<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 :

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 terminal=pdfcairo file_name="C:/Users/kraska/AppData/Roaming/SMath/extensions/plugins/44011c1e-5d0d-4533-8e68-e32b5badce41/GnuPlot/gee2xbyi" font="Arial" font_size=12 title="" background_color="#fefefe" enhanced3d = true xu_grid=360 yv_grid=40 xlabel="x" ylabel="y" zlabel="z" proportional_axes=xyz "set term pdfcairo enhanced size 4.5, 3.2" "set encoding utf8" "set style line 100 lc rgb 'grey' lt -1 lw 0.1" "set grid xtics ls 100" "set grid ytics ls 100" "set pm3d lighting depthorder base" "set view 54, 150, 2.3, 0.8" plot3D SAMPLE PLOT <titleState>Custom</titleState> <border>Disable</border> <borderVal>4095</borderVal> <textSize>12</textSize> <textSizeState>Disable</textSizeState> <textFont>Arial</textFont> <contour>Disable</contour> <contourType /> <contourLevels>5</contourLevels> <bgColor>#fefefe</bgColor> <enhanced3dState>Enable</enhanced3dState> <pm3d>Enable</pm3d> <pm3dpalette>color</pm3dpalette> <gridXu>360</gridXu> <gridYv>20</gridYv> <gridState>Custom</gridState> 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