<content> <p>Draw-Descriptions (Maxima)</p> </content>

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<content> <p>Draw-Descriptions (Maxima)</p> </content> <content> <p>Draw-Descriptions (Maxima)</p> </content>

dOptionen

Options

[Maxima Draw] adapt_depth (=10): maximale Teilungstiefe der adaptive Kurvenabtastung. [Maxima Draw] adapt_depth (=10): maximum number of adaptive interval splits when drawing curves. addapt_depth adapt_depth :

[Maxima Draw3D] axis_3d (=true): Achsen zeichnen, false: keine Achsen zeichnen. Globale Option. [Maxima Draw3D] axis_3d (=true): draw axes, false: don't draw axes. Global option. axis_3d axis_3d :

[Maxima Draw2D] axis_bottom (=true): untere Achse zeichnen, false: Achse nicht zeichnen. Globale Option. [Maxima Draw2D]  axis_bottom (=true): draw bottom axis, false: don't draw axis. Global option. axis_bottom axis_bottom :

[Maxima Draw2D] axis_top (=true): obere Achse zeichnen, false: Achse nicht zeichnen. Globale Option. [Maxima Draw2D] axis_top (=true): draw top axis, false: don't draw axis. Global option. axis_top axis_top :

[Maxima Draw2D] axis_left (=true): linke Achse zeichnen, false: Achse nicht zeichnen. Globale Option. [Maxima Draw2D] axis_left (=true): draw left axis, false: don't draw axis. Global option. axis_left axis_left :

[Maxima Draw2D] axis_right (=true): rechte Achse zeichnen, false: Achse nicht zeichnen. Globale Option. [Maxima Draw2D] axis_right (=true): draw right axis, false: don't draw axis. Global option. axis_right axis_right :

[Maxima Draw] background_color (=white): Hintergrundfarbe des Diagramms. Globale Option. [Maxima Draw] background_color (=white): background color of the plot. Global option. background_color background_color :

[Maxima Draw] border (=true): Ränder von polygon(), ellipse(), quadrilateral(), triangle() oder rectangle() zeichnen. [Maxima Draw] border (=true): draw borders of polygon(), ellipse(), quadrilateral(), triangle() or rectangle()objects. border border :

[Maxima Draw3D] capping(=false): Endflächen an tube()-Objekten, false, true oder Liste mit zwei Werten. [Maxima Draw3D] capping (=false): indicates if extremes of the tube object remain open (false) or are closed (true). If only one end is to be closed, provide a list with two entries. capping capping :

[Maxima Draw] cbrange (=auto): Farbbereich Liste [min,max]. Globale Option. [Maxima Draw] cbrange (=auto): color bar range, list [min,max]. Global option. cbrange cbrange :

[Maxima Draw] cbtics (=auto): Marken an der Farblegende. Globale Option. [Maxima Draw] cbtics (=auto): color bar tick marks. Global option. cbtics cbtics :

[Maxima Draw] color (=blue): Farbe für Linien, Punkte, Kanten und Text, als Namen oder hexadezimal in der Form RGB "#rrggbb" oder RGBA "#rrggbbaa". A steht für Transparenz. [Maxima Draw] color (=blue): color for lines, points, borders and text, given as name, as RGB "#rrggbb" or as RGBA "#rrggbbaa" with A (alpha) being transparency. color color :

[Maxima Draw] colorbox (=true): Farblegende ohne Beschriftung zeichnen, false: keine Farblegende zeichnen, text: Farblegende mit text als Beschriftung zeichnen. Globale Option. [Maxima Draw] colorbox (=true): draw color scale without label, false: no colorbar, string: draw colorbar with string as label. Global option. colorbox colorbox :

[Maxima Draw3D] contour (=none): Art der Höhenlinien. none: keine, base: auf der xy-Ebene, surface: auf der 3D-Fläche, both: auf xy-Ebene und Fläche, map: auf xy-Ebene und Blickrichtung senkrecht. Globale Option. [Maxima Draw3D] contour (=none): contour lines. none: no contours, base: on xy plane, surface: on 3D surface, both: on xy plane and surface, map: on xy plane plus vertical view. Global option. See also contour_levels. contour contour :

[Maxima Draw3D] contour_levels (=5): Werte für die Höhenlinien. Anzahl oder Liste [min, Δ, max], oder set(L1, L2,...) mit Werten. Globale Option. [Maxima Draw3D] contour_levels (=5): contour levels. number oder list [min, Δ, max], or set(L1, L2,...) of values. Global option. contour_levels contour_levels :

[Maxima Draw] data_file_name (="data.gnuplot"): Kurvendaten für Gnuplot [Maxima Draw] data_file_name (="data.gnuplot"): internal data file name for gnuplot data_file_name data_file_name :

[Maxima Draw] delay (=5) Zeitschritt in animierten gif-Dateien. Nur für originale Maxima(draw2d/3d). Globale Option. [Maxima Draw]  delay (=5): time step in animted gif files. Applies only for native Maxima(draw2d/3d). Global option. delay delay :

[Maxima Draw] dimensions (= 600 x 500): Liste [Breite,Höhe]. Nur für originale Maxima(draw2d/3d). Globale Option. [Maxima Draw] dimensions (= 600 x 50:0): list [width, height]. Applies only for native Maxima(draw2d/3d). Global option. dimensions dimensions :

[Maxima Draw] draw_realpart (=true) Realteil zur Darstellung komplexer Werte verwenden. [Maxima Draw] draw_realpart (=true) use real part for plotting complex values. draw_realpart draw_realpart :

[Maxima Draw] enhanced3d (=none) Farbpalette, none: 3D Flächen als Gitter zeichnen (Steuerung mit color und surface_hide), true: Farbe anhand des z-Werts, Liste  [f(x,y,z), x, y, z]: Farbe anhand der Funktion f, Liste [f(u,v),u,v]: Farbe anhand der Flächenparameter oder Liste [f(k), k ]: Farbe anhand der Punktnummer in points() Objekten. [Maxima Draw3D] enhanced3d (=true) coloring of surfaces by z-value, (control using color und surface_hide), false: draw 3D surfaces as grid, or list [f(x,y,z), x, y, z]: coloring according to function f or list [f(u,v),u,v] or list [f(k), k ] (k being the point counter in points() objects). enhanced3d enhanced3d :

[Maxima Draw2D] error_type (=y) Art der Fehlerbalken in errors():  x, y, xy oder boxes. [Maxima Draw2D] error_type (=y) type of error bars in errors():  x, y, xy or boxes. error_type error_type :

[Maxima Draw] file_name (="maxima_out") Grafik-Ausgabedatei, Endung wird gemäß Ausgabeformat ergänzt. Globale Option. [Maxima Draw] file_name (="maxima_out") output file, extension is set according to format. Global option. Only for experts. file_name file_name :

[Maxima Draw2D] fill_color (=red) Füllfarbe für Objekte polygon() und explicit(). [Maxima Draw2D] fill_color (=red) fill color for polygons and explicit functions y(x). fill_color fill_color :

[Maxima Draw] fill_density (=0) Deckungsgrad der Füllfarbe in bars()-Objekten, zwischen 0 und 1. Für andere Objekte benutzen Sie option color mit der Transparenz aa in "rrggbbaa". [Maxima Draw] fill_density (=0) opacity of the fill color in bars()-objects, 0..1. For other objects use option color with transparency aa in "#rrggbbaa". fill_density fill_density :

[Maxima Draw2D] filled_func (=false): explizite Funktionen y(x) werden nicht gefüllt, true: Füllung bis zur Unterkante des Diagramms, g(x): Füllung zwischen g(x) und y(x). [Maxima Draw2D] filled_func (=false): don't fill explicit functions y(x), true: fill between the next explicit() object and the bottom border of the diagram, g(x): fill between g(x) and the next explicit() object. filled_func filled_func :

[Maxima Draw] gnuplot_file_name (="maxout.gnuplot"): Kommandodatei für Gnuplot. Gobale Option nur für Experten. [Maxima Draw] gnuplot_file_name (="maxout.gnuplot"): name of the temporary gnuplot command file. Global option. Only for experts. gnuplot_file_name gnuplot_file_name :

[Maxima Draw] gnuplot_term (): Ausgabe-Format-Spezifikation (Text nach "set terminal"). Gobale Option nur für Experten. [Maxima Draw] gnuplot_term (): gnuplot terminal options (arguments of "set terminal"). Global option. Only for experts. Use name argument of Draw2D() or Draw3D for format specification. gnuplot_term gnuplot_term :

[Maxima Draw] gnuplot_out_file (=maxplot.xxx) Ausgabedateiname (xxx abhängig vom Format). Gobale Option nur für Experten. [Maxima Draw] gnuplot_out_file (=maxplot.xxx) output file name (xxx depends on terminal). Global option. Only for experts. gnuplot_out_file gnuplot_out_file :

[Maxima Draw] grid (=true): Gitterraster zeichnen, false: kein Gitter zeichnen. Global option. [Maxima Draw] grid (=true): draw xy grid, false: don't draw grid. Global option. grid grid :

[Maxima Draw] head_angle (=45) Pfeilwinkel an vector()-Objekten (halber Öffnungswinkel in °) [Maxima Draw] head_angle (=45): head angle of vector()-objects (angle between border and vector  direction in °) head_angle head_angle :

[Maxima Draw] head_both (=false) oder true: ob bei vector()-Objekten zwei Pfeilspitzen gezeichnet werden. [Maxima Draw] head_both (=false): don't draw a second head of vector() objects, true: draw a second head. head_both head_both :

[Maxima Draw] head_length (=2) Länge der Pfeilspitze bei vector()-Objekten in Einheiten der x-Achse. [Maxima Draw] head_length (=2): head length of vector() objects in units of the x-axis. head_length head_length :

[Maxima Draw] head_type (=filled) Spitzenform bei vector()-Objekten: filled, empty oder nonfilled. [Maxima Draw] head_type (=filled) head type of vector() objects: filled ▲, empty ∆ or nofilled Λ. head_type head_type :

[Maxima Draw] interpolate_color (=false) Farbglättung auf Facetten. true: Optimale Glättung, Liste [n, m] Zahl der Unterteilung. false: Einheitliche Farbe. [Maxima Draw] interpolate_color (=false) color smoothing on facets. true: optimal smoothing, list [n, m] number of subdivisions. false: homogeneous color. interpolate_color interpolate_color :

[Maxima Draw] ip_grid (=Liste [50, 50] ): Primäres Gitter für die Darstellung impliziter Funktionen, als Liste angeben. [Maxima Draw] ip_grid (= list[50, 50] ): primary grid for sampling of implicit functions, two-element list. ip_grid ip_grid :

[Maxima Draw3D] ip_grid_in (= Liste [5, 5] ): Sekundäres Gitter für die Darstellung impliziter Funktionen. [Maxima Draw3D] ip_grid_in (= list [5, 5] ): secondary grid for sampling of implicit functions. ip_grid_in ip_grid_in :

[Maxima Draw] key (=""): Legendeneintrag für das nachfolgende Grafikobjekt. [Maxima Draw] key (=""): legend key for subsequent graphics object. key key :

[Maxima Draw] key_pos (="", entspricht top_right): Legendenposition. Gültige Werte: top_left, top_center, top_right, center_left, center, center_right, bottom_left, bottom_center, bottom_right. Globale Option. [Maxima Draw] key_pos (="", equivalent to top_right): position of the legend. Valid options: top_left, top_center, top_right, center_left, center, center_right, bottom_left, bottom_center, bottom_right. Global option key_pos key_pos :

[Maxima Draw] label_alignment (=center), left oder right: horizontale Ausrichtung von Beschriftungen. [Maxima Draw] label_alignment (=center), left or right: alignment of text labels. label_alignment label_alignment :

[Maxima Draw] label_orientation (=horizontal) oder vertical: Ausrichtung von Texten. [Maxima Draw] label_orientation (=horizontal) or vertical: orientation of text labels. label_orientation label_orientation :

[Maxima Draw] line_type (=solid) Linienart. Alternativen: dots (für alle Formate) oder wenn von Gnuplot unterstützt: dashes, short_dashes, short_long_dashes, short_short_long_dashes. [Maxima Draw] line_type (=solid) line type. Other options: dots (for all formats) or if supported by Gnuplot: dashes, short_dashes, short_long_dashes, short_short_long_dashes. line_type line_type :

[Maxima Draw] line_width (=1) Linienstärke, >0. [Maxima Draw] line_width (=1) line width, >0. line_width line_width :

[Maxima Draw] logcb (=false) oder true: logarithmische Farbskale. Globale Option. [Maxima Draw] logcb (=false) or true: log scale of the colorbar. Global option. logcb logcb :

[Maxima Draw] logx (=false) oder true: logarithmische x-Achse. Globale Option. [Maxima Draw] logx (=false) or true: log scale of x-axis. Global option. logx logx :

[Maxima Draw2D] logx_secondary (=false) oder true: logarithmische zweite x-Achse. Globale Option. [Maxima Draw2D] logx_secondary (=false) or true: log scale of secondary x-axis. Global option. logx_secondary logx_secondary :

[Maxima Draw] logy (=false) oder true: logarithmische y-Achse. Globale Option. [Maxima Draw] logy (=false) or true: log scale of y-axis. Global option. logy logy :

[Maxima Draw2D] logy_secondary (=false) oder true: logarithmische zweite y-Achse. Globale Option. [Maxima Draw2D] logy_secondary (=false) or true: log scale of secondary y-axis. Global option. logy_secondary logy_secondary :

[Maxima Draw] logz (=false) oder true: logarithmische z-Achse. Globale Option. [Maxima Draw] logz (=false) or true: log scale of z-axis. Global option. logz logz :

[Maxima Draw] nticks (=29): Punktezahl für die Kurvenabtastung [Maxima Draw] nticks (=29): number of points for curve sampling (explicit and parametric) nticks nticks :

[Maxima Draw] palette (=color) Standard-Farbpalette, gray: Graustufen, Liste [Farbe1, Farbe2...]: Interpolation über die gegebenen Farben. Globale Option. [Maxima Draw] palette (=color) default color palette, gray: gray levels, or list [color1, color2...]: interpolation over the given set of colors. Global option. palette palette :

[Maxima Draw] point_size (=1) Größe von points()-Symbolen. [Maxima Draw] point_size (=1) size of points()-symbols. point_size point_size :

[Maxima Draw] point_type (=1) Symbol für points()-Objekte, Name oder Kennzahl: none -1, dot 0, plus 1, multiply 2, asterisk 3, square 4, filled_square 5, circle 6, filled_circle 7, up_triangle 8, filled_up_triangle 9, down_triangle 10, filled_dwn_triangle 11, diamant 12, filled_diamant 13 [Maxima Draw] point_type (=1) type of points() objects, name or key number: none -1, dot 0, plus 1, multiply 2, asterisk 3, square 4, filled_square 5, circle 6, filled_circle 7, up_triangle 8, filled_up_triangle 9, down_triangle 10, filled_down_triangle 11, diamant 12, filled_diamant 13 point_type point_type :

[Maxima Draw] points_joined (=false), true oder impulses: Verbindungsart von points()-Objekten [Maxima Draw] points_joined (=false), true or impulses: how to join points points_joined points_joined :

[Maxima Draw] proportional_axes (=none), xy oder xyz: Achsen mit gleicher Skalierung). Globale Option. [Maxima Draw] proportional_axes (=none), xy or xyz: set axes of equal scaling). Global option. proportional_axes proportional_axes :

[Maxima Draw3D] surface_hide (=false) oder true: Ausblendung verdeckter Flächen bei Linienrasterdarstellung. Nur relevant wenn enhanced3D=false. [Maxima Draw3D] surface_hide (=false) or true: handling of hidden lines in surface grids. Only relevant if enhanced2d=false. surface_hide surface_hide :

[Maxima Draw] terminal (=screen) Ausgabeart. screen: Separates Ausgabefenster, png, jpg, gif, eps, svg, pdf, animated_gif: Gnuplot Ausgabeformat. Globale Option. Nur für Experten. Nutzen Sie das Name-Argument von Draw2D()/3D(). [Maxima Draw] terminal (=screen) separate interactive terminal window,  png, jpg, gif, eps, svg, pdf, animated_gif: gnuplot output format. Global option. Only for experts. Use name argument of Draw2D/3D instead. terminal terminal :

[Maxima Draw] title (="") Überschrift für das Diagramm. Globale Option. [Maxima Draw] title (="") headline for the plot. Global option title title :

[Maxima Draw] transform (=none) Koordinatentransformation, für 2D als Liste [f1(x,y), f2(x,y), x, y], für 3D als Liste [f1(x,y), f2(x,y), f3(x, y, z), x, y, z] [Maxima Draw] transform (=none) co-ordinate transformation, for 2D as list [f1(x,y), f2(x,y), x, y], for 3D as list [f1(x,y), f2(x,y), f3(x, y, z), x, y, z] transform transform :

[Maxima Draw] transparent (=false) oder true: ob Rechtecke, Polygone oder Ellipsen transparent sein sollen (bei false werden sie gefüllt). Für echte Transparenz nutzen Sie die Option color mit dem Transparenzwert aa der RGBA Farbspezifikation "#rrggbbaa". [Maxima Draw] transparent (=false) fill objects with border, true: don't fill (make them transparent). For actual transparency use RGBA color "rrggbbaa" with the color option. transparent transparent :

[Maxima Draw] unit_vectors (=false) oder true: Vektoren in originaler Länge oder als Einheitsvektoren zeichnen. [Maxima Draw] unit_vectors (=false) draw vectors in original length, true: draw unit vectors. unit_vectors unit_vectors :

[Maxima Draw] user_preamble (="") Zeichenkette oder Liste von Zeichenketten, die unmittelbar vor dem eigentlichen Diagrammbefehl an Gnuplot übergeben werden. Globale Option. [Maxima Draw] user_preamble (="") string or list of strings, gnuplot commands placed  immediately before the plot commands. Global option user_preamble user_preamble :

[Maxima Draw] view (=(60,30)) Blickrichtung (Zenithwinkel von z aus, Azimutwinkel von x aus). Globale Option. [Maxima Draw] view (=(60,30)) view direction (zenith angle from z, azimut angle from x). Global option. view view :

[Maxima Draw] wired_surface (=false) oder true: Gitternetz auf eingefärbten Flächen anzeigen oder nicht. Globale Option. [Maxima Draw] wired_surface (=false) no grid on colored surfaces, true: draw grid on surfaces. No color control. Global option. wired_surface wired_surface :

[Maxima Draw3D] x_voxel (=10) x-Bereichsteilung für den Marching-Cubes-Algorithmus bei implizit gegebenen Flächen. [Maxima Draw3D] x_voxel (=10) number of x steps for the Marching Cubes algorithm (for implicit 3D functions). Also used for region(). x_voxel x_voxel :

[Maxima Draw3D] y_voxel (=10) y-Bereichsteilung für den  Marching-Cubes-Algorithmus bei implizit gegebenen Flächen. [Maxima Draw3D] y_voxel (=10) number of y steps for the Marching Cube algorithm (for implicit 3D functions). Also used for region(). y_voxel y_voxel :

[Maxima Draw3D] z_voxel (=10) z-Bereichsteilung für den Marching-Cubes-Algorithmus bei implizit gegebenen Flächen. [Maxima Draw3D] z_voxel (=10) number of z steps for the Marching Cube algorithm (for implicit 3D functions). z_voxel z_voxel :

[Maxima Draw] xaxis (=false) oder true: Anzeige der x-Achse. Globale Option. [Maxima Draw] xaxis (=true): draw x-axis. false: don't draw x-axis. Global option xaxis xaxis :

[Maxima Draw] yaxis (=false) oder true: Anzeige der y-Achse. Globale Option. [Maxima Draw] yaxis (=true): draw y-axis. false: don't draw y-axis. Global option yaxis yaxis :

[Maxima Draw3D] zaxis (=false) oder true: Anzeige der z-Achse. Globale Option. [Maxima Draw3D] zaxis (=true): draw x-axis. false: don't draw x-axis. Global option. zaxis zaxis :

[Maxima Draw] xaxis_color (=black) Farbe x-Achse. Globale Option. [Maxima Draw] xaxis_color (=black) color of the x-axis. Global option. xaxis_color xaxis_color :

[Maxima Draw] yaxis_color (=black) Farbe y-Achse. Globale Option. [Maxima Draw] yaxis_color (=black) color of the y-axis. Global option. yaxis_color yaxis_color :

[Maxima Draw3D] zaxis_color (=black) Farbe z-Achse. Globale Option. [Maxima Draw3D] zaxis_color (=black) color of the z-axis. Global option zaxis_color zaxis_color :

[Maxima Draw2D] xaxis_secondary (=false) oder true: Nutzung einer zweiten x-Achse (oben) für die nachfolgenden Objekte. [Maxima Draw2D] xaxis_secondary (=false) or true: use the secondary x-axis (top) for subsequent objects. xaxis_secondary xaxis_secondary :

[Maxima Draw2D] yaxis_secondary (=false) oder true: Nutzung einer zweiten y-Achse (rechts) für die nachfolgenden Objekte [Maxima Draw2D] yaxis_secondary (=false) or true: use the secondary y-axis (right) for subsequent objects yaxis_secondary yaxis_secondary :

[Maxima Draw] xaxis_type (=dots) oder solid: Linienart der x-Achse. Globale Option. [Maxima Draw] xaxis_type (=solid) or dots. Global option. xaxis_type xaxis_type :

[Maxima Draw] yaxis_type (=dots) oder solid: Linienart der y-Achse. Globale Option. [Maxima Draw] yaxis_type (=solid) or dots. Global option. yaxis_type yaxis_type :

[Maxima Draw3D] zaxis_type (=dots) oder solid: Linienart der z-Achse. Globale Option. [Maxima Draw3D] zaxis_type (=solid) or dots. Global option. zaxis_type zaxis_type :

[Maxima Draw] xaxis_width (=1) Linienbreite der x-Achse. Globale Option. [Maxima Draw] xaxis_width (=1). Global option xaxis_width xaxis_width :

[Maxima Draw] yaxis_width (=1) Linienbreite der y-Achse. Globale Option. [Maxima Draw] yaxis_width (=1). Global option yaxis_width yaxis_width :

[Maxima Draw3D] zaxis_width (=1) Linienbreite der z-Achse. Globale Option. [Maxima Draw3D] zaxis_width (=1). Global option. zaxis_width zaxis_width :

[Maxima Draw] xlabel (="") x-Achsenbeschriftung. Globale Option. [Maxima Draw] xlabel (="") label of the x-axis. Global option xlabel xlabel :

[Maxima Draw] ylabel(="") y-Achsenbeschriftung. Globale Option. [Maxima Draw] ylabel (="") label of the y-axis. Global option ylabel ylabel :

[Maxima Draw] zlabel (="") z-Achsenbeschriftung. Globale Option. [Maxima Draw] zlabel (="") label of the z-axis. Global option zlabel zlabel :

[Maxima Draw] xlabel_secondary (="") Beschriftung der zweiten x-Achse. Globale Option. [Maxima Draw] xlabel_secondary (="") label of the secondary x-axis. Global option xlabel_secondary xlabel_secondary :

[Maxima Draw] ylabel_secondary (="") Beschriftung der zweiten y-Achse. Globale Option. [Maxima Draw] ylabel_secondary (="") label of the secondary y-axis. Global option ylabel_secondary ylabel_secondary :

[Maxima Draw] xrange (=auto) oder Liste [min,max]: x-Achsenbereich. Globale Option. [Maxima Draw] xrange (=auto) or list [min,max]: range of the x-axis. Global option xrange xrange :

[Maxima Draw] yrange (=auto) oder Liste [min,max]: y-Achsenbereich. Globale Option. [Maxima Draw] yrange (=auto) or list [min,max]: range of the y-axis. Global option yrange yrange :

[Maxima Draw] zrange (=auto) oder Liste [min,max]: z-Achsenbereich. Globale Option. [Maxima Draw] zrange (=auto) or list [min,max]: range of the z-axis. Global option. zrange zrange :

[Maxima Draw] xrange_secondary (=auto) oder Liste [min,max]: Bereich der zweiten (oberen) x-Achse. Globale Option. [Maxima Draw] xrange_secondary (=auto) or list [min,max]: range of the secondary (top) x-axis. Global option. xrange_secondary xrange_secondary :

[Maxima Draw] yrange_secondary (=auto) oder Liste [min,max]: Bereich der zweiten (rechten) y-Achse. Globale Option. [Maxima Draw] yrange_secondary (=auto) or list [min,max]: range of the secondary (right) y-axis. Global option. yrange_secondary yrange_secondary :

[Maxima Draw] xtics (=auto): automatische x-Achsmarken, none: keine, Zahl: Schrittweite, Liste mit [min, Schritt, max], oder set() von Zahlen: explizite Vorgabe der Zahlenwerte, oder set() von Listen ["text",pos] mit Vorgabe von Beschriftung und Position. Globale Option. [Maxima Draw] xtics (=auto): automatic x ticks, none: no x ticks, number: tick interval, list of [min, step, max], or set() of numbers: explicit tick values, or set() of lists ["text",pos] for spec of position and text. Global option xtics xtics :

[Maxima Draw] ytics (=auto): automatische y-Achsmarken, none: keine, Zahl: Schrittweite, Liste mit [min, Schritt, max], oder set() von Zahlen: explizite Vorgabe der Zahlenwerte, oder set() von Listen ["text",pos] mit Vorgabe von Beschriftung und Position. Globale Option. [Maxima Draw] ytics (=auto): automatic y ticks, none: no y ticks, number: tick interval, list of [min, step, max], or set() of numbers: explicit tick values, or set() of lists ["text",pos] for spec of position and text. Global option. ytics ytics :

[Maxima Draw] ztics (=auto): automatische z-Achsmarken, none: keine, Zahl: Schrittweite, Liste mit [min, Schritt, max], oder set() von Zahlen: explizite Vorgabe der Zahlenwerte, oder set() von Listen ["text",pos] mit Vorgabe von Beschriftung und Position. Globale Option. [Maxima Draw] ztics (=auto): automatic z ticks, none: no x ticks, number: tick interval, list of [min, step, max], or set() of numbers: explicit tick values, or set() of lists ["text",pos] for spec of position and text. Global option. ztics ztics :

[Maxima Draw] xtics_secondary (=auto): automatische Marken an der sekundären (oberen) x-Achse, none: keine, Zahl: Schrittweite, Liste mit [min, Schritt, max], oder set() von Zahlen: explizite Vorgabe der Zahlenwerte, oder set() von Listen ["text",pos] mit Vorgabe von Beschriftung und Position. Globale Option. [Maxima Draw] xtics_secondary (=auto): automatic ticks on the secondary (top) x-axis, none: no ticks, number: tick interval, list of [min, step, max], or set() of numbers: explicit tick values, or set() of lists ["text",pos] for spec of position and text. Global option. xtics_secondary xtics_secondary :

[Maxima Draw] ytics_secondary (=auto): automatische Marken an der sekundären (rechten) y-Achse, none: keine, Zahl: Schrittweite, Liste mit [min, Schritt, max], oder set() von Zahlen: explizite Vorgabe der Zahlenwerte, oder set() von Listen ["text",pos] mit Vorgabe von Beschriftung und Position. Globale Option. [Maxima Draw] ytics_secondary (=auto): automatic ticks on the secondary (right) y-axis, none: no ticks, number: tick interval, list of [min, step, max], or set() of numbers: explicit tick values, or set() of lists ["text",pos] for spec of position and text. Global option. ytics_secondary ytics_secondary :

[Maxima Draw] xtics_axis (=false): x-Achsmarken am Rand, true: an der Achse. Globale Option. [Maxima Draw] xtics_axis (=false): ticks on the boundary, true: ticks on the axis. Global option. xtics_axis xtics_axis :

[Maxima Draw] ytics_axis (=false): y-Achsmarken am Rand, true: an der Achse. Globale Option. [Maxima Draw] ytics_axis (=false): ticks on the boundary, true: ticks on the axis. Global option. ytics_axis ytics_axis :

[Maxima Draw] ztics_axis (=false): z-Achsmarken am Rand, true: an der Achse. Globale Option. [Maxima Draw] ztics_axis (=false): ticks on the boundary, true: ticks on the axis. Global option. ztics_axis ztics_axis :

[Maxima Draw] xtics_secondary_axis (=false): Marken an der sekundaren (oberen) x-Achse am Rand, true: an der Achse. Globale Option. [Maxima Draw] xtics_secondary_axis (=false): ticks on the secondary (top) boundary, true: ticks on the axis. Global option. xtics_secondary_axis xtics_secondary_axis :

[Maxima Draw] ytics_secondary_axis (=false): Marken an der sekundaren (rechten) y-Achse am Rand, true: an der Achse. Globale Option. [Maxima Draw] ytics_secondary_axis (=false): ticks on the secondary (right) boundary, true: ticks on the axis. Global option. ytics_secondary_axis ytics_secondary_axis :

[Maxima Draw] xtics_rotate (=false): x-Achsmarken nicht drehen, true: um 90° drehen. Globale Option. [Maxima Draw] xtics_rotate (=false): horizontal tick labels, true: vertical tick labels. Global option. xtics_rotate xtics_rotate :

[Maxima Draw] ytics_rotate (=false): y-Achsmarken nicht drehen, true: um 90° drehen. Globale Option. [Maxima Draw] ytics_rotate (=false): horizontal tick labels, true: vertical tick labels. Global option. ytics_rotate ytics_rotate :

[Maxima Draw] ztics_rotate (=false): z-Achsmarken nicht drehen, true: um 90° drehen. Globale Option. [Maxima Draw] ztics_rotate (=false): horizontal tick labels, true: vertical tick labels. Global option. ztics_rotate ztics_rotate :

[Maxima Draw] xtics_rotate_secondary (=false): Marken an der sekundären (oberen) x-Achse nicht drehen, true: um 90° drehen. Globale Option. [Maxima Draw] xtics_rotate_secondary (=false): don't rotate ticks of the secondary (top) x-axis, true: rotate by 90°. Global option. xtics_rotate_secondary xtics_rotate_secondary :

[Maxima Draw] ytics_rotate_secondary (=false): Marken an der sekundären (rechten) y-Achse nicht drehen, true: um 90° drehen. Globale Option. [Maxima Draw] ytics_rotate_secondary (=false): don't rotate ticks of the secondary (right) y-axis, true: rotate by 90°. Global option ytics_rotate_secondary ytics_rotate_secondary :

[Maxima Draw] xu_grid (=30) Abtastpunkte in x- oder u-Richtung [Maxima Draw] xu_grid (=30) sampling points in x- or u-direction xu_grid xu_grid :

[Maxima Draw] yv_grid (=30) Abtastpunkte in y- oder v-Richtung [Maxima Draw] yv_grid (=30) sampling points in y- or v-direction yv_grid yv_grid :

[Maxima Draw3D] xyplane (=false): xy-Koordinatenebene automatisch anordnen, Zahl: z-Koordinate. Globale Option. [Maxima Draw3D] xyplane (=false): automatic allocation of the xy-plane, real number: xy-plane intersects the z-axis at this level. Global option. xyplane xyplane :

Objekte

[Maxima Draw2D] bars(b1,b2...) Säulendiagramm, Säulen gegeben durch Listen (x,h,w). Optionen: key, color, fill_color, fill_density und line_width. [Maxima Draw2D] bars(b1,b2...) vertical bars, given as lists [x,h,w]. Options: key, color, fill_color, fill_density and line_width._bars_bars :

[Maxima Draw3D] elevation_grid(Matrix,x0,y0,w,h) Stellt eine Matrix in 3D dar. M_11 bei x=x0,y=y0+h, M_mn bei x=x0+w,y=y0. Optionen:  xu_grid, yv_grid, line_type, key, wired_surface, enhanced3d und color [Maxima Draw3D] elevation_grid(matrix,x0,y0,w,h) Draws a m x n-matrix in 3D. M_11 at x=x0,y=y0+h, M_mn at x=x0+w,y=y0. Options:  xu_grid, yv_grid, line_type, key, wired_surface, enhanced3d and color_1_2_3_4_5 elevation_grid_1_2_3_4_5 elevation_grid :

[Maxima Draw3D] elevation_grid(Matrix,x0,y0,w,h) Stellt eine Matrix in 3D dar. M_11 bei x=x0,y=y0+h, M_mn bei x=x0+w,y=y0. Optionen:  xu_grid, yv_grid, line_type, key, wired_surface, enhanced3d and color [Maxima Draw3D] cylindrical(r(φ,z),z,zmin,zmax,φ,φmin,φmax) Draws a function in cylindrical coordinates. Options: xu_grid, yv_grid, line_type, key, wired_surface, enhanced3d and color._1_2_3_4_5_6_7 cylindrical_1_2_3_4_5_6_7 cylindrical :

[Maxima Draw2D] ellipse(xc,yc,a,b,φ1,φ2) Ellipse, zentriert bei xc, yc mit den Halbachsen a und b, von Startwinkel φ1 und Winkelbreite dφ. In Maxima 5.47 wird die Breite doppelt gezählt. Optionen nticks, transparent, fill_color, border, line_width, line_type, key und color. [Maxima Draw2D] ellipse(xc,yc,a,b,φ1,dφ) plots an ellipse centered at [xc, yc] with horizontal and vertical semi axis a and b, respectively, starting at φ1 with an amplitude dφ. Note that in Maxima 5.47 the amplitude is doubled. Options: nticks, transparent, fill_color, border, line_width, line_type, key and color. #1 #2 #3 #4 #5 #6 ellipse #1 #2 #3 #4 #5 #6 ellipse :

[Maxima Draw2D] errors(e1,e2...) Punkte mit Fehlerbalken, horizontal, vertical oder beide, abhängig von der Option error_type. Argumente für error_type = x: Listen [x, y, xdelta] oder [x, y, xlow, xhigh]. Argumente für error_type = y; Listen [x, y, ydelta] oder [x, y, ylow, yhigh]. Argumente für error_type = xy oder error_type = boxes: Listen [x, y, xdelta, ydelta] oder [x, y, xlow, xhigh, ylow, yhigh]. Optionen: error_type, points_joined, line_width, key, line_type, color, fill_density, xaxis_secondary, and yaxis_secondary [Maxima Draw2D] errors(e1,e2...) Draws points with error bars, horizontally, vertically or both, depending on the value of option error_type. If error_type = x, arguments are lists [x, y, xdelta] or [x, y, xlow, xhigh]. If error_type = y, arguments are lists [x, y, ydelta] or [x, y, ylow, yhigh]. If error_type = xy or error_type = boxes, arguments are lists [x, y, xdelta, ydelta] or [x, y, xlow, xhigh, ylow, yhigh]. Options: error_type, points_joined, line_width, key, line_type, color, fill_density_errors_errors :

[Maxima Draw2D] explicit(f,x,xmin,xmax) zeichnet eine Funktion f von x von xmin bis xmax. Optionen: nticks, adapt_depth, draw_realpart, line_width, line_type, key, filled_func, fill_color und color [Maxima Draw2D] explicit(f,x,xmin,xmax) plots explicit function f, with variable x taking values from xmin to xmax. Options: nticks, adapt_depth, draw_realpart, line_width, line_type, key, filled_func, fill_color and color_1_2_3_4 explicit_1_2_3_4 explicit :

[Maxima Draw3D] explicit(f,x,xmin,xmax,y,ymin,ymax) zeichnet eine Funktion f von x und y im Bereich xmin bis xmax und ymin bis ymax Optionen: draw_realpart, xu_grid, yv_grid, line_type, line_width, key, wired_surface, enhanced3d und color. [Maxima Draw3D] explicit(f,x,xmin,xmax,y,ymin,ymax) plots the explicit function f, with variable x taking values from xmin to xmax and variable y taking values from ymin to ymax. Options: draw_realpart, xu_grid, yv_grid, line_type, line_width, key, wired_surface, enhanced3d, and color. #1 #2 #3 #4 #5 #6 #7 explicit #1 #2 #3 #4 #5 #6 #7 explicit :

[Maxima Draw2D] image(Matrix,x0,y0,w,h)  Farbdarstellung einer m x n Matrix entsprechend der Option palette im Gebiet (x0,y0) bis (x0+w,y0+h) [Maxima Draw2D] image(matrix,x0,y0,w,h)  plots matrix in the rectangular region from vertex (x0,y0) to (x0+w,y0+h) according to global option palette. #1 #2 #3 #4 #5 image #1 #2 #3 #4 #5 image :

[Maxima Draw2D] implicit(f,x,xmin,xmax,y,ymin,ymax) 2D Darstellung einer implizit gegebenen Funktion im angegebenen Variablenbereich (marching squares Algorithmus). Optionen: ip_grid, ip_grid_in, line_width, line_type, key, color [Maxima Draw2D] implicit(f,x,xmin,xmax,y,ymin,ymax) plots the implicit function defined by f, with variable x taking values from xmin to xmax, and variable y taking values from ymin to ymax. Options: ip_grid, ip_grid_in, line_width, line_type, key, color #1 #2 #3 #4 #5 #6 #7 implicit #1 #2 #3 #4 #5 #6 #7 implicit :

[Maxima Draw3D] implicit(f,x,xmin,xmax,y,ymin,ymax,z,zmin,zmax) 3D Darstellung einer implizit gegebenen Funktion im angegebenen Variablenbereich (Marching Cubes Algorithmus). Optionen: x_voxel, y_voxel, z_voxel, ip_grid_in, line_width, line_type, wired_surface, enhanced3D, color [Maxima Draw] [Maxima Draw3D] implicit(f,x,xmin,xmax,y,ymin,ymax,z,zmin,zmax) plots the implicit surface defined by f, with variable x taking values from xmin to xmax, variable y taking values from ymin to ymax and variable z taking values from zmin to zmax. This object implements the marching cubes algorithm. Options: x_voxel, y_voxel, z_voxel, line_width, line_type, key, wired_surface, enhanced3d,and color #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 implicit #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 implicit :

[Maxima Draw2D and 3D] label(list) Beschriftung, im 2D gegeben als Liste (text,x,y), im 3D als Liste (text,x,y,z), spezielle Optionen: color, label_alignment, label_orientation [Maxima Draw2D and 3D] label(list) text label specified in 2D as list [text,x,y], in 3D as list [text,x,y,z], Options: color, label_alignment, label_orientation_label_label :

[Maxima Draw2D] parametric(fx,fy,p,pmin,pmax) parametrisch gegebenen Funktion. Optionen: nticks, line_width, line_type, key, color, enhanced3d [Maxima Draw2D] parametric(fx,fy,p,pmin,pmax) plots the parametric function fx, fy  with parameter p taking values from pmin to pmax. Options: nticks, line_width, line_type, key, color, enhanced3d #1 #2 #3 #4 #5 parametric #1 #2 #3 #4 #5 parametric :

[Maxima Draw3D] parametric(fx,fy,fz,p,pmin,pmax) Parametrische Funktion, beschrieben durch drei Koordinatenfunktionen des Parameters p im gegebenen Bereich von pmin bis pmax. Optionen: nticks, line_width, line_type, key, color, enhanced3d [Maxima Draw3D] parametric(fx,fy,fz,p,pmin,pmax) parametric function, described by three coordinate functions of parameter p, taking values from pmin to pmax. Options: nticks, line_width, line_type, key, color, enhanced3d #1 #2 #3 #4 #5 #6 parametric #1 #2 #3 #4 #5 #6 parametric :

points(matrix) Punkte, 2D: n x 2 oder 2 x n -> n Punkte bei (x,y) oder 1 x n oder n x 1 -> n Punkte bei (index,y). 3D: n x 3 oder 3 x n -> Punkte bei (x,y,z). Spezielle Optionen: points_joined, point_type, point_size [Maxima Draw2D/3D] [Maxima Draw2D and 3D] points(matrix) 2D: n x 2 or 2 x n -> n points at (x,y) or 1 x n or n x 1 -> n points at (index,y). 3D: n x 3 or 3 x n -> points at (x,y,z). Specific options: points_joined, point_type, point_size, enhanded #1 points #1 points :

[Maxima Draw2D] points(lx,ly) Punkte, gegeben durch zwei Listen der x- und y-Koordinaten. Spezielle Optionen: points_joined, point_type, point_size [Maxima Draw2D] [Maxima Draw2D] points(lx,ly) points given by two lists, special options: points_joined, point_type, point_size #1 #2 points #1 #2 points :

[Maxima Draw3D] points(lx,ly,lz) zeichnet Punkte, gegeben durch drei Listen, spezielle Optionen: points_joined, point_type, point_size [Maxima Draw3D] points(lx,ly,lz) points given by three lists, pecial options: points_joined, point_type, point_size #1 #2 #3 points #1 #2 #3 points :

[Maxima Draw2D] polar(r,φ,φmin,φmax) zeichnet Funktionen, die in Polarkoordinaten gegeben sind (Radius r als Funktion des Winkels φ). Spezielle Optionen: key, nticks, line_width, line_type, color [Maxima Draw2D] polar(r,φ,φmin,φmax) draws functions given in polar co-ordinates (radius r as function of angle φ). Special options: key, nticks, line_width, line_type, color #1 #2 #3 #4 polar #1 #2 #3 #4 polar :

[Maxima Draw2D] polygon(matrix)  Polygon durch die als n x 2 Matrix gegebenen Punkte. Spezielle Optionen: transparent, fill_color, border, line_width, key, line_type color [Maxima Draw2D] polygon(matrix)  polygon defined by matrix (each row one point). Special options: transparent, fill_color, border, line_width, key, line_type color #1 polygon #1 polygon :

[Maxima Draw2D]  polygon(lx,ly)  Zeichnet ein Polygon durch die als x- und y-Listen gegebenen Punkte. Spezielle Optionen: transparent, fill_color, border, line_width, key, line_type color [Maxima Draw2D]  polygon(lx,ly)  polygon defined by two lists. Special options: transparent, fill_color, border, line_width, key, line_type, color #1 #2 polygon #1 #2 polygon :

[Maxima Draw2D/3D] quadrilateral(l1,l2,l3,l4) Viereck, gegeben durch Listen der Eckkoordinaten 2D: [x,y] oder 3D: [x,y,z]. Spezielle Optionen: transparent, fill_color, border, line_width, key, line_type, color, enhanced3D (nur 3D) [Maxima Draw2D/3D] quadrilateral(l1,l2,l3,l4) quadrilateral given by lists  of vertex coordinates: [x,y] in 2D or [x,y,z] in 3D. Special options: transparent, fill_color, border, line_width, key, line_type, color, enhanced3D (3D only) #1 #2 #3 #4 quadrilateral #1 #2 #3 #4 quadrilateral :

[Maxima Draw2D] rectangle(l1,l2) Rechteck gegeben durch Koordinatenlisten [x,y] gegenüberliegender Ecken. Spezielle Optionen: transparent, fill_color, border, line_width, key, line_type, color [Maxima Draw2D] rectangle(l1,l2) specified by lists [x,y] of opposite corners. Special options: transparent, fill_color, border, line_width, key, line_type, color #1 #2 rectangle #1 #2 rectangle :

[Maxima Draw2D] region(expr,x,xmin,xmax,y,ymin,ymax) Füllt das Gebiet wo expr wahr (true) ist im Bereich von xmin, xmax, ymin, ymax. Optionen: fill_color, key, x_voxel, y_voxel [Maxima Draw2D] region(expr,x,xmin,xmax,y,ymin,ymax) Fills the region where expr is true in the domain given by xmin, xmax, ymin, ymax. Options: fill_color, key, x_voxel, y_voxel #1 #2 #3 #4 #5 #6 #7 region #1 #2 #3 #4 #5 #6 #7 region :

[Maxima Draw2D] region(expr,x,xmin,xmax,y,ymin,ymax) Füllt das Gebiet wo expr wahr (true) ist im Bereich von xmin, xmax, ymin, ymax. Optionen: fill_color, key, x_voxel, y_voxel [Maxima Draw3D] spherical(r, a, mina, maxa, z, minz, maxz) plots function r(a, z) defined in spherical coordinates, with azimuth a taking values from mina to maxa and zenith z taking values from minz to maxz. Options: xu_grid, yv_grid, line_type, key, wired_surface, enhanced3d, and color. #1 #2 #3 #4 #5 #6 #7 spherical #1 #2 #3 #4 #5 #6 #7 spherical :

[Maxima Draw2D/3D] triangle(l1,l2,l3)  Dreieck, gegeben durch Koordinatenlisten [x,y] (2D) oder [x,y,z] (3D) der Ecken. Spezielle Optionen: transparent, fill_color, border, line_width, key, line_type, color [Maxima Draw2D/3D] triangle(l1,l2,l3)  given by co-ordinate lists [x,y] (2D) or [x,y,z] (3D) of the corners.Options: transparent, fill_color, border, line_width, key, line_type, color #1 #2 #3 triangle #1 #2 #3 triangle :

[Maxima Draw3D]  tube(fx,fy,fz,fr,p,pmin,pmax)  Röhre, gegeben durch Mittellinie mit fx,fy,fz und den Radius fr als Funktionen des Parameters p. Spezielle Optionen:  xu_grid, yv_grid, line_type, line_width, key, wired_surface, enhanced3d, color, capping. [Maxima Draw3D]  tube(fx,fy,fz,fr,p,pmin,pmax)  tube, given by functions fx,fy,fz of the centerline and radius function fr of the parameter p from pmin to pmax. Special options:  xu_grid, yv_grid, line_type, line_width, key, wired_surface, enhanced3d, color, capping. #1 #2 #3 #4 #5 #6 #7 tube #1 #2 #3 #4 #5 #6 #7 tube :

[Maxima Draw2D/3D]  vector(l1,l2)  Pfeil,  gegeben durch Koordinatenlisten (x,y) (2D) oder (x,y,z) (3D) der Endpunkte. Spezielle Optionen:  head_both, head_length, head_angle, head_type, line_width, line_type, key color. [Maxima Draw2D/3D]  vector(l1,l2)  given by co-ordinate lists [x,y] (2D) or [x,y,z] (3D) of the end points. Spezielle Optionen:  head_both, head_length, head_angle, head_type, line_width, line_type, key color. #1 #2 vector #1 #2 vector :

Sonstige

[Maxima]  set(e1, e2,...) Maxima set (Liste in geschweiften Klammern) [Maxima]  set(e1, e2,...) Maxima set (in Maxima this is a list in curly braces)_set_set :

[Maxima] args(expr) Liste der Argumente von expr [Maxima] args(expr) list of the top level arguments of expr. Extracts lines from matrices._args_args :

[Maxima] apply(f, Liste) Macht die Elemente der Liste zu Argumenten von f. apply(f,args(matrix)) wandelt eine Matrix in Argumente einer Funktion um -> f(row1, row2,...) [Maxima] apply(f, list) applies the elements of list as arguments to f. apply(f,args(matrix)) converts a matrix into arguments of a function -> f(row1, row2,...)_args_args :

[Maxima] true true :

[Maxima] false false :

L 2 x - 2 y * + 3 z * - x 2^y 2^+ z 2^+ 22^- x 2^12^/ y 2^22^/ - z 1 - (2^12^/ + 31 sys x y z 31 sys Solve :

L 3 el x 0.5555374857700439 ≡ y 1.341250605913718 ≡ z 1.37565445026178 ≡ 31 sys

L 4 el x 0.24034617762208943 - ≡ y 1.2243905230628362 ≡ z 1.5630423685553854 ≡ 31 sys

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