data:image/png;base64,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

productPrice0

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _ 0

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _ 0

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

1bf

Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _

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E9p3kYYPlzIuGDJe1HM4d5KBh8Q6R8emS67/MAKx5UsOti6PbtxEj4YqTzm7Cjh3kYHCko3w9hJRAgvqNk+oOp97Nf9QWa10biZdDqdU6KghXvjoTzIVa/tH4tEl8C93+ntxqbbwjEp0daizQn4eI4MyfAFyPxN2FbD3NS7ebEocD0ZZH4edi293kDVq0KdP5iZv6KSHw/bDsmnjdm66EBfJjJXrmcZ8bAJZNtntDrb/VDDV4a4JXAfwAvWd5m6G7Y1ts/ou69ljYbfJ3ABZNTbJnD4+Z815UNLo1wR4RNAZ4VAl+KkbdNtjntUPttNlnDDt4b4PQId4XIl7dO83/2FNAc5H0hsnxrm1d1w7jNFwJcuLXN2JyLTOgBS1j+zJJwSUm753kYoPFS4JVxZh5K2kPMcR4KGl8vCRfAVF/noaBxKXBHCZuKah4IX4rEt5W0DzkP1S9ZBY1vFcSX7WL6Wlj+qILw1ZLiUbD9roRauEilrHxmoLwk0up5TqD54kD5+kjz6XDXVMHQhyHsKJn85R6LXhoY+nrszslkn+dk6NKSeAeETQXhWSXllwLhbZHWYeakuaagfG8knA7xrkD4cklr7/NGQfN9EJfvftegG8ZfiMQLYdtR/bxxsP4ZwIeZ7GaDy2PgltYU7+zxHwErlvG4ZQNMfn+KbzcbxHkEMM1G99XiiZNtXtvruvO5X7PBJ2LgNiJbqgCebPOm447j5O3b+e/DHG+wOcjpk1Pc0mzwWyGyYr8AbnBHKHje1u18sTpOdz+BlZNT/Op86kzlMQWNyyHcUjLV8zzAssfBwCRMfbugEecRwBQ0uvNQ0u7rPBQ0PgHhtpK4pQrgkvab4LiTYfvh5mEAlp0KO74606vjCxrfKeHx0L4zlf4tVh0FzctL4i3QmsOcHP9gKJfB1Ld21zn0awXxjJJtL+y17oLmzJxM9nlOhj5Rwm3Alt0BPNnrnAxC83SYvKWg+VsQV+wbwIGhOyLF82Br93lj937KlSXbjurnDQO41wk+4H7NBnfOvBKdmM8h5hvA1SvvsuCdrSlOn8+6vT5mVYNHlnQ/910NfC8GLm5N8Rmg0+sxmg3eMksAd8olPGRgF28v4aYisjQWPHtyip/t9bgp3q+gcefMK9F5zcN8A3j3K+/inSVTfZ0HaDxyAD4dYXWgOgchXNxhag7z0HhkARcGODsS/7pk+r0p9rHfNQVW3Bkpqlei85oTWP2IQOeGSLwAWuO919t95f3OSKvPc7LqkYHOD5434GJozWFOuuH6llkCuBOJDykI3ecNiEsD4dmR1lH9vGEA9z7B+92z2aBdwGn3tbljPoeYbwAfv4wnDhRcM9nm5F7XHWrwtgA/Ut0/Bj7VmuKqnh+7+/PqVxPYFSPNVpsNvZ48dZAAbselPDzs5N0hcgOBWEbOa03zgl5rSvF+BY12SfX2fHte8zDfAIZlTyworilp9zwPBY23MTMPgfCpDlM9z0P1eXVJeDWEXRCbkXaP8zB4SkF8USScHYjbS9ovBnak2Mt+1hQYakcGToP75jEnxz8xUFwVCb8Nk5fPrc7uY6+JtOYwJ0Nvg9B93iiJn4JWz3MCQ88J8GpgV/VGV6TV45wcNIDbER5ewLtL4g0QYoDzIq2j+nnDAJ7bFO+9d7PBNiJPnpzmG/M5xHwDeNVxPKVT8plWm1N6XXdokJFQUp0AU50Bc/vWaf79MI9dsnKQ526d4oqhBufseQu62eD2Tsn59+/gy72sfZAA/uZAyXPv3dF9m6p6C/qtRJqT0/zvXo6Z6n0KGttK4pNhel7zMP8APu4pBeVnSto9zwMMjiyh7M7DLrgdpg87DwMMPrfD1BXQOGfPW9AFjdtLyvNhx6HmoQHLHgk7vrand4FG9TnypR3af5lqP/tVV2BoWyQ8GSbnOCfHbwgUH47wUmjdMPf6jntKYOAzkdYc5mRoZObEueqZ43bYetg5gRXPhW1XwNA5e96CDgzdHrtzcn9PzxsHeQX8zUj5XLi/+7xR0HwrlM2SbUf184YBPPdJ7j6i2eBrBbz2vjab5nOI+QbwzBnUF022GZnPur0+prmcW4FLy8BtBZxXDHBl2eGzcSmPb7W4p5fjzBrAy3lX9e+nCtwHw/FTDW6JgVe2prixl2Omep+CRncedtGe1zzMN4CrV6QQLurQ7us8FCzfPQ+E7jyUDFxZ0PlsydLHQ+sQ8zB0QsHOb5eUT5kJ4cGCxj8H4q91mP77VPvZr7oCQ9UZzK+FbXOYkz2vXlkPrb2/yMytxu4r0osirb7OSaB5a4RLobytoDivpHNloPhs7H7mf6g5+cFuZgvggqF3QSx2B+6Djw9svyV2T2htHdXPGwbw3KZ4772HGnwwwN2TbX6710M0G7wJeNHM/U+l++qDuDOwYWqK/+rlOM1B3hMj21ttLurl/vO9z5pBHt6J/G6Es6H76vnWAi6+r83mQx1z5szmPZ9vnbj7RTd3R7iqqnloiBPDTv6uOmaAJpHLt07zxvnWmcrjChrV12ruLmn3PA8FjVnnoSRsgKme5qFg8D3sfku3r/MAgw8viNXXQrrzEOHWAbh4F+1DzkPVnwEaL4nw9gDfjvAI4O9K2q/p9dsDqfR4IeooGPoghLtLJucwJ0Mfj3A+sO/Xju6ceVu3p7IKVrwHwvaS1iLMyZL9njci8WLYdpg56Z7Z/IDnjQBX7a556MTA7ueN6vVPgMtLWkf984YB3NP4PvBOq5fxpE7B1ZPt7merfb0Ix57VV8PKToNvBDhra7v7Vaa+36q3vMsO50xO8ycLuVj1PePBbWyf49ewFrKEBT7WsicVFFeXtBdtHqono4LGN0o4C9qLMg9QveXdOadkeq7zUMDxJ8H99+b42e8Phu34JwWKqyOtRZyT1SsDu74RKc6CrYs2J7DkHJic65wc5t/ligfB4Ha4ey5f31zgf+v9P5xfQ+rBuNngA8A91Vd0erj7Ed+l+x3aku1bp/mNIz6YB1hwgYJGdx52f0Wn/7eCwfdBub1k2nnoP/eCrVDQnJmT6is6/b/NfIe2evXrnPSfe0FWMIB7YHwoHNca5HWtKaqvVPT1VXB1Kcqdg7ymNcUleb+C6KEx9d3luILB15VM9X0edl+KcvA1JVPOQ339nufKDz0OWq+D1iLMyZom7HwNtJyTeXarjocZwHWou6YCCiigQPYCBnD2IyCAAgoooEAdAgZwHequqYACCiiQvYABnP0ICKCAAgooUIeAAVyHumsqoIACCmQvYABnPwICKKCAAgrUIWAA16HumgoooIAC2QsYwNmPgAAKKKCAAnUIGMB1qLumAgoooED2AgZw9iMggAIKKKBAHQIGcB3qrqmAAgookL2AAZz9CAiggAIKKFCHgAFch7prKqCAAgpkL2AAZz8CAiiggAIK1CFgANeh7poKKKCAAtkLGMDZj4AACiiggAJ1CBjAdai7pgIKKKBA9gIGcPYjIIACCiigQB0CBnAd6q6pgAIKKJC9gAGc/QgIoIACCihQh4ABXIe6ayqggAIKZC9gAGc/AgIooIACCtQhYADXoe6aCiiggALZCxjA2Y+AAAoooIACdQgYwHWou6YCCiigQPYCBnD2IyCAAgoooEAdAgZwHequqYACCiiQvYABnP0ICKCAAgooUIeAAVyHumsqoIACCmQvYABnPwICKKCAAgrUIWAA16HumgoooIAC2QsYwNmPgAAKKKCAAnUIGMB1qLumAgoooED2AgZw9iMggAIKKKBAHQIGcB3qrqmAAgookL2AAZz9CAiggAIKKFCHgAFch7prKqCAAgpkL2AAZz8CAiiggAIK1CFgANeh7poKKKCAAtkLGMDZj4AACiiggAJ1CBjAdai7pgIKKKBA9gIGcPYjIIACCiigQB0CBnAd6q6pgAIKKJC9gAGc/QjMH2DdGZzIEhr7HqEoiGPj/Nf8j7q4j1y3jiWbN9MB4uKu7GoKKJC7gAGc+wQcwf5Hh7kO2HDAITpjEyw5gsMu2kPXnc2Ti5JbgZePTfDxRVvYhRRQQAHAAD7CMRgZYXCw5PVzPczyNfzx1VczPdfHHer+64f5OIGxTeP8+UIe92DHWj/CG4k8vvp5gJOAZwPJB/C553LSsl2sjZG1wJtC5M8puGrHDrZcfwv/3S+7DWt5cBn4ReCMACuByRj4SoQrNo/zjUOt+8yn8dCyw8+X8NTuYyMtAv8SB7hy0438W79q9rgKKNA/AQP4CG2rt2GLpdw918OUDVZv3sx9c33cwe6/4cd4VFzCNwm8Z2ycNyzUcXs9zrq1DBeB8ZQDuArepTt5H3A+MDDL3krgyrLBqxeyN9U6oyP8ApEPAytmWbcTAm/fOM7vzOa9fi3/KwTeBfu/3T9z35LAxWPjXNRrr7yfAgqkIWAAH2Ef1q2jUUzzmgccJvIy4HTgVsIDX5EuX8UHFvIV8IZhLojwfw3g2Rv6ghcwcM+3uBl4KvB14N1AG/gY8I4I3wnwG8BDCFw/Ns6PL9TnwhvO4txYsBkogKsivGtph//sLOXhZcmrA7y0W3XkFWNbuvXsvY2O8AIin575Hx8rIx9eVvJfOwoeNgCvioFXzPzswrEJPnKE4+zDFVBgEQUM4D5hbxjmygjnR/jUpgl+vtdlnjHMYzuBM4msKmArkVs3buHLBwuD0bP5kXIXq4uCXwZeRuTyMnbDZe8tRKY2fZ5/OVgNP/ETLG/fxzkFPJqSRllwd4zctHmCO3qt+0heAVefxbKLJQF2brqZf+51zbncb/QsRinYCHx/51JOu/567j7wM+DKkpIvAs0IP7Npgs/MZY2D3Xd0hI1ERglcMzbOsw7s5egwfwU8D/j82ARn7RfAw3wVeGwIfHjjOK8+cI3REa4g8nzg5rGJ7lvq3hRQ4CgRMID71Ki5BvAz1/KYXYGPBjjnwJJC5Euh4BXXjXdPGNrvtmGEv4mRnznkNiL/NraF02a7z4a1vCgGLgFOOODn1VnBnywbvGrz5u4rxUPejiSAR4e7b8VXn4neMzbBiYdbaz4/Xz/CK0LkUuDasQnOq44x20lY64d5R4AnRLh00wR/PZ+19n3Mm6G4YS23xMApIfBLG8f5mwOPuX6El4TIJ4jcN7aF1Xt+/rSnMbS8wybgpCLwU9eNd38R2+82Otx9p6V61fw/YxOcfKT1+ngFFFg8AQO4T9ZzCeDRER5WvdKtnmiBG2Pkj0PgP4CHQfcEr+pM4xYFTxm7iW/uW/LoWl5M6J4I9ZPAE0LgCzF2X+ntvQX47saJ/V8VVz/csJb1MXTvW83Bn4XA38YO2+MAZ4TIb1WhGAN/ummc1x2OKfUA3jDCs2LkauCO5as5rXr7P5WzoGc+4/1TDvGL0sH8R0d4A5E/JjI+toWzD9cnf66AAukIGMB96sWcAngtlxH4heptxBMmOfeKr7JjT1nV91SLKT5HYAT467GJ7glED7hVZ0B3P0ucw0lY+7x6/szYxP6vove+KoP2zAljh3wVnHoAd99mv5evBzgF+KcY+fU4QCeBryGFDSPcHCM/NpfeVQOwbphHFnA98PAYeOGmcS7r0zh7WAUU6IOAAdwH1O6ryx4/A555m/F7wLKDfe64YYSfjZGrgF3TA6y58UZaB5Y9nwAePZsfimX3yfs7B34NpvraS6ez+4IaIfL4jVu6n0Ue9HaEAfxaAssDTG0c50N9agnrz+L0UPC3wA/NrPF9YE0I/ENZ8pGlU1x3zW3c36/1Z/3FaYSLQuTtwNadO3nsob4GVb1TEkrOjYEh4EzghcBgCLxl4zi/u5h1u5YCChy5gAF85IazHqHXAF4/wo+H2D1DluWRlVdvYfLAA244ixNiQRXShJKnb7y5+6pnv9t8Ani2ws84g6XLlrGkCYM7I/dU9ylLztx8M1/oVwD3qQWzHvanz+C47ct4cYSfI3Y/b1+2zx23A3++pODN19zEd/td1/phfjXAeypi4KfGJrpvkR/0ts8vYnvu868x8NpN4/xTv2v1+AoosPACBvDCm3aP2GsAbxj5CaghAAAIJElEQVTmhRH+Erh3bII1BytndJgqHAYjXLBpYu/XUvbefb4BvH6YpwGvDXT/+9ADAql7/GMpgPf13XA2Z8aSmyN8MkSWEfipyhi4i4Kzx27izn6MR3Vi1ueG+aMAvwbsiPDi2Xp64NrPGOGJJbySyPERTgt0P5aovs982Y4lvO6GG7i3H/V6TAUU6I+AAdwf154DeHSYXwI+EgL/vXG8G4Cz3kbXci+BVQFetnGCTxx4p/kE8Ia1vH7mDOjqcNXJXdcT+B6R6RipLg7RvTDEsRrAB56E1b3aVMnfdj+PhU+PTXDBQo9H9ZFDo+TyGLsnzd1TwPOvm9j9Dshcb+edxQ93Ci6LMAzcdO4E575196tpbwoocBQIGMB9alKvr4DXD/NzAT4FbBub6H62N9stjA53vwpUfU58/mxfj5lrAM+cef3v3Ve8kUvO3cKv7/vkXZ20NH3v7q8fHSsBXF2M44orun94oXs7yNeQ1gaYqM46H5uguZDjce4ZnLx0Kf8IPBH48pKSn7nm5u7Z7vO+zRzzW9Ur4Rh4xqbx/c+An/eBfaACCvRdwADuE3GvATw63L0y0y3doOtw8ubP8z8HlvSMp/GIsrP77dAi8KTZvg861wDe563v6anA6vFxpvZdt7ogSMnuE6+O9gA+5xxWL9vJPxB4Utnh0XuMZwvg887klF0D/Gd1sYxzJ1iyUK8oZ45bfeXrMRE+u2OAn5vtZLr9ejDCEzslzw8Fx42N88aDjerocPf61Q8BvBpWn/49e1gF+iFgAPdDdQ6fAVcnPa1c2n0CPWHmQg3VxSL2u40Ody91+QHguyecwkP3fRW3546jw3y0+qs+Ad6/caJ7VaxD3tav5cIQumccf3dsggfPsuY7gV9fjADesJZLy8DxASbHJrjwcLXP5+ejI3yl+sMR+36vedZXwD9w+ebYBI+ez1oHPmbdOlYU7e51sp9QfZWsbHDB5s3sOtyxZy5h+bnqfiFy+sYt3HbgY2aub909YWwhr951uNr8uQIKHLmAAXzkhrMeoddXwNWDR4f5fehe+OLOnUs5s7pM4p6Dnnc2D9pV8nngESHw5oN93WT9Wt4685ntbWWDMw73BL/PpRmrCxQ/7roJ/nXPmjNvi/8RsKq6LGP1V44Od4buEX4Nqf9XwlrLs0Pg72cuOvKx2OFdYYDlM+8+vLzs8Nmi4EUE3gocF+H1myZ470KMx55fjqq3nZev5sxerwFenax1/XC3Lz8K/DOBn9z3by0/4wxWxmV8cubz5K3LI4+Y7Sz6hdiDx1BAgYUXMIAX3rR7xLkEcPdPGsbuV0mq73beFWP3qlTfDpEfjoGXz7y9eOMJk4zue5GOfUtfP0z12eVN3Qv+B/6dyBeIBEL3zOrHxMg7Nm3h/Xse84LHseyeJl+pfrbPmjurj0aBHw+Bn44lFxF4evWZaISP7ljCldWZtutH2BDiA/76ThXUVf3VJSzH9q0tBv5x0zhVoM96W4xLUVYLrx/hF0PsGlSXvaxu1avQ6m8XVxc+2fN1pF0x8vZNW3jzQoxGdYnRTuj+ucAQ4VuhOsP6ELcQuXDfV7ozfb0WuucHtCNsDvBt4MFEzq1OzKvMA7x44wSfXIiaPYYCCiyOgAHcJ+e5BHBVQveCHCW/V/1FnJkn2z2V3RvhI+3AWw78nPbA0kfX8nJC96IO1eeBe27VWbFfi5E3btrC/9svtNdyRghcPhPCe350O/CG6hXvzHeUqz8UUF0nujMQeey1W/j6zPWj/6JXugif2DTRvWZxrQFcLb5uHauKNs8PkZEYutfHri7fWP1N3uqPQNwyMMBfXXdj9zPgBbk9Yy3nlOGB39s+2MGLyLnXbeGGfX/eDfGCtxN5zgFfE4tEro8Fv+N3gRekXR5EgUUVMIAXlfvwi1WfCQ8t59Qi0iwi964+hdtn+8z3EEcKz3waJ5e7OLETmJwOfOcwwR3WjXBqUV0Raif/s/ELVGdG773N/DGBkzqD3HO4t7UPv7u07pHKtaB7VanOTN95Hz9KyUoK7p8q+MbhTuTq9djeTwEFFl/AAF58c1dMRGDDj/EoBrpvSb9r4xaqt3m9KaCAAosmYAAvGrULKaCAAgoo8AMBA9hpUEABBRRQoAYBA7gGdJdUQAEFFFDAAHYGFFBAAQUUqEHAAK4B3SUVUEABBRQwgJ0BBRRQQAEFahAwgGtAd0kFFFBAAQUMYGdAAQUUUECBGgQM4BrQXVIBBRRQQAED2BlQQAEFFFCgBgEDuAZ0l1RAAQUUUMAAdgYUUEABBRSoQcAArgHdJRVQQAEFFDCAnQEFFFBAAQVqEDCAa0B3SQUUUEABBQxgZ0ABBRRQQIEaBAzgGtBdUgEFFFBAAQPYGVBAAQUUUKAGAQO4BnSXVEABBRRQwAB2BhRQQAEFFKhBwACuAd0lFVBAAQUUMICdAQUUUEABBWoQMIBrQHdJBRRQQAEFDGBnQAEFFFBAgRoEDOAa0F1SAQUUUEABA9gZUEABBRRQoAYBA7gGdJdUQAEFFFDAAHYGFFBAAQUUqEHAAK4B3SUVUEABBRQwgJ0BBRRQQAEFahAwgGtAd0kFFFBAAQUMYGdAAQUUUECBGgQM4BrQXVIBBRRQQAED2BlQQAEFFFCgBgEDuAZ0l1RAAQUUUMAAdgYUUEABBRSoQcAArgHdJRVQQAEFFDCAnQEFFFBAAQVqEDCAa0B3SQUUUEABBQxgZ0ABBRRQQIEaBAzgGtBdUgEFFFBAAQPYGVBAAQUUUKAGAQO4BnSXVEABBRRQ4P8DJBSu4Rj5rp4AAAAASUVORK5CYII=Meow Mix Kibble,Mouse Toy,Pink Bell Collar,Pink Bell Collar4230