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+#### MATPLOTLIBRC FORMAT
+
+## NOTE FOR END USERS: DO NOT EDIT THIS FILE!
+##
+## This is a sample matplotlib configuration file - you can copy it
+## in your system in site-packages/matplotlib/mpl-data/stylelib/
+## (which is related to your Python installation location).
+##
+## See https://matplotlib.org/stable/tutorials/introductory/customizing.html#the-matplotlibrc-file
+## for more details on the paths which are checked for the configuration file.
+##
+## Blank lines, or lines starting with a comment symbol, are ignored, as are
+## trailing comments.  Other lines must have the format:
+##     key: val  # optional comment
+##
+## Formatting: Use PEP8-like style (as enforced in the rest of the codebase).
+## All lines start with an additional '#', so that removing all leading '#'s
+## yields a valid style file.
+##
+## Colors: for the color values below, you can either use
+##     - a matplotlib color string, such as r, k, or b
+##     - an rgb tuple, such as (1.0, 0.5, 0.0)
+##     - a hex string, such as ff00ff
+##     - a scalar grayscale intensity such as 0.75
+##     - a legal html color name, e.g., red, blue, darkslategray
+##
+## Matplotlib configuration are currently divided into following parts:
+##     - LINES
+##     - PATCHES
+##     - HATCHES
+##     - BOXPLOT
+##     - FONT
+##     - TEXT
+##     - LaTeX
+##     - AXES
+##     - DATES
+##     - TICKS
+##     - GRIDS
+##     - LEGEND
+##     - FIGURE
+##     - IMAGES
+##     - CONTOUR PLOTS
+##     - ERRORBAR PLOTS
+##     - HISTOGRAM PLOTS
+##     - SCATTER PLOTS
+##     - AGG RENDERING
+##     - PATHS
+##     - SAVING FIGURES
+##     - INTERACTIVE KEYMAPS
+##     - ANIMATION
+
+##### CONFIGURATION BEGINS HERE
+
+## ***************************************************************************
+## * LINES                                                                   *
+## ***************************************************************************
+## See https://matplotlib.org/api/artist_api.html#module-matplotlib.lines
+## for more information on line properties.
+lines.linewidth: 	   0.75        # line width in points
+lines.linestyle: 	   -           # solid line
+lines.color:     	   C0          # has no affect on plot(); see axes.prop_cycle
+lines.marker:          None        # the default marker
+lines.markerfacecolor: auto        # the default marker face color
+lines.markeredgecolor: auto        # the default marker edge color
+lines.markeredgewidth: 1.0         # the line width around the marker symbol
+lines.markersize:      6           # marker size, in points
+lines.dash_joinstyle:  round       # {miter, round, bevel}
+lines.dash_capstyle:   butt        # {butt, round, projecting}
+lines.solid_joinstyle: round       # {miter, round, bevel}
+lines.solid_capstyle:  projecting  # {butt, round, projecting}
+lines.antialiased: True            # render lines in antialiased (no jaggies)
+
+## The three standard dash patterns.  These are scaled by the linewidth.
+lines.dashed_pattern: 3.7, 1.6
+lines.dashdot_pattern: 6.4, 1.6, 1, 1.6
+lines.dotted_pattern: 1, 1.65
+lines.scale_dashes: True
+
+markers.fillstyle: full  # {full, left, right, bottom, top, none}
+
+pcolor.shading : flat
+
+## ***************************************************************************
+## * PATCHES                                                                 *
+## ***************************************************************************
+## Patches are graphical objects that fill 2D space, like polygons or circles.
+## See https://matplotlib.org/api/artist_api.html#module-matplotlib.patches
+## for more information on patch properties.
+patch.linewidth:       1      # edge width in points.
+patch.facecolor:       C0
+patch.edgecolor:       black  # if forced, or patch is not filled
+patch.force_edgecolor: False  # True to always use edgecolor
+patch.antialiased:     True   # render patches in antialiased (no jaggies)
+
+
+## ***************************************************************************
+## * HATCHES                                                                 *
+## ***************************************************************************
+hatch.color:     black
+hatch.linewidth: 1.0
+
+
+## ***************************************************************************
+## * BOXPLOT                                                                 *
+## ***************************************************************************
+boxplot.notch:       False
+boxplot.vertical:    True
+boxplot.whiskers:    1.5
+boxplot.bootstrap:   None
+boxplot.patchartist: False
+boxplot.showmeans:   False
+boxplot.showcaps:    True
+boxplot.showbox:     True
+boxplot.showfliers:  True
+boxplot.meanline:    False
+
+boxplot.flierprops.color:           black
+boxplot.flierprops.marker:          o
+boxplot.flierprops.markerfacecolor: none
+boxplot.flierprops.markeredgecolor: black
+boxplot.flierprops.markeredgewidth: 1.0
+boxplot.flierprops.markersize:      6
+boxplot.flierprops.linestyle:       none
+boxplot.flierprops.linewidth:       1.0
+
+boxplot.boxprops.color:     black
+boxplot.boxprops.linewidth: 1.0
+boxplot.boxprops.linestyle: -
+
+boxplot.whiskerprops.color:     black
+boxplot.whiskerprops.linewidth: 1.0
+boxplot.whiskerprops.linestyle: -
+
+boxplot.capprops.color:     black
+boxplot.capprops.linewidth: 1.0
+boxplot.capprops.linestyle: -
+
+boxplot.medianprops.color:     C2
+boxplot.medianprops.linewidth: 1.0
+boxplot.medianprops.linestyle: -
+
+boxplot.meanprops.color:           C2
+boxplot.meanprops.marker:          ^
+boxplot.meanprops.markerfacecolor: C2
+boxplot.meanprops.markeredgecolor: C2
+boxplot.meanprops.markersize:       6
+boxplot.meanprops.linestyle:       --
+boxplot.meanprops.linewidth:       1.0
+
+
+## ***************************************************************************
+## * FONT                                                                    *
+## ***************************************************************************
+## The font properties used by `text.Text`.
+## See https://matplotlib.org/api/font_manager_api.html for more information
+## on font properties.  The 6 font properties used for font matching are
+## given below with their default values.
+##
+## The font.family property has five values:
+##     - 'serif' (e.g., Times),
+##     - 'sans-serif' (e.g., Helvetica),
+##     - 'cursive' (e.g., Zapf-Chancery),
+##     - 'fantasy' (e.g., Western), and
+##     - 'monospace' (e.g., Courier).
+## Each of these font families has a default list of font names in decreasing
+## order of priority associated with them.  When text.usetex is False,
+## font.family may also be one or more concrete font names.
+##
+## The font.style property has three values: normal (or roman), italic
+## or oblique.  The oblique style will be used for italic, if it is not
+## present.
+##
+## The font.variant property has two values: normal or small-caps.  For
+## TrueType fonts, which are scalable fonts, small-caps is equivalent
+## to using a font size of 'smaller', or about 83%% of the current font
+## size.
+##
+## The font.weight property has effectively 13 values: normal, bold,
+## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
+## 400, and bold is 700.  bolder and lighter are relative values with
+## respect to the current weight.
+##
+## The font.stretch property has 11 values: ultra-condensed,
+## extra-condensed, condensed, semi-condensed, normal, semi-expanded,
+## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
+## property is not currently implemented.
+##
+## The font.size property is the default font size for text, given in pts.
+## 10 pt is the standard value.
+##
+## Note that font.size controls default text sizes.  To configure
+## special text sizes tick labels, axes, labels, title, etc, see the rc
+## settings for axes and ticks.  Special text sizes can be defined
+## relative to font.size, using the following values: xx-small, x-small,
+## small, medium, large, x-large, xx-large, larger, or smaller
+
+font.family:  Verdana
+font.style:   normal
+font.variant: normal
+font.weight:  normal
+font.stretch: normal
+font.size:    12.0
+
+font.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif
+font.sans-serif: DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
+font.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, cursive
+font.fantasy:    Comic Neue, Comic Sans MS, Chicago, Charcoal, ImpactWestern, Humor Sans, xkcd, fantasy
+font.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace
+
+
+## ***************************************************************************
+## * TEXT                                                                    *
+## ***************************************************************************
+## The text properties used by `text.Text`.
+## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text
+## for more information on text properties
+text.color: black
+
+
+## ***************************************************************************
+## * LaTeX                                                                   *
+## ***************************************************************************
+## For more information on LaTex properties, see
+## https://matplotlib.org/tutorials/text/usetex.html
+text.usetex: False  # use latex for all text handling. The following fonts
+                     # are supported through the usual rc parameter settings:
+                     # new century schoolbook, bookman, times, palatino,
+                     # zapf chancery, charter, serif, sans-serif, helvetica,
+                     # avant garde, courier, monospace, computer modern roman,
+                     # computer modern sans serif, computer modern typewriter
+                     # If another font is desired which can loaded using the
+                     # LaTeX \usepackage command, please inquire at the
+                     # matplotlib mailing list
+text.latex.preamble:   # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES
+                        # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP
+                        # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.
+                        # text.latex.preamble is a single line of LaTeX code that
+                        # will be passed on to the LaTeX system. It may contain
+                        # any code that is valid for the LaTeX "preamble", i.e.
+                        # between the "\documentclass" and "\begin{document}"
+                        # statements.
+                        # Note that it has to be put on a single line, which may
+                        # become quite long.
+                        # The following packages are always loaded with usetex, so
+                        # beware of package collisions: color, geometry, graphicx,
+                        # type1cm, textcomp.
+                        # Adobe Postscript (PSSNFS) font packages may also be
+                        # loaded, depending on your font settings.
+
+## FreeType hinting flag ("foo" corresponds to FT_LOAD_FOO); may be one of the
+## following (Proprietary Matplotlib-specific synonyms are given in parentheses,
+## but their use is discouraged):
+## - default: Use the font's native hinter if possible, else FreeType's auto-hinter.
+##            ("either" is a synonym).
+## - no_autohint: Use the font's native hinter if possible, else don't hint.
+##                ("native" is a synonym.)
+## - force_autohint: Use FreeType's auto-hinter.  ("auto" is a synonym.)
+## - no_hinting: Disable hinting.  ("none" is a synonym.)
+text.hinting: force_autohint
+
+text.hinting_factor: 8  # Specifies the amount of softness for hinting in the
+                         # horizontal direction.  A value of 1 will hint to full
+                         # pixels.  A value of 2 will hint to half pixels etc.
+text.kerning_factor : 0  # Specifies the scaling factor for kerning values. This
+                          # is provided solely to allow old test images to remain
+                          # unchanged. Set to 6 to obtain previous behavior. Values
+                          # other than 0 or 6 have no defined meaning.
+text.antialiased: True  # If True (default), the text will be antialiased.
+                         # This only affects the Agg backend.
+
+## The following settings allow you to select the fonts in math mode.
+#mathtext.fontset: dejavusans  # Should be 'dejavusans' (default),
+                               # 'dejavuserif', 'cm' (Computer Modern), 'stix',
+                               # 'stixsans' or 'custom' (unsupported, may go
+                               # away in the future)
+## "mathtext.fontset: custom" is defined by the mathtext.bf, .cal, .it, ...
+## settings which map a TeX font name to a fontconfig font pattern.  (These
+## settings are not used for other font sets.)
+mathtext.bf:  sans:bold
+mathtext.cal: cursive
+mathtext.it:  sans:italic
+mathtext.rm:  sans
+mathtext.sf:  sans
+mathtext.tt:  monospace
+mathtext.fallback: cm  # Select fallback font from ['cm' (Computer Modern), 'stix'
+                        # 'stixsans'] when a symbol can not be found in one of the
+                        # custom math fonts. Select 'None' to not perform fallback
+                        # and replace the missing character by a dummy symbol.
+mathtext.default: regular  # The default font to use for math. (default: it)
+                       # Can be any of the LaTeX font names, including
+                       # the special name "regular" for the same font
+                       # used in regular text.
+
+
+## ***************************************************************************
+## * AXES                                                                    *
+## ***************************************************************************
+## Following are default face and edge colors, default tick sizes,
+## default fontsizes for ticklabels, and so on.  See
+## https://matplotlib.org/api/axes_api.html#module-matplotlib.axes
+axes.facecolor:     white   # axes background color
+axes.edgecolor:     black   # axes edge color
+axes.linewidth:     0.75     # edge linewidth
+axes.grid:          False   # display grid or not
+axes.grid.axis:     both    # which axis the grid should apply to
+axes.grid.which:    major   # gridlines at {major, minor, both} ticks
+axes.titlelocation: center  # alignment of the title: {left, right, center}
+axes.titlesize:     large   # fontsize of the axes title
+axes.titleweight:   normal  # font weight of title
+axes.titlecolor:    auto    # color of the axes title, auto falls back to
+                             # text.color as default value
+axes.titley:        None    # position title (axes relative units).  None implies auto
+axes.titlepad:      6.0     # pad between axes and title in points
+axes.labelsize:     medium  # fontsize of the x any y labels
+axes.labelpad:      4.0     # space between label and axis
+axes.labelweight:   normal  # weight of the x and y labels
+axes.labelcolor:    black
+axes.axisbelow:     line    # draw axis gridlines and ticks:
+                             #     - below patches (True)
+                             #     - above patches but below lines ('line')
+                             #     - above all (False)
+
+axes.formatter.limits: -5, 6  # use scientific notation if log10
+                               # of the axis range is smaller than the
+                               # first or larger than the second
+axes.formatter.use_locale: False  # When True, format tick labels
+                                   # according to the user's locale.
+                                   # For example, use ',' as a decimal
+                                   # separator in the fr_FR locale.
+axes.formatter.use_mathtext: False  # When True, use mathtext for scientific
+                                     # notation.
+axes.formatter.min_exponent: 0  # minimum exponent to format in scientific notation
+axes.formatter.useoffset: True  # If True, the tick label formatter
+                                 # will default to labeling ticks relative
+                                 # to an offset when the data range is
+                                 # small compared to the minimum absolute
+                                 # value of the data.
+axes.formatter.offset_threshold: 4  # When useoffset is True, the offset
+                                     # will be used when it can remove
+                                     # at least this number of significant
+                                     # digits from tick labels.
+
+axes.spines.left:   True  # display axis spines
+axes.spines.bottom: True
+axes.spines.top:    False
+axes.spines.right:  False
+
+axes.unicode_minus: True  # use Unicode for the minus symbol rather than hyphen.  See
+                           # https://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
+axes.prop_cycle: cycler('color', ['004e73', 'fdca00' , 'e9503e', 'afcc50', '898989', '50b695', 'ee7a34', '000000ff'])
+                  # color cycle for plot lines as list of string colorspecs:
+                  # single letter, long name, or web-style hex
+                  # As opposed to all other paramters in this file, the color
+                  # values must be enclosed in quotes for this parameter,
+                  # e.g. '1f77b4', instead of 1f77b4.
+                  # See also https://matplotlib.org/tutorials/intermediate/color_cycle.html
+                  # for more details on prop_cycle usage.
+axes.autolimit_mode: data  # How to scale axes limits to the data.  By using:
+                            #     - "data" to use data limits, plus some margin
+                            #     - "round_numbers" move to the nearest "round" number
+axes.xmargin:   .05  # x margin.  See `axes.Axes.margins`
+axes.ymargin:   .05  # y margin.  See `axes.Axes.margins`
+polaraxes.grid: True  # display grid on polar axes
+axes3d.grid:    True  # display grid on 3d axes
+
+
+## ***************************************************************************
+## * AXIS                                                                    *
+## ***************************************************************************
+xaxis.labellocation: center  # alignment of the xaxis label: {left, right, center}
+yaxis.labellocation: center  # alignment of the yaxis label: {bottom, top, center}
+
+
+## ***************************************************************************
+## * DATES                                                                   *
+## ***************************************************************************
+## These control the default format strings used in AutoDateFormatter.
+## Any valid format datetime format string can be used (see the python
+## `datetime` for details).  For example, by using:
+##     - '%%x' will use the locale date representation
+##     - '%%X' will use the locale time representation
+##     - '%%c' will use the full locale datetime representation
+## These values map to the scales:
+##     {'year': 365, 'month': 30, 'day': 1, 'hour': 1/24, 'minute': 1 / (24 * 60)}
+
+date.autoformatter.year:        %Y
+date.autoformatter.month:       %Y-%m
+date.autoformatter.day:         %Y-%m-%d
+date.autoformatter.hour:        %m-%d %H
+date.autoformatter.minute:      %d %H:%M
+date.autoformatter.second:      %H:%M:%S
+date.autoformatter.microsecond: %M:%S.%f
+
+## ***************************************************************************
+## * TICKS                                                                   *
+## ***************************************************************************
+## See https://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick
+xtick.top:           False   # draw ticks on the top side
+xtick.bottom:        True    # draw ticks on the bottom side
+xtick.labeltop:      False   # draw label on the top
+xtick.labelbottom:   True    # draw label on the bottom
+xtick.major.size:    3.5     # major tick size in points
+xtick.minor.size:    2       # minor tick size in points
+xtick.major.width:   0.8     # major tick width in points
+xtick.minor.width:   0.6     # minor tick width in points
+xtick.major.pad:     3.5     # distance to major tick label in points
+xtick.minor.pad:     3.4     # distance to the minor tick label in points
+xtick.color:         black   # color of the tick labels
+xtick.labelsize:     medium  # fontsize of the tick labels
+xtick.direction:     out     # direction: {in, out, inout}
+xtick.minor.visible: False   # visibility of minor ticks on x-axis
+xtick.major.top:     True    # draw x axis top major ticks
+xtick.major.bottom:  True    # draw x axis bottom major ticks
+xtick.minor.top:     True    # draw x axis top minor ticks
+xtick.minor.bottom:  True    # draw x axis bottom minor ticks
+xtick.alignment:     center  # alignment of xticks
+
+ytick.left:          True    # draw ticks on the left side
+ytick.right:         False   # draw ticks on the right side
+ytick.labelleft:     True    # draw tick labels on the left side
+ytick.labelright:    False   # draw tick labels on the right side
+ytick.major.size:    3.5     # major tick size in points
+ytick.minor.size:    2       # minor tick size in points
+ytick.major.width:   0.8     # major tick width in points
+ytick.minor.width:   0.6     # minor tick width in points
+ytick.major.pad:     3.5     # distance to major tick label in points
+ytick.minor.pad:     3.4     # distance to the minor tick label in points
+ytick.color:         black   # color of the tick labels
+ytick.labelsize:     medium  # fontsize of the tick labels
+ytick.direction:     out     # direction: {in, out, inout}
+ytick.minor.visible: False   # visibility of minor ticks on y-axis
+ytick.major.left:    True    # draw y axis left major ticks
+ytick.major.right:   True    # draw y axis right major ticks
+ytick.minor.left:    True    # draw y axis left minor ticks
+ytick.minor.right:   True    # draw y axis right minor ticks
+ytick.alignment:     center_baseline  # alignment of yticks
+
+
+## ***************************************************************************
+## * GRIDS                                                                   *
+## ***************************************************************************
+grid.color:     b0b0b0  # grid color
+grid.linestyle: -       # solid
+grid.linewidth: 0.8     # in points
+grid.alpha:     1.0     # transparency, between 0.0 and 1.0
+
+
+## ***************************************************************************
+## * LEGEND                                                                  *
+## ***************************************************************************
+legend.loc:           best
+legend.frameon:       False     # if True, draw the legend on a background patch
+legend.framealpha:    0.8      # legend patch transparency
+legend.facecolor:     inherit  # inherit from axes.facecolor; or color spec
+legend.edgecolor:     0.8      # background patch boundary color
+legend.fancybox:      True     # if True, use a rounded box for the
+                                # legend background, else a rectangle
+legend.shadow:        False    # if True, give background a shadow effect
+legend.numpoints:     1        # the number of marker points in the legend line
+legend.scatterpoints: 1        # number of scatter points
+legend.markerscale:   1.0      # the relative size of legend markers vs. original
+legend.fontsize:      medium
+legend.title_fontsize: None    # None sets to the same as the default axes.
+
+## Dimensions as fraction of fontsize:
+legend.borderpad:     0.4  # border whitespace
+legend.labelspacing:  0.5  # the vertical space between the legend entries
+legend.handlelength:  2.0  # the length of the legend lines
+legend.handleheight:  0.7  # the height of the legend handle
+legend.handletextpad: 0.8  # the space between the legend line and legend text
+legend.borderaxespad: 0.5  # the border between the axes and legend edge
+legend.columnspacing: 2.0  # column separation
+
+
+## ***************************************************************************
+## * FIGURE                                                                  *
+## ***************************************************************************
+## See https://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure
+figure.titlesize:   large     # size of the figure title (``Figure.suptitle()``)
+figure.titleweight: normal    # weight of the figure title
+figure.figsize:     6.69, 4.6  # figure size in inches default: 6.4, 4.8
+figure.dpi:         220       # figure dots per inch
+figure.facecolor:   white     # figure facecolor
+figure.edgecolor:   white     # figure edgecolor
+figure.frameon:     True      # enable figure frame
+
+## The figure subplot parameters.  All dimensions are a fraction of the figure width and height.
+figure.subplot.left:   0.125  # the left side of the subplots of the figure
+figure.subplot.right:  0.9    # the right side of the subplots of the figure
+figure.subplot.bottom: 0.11   # the bottom of the subplots of the figure
+figure.subplot.top:    0.88   # the top of the subplots of the figure
+figure.subplot.wspace: 0.2    # the amount of width reserved for space between subplots,
+                               # expressed as a fraction of the average axis width
+figure.subplot.hspace: 0.2    # the amount of height reserved for space between subplots,
+                               # expressed as a fraction of the average axis height
+
+## Figure layout
+figure.autolayout: False  # When True, automatically adjust subplot
+                           # parameters to make the plot fit the figure
+                           # using `tight_layout`
+figure.constrained_layout.use: False  # When True, automatically make plot
+                                       # elements fit on the figure. (Not
+                                       # compatible with `autolayout`, above).
+figure.constrained_layout.h_pad:  0.04167  # Padding around axes objects. Float representing
+figure.constrained_layout.w_pad:  0.04167  # inches. Default is 3./72. inches (3 pts)
+figure.constrained_layout.hspace: 0.02     # Space between subplot groups. Float representing
+figure.constrained_layout.wspace: 0.02     # a fraction of the subplot widths being separated.
+
+
+## ***************************************************************************
+## * IMAGES                                                                  *
+## ***************************************************************************
+image.aspect: equal                # {equal, auto} or a number
+image.interpolation:  antialiased  # see help(imshow) for options
+image.cmap:   viridis              # A colormap name, gray etc...
+image.lut:    256                  # the size of the colormap lookup table
+image.origin: upper                # {lower, upper}
+image.resample:  True
+image.composite_image: True  # When True, all the images on a set of axes are
+                              # combined into a single composite image before
+                              # saving a figure as a vector graphics file,
+                              # such as a PDF.
+
+
+## ***************************************************************************
+## * CONTOUR PLOTS                                                           *
+## ***************************************************************************
+contour.negative_linestyle: dashed  # string or on-off ink sequence
+contour.corner_mask:        True    # {True, False, legacy}
+contour.linewidth:          None    # {float, None} Size of the contour
+                                     # linewidths. If set to None,
+                                     # it falls back to `line.linewidth`.
+
+
+## ***************************************************************************
+## * ERRORBAR PLOTS                                                          *
+## ***************************************************************************
+errorbar.capsize: 0  # length of end cap on error bars in pixels
+
+
+## ***************************************************************************
+## * HISTOGRAM PLOTS                                                         *
+## ***************************************************************************
+hist.bins: 10  # The default number of histogram bins or 'auto'.
+
+
+## ***************************************************************************
+## * SCATTER PLOTS                                                           *
+## ***************************************************************************
+scatter.marker: o         # The default marker type for scatter plots.
+scatter.edgecolors: face  # The default edge colors for scatter plots.
+
+
+## ***************************************************************************
+## * AGG RENDERING                                                           *
+## ***************************************************************************
+## Warning: experimental, 2008/10/10
+agg.path.chunksize: 0  # 0 to disable; values in the range
+                        # 10000 to 100000 can improve speed slightly
+                        # and prevent an Agg rendering failure
+                        # when plotting very large data sets,
+                        # especially if they are very gappy.
+                        # It may cause minor artifacts, though.
+                        # A value of 20000 is probably a good
+                        # starting point.
+
+
+## ***************************************************************************
+## * PATHS                                                                   *
+## ***************************************************************************
+path.simplify: True  # When True, simplify paths by removing "invisible"
+                      # points to reduce file size and increase rendering
+                      # speed
+path.simplify_threshold: 0.111111111111  # The threshold of similarity below
+                                          # which vertices will be removed in
+                                          # the simplification process.
+path.snap: True  # When True, rectilinear axis-aligned paths will be snapped
+                  # to the nearest pixel when certain criteria are met.
+                  # When False, paths will never be snapped.
+path.sketch: None  # May be None, or a 3-tuple of the form:
+                    # (scale, length, randomness).
+                    #     - *scale* is the amplitude of the wiggle
+                    #         perpendicular to the line (in pixels).
+                    #     - *length* is the length of the wiggle along the
+                    #         line (in pixels).
+                    #     - *randomness* is the factor by which the length is
+                    #         randomly scaled.
+path.effects:
+
+
+## ***************************************************************************
+## * SAVING FIGURES                                                          *
+## ***************************************************************************
+## The default savefig params can be different from the display params
+## e.g., you may want a higher resolution, or to make the figure
+## background white
+savefig.dpi:       figure      # figure dots per inch or 'figure'
+savefig.facecolor: auto        # figure facecolor when saving
+savefig.edgecolor: auto        # figure edgecolor when saving
+savefig.format:    png         # {png, ps, pdf, svg}
+savefig.bbox:      standard    # {tight, standard}
+                                # 'tight' is incompatible with pipe-based animation
+                                # backends (e.g. 'ffmpeg') but will work with those
+                                # based on temporary files (e.g. 'ffmpeg_file')
+savefig.pad_inches:   0.1      # Padding to be used when bbox is set to 'tight'
+
+savefig.transparent: False     # setting that controls whether figures are saved with a
+                                # transparent background by default
+savefig.orientation: portrait  # Orientation of saved figure
+
+### ps backend params
+ps.papersize:      letter  # {auto, letter, legal, ledger, A0-A10, B0-B10}
+ps.useafm:         False   # use of afm fonts, results in small files
+ps.usedistiller:   False   # {ghostscript, xpdf, None}
+                            # Experimental: may produce smaller files.
+                            # xpdf intended for production of publication quality files,
+                            # but requires ghostscript, xpdf and ps2eps
+ps.distiller.res:  6000    # dpi
+ps.fonttype:       3       # Output Type 3 (Type3) or Type 42 (TrueType)
+
+### PDF backend params
+pdf.compression:    6  # integer from 0 to 9
+                        # 0 disables compression (good for debugging)
+pdf.fonttype:       3  # Output Type 3 (Type3) or Type 42 (TrueType)
+pdf.use14corefonts : False
+pdf.inheritcolor:   False
+
+### SVG backend params
+svg.image_inline: True  # Write raster image data directly into the SVG file
+svg.fonttype: path      # How to handle SVG fonts:
+                         #     path: Embed characters as paths -- supported
+                         #           by most SVG renderers
+                         #     None: Assume fonts are installed on the
+                         #           machine where the SVG will be viewed.
+svg.hashsalt: None      # If not None, use this string as hash salt instead of uuid4
+
+### pgf parameter
+## See https://matplotlib.org/tutorials/text/pgf.html for more information.
+pgf.rcfonts: True
+pgf.preamble:  # See text.latex.preamble for documentation
+pgf.texsystem: xelatex
+
+## ***************************************************************************
+## * INTERACTIVE KEYMAPS                                                     *
+## ***************************************************************************
+## Event keys to interact with figures/plots via keyboard.
+## See https://matplotlib.org/users/navigation_toolbar.html for more details on
+## interactive navigation.  Customize these settings according to your needs.
+## Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '')
+keymap.fullscreen: f, ctrl+f   # toggling
+keymap.home: h, r, home        # home or reset mnemonic
+keymap.back: left, c, backspace, MouseButton.BACK  # forward / backward keys
+keymap.forward: right, v, MouseButton.FORWARD      # for quick navigation
+keymap.pan: p                  # pan mnemonic
+#keymap.zoom: o                 # zoom mnemonic
+keymap.save: s, ctrl+s         # saving current figure
+keymap.help: f1                # display help about active tools
+keymap.quit: ctrl+w, cmd+w, q  # close the current figure
+keymap.quit_all:               # close all figures
+keymap.grid: g                 # switching on/off major grids in current axes
+keymap.grid_minor: G           # switching on/off minor grids in current axes
+keymap.yscale: l               # toggle scaling of y-axes ('log'/'linear')
+keymap.xscale: k, L            # toggle scaling of x-axes ('log'/'linear')
+keymap.copy: ctrl+c, cmd+c     # Copy figure to clipboard
+
+
+## ***************************************************************************
+## * ANIMATION                                                               *
+## ***************************************************************************
+animation.html: none  # How to display the animation as HTML in
+                       # the IPython notebook:
+                       #     - 'html5' uses HTML5 video tag
+                       #     - 'jshtml' creates a Javascript animation
+animation.writer:  ffmpeg        # MovieWriter 'backend' to use
+animation.codec:   h264          # Codec to use for writing movie
+animation.bitrate: -1            # Controls size/quality tradeoff for movie.
+                                  # -1 implies let utility auto-determine
+animation.frame_format: png      # Controls frame format used by temp files
+animation.ffmpeg_path:  ffmpeg   # Path to ffmpeg binary. Without full path
+                                  # $PATH is searched
+animation.ffmpeg_args:           # Additional arguments to pass to ffmpeg
+animation.convert_path: convert  # Path to ImageMagick's convert binary.
+                                  # On Windows use the full path since convert
+                                  # is also the name of a system tool.
+animation.convert_args:          # Additional arguments to pass to convert
+animation.embed_limit:  20.0     # Limit, in MB, of size of base64 encoded
+                                  # animation in HTML (i.e. IPython notebook)
diff --git a/Kennlinien_und_Fitting.ipynb b/Kennlinien_und_Fitting.ipynb
index 32cc799dda49a2ecd66286786cf4a0f8f2a2d1f2..c0d54ea4cf0e87dd38d6d99902adafe6513412fb 100644
--- a/Kennlinien_und_Fitting.ipynb
+++ b/Kennlinien_und_Fitting.ipynb
@@ -1,27 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pumpenkennlinien importieren und auf Pumpengleichung Fitten\n",
-    "\n",
-    "aus Webplotdigitizer .csv datei eines Plots extrahieren\n",
-    "\n",
-    "mit pandas read_csv importieren \n",
-    "-> rechenspaß \n",
-    "->Profit!!!\n",
-    "\n",
-    "\n",
-    "Q^2 -> Q_sq quit\n",
-    "Multiple Linear Regression with Intercept\n",
-    "\n",
-    "Q^3, Q^2\n",
-    "\n",
-    "pandas -> read_csv\n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -51,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,44 +105,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.04326332  0.18706448  8.74223069]\n",
-      "R^2: 0.9993376472269206\n",
-      "[-0.04427426  0.22583718  8.47417639]\n",
-      "R^2: 0.9996437866771651\n",
-      "[-0.04551814  0.25487938  8.35225635]\n",
-      "R^2: 0.9996824413373676\n",
-      "[-0.04337133  0.2282963   8.41669451]\n",
-      "R^2: 0.9997607916622331\n",
-      "[-0.04486213  0.24504513  8.38508942]\n",
-      "R^2: 0.9995310406285615\n",
-      "[-0.04560908  0.26110027  8.36778663]\n",
-      "R^2: 0.999203487869221\n",
-      "[-0.05301025  0.30409333  8.32747118]\n",
-      "R^2: 0.989680503933434\n",
-      "[-0.08310832  0.34740007  8.36358287]\n",
-      "R^2: 0.9616275516077178\n",
-      "[0.072644   0.14528799 8.40382273]\n",
-      "R^2: 1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -212,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -225,9 +168,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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55RdZvny57Nu3T0RETp48KXfccYfUqlVLBg0aZEe1EUa9evXkjjvukDvuuEPOPffckNtkZWXJoEGD5KeffhIRkS1btsj//M//yMMPP2xlVRGDAwcOSP/+/bX3JNxF7/0MY2g5y+7duyUtLS0olLziiitk6tSpYXsT5OTkWFQ7RCIhISGm54riYAvO8NlnnwWFkm3atJF3331XOnXqVGbbffv2yd133y0ff/yxiPw5hNGYMWPkk08+say+CG/y5MlBoeRll10mb7zxhjRq1Chou8LCQnn66adl4sSJEggE5MSJE3LttddKRkaG1KxZ0+pqO48CEFJubq6qUqWKEhElIuq6664rd/sFCxZo24qImjlzpkU1RaR8Pl/QMQpV4HyzZ89WIqI6duyoXnnlFXX48OGQ2xUUFKgJEyYEHd8GDRqoo0ePWlxjhJKTk6MefvjhiI9Hdna2SkxM1I5lp06dTK4hjHLy5EmVmpoa8po7YsQIu6uHECZOnKgdo8qVK9tdHcSof//+2vH0+XzqmWeesbtKsNB7770XdPxhv0svvVQ7JmeccYbKzc0td/tAIKD69eunvaZSpUpqz549FtUW4ezatUtVrVpVOy69evVSp06dKvc1zz33XNB90LPPPmtRbZ2NqZ2AMObPny8nT54UERGfzydPP/10udtfddVV0r17d235rbfeMrN6iIJiTB1PaNCggbz77ruyceNGufPOO6VWrVoht0tOTpYpU6bI6NGjtXV79uwJ+lYT9mnatKlMmzYt4smJWrVqJZdccom2XLo7IpxJKSW33nqrrF69WkREa9kMwBrz58+Xzz//XFt+9NFHZezYsTbWCFY6deqUPPbYY9pyenq6jbWByJ/HpPgzUUTk6quvloYNG5b7Gp/PJw899JC2XFRUJP/9739NqyMi89Zbb8mJEye05enTp0tiYvmdkh944AHp0qWLtvzCCy/I6dOnTaujWxBMAmGUvIm76KKLpFmzZhW+ZujQodq/N2zYwNiFgAkuu+wyufHGG8Xn80W0fenuvl999ZUZ1YIF6tWrp/371KlTNtYEkRo/frw2ZEaTJk1k0aJFzKoOWEQpJePGjdOWO3fuLJMmTbKvQrDca6+9pn2RV6lSJXn88cdtrhHy8vKCJp8qeW9TnpYtWwYtHzx40NB6Qb8vvvhC+3ezZs3koosuiuh19913n/bvXbt2yZo1a4yumusQTAIhBAKBoG+yevbsGdHrLr74Yu3fSikCEIdRSpUpU6dOtbtaMFnjxo3lrLPO0pZ3795tY20Qi19//VX7d5s2bWysCSLx6quvyrRp00RE5IwzzpAlS5ZU2CoEgHFWrVoVNIHjxIkTpVKlSjbWCFb6448/ZMqUKdryLbfcIq1bt7axRhCRMj1FMjMzI3pdfn5+0DKT39iv5FwU7dq1i/h1l112WdDyN998Y1id3IpgEghh586dQc2yww0MXlqbNm2CbviKJ2oAYK+EhP/9uKNLvztt3bpV1q1bpy2XbKEO5/n8889l1KhRIvLnRCgff/yxXHDBBTbXCogvJYcVatiwoQwcONC+ysBy06dPlz179ojIn8PbTJw40eYaQUSkVq1aQV+ufvHFF7Jy5coKX7d27Vrt37Vr15Zu3bqZUj9Ebv/+/dq/ww0tFUpKSkrQF7VZWVmG1suNCCaBEEp++yHyZ4urSCQnJ0tKSkrY/QCw3h9//BH0LXOkXWbgHLm5uZKenq51ferYsaOMHDnS5lohnE2bNkl6ero2ZtJrr70mf/nLX2yuFRB/Vq1apf17wIABEQ+BAvfbv3+/PPPMM9ryiBEjpEmTJjbWCCWNGTNG+7dSSi6//HKZPXt22C/PT58+LS+//LK2PHr06JhmaYcxSoaRhw8f1vXaBg0aaP/+/fffDauTWxFMAiHs2rUraLlOnToRv7ZkMFl6PwCs9/XXXweN5dO5c2cba4NIBAIBOXjwoKxZs0bGjRsnbdq0kR9++EFERC688EJZsmQJN+QOtXPnThkwYIAcO3ZMREQmTZokt9xyi821AuLPgQMH5LffftOWO3furA0zNHLkSOnQoYOkpKRIUlKS1KxZU1q2bCmDBg2S999/n4kYPOCJJ56QI0eOiIhItWrVZPz48TbXCCWNGDFChgwZoi0fP35cbrvtNrnoootk3rx5QeNoK6Vk1KhR8uOPP4rIn5PIlRw7FvYpGS4W36dGqnr16tq/GS9UpPwpg4A49ccffwQt16xZM+LXlrzIlN4PAOvNmjVL+7fP55PLL7/cxtqgIuedd17IYTAaNmwoo0aNkrFjxxJKOtThw4flb3/7m+Tm5orIn+OZ0XXQ/QoLC6Vx48Zy6NAhCQQCkpycLDVq1JDGjRvLBRdcIP3795eBAwdKtWrV7K4qSti0aVPQ8q+//ioXXnhhyPHsjh07JseOHZPt27fLxx9/LP/4xz9k9uzZEU/kAGf57bff5KWXXtKWR48eHRSgwH4+n0/eeecdad68uUydOlWKiopERGTjxo0yZMgQGT16tFx77bVy5ZVXyqxZs2ThwoUi8ud8BgsWLGASOYe49NJLZfPmzSLy5zV22bJl0rdv34heW7LRxMmTJ02pn5vQYhIIoXSgmJycHPFrk5KSwu4HgLVWrFih3cyJiAwcOFCaNWtmX4UQlbp168ojjzwit912G6GkQ506dUquvfZabZykv/zlL/Laa6/ZXCsYQSklubm5cvz4cTl58qQcOXJEcnNz5bvvvpO33npLBg8eLM2bN5c333zT7qqihOKxBYtNmzatTCgZbiKczZs3S+/evWXp0qWm1Q/mmThxohQUFIjInxOPPfzwwzbXCKEkJCTIlClTZMmSJZKYGNxebP/+/fLqq6/KgAEDtPvYZ555RlatWhU0oSPsdf311wct33bbbbJly5YKX7dz507ZuXOntqwna/AqgkkghOIP82KlPyzKUzKYLDmBDgBr7du3T4YNG6YtV61aVZ566in7KoSIVK5cuUz4mJeXJ/fee680bdpURowYoXVPg3PceeedsmLFChERadu2rXz88cdBn4fwtr1798rtt98uY8aMYYIxhwg13lmnTp3k2Wefle+++06OHTsmp0+flj/++EO2b98u77zzjrRv317b9vjx43LDDTfI7t27raw2YrR582Z5++23teUHH3xQateubWONEI5SSp577jm55ppr5PTp01K7dm25//77gybGKempp56SV155JailHeyVmpoqgwYN0pZ///136d69u0yePFkyMjK0LtqnTp2S7du3y7x58yQ9PV1atGgRFEyeccYZltfdaQgmgRBKPxQXN6+PRMkxQapWrWpYnQBErqCgQK699tqgcV5feOGFsDd7cI6MjAw5efKkHD9+XHbs2CHLli2T4cOHS1JSkpw6dUpeffVV6dWrlxw6dMjuquL/mzx5sjb7b8OGDWXJkiXcZLvchRdeKFOmTJFPPvlENm7cKDk5OXLs2DEpLCyU/Px8WbdunTz//PNBQZaIyIsvvhg0fAbsU/oLnPnz58vGjRvlwQcflC5dumhDD1WrVk2aN28uQ4cOlfXr18s111yjvebQoUMyYcIES+uN2IwfP14LrurWrSv333+/zTVCKEVFRXLDDTfI2LFj5Y8//pBOnTpJVlaWPP/88/Ljjz/Kpk2b5IEHHghqHZmfny+jRo2Syy+/nF55DjJr1izp0qWLtnzkyBGZNGmSXHjhhVKnTh1JTEyU5ORkadmypQwZMkQ+/PDDoLxAhGBShGASCKnkOJEiomsQ8JIXGsZbAqwXCATkxhtvlG+//VZbN2TIELnzzjttrBX0qlq1qjRr1kz++te/yquvvhrU1SkrK4uHLYd47733ZNKkSSIiUqNGDVm8eLGcc8459lYKMbvqqqtkwoQJcvXVV0unTp2kadOmUr16dUlKSpKUlBTp2rWr3H///fL999/LE088EfTahx9+mIdmBygsLAxa7tq1a4WvqVy5ssyZMyfoPfzJJ5+UeYiGM61Zs0Y+++wzbfmRRx6RGjVq2FgjhPPwww/L/PnzRUTknHPOkcWLF0ujRo20n3fo0EGee+45+f3332XmzJlBk6suWbJErrvuOlqnO0RKSoqsWrVKhg0bJj6fr8zPSzdwqlWrlvzjH/8Imly3YcOGptfT6QgmgRBKB5PHjx+P+LUlb8ZL7weAuZRScuedd8rHH3+srUtLS2PsMw/o06ePDB8+XFueM2dOmTHUYL1ly5aJyJ9j1X3wwQfSsWNHm2sEKyUkJMj48ePljjvu0NYdPXo0aGxf2KP05BiRdv+sUaOGDB06VFs+dOhQyAlz4DyPPPKI9u/GjRvL3XffbWNtEM7mzZtl+vTp2vLEiRPDTk6UnJwsd999t2RlZUnnzp219Z9//rnMmzfP9LoiMtWrV5fZs2fL999/L/fee6907txZateuLYmJiVK1alVp2rSpXHnllfLKK6/Izp075d5775UDBw5or+feiWASCKlx48ZBy3q6DObn54fdDwDzKKVk5MiR8sYbb2jrevToIZ999hkTpnjE1Vdfrf1bKSUrV660sTYoKRAIyNVXXy1VqlSpsPz666/a615//fWgn/3jH/+w8a9AtB588MGg5dWrV9tUExQrPUGGni/ZSz8k8yWQ8y1evDjofffYY48xc7NDvf7669oXBVWqVJGbbrqpwtc0aNBA3n///aAJq2bPnm1aHRGdDh06yAsvvCAbNmyQAwcOyKlTp+T48eOSk5Mj//rXv+TOO++UWrVqSUZGRtDrSobO8YpgEgihVatWQcuRDvxdUFAQ9O1H6f0AMEcgEJDhw4fLK6+8oq275JJLZOnSpXRj8pDSXYSZlME5lFJSUFAQUSmpqKgo6Gd6hk6Bc5x33nlB11qCLPs1adIkaFnPMSk9WQoTjjlbIBCQcePGacvNmzeX2267zcYaoTxr1qzR/t28efOIZ2Ru2bKldOvWTVv+/vvvDa8brLFkyRLt39WrV6fFpBBMAiE1adIk6FvGrKysiF63ZcuWoHEkWrdubXjdAAQ7deqUDB06NKil5IABA2Tp0qVSq1YtG2sGo5Ue54yZKQHnKHm9ZYxJ+7Vr1y5oefPmzRG/tnQQWXJ8OzjP3Llz5YcfftCWJ02aJElJSTbWCOUp2YhF75fnJb9wOHr0qGF1gnVOnz4tc+fO1ZYHDRrEvBRCMAmElJCQIKmpqdry119/HdHrSk62IfJniy0A5jl27JgMHDgwaJyd66+/XhYsWCBVq1a1sWYww88//xy0zGDh9nvrrbdEKaWrNG3aVHv9iBEjgn5WPJEO3EUpJQcPHtSW69ata2NtICJSr149adOmjba8ePHiiF9b+gv5li1bGlYvGKuwsFD+/ve/a8vnn3++3HjjjTbWCBUpGfT//PPPZSZHKc/hw4e1f3Oddac5c+YEDWlz66232lgb5yCYBMLo37+/9u///Oc/smPHjgpfUzy7mohI+/btGWMSMNGePXukd+/e2uQbIn+GHHPnzqWlgEe9//77Qcslv0ACYJ+srCw5ceKEtnz++efbWBsUGzJkiPbv5cuXR9z185NPPtH+3apVK2nevLnhdYMxXn75ZcnJydGWp0yZIgkJPOI72QUXXKD9+8CBA7Jo0aKIXnfixAn597//rS136tTJ8LrBXDk5OUFjMvfp00cuvfRSG2vkHFy1gDDS09O1CTOUUvLoo4+Wu/2SJUuCWkxGMpAxgOhs2bJFunfvLhs3btTWTZkyRWbNmsUNuUsMGjRIsrOzI97+s88+kw8//FBb7tWrV1DLOwD2efPNN4OWS365C/uMHDkyqIvgsGHD5NixY+W+5s033wyamGHkyJGm1Q+xOXr0qDzxxBPacufOneWaa66xsUaIxLXXXhu0fO+998revXsrfN2kSZOChlko+cUDnG/Hjh3Sr18/rdVrlSpVZNasWTbXyjl4egPCaNy4sdx+++3a8rx58+TRRx8NOTD/qlWrZOjQodpyvXr15O6777aknkC8+frrr6VXr15aN4jk5GR55513ZMKECTbXDHp8/PHH0rZtW0lPT5fly5eHnfTkxIkTMn36dElPTxellIiI+Hw+eeqpp6ysLhA3nnrqKVmwYEHE269Zs0ZmzpypLffo0UMuvPBCE2oGvVJSUuShhx7SljMzM8Xv98u2bdvKbFtUVCQzZ86Uu+66S1vXunVr7mcd7LnnnpO8vDxtuWRICef629/+Jr169dKWf/31V+nZs6d88803Ibc/evSo3H///fL0009r67p37y6DBw82va6IzJ49e8pM7lesoKBAXnzxRbnooou0L+QTEhLktddekxYtWlhZTUfzqeK7fABl7N+/X7p27Sq//PKLtu7ss8+W/v37S6NGjeTw4cOyfv36oNnVfD6ffPTRR3xj6RLTpk0LmsmQS6Kz7d27V5o0aSKFhYUiInLmmWfKggULpHfv3vZWDLr5fL6g5erVq0uvXr2kVatWUrduXSkoKJAtW7bIqlWr5NChQ0HbPv3000EP23CXZs2aaV8sjBgxghYDDjNs2DCZM2eOdO7cWe6++24ZNGhQyInEioqK5O2335bRo0drk90kJCTIypUr6ZrmIIFAQC677DJZvny5tq5SpUrSp08f6dSpk1SpUkV27doly5YtCxr3rE6dOrJ27VomcnSoffv2SYsWLbQWsKmpqWGDLTjPjh075OKLL5bc3Nyg9R07dpSLL75Y6tWrJydOnJAtW7bI8uXLg1o6N23aVFavXi3nnHOO1dVGGLNmzZLx48eL3++X8847TypXriz79++Xbdu2yapVq+T48ePatgkJCTJ79my5+eabbayx8xBMAhXYsmWL9OvXT3bu3FnhtomJifLCCy/IqFGjLKgZ9OrevXuZdbm5uUHHtlu3bmW2eeyxx2TAgAGm1g2RycnJkXPPPVdbrlSpkiQmJureT9OmTeWnn34ysmrQKSkpKWwryXAqV64s06dPp2uhyxFMOltxMFksMTFRLrroImnfvr3Ur19flFKSk5MjX331lezatSvotU8++WTQl31whiNHjsjQoUNl4cKFEW3fpk0bWbBggfyf//N/TK4ZojVmzBh58cUXteVvvvmGcZddJjs7W9LT04OGTqjIxRdfLHPnzg2anRv2mzVrVkT3pu3bt5eZM2fKxRdfbEGt3EX/0xwQZ9q0aSNZWVny6KOPyjvvvBM0G1qxhIQEufTSS+XZZ59lIGIHW7duXVTblOwmA2cpKirSNZthsXDdLWCdXbt2yfz582Xx4sWyevXqoIkzSqtataoMGTJEHnroIR6UAYudPn1a1q5dK2vXrg27TVJSkvzzn//kSwOHqlWrlnz66afy7rvvypNPPilbt24NuV39+vXl/vvvl9GjRweNTQln2bFjh7zyyiva8mWXXUYo6UKtW7eW9evXyyuvvCIzZswod9ztdu3ayf333y+33HILY6m7jM/nk86dO8vw4cPl9ttvl0qVKtldJUeixSSgQ2FhoXzzzTeSk5MjeXl5UrNmTWnYsKH06tVLGjRoYHf1UIHSXUcjNXv2bBk2bJixlUFUSreYjFbTpk2DZrGEvU6dOiU//fST/Pjjj7Jv3z45cuSIJCUlSUpKirRp00a6dOnCTOseQotJZztw4IDMmzdPPv30U/n222/L/dKgWrVqMmjQIHnsscekZcuWFtYSscjOzpb169fL3r175dSpU1K3bl3p0KGDdO7cOep7JVhn6NChMnfuXG15w4YN0rlzZxtrBCPs2LFDe18ePXpUatSoIQ0aNJAePXrQQtLhDh8+LN9++61s375djh49KgkJCXLmmWdK8+bNpXPnznLWWWfZXUXHI5gEAAAAUEZhYaH89NNPsnXrVu1huVKlSpKSkiItW7aUHj16SHJyst3VBAAALkYwCQAAAAAAAMByDFAAAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHIEkwAAAAAAAAAsRzAJAAAAAAAAwHL/D3dRxj4vk1UgAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "<Figure size 1471.8x1012 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -251,14 +194,12 @@
     "\n",
     "LR_H = LinearRegression(fit_intercept=False).fit(X , df['H'].to_numpy(float))\n",
     "LR_P = LinearRegression().fit(X2,df['P'].to_numpy(float))\n",
-    "\n",
+    "plt.style.use('FST.mplstyle')\n",
     "fig1, (ax1,ax2) = plt.subplots(2,1,sharex=True,gridspec_kw={'hspace':0})\n",
     "ax1.set_xticks(np.linspace(0,10,11))\n",
     "ax1.set_xticks(np.linspace(0,10,21),minor=True)\n",
-    "ax1.set_title('Förderhöhe Kennlinie',loc='center')\n",
-    "ax1.set_ylabel('$H$ in m')\n",
-    "ax2.set_title('Leistungskennlinien',loc='center',y=-0.25)\n",
-    "ax2.set_ylabel('$P$ in kW')\n",
+    "ax1.set_ylabel('Höhe $H$ in m')\n",
+    "ax2.set_ylabel('Leistung $P$ in kW')\n",
     "\n",
     "#muss für jeden bereich fon n einzeln geplottet werden\n",
     "n=set()\n",
@@ -269,8 +210,8 @@
     "    n_plt=n.pop()\n",
     "    X_plt=np.append(np.append(Q*Q, Q*n_plt,axis=1),np.ones((181,1))*n_plt**2,axis=1) \n",
     "    X2_plt=np.append(np.append(np.append(Q*Q*Q, Q*Q*n_plt,axis=1),Q*(n_plt**2),axis=1),np.ones((181,1))*(n_plt**3),axis=1)\n",
-    "    ax1.plot(Q,LR_H.predict(X_plt),'o',ms=1.5)\n",
-    "    ax2.plot(Q,LR_P.predict(X2_plt),'o',ms=1.5)\n",
+    "    ax1.plot(Q,LR_H.predict(X_plt),'ko',ms=1.5)\n",
+    "    ax2.plot(Q,LR_P.predict(X2_plt),'ko',ms=1.5)\n",
     "print(f'R^2{LR_H.score(X,df['H'])}')\n",
     "print(f'R^2{LR_P.score(X2,df['P'])}')"
    ]
@@ -299,40 +240,14 @@
     "Ziel einen Fit mit n als zweite Variable\n",
     "multiple linear REgression\n",
     "\n",
-    "Ziel2 den FST custom stil zum plotten des Kennfelds einbinden\n",
+    "Ziel2 den FST custom stil zum plotten des Kennfelds einbinden -done\n",
     "\n",
     "Ziel3 eine Präsi erstellen anhand des Leitfadens im studierende Starterpaket\n",
     "\n",
-    "schritt 4 Profit "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    LinearRegression(fit_intercept=False)\n",
-       "1    LinearRegression(fit_intercept=False)\n",
-       "2    LinearRegression(fit_intercept=False)\n",
-       "3    LinearRegression(fit_intercept=False)\n",
-       "4    LinearRegression(fit_intercept=False)\n",
-       "5    LinearRegression(fit_intercept=False)\n",
-       "6    LinearRegression(fit_intercept=False)\n",
-       "7    LinearRegression(fit_intercept=False)\n",
-       "8    LinearRegression(fit_intercept=False)\n",
-       "Name: Q-h_fit, dtype: object"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results['Q-h_fit']"
+    "schritt4 mit dem pymo packet Optimierungsgleichung formulieren Anlagen kennlinie definieren\n",
+    "\n",
+    "         für verschiedene durchflüsse optimale drehzahl berechnen.\n",
+    "\n"
    ]
   }
  ],