8ISQDx/YGr68KCXkRHB70CSUN3Ir2k/ul9ee3id3pySA6X+SefAbVyYy/CLol8U8gKdER3w+JzdQ4uhK8sA8Ct6O95ZZsL/qQODbpQkeHFChSyuO/FCifVbkfqHLfCwIXQOlAew0BKGt2BiXhQBeJ70X/F0AZq35OE6eIf7gIFBUYycBhtDIKgVdDYPTPEopg/4jUMrkPPSL+GjoFueRO9I8isYkd+/dFQpUAD6xAQnMFd6MV/RKUjW/KPfZFLQUCJwP9AQnFSD+A4u+f1VJivkGeeyUSpMiUqknB5lc1Kdj8qiYl7N2rmhTluX+Jz/a4qknB3r2qSREoJVRN0G+lc2uJnD7y7qVeDp0V5G1kl95XVZB/Ut4HxHe9LwAAIABJREFUSoCCVsxOZfetAgkS72npEPfjqIlOEdQPm81d4mblPQFtEHeSwKdRj7h/+NtbesX9w9fW0s/vGokXEXqFe8k+tErccBK9aI0IHOtu7hL34wgNLO4pLV9B93ZzL3gk6hbPnNoPoEfFnX5ob9QnbkeDVVWDInB/S5sPAq+W4lo9LZ1CN6TvPnS0l3vt/4PWi8ea5zB0/yD3Rv4R9Yrch/dFkPsKNCACD0DgyBNor63iFD3oI5A7OmkWKCejDX0idwVK9b7oMfHL3liBnhW/Z4DmLpB9Gq18S2Tciw5SoHQCKOi/xaGhq5EpHg3BrmYJin1/AGXzXugp/rhI0qHEiwkBClpZAMUvQUGfFKBEzeZOcWjHL9FlAhRdgVJ/MHpIJOBfXQRKvwQFrZCB+1rb4YK2dIjAvvsvu8PNva57Lr27nXtNd156nwCl8U+X/1X8xpFZLWXuHYXZxbAFlruDjAnZUcWkhKpJNhuoYfujmjLZ7JDdQKc2ttHT9LKxV0M39cQ7Ox7NZjPNzk8jb81INptm73c03KFsdoJJX+juWDbbw3vqJw9ks1G3jla5PZPZ7KjNYKUmU9kse4Wk6Wyjp+hns4ENXTSw2GV3Pw3c5jwSeWv92ayc5NqWzU56PHsj3R3NZsfdnqoq3Z3MZmNutivcM5HNhtwGWq3p6Wx2pIYXP9DAvDwcXUEDiyn8O2lgMUP5NPWaXB+nuQey2ZTu3pcGDvPAK5GuJbLZBKth8bjHs9lGvk5zNj1t0OZFH3E10dyHag10krMAikk9Map9WIFCc0/w7fZH1NBD+fDl6KZsNqnpNHcNZ7M6n1c4ayybHaOgrNE4KDz3VgEK+rGjkwbuaqAEqe+ngVvpoJuDIl6Uit6hP5sXLhzlzmYH+cLfCfT6xCnaK91uBoqnCBR0CAfFV0dPwQOLwp7baWAhHPsUxViMyN9koGirJSgaA8UtQEErOCgRVmDi1lMcFNZSaO5iDuGX9BRCoftGBcpDtH0IkedXqWfwlkJ/9oTbzYpOaEspanyL+wa8d7NhSsmY7GSlVE1SYgLb20ESjXwWy+iH1xF7guQhOdXhYK8jZhM29RPwOuKaDOyq0Wam5VTHyfnparlQSKbsfJq3qRVeR2z0kojGN9LpfgJTqttJryw1weodzUEi18S2TxMQbcjPKJ0Npq7BZjSbyaSYQ/GaBF7U/iCBaZo3yT3S05m6BpvFqkux1xGzWSxbju2A21HVxE1g5vObYu/YR8ymTsLqRdBJxaAECCzONAAoDRNEqgmjMJH1N0ek83IWy00m5R+/IkBZw0AZFzOfRg9ha7Dox+xN037dvJpN34bhHc0OTH4qjw0QmHwfz8v6m2On+d6xlQyUbE0xKOgQBkpcLNkZg+RVeegb5D7pechl0htkF3Q14lunJCjaDN87toLlnpNLHU1cQ4Y5nQRUI+4kb0vvRQKT73XkWul1Ezm/X6tainuHkt3lIwiXMeF1JZucxaUmpVRN5KS1aZ/hi+CnMrRhrdyjtn5+Iw+lJh052NbcDyIYRkC9K8bJRDB4qUmKvXeerYPUT8M6SA28h5sGPl4e8GUCpSat0IzQH9NQVeGPy3INPQL7pV0TA6ZoC44cLILX5uG1AusIrK6foZRBLs5DqYmZcUh1dx+IVxtjIaB5Uq1KjrH/5qomAhRQNZEMBVDOK4CiVhX+rBaHNkakZJE7phbgfByUNQwUWPis4dtWKUFMrgxyNV9gUfthW1Nw6M1+8J4EnSC0PQilJuMDstTEkeUvLjqE7epVby5QoJxOoJbhoiwEuYqBwgjiHVSgjDJQGEG0xJDEvXEKWkrdzCVwLLzGHp1EYGf912fgj78C/RuzbxJKTUbmJsjulrtf+C5EG7iMCa8roQQpKjVpKqFqoupqJqAWy6FEMOzqxx6ThjUxPa60KtQefxPm1NEj/PKwJ8hYkyRIYe++MwsH1Cl9ApSFUhNdFT98PQRVFR0FVZNueagRBNEGPQ5t1pUuCHiowEpF6LhJWEnXo7CS3lZK1UStpFfPoWoiQMkrUKYUKKqK6SJVxXQtIzLP3aekXt5skgRxK1AaM4YkSB1Tg+AEqckpZRDYj4u+pipCLlWlXffwTflsJT3QAqBwCQZeapICtBtUxkipCB2nNJs+z6ReVvNblAKlh2/KpwQxhuG9UkYYwrnHT4BjleIGUqc4WskTnZKYrWrSNXe5++5ulhUEWVAUIWPC60ooQYpKTUqpmiitipSS/clDW6hV7fjYjLNYwINbq9Kq6IGeGHrKlATRw82SINo4tGNHDjy7Cnz0NJSa6FBCiy6MAEG6/F5QNemFCzXaJtuCO6mUQTKqIqTQFhRVTkg7JUG8jNxC1SSoVE0GZBBvQJVMNXglQVwpjyRIQx6qKopAySpQVN3mjzaDd7NSNRm+Dj7bBrI/2rgiCAeFEcRO7PAEmZYrz6ZHPZC+XQPe1eqB9JdeIMhouwlSL4YkiJB6YVZfuB2pO97xqtzrHHafWV0CFEYQ7whUWuhFqiYnwbHqJlRQZDlWVbOcocqYWkLQUrqL2+mydbGEjAlfNlcE4aUmTSVUTQaggIB0mtDFgmezQaAi5Bfs8c/bQm/eLgQ5DH9GdrY8UXXz7w1BqUluyCtLTfJw8/dAp5SeAsoBL2GBOUG61cV7hImP8KqK0KQTXjwSlS3KkRkRVRVd9pk+FRhu/p8jUBFygZJhu5zIUhPvwLRdlpoE07LURIsn5GVsTKub/yQXH6EEqSZdkiBuGHqZXgXKzwqgqA7LM6pmqwbebOqcUIosiWGparIzKIwgOnuac4I0K40QU4HysKrUeku92KqFKbIwgjRMcUUWLvXSJwniIe3qFCDY900FyqWqRd/DxEcYQYwgEx+RoGiCII2pUUmzuulB1VLgiXgCSGCic5Qiy09UD/RW0KYz/FnZ2XJHyoIgQsZkR4LwUpNSqibTon14/GRKlJo4w2RcDNzrkwRqFIZJQONtYXuOyMJzT550eGVzJ1Il7vuEXVpWamISNqqmBPH4uN4mu/OMkUkxznPEld5mHxlzcYLQIaJUBzx6ipiiqsJDSIuX17A0gc6Gt4PkPaKqop/kRPGDFiCwR8RO4DVAnQQKN0aJ380JQoeIvbpQNcmTdlFqYhD5DPW2kRnZem7ioFCCeIbJlI0TpABKQ5LAa4CGSFCAUp0j8n1rx2WJfJXOWYQ0N7Hcvc0kL4usrhKgrGGinVmhEeIaJZO1nCANcaYmwwhinySyYNaWgRmBo9NEys+dngc1mW9yUChBjA6hyKJAoQShoKRFA22MKhHSdgKViUGYJjlygk2TMIJoedJmFIFCCUJByQuu6j1kWs5SDCu52i0EKnG8BMQ1fWqaJEEGxLEU7U7VUoqsElRNZphqhVP38NLFcY8LnehhGzPjbs3QPGwb6jY2Qfm5VsJUKw5FLqZQQXy1Hr2xeYpm01OnGw2tdLif5s3iSibl0epAq+r6aKJT3kZ0UC1TcpjUXbqnmu1WTbpd7AUt1Ktlo7qT2FbO0erDaGC2R5TvNzwvQL0++yrkaMnSBLvqq6rqO2meubYGQ6/roYEzzY1ode2gDOy2sQPamDbL2neo52DjkeM16r3FSv5OZxtn/TUfQ06D7REdqN0XOZozXJtlBWpop0PD6fYG3VNn0sBi98fdeQ4K+gcFyvECFI9muLlsBwflVAHKpyQonN3fot+b5hJ3P8Y0906WexfNPcGr1m6j3lSTEx0oQWG4c1DoKb7PQYm6f4s0d4KD4jEEKI8DKHkuYXZJnALAb+G/STFQHGjlTqCkdBf6JAcF6yxjpuTQyEA5jhVwv81AOa2DerwM8RyWyoB9P9TYAqA4JChoBQcl2+zQdTsTeEl7oaWY7Hlx5Cbq8RcCHcNeNrOdgXIK2wnNX0j8RXaEr8ajO5tYS+m167qjbce51UrZk07S4aSMO4nOFBTMT4QmpEJFx9t9wpv5LJISHdOJsJS5yMUjcmY7XlcPShZolfyxU+iDMnAqBCtEk6Fx4U13vd0rT3YsBJ7StwXkKfZBUrckV1UlT5GNxHMQeP9pCCxzz/ehr0qdjUfQFvFZrm1Lv/ydhyAp0TG9H4LcVyIIHI3JwKHtbinqkUYfU6D85yxQLkYSlPy/Qe6pA5DULRl32mRXIltVJQNHtrsmIPeDZ4OCLpXhbgWllTwOASgfQnLyZ2Jv2EWfbLDH5SnQSgXKB6Yh90MhdzjFzDPoVQnK55EEJeN9Z1iC8oESoKAVMnAmHIfA4YQMPEBHYCJwG7pZ/DX3PQAl27xVKtzMFLcUfkErULSh2JSqSSkrpWpSyuZXNSlhJ5QIvFBVk6LPZquaFGyhqiZF4WarmhRsflWTgpXMfWGqJgUrpWpSdOjCVE1qEKwRf6NEkA+UAKVqxezvFZ32dOlMoUel9+uFtI8KJEjW194jRD0CG9Bn+LujyWR/e5+44+He9gFx841/Gg2Ku0aku31I/J7Rrg4/zykf6OwM8h+ffgqt3Mz/mPOhg4WoR2awvVc8X1I0sBDGGO9t7xf3SP2f0ANC0Sna3e4Tt63Q3miYB56hD+wX+clm/B1dQphjegitFvfo7GB7j7iV+u9F69q4N/EYekfchpM0d3EDa1iD/iq+984q9Lj46+gKNCIEPEY6Okdl4HZTZBL/KzpQiHpMDaCTkzuB0nYiuk/cfF0fRX8RqUQOQEMi9zEAZfpFhF4Wv2K4QwamoKzZGRTfPegMIaEyvh5JUBK97YMid/v+6OHkjqDkg52dAZ575lm04g3uTTyK9t4uQUGHiB+bHmjvFRIqPT9Dl/YJUM5CEpRYT7svuyMo+ddXoOezAhS0QlzQ6WEAhbUUkXtwA/qUl3v9t6Jvibkp/AMELaWnfVA8/23X//JRfrnzm9b/9hn22dy1WKnFsDkJYl6y/soHdrXfEI/t/Ml0KjXEhnJufSKVEvtbf0S9TjY/5WinJ2xhSzz1PanUhFej49HawVQK87IB20gqlWB6IIYWSqUirFxDd0VTqTquB/K54VTKX0MH6W59PJXiBSaadzKVMtl0RmMbDdzGpvrrTRpOlDw8nkqN62zCoMafSiVd+t7I6x6lmfAVjqObU6kQU1/RtUQqNWKj41GPG6dSg7U8MM1YiBH8jJ6ivfFhtLmhM5WabGa51/WmUjHxhoGXUilRB4K2p1Jxp85KTcIQ2BWnf2ULIDww70ajr8dSqb46Okh3NtPAnWzutZGCMilEQO+heYo6kGcYKJ4DkMlBcQEoLXx/ybEdqdSo28teTJNMpYYpKGvcHgrKgB1AEVomv+ag0EF6AwVl0mCjdHs/RZYrVaLXGSgeCopGQeECL7oWSaU0vvB3Ev2e0MM4i/7VZ6eDdM2goPDiGGcLDSw22V/LQTkLmfUUlAmOdu0QzU68b2QbPYCvcKz1pFJhl4FW6K5YMSjDbPpfYy1FTKf8kP4KIbp/BWspjZcg3lImWYGJaactJfQl/te36IXi8x9HNKZSe7BYcd4NU+YDhDz/+i4IokpNVJhpHBIzcE1e9fLnG3G/mH3w9MK7gl3DTF2DV1WMwi6y2gi8K7g6npRr6s4kCFp/gW/XYdO8BqhreFuxT0yE6N24W0xKaT4M+1EdsAPOHmYrumwWqzqekHuY1sbkdh3ThaN2OeWPR2XgZgwlD7ezPUxsR6GnH8s5NucIBhXnVgxa9j5QNXElEpA7jsn1Hw3DtPWP8IiTz2J52/GAG0AB1Yi31BuxPVhuubVHQL1geyImtwUek4jbIHBYlJpQUKQyiLdFachsYHIpvNTEx3RY+ARUAN6IjToBFFtc6bAkYTrpFAxTv9/gGjLoELOpBQ+LVIwu/IT86zMMlLPYris/vFW6PoS/qkCRG7sODzNQ+DSvAsWNQ2Iar8nAoCF8HYbXwTyCu3TzEsRfCN0icqctBTaQ9uLTpDe6J6t5d0kQvJ7XkTDZktfd5LkOWWXCJUx4cckdv21nFSeb7/3zrRGyfpITBLZ+1iWgXAMVaVWA52bbJ/mUf1MMSk06YV+10QvKIB6/epFLO1/PowRpSMj5867aEiIYbjW9/30QwTDbmAgGawtGD0zVotf6JH20Ub5ew6oqYrBeU5uEa3xEkaoJqIx71NLEpSDnjO4KyKoKfWAQ6oQCsKe0MQKSOCgMK+m2otyhXONspW7xk5hD7knvAEUDb6/SdN8iRZxNZ4iDwkpNVBVALWzeQ0ex3Pk6iFspgxhqP+z6EIDS7YeyNJ96EYIGOkHIxxfrWalJEjLWMCx9n8zW7s7iW9xh57TZrJZV7oSKNvQ821nPa7FGTMg9AotYdQn1ZgvV8I9luV/Cc09CHUaTWou8Se2cfmyRVE3mJMicnShGEPIrXkfCZEte4gQRVSYvMAkTXlzS9BKvONm8gdg2x25mhxWpmowVBDxUVUVSaVWEnbNVTfrVVnzQ8jdNuKeil0HVpCDg0RiD1eh6tce/BlYw0HFqj3+RgId6M89tQGSjjy9us6qKEQjsCqsXj8RA1aQ2AavRmlrmPgMeNOiHStWkTdVS9Cot/yH1jg3eW2AEaUyo+htVd7W2B7yTGCicIJ4CKOrFEvfAGqMxyEFZA9oKHJRoQeoFSk1scSg10VTd1ZcLoHTDKTrVm3meuhi8bUrVJFRC1STRIAmiKeUFDcTK0A9UDcu1XJ6IlZoAt03Dp/g2qgp7giowUzURBFGqJi5YREPng1g9umJuVZPsohBkzqE+I8j4el5HwmRL6BPk2Q65RsglTHhxSdNLvOJkczUZv2lTzY4EcYVVuYZqxzWqHWsxpWqi2rE+CBcK2hi9bSoBj01K1aRIJQY8O4bmpqlb5EljcAV0JeDR+qhqZKod8wVdXmqi9FWcUfUSmqR6gU4SnnSaegnNOTXg/bIfCNIJ5Pb2QTvW/fBAQh1cI4SVmiRtXQAK/HGdIvcZQXiBTkHAo00VqP0VyK37CqompaReoNREK0HuC4aV1Iticpd6f96L0ItEdUCQxiicoq6gJ8TueIIgqh27ocOEfq50nG7lT2lWatJbAKXQUlQtVgQKQnnunCCuuMpdkfsS9S6yPyxrF+uF13gdCZMtedlGrgeCbGQSJnztvGkjrzjZbCfk+iv5hMM0hn5FNYZSpBNLaFU0MY8RpKkjKZ8vel8MCgj8oxJtZ0j1KwI9UtXEloTCVRf2zgpsYLgdXZOUvPT2xGWpicenXsvmhutTH+2TVRU1Sbk+SwPLd2egU1lgThAPjJDMFtU+/hwG74UIVFUEAjIVR3hQ/m57HCrlURLeUehSr4RyK0WWnyjxkZuTUtVE74UeqHtYvVWoBRRZ6uK9UtWkGBSQMTqT3axELZbKvU3d/P+SUKCE5fPFFVSKPQOqsxVloPAnCG5Sp4D6m/NZT4gTpBl6oN5OBcrfVA/UHoAxSBh6oHVx04CW8jv5teOVKOPZDHdOEC/0nps6VFXWY+pO8vbyEeQH1161IcvrSJhsSeiqyy8FgnAJE04Q/6XvsIoTRpA3biGCIPKurvVhqO/cjMdE/7RuFI+I24EtggdcnCDOOO4S7ciZxK3yzSuYaYSwC2tgECBcj7ks5sGmuwfHxbVo9OGweHDUB3BAkKs2rMaoftwj2pEzgdt0oWqCsayquBDLR53RKgKvNj1dWMqRugYwdHaq8WgdJ4h9DA+LdmSLgs7p2hCWgminJnCTqKrwYOzhuXubsGyWng4cksIpj+NoNSeIYxiHJChBDMX93TBGPSKITbdQNUnS3LskKLJQ6RJQZNHbRO5rOChi0sHpw1CwW8dAuVrosPhEKtUxLOtAj44UgeLluHt1DKqMv8ZxeVe/Q+ibHMJAiYg7jsOPodjYi4cdnCDVUdwnbgyuOJYvcjs5AfeZr3FQWKmJV+XeAWhrvXhQhnsTwwPOhUcaOEFsYaYmIwNLndPjYnDz/WLlbLl9pV4SBEc8jYZWzXbVtrCu99pNmGmEuHQX1wMZdmm6U2PbNge1Q1EjU9fAvTZNbzBiGCc77R69numBJFrqPB57WxLjIN/2dBf1oroDHWRjsh1hd6OucemLoNNJA7PtmCP0FE6uZfIa64R/uV0GduhsU2lXzSpUz/RAYvylh1dwGZJ63WPvpIFjhgOttjElh7DmMDSNyXZ4WbnTurdZ7s670Ytch2WInqKRa5nwqorz2BF8UHOJn+beYV+B6lpk7m57e5LpmzTo7homWtDLZUiewUyGxImOdzHBhaACZSsb8pzOBA9eYpPaX2FCH701+6MGpmWCuygoXCQlwjfZrae/J+6t97hr2dcYKAfuBIqbbdc4ejsH5TfIxUHxaRQepgci2H1BPwdlb1TPtEyS7XaPh+c+xmVIbqHeEJch+asABX3CxUAZc7FTsO2ydYw/JzRwUM5AjS4GSn+1Zjg8EQUK/V6Cv7/t12MMlHq0ogAKV72JeBy6xrVMWtnEwtrXqedk87fH1lLP77wQObVR3lI0o5HnznvIFw3SjLnS2M9GK4Yg1TdMA0FogxseE9u/cd+WVumFh6UaBQ4NS/2M5GeQVCtJjvqluEUiIMUtcFx5QZtT6mzE0MEyXHQ4JL3IsBTmoKeQgRMtWwZUYDjF3kieIlxnl4IkiZEAnAztL8MVcu/dIoU58POocefc4x9DUlQkqm2XgiiJlSgxK/CIFObAQ1u9MoEoOk1lDKC0bZFKK4lPo0GZ8YFoFihjdXWhnQPH0JpZoHRvaZeB/wi6JTR3iWLceEeKiiT3UacYCSgFk5XyN/q3u2Nwin9WaEPGnVu6pHcOCKIkx4bhgH2RBCXSUAtooxWzQCmg3Y+OlZ65pUMG/okKrHKPHYCkSkzEUcN39lcIQcB2VjWZ1+ZXNSll86qazG/7zHuyUqomygqqJgU7pES4UqomJU82W9WkYKVUTXYRroSqibJSqiYFKwVKQdWk6LPZqiYF+2qJIB8oFXheVRM63NvZflYiyJqdP6tAgkQ6PM1CvyLcrrcKwgfbPK1C8GKkVW8Xd6E3P4RuFjIkA01GF7/3JHtpd5jfZxLdurdH6Jt0GU1C3GLwVrTvKyqwuAuOtemtUvCiRW8TN6thegpx3xxsNjqFbMdTK9Gf+R0n2ePVu8UpTMPby08xfDta9SI/gObeIh4NoTa9RQRu/Db6qcY9vwr82oHoFpFUf5PRyW++8Uer0IaQyB0CxzuNJiHhElWgdNyA/nmbAiWgchegvPVhAOXFfdDtQ/OCwj3fn9C+L88GReZefya6Vt8JFF+zLkF5moIS3BGUsXvRise5F6OnEA/inpvRmjcBFIl2sNXTJi7o9s+g69tkYE8Hv+cnn1+F7hgRuTfJ3MP3o6pHYvOAcogAZVSBUnssuq6Je0MtMjDeOAuUuXcU5hbD3mO5+8yGq67axAbqBRNlJzSrYTb4dBtTuVwfm4DRmrK5XBcbjznbc7lsMxtxOXpyuXExE/J4Lpdys1F6TTCXw0zwwlsdyeXimpfVVyRzuRAbGeraRC4nyjW+EMvlBtmIXPNmcjmTDZydbfSXtLHAjWYul/Gy0WL9QC6X9rDAtX6ak1jSrcnlkiywV4vnchFW8qC7cS63if/xjFAu56vjudPAPTz3Zpq7WIm5kubexAI39NEgYiz7XC43yaRRzJpRegBf9jnCncvF2K8wNBp4lOfunqQJ8MAeCooYaF6UpqBA7tlmHpiBImpYHsvlwmLLxxv0M3YKr60ASoKB4pWgCPmEM6PFoPz/7V1rkBxVFZ4f/lL5g/4QBasiEbBKCiwWCFIhqImAaIGR4pmK8RFDISqQBJTKgxiJysPwSEE0FIkUYCBiNOSxCXFmdnd2dme6e2b2vZvNvnezr9n3vHZ6uo99u+853ZNMercIYXfwfj9279y5c/vM1/d099x7zndPsI5DrGNLSn2TUbJJURgp5TYpPiRFMkixRGKva1fVfja5KEspVbWmJO6cUtWTbIolxEhpME9oo6pmrMCe7UYpwqY9yruNr22lrB9lHbMTKhvWyebqTIlxfgZtUsqRFEv64X4nKdOWlsk2o2R2HOg0Tpk1Y3OAkxLxDavOhbyPNmHqnBB+BeCJAaeDaBh2kuDTknUwwiMTWoHr1is90Ma3fB4GVHGuwyTdigzmvflUUgbRVb+1XKAATnU8AOPVZl20GQasdkonbkIsDUCzNRtZNY45exUpWMo/O2Z3nOOTwDJgNMtymLTmqWINMMw3K24DlPB/BzchDo3Aal53Am0vm9ZxLj+l8flOyZRLMRlwkIJ5QFvgFCelG9qJlN/wd2uBawh7+pAUr5MUsh3VhO83NWSYxU0wyOfnOgH34vgP8Mn36jEkJZAEXL20SdFQ52KxKZdi9gc4M/4wxImUHp7j1ge4i3o5ZncGpwDjb4aAd+zNZfgCxyLNQUoAScEV980OUrbzuqNMQ8Y8oROYRufpxY59eQvd88hBmta2wbR27Omtm1UzGuXIS9ufe8zKvMCc4wqNtnRxCHhgKUob2j+cxYWgNlzMiPahMogygiIYoeQW/MQkxiiUOeRS7EOQgAeJYJyg4IfXMdFWGerjYzs8SSvUcVQ1KdftDe15OJFnCeByZ4wypx/P8PWFaAepLOzHnFJ5DHNKq9K4eFiRuxfbURasQqUIpb0/RNvR/HkKSeklUuI9RAqt7JG6e5luS73gktAywHXUmhwud7aQysJrSEpk6B2sq8Xs46ppdFpP1ibFth3fvEvDNeBGUll4GbOPI/24nZanGkmpJlICOUy09ThkavjEuOd2HWNY6mkd5E9TGH/T6xyV8yncPbTlofe0YzthT70ZjXJkBw9c1GikSgnSVnAIeOCYjZDozE0URVRrq5qg8EFLF/pbZHA5fiJmayvgmDUFPEz4srgmptD6ZIyED9Z2Y8ftWIicwvA9TxVtoZTCQzgEPDR0ZIWED5aNYLs6Ej7YRmoQPbQFCQl4hKdQnsiTwDFbAThmI3iIOVyXAAAJZ0lEQVQv8yymReP7iJRmIqXTJgVvNJ4oSb1M4wcC+VIvJmSKdIhQmMyjeBFqad+Kde9Stv/ozVjXZZOCY7aMVE2uS2JUg0K7eK3uIxUOugjtOVPVxEEKBUTYUi8lGTqhdBG61ybl/KmanFMv3ZOQ2iYd88H+mBmNcsTLHUQngYZQGk9ZpY5RFWU0jhW6RN5Kp6wBL5EtJ3EcR3tRi0CJr8JPtNqeRxF6gKdM1nC4yXTKauiUbcb7RksXjeNBXDX31Ea4nVVZ7NihJ0TOLdM4Xk6SRY0U/PAijuNIPwl40P5o1WkUOfCoGPto265QsOItpFqxxo2USJw2/zlxJikOqRdybjmJIQc1FNm0ySaFdqM7RAINE7ijmyeOX7aC7tI+0oD4Jo3jKI3jtaTO00670e3D+4ZCoaGhFO7o5iEdJ7+talJAsugXw/gdW52jch49YvneBvhrgK2mx8xolCPlENtnvYWXI5kk5BQS8IjR/aUJMJTxJRQFUvpJq2IswdkOpvD2X56jnVd0HNsy4DU6SvfmejrESXwmivSShJw/zUdKdWKMj5RAlsZ2DhVZZHpQVABDrFdSxy2AESG7NH7FUwZxX01PDYZGBDNDaLvWzTuWAB8Ul5AiS4xsb6RnohfpYeufKr+mhMeSfKRUpUYw1CRHUXt6mzXemiUSH4mSIssaeiZqLUCKF0kJJWhsj+KDol/HsV0CNLbphNYA7tGzEX8WRLpIkeUQytSEJijEp38YsxuIFC/8nr95E5EShdt43aNoe7SdvHEfPj2HR52jch45yPSOzet3mFomMTMaxXCQ3rVWojOX9KweQJ2NylFIWj5QlgAegu6dRpHJkhT0WENV0sG6DEZjAPWMvOZII/DRI7eBytWT9oJqHaIqDmmr44pJmLQuw/40xK1xJKnAo3ONjvkOTz8AaDTvEpF6gJh5fpQToN1svbsbFVmqhmDasj0wDhh00gE8KsqbBR7Ncv008HArBYBHVT5gCqewqhYUTpE6kZRQP+BzdBBSlnOVJ2DC8gFfBriYW0kS9nA/0sC6YUajREqDQYrpDUob5Pgd6W0kJRg3hVMsUnBY9sJIsJmTcopI4Y89tzNSzKFdR2oym1F8ROqGNA/cPOIgJcHZTgLu2jsCPFkhrAHfCHKpDla4VbQGYKVVt55I6ShASiWRMgUY0T4APFRMysEbnJScgxQH5pGDuCFbWymFTOmLdDQoV0vMyaeUKrlaZsma43KVXGXqgZh736xgk8P93pAcaGATEl3+sFTeojHJhHIpXNauMy2GCinkY/moYyzC9GqWxJWJVUrVMkvWTCrGIWSWQDph/A+aeiAjxkErmfQFDMghqbKOZXya1P7WOKzeURYOlzF1B63FOIS/y3hz0lTBeg+YlAeznQkZpCKGnRJLNG1gF8llLCt91DhoMMqCMs29b37CUmP7fCG5osn4QWhdabcyKY/W8nDY38lsbwpIIS+7cGTrApyU3h8ZzRYFjVIin5Qos91bgqSYYje/ZN+s2+dCyviDRIphe7VEpEjsozVMm+W2Vk5K0CRlkJFSz0gxAxIfzyMlYwrh/s0oqQ0BOexlEy9DbCb3muMmKUFOyiSzWGHJr9Vs+u6HTFNhiPHOtEzA/K3/CHu30+i4vNWwPWtOd+3QOCmmlolFyiCScn1lHikK8/3vMz2IuNEqyFRvLE1gIqWiJf8ndJE4iEHBBFf8AHWCa1BAdhx1uKfHcW7uVIwrbkAKVyz1JK786FMJLE1is3RNNx0CO1ap4yx1nJnAKYwU6qvo7fW8nZ6YsjvmpenazgIdcxkUGI2Moe0TeIjeGNcIgSTarrU2qqfbrpHtNilTUcxUdielqxZNcSUlU5AUtH0kMo7tiJS0Gylq40n8PpP0FWOogWt3bNs+FJ2kjvEQHXW8nZ6kjptaqePZkRKPTJxhewFSCEXjIAICc4FCDoL3GC13WoWObqhjDa2a02c+6jgTgr1Ab5doIwfdLtEyj2aXaPraNi9HpYIdY6lQx/psOy5wiGwB2wt1XOAQudnaTt2dCyluts+alAK2F+q4kO0fNSluthfqGCHuIAICLhAOIiDgAuEgAgIuEA4iIOCCwg6yb926B18fXrluXV/u2adeNyrsMq9Ibntqe8bZwkLeCwGBosdZ7yDbTzXuNf4F9sEzPQB2mVf8+yC8/YGzhYW8FwICRY+zOUjLq+D7y8s+2NMEh33gKPOK5lE44He2sJD3QkCg6HE2B3lmFELHhzfU7ugC3/vgKPMKAOl3KWcLC3kvBASKHmdxkMmt5j/vvt3GLcHrLGPFru3J01sYyHshIFD0OIuD+A4AvFkLuwOBf8HzPc4yrwiw3+LOFhbyXggIFD3O4iDPdgP0b9zwgp574Zm/g7PMK1595MknK5wtLOS9EBAoeoh1EAEBFwgHERBwgXAQAQEXCAcREHCBcBABARfM5CCHrrrkqpd5ObGVqktXnDeLBATmEWZwkJELQtCzeK/1In4F1SdOnUebBM4d2ZmbCMwGMzhI2+fHAWrC8PyCyzfB3Z9+yKia+PHXFv/3+Iq3rr56weVWvcD8wYZLb/xeqX/V8mfhqUsXboGDqwBWHdx591e+vEa4zIfCTI9Yj154365h8F47OnXLa9Yd5JWV4Ft/3HjEyn3rXav+YzBTYHYovVHt+Eyp/8IOOHxVMnllKXeQC/uz335lrm0rTsz4Iz2+f/VF72+8dOnSy39mOUjdxeuO68xBHlsLVv35t1Jgllj3HMCtpf5bANb/AWDTE9xB7gHYfddc21acmMFB3t9h/PnH0j8+D5BM8N8gw28sv81wkL1LVLDqz7+VArPEI8YJWVTqN5xh/dMAW9YzB7nz4M77AN68c65tK07M4CDKF+pg/Fe/lr4+nl4sxb/KqjZt1Hou+GBF3WUDAFb9x2GnwKxwYHGu81Omgxz6Rjp55eHKa7Xhzx7c+bkh9bsvzLVtxYmZHrGOXvulL65JwosLL9kIuWuM6xF03XDxkr3HV9xzUUlJSc6sF5g/eGLhHd+pYg4Cmy+7bAvAyoULlh3ceccNC36amfGzAgUgFgo/UQi9BOkr4qfX7twwF7Z8MiAc5BOF5M+X3PDWGbXCQT48hIP8H2BseK4tKF4IBxEQcIFwEAEBFwgHERBwgXAQAQEXCAcREHCBcBABARcIBxEQcIFwEAEBFwgHERBwgXAQAQEXCAcREHDB/wCbsUMnnjSOigAAAABJRU5ErkJggg==\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R -w 800 -h 300\n", + "upset(\n", + " movies, genres,\n", + " width_ratio=0.1,\n", + " min_size=10,\n", + " mode='inclusive_union',\n", + " base_annotations=list('Size'=(intersection_size(counts=FALSE, mode='inclusive_union'))),\n", + " intersections='all',\n", + " max_degree=3\n", + ")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -343,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -394,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -443,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -492,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -711,7 +755,7 @@ "title 9.955906e-01 " ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -747,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -775,7 +819,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -884,7 +928,7 @@ "r9 1.969511e-92 " ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -902,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -954,7 +998,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -996,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1030,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1074,7 +1118,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1114,7 +1158,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1143,7 +1187,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1174,7 +1218,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1217,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1255,7 +1299,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1293,7 +1337,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1338,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1380,7 +1424,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1423,7 +1467,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1463,7 +1507,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1496,7 +1540,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1534,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1595,7 +1639,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1631,7 +1675,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1674,7 +1718,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1712,7 +1756,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1753,7 +1797,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1797,7 +1841,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1828,7 +1872,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1859,7 +1903,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1897,7 +1941,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1922,7 +1966,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1954,7 +1998,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1999,7 +2043,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -2074,7 +2118,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -2107,7 +2151,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -2132,7 +2176,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -2187,7 +2231,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -2216,7 +2260,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -2265,7 +2309,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -2330,7 +2374,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -2355,7 +2399,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -2387,7 +2431,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -2412,7 +2456,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2437,7 +2481,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2469,7 +2513,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2494,7 +2538,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -2503,7 +2547,7 @@ "['Action', 'Animation', 'Comedy', 'Drama', 'Documentary', 'Romance', 'Short']" ] }, - "execution_count": 56, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -2514,7 +2558,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -2553,7 +2597,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -2604,7 +2648,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -2625,7 +2669,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -2672,7 +2716,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -2720,7 +2764,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -2774,7 +2818,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -2818,7 +2862,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -2861,7 +2905,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -2904,7 +2948,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -2925,7 +2969,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -2981,7 +3025,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -3017,7 +3061,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -3060,7 +3104,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "metadata": {}, "outputs": [ {